Systems and methods for analyte processing

Abstract

In one aspect disclosed herein is a method comprising: coupling a biomolecule to a surface; detecting the biomolecule using surface-enhanced Raman spectroscopy (SERS); and based at least in part on the detecting in b), identifying the biomolecule at an accuracy of at least 75%. In another aspect disclosed herein is a method comprising: coupling an analyte on a surface; detecting the analyte using surface-enhanced Raman spectroscopy (SERS) to obtain analyte data; and identifying the analyte by applying a trained machine learning algorithm to the analyte data, wherein the trained machine learning algorithm does not comprise comparing the analyte data to a database comprising data associated with a reference molecule.

Description

SYSTEMS AND METHODS FOR ANALYTE PROCESSING CROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application No.63 / 709,992 filed on October 21, 2024, U.S. Provisional Application No. 63 / 736,562 filed on December 19, 2024, and U.S. Provisional Application No. 63 / 803,417 filed on May 9, 2025, the entirety of each is incorporated herein by reference.BACKGROUND

[0002] Proteins may play an integral role in cell biology and physiology, performing and facilitating many different biological functions. The repertoire of different protein molecules is extensive, and may be much more complex than the genome and transcriptome, due to additional diversity introduced by post-translational modifications (PTMs) and protein splicing. Additionally, proteins within a cell can dynamically change (e.g., in expression level and modification state) in response to the environment, physiological state and disease state.SUMMARY

[0003] Aspects of the present disclosure teach certain benefits in construction and use which give rise to the exemplary advantages described below.

[0004] In one aspect disclosed herein is a method comprising: a) coupling a biomolecule to a surface; b) detecting the biomolecule using surface-enhanced Raman spectroscopy (SERS); and c) based at least in part on the detecting in b), identifying the biomolecule at an accuracy of at least 75%.

[0005] In some embodiments, the detecting is performed at single molecule resolution.

[0006] In some embodiments, the identifying comprises using a trained machine learning algorithm.

[0007] In some embodiments, the biomolecule is selected from the group consisting of a nucleic acid and a polypeptide. In some embodiments, the polypeptide comprises a modified residue with a modification. In some embodiments, the modification is noncovalently bound to the modified residue. In some embodiments, the modification is covalently bound to the modified residue. In some embodiments, the modification is a ligand. In some embodiments, the modification is a post-translational modification. In some embodiments, the identifying comprises identifying the modification. In some embodiments, the identifying comprisesidentifying the modified residue. In some embodiments, the polypeptide comprises an additional modified residue with an additional modification. In some embodiments, the modification and the additional modification are different. In some embodiments, the modification and the additional modification are the same. In some embodiments, the identifying comprises identifying the additional modification. In some embodiments, the identifying comprises identifying the additional modified residue.

[0008] In some embodiments, the method further comprises detecting a normalization reference molecule using SERS. In some embodiments, the method further comprises comparing the normalization reference molecule and the biomolecule. In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the normalization reference molecule is graphene. In some embodiments, the normalization reference molecule is coupled to the biomolecule. In some embodiments, the biomolecule does not have a label.

[0009] In another aspect disclosed herein is a method comprising: a) coupling an analyte on a surface; b) detecting the analyte using surface-enhanced Raman spectroscopy (SERS) to obtain analyte data; and c) identifying the analyte by applying a trained machine learning algorithm to the analyte data; wherein the trained machine learning algorithm does not comprise comparing the analyte data to a database comprising data associated with a reference molecule.

[0010] In some embodiments, the trained machine learning algorithm does not comprise comparing a spectrum of the analyte to a reference spectrum of the reference molecule. In some embodiments, the reference molecule is a reference protein or a reference amino acid. In some embodiments, the method further comprises comparing the analyte data to data in a database. In some embodiments, the comparing the analyte data to data in a database uses an additional trained machine learning algorithm.

[0011] In some embodiments, the detecting is performed at single molecule resolution.

[0012] In some embodiments, the method further comprises detecting a normalization reference molecule using SERS. In some embodiments, the method further comprises comparing the normalization reference molecule and the analyte. In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the normalization reference molecule is graphene. In someembodiments, the normalization reference molecule is coupled to the analyte. In some embodiments, the analyte does not have a label.

[0013] In some embodiments, the analyte comprises a polymer, and wherein the polymer comprises subunits. In some embodiments, the method further comprises identifying a sequence of the subunits of the polymer.

[0014] In another aspect disclosed herein is a method comprising: a) coupling a polymer to a surface, wherein the polymer comprises subunits; b) detecting the polymer using surface-enhanced Raman spectroscopy (SERS); and c) based at least in part on the detecting in b) identifying the polymer at an accuracy of at least 75%.

[0015] In some embodiments, the identifying comprises using a trained machine learning algorithm.

[0016] In another aspect disclosed herein is a method comprising: a) coupling a polymer to a surface wherein the polymer comprises subunits; b) detecting the polymer using surface-enhanced Raman spectroscopy (SERS); c) quantifying a subunit composition of the polymer; and d) identifying the polymer.

[0017] In some embodiments, the method further comprises detecting a normalization reference molecule using SERS. In some embodiments, the method further comprises comparing the normalization reference molecule and the polymer. In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the normalization reference molecule is graphene. In some embodiments, the normalization reference molecule is coupled to the polymer. In some embodiments, the polymer does not have a label.

[0018] In some embodiments, the quantifying comprises using a trained machine learning algorithm.

[0019] In some embodiments, the detecting is performed at single molecule resolution.

[0020] In some embodiments, the method further comprises cleaving a terminal subunit from the polymer to generate a n-1 polymer, wherein the n-1 polymer comprises a new terminal subunit. In some embodiments, the method further comprises detecting the n-1 polymer using surface-enhanced Raman spectroscopy (SERS).

[0021] In some embodiments, the polymer is a polypeptide. In some embodiments, the terminal subunit is an N-terminal amino acid. In some embodiments, the terminalsubunit is a C-terminal amino acid. In some embodiments, the method further comprises determining a sequence of amino acids of the polypeptide. In some embodiments, the polypeptide comprises a modified residue with a modification. In some embodiments, the modification is noncovalently bound to the modified residue. In some embodiments, the modification is covalently bound to the modified residue. In some embodiments, the modification is a ligand. In some embodiments, the modification is a post-translational modification. In some embodiments, the identifying comprises identifying the modification. In some embodiments, the identifying comprises identifying the modified residue. In some embodiments, the polypeptide comprises an additional modified residue with an additional modification. In some embodiments, the modification and the additional modification are different. In some embodiments, the modification and the additional modification are the same. In some embodiments, the identifying comprises identifying the additional modification. In some embodiments, the identifying comprises identifying the additional modified residue.

[0022] In some embodiments, the polymer is a nucleic acid.

[0023] In some embodiments, the surface is a functionalized surface comprising a plurality of features. In some embodiments, the plurality of features comprises pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof. In some embodiments, the plurality of features has a density of at least 1 million features per cm2on the surface. In some embodiments, features of the plurality of features are uniform or stochastic.

[0024] In another aspect disclosed herein is a method for detecting an interaction between a first protein and a second protein, the method comprising: a) coupling a cross-linked sample to a surface, wherein the cross-linked sample comprises the first protein and the second protein; and b) detecting the first protein and the second protein from the cross-linked sample using surface-enhanced Raman spectroscopy (SERS).

[0025] In some embodiments, the method further comprises purifying the first protein from a cross-linked biological sample to generate the cross-linked sample.

[0026] In some embodiments, the cross-linked biological sample comprises a cell. In some embodiments, the cross-linked biological sample comprises cell secretions.

[0027] In some embodiments, the method further comprises identifying the second protein, wherein the identifying has an accuracy of at least 75%.

[0028] In some embodiments, the identifying comprises a trained machine learning algorithm. In some embodiments, the trained machine learning algorithm does not comprise comparing data obtained from detecting the first protein or data obtained from detecting the second protein to data associated with a reference molecule. In some embodiments, the trained machine learning algorithm does not comprise comparing a first spectrum of the first protein or a second spectrum of the second protein to a reference spectrum of the reference molecule. In some embodiments, the reference molecule is a reference protein or a reference amino acid.

[0029] In some embodiments, the detecting is performed at single molecule resolution.

[0030] In some embodiments, the method further comprises coupling a sample to the surface, wherein the sample is not cross-linked and comprises the first protein. In some embodiments, the sample does not comprise the second protein. In some embodiments, the method further comprises purifying the first protein from a biological sample to generate the sample. In some embodiments, the method further comprises detecting the first protein from the sample using SERS. In some embodiments, the detecting the first protein from the sample using SERS is performed at single molecule resolution.

[0031] In some embodiments, the method further comprises identifying the second protein based at least in part on the detecting the first protein and the second protein from the cross-linked sample using SERS and the detecting the first protein from the sample using SERS.

[0032] In some embodiments, the surface is a functionalized surface comprising a plurality of features. In some embodiments, the plurality of features comprises pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof. In some embodiments, the plurality of features has a density of at least 1 million features per cm2on the surface. In some embodiments, features of the plurality of features are uniform or stochastic.

[0033] In some embodiments, the method further comprises detecting a normalization reference molecule using SERS. In some embodiments, the method further comprises comparing the normalization reference molecule and the first protein. In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the normalization reference molecule is graphene.In some embodiments, the normalization reference molecule is coupled to the first protein. In some embodiments, the first protein does not have a label.

[0034] In another aspect disclosed herein is a method comprising: a) coupling a polymer to a surface, wherein the polymer comprises subunits, wherein the polymer is a polypeptide; b) sequencing the polymer; c) quantifying a subunit composition of the polymer with an accuracy of at least 75%; and d) identifying the polymer.

[0035] In some embodiments, b) comprises sequencing the polymer with aid of SERS. In some embodiments, c) comprises quantifying the subunit composition with aid of SERS. In some embodiments, the method further comprises detecting a normalization reference molecule using SERS. In some embodiments, the method further comprises comparing the normalization reference molecule and the polymer. In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the normalization reference molecule is graphene. In some embodiments, the normalization reference molecule is coupled to the polymer. In some embodiments, the polymer does not have a label.

[0036] In some embodiments, the quantifying comprises using a trained machine learning algorithm.

[0037] In some embodiments, the detecting is performed at single molecule resolution.

[0038] In some embodiments, the method further comprises cleaving a terminal subunit from the polymer to generate a n-1 polymer, wherein the n-1 polymer comprises a new terminal subunit. In some embodiments, the method further comprises detecting the n-1 polymer using surface-enhanced Raman spectroscopy (SERS). In some embodiments, the terminal subunit is an N-terminal. In some embodiments, the terminal subunit is a C-terminal amino acid. In some embodiments, the polypeptide comprises a modified residue with a modification. In some embodiments, the modification is noncovalently bound to the modified residue. In some embodiments, the modification is covalently bound to the modified residue. In some embodiments, the modification is a ligand. In some embodiments, the modification is a post-translational modification. In some embodiments, the identifying comprises identifying the modification. In some embodiments, the identifying comprises identifying the modified residue. In some embodiments, the polypeptide comprises an additional modified residue with an additional modification. In some embodiments, the modification and the additional modification are different.In some embodiments, the modification and the additional modification are the same. In some embodiments, the identifying comprises identifying the additional modification. In some embodiments, the identifying comprises identifying the additional modified residue.

[0039] In some embodiments, the surface is a functionalized surface comprising a plurality of features. In some embodiments, the plurality of features comprises pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof. In some embodiments, the plurality of features has a density of at least 1 million features per cm2on the surface. In some embodiments, features of the plurality of features are uniform or stochastic.

[0040] The present disclosure provides systems and methods of single molecule peptide sequencing. Embodiments include an aminopeptidase-pore complex paired with a biosensor, together comprised of (a) an aminopeptidase, (b) a channel such as a protein based pore which penetrates and traverses a scaffold such as a lipid bilayer and (c) a binding and detection domain that includes a high-affinity amino acid binder, such as a singular, universally binding or plurality of amino acid residuespecific molecularly imprinted polymers (MIPs) overlayed on a biosensor.

[0041] In embodiments, the peptide sequencing methods include contacting a single peptide molecule with an aminopeptidase-pore complex in a lipid bilayer or other pore scaffold. In aspects, the peptide molecule is degraded by an aminopeptidase that removes an amino acid from the amino-terminal portion of the peptide molecule. In aspects, the method also includes detecting a signal indicative of association of the cleaved amino acid with a specific molecularly imprinted polymer (MIP).

[0042] In embodiments, the methods also include detecting a sequential series of signals (e.g., pulses or spectra) after association of the terminal amino acid molecules with a high-affinity binder, thereby obtaining sequence information about the peptide molecule. In embodiments, the amino acid sequence of some, most or all of the single peptide molecule is determined, as well as which amino acids have PTMs and the identity of those PTMs.

