Introduction of bioorthogonal conjugation sites using noncanonical amino acids for rapid assembly and screening of therapeutically loaded nanobody assemblies

Abstract

The present disclosure includes bioorthogonal conjugation systems and methods of producing same. The systems include at least one ncAA that participates in an orthogonal click reaction owing to its reactive group. The system further includes at least one plasmid having a gene for one or more of: an aminoacyl-tRNA synthetase, a tRNA, and a nanobody. Each nanobody includes suppression stop codons that define locations for conjugation of a ncAA, such that reactive groups of the ncAAs are incorporated at each defined location of the nanobody. Locations are not restricted to N and C terminals of the peptide, facilitating nanobody functionality post-conjugation. Resulting nanobodies are modified with the reactive groups of the ncAA in one or more locations, such that multiple cargos can be conjugated to each nanobody. Additionally, nanobodies can be conjugated to other nanobodies having corresponding reactive groups to form assemblies, which may be used as therapeutic nanobody libraries.

Description

INTRODUCTION OF BIOORTHOGONAL CONJUGATION SITES USING NONCANONICAL AMINO ACIDS FOR RAPID ASSEMBLY AND SCREENING OF THERAPEUTICALLY LOADED NANOBODY ASSEMBLIESCROSS REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to United States Provisional Patent Application Number 63 / 713,855 filed on October 30, 2024, the entire contents of which are hereby incorporated by reference.SEQUENCE LISTING

[0002] An electronic sequence listing (069596-00103.xml; size 24.0 KB; date of creation October 30, 2025) submitted herewith is incorporated by reference in its entirety.FIELD

[0003] The present disclosure relates to systems and methods of introducing conjugation sites using noncanonical amino acids for generation of nanobody assemblies.BACKGROUND

[0004] Bispecific antibodies and antibody fragments have become increasingly relevant therapeutic molecules in immunotherapy. However, these dual treatments are more expensive and associated with a higher occurrence of adverse events compared to single-agent therapies. Thus, over the past two decades, recombinant variable domains of heavy-chain only antibodies - referred to as nanobodies - have become an increasingly valuable diagnostic and therapeutic tool in cancer and other rare diseases as alternative bispecific therapeutics. However, a challenge in the clinical adoption of these nanobody therapies has been the creation of stable, chemically defined, and highly specific conjugation sites within the nanobody. Stable nanobody-protein conjugates formed through genetic fusion are restricted to linkages on the N and C terminals, which can be deleterious to their binding affinity and activity in vivo. Exploration of the nanobody-protein conjugate combinatorial design space is also limited by the cost and labor of genetic fusion, which requires the generation of an individual plasmid per new nanobody-protein combination.SUMMARY

[0005] In an aspect of the present disclosure, a bioorthogonal conjugation system is provided. The system includes at least one non-canonical amino acid (ncAA) configured for participating in an orthogonal click reaction and at least one plasmid comprising a gene for one or more of: an aminoacyl-tRNA synthetase (aaRS), a tRNA, and a nanobody having at least one suppression stop codon that defines a location for conjugation of at least one ncAA.

[0006] In some embodiments, at least one ncAA is a phenylalanine derived azide containing amino acid or a tetrazine containing amino acid. In some instances, the phenylalanine derived azide containing amino acid is 4-Azido-L-phenyl alanine (pAzF). In some instances, the tetrazine containing amino acid is 3-(6-methyl-s-tetrazin-3-yl)phenylalanine (Tet-3.0-Me) or (S)-2-amino- 3-(6-phenyl-l,2,4,5-tetrazin-3-yl)propanoic acid (Tet-4.0-Ph).

[0007] In some embodiments, at least one ncAA is capable of participating in an orthogonal click chemistry reaction. In some instances, the at least one ncAA is capable of participating in spontaneous copper-free Diels- Alder cycloaddition reactions with trans-cyclooctene (TCO) and dibenzo cyclooctyne (DBCO) groups. In some instances, at least one ncAA is capable of participating in spontaneous copper-free azide-alkyne cycloaddition reactions with bicyclo[6.1.0]non-4-yne (BCN) and dibenzo cyclooctyne (DBCO) groups.

[0008] In some embodiments, the location for conjugation of at least one ncAA is surface- exposed on an expressed nanobody of the bioorthogonal conjugation system. In some embodiments, the system includes a first plasmid having genes for a first aaRS and a first tRNA, and a second plasmid having a gene for the nanobody with a first suppression stop codon. In some instances, the first tRNA is configured to incorporate a first ncAA at the first suppression stop codon. In some cases, the nanobody further includes a second suppression stop codon, and further includes a third plasmid having genes for a second aaRS and a second tRNA. In some cases, the second tRNA is configured to incorporate a second ncAA at the second suppression stop codon, such that an expressed nanobody includes dual, bioorthogonal conjugation sites at the first ncAA and second ncAA.

[0009] In some embodiments, the system further includes one plasmid having genes for the aaRS, the tRNA, and the nanobody with the suppression stop codon. In some instances, the tRNA is configured to incorporate at least one ncAA at the suppression stop codon such that an expressed nanobody includes a biorthogonal conjugation site at the ncAA.

[0010] In another aspect of the present disclosure, there is presented a modified protein for modular conjugation, the modified protein having at least one bioorthogonal conjugation site where a ncAA is located, the ncAA capable of participating in an orthogonal click chemistry reaction.

[0011] In some embodiments, the modified protein has at least two bioorthogonal conjugation sites, each where a different ncAA is located. In some instances, each bioorthogonal conjugation site is configured to conjugate a different cargo.

[0012] In yet another aspect of the present disclosure, a protein assembly is presented that includes one or more modified protein of presented embodiments. In some embodiments, each bioorthogonal conjugation site is configured to conjugate a different cargo. In some embodiments, the one or modified proteins are assembled with a linker selected from the group consisting of TCO-PEG-TCO, DBCO-PEG-DBCO, and TCO-PEG-DBCO.

[0013] In yet another aspect of the present disclosure, there is presented a method expressing a nanobody with one or more bioorthogonal conjugation sites. The method includes transforming into a bacterial host at least one plasmid having a gene for one or more of an aaRS, a tRNA, and a nanobody having at least one suppression stop codon that defines a location for conjugation of the at least one ncAA. The method includes expressing the nanobody with a bioorthogonal conjugation site at each incorporated ncAA.

[0014] In some embodiments, the bacterial host is transformed with one plasmid having genes for the aaRS, the tRNA, and the nanobody with the suppression stop codon. In some embodiments, the bacterial host is co-transformed with a first plasmid having genes for a first aaRS and a first tRNA, a second plasmid having genes for a second aaRS and a second tRNA, and a third plasmid having a gene for the nanobody with a first suppression stop codon and a second suppression stop codon.

[0015] In yet another aspect of the present disclosure, there is provided a method of treating or preventing a condition in a subject in need thereof. The method includes administering to the subject a therapeutically effective amount of a modified protein. The modified protein includes at least one cargo conjugated at a bioorthogonal conjugation site of the modified protein where a noncanonical amino acid (ncAA) is located.

[0016] In some embodiments, the condition is cancer or an autoimmune disease. In some embodiments, the modified protein penetrates the blood-brain barrier. In some embodiments, theat least one cargo is conjugated via orthogonal click chemistry reaction. In some embodiments, the at least one cargo is selected from the group consisting of small molecules, oligonucleotides, and peptides. In some embodiments, the modified protein has at least two bioorthogonal conjugation sites, each where a different ncAA is located. In some instances, a different cargo is located at each bioorthogonal conjugation site.BRIEF DESCRIPTION OF THE FIGURES

[0017] The present disclosure same can be better understood, by way of example only, with reference to the following drawings. The elements of the drawings are not necessarily to scale relative to each other, emphasis instead being placed upon clearly illustrating the principles of the disclosure.