[0043] Embodiments also include a method of sequencing a peptide. The method can include (a) obtaining an aminopeptidase-pore complex embedded in a scaffold such as a lipid bilayer, (b) contacting the first amino-terminal (N-terminal) amino acid of a peptide with the aminopeptidase portion of the aminopeptidase-pore complex, thereby cleaving the first N-terminal amino acid from the peptide and liberating thefirst N-terminal amino acid from the peptide, (c) permitting the liberated first N-terminal amino acid to pass through the channel and into the detection domain, and binding a MIP specific to amino acid(s); wherein after the first N-terminal amino acid contacts the MIP the optical signal of the amino acid is detected, (d) registering and recording the first optical signal, thereby identifying the first N-terminal amino acid in the peptide. The obtaining in (a), contacting in (b), permitting in (c), and / or registering in (d) can be repeated for a second amino acid (and subsequent amino acids) until each amino acid of the peptide is individually cleaved and recognized.

[0044] In aspects, the system and methods described herein uses Surface-enhanced Raman spectroscopy or surface-enhanced Raman scattering (SERS).

[0045] In aspects, the system does not include a nanopore component (e.g., to immobilize the sample peptide and provide single molecule architecture).

[0046] Embodiments also include a method of sequencing a peptide that includes (a) attaching a peptide to a functionalized surface, (b) measuring a first composition and abundance of amino acids in the peptide, (c) liberating a single or multiple N- or C-terminal amino acid(s) from the peptide, (d) measuring a second composition and abundance of amino acids in the peptide and (e) comparing the first composition and abundance and the second composition and abundance to identify the liberated N-terminal amino acid(s). The method can be repeated to determine the complete peptide sequence.

[0047] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.INCORPORATION BY REFERENCE

[0048] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference.BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings of which:

[0050] FIG. 1 depicts an example single protein detection channel according to embodiments.

[0051] FIG. 2 is a flow chart that depicts an example workflow of using the protein detection channel to sequence a peptide.

[0052] FIG. 3 shows an example method of sequencing single polypeptides using Raman spectroscopy.

[0053] FIG. 4 shows an example method of sequencing single polypeptides.

[0054] FIG. 5 is a flowchart that shows an example workflow in a method of analysis of protein-protein interaction.

[0055] FIG. 6 is a flowchart that shows an example workflow in a method of analysis of protein-protein interaction (without a cross-linker).

[0056] FIG. 7 shows an example computer system that is programmed or otherwise configured to implement methods provided herein.DETAILED DESCRIPTION

[0057] While various embodiments of the invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

[0058] Recognized herein is a need for improved techniques relating to macromolecule sequencing and / or analysis, with applications to protein sequencing and / or analysis, as well as to products, methods, and kits for accomplishing the same. Recognized herein is a need for proteomics technology that is single molecule, highly-parallelized, accurate, sensitive, and high-throughput. The present disclosure fulfills these and other needs.Definitions

[0059] Throughout this application, various embodiments may be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1 , 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0060] As used in the specification and claims, the singular forms “a”, “an” and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a sample” includes a plurality of samples, including mixtures thereof.

[0061] As used herein, the term “about” a number refers to that number plus or minus 10% of that number. The term “about” a range refers to that range minus 10% of its lowest value and plus 10% of its greatest value.

[0062] Whenever the term “at least,” “greater than,” or “greater than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “at least,” “greater than” or “greater than or equal to” applies to each of the numerical values in that series of numerical values. For example, greater than or equal to 1 , 2, or 3 is equivalent to greater than or equal to 1 , greater than or equal to 2, or greater than or equal to 3.

[0063] Whenever the term “no more than,” “less than,” or “less than or equal to” precedes the first numerical value in a series of two or more numerical values, the term “no more than,” “less than,” or “less than or equal to” applies to each of the numerical values in that series of numerical values. For example, less than or equal to 3, 2, or 1 is equivalent to less than or equal to 3, less than or equal to 2, or less than or equal to 1.

[0064] The term “polypeptide” can refer to a protein. A polypeptide can be a dipeptide or a tripeptide. A polypeptide may be a chain of 2 or more amino acids, a chain of 3 or more amino acids, a chain of 3 or more amino acids, a chain of 4 or more amino acids, a chain of 5 or more amino acids, a chain of 6 or more amino acids, a chain of 7 or more amino acids, a chain of 8 or more amino acids, a chain of 9 or more amino acids, a chain of 10 or more amino acids, a chain of 20 or more amino acids, a chain of 30or more amino acids, a chain of 40 or more amino acids, a chain of 50 or more amino acids, a chain of 60 or more amino acids, a chain of 70 or more amino acids, a chain of 80 or more amino acids, a chain of 90 or more amino acids, a chain of 100 or more amino acids, a chain of 150 or more amino acids, a chain of 200 or more amino acids, a chain of 250 or more amino acids, a chain of 300 or more amino acids, a chain of 350 or more amino acids, a chain of 400 or more amino acids, a chain of 450 or more amino acids, a chain of 500 or more amino acids, a chain of 550 or more amino acids, a chain of 600 or more amino acids, a chain of 650 or more amino acids, a chain of 700 or more amino acids, a chain of 750 or more amino acids, a chain of 800 or more amino acids, a chain of 850 or more amino acids, a chain of 900 or more amino acids, a chain of 950 or more amino acids, or a chain of 1000 or more amino acids. The polypeptide can refer to a protein that is denatured. The polypeptide can refer to a protein that is folded. The protein can have one or more secondary structures (e.g., alpha helices and beta sheets) and / or one or more disordered domains (e.g., intrinsically disordered regions).

[0065] The term “protease” can refer to an enzyme that catalyzes proteolysis, breaking down proteins into smaller polypeptides or single amino acids, and spurring the formation of new protein products. They do this by cleaving the peptide bonds within proteins by hydrolysis, a reaction where water breaks bonds. Proteases are involved in numerous biological pathways, including digestion of ingested proteins, protein catabolism (breakdown of old proteins) and cell signaling.

[0066] The term “aminopeptidase” can refer to an enzyme that removes amino acids from the N-terminus of peptides or proteins. They are found in many subcellular organelles, in the cytoplasm, and as membrane components.Aminopeptidases are involved in a variety of cellular processes, including protein synthesis and degradation, and metabolism.

[0067] The term “molecularly imprinted polymer” or “MIP” can refer to a polymer that has been processed using the molecular imprinting technique which leaves cavities in the polymer matrix with an affinity for a chosen "template" molecule. The process can involve initiating the polymerization of monomers in the presence of a template molecule that is extracted afterwards, leaving behind complementary cavities. These polymers have affinity for the original molecule and have been used in applications such as chemical separations, catalysis, or molecular sensors.

[0068] The term “spectral signal” can refer to a plot of a material’s Raman signal intensity versus wavelength (i.e. Raman shift) that provide a spectral fingerprint for identifying molecules by revealing their vibrational modes. In laboratory settings, Raman spectroscopy can be used to analyze and quantify substances.

[0069] The term "administration" can refer to the introduction of an amount of a predetermined substance into a patient by a certain suitable method. The composition disclosed herein may be administered via any of the common routes, as long as it is able to reach a desired tissue, for example, but is not limited to, intraperitoneal, intravenous, intramuscular, subcutaneous, intradermal, oral, topical, intranasal, intrapulmonary, or intrarectal administration. However, since peptides are digested upon oral administration, active ingredients of a composition for oral administration may be coated or formulated for protection against degradation in the stomach.

[0070] The term “medicament,” “active agent” or “active ingredient” can refer to a substance, compound, or molecule, which is biologically active or otherwise, that induces a biological or physiological effect on a subject to which it is administered. In other words, “active agent” or “active ingredient” can refer to a component or components of a composition to which the whole or part of the effect of the composition is attributed. An active agent can be a primary active agent, or in other words, the component(s) of a composition to which the whole or part of the effect of the composition is attributed. An active agent can be a secondary agent, or in other words, the component(s) of a composition to which an additional part and / or other effect of the composition is attributed.

[0071] The term “pharmaceutical composition” can include the combination of an active agent with a carrier, inert or active, in a sterile composition suitable for diagnostic or therapeutic use in vitro, in vivo or ex vivo. In one aspect, the pharmaceutical composition is substantially free of endotoxins or is non-toxic to recipients at the dosage or concentration employed.

[0072] In an embodiment, a “subject” of diagnosis or treatment is, without limitation, a prokaryotic or a eukaryotic cell, a tissue culture, a tissue, or an animal, e.g., a mammal, including a human. Non-human animals subject to diagnosis or treatment include, for example, without limitation, a simian, a murine, a canine, a leporid, such as a rabbit, livestock, sport animals, and pets.

[0073] The terms “treating,” “treatment” and the like can be used herein, without limitation, to refer to obtaining a desired pharmacologic and / or physiologic effect. The effect may be prophylactic in terms of completely or partially preventing a disorder or sign or symptom thereof, and / or may be therapeutic in terms of amelioration of the symptoms of the disease or infection, or a partial or complete cure for a disorder and / or adverse effect attributable to the disorder.

[0074] The term “prognosis” can refer to the likely outcome or course of a disease and / or the chance of recovery or recurrence. This contrasts with a “diagnosis” which can refer to identifying an ailment or disease, such as from examining a subject.

[0075] The term “linker” can refer to a functional group that covalently bonds two or more moieties in a compound or material. For example, the linker can serve to covalently bond a first moiety to a second moiety (e.g., an active agent).

[0076] The term "biomarker" can refer to a DNA, RNA, protein, carbohydrate, or glycolipid-based molecular marker, the expression or presence of which in a sample can be detected by methods (e.g., methods disclosed herein) and is predictive or prognostic of the effective responsiveness or sensitivity of a mammalian subject with an ailment. Biomarkers may be present in a test sample but absent in a control sample, absent in a test sample but present in a control sample, or the amount or of biomarker can differ between a test sample and a control sample. For example, protein biomarkers can be present in such a sample, but not in a control sample, or certain biomarkers are seropositive in the sample, but seronegative in a control sample. Also, expression of such a biomarker may be determined to be higher than that observed from a control sample. The terms "marker" and "biomarker" can be used herein interchangeably.

[0077] The amount of the biomarker can be measured in a test sample and compared to the “normal control level,” utilizing techniques such as reference limits, discrimination limits, or risk defining thresholds to define cutoff points and abnormal values for an ailment. The normal control level can comprise the level of one or more biomarkers or combined biomarker indices that can be found in a subject not suffering from the ailment. Such normal control level and cutoff points can vary based on whether a biomarker is used alone or in a formula combining with other biomarkers into an index. Alternatively, the normal control level can be a database of biomarker patterns from previously tested subjects who did not experience the ailment over a clinically relevant time.

[0078] Tests to measure biomarkers and biomarker panels can be implemented on a variety of diagnostic test systems. Diagnostic test systems are apparatuses that can include compositions, systems, and / or methods for obtaining test results from biological samples. Examples of such compositions, systems, and / or methods include modules that automate the testing (e.g., biochemical, immunological, nucleic acid detection assays). Some diagnostic test systems are designed to handle multiple biological samples and can be programmed to run the same or different tests on each sample. Diagnostic test systems can include compositions, systems, and / or methods for collecting, storing, and / or tracking test results for each sample, such as in a data structure or database. Examples include physical and electronic data storage devices (e.g., hard drives, flash memory, magnetic tape, paper printouts). Diagnostic test systems can also include compositions, systems, and / or methods for reporting test results. Examples of compositions, systems, and / or methods for reporting can include visible display, a link to a data structure or database, or a printer. The compositions, systems, and / or methods for reporting can be a data link to send test results to an external device, such as a data structure, data base, visual display, or printer.

[0079] The term "detecting" or "determining" with respect to a biomarker value can includes the use of both an instrument to observe and record a signal corresponding to a biomarker value and material(s) to generate that signal. In various embodiments, the biomarker value is detected using any suitable method, including fluorescence, chemiluminescence, surface plasmon resonance, surface acoustic waves, mass spectrometry, infrared spectroscopy, Raman spectroscopy, atomic force microscopy, scanning tunneling microscopy, electrochemical detection methods, nuclear magnetic resonance, quantum dots, and the like.

[0080] The term “amino acid” can refer to either natural and / or unnatural, post-translationally modified (PTM) or synthetic amino acids, including both the D and L optical isomers, amino acid analogs and peptidomimetics. In some aspects, the term “amino acid” can refer to monomeric amino acids. Non-limiting examples of naturally existing amino acids or derivative forms that are used in various embodiments include the following (with three letter, and one letter code abbreviations included): Alanine (Ala, A); Arginine (Arg, R); Asparagine (Asn, N); Aspartic acid (Asp, D); Cysteine (Cys, C); Glutamic acid (Glu, E); Glutamine (Gin, Q); Glycine (Gly, G); Histidine (His, H); Isoleucine (lie, I); Leucine (Leu, L); Lysine (Lys, K); Methionine(Met, M); Phenylalanine (Phe, F); Proline (Pro, P); Selenocysteine; Serine (Ser, S); Threonine (Thr, T); Tryptophan (Trp, W); Tyrosine (Tyr, Y); Valine (Vai, V); Citrulline; Cystine; Gama aminobutyric acid (GABA); Ornithine; Theanine; Betaine; Carnitine; Carnosine; Creatine; Hydroxyproline; Hydroxytryptophan; N-acetyl cysteine; S-Adenosyl methionine (SAM-e); Taurine; and Tyramine. Post-translational modifications may include reversible and irreversible PTMs such as phosphorylation, ubiquitination, glycosylation, SUMOylation, methylation, acetylation, citrullination, lipidation, oxydation or any other type of PTM on amino acids.

[0081] The term “surface-enhanced Raman spectroscopy” or “surface-enhanced Raman scattering” or “SERS” can refer to a surface-sensitive technique that enhances Raman scattering from molecules applied to rough or patterned metal surfaces or by nanostructures such as plasmonic-magnetic silica nanotubes. The enhancement factor can be as much as 1O10to 1014, with enhancement of 1O10sufficient to detect single molecules.

[0082] The term “Raman spectroscopy tags” can refer to chemical bonds that are attached to molecules of interest to help identify them in Raman spectra. Cell-silent Raman tags have a unique vibrational frequency in the Raman spectrum's cell-silent region, which is between 1800-2800 cm-1. This region is free of bands from biological molecules, so the tags can help improve the signal-to-noise ratio and chemical contrast of Raman spectra. Tags with functional groups like alkyne, nitrile, or a carbon-deuterium bond can be used as cell-silent Raman tags. Gap-enhanced Raman tags (GERTs) are probes for surface-enhanced Raman scattering (SERS) spectroscopy. They are made with plasmonic metals like gold or silver, and the gaps between the metal core-shell structures create strong electromagnetic fields that enhance SERS responses. Alkyne tags are considered the most suitable for Raman imaging.

[0083] The term “Surface Plasmon Resonance” or “SPR” can refer to an optical technique that measures molecular interactions in real time. It utilizes a phenomenon that occurs where electrons in a thin metal sheet become excited by light that is directed to the sheet with a particular angle of incidence, and then travel parallel to the sheet.

[0084] The term “graphene” can refer to a carbon allotrope comprised of a single layer of atoms arranged in a planar nanostructure.

[0085] The term “crosslinking” can refer to a process of chemically joining two or more molecules by a covalent bond. Selectable targets for crosslinking on proteins include: primary amines, carboxyls, sulfhydryls and carbonyls. Primary amines (-NH2): This group exists at the N-terminus of each polypeptide chain and in the side chain of lysine (Lys, K) residues. Carboxyls (-COOH): This group exists at the C-terminus of each polypeptide chain and in the side chains of aspartic acid (Asp, D) and glutamic acid (Glu, E). Sulfhydryls (-SH): This group exists in the side chain of cysteine (Cys, C). Often, as part of a protein's secondary or tertiary structure, cysteines are joined together between their side chains via disulfide bonds (-S-S-). Carbonyls (-CHO): These aldehyde groups can be created by oxidizing carbohydrate groups in glycoproteins.

[0086] The term "labeling" can refer to any form of crosslinking or modification whose purpose is to attach a chemical group (e.g., a fluorescent molecule) to aid in detection or analysis of a molecule. The entire set of crosslinking and modification methods for use with proteins and other biomolecules in biological research is often called "bioconjugation."

[0087] The term “SpyTag / SpyCatcher” can refer to a system for irreversible conjugation of recombinant proteins. The peptide SpyTag (13 amino acids) spontaneously reacts with the protein SpyCatcher (12.3 kDa) to form an intermolecular isopeptide bond between the pair. DNA sequence encoding either SpyTag or SpyCatcher can be recombinantly introduced into the DNA sequence encoding a protein of interest, forming a fusion protein. These fusion proteins can be covalently linked when mixed in a reaction through the SpyTag / SpyCatcher system. Using the Tag / Catcher pair, bioconjugation can be achieved between two recombinant proteins that may otherwise be restrictive or impossible with direct genetic fusion between the two proteins. For example, issues regarding protein folding, suboptimal expression host, and specialized post-translational modifications can be alleviated by separating the production of the proteins with the modularity of the Tag / Catcher system.

[0088] Following SpyTag / SpyCatcher, the fully orthogonal pair SnoopTag / SnoopCatcher was developed from the RrgA protein of Streptococcus pneumoniae that has no cross-reactivity with SpyTag / SpyCatcher. Note that SnoopTag / SnoopCatcher forms an isopeptide bond between a Lys-Asn instead of Lys-Asp found in SpyTag / SpyCatcher. The same domain from RrgA can be split in adifferent way to that used to create SnoopTag / SnoopCatcher, to generate a new pair called DogTag / DogCatcher. Unlike SpyTag and SnoopTag which have extended structures, the region of RrgA used to create DogTag forms a (3-hairpin and so predisposed for insertion into protein loops. This ability has been exploited to fluorescently label an internal loop of the mammalian TRPC5 membrane channel protein which cannot be modified at the protein termini, without impacting on the channel properties of TRPC5. DogTag has been successfully coupled to DogCatcher when inserted into soluble proteins (superfolder GFP, HaloTag, and Gre2p).

[0089] As applicable, the terms "about" or "generally", as used herein in the specification and appended claims, and unless otherwise indicated, can refer to a margin of + / - 20%. Also, as applicable, the term "substantially" as used herein in the specification and appended claims, unless otherwise indicated, can refer to a margin of + / - 10%. It is to be appreciated that not all uses of the above terms are quantifiable such that the referenced ranges can be applied.

[0090] Many useful compounds and the like can be found in Remington’s Pharmaceutical Sciences (13thEd), Mack Publishing Company, Easton, PA (which is incorporated by reference herein in its entirety) — a reference for various types of administration. As used herein, the term “formulation(s)” can refer to a combination of at least one active ingredient with one or more other ingredient, also commonly referred to as excipients, which may be independently active or inactive. The term “formulation” may or may not refer to a pharmaceutically acceptable composition for administration to humans or animals and may include compositions that are useful intermediates for storage or research purposes.

[0091] As the patients and subjects of the methods disclosed herein are, in addition to humans, veterinary subjects, formulations suitable for these subjects are also appropriate. Such subjects include livestock and pets as well as sports animals such as horses, greyhounds, and the like.DETAILED DESCRIPTION

[0092] Embodiments of the compositions, systems, and / or methods disclosed herein include an aminopeptidase-pore complex that includes (a) an aminopeptidase, (b) a protein-based pore which forms a channel that penetrates and traverses a lipid bilayer and (c) a binding and detection domain that includes a plurality of amino acid residue-specific molecularly imprinted polymers (MIPs). The complex can be used to determine the sequence of a sample peptide.

[0093] In aspects, the system includes one or more parallel channels that have a modified aminopeptidase-pore complex embedded in a scaffold (e.g., lipid bilayer), which will capture and cleave sample proteins into their constituent amino acids, releasing free amino acids into the detection channel. In aspects, an osmotic gradient provides a direction of flow, and as the amino acids flow down a confined channel they can be bound and sequestered by residue-specific molecularly imprinted polymers (MIPs) and subsequently detected by Raman spectroscopy. This allows determination of the sequence of each protein fed into the channel.

[0094] FIG. 1 is a schematic of a single protein detection channel according to embodiments. The aminopeptidase-pore complex is embedded in a lipid bilayer and separates the “cis” region from the “trans” region. A sample protein is depicted on the “cis” side of the protein channel.

[0095] Individual amino acids are cleaved from the N-terminal portion of the protein. Each amino acid can thereafter be bound and immobilized by amino acid specific molecularly imprinted polymers (“MIPs”). In aspects, an optical or electrochemical signal is measured after an amino acid binds a complementary MIP. The optical or electrochemical signal (unique for each amino acid / MIP) is detected and recorded. The process is repeated for each amino acid in the peptide. In aspects, a polymer, high-affinity chemical coating, or other binder (i.e. , non-MIP) is used.

[0096] FIG. 2 is a flow chart 100 that depicts the workflow of determining the sequence of a peptide using a protein detection channel according to embodiments. The 105 entails assembly of the aminopeptidase-pore complex into a lipid bilayer.

[0097] Next, a peptide is added which contacts the complex 110. Specifically, a first amino-terminal (N-terminal) amino acid of the peptide binds with the aminopeptidase portion of the aminopeptidase-pore complex. The aminopeptidase then cleaves the first N-terminal amino acid from the peptide 115 and liberates the first N-terminal amino acid from the peptide. The liberated N-terminal amino acid passes through the channel and into a detection domain. There, it contacts an MIP that may or may not be specific to the first N-terminal amino acid 120.

[0098] When the first N-terminal amino acid binds the MIP specific to the first N-terminal amino acid, an optical or electrochemical signal is elicited, unique to each amino acid 125. The signal can be detected and recorded, thereby identifying the first N-terminal amino acid in the peptide. The process can be repeated for a secondamino acid (and subsequent amino acids) until each amino acid of the peptide is individually cleaved and recognized.

[0099] FIG. 3 also depicts a single protein detection channel according to embodiments. Cleaved amino acids are in the “trans” region of the pore complex (e.g. the detection channel). Individual amino acids can be identified using Raman spectroscopy. In aspects, gold surface enhancement is used for sensitivity.Lipid Bilayer

[0100] Lipid membranes regulate the flow of nutrients and communication signaling between cells and protect the sub-cellular structures. Recent attempts to fabricate artificial systems using nanostructures that mimic the physiological properties of natural lipid bilayer membranes (LBM) fused with transmembrane proteins have helped demonstrate the importance of temperature, pH, ionic strength, adsorption behavior, conformational reorientation and surface density in cellular membranes which all affect the incorporation of proteins on solid surfaces.

[0101] As described above, the system can use a lipid bilayer to separate the cis region from the trans region. In aspects, an aminopeptidase-pore complex is embedded in the lipid bilayer.Aminopeptidase-pore complex

[0102] In aspects, the aminopeptidase-pore complex is made of Streptomyces griseus Aminopeptidase or “SGAP” (i.e. , the aminopeptidase) and alpha-hemolysin (aHL, the pore). The complex may be expressed as a fusion protein with a short linker sequence or utilize a conjugation system such as DogTag / DogCatcher. FIG. 1 shows SGAP on the cis side of the bilayer. Alternatively, it can up be on the trans side to prevent stray amino acids remaining on the cis side of the pore.

[0103] Although the methods described herein use an aminopeptidase, Applicants proposed that the methods disclosed herein can use a protease or chemical or mechanical degradation. For example, the peptide can be cut into di- or tri-peptides etc. and still detected by SERS.Molecularly Imprinted Polymers (MIPs)

[0104] Molecularly imprinted polymers (MIPs) are synthetic materials that can selectively bind to a specific molecule, similar to the way antibodies specifically bind to antigens in biological systems. MIPs are made using a technique called molecular imprinting, which creates cavities in the polymer that are shaped to bind specific template molecules.

[0105] Molecularly imprinted polymers (MIPs) are synthetic receptors that mimic the binding of natural antibodies. In other words, MIPs can selectively bind to the target molecule and qualify as bio-inspired synthetic materials. Today, MIPs are being developed further for biological applications. High cost and time-consuming techniques are compelling factors for the field of biochemistry, biomedicine and biotechnology (3B), and there is an urgent need for an alternative, cheap, easy to produce, fast, effective and high-affinity method in these fields. MIPs stand out as a promising way for this purpose. MIPs have superiorities such as specific recognition specificity, excellent sensitivity, selectivity, and reusability.

[0106] In aspects, an electrochemical or optical signal is elicited from an amino acid bound to a complementary MIP overlayed on the detection surface.

[0107] In embodiments, association of one or more amino acids with the detection surface results in a characteristic signal that is unique for each amino acid exposed or cleaved from the terminus. In embodiments, a signal with a characteristic pattern results from an individual association event between a detection surface and an amino acid exposed or cleaved from the terminus. In embodiments, the characteristic pattern corresponds to a reversible or irreversible binding interaction between the detection surface and amino acid exposed or cleaved from the terminus of the single polypeptide molecule. In embodiments, the characteristic pattern is indicative of the amino acid exposed or cleaved from the terminus of the single peptide molecule and an amino acid at a contiguous position (e.g., amino acids of the same type or different types).