[0018] FIG. l is a schematic representation of an exemplary system of the present disclosure for modifying proteins for position-independent conjugation. Noncanonical amino acids (ncAAs) are incorporated into peptides using select tRNA / aaRS pairs. Such systems employ one or more suppression stop codon, such as the amber and ochre stop codons, to add the ncAAs with special reactive groups to the peptide at precise locations. The resulting exemplary peptide includes dual bioorthogonal conjugation sites where the ncAAs with special reactive groups are incorporated.

[0019] FIG. 2 is a schematic representation of the expression of nanobodies, which exhibit levels of binding and specificity similar to antibodies while maintaining a size of approximately 15 kDa. Nanobodies are the recombinantly expressed variable heavy chain of HCAbs, which are found in Camelids and some sharks. Such nanobodies may exhibit increased diffusion through the extracellular matrix relative to antibodies owing to their smaller size.

[0020] FIG. 3 is a schematic representation of typical applications of nanobodies using genetic fusion. Such genetic fusion methods of previous use limit linkages to the ends of the peptides, though these end-to-end linkages can interfere with binding. Further, such previous methods may require many plasmids to produce protein libraries, which increases time and labor.

[0021] FIG. 4 is a schematic representation of protein fusion limiting structure driven function. As previous fusion techniques limit protein assemblies to having junctions at N or C terminals, this limitation can impact protein function and stability due to steric hinderance and biochemical conflicts at interfaces.

[0022] FIG. 5 is a schematic representation of previous molecular bioconjugation approaches. In efforts to avoid or mitigate functional limitations brought about by fusion protein approaches, modular approaches have been employed. Such approaches, however, still rely on either genetic fusion (HaloTag and Spycatcher) or N and C terminal restrictions (Sortase).

[0023] FIG. 6 is a schematic representation of an exemplary rapid assembly approach of preparing therapeutically loaded modular protein assemblies according to the present disclosure. Such approaches allow for continual evaluation of ncAA placement in peptides, with effective designs eventually identified to generate desired modular immunotherapy assemblies.

[0024] FIG. 7 is a schematic representation of an exemplary system of the present disclosure for modifying proteins for position-independent conjugation. ncAAs are incorporated into peptides using select tRNA / aaRS pairs. Such systems employ a suppression stop codon, such as the amber stop codon, to add the ncAA with special reactive groups to the peptide at a precise location. The resulting exemplary peptide includes at least one bioorthogonal conjugation site where each ncAA with special reactive group is incorporated.

[0025] FIG. 8 is a schematic representation of the suppression vectors pUltra and pDule, which are configured for amber suppression in the disclosed methods. pUltra requires mutagenesis to enable amber suppression.

[0026] FIG. 9 is a schematic representation of exemplary proteins that are modified for position independent conjugation using 4-Azido-Lphenylalanine (pAzF) (left), bicyclo[6.1.0]non-4-yn-9-ylmethanol lysine (BCNK) (middle), and 3-(6-methyl-s-tetrazin-3- yl)phenylalanine (Tet-3.0-Me) (right) amino acids containing reactive groups.

[0027] FIG. 10 is a graphical representation of LC-MS spectra data for Boc-protected Tet- 3.0-Me following purification using flash chromatography.

[0028] FIG. 11 is a graphical representation of LC-MS spectra data for crude Boc-protected Tet-4.0-Ph.

[0029] FIG. 12 is a schematic representation of proteins modified with ncAAs having reactive groups, along with corresponding conjugation reactive groups to allow the position independent conjugations of the present disclosure. Azide displaying ncAAs can participate in spontaneous copper-free azide-alkyne cycloaddition reactions with BCN and DBCO groups; BCN displaying ncAAs can participate in spontaneous copper-free azide-alkyne cycloaddition reactions with azide groups; tetrazine displaying ncAAs can participate in IEDDA click reactionsbetween the diene (e.g. a 1,2, 4, 5 tetrazine) and a dienophile (e g. a strained trans-cyclooctene (sTCO)).

[0030] FIG. 13 is a schematic representation of an IEDDA click chemistry conjugation reaction between tetrazine containing ncAAs and strained trans-cyclooctene (sTCO) reactive groups to form a cycloaddition product.

[0031] FIG. 14 is a schematic representation of conjugation of proteins with positionindependent methods of the present disclosure.

[0032] FIG. 15 is a schematic representation of noncanonical amino acids offering scalable conjugation by insertion into peptides. The ncAAs may present reactive groups in a position independent manner, where peptides displaying corresponding reactive groups may be conjugated as protein assemblies. Alternatively, or in addition, therapeutic cargo may be conjugated to reactive group-di splaying peptides at predetermined locations afforded by the ncAA incorporation methods of the present disclosure.

[0033] FIG. 16 is a schematic representation of noncanonical amino acids incorporated into proteins at preselected positions, where the modified proteins are configured into protein assembly libraries. A 1 :2 plasmid to assembly ratio is possible in generating such protein assembly libraries.

[0034] FIG. 17 is a schematic representation of noncanonical amino acids for modular conjugation systems. The ncAAs may present reactive groups in a position independent manner such that proteins can be conjugated together as assemblies, and such that multiple cargos can be conjugated to proteins in a one pot assembly format.

[0035] FIG. 18 is a gel image representation of the expression of wild type nEGFRvIII. Lane 1 represents a 10 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. Lane 2 represents a 25 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. Lane 3 represents a 50 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. Lane 4 represents a flowthrough from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII wild type following 1 hr incubation with purified bdSENPl protease to remove the poly-histidine tag. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. Lane 5 represents a 150 mM imidazole wash from IMAC columnloaded with clarified lysate of expression culture for nEGFRvIII Wild type. The strong band at approximately 32 kDa indicates removal of the bdSENPl protease while the light band just under the 15 kDa marker indicates target nEGFRvIII nanobody not removed during the elution step. Lane 6 represents a Bio-Red Precision Plus Protein Standard.

[0036] FIG. 19 is a gel image and graphical representation of single site incorporation of noncanonical amino acids via SDS PAGE (left panel) and a plot of fluorescence marking the EGFRvIII variant. For the gel image, Lane 1 represents flowthrough from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII QI Tet-3.0-Me following 1 hr incubation with purified bdSENPl protease to remove the poly-histidine tag. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. Lane 2 represents a purified wild type nEGFRvIII. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. This band is similar in size to the band in lane 1, indicating they are of identical size and therefore identity. Lane 3 represents a Bio-Red Precision Plus Protein Standard. Regarding the right panel plot, fluorescence demonstrates labeling of iFluor® 488 TCO dye to only the nEGFRvIII variant with the first amino acid (QI) replaced with Tet- 3.0-Me via ncAA incorporation. Excitation and emission wavelengths, selected based on maximum values for the dye, are 476 nm / 516 nm, respectively. This, along with the SDS-PAGE gel shift confirms the expression of the nEGFRvIII variant and the functionality of the Tet-3.0- Me ncAA at this location.

[0037] FIG. 20 is a gel image and graphical representation of single site incorporation of noncanonical amino acids via SDS PAGE (left panel) and a plot of fluorescence marking the EGFRvIII variant. For the gel image, Lane 1 represents flowthrough from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII S124 pAzF following 1 hr incubation with purified bdSENP l protease to remove the poly-histidine tag. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. Lane 2 represents purified wild type nEGFRvIII. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. This band is similar in size to the band in lane 1, indicating they are of identical size and therefore identity. Lane 3 represents a Bio-Red Precision Plus Protein Standard. Regarding the right panel plot, fluorescence demonstrates labeling of sulfo-DBCO Cy5dye to only the nEGFRvIII variant with the last amino acid (SI 24) replaced with pAzF via ncAA incorporation. Excitation and emission wavelengths, selected based on maximum values for the dye, are 647 nm / 655 nm, respectively. This, along with the SDS-PAGE gel shift confirms the expression of the nEGFRvIII variant and the functionality of the pAzF ncAA at this location.