[0108] Although the methods described herein use multiple MIPs, Applicants propose that a single MIP may have affinity for more than one amino acid. In this regard, it is possible to use a single “universal” MIP that can bind any amino acid with the compositions, systems, and / or methods disclosed herein.Single Molecule Peptide Sequencing without Channels or Nanopores

[0109] In other embodiments, an alternative method is used to sequence a peptide. This method does not require a nanopore or channel component. An example of a method disclosed herein is depicted in FIG. 4.

[0110] In Fig. 4, peptides are attached to a functionalized surface modified with gold nanofeatures and covered in a reference chemical, such as graphene. The reference can act as a known marker against which to normalize the amino acid composition of the peptide (e.g., to quantify the number of each amino acid). SERSalone may detect the composition (e.g., the constituent amino acids in the peptide) but may not detect the quantity of each constituent amino acid. The reference chemical can also be a tag that is added to the protein before attachment to the functionalized surface. After attachment, the first cycle can begin. In the first cycle (cycle 1), surface-enhanced Raman spectroscopy (SERS) is used to measure the composition and relative abundance of amino acids in each peptide at each SERS hotspot. In this example (FIG. 4), the spectra indicate the following combination of amino acids: CDRIAL. If multiple C’s, D’s, R’s, etc. are present, the reference chemical can be used to determine the relative abundance of each amino acid in the peptide.

[0111] Next, FIG.4 depicts that a single or multiple terminal amino acid(s) is cleaved (e.g., via peptidase such as an aminopeptidase or a carboxypeptidase, a protease or Edman degradation). In the next cycle (cycle 2), SERS is again used to measure the composition and relative abundance of amino acids in the peptide. In this example, the spectra indicate the following combination of amino acids: RCAID. Again, if multiple C’s, D’s, R’s, etc. are present, the reference chemical can be used to determine the relative abundance of each amino acid in the peptide.

[0112] Next, FIG.4 depicts that the spectra from cycle 2 are compared to that of cycle 1 to determine the amino acid (or amino acids) that was cleaved off the terminus (e.g., L). The cycles continue with iterative rounds of amino acid cleavage, SERS measurement, and amino acid analysis. A third round is also depicted in FIG.4. The method can be repeated for each amino acid in the peptide to determine the complete peptide sequence of any peptide. Comparison to the reference chemical in order to deconvolute the relative abundance of each amino acid can allow analysis of complex and long polypeptides and quantification of amino acid compositions (e.g., the number of each amino acid).

[0113] FIG. 4 also depicts an example comparison of the spectra obtained after each cycle. In some aspects, a trained ML algorithm is used to deconvolute the signals to determine the relative composition and abundance of amino acids.Thereafter, single molecule peptide sequencing can entail calculating the amino acid difference between each cycle. Proof of the presence of only a single peptide molecule may or may not require a control analyte (that is not an amino acid or composed of amino acids but may be a wide array of other biological or non-biological chemicals with distinct and well-characterized Raman spectra) thatattaches to the functionalized surface and acts as a control (e.g., graphene). This control can occupy hotspots alone as well as coincide with peptides. In some embodiments, other hotspots can be empty. The ratio of empty vs. control analyte occupied vs. control analyte and peptide occupied hotspots can act to prove the presence of peptide sequencing at single molecule resolution.

[0114] In some aspects disclosed herein is a method comprising coupling a biomolecule to a surface. In some embodiments, the method further comprises detecting the biomolecule using surface-enhanced Raman spectroscopy (SERS). In some embodiments, the method further comprises identifying the biomolecule.Identification of the biomolecule can be based at least in part on the detecting. In some embodiments, identification of the biomolecule has an accuracy of at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. In some embodiments, identifying the biomolecule has an accuracy of at least 75%. In some embodiments, the detecting is performed at single molecule resolution.

[0115] In some embodiments, the identifying comprises using a trained machine learning algorithm. The trained machine learning algorithm can comprise supervised learning or unsupervised learning. Non-limiting examples of a trained machine learning algorithm include a neural network, a support vector machine, t-SNE, PCADeep, transformer-based architectures, or a combination thereof. In some embodiments, the trained machine learning algorithm does not comprise comparing the biomolecule to a database comprising data associated with a reference molecule (e.g., a reference protein or a reference amino acid). In some embodiments, the data associated with the reference molecule comprises genomic data or transcriptom ic data. In some embodiments, the trained machine learning algorithm does not comprise comparing a spectrum of the biomolecule (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule. In some embodiments, the trained machine learning algorithm comprise comparing a spectrum of the biomolecule (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule.

[0116] In some embodiments, the biomolecule is a nucleic acid or a polypeptide. The nucleic acid can comprise deoxyribonucleic acids, ribonucleic acids, or a combination thereof. In some examples, the nucleic acid is DNA or RNA.

[0117] In some embodiments, the biomolecule (e.g., the polypeptide) can be modified. For example, the polypeptide can comprise a modified residue with a modification. In some embodiments, the identifying comprises identifying the modified residue. In some embodiments, the identifying comprises identifying the modification. The modification can be covalently or noncovalently bound to the modified residue. The modification can be a ligand (e.g., a ligand that binds to the polypeptide at the modified residue). The ligand can be a small molecule (e.g., a chemical, a drug). The modification can also be a post-translational modification. Non-limiting examples of post-translational modifications include phosphorylation, acetylation, SUMOylation, ubiquitination, glycosylation, nitrosylation (e.g., S-nitrosylation), citrullination, deamidation, UFMylation, prenylation, myristoylation, palmitoylation (e.g., S-palmitoylation), tyrosine sulfation, formylation, carboxylation, methylation, neddylation, biotinylation, oxidation, or lipidation.

[0118] In some embodiments, the polypeptide comprises an additional modified residue. In some embodiments, the additional modified residue comprises an additional modification. The modification and the additional modification can be the same or different. In some embodiments, the additional modified residue and / or the additional modification is identified.

[0119] In some embodiments, the method further comprises detecting a normalization reference molecule (e.g., graphene) using SERS. The normalization reference molecule can be a control or a reference described elsewhere herein. In some embodiments, the method further comprises comparing the normalization reference molecule and the biomolecule. Comparison (e.g., comparison of the spectra from detection using SERS) of the normalization reference molecule and the biomolecule can allow for quantification of the composition of the biomolecule (e.g., the number of each amino acid in a polypeptide). In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the biomolecule does not have a label. The surface can comprise a single layer of the normalization reference molecule. In some examples, the normalization reference molecule can be coupled to the biomolecule.

[0120] In another aspect disclose herein is a method comprising coupling an analyte to a surface. In some embodiments, the method further comprises detecting the analyte using surface-enhanced Raman spectroscopy (SERS). In some embodiments, the method further comprises identifying the analyte. Identification ofthe analyte can be based at least in part on the detecting and using a trained machine learning algorithm. The trained machine learning algorithm can comprise supervised learning or unsupervised learning. Non-limiting examples of a trained machine learning algorithm include a neural network, a support vector machine, t-SNE, PCADeep, transformer-based architectures, ora combination thereof. In some embodiments, the trained machine learning algorithm does not comprise comparing the analyte to a database comprising data associated with a reference molecule (e.g., a reference protein or a reference amino acid). In some embodiments, the data associated with the reference molecule comprises genomic data or transcriptom ic data. In some embodiments, the trained machine learning algorithm does not comprise comparing a spectrum of the analyte (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule. In some embodiments, the trained machine learning algorithm comprise comparing a spectrum of the biomolecule (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule. In some embodiments, the detecting is performed at single molecule resolution. In some embodiments, the analyte is a polymer. The polymer can be comprised of subunits.

[0121] In some embodiments, the method further comprises comparing the analyte data to data in a database. Comparing the analyte data to data in a database can comprise using an additional trained machine learning algorithm.

[0122] In some embodiments, the method further comprises detecting a normalization reference molecule (e.g., graphene) using SERS. The normalization reference molecule can be a control or a reference described elsewhere herein. In some embodiments, the method further comprises comparing the normalization reference molecule and the analyte. Comparison (e.g., comparison of the spectra from detection using SERS) of the normalization reference molecule and the analyte can allow for quantification of the composition of the analyte (e.g., the number of each subunit in a polymer). In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the analyte does not have a label. The surface can include a single layer of the normalization reference molecule. In some examples, the normalization reference molecule can be coupled to the analyte.

[0123] In some aspects disclosed herein is a method comprising coupling a polymer to a surface. The polymer can be comprised of subunits. In someembodiments, the method further comprises detecting the polymer using surface-enhanced Raman spectroscopy (SERS). The detecting can be performed at single molecule resolution. In some embodiments, the method further comprises identifying the polymer. Identification of the polymer can be based at least in part on the detecting. In some embodiments, identification of the polymer has an accuracy of at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. In some embodiments, identifying the polymer has an accuracy of at least 75%. Identification of the polymer can be based at least in part on the detecting and using a trained machine learning algorithm. The trained machine learning algorithm can comprise supervised learning or unsupervised learning. Non-limiting examples of a trained machine learning algorithm include a neural network, a support vector machine, t-SNE, PCADeep, transformer-based architectures, ora combination thereof. In some embodiments, the trained machine learning algorithm does not comprise comparing the polymer to a database comprising data associated with a reference molecule (e.g., a reference protein or a reference amino acid). In some embodiments, the data associated with the reference molecule comprises genomic data or transcriptom ic data. In some embodiments, the trained machine learning algorithm does not comprise comparing a spectrum of the polymer (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule. In some embodiments, the trained machine learning algorithm comprise comparing a spectrum of the biomolecule (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule.

[0124] In some aspects disclosed herein is a method comprising coupling a polymer to a surface. The polymer can be comprised of subunits. In some embodiments, the method further comprises detecting the polymer using surface-enhanced Raman spectroscopy (SERS). The detecting can be performed at single molecule resolution. In some embodiments, the method further comprises quantifying a subunit composition of the polymer. In some embodiments, the method further comprises identifying the polymer.

[0125] In some aspects disclosed herein is a method comprising coupling a polymer to a surface, wherein the polymer comprises subunits, wherein the polymer is a polypeptide. In some embodiments, the method further comprises sequencing the polymer. In some embodiments, the method further comprises quantifying asubunit composition of the polymer with an accuracy of at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. In some embodiments, the method comprises quantifying a subunit composition of the polymer with an accuracy of at least 75%. In some embodiments, the method further comprises identifying the polymer. In some embodiments, sequencing the polymer comprises sequencing the polymer with aid of SERS. In some embodiments, quantifying the subunit composition of the polymer comprises quantifying the subunit composition with aid of SERS.

[0126] For example, the polymer can be a polypeptide comprising subunits of amino acids. The method can comprise quantifying an amino acid composition of the polypeptide. Quantifying the amino acid composition can include identifying the number of each amino acid (e.g., arginine, histidine, lysine, aspartic acid, glutamic acid, serine, threonine, asparagine, glutamine, cysteine, glycine, proline, alanine, valine, isoleucine, leucine, methionine, phenylalanine, tyrosine, tryptophan, or variants thereof) in the polypeptide. A variant of an amino acid can comprise a post-translationally modified amino acid.

[0127] In some embodiments, the method further comprises detecting a normalization reference molecule (e.g., graphene) using SERS. The normalization reference molecule can be a control or a reference described elsewhere herein. In some embodiments, the method further comprises comparing the normalization reference molecule and the polymer. Comparison (e.g., comparison of the spectra from detection using SERS) of the normalization reference molecule and the polymer can allow for quantification of the composition of the polymer (e.g., the number of each amino acid in a polypeptide). In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the polymer does not have a label. The surface can comprise a single layer of the normalization reference molecule. In some examples, the normalization reference molecule can be coupled to the polymer.

[0128] In some embodiments, the quantifying comprises using a trained machine learning algorithm. The trained machine learning algorithm can comprise supervised learning or unsupervised learning. Non-limiting examples of a trained machine learning algorithm include a neural network, a support vector machine, t-SNE, PCADeep, transformer-based architectures, or a combination thereof. In someembodiments, the trained machine learning algorithm does not comprise comparing the polymer to a database comprising data associated with a reference molecule (e.g., a reference protein or a reference amino acid). In some embodiments, the data associated with the reference molecule comprises genomic data or transcriptom ic data. In some embodiments, the trained machine learning algorithm does not comprise comparing a spectrum of the polymer (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule. In some embodiments, the trained machine learning algorithm comprise comparing a spectrum of the biomolecule (e.g., a spectrum from detection of the analyte using SERS) to a reference spectrum of the reference molecule.

[0129] In some embodiments, the method further comprises cleaving a terminal subunit from the polymer. In some embodiments, the polymer is a polypeptide. In some cases, when the polymer is a polypeptide, the subunits can be amino acids. In some embodiments, the terminal subunit is an N-terminal subunit. In some embodiments, the terminal subunit is a C-terminal subunit. Cleaving can comprise using a protease. The protease can be a peptidase (e.g., an aminopeptidase or a carboxypeptidase). Cleaving can also comprise a chemical reaction (e.g., Edman degradation). Cleavage of the terminal subunit from the polymer can generate a n-1 polymer that comprises a new terminal subunit. The method can further comprise detecting the n-1 polymer using SERS. A subunit composition of the n-1 polymer can be determined or quantified based at least in part on the detection of the n-1 polymer using SERS. The terminal subunit cleaved from the polymer can be identified by comparison of the detection of the polymer and the detection of the n-1 polymer. In some embodiments, the method comprises determining a sequence of subunit (e.g., amino acid residues) of the polymer (e.g., polypeptide) based at least in part on detecting the n-1 polymer using SERS. For example, detection of the polymer and detection of the n-1 polymer can enable quantification of the subunit compositions of the polymer and the n-1 polymer. Comparison of the polymer and the n-1 polymer based on their detections or their subunit compositions can identify the terminal subunit cleaved from the polymer. The method can encompass successive cleavage(s) of subunits from the terminus of the n-1 polymer to generate a n-2 polymer, a n-3 polymer, a n-4 polymer, and so on. The sequence of the polymer can be determined by identification of each subunit cleaved and the order of each subunit’s cleavage.

[0130] The polymer can be a nucleic acid. The nucleic acid can comprise deoxyribonucleic acids, ribonucleic acids, or a combination thereof. In some cases, when the polymer is a nucleic acid, the subunits can be nucleobases. In some examples, the nucleic acid is DNA or RNA.

[0131] In some embodiments, the polymer (e.g., the polypeptide) can be modified. The polymer can comprise a modified subunit with a modification. For example, the polypeptide can comprise a modified residue with a modification. In some embodiments, the identifying comprises identifying the modified subunit (e.g., modified residue). In some embodiments, the identifying comprises identifying the modification. The modification can be covalently or noncovalently bound to the modified subunit (e.g., modified residue). The modification can be a ligand (e.g., a ligand that binds to the polypeptide at the modified residue). The ligand can be a small molecule (e.g., a chemical, a drug). The modification can also be a post-translational modification. Non-limiting examples of post-translational modifications include phosphorylation, acetylation, SUMOylation, ubiquitination, glycosylation, nitrosylation (e.g., S-nitrosylation), citrullination, deamidation, UFMylation, prenylation, myristoylation, palmitoylation (e.g., S-palmitoylation), tyrosine sulfation, formylation, carboxylation, methylation, neddylation, biotinylation, oxidation, or lipidation.

[0132] In some embodiments, the polypeptide comprises an additional modified residue. In some embodiments, the additional modified residue comprises an additional modification. The modification and the additional modification can be the same or different. In some embodiments, the method comprises identifying the additional modified residue. In some embodiments, the method comprises identifying the additional modification.

[0133] In some embodiments, the surface is a functionalized surface. The functionalized surface can comprise a plurality of features. The plurality of features can comprise pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof. Features of the plurality of features can be arrayed on the surface in a repeated or orderly pattern, or in a stochastic manner. In some embodiments, the plurality of features is uniform. For example, the plurality of features consists of one structure (e.g., bowties) and are arrayed on the surface in an orderly pattern. The plurality of features can be etched on the surface. The plurality of features can be made with lithography on a slide (e.g., glass slide) orsilicon wafer. The plurality of features can be coated with gold or silver. The plurality of features can be engineered through semiconductor processing. In some embodiments, the plurality of features is nanofabricated. One or more features of the plurality of features can enhance signals generated by SERS. The one or more features can generate one or more hotspots for detection of signals generated by SERS. In some embodiments, the density of the plurality of features on the surface is at least about 100,000 features per cm2, at least about 500,000 features per cm2, at least about 1 million features per cm2, at least about 2 million features per cm2, at least about 3 million features per cm2, at least about 4 million features per cm2, at least about 5 million features per cm2, at least about 10 million features per cm2, at least about 20 million features per cm2, at least about 30 million features per cm2, at least about 40 million features per cm2, at least about 50 million features per cm2, at least about 100 million features per cm2, at least about 200 million features per cm2, at least about 300 million features per cm2, at least about 400 million features per cm2, at least about 500 million features per cm2, at least about 600 million features per cm2, at least about 700 million features per cm2, at least about 800 million features per cm2, at least about 900 million features per cm2, at least about 1 billion features per cm2, at least about 2 billion features per cm2, at least about 5 billion features per cm2, at least about 10 billion features per cm2, at least about 20 billion features per cm2, at least about 30 billion features per cm2, at least about 40 billion features per cm2, at least about 50 billion features per cm2, at least about 60 billion features per cm2, at least about 70 billion features per cm2, at least about 80 billion features per cm2, at least about 90 billion features per cm2, or at least about 100 billion features per cm2. In some embodiments, the density of the plurality of features on the surface is at least about 1 billion features per cm2. In some embodiments, the density of the plurality of features on the surface is at least about 1 million features per cm2. In some embodiments, the density of the plurality of features on the surface is at least about 5 million features per cm2. In some embodiments, the plurality of features has a density of 1 million to 1 billion features per cm2on the surface. In some embodiments, the plurality of features has a density of 5 million to 1 billion features per cm2on the surface. In some embodiments, the plurality of features has a density of 10 million to 1 billion features per cm2on the surface.Detection of Protein-Protein Interactions

[0134] In some aspects disclosed herein is a method for detecting an interaction between proteins (e.g., a first protein and a second protein). The method can comprise coupling a cross-linked sample to a surface. The cross-linked sample can comprise the first protein and the second protein. In some embodiments, the method further comprises detecting the first protein and the second protein from the crosslinked sample using SERS. In some embodiments, detecting the first protein and the second protein from the cross-linked sample using SERS is performed at single molecule resolution.

[0135] In some embodiments, the method further comprises purifying the first protein from a cross-linked biological sample to generate the cross-linked sample. Purifying can comprise enrichment of the first protein using a binder that binds to the first protein. For example, purifying can comprise immunoprecipitation and the binder can comprise an antibody that binds to the first protein. In some embodiments, the cross-linked biological sample comprises a cell, cell secretions (e.g., supernatant of a cell culture), or both. The cross-linked biological sample can be generated by treating a non-cross-linked biological sample with a cross-linker. Treating a non-cross-linked biological sample with a cross-linker can generate covalent bonds between biomolecules in close proximity to one another. For example, a covalent bond can be formed between two interacting proteins (e.g., the first protein and the second protein) following cross-linking. A cross-linker can be a chemical reagent. The cross-linker can be a homobifunctional cross-linker, a heterobifunctional crosslinker, a photoreactive cross-linker, or a combination thereof. Non-limiting examples of a homobifunctional cross-linker include disuccinimidyl suberate, disuccinimidyl tartrate, dithiobis succinimidyl propionate, and maleimide containing cross-linkers (e.g., bismaleimidoethane, 1 ,4-bismaleimidobutane, bismaleimidohexane, dithiobismaleimidoethane). Non-limiting examples of a heterobifunctional cross-linker can include MDS (m-Maleimidobenzoyl-N-hydroxysuccinimide ester), GMBS (N-y-Maleimidobutyryloxysuccinimide ester), EMCS (N-(£-Maleimidocaproyloxy) succinimide ester), and sulfo-EMCS (N-(£-Maleimidocaproyloxy) sulfo succinimide ester). A photoreactive cross-linker can be an aryl-azide (e.g., N-((2-pyridyldithio)ethyl)-4-azidosalicylamide, N-5-Azido-2-nitrobenzyloxysuccinimide, sulfosuccinimidyl 6-(4'-azido-2'-nitrophenylamino)hexanoate), a diazirine (e.g., a NHS-ester diazirine, an azipentanoate), or a combination thereof.

[0136] In some embodiments, the method comprises identifying the second protein with an accuracy of at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 97%, at least 98%, or at least 99%. In some embodiments, identifying the second protein has an accuracy of at least 75%. The identifying can comprise a trained machine learning algorithm. In some embodiments, the trained machine learning algorithm does not comprise comparing the first protein (e.g., data obtained from detecting the first protein) or the second protein (e.g., data obtained from detecting the second protein) to a database comprising data associated with a reference molecule (e.g., a reference protein or a reference amino acid). In some embodiments, the data associated with the reference molecule comprises genomic data or transcriptom ic data. In some embodiments, the trained machine learning algorithm does not comprise comparing neither of the first protein nor the second protein to a database comprising data associated with a reference molecule. In some cases, the trained machine learning algorithm does not comprise comparing a first spectrum of the first protein or a second spectrum of the second protein to a reference spectrum of the reference molecule. In some cases, the trained machine learning algorithm does not comprise comparing neither a first spectrum of the first protein nor a second spectrum of the second protein to a reference spectrum of the reference molecule. The trained machine learning algorithm can comprise supervised learning or unsupervised learning. Non-limiting examples of a trained machine learning algorithm include a neural network, a support vector machine, t-SNE, PCADeep, transformer-based architectures, ora combination thereof.

[0137] In some embodiments, the method comprises coupling a sample to the surface. The sample can comprise the first protein and can be not cross-linked. In some embodiments, the sample does not comprise the second protein. The method can comprise purifying the first protein from a biological sample to generate the sample. Purifying can comprise enrichment of the first protein using a binder that binds to the first protein. For example, purifying can comprise immunoprecipitation and the binder can comprise an antibody that binds to the first protein. The biological sample can comprise a cell, cell secretions (e.g., supernatant of a cell culture), or both. In some embodiments, the method comprises detecting the first protein from the sample using SERS. In some cases, the detecting the first protein from the sample using SERS is performed at single molecule resolution.

[0138] In some embodiments, the method comprises identifying the second protein based at least in part on the detecting the first protein and the second protein from the cross-linked sample using SERS and the detecting the first protein from the sample using SERS. In some cases, the method comprises identifying the second protein as an interaction partner of the first protein.

[0139] In some embodiments, the surface is a functionalized surface. The functionalized surface can comprise a plurality of features. The plurality of features can comprise pillars, trees, , bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof. Features of the plurality of features can be arrayed on the surface in a repeated or orderly pattern, or in a stochastic manner. In some embodiments, the plurality of features is uniform. For example, the plurality of features consists of one structure (e.g., bowties) and are arrayed on the surface in an orderly pattern. The plurality of features can be etched on the surface. The plurality of features can be made with lithography on a slide (e.g., glass slide) or silicon wafer. The plurality of features can be coated with gold or silver. The plurality of features can be engineered through semiconductor processing. In some embodiments, the plurality of features is nanofabricated. One or more features of the plurality of features can enhance signals generated by SERS. The one or more features can generate one or more hotspots for detection of signals generated by SERS. In some embodiments, the density of the plurality of features on the surface is at least about 100,000 features per cm2, at least about 500,000 features per cm2, at least about 1 million features per cm2, at least about 2 million features per cm2, at least about 3 million features per cm2, at least about 4 million features per cm2, at least about 5 million features per cm2, at least about 10 million features per cm2, at least about 20 million features per cm2, at least about 30 million features per cm2, at least about 40 million features per cm2, at least about 50 million features per cm2, at least about 100 million features per cm2, at least about 200 million features per cm2, at least about 300 million features per cm2, at least about 400 million features per cm2, at least about 500 million features per cm2, at least about 600 million features per cm2, at least about 700 million features per cm2, at least about 800 million features per cm2, at least about 900 million features per cm2, at least about 1 billion features per cm2, at least about 2 billion features per cm2, at least about 5 billion features per cm2, at least about 10 billion features per cm2, at least about 20 billion features per cm2, at least about 30 billion features per cm2, at least about 40 billionfeatures per cm2, at least about 50 billion features per cm2, at least about 60 billion features per cm2, at least about 70 billion features per cm2, at least about 80 billion features per cm2, at least about 90 billion features per cm2, or at least about 100 billion features per cm2. In some embodiments, the density of the plurality of features on the surface is at least about 1 billion features per cm2. In some embodiments, the density of the plurality of features on the surface is at least about 1 million features per cm2. In some embodiments, the density of the plurality of features on the surface is at least about 5 million features per cm2. In some embodiments, the plurality of features has a density of 1 million to 1 billion features per cm2on the surface. In some embodiments, the plurality of features has a density of 5 million to 1 billion features per cm2on the surface. In some embodiments, the plurality of features has a density of 10 million to 1 billion features per cm2on the surface.