[0038] FIG. 21 is a graphical representation of dual site incorporation of noncanonical amino acids. The plots demonstrate the labeling of iFluor® 488 TCO and sulfo-DBCO Cy5 dye to only the nEGFRvIII variant with the first amino acid (QI) replaced with Tet-3.0-Me and last amino acid (S124) replaced with pAzF via ncAA incorporation. Excitation and emission wavelengths, selected based on maximum values for the dye, are 476 nm / 516 nm (left plot) and 647nm / 655nm (right plot), respectively. This, along with the SDS-PAGE gel shift confirms the expression of the nEGFRvIII variant and the functionality of the Tet-3.0-Me and pAzF ncAA at this location.

[0039] FIG. 22 is an image representation of target affinity retention following dual incorporation. Noncanonical amino acids incorporated into nEGFRvIII were dual labeled with fluorophores and incubated with EGFR positive EMT6 cells. iFluor® 488 TCO is visualized in the top image, while sulfo-DBCO Cy5 dye is visualized in the bottom image. Colocalization of fluorescence from the labels is greater than 95%.

[0040] FIG. 23 A is a schematic representation of construction of a single plasmid GCE system. Chemical structures of the two ncAAs used in the present disclosure, referred to as Tet3.0 and AzK, are shown.

[0041] FIG. 23B is a schematic representation of construction of a single plasmid GCE system. The pRESS plasmid used to express proteins of interest with site-specific ncAA incorporation is displayed.

[0042] FIG. 23C is a schematic representation of construction of a single plasmid GCE system. The reporter pRESS MBP-mCherry and pRESS-Tet3.0 MBP-mCherry plasmids after construction using the Bsal or PaqCI sites for golden gate assembly are shown.

[0043] FIG. 25 is a schematic representation of the SUPER pET plasmid.

[0044] FIG. 24A is a graphical representation of improved conjugation efficiency with bifunctional linkers. The graph shows conversion percentage for QlpAzF and QI AzK with nCD276.

[0045] FIG. 24B is a gel image representation of improved conjugation efficiency with bifunctional linkers. The higher molecular weight species in lanes 5, 6, and 7 represents nCD276-PEG12-C21 as a bifunctional product with the PEG12 linker.

[0046] FIG. 24C is a schematic representation of improved conjugation efficiency with bifunctional linkers. The schematic shows the bifunctional linking of the modified proteins using a PEG12 linker.

[0047] FIG. 26A is a schematic representation of crosslinking and characterization of fusions using nanobody building blocks. Wild-type nanobodies nPD-LlWT, nCTLA4WTand genetic fusions PN-Ccand Pc-CNproduced as controls.

[0048] FIG. 26B is a schematic representation of crosslinking and characterization of fusions using nanobody building blocks. Mechanistic action of dual-administration and single-agent bispecific immune checkpoint blockades against PD-L1 and CTLA-4.

[0049] FIG. 26C is a schematic representation of crosslinking and characterization of fusions using nanobody building blocks. Crosslinking of nPD-LlQ1Azk, nPD-Llsl l6Azk, nCTLA-4Q1Tet3 0, and nCTLA-4sl23Tet3 0using the commercial TCO-PEG12-DBCO linker at physiological conditions to create genetic fusion mimics ( l)-(4).

[0050] FIG. 27A is a graphical representation of crosslinking and characterization of fusions using nanobody building blocks. ESI-MS data validating ncAA incorporation into nPD-LlQ1Azk(middle plot) and nPD-Llsl l6Azk(bottom plot) with comparison to nPD-Ll WT (top plot).

[0051] FIG. 27B is a graphical representation of crosslinking and characterization of fusions using nanobody building blocks. ESI-MS data validating ncAA incorporation into nCTLA- 4QiTet3 o (middle plot)and nCTLA-4sl23Tet3 0(bottom plot) with comparison to nCTLA-4 WT (top plot).

[0052] FIG. 27C is a graphical representation of crosslinking and characterization of fusions using nanobody building blocks. Flow cytometry may evaluate (1 )-(4) target extracellular CTLA-4 and PD-L1 through co-localization of MDA-MB-231 cells overexpressing PD-L1 and Jurkat cells overexpressing CTLA-4 / PD1.

[0053] FIG. 28 A is a schematic representation of the mutation of nanobody framework residues. Structure of nPD-Ll modeled in Rosetta with sites for ncAA incorporation highlighted, with the favorability of these sites for mutation quantified using scored for RMSD and AAG.

[0054] FIG. 28B is a graphical representation of the mutation of nanobody framework residues. ESI-MS data validating the ncAA incorporation for nPD-LlR45AzKand nPD-LlK75Azk.

[0055] FIG. 29 is a schematic representation of the impact of bispecific topology on function in vitro. Crosslinking of nPD-LlR45Azk, nPD-LlK75Azk, nCTLA-4Q1Tet3 0, and nCTLA-4sl23Tet3 0to produce the geometrically unique fusions (5)-(8).

[0056] FIG. 30A is a gel image representation of the impact of bispecific topology on function in vitro. SDS PAGE image data (right panel) demonstrates the generation of bi specific nanobodies as demonstrated in the lane schematics in the left panel.

[0057] FIG. 30B is a graphical representation of the impact of bispecific topology on function in vitro. Flow cytometry may evaluate fusion mediated codocalization of MDA-MB- 231 cells with CD3+ T-cells isolated from PBMCs.DETAILED DESCRIPTION

[0058] Embodiments described herein can be understood more readily by reference to the following detailed description and examples. Elements, apparatus and methods described herein, however, are not limited to the specific embodiments presented in the detailed description and examples. It should be recognized that these embodiments are merely illustrative of the principles of the present disclosure. Numerous modifications and adaptations will be readily apparent to those of skill in the art without departing from the spirit and scope of the disclosure.

[0059] In addition, all ranges disclosed herein are to be understood to encompass any and all subranges subsumed therein. For example, a stated range of “1.0 to 10.0” should be considered to include any and all subranges beginning with a minimum value of 1.0 or more and ending with a maximum value of 10.0 or less, e.g., 1.0 to 5.3, or 4.7 to 10.0, or 3.6 to 7.9.

[0060] All ranges disclosed herein are also to be considered to include the end points of the range, unless expressly stated otherwise. For example, a range of “between 5 and 10,” “from 5 to 10,” or “5-10” should generally be considered to include the end points 5 and 10.

[0061] Further, when the phrase “up to” is used in connection with an amount or quantity, it is to be understood that the amount is at least a detectable amount or quantity. For example, a material present in an amount “up to” a specified amount can be present from a detectable amount and up to and including the specified amount.

[0062] Additionally, in any disclosed embodiment, the terms “substantially,” “approximately,” and “about” may be substituted with “within [a percentage] of’ what is specified, where the percentage includes 0.1, 1, 5, and 10 percent.

[0063] Disjunctive language such as the phrase “at least one of X, Y, or Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to present that an item, term, etc., may be either X, Y, or Z, or any combination thereof (e.g., X, Y, and / or Z). Thus, such disjunctive language is not generally limited to, and should not, imply that certain embodiments require at least one of X, at least one of Y, or at least one of Z to each be present.

[0064] The terms “prevention”, “prevent”, “preventing”, “suppression”, “suppress” and “suppressing” as used herein refer to a course of action (such as delivery or administration of a drug, prodrug, or compound) initiated prior to the onset of a clinical manifestation of a disease state or condition so as to prevent or reduce such clinical manifestation of the disease state or condition. Such preventing and suppressing need not be absolute to be useful.