[0140] In some embodiments, the method further comprises detecting a normalization reference molecule (e.g., graphene) using SERS. The normalization reference molecule can be a control or a reference described elsewhere herein. In some embodiments, the method further comprises comparing the normalization reference molecule and the first protein. Comparison (e.g., comparison of the spectra from detection using SERS) of the normalization reference molecule and the first protein can allow for quantification of the composition of the first protein (e.g., the number of each amino acid in the first protein). In some embodiments, the surface comprises the normalization reference molecule. In some embodiments, the first protein does not have a label. The surface can comprise a single layer of the normalization reference molecule. In some examples, the normalization reference molecule can be coupled to the first protein.Methods of Use

[0141] Methods for identifying (e.g., diagnosing) a disease, disorder, condition, or symptom thereof or a condition related thereto are provided herein. For example, the system and methods described herein can detect mutations and / or PTMs in proteins as well as biomarkers. Preferred, but non-limiting embodiments are directed to methods for treating, preventing, inhibiting, or ameliorating a disease, disorder, condition, or symptom described below.

[0142] In embodiments, the disease or ailment is one or more of atherosclerosis, osteoarthritis, osteoporosis, hypertension, arthritis, cataracts, cancer, Alzheimer’s disease, chronic obstructive pulmonary disease (COPD) and idiopathic pulmonaryfibrosis. Other ailments (including age-related conditions) associated with age or senescence include hair graying, sarcopenia, adiposity, neurogenesis, fibrosis, and glaucoma. Still other ailments include cardiovascular disease (e.g., atherosclerosis, angina, arrhythmia, cardiomyopathy, congestive heart failure, coronary artery disease, carotid artery disease, endocarditis, coronary thrombosis, myocardial infarction, hypertension, aortic aneurysm, cardiac diastolic dysfunction, hypercholesterolemia, hyperlipidemia, mitral valve prolapsed, peripheral vascular disease, cardiac stress resistance, cardiac fibrosis, brain aneurysm, and stroke). An ailment can also be an inflammatory or autoimmune disease or disorder (e.g., osteoarthritis, osteoporosis, oral mucositis, inflammatory bowel disease or kyphosis). An ailment can also be a neurodegenerative disease (e.g., Alzheimer's disease, Parkinson's disease, Huntington's disease, dementia, mild cognitive impairment, or motor neuron dysfunction). An ailment can be a metabolic disease (e.g., diabetes, diabetic ulcer, metabolic syndrome, or obesity). An ailment can also be a pulmonary disease (e.g., pulmonary fibrosis, chronic obstructive pulmonary disease, asthma, cystic fibrosis, emphysema, bronchiectasis, or age-related loss of pulmonary function). An ailment can also be an eye disease or disorder (e.g., macular degeneration, glaucoma, cataracts, presbyopia, or vision loss). An ailment can be renal disease, renal failure, frailty, hearing loss, muscle fatigue, skin conditions, skin wound healing, liver fibrosis, pancreatic fibrosis, oral submucosa fibrosis or sarcopenia. An ailment can also be a dermatological disease or disorder (e.g., eczema, psoriasis, hyperpigmentation, nevi, rashes, atopic dermatitis, urticaria, diseases or disorders related to photosensitivity or photoaging).

[0143] In certain embodiments, a peptide, polypeptide, or protein can be fragmented. For example, the fragmented peptide can be obtained by fragmenting a protein from a sample, such as a biological sample. The peptide, polypeptide, or protein can be fragmented by any method, including fragmentation by a protease or endopeptidase. In some embodiments, fragmentation of a peptide, polypeptide, or protein is targeted by use of a specific protease or endopeptidase. A specific protease or endopeptidase binds and cleaves at a specific consensus sequence (e.g., TEV protease which is specific for E LYFQ\S consensus sequence). In other embodiments, fragmentation of a peptide, polypeptide, or protein is non-targeted or random by use of a non-specific protease or endopeptidase. A non-specific protease may bind and cleave at a specific amino acid residue rather than a consensussequence (e.g., proteinase K is a non-specific serine protease). Proteinases and endopeptidases are examples of such that can be used to cleave a protein or polypeptide into smaller peptide fragments include proteinase K, trypsin, chymotrypsin, pepsin, thermolysin, thrombin, Factor Xa, furin, endopeptidase, papain, pepsin, subtilisin, elastase, enterokinase, Genenase™ I, Endoproteinase LysC, Endoproteinase AspN, Endoproteinase GluC, etc. (Granvogl et al., 2007, Anal Bioanal Chem 389: 991- 1002). In certain embodiments, a peptide, polypeptide, or protein is fragmented by proteinase K, or optionally, a thermolabile version of proteinase K to enable rapid inactivation. Proteinase K is quite stable in denaturing reagents, such as urea and SDS, enabling digestion of completely denatured proteins.

[0144] Chemical reagents can also be used to digest proteins into peptide fragments. A chemical reagent may cleave at a specific amino acid residue (e.g., cyanogen bromide hydrolyzes peptide bonds at the C-terminus of methionine residues). Chemical reagents for fragmenting polypeptides or proteins into smaller peptides include cyanogen bromide (CNBr), hydroxylamine, hydrazine, formic acid, BNPS- skatole [2-(2-nitrophenylsulfenyl)-3-methylindole], iodosobenzoic acid, vNTCB +Ni (2- nitro-5-thiocyanobenzoic acid).

[0145] In certain embodiments, following enzymatic or chemical cleavage, the resulting peptide fragments are approximately the same desired length (e.g., from about 10 amino acids to about 70 amino acids, from about 10 amino acids to about 60 amino acids, from about 10 amino acids to about 50 amino acids, about 10 to about 40 amino acids, from about 10 to about 30 amino acids, from about 20 amino acids to about 70 amino acids, from about 20 amino acids to about 60 amino acids, from about 20 amino acids to about 50 amino acids, about 20 to about 40 amino acids, from about 20 to about 30 amino acids, from about 30 amino acids to about 70 amino acids, from about 30 amino acids to about 60 amino acids, from about 30 amino acids to about 50 amino acids, or from about 30 amino acids to about 40 amino acids).Computer Systems

[0146] The present disclosure provides computer systems that are programmed to implement methods of the disclosure. FIG. 7 shows a computer system 701 that is programmed or otherwise configured to configured to process analytes (e.g., for the purpose of detecting and / or sequencing the analytes). The computer system 701can regulate various aspects of the systems and methods of the present disclosure, such as, for example, detecting and / or sequencing the analytes (e.g., biomolecules, polymers, polypeptides, nucleic acids). The computer system 701 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0147] The computer system 701 includes a central processing unit (CPU, also “processor” and “computer processor” herein) 705, which can be a single core or multi core processor, or a plurality of processors for parallel processing. The computer system 701 also includes memory or memory location 710 (e.g., randomaccess memory, read-only memory, flash memory), electronic storage unit 715 (e.g., hard disk), communication interface 720 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 725, such as cache, other memory, data storage and / or electronic display adapters. The memory 710, storage unit 715, interface 720 and peripheral devices 725 are in communication with the CPU 705 through a communication bus (solid lines), such as a motherboard. The storage unit 715 can be a data storage unit (or data repository) for storing data. The computer system 701 can be operatively coupled to a computer network (“network”) 730 with the aid of the communication interface 720. The network 730 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 730 in some cases is a telecommunication and / or data network. The network 730 can include one or more computer servers, which can enable distributed computing, such as cloud computing. The network 730, in some cases with the aid of the computer system 701, can implement a peer-to-peer network, which may enable devices coupled to the computer system 701 to behave as a client or a server.

[0148] The CPU 705 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 710. The instructions can be directed to the CPU 705, which can subsequently program or otherwise configure the CPU 705 to implement methods of the present disclosure. Examples of operations performed by the CPU 705 can include fetch, decode, execute, and writeback.

[0149] The CPU 705 can be part of a circuit, such as an integrated circuit. One or more other components of the system 701 can be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0150] The storage unit 715 can store files, such as drivers, libraries and saved programs. The storage unit 715 can store user data, e.g., user preferences and user programs. The computer system 701 in some cases can include one or more additional data storage units that are external to the computer system 701, such as located on a remote server that is in communication with the computer system 701 through an intranet or the Internet.

[0151] The computer system 701 can communicate with one or more remote computer systems through the network 730. For instance, the computer system 701 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 701 via the network 730.

[0152] Methods as described herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 701, such as, for example, on the memory 710 or electronic storage unit 715. The machine executable or machine readable code can be provided in the form of software. During use, the code can be executed by the processor 705. In some cases, the code can be retrieved from the storage unit 715 and stored on the memory 710 for ready access by the processor 705. In some situations, the electronic storage unit 715 can be precluded, and machine-executable instructions are stored on memory 710.

[0153] The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code, or can be compiled during runtime. The code can be supplied in a programming language that can be selected to enable the code to execute in a pre-compiled or as-compiled fashion.

[0154] Aspects of the systems and methods provided herein, such as the computer system 701, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” typically in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can bestored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non-transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0155] Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) datacommunications. Common forms of computer-readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge,a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0156] The computer system 701 can include or be in communication with an electronic display 735 that comprises a user interface (III) 740 for providing, for example, data associated with the analytes (e.g., spectra of the analytes, subunit compositions of the analytes, sequences of the analytes). Examples of Ill’s include, without limitation, a graphical user interface (GUI) and web-based user interface.

[0157] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 705. The algorithm can, for example, process data associated with the analytes, manage reagents, or manage power to the device.EXAMPLES

[0158] The following non-limiting examples are provided for illustrative purposes only to facilitate a more complete understanding of representative embodiments now contemplated. These examples are intended to be a mere subset of all possible contexts in which the components of the formulation may be combined. Thus, these examples should not be construed to limit any of the embodiments described in the present specification, including those pertaining to the type and amounts of components of the formulation and / or methods and uses thereof.Example 1Determination of Sequence of Sample Peptide

[0159] In this example, a technician wishes to determine the sequence of a peptide that is 50 to 100 amino acids in length. Methods such as mass spectrometry and Edman degradation may be impractical or fail. The technician utilizes the aminopeptidase-pore complex described herein.

[0160] After assembly of the complex, a peptide is added to the solution. As shown in FIG. 2, a first amino-terminal (N-terminal) amino acid of the peptide binds with the aminopeptidase portion of the aminopeptidase-pore complex. The aminopeptidase then cleaves the first N-terminal amino acid from the peptide andliberates the first N-terminal amino acid from the peptide. The liberated N-terminal amino acid passes through the channel and into a detection domain. There, it binds a MIP that is specific to the first N-terminal amino acid. The amino acid is detected using SERS. The process can be repeated for each amino acid in the peptide.

[0161] Although the method is described utilizing the N-terminal portion of a peptide, Applicants propose that the C-terminal portion can be similarly used. In aspects, a peptide binds via its C-terminal portion and individual amino acids are cleaved therefrom.Example 2Analysis of Protein-Protein Interaction using the SERS biosensor

[0162] In aspects, the SERS biosensor can be used to identify and characterize interactions between proteins in a high-throughput manner.

[0163] Protein-protein interactions of a specific protein of interest (POI) can be identified using the following approach. First, a POI is tagged and purified. The purified POI is then applied to the biosensor and scanned to obtain its unique surface-enhanced Raman spectroscopy (SERS) spectra. After scanning, a cell lysate or cell protein fraction may be applied and incubated on the biosensor to bind the purified POI. The lysate or fraction is gently washed to remove unbound proteins and the surface is re-scanned to identify any bound proteins.

[0164] FIG. 5 is a flowchart 200 that shows the workflow in a method of analysis of protein-protein interaction using the SERS biosensor. A protein of interest is identified 205 and spectra of that protein are obtained 207. Cells containing the same tagged POI construct are treated with a protein crosslinker that covalently links bound proteins 210. The POI is purified (along with its crosslinked binding partner(s)) and applied to the biosensor. New spectra are obtained and the spectra of the POI can be subtracted from the new observed spectra 220. These “POI subtracted” spectra can be analyzed to determine an amino acid composition. Interacting proteins are identified using a list of candidates of known amino acid composition 225. This method is simplified with the generation a protein spectra database, to identify “POI subtracted” spectra, and allows for a higher throughput version of this method without tags, as described below.Generation of Protein Spectra Database

[0165] In aspects, a database is set up so that spectra can be subtracted as described above. To do so, interacting proteins are covalently crosslinked in vivo and then whole protein lysates are purified and loaded onto the biosensor. After scanning, data is processed to identify proteins in the hotspots, including crosslinked binding partners. A robust database of protein spectral fingerprints will improve the capabilities to identify proteins. Deconvolution of the spectral signals can reveal binding partners and thus, protein-protein interactions. This can lead to a holistic protein “interactome.”

[0166] Another method entails an in vitro binding assay that can be used to identify protein-protein interactions without crosslinking proteins as shown in FIG. 6 (300). Purified POIs (e.g., an antibody or preparation of many antibodies) are loaded onto the biosensor 310. The biosensor is then scanned, revealing which POIs are at which hotspots. Then, another preparation of proteins is applied to the biosensor, incubated and then the biosensor is gently washed to remove any unbound proteins. The biosensor is then scanned a second time and the signals deconvoluted to reveal binding partners and protein-protein interactions (e.g., epitopes). After enough data is collected, one can identify where the proteins are interacting, which can be fine mapping of the epitope or protein interaction.

[0167] While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the invention be limited by the specific examples provided within the specification. While the invention has been described with reference to the aforementioned specification, the descriptions and illustrations of the embodiments herein are not meant to be construed in a limiting sense. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. Furthermore, it shall be understood that all aspects of the invention are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is therefore contemplated that the invention shall also cover any such alternatives, modifications, variations or equivalents. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.EMBODIMENTS1. An aminopeptidase-pore complex combined with a biosensor comprised of:a) an aminopeptidase;b) a protein-based or solid-state pore which forms a channel that penetrates and traverses a scaffold such as a lipid bilayer;c) a receptor domain comprising a single, universally binding or plurality of amino acid residue-specific molecularly imprinted polymers (MIPs) or other high-affinity binders coated over a SERS-functionalized surface.2. The aminopeptidase-pore complex of embodiment 1 , wherein the aminopeptidase is Streptomyces griseus aminopeptidase I.3. The aminopeptidase-pore complex of embodiment 1 , wherein the proteinbased pore comprises one or more a-haemolysin (a-HL) peptides.4. The aminopeptidase-pore complex of embodiment 3, wherein the one or more a-HL peptides are Staphylococcus aureus a-HL.5. The aminopeptidase-pore complex of embodiment 1 , wherein the proteinbased pore comprises seven a-HL peptides.6. The aminopeptidase-pore complex of embodiment 1 , wherein the aminopeptidase-pore complex comprises a fusion protein of an aminopeptidase and an a-HL peptide.7. The aminopeptidase-pore complex of embodiment 6, wherein the aminopeptidase and a-HL peptide are directly linked such that the fusion protein lacks a linker between the aminopeptidase and the a-HL peptide.8. The aminopeptidase-pore complex of embodiment 6, wherein the aminopeptidase and a-HL peptide are indirectly linked via a linker between the aminopeptidase and the a-HL peptide.9. The aminopeptidase-pore complex of embodiment 8, wherein the linker comprises one or more amino acids.10. The aminopeptidase-pore complex of embodiment 9, wherein the one or more amino acids comprise glycine and / or serine.11. The aminopeptidase-pore complex of embodiment 8, wherein the fusion protein is expressed from a single mRNA as one polypeptide.12. The aminopeptidase-pore complex of embodiment 8, wherein the fusion protein is not expressed from a single mRNA as one polypeptide.13. The aminopeptidase-pore complex of embodiment 3, wherein the aminopeptidase and the one or more a-HL peptides are directly or indirectly linked post-translationally.14. The aminopeptidase-pore complex of embodiment 1 , wherein the aminopeptidase and the one or more a-HL peptides are linked using a conjugation system.15. The aminopeptidase-pore complex of embodiment 1 , wherein the aminopeptidase-pore complex comprises more than one fusion protein of an aminopeptidase and an a-HL peptide.16. The aminopeptidase-pore complex of embodiment 1 , wherein when seven a-HL peptides contact, they form the protein-based pore.17. The aminopeptidase-pore complex of embodiment 16, wherein when the protein-based pore contacts a lipid bilayer, the protein-based pore forms a channel that penetrates and traverses the lipid bilayer.18. The aminopeptidase-pore complex of embodiment 1 , wherein the channel permits traversal of amino acids.19. The aminopeptidase-pore complex of embodiment 18, wherein traversal of amino acids through the channel is at least about 70% efficient relative to the a-HL when not associated with an aminopeptidase-pore complex.20. The aminopeptidase-pore complex of embodiment 1 , wherein the aminopeptidase is N-terminal to the a-HL peptide.21. The aminopeptidase-pore complex of embodiment 1 , wherein the aminopeptidase is C-terminal to the a-HL peptide.22. The aminopeptidase-pore complex of embodiment 1 , wherein the detection domain comprises a first MIP that is specific to a first amino acid residue.23. The aminopeptidase-pore complex of embodiment 1 , wherein the detection domain comprises a second MIP that is specific to a second amino acid residue. 24. A method of sequencing a peptide using the aminopeptidase-pore complex of embodiment 1.25. A method of sequencing a peptide, the method comprising steps of:a) obtaining an aminopeptidase-pore complex embedded in a lipid bilayer or other pore scaffold,b) contacting a first amino-terminal (N-terminal) amino acid of a peptide with the aminopeptidase portion of the aminopeptidase-pore complex, therebycleaving the first N-terminal amino acid from the peptide and liberating the first N-terminal amino acid from the peptide;c) permitting the liberated first N-terminal amino acid to pass through the channel and into the detection domain, and contact an MIP specific to the first N-terminal amino acid; wherein when the first N-terminal amino acid contacts the MIP specific to the first N-terminal amino acid, the amino acid produces a first signal;d) registering and recording the first signal, thereby identifying the first N- terminal amino acid in the peptide;e) contacting a second N-terminal amino acid of the peptide with the aminopeptidase portion of the aminopeptidase-pore complex, which cleaves the second N-terminal amino acid from the peptide and liberates the second N-terminal amino acid from the peptide; wherein the second amino acid in the peptide’s linear order becomes the second N-terminal amino acid once the first N-terminal amino acid is liberated from the peptide;f) permitting the liberated second N-terminal amino acid to pass through the channel and into the detection domain, and contact an MIP specific to the second N-terminal amino acid; wherein when the second N-terminal amino acid contacts the MIP specific to the second N-terminal amino acid, the amino acid produces a second signal;g) registering and recording the second signal, thereby identifying the second N- terminal amino acid in the peptide;h) contacting an nth N-terminal amino acid of the peptide with the aminopeptidase portion of the aminopeptidase-pore complex, which cleaves the nth N-terminal amino acid from the peptide and liberates the nth N- terminal amino acid from the peptide; wherein the nth amino acid in the peptide’s linear order becomes the nth N-terminal amino acid once the n-1st N-terminal amino acid is liberated from the peptide;i) permitting the liberated nth N-terminal amino acid to pass through the channel and into the detection domain, and contact an MIP specific to the nth N- terminal amino acid; wherein when the nth N-terminal amino acid contacts the MIP specific to the nth N-terminal amino acid, the amino acid produces an nth signal;j) registering and recording the nth signal, thereby identifying the nth N-terminal amino acid in the peptide; wherein when the first N-terminal amino acid, the second N-terminal amino acid, and the nth N-terminal amino acid are identified, the linear order of amino acids in the peptide is determined.26. The method of embodiment 25, wherein the first amino acid residue is different from the second amino acid residue.27. The method of any of embodiment 25, wherein the detection domain comprises an nth MIP that is specific to an nth amino acid residue.28. The method of embodiment 25, wherein the first amino acid residue, the second amino acid residue, and the nth amino acid residue are different.29. The method of embodiment 25, wherein the first amino acid residue to the nth amino acid residue comprise one or more of alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine.30. The method of embodiment 25, wherein the first amino acid residue to the nth amino acid residue comprise each of alanine, arginine, asparagine, aspartic acid, cysteine, glutamine, glutamic acid, glycine, histidine, isoleucine, leucine, lysine, methionine, phenylalanine, proline, serine, threonine, tryptophan, tyrosine, and valine.31. The method of embodiment 25, wherein the first MIP, the second MIP, and the nth MIP bind individual amino acids that result in different signals.32. The method of embodiment 25, wherein a first signal from the first N-terminal amino acid is registered before a second signal from the second N-terminal amino acid, thereby permitting the identification of sequential amino acids in the peptide, with the first N-terminal amino acid preceding the second N-terminal amino acid in the peptide.33. The method of embodiment 25, wherein an n-1st signal from the n-1st N-terminal amino acid is registered before an nth signal from the nth N-terminal amino acid, thereby permitting the identification of sequential amino acids in the peptide, with the n-1st N-terminal amino acid preceding the nth N-terminal amino acid in the peptide.34. The method of embodiment 25, wherein the detection domain side of the lipid bilayer comprises a buffer.35. The method of embodiment 25, wherein the buffer has an osmolarity such that an osmotic gradient is established across the membrane which provides directional flow through the channel.36. The method of embodiment 25, wherein the buffer is flowed in a direction that promoted flow of the amino acids through the channel and / or removes amino acids from the detection domain.37. The method of embodiment 25, wherein the buffer is a phosphate and / or a buffer at substantially neutral pH.38. The method of embodiment 25, wherein each signal is registered and recorded by a computer.39. The method of embodiment 25, wherein the method identifies the linear order of amino acids in the peptide in an unbiased way.40. The method of embodiment 25, wherein the aminopeptidase-pore complex identifies the linear order of amino acids of one peptide at a time.41. The method of embodiment 25, wherein the lipid bilayer comprises 1 ,2-diphytanoyl-sn-glycero-3-phosphocholine52 (DPhPC).42. An aminopeptidase-pore complex and a biosensor comprised of:a) an aminopeptidase;b) a solid-state pore which forms a channel that penetrates and traverses a lipid bilayer or other pore scaffold;c) a receptor domain comprising a single, universally binding or plurality of amino acid residue-specific molecularly imprinted polymers (MIPs) or other high- affinity binders.43. A method of sequencing a peptide, the method comprising steps of:a) coupling a peptide to a functionalized surface,b) measuring a first composition and abundance of amino acids in the peptide, c) liberating an N-terminal amino acid from the peptide;d) measuring a second composition and abundance of amino acids in the peptide,e) comparing the first composition and abundance and the second composition and abundance to identify the N-terminal amino acid.44. The method of embodiment 43, wherein the steps of b) to e) are repeated for each amino acid in the peptide to determine the sequence of amino acids in the peptide.45. The method of embodiment 43, wherein the functionalized surface is modified with gold nanofeatures and covered in graphene or another reference chemical to measure the relative abundance of amino acids in the peptide.46. The method of embodiment 43, wherein surface-enhanced Raman spectroscopy (SERS) is used in the steps of measuring the first composition and abundance and second composition and abundance of amino acids in the peptide.47. The method of embodiment 43, wherein an algorithm is used in the step of comparing the first composition and abundance and the second composition and abundance to identify the N-terminal amino acid.48. The method of embodiment 43, wherein a control analyte is used to prove SERS spectra and resulting sequences are obtained from single molecules of peptides.49. A method of identifying an interaction between a first protein and a second protein, the method comprising steps of:a) Identifying a protein of interest (POI),b) Tagging and purifying the POI,c) Applying the POI to the biosensor of claim 1 to obtain a first spectra, d) Treating cells containing the POI with a cross-linker,e) Purifying the POI with cross-linked proteins,f) Applying the cross-linked POI-protein complexes to the biosensor of claim 1 to obtain a second spectra,g) comparing the first spectra and the second spectra to determine an amino acid composition.50. The method of embodiment 49, further comprising a step of generating a protein spectra database, wherein the database is comprised of spectra of known protein spectral fingerprints.51. The method of embodiment 49, wherein an algorithm is used in the step of comparing the first spectra and the second spectra to determine an amino acid composition.52. A method of identifying an interaction between a first protein and a second protein, the method comprising steps of:a) Identifying a first protein of interest (POI),b) Purifying the first POI,c) Applying the first POI to the biosensor of claim 1 to obtain a first spectra,d) Applying a cell lysate or other preparation of potential binding proteins to the biosensor containing the scanned POI,e) Washing the biosensor to remove unbound proteins,f) Obtaining a second spectra,g) Comparing the first spectra and the second spectra to identify binding partners and / or protein interactions.53. The method of embodiment 52, wherein the first POI is comprised of an antibody preparation.54. The method of embodiment 52, wherein an algorithm is used in the step of comparing the first spectra and the second spectra to determine an amino acid composition.

Claims

1. CLAIMS2.What is claimed is:

1. A method comprising:4.a) coupling a biomolecule to a surface;5.b) detecting the biomolecule using surface-enhanced Raman spectroscopy (SERS); and6.c) based at least in part on the detecting in b), identifying the biomolecule at an accuracy of at least 75%.