[0065] The terms “treatment”, “treat” and “treating” as used herein refers a course of action (such as delivery or administration of a drug, prodrug, or compound) initiated after the onset of a clinical manifestation of a disease state or condition so as to eliminate or reduce such clinical manifestation of the disease state or condition. Such treating need not be absolute to be useful.

[0066] In this disclosure terms such as “administering” or “administration” include acts such as prescribing, dispensing, giving, or taking a substance such that what is prescribed, dispensed, given, or taken is actually contacts the patient’s body externally or internally (or both). It is specifically contemplated that instructions or a prescription by a medical professional to a subject or patient to take or otherwise self-administer a substance is an act of administration.

[0067] The term “in need of treatment” as used herein refers to a judgment made by a caregiver that a patient requires or will benefit from treatment. This judgment is made based on a variety of factors that are in the realm of a caregiver's expertise, but that includes the knowledge that the patient is ill, or will be ill, as the result of a condition that is treatable by a method or compound of the present disclosure.

[0068] The term “in need of prevention” as used herein refers to a judgment made by a caregiver that a patient requires or will benefit from prevention. This judgment is made based on a variety of factors that are in the realm of a caregiver's expertise, but that includes theknowledge that the patient will be ill or may become ill, as the result of a condition that is preventable by a method or compound of the disclosure.

[0069] The term “individual”, “subject” or “patient” as used herein refers to any animal, including mammals, such as mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, or primates, and humans. The term may specify male or female or both, or exclude male or female.

[0070] The term an “effective amount,” “sufficient amount” or “therapeutically effective amount” as used herein is an amount of a compound of the disclosure that is sufficient to achieve a beneficial or desired result, including clinical results. In certain embodiments, an effective amount is an amount of the compound of the disclosure that avoids or substantially attenuates undesirable side effects.

[0071] In certain embodiments, the “effective amount,” “sufficient amount” or “therapeutically effective amount” in the context of the present disclosure increases an immune response in a subject suffering from cancer or an autoimmune disease or disorder by at least 5%, preferably at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, 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%, or 99%. In some embodiments, the “effective amount,” “sufficient amount” or “therapeutically effective amount” in the context of the present disclosure increases the survival rate of a subject suffering from a disease or disorder by at least 5%, preferably at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, 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%, or 100%. In each of the foregoing, when a reduction or increase is specified, such reduction or increase may be determined with respect to a subject that has not been treated with a compound of the disclosure and that is diagnosed as suffering from a disease or disorder.

[0072] The term “tissue” as used herein refers to an organ, part of an organ, cellular structure(s), and / or group of cells in the body of a subject. Including, but not limited to, lung, an embryo, a fetus, placenta, liver, kidney, spleen, brain, testis, or uterus.

[0073] The term “protein,” “peptide,” “polypeptides” and “oligopeptides” refers to chains of amino acids (typically L-amino acids) whose alpha carbons are linked through peptide bonds formed by a condensation reaction between the carboxyl group of the alpha carbon of one aminoacid and the amino group of the alpha carbon of another amino acid. Typically, the amino acids making up a protein are numbered in order, starting at the amino terminal residue and increasing in the direction toward the carboxy terminal residue of the protein.

[0074] The term “nucleotide” as used herein refers to any such known groups, natural or synthetic. It includes conventional DNA or RNA bases (A, G, C, T, U), base analogs (e.g., inosine, 5-nitroindazole and others), imidazole-4-carboxamide, pyrimidine or purine derivatives (e.g., modified pyrimidine base 6H,8H-3,4-dihydropyrimido[4,5-c][l,2]oxazin-7-one (sometimes designated "P" base that binds A or G)) and modified purine base N6-methoxy-2,6- diaminopurine (sometimes designated "K" base that binds C or T), hypoxanthine, N-4-methyl deoxyguanosine, 4-ethyl-2'-deoxycytidine, 4,6-difluorobenzimidazole and 2,4-difluorobenzene nucleoside analogues, pyrene-functionalized LNA nucleoside analogues, deaza- or aza-modified purines and pyrimidines, pyrimidines with substituents at the 5 or 6 position and purines with substituents at the 2, 6 or 8 positions, 2-aminoadenine (nA), 2-thiouracil (sU), 2-amino-6- methylaminopurine, O-6-methylguanine, 4-thio-pyrimidines, 4-amino-pyrimidines, 4- dimethylhydrazine-pyrimidines, O-4-alkyl-pyrimidines and hydrophobic nucleobases that form duplex DNA without hydrogen bonding. Nucleobases can be joined together by a variety of linkages or conformations, including phosphodiester, phosphorothioate or methylphosphonate linkages, peptide-nucleic acid linkages.

[0075] The term “polynucleotide” as used herein refers to a multimeric compound comprising nucleotides linked together to form a polymer, including conventional RNA, DNA, LNA, BNA, copolymers of any of the foregoing, and analogs thereof.

[0076] The term “nucleic acid” as used herein refers to a single stranded polynucleotide or a duplex of two polynucleotides. Such duplexes need not be annealed at all locations and may contain gaps or overhangs.

[0077] Since 2014, the FDA has approved 10 additional bispecific antibody-based therapies for treating cancer, hematologic, and ocular diseases. For solid tumors, dual administration of antibodies inhibiting programmed death 1 (PD-l) / programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte-associated protein 4 (CTLA-4) immune checkpoints has demonstrated efficacy and safety in several cancer types. However, this dual treatment regimen is more expensive and associated with a higher occurrence of adverse events compared to single-agent therapies, making PD-1 / PD-L1 and CTLA-4 increasingly investigated targets for a bispecifictherapy. Recently, a novel anti-PD-Ll and CTLA-4 bispecific antibody, KN046, was well tolerated and displayed promising anti-tumor efficacy in patients with advanced solid tumors in a phase I clinical trial.

[0078] Another rising topic in the field of bispecific antibodies has been the development of novel variable heavy chain-only single domain antibody, or nanobody, based bispecific alternatives. A significant motivation for this trend is the structural simplicity and high physicochemical stability of nanobodies that enable efficient production in microbial hosts. This results in significantly lower manufacturing cost, complexity, and time than standard monoclonal antibodies and antibody fragment formats, such as single-chain variable fragments. Most nanobody conjugates in development leverage flexible peptide linkers to genetically fuse domains. While this approach offers a stable covalent linkage, it must follow canonical N- to C- terminal expression rules and only produces fusions that differ in the linear arrangement of domains. Therefore, in two-component systems, like bispecific single-chain variable fragments, only two isoforms (A-B and B-A) are possible. This limitation has led to the therapeutic impact of domain topology for bispecific molecules being largely understudied. The importance of geometric control over targeting domains is highlighted by demonstrating orientation-dependent improvement of antigen affinity or virus neutralization for a nanobody surface-functionalized nanoparticles and tri-specific nanobody conjugates. To achieve this geometric control, individual antigen-targeting domains with reactive handles can be expressed independently, followed by covalent crosslinking through polymeric linkers. However, to ensure high product homogeneity, these handles must be orthogonal to each other, and ideally, react rapidly under physiological conditions.

[0079] To address this requirement, methods such as strain-promoted azide-alkyne cycloaddition (SPAAC) and inverse electron-demand Diels-Alder cycloaddition (IEDDA) reactions are utilized. SPAAC leverages strained cyclooctenes (e.g. a dibenzocyclooctyne (DBCO)) to spontaneously conjugate azide and cycloalkyne residues to produce a tetrazole cycloaddition product. IEDDA click reactions rely on the cycloaddition between a molecule containing a diene (e.g. a 1,2, 4, 5 tetrazine) and one containing a dienophile (e.g. a strained transcyclooctene (sTCO)). IEDDA click reactions are extremely selective and demonstrate rapid conjugation kinetics, with reported second-order rate constants on the order of 106M'1s'1.

[0080] Herein, various non-canonical amino acids (ncAAs) with residues capable of participating in SPAAC and IEDDA have been synthesized and characterized, along with the corresponding aminoacyl-tRNA synthetase / suppressor tRNA pair (aaRS / tRNA) needed to achieve genetic code expansion (GCE). Leveraging the site-specific control over ncAA incorporation, GCE technology is used in a modular platform to generate eight geometrically distinct fusions of an anti-PD-Ll (nPD-Ll) and anti-CLTA-4 nanobody (nCTLA-4). Using an azide- and tetrazine-modified lysine and phenylalanine, four sites within nPD-Ll and two sites within nCTLA-4 were independently mutated to create fusions with unique topologies, crosslinked through a commercially available heterobifunctional linker. This platform may be used for high-throughput drug discovery applications with the high reaction efficiency, product homogeneity, and facile purification of the fusions. A parallel in vitro screening of these novel molecules may rapidly inform critical decisions on domain orientation when engineering new bispecific immunotherapeutics.

[0081] The present disclosure is directed to a platform to accelerate the generation of nanobody assembly libraries - specifically mono- and bi-specific conjugates of nanobody domains - without the need for genetic fusion to improve the retention of binding activity (FIG. 1). The platforms and methods utilize: (1) the synthesis of functionalized noncanonical amino acids (ncAAs) capable of orthogonal click reactions and (2) suppression and reprogramming of stop codons for incorporation of these ncAAs into nanobodies to provide defined, stable, bioorthogonal conjugation sites. The phenylalanine derived tetrazine containing amino acids, referred to as Tet-3.0-Me and Tet-4.0-Ph, are synthesized according to procedures known in the art. These amino acids can participate in spontaneous copper-free Diels- Alder cycloaddition reactions with trans-cyclooctene (TCO) and dibenzo cyclooctyne (DBCO) groups. Similarly, the phenylalanine derived azide containing amino acids, referred to as 4-Azido-Lphenylalanine (pAzF) (purchased from BroadPharm), may be purchased from commercial vendors and can participate in spontaneous copper-free azide-alkyne cycloaddition reactions with Bicyclononyne (BCN) and DBCO groups.

[0082] In biology, the function of macromolecules is dictated by their function. Alternations or modifications to these structures often result in a loss of function, which can be irreversible. Examples include carbohydrate isomer configurations, DNA / nucleic acids, and proteins. In proteins the primary structure (AA sequence), secondary, tertiary, and quaternary structure allimpact the end function of proteins, and modifications or alterations at any stage can impact function. A relevant protein function is binding and activation / inhibition of target receptors by targeting proteins (antibodies, single-chain variable fragments, nanobodies, etc.). The use of antibodies is one way to reinvigorate the natural anticancer immune responses. They can serve as both therapeutic molecules by blocking / activating ligands that result in an anti-cancer immune response, and they can also deliver drugs directly to tumors due their ability to target specific surface markers / antigens. However, antibodies are relatively large (approximately 150 kDa), and this size can be a problem at a tumor site with narrow and often difficult to penetrate blood vessels are present. If antibodies do manage to leave the blood vessels and move toward the tumor, their large size makes passive diffusion through the extracellular matrix (ECM) and into the tumor very challenging.

[0083] One potential solution to this size issue is the use of nanobodies, which are the recombinantly expressed variable heavy chain (the part that does the binding) of HCAbs, which are found in Camelids and some sharks (FIG. 2). Nanobodies provide the same degree of antigen specificity and affinity as mAbs but are approximately a tenth of the size of antibodies (about 15 kDa), making passive diffusion easier. They are also generally cationic, which aids in transport across the negatively blood vessels and ECM.

[0084] Most applications using nanobodies require them to be conjugated to either another nanobody, protein, or small molecules. Genetic fusion is the most common way to link two proteins; however, it is limited to end-to-end linkages, which may be harmful by interfering with binding. Genetic fusion also requires more plasmids and labor for applications like screening multiple combinations of therapeutic nanobodies (FIG. 3). For example, for five nanobodies, ten plasmids are needed; for six nanobodies, 15 plasmids are needed. Further, if the orientation is to be changed, even more plasmids are needed.

[0085] Genetic fusion / fusion protein strategies have been a work horse in molecular / synthetic biology for generating protein constructs that confer dual functionality, for example enzymes with a florescent domain or bispecific binding proteins (nanobodies). However, these fusion techniques limit protein assemblies to junctions at the N and C terminals and can have negative impacts on function and stability of the protein domains due to steric limitations and biochemical conflicts at the interface of the proteins (FIG. 4). To circumvent the limitations of fusion proteins and offer a more customizable approach to creating protein assemblies, more modularapproaches for protein-protein / peptide conjugation have been created. These include the Spycatcher / Spytag, HaloTag, and SortAse approaches which also offer methods to load drugs onto proteins with high site specificity (FIG. 5). However, the major limitation to all of these approaches is an inherent reliance on either genetic fusion (Spycatcher / HaloTag) or remaining restricted to N and C terminals (SortAse).

[0086] The ncAA approach of the present disclosure can have a wide range of functional / reactive groups and thus allows for addition of amino acids with special reactive groups to the proteins. The disclosed methods allow precise control over amino acid location and can provide sites for conjugation of the proteins to small molecules. This results in a reduction of plasmids needed to generate therapeutic nanobody libraries (FIG. 6). Such libraries may, in some instances, include a therapeutic nanobody panel including nanobody assemblies where a first nanobody of the nanobody assembly includes tumor cell engaging domains, and a second nanobody of the nanobody assembly includes immune cell engaging domains. Further, libraries may be screened in vitro, and novel candidates may be evaluated in vivo.Sequences

[0087] The sequences described in Table 1 are exemplary design sequences of the present disclosure.Table 1. Design sequences of the present disclosureCompounds of the Disclosure

[0088] The modified proteins and protein assemblies of the disclosure can be produced using various techniques known in the art and techniques and methods described herein. For recombinant production of polypeptides or nanobodies of the disclosure including a heterologous amino acid sequence, nucleic acid encoding polypeptides or nanobodies of the disclosure and a desired heterologous amino acid sequence can be synthesized and inserted into one or more vectors for further cloning and / or expression in host cells. Polypeptides and nanobodies of the disclosure lacking a heterologous sequence may be produced in the same manner, with the exception the cDNA does not encode the heterologous amino acid sequence. In some embodiments, polypeptides and nanobodies of the disclosure having one or more amino acid substitutions, insertions or deletions are generated by site-directed mutagenesis or other methods known in the art. Such nucleic acid may be readily isolated and sequenced using conventional procedures. Expression vectors comprising polypeptides and nanobodies of the disclosure can be transfected into the host cells or stably expressed in the host cells. Suitable host cells for cloning or expression of the polypeptides and nanobodies of the disclosure include prokaryotic or eukaryotic cells. Expression of the polypeptides and nanobodies of the disclosure can be achieved using yeast, insect, or mammalian expression systems. The expressed polypeptides and nanobodies can be purified using methods known in the art. For example, polypeptides or nanobodies of the disclosure may be purified by affinity chromatography using an appropriatemonoclonal antibody or using the optional sequence tag disclosed herein. The purified polypeptides and nanobodies can be verified by using SDS PAGE or Western Blot analysis.

[0089] A skilled artisan will be able to determine suitable substitutions, insertions and deletions, including combinations thereof, of a polypeptide or nanobody using techniques known in the art. For identifying suitable areas of a polypeptide or nanobody that may be changed without destroying activity, one skilled in the art may target areas not believed to be important for activity. For example, when homologous polypeptides with similar activities from the same species or from other species are known, one skilled in the art may compare the amino acid sequence of a polypeptide described herein to such homologous polypeptides. With such a comparison, one can identify residues and portions of the molecules that are conserved among similar polypeptides. It will be appreciated that changes in areas of a polypeptide described herein that are not conserved relative to such homologous polypeptide would be less likely to adversely affect the biological activity and / or structure of a polypeptide described herein. One skilled in the art would also know that, even in relatively conserved regions, one may substitute chemically similar amino acids for the naturally occurring residues while retaining activity (for example, conservative amino acid substitutions). Therefore, even areas that may be important for biological activity or for structure may be subject to such amino acid substitutions without destroying the biological activity or without adversely affecting the polypeptide structure.

[0090] The deletions, insertions, and substitutions can be selected, as would be known to one of ordinary skill in the art, to generate a desired polypeptide or nanobody variant. For example, it is not expected that deletions, insertions, and substitutions in a non-functional region of a polypeptide or nanobody would alter activity. Likewise conservative amino acid substitutions and / or substitution of amino acids with similar hydrophilic and / or hydropathic index values are expected to be tolerated in a conserved region and polypeptide activity may be conserved with such substitutions.Medicaments and Pharmaceutical Compositions

[0091] Useful compositions of the present disclosure may comprise one or more compounds of the disclosure as described above. In one embodiment, such compounds are in the form of compositions, such as but not limited to, pharmaceutical compositions and medicaments. The compositions disclosed may comprise one or more of such compounds, in combination with a pharmaceutically acceptable carrier. To form a pharmaceutically acceptable composition suitablefor administration, such compositions will contain a therapeutically effective amount of a compound(s).

[0092] The compositions of the disclosure may be used in the treatment and prevention methods of the present disclosure. Such compositions are administered to a subject in amounts sufficient to deliver a therapeutically effective amount of the compound(s) so as to be effective in the treatment and prevention methods disclosed herein. The therapeutically effective amount may vary according to a variety of factors such as, but not limited to, the subject’s condition, weight, sex and age. Other factors include the mode and site of administration. The compositions may be provided to the subject in any method known in the art. Exemplary routes of administration include, but are not limited to, subcutaneous, intravenous, topical, epicutaneous, intramuscular, and pulmonary. The compositions of the present disclosure may be administered only one time to the subject or more than one time to the subject. Furthermore, when the compositions are administered to the subject more than once, a variety of regimen may be used, such as, but not limited to, one per day, once per week, once per month or once per year. The compositions may also be administered to the subject more than one time per day. The therapeutically effective amount of the modified proteins and protein assemblies and appropriate dosing regimens may be identified by routine testing in order to obtain optimal activity, while minimizing any potential side effects. In addition, co-administration or sequential administration of other agents may be desirable.

[0093] The compositions of the present disclosure may be administered systemically, such as by intravenous administration, or locally such as by subcutaneous injection. The compositions of the present disclosure may further comprise agents which improve the solubility, half-life, absorption, etc. of the compound(s). Furthermore, the compositions of the present disclosure may further comprise agents that attenuate undesirable side effects and / or or decrease the toxicity of the compounds(s).

[0094] The compositions of the present disclosure can be administered in a wide variety of dosage forms for administration. For example, compositions can be administered in forms, such as, but not limited to, solutions, suspensions, emulsions, or solutions for intravenous administration or injection. Any of the foregoing may be modified to provide for timed release and / or sustained release formulations.

[0095] In the present disclosure, the compositions may further comprise a pharmaceutically acceptable carrier. Such carriers include, but are not limited to, vehicles, adjuvants, surfactants, suspending agents, emulsifying agents, diluents, excipients, binders, lubricants, and buffering agents. Typically, the pharmaceutically acceptable carrier is chemically inert to the active compounds and has no detrimental side effects or toxicity under the conditions of use. The pharmaceutically acceptable carriers can include polymers and polymer matrices. The nature of the pharmaceutically acceptable carrier may differ depending on the particular dosage form employed and other characteristics of the composition.

[0096] Formulations suitable for parenteral administration include aqueous and non-aqueous, isotonic sterile injection solutions, which can contain anti-oxidants, buffers, bacteriostats, and solutes that render the formulation isotonic with the blood of the patient, and aqueous and nonaqueous sterile suspensions that can include suspending agents, solubilizers, thickening agents, stabilizers, and preservatives. The compound(s) may be administered in a physiologically acceptable diluent, such as a sterile liquid or mixture of liquids, including water, saline, aqueous dextrose and related sugar solutions, an alcohol, such as ethanol, isopropanol, or hexadecyl alcohol, glycols, such as propylene glycol or polyethylene glycol such as poly(ethyleneglycol) 400, glycerol ketals, such as 2,2-dimethyl-l,3-dioxolane-4-methanol, ethers, an oil, a fatty acid, a fatty acid ester or glyceride, or an acetylated fatty acid glyceride with or without the addition of a pharmaceutically acceptable surfactant, such as, but not limited to, a soap, an oil or a detergent, suspending agent, such as, but not limited to, pectin, carbomers, methylcellulose, hydroxypropylmethylcellulose, or carboxymethylcellulose, or emulsifying agents and other pharmaceutical adjuvants. Suitable preservatives and buffers can be used in such formulations. Methods of Treatment and Prevention

[0097] The teachings of the present disclosure provide for the treatment and / or prevention of cancer or autoimmune disease in a subject in need of such treatment. Cancers and autoimmune disease include breast cancer, brain cancer, lung cancer, colorectal cancer, prostate cancer, cervical cancer, melanoma, kidney cancer, pancreatic cancer, thyroid cancer, bladder cancer, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s disease, systemic lupus erythematosus, neuromyelitis optical, and autoimmune encephalitis.

[0098] The method of treatment and / or prevention comprises administering to the subject any of the compounds disclosed herein. The method will often further comprise identifying a subject in need of such treatment or prevention.

[0099] Such targeted delivery interactions are accomplished by administering a compound or pharmaceutical composition containing at least one modified protein or protein assembly. Any cargo conjugated to the protein or protein assembly configured to modulate a desired immune response may be utilized.

[0100] The following Examples are exemplary of the systems and methods described herein and should not be considered limiting unless expressly stated.EXAMPLE 1Bioorthogonal Conjugation Sites Using Noncanoni cal Amino Acids

[0101] In one embodiment, an amber suppression stop codon is used to create a defined site for conjugation (FIG. 7). Amber suppression repurposes the “amber” stop codon to incorporate substantially any amino acid. The system works by introducing an amber tRNA that recognizes the amber site and an amber aminoacyl-tRNA synthetase that charges the tRNA with the given amino acids. These amino acids can have a wide range of functional / reactive groups and thus allows for addition of amino acids with special reactive groups to the proteins.

[0102] To enable the incorporation of these purchased and synthesized ncAAs, several plasmids harboring genes for aminoacyl-tRNA synthetases (aaRS) that attach ncAAs to transfer RNA (tRNA) were purchased and modified. During protein expression, tRNA then add the ncAA to the growing protein chain at assigned stop codons. All plasmids were purchased from Addgene, and modifications were made using gene fragments (Gblocks) and oligo primers purchased from IDT. Two plasmids capable of incorporating Tet-3.0-Me at TAA and TAG were acquired, referred to as pUltra-Tet3.0[TAA] (Plasmid #164580) and pDule-Tet3.0 (Plasmid #174080), respectively (FIG. 8). Additionally, the plasmid pEVOL-pAzF[TAG] (Plasmid #164579) which is capable of incorporating of pAzF at TAG codons was purchased. To enable the incorporation of Tet-4.0-Ph into both TAG and TAA codon sites a Gblock was purchased for an aaRS capable of incorporating Tet-4.0-Ph, developed previously and referred to as RS-E1, as well as the tRNA sequence from pUltra-Tet3.0[TAA] modified to recognize the TAG codon. Gblocks were inserted into plasmids using designed oligo primers and the NEBuilder® HiFiDNA Assembly Master Mix kit. Proteins modified by position independent conjugation are shown in FIG. 9.

[0103] Various plasmids were constructed for the direct incorporation of immunomodulating nanobodies featuring a genetically engineered ncAA into the expression platform. These plasmids feature nanobody sequences with an N-terminal poly-histidine tag, for improved immobilized metal affinity chromatography (IMAC) purification, which is followed by a protease cleavage tag, referred to as bdSUMO and developed previously, to achieve a polyhistidine tag free nanobody (FIG. 7). LC-MS spectra data for Boc-protected Tet-3.0-Me following purification using flash chromatography is shown in FIG. 10. The expected mass of Boc-protected Tet-3.0-Me is 360, however the hoc group (that has a mass of approximately 100) is removed by ionization so a peak was observed at 260. In FIG. 11, LC-MS spectra data for Crude Boc-protected Tet-4.0-Ph is shown. The expected mass is 345, however the reduced form (+2) of the product is observed at 348. The largest peak present is the starting material.

[0104] FIG. 12 shows proteins modified by ncAAs, which can participate in spontaneous copper-free cycloaddition reactions with DBCO and sTCO, for example. Tetrazine amino acids, for example, enable IEDDA conjugation, where IEDDA is a cycloaddition reaction between 1,2, 4, 5 tetrazine and dienophiles that is highly specific and extremely fast under physiological conditions. Tet-3.0-Me has a reported k of approximately 8 x 104M^s'1with sTCO when incorporated into proteins (FIG. 13). The resulting modified proteins from FIG. 12 are shown in FIG. 14.

[0105] Noncanonical amino acids incorporated as described offer a scalable conjugation, as shown in FIG. 15. For example, a 1 :2 plasmid to assembly ratio can be used to generate a protein assembly library (FIG. 16). Modular conjugation is possible, where different cargos can be incorporated upon distinct sites on protein assemblies (FIG. 17). Further, incorporation of noncanonical amino acids can be designed via modeling and folding analysis. Given that the sequence fold in alpha fold, the output is PDB. PyMol is used to visualize PDB, pull out surface exposure, and determine how much of the amino acid is exposed or can be exposed (1 fully exposed vs 0 is not). Python was used to write a sorting algorithm to rank surface exposure. For position and distance, PyMol provides (1) position of residue (2) xyz coordinates of alpha carbons. Then, the data is sorted to find residue positions that correlate with CDR and placed in a list: CDR residues and non-CDR residues. A pairwise function is used to find pairwise distancebetween every non-CDR and CDR point. It takes an average of non-CDR points to find ones that are on average farthest from all CDR points and uses this to rank top positions.

[0106] For example, within the sequence of the presently disclosed nanobodies, amino acid sites were computationally selected to be reassigned for ncAA incorporation based on a model that evaluates the surface exposure of the various residues on the nanobody, the distance between conjugation site and the binding regions, and the relative hydrophobicity of the side chains. This model is built from scripts interfacing PyMol and Python to extract and screen the nanobodytarget complex and evaluate each amino acid in the nanobody chain for these attributes. See Table 2 for key function blocks and Appendix A for the code.Table 2. Key function blocks of model for computational selection of ncAA incorporation sites.

[0107] In addition to prime candidates determined by this model, the first and last amino acids in the chain were also modified to act as a comparison to traditional genetic fusion. Mutations to the sequence are done using designed oligos and the NEB Q5® Site-Directed Mutagenesis Kit. These sequences include genes for anti-ABCC3, anti-Albumin, anti-PDLl, anti-CLTA4, and anti-EGFRvIII nanobodies and a transferrin peptide. For example, albumin can be used for “hitchhiking” as an approach for enhanced tumor targeting. CLTA4 blockades have elicited strong anti-tumor immune responses. Anion transporter ABCC3 is known to play a role in multiple drug resistance in glioblastoma. Additionally, transferrin has been used as a shuttle peptide to increase entry across the blood brain barrier.

[0108] An exemplary embodiment of conjugation locations based on proximity to the CDR can involve conjugation sites distant from a binding region, which are typically surface exposed.For anti-CLTA4, for instance, an N-to-N terminal conjugation can be successful, though other locations are possible.

[0109] Following mutation of nanobody sequences, these designed expression plasmids are cotransformed with aaRS / tRNA harboring plasmids into bacterial hosts for expression of nanobodies. Purified nanobodies are then reacted in a one-pot assembly with a TCO-PEG-TCO, DBCO-PEG-DBCO, or TCO-PEG-DBCO linker to create mono- or bispecific nanobody assemblies. The orthogonality of encoded conjugation sites also enables this platform to be a modular, high throughput means for rapid cargo loading of nanobody assemblies, which has the versatility to include small molecules, oligonucleotides, and peptides / proteins that have been modified to include click-capable functional groups such as TCO and DBCO.

[0110] Examples and analysis of conjugations are provided in FIGS. 18-21. In FIG. 18, expression of wild type nEGFvIII is shown. Lane 1 : 10 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. Lane 2: 25 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. Lane 3: 50 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. Lane 4: Flowthrough from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII wild type following Ihr incubation with purified bdSENPl protease to remove the poly-histidine tag. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. Lane 5: 150 mM imidazole wash from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII Wild type. The strong band at approximately 32 kDa indicates removal of the bdSENPl protease while the light band just under the 15 kDa marker indicates target nEGFRvIII nanobody not removed during the elution step. Lane 6: Bio- Red Precision Plus Protein Standard.

[0111] In FIG. 19, single site incorporation of ncAAs is shown. In the left panel, SDS PAGE is provided: Lane 1 : Flowthrough from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII QI Tet-3.0-Me following Ihr incubation with purified bdSENPl protease to remove the poly-histidine tag. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. Lane 2: Purified Wild-Type nEGFRvIII. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. This band issimilar in size to the band in lane 1 , indicating they are of identical size and therefore identity. Lane 3: Bio-Red Precision Plus Protein Standard. In the right panel, the graphical analysis demonstrates labeling of iFluor® 488 TCO dye to only the nEGFRvIII variant with the first amino acid (QI) replaced with Tet-3.0-Me via ncAA incorporation. Excitation and Emission wavelengths, selected based on maximum values for the dye, are 476 nm / 516 nm, respectively. This, along with the SDS-PAGE gel shift confirms the expression of the nEGFRvIII variant and the functionality of the Tet-3.0-Me ncAA at this location.

[0112] In FIG. 20, single site incorporation of ncAAs is further examines. In the left panel, SDS PAGE shows: Lane 1 : Flowthrough from IMAC column loaded with clarified lysate of expression culture for nEGFRvIII S124 pAzF following Ihr incubation with purified bdSENPl protease to remove the poly-histidine tag. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. Lane 2: Purified Wild-Type nEGFRvIII. The band just under the 15 kDa marker indicates the expression of a protein of the approximate size (13.4 kDa) of the target nEGFRvIII nanobody. This band is similar in size to the band in lane 1, indicating they are of identical size and therefore identity. Lane 3: Bio-Red Precision Plus Protein Standard. In the right panel, the graphical analysis demonstrates labeling of Sulfo-DBCO Cy5 dye to only the nEGFRvIII variant with the last amino acid (SI 24) replaced with pAzF via ncAA incorporation. Excitation and Emission wavelengths, selected based on maximum values for the dye, are 647 nm / 655 nm, respectively. This, along with the SDS-PAGE gel shift confirms the expression of the nEGFRvIII variant and the functionality of the pAzF ncAA at this location.

[0113] FIG. 21 displays analysis of dual site incorporation of ncAAs, with Tet-3.0-me and pAzF evaluated. The analysis demonstrates labeling of iFluor® 488 TCO and Sulfo-DBCO Cy5 dye to only the nEGFRvIII variant with the first amino acid (QI) replaced with Tet-3.0-Me and last amino acid (S 124) replaced with pAzF via ncAA incorporation. Excitation and Emission wavelengths, selected based on maximum values for the dye, are 476 nm / 516 nm and 647nm / 655nm, respectively. This, along with the SDS-PAGE gel shift confirms the expression of the nEGFRvIII variant and the functionality of the Tet-3.0-Me and pAzF ncAA at this location.

[0114] In FIG. 21, retention of function following ncAA incorporation is shown. nEGFR QlTet3.0 and S124pAzF with iFluor® 488 TCO and Sulfo-DBCO Cy5 dye are attached andincubated with EGFR positive EMT6 cells. Cells are also stained with membrane stain and nucleus stain. The image shows accumulation of TCO and DBCO on the surface of cells indicating a retainment of function (FIG. 22).EXAMPLE 2Modular Nanobody Conjugates with Controlled Topology Using Genetically Encoded Non- Canonical Amino Acids

[0115] Here, the application of the 6-butyl-l,2,4,5-tetrazine-containing phenylalanine derivative (Tet3.0 Bu; Tet3.0) is evaluated to create an orthogonal aaRS / tRNA pair for incorporation into peptides (FIG. 7 and FIG. 23A-C) due to its high incorporation efficiency and rapid reaction kinetics with strained alkynes. To control the homogeneity of the bispecific conjugate products, the azide-containing pyrrolysine derivative NE-((2azidoethoxy)carbonyl)-L- lysine (AzK) was selected (FIG. 23 A), a substrate of the well-characterized wild-type Methanosarcina mazei pyrrolysyl-tRNA synthetase / tRNA pair (AT / iiPylRS / AT / iitRN Acv \l>vl). AzK serves as an orthogonal reaction site to Tet3.0, demonstrating rapid and selective cycloaddition with DBCO. The conjugate pair of nCD276 QIAzK and C21 ElTet3.0 Bu is evaluated in FIG. 24A-C, where the percent conversion of AzK exceeded that of pAzF.

[0116] Low ncAA incorporation efficiency, resulting in yields 30-80% lower than wild-type expression, remains a barrier to the widespread application of GCE technology. While others have attempted to address this challenge using approaches such as orthogonal ribosome systems or genome-wide knockout of release factor 1 (RF1) in expression hosts, the present disclosure reduces the metabolic burden associated with commonly used multi-plasmid systems for GCE using other approaches. The colocalization of the ATmPylRS, A / RNAcuAPyl, and protein of interest (POI) genes, all under control of the T7 / aco promoter (T7p) and terminator (T7t) has previously been shown to result in a significant elevation of EGFP yield in the presence of AzK. Therefore, the present disclosure uses pRESS, a prokaryotic recombinant expression and suppression system, based on the established pET28b(+) plasmid (FIG. 22B, FIG. 25). pRESS contains genes for A7 / ; / PylRS, AT / vztRN Acu \Pvl, and the POI, all under control of T7p / T7t and regulated by the lacl gene. Notably, to ensure proper 5’ and 3’ processing of the A / MtRNAcuAPylprecursor processing, the proK promoter and terminator were retained in the gene cassette and flanked by T7p / T7t, rather than replacing them. pRESS contains orthogonal golden gate cut sites,Bsal and PaqCI, flanking the POI and A / wPylRS sequences, offering a modular platform for incorporating a wide range of ncAAs through insertion of mutant A / mPylRS genes (FIG. 23B).

[0117] To demonstrate the modularity and reduced metabolic burden of pRESS, pRESS- Tet3.0 was created, which contains the Methanosarcina barker / -derived pyrrolysyl-tRNA synthetase (A / Z>Tet3.0RS). A his-tagged maltose-binding protein (MBP) domain, followed by a tobacco etch virus (TEV) protease cleavage site and a glycine-serine linker (G4S), were fused to the N-terminus of mCherry to create the fluorescent reporter MBP-mCherry. Wild-type (MBP- mCherryWT) and mutant MBP-mCherry (MBP-mCherryG381TAG), harboring the TAG codon at position G381, were inserted into pRESS and pRESS-Tet3.0 and transformed into the E. coli expression strain BL21(DE3) (FIG. 23C). In parallel, MBP-mCherryWTand MBP- mCherryG381TAGwere inserted into the parental pET28b(+) plasmid and co-transformed into BL21(DE3) cells along with pUltraI-Tet3.0 [TAG], a derivative of the pUltraI-Tet3.0 [TAA] plasmid that is modified to recognize the TAG rather than the TAA codon. Mean fluorescence intensity (MFI) of each culture, normalized to OD600was measured 24 hours post-induction. The data demonstrates improved MBP-mCheriyG381TAGexpression using pRESS.

[0118] Following the validation of pRESS, constructs were inserted to produce a previously characterized anti-PD-Ll (nPD-Ll) and anti-CTLA-4 (nCTLA-4) nanobody containing AzK and Tet3.0 sites, respectively, for the creation of a small panel of unique bispecific molecules. Alone, nPD-Ll and nCTLA-4 act as antagonists against their respective immune checkpoint, leading to improved T-cell activation and function in immunologically suppressive environments, such as the tumor microenvironment (FIG. 26B). As a fusion, this molecule functions as both a dual immune checkpoint blockade and a redirector of CTLA-4 positive T-cells towards cells with high expression of PD-L1, leading to increased immunological activity in suppressive tumors (FIG. 26B).

[0119] A cleavable N-terminus Brachypodium distachyon-derived small ubiquitin-like modifier (bdSUMO) domain and C-terminus 6xHis tag were added to address challenges in N- or C-terminally placed ncAAs (FIG. 7). Insertion of a stop codon following the canonical ATG start codon results in translation being shifted to a downstream initiation site, making N-terminal ncAA placement non-trivial. Conversely, pre-mature truncation products for C-terminus ncAA sites would be nearly identical in size and chemical composition to the full-length ncAA- containing target protein, making their separation challenging. By including the ncAAincorporation site prior to the 6xHis tag, only full-length products can be easily purified via immobilized metal affinity chromatography. As controls, wild-type nanobodies (nPD-LlWTand nCTLA4WT) and genetic fusions (PN-Ccand Pc-CN) were also produced (FIG. 26A).

[0120] To investigate the capacity to make functional ncAA -based bispecific conjugates, molecules were created that mimic the geometric orientation of the genetic fusions. The first and last amino acids of the canonical nPD-Ll and nCTLA-4 sequences were replaced with the TAG codon to produce nPD-LlQ1Azk, nPD-Llsll6Azk, nCTLA-4Q1Tet3 0, and nCTLA-4sl23Tet3 0(FIG. 26C). ESI-MS analysis of these samples revealed high homogeneity and ncAA incorporation (FIG. 27A-B), matching the results seen with the MBP-mCherry reporter.

[0121] To build the bispecific panel, all possible permutations of mutant nPD-Ll and nCTLA-4 were reacted with a commercially available heterobifunctional TCO-PEG12-DBCO linker in a 1 : 1 : 1 molar ratio and incubated overnight at room temperature under physiological conditions (FIG. 26C). However, other linkers, such as TCO-PEG6-DBCO, are possible. The resulting isoforms (1 )-(4) were produced with high efficiency and product homogeneity, as characterized by ESI-MS, and were easily separated from the remaining starting material using size exclusion chromatography.

[0122] After establishing the ncAA-based method of conjugating nanobodies results in a functional molecule, the impact of diversifying the domain orientations on isoform functionality was studied. To help identify prime candidate sites for non-terminal ncAA insertion within nPD- Ll, in silico modeling of potential mutant nPD-Ll structures containing AzK was performed using Rosetta