2. The method of any one of the preceding claims, wherein the detecting is performed at single molecule resolution.

3. The method of any one of the preceding claims, wherein the identifying comprises using a trained machine learning algorithm.

4. The method of any one of the preceding claims, wherein the biomolecule is selected from the group consisting of a nucleic acid and a polypeptide.

5. The method of claim 4, wherein the polypeptide comprises a modified residue with a modification.

6. The method of claim 5, wherein the modification is noncovalently bound to the modified residue.

7. The method of claim 5, wherein the modification is covalently bound to the modified residue.

8. The method of any one of claims 5-7, wherein the modification is a ligand.

9. The method of claim 7, wherein the modification is a post-translational modification.

10. The method of any one of claims 5-9, wherein the identifying comprises identifying the modification.

11. The method of any one of claims 5-10, wherein the identifying comprises identifying the modified residue.

12. The method of any one of claims 5-11 , wherein the polypeptide comprises an additional modified residue with an additional modification.

13. The method of claim 12, wherein the modification and the additional modification are different.

14. The method of claim 12, wherein the modification and the additional modification are the same.

15. The method of any one of claims 12-14, wherein the identifying comprises identifying the additional modification.

16. The method of any one of claims 12-15, wherein the identifying comprises identifying the additional modified residue.

17. The method of any one of the preceding claims, further comprising detecting a normalization reference molecule using SERS.

18. The method of claim 17, further comprising comparing the normalization reference molecule and the biomolecule.

19. The method of claim 17 or claim 18, wherein the surface comprises the normalization reference molecule.

20. The method of claim 19, wherein the normalization reference molecule is graphene.

21. The method of claim 17 or claim 18, wherein the normalization reference molecule is coupled to the biomolecule.

22. The method of any one of claims 1-20, wherein the biomolecule does not have a label.

23. A method comprising:27.a) coupling an analyte on a surface;28.b) detecting the analyte using surface-enhanced Raman spectroscopy (SERS) to obtain analyte data; and29.c) identifying the analyte by applying a trained machine learning algorithm to the analyte data;30.wherein the trained machine learning algorithm does not comprise comparing the analyte data to a database comprising data associated with a reference molecule.

24. The method of claim 23, wherein the trained machine learning algorithm does not comprise comparing a spectrum of the analyte to a reference spectrum of the reference molecule.

25. The method of claim 23 or claim 24, wherein the reference molecule is a reference protein or a reference amino acid.

26. The method of any one of claims 23-25, further comprising comparing the analyte data to data in a database.

27. The method of claim 26, wherein the comparing the analyte data to data in a database uses an additional trained machine learning algorithm.

28. The method of any one of claims 23-27, wherein the detecting is performed at single molecule resolution.

29. The method of any one of claims 23-28, further comprising detecting a normalization reference molecule using SERS.

30. The method of claim 29, further comprising comparing the normalization reference molecule and the analyte.31.The method of claim 29 or claim 30, wherein the surface comprises the normalization reference molecule.

32. The method of claim 31, wherein the normalization reference molecule is graphene.

33. The method of claim 31 or claim 32, wherein the normalization reference molecule is coupled to the analyte.

34. The method of any one of claims 23-32, wherein the analyte does not have a label.

35. The method of any one of claims 23-34, wherein the analyte comprises a polymer, and wherein the polymer comprises subunits.

36. The method of claim 35, further comprising identifying a sequence of the subunits of the polymer.

37. A method comprising:44.a) coupling a polymer to a surface, wherein the polymer comprises subunits;45.b) detecting the polymer using surface-enhanced Raman spectroscopy (SERS); and46.c) based at least in part on the detecting in b) identifying the polymer at an accuracy of at least 75%.

38. The method of claim 37, wherein the identifying comprises using a trained machine learning algorithm.

39. A method comprising:49.a) coupling a polymer to a surface wherein the polymer comprises subunits;50.b) detecting the polymer using surface-enhanced Raman spectroscopy (SERS);51.c) quantifying a subunit composition of the polymer; and52.d) identifying the polymer.

40. The method of any one of claims 37-39, further comprising detecting a normalization reference molecule using SERS.

41. The method of claim 40, further comprising comparing the normalization reference molecule and the polymer.

42. The method of claim 40 or claim 41 , wherein the surface comprises the normalization reference molecule.

43. The method of claim 42, wherein the normalization reference molecule is graphene.

44. The method of claim 40 or claim 41 , wherein the normalization reference molecule is coupled to the polymer.

45. The method of any one of claims 37-43, wherein the polymer does not have a label.

46. The method of any one of claims 39-45, wherein the quantifying comprises using a trained machine learning algorithm.

47. The method of any one of claims 37-46, wherein the detecting is performed at single molecule resolution.

48. The method of any one of claims 35-47, further comprising cleaving a terminal subunit from the polymer to generate a n-1 polymer, wherein the n-1 polymer comprises a new terminal subunit.

49. The method of claim 48, further comprising detecting the n-1 polymer using surface-enhanced Raman spectroscopy (SERS).

50. The method of any one of claims 35-49, wherein the polymer is a polypeptide.

51. The method of any one of claims 48-50, wherein the terminal subunit is an N- terminal amino acid.

52. The method of any one of claims 48-50, wherein the terminal subunit is a C- terminal amino acid53. The method of claim 51 or claim 52, further comprising determining a sequence of amino acids of the polypeptide.

54. The method of any one of claims 50-53, wherein the polypeptide comprises a modified residue with a modification.

55. The method of claim 54, wherein the modification is noncovalently bound to the modified residue.

56. The method of claim 54, wherein the modification is covalently bound to the modified residue.

57. The method of any one of claim 54-56, wherein the modification is a ligand.

58. The method of claim 56, wherein the modification is a post-translational modification.

59. The method of any one of claims 54-58, wherein the identifying comprises identifying the modification.

60. The method of any one of claims 54-59, wherein the identifying comprises identifying the modified residue.61.The method of any one of claims 54-60, wherein the polypeptide comprises an additional modified residue with an additional modification.

62. The method of claim 61, wherein the modification and the additional modification are different.

63. The method of claim 61, wherein the modification and the additional modification are the same.

64. The method of any one of claims 61 -63, wherein the identifying comprises identifying the additional modification.

65. The method of any one of claims 61 -64, wherein the identifying comprises identifying the additional modified residue.

66. The method of any one of claims 35-49, wherein the polymer is a nucleic acid.

67. The method of any one of the preceding claims, wherein the surface is a functionalized surface comprising a plurality of features.

68. The method of claim 67, wherein the plurality of features comprises pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof.

69. The method of claim 67 or claim 68, wherein the plurality of features has a density of at least 1 million features per cm2on the surface.

70. The method of any one of claims 67-69, wherein features of the plurality of features are uniform or stochastic.71.A method for detecting an interaction between a first protein and a second protein, the method comprising:82.a) coupling a cross-linked sample to a surface, wherein the cross-linked sample comprises the first protein and the second protein; and b) detecting the first protein and the second protein from the cross-linked sample using surface-enhanced Raman spectroscopy (SERS).

72. The method of claim 71 , further comprising purifying the first protein from a cross-linked biological sample to generate the cross-linked sample.

73. The method of claim 71 or claim 72, wherein the cross-linked biological sample comprises a cell.

74. The method of any one of claims 71-73, wherein the cross-linked biological sample comprises cell secretions.

75. The method of any one of claims 71-74, further comprising identifying the second protein, wherein the identifying has an accuracy of at least 75%.

76. The method of any one of claims 71-75, wherein the identifying comprises a trained machine learning algorithm.

77. The method of claim 76, wherein the trained machine learning algorithm does not comprise comparing data obtained from detecting the first protein or data obtained from detecting the second protein to data associated with a reference molecule.

78. The method of claim 77, wherein the trained machine learning algorithm does not comprise comparing a first spectrum of the first protein or a second spectrum of the second protein to a reference spectrum of the reference molecule.

79. The method of claim 77 or claim 78, wherein the reference molecule is a reference protein or a reference amino acid.

80. The method of any one of claims 71-79, wherein the detecting is performed at single molecule resolution.

81. The method of any one of claims 71 -80, further comprising coupling a sample to the surface, wherein the sample is not cross-linked and comprises the first protein.

82. The method of claim 81 , wherein the sample does not comprise the second protein.

83. The method of claim 81 or claim 82, further comprising purifying the first protein from a biological sample to generate the sample.

84. The method of any one of claims 81-83, further comprising detecting the first protein from the sample using SERS.

85. The method of claim 84, wherein the detecting the first protein from the sample using SERS is performed at single molecule resolution.

86. The method of claim 84 or claim 85, further comprising identifying the second protein based at least in part on the detecting the first protein and the second protein from the cross-linked sample using SERS and the detecting the first protein from the sample using SERS.

87. The method of any one of claims 71 -86, wherein the surface is a functionalized surface comprising a plurality of features.

88. The method of claim 87, wherein the plurality of features comprises pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof.

89. The method of claim 87 or claim 88, wherein the plurality of features has a density of at least 1 million features per cm2on the surface.

90. The method of any one of claims 87-89, wherein features of the plurality of features are uniform or stochastic.

91. The method of any one of claims 71 -90, further comprising detecting a normalization reference molecule using SERS.

92. The method of claim 91 , further comprising comparing the normalization reference molecule and the first protein.

93. The method of claim 91 or claim 92, wherein the surface comprises the normalization reference molecule.

94. The method of claim 93, wherein the normalization reference molecule is graphene.

95. The method of claim 91 or claim 92, wherein the normalization reference molecule is coupled to the first protein.

96. The method of any one of claims 71-94, wherein the first protein does not have a label.

97. A method comprising:107.a) coupling a polymer to a surface, wherein the polymer comprises subunits, wherein the polymer is a polypeptide;108.b) sequencing the polymer;109.c) quantifying a subunit composition of the polymer with an accuracy of at least 75%; and110.d) identifying the polymer.

98. The method of claim 97, wherein b) comprises sequencing the polymer with aid of SERS.

99. The method of claim 97 or claim 98, wherein c) comprises quantifying the subunit composition with aid of SERS.

100. The method of any one of claims 97-99, further comprising detecting a normalization reference molecule using SERS.

101. The method of claim 100, further comprising comparing the normalization reference molecule and the polymer.

102. The method of claim 100 or claim 101 , wherein the surface comprises the normalization reference molecule.

103. The method of claim 102, wherein the normalization reference molecule is graphene.

104. The method of claim 102 or claim 103, wherein the normalization reference molecule is coupled to the polymer.

105. The method of any one of claims 97-104, wherein the polymer does not have a label.

106. The method of any one of claims 97-105, wherein the quantifying comprises using a trained machine learning algorithm.

107. The method of any one of claims 97-106, wherein the detecting is performed at single molecule resolution.

108. The method of any one of claims 97-107, further comprising cleaving a terminal subunit from the polymer to generate a n-1 polymer, wherein the n-1 polymer comprises a new terminal subunit.

109. The method of claim 108, further comprising detecting the n-1 polymer using surface-enhanced Raman spectroscopy (SERS).

110. The method of claim 108 or claim 109, wherein the terminal subunit is an N-terminal.

111. The method of claim 108 or claim 109, wherein the terminal subunit is a C-terminal amino acid.

112. The method of any one of claims 97-111, wherein the polypeptide comprises a modified residue with a modification.

113. The method of claim 112, wherein the modification is noncovalently bound to the modified residue.

114. The method of claim 112, wherein the modification is covalently bound to the modified residue.

115. The method of any one of claim 112-114, wherein the modification is a ligand.

116. The method of claim 112, wherein the modification is a post- translational modification.

117. The method of any one of claims 112-116, wherein the identifying comprises identifying the modification.

118. The method of any one of claims 112-117, wherein the identifying comprises identifying the modified residue.

119. The method of any one of claims 112-118, wherein the polypeptide comprises an additional modified residue with an additional modification.

120. The method of claim 119, wherein the modification and the additional modification are different.

121. The method of claim 119, wherein the modification and the additional modification are the same.

122. The method of any one of claims 119-121 , wherein the identifying comprises identifying the additional modification.

123. The method of any one of claims 119-122, wherein the identifying comprises identifying the additional modified residue.

124. The method of any one of claims 97-123, wherein the surface is a functionalized surface comprising a plurality of features.

125. The method of claim 124, wherein the plurality of features comprises pillars, trees, bowties, pyramids, wells, duckbills, crystals, pores, ora combination thereof.

126. The method of claim 124 or claim 125, wherein the plurality of features has a density of at least 1 million features per cm2on the surface.

127. The method of any one of claims 124-126, wherein features of the plurality of features are uniform or stochastic.

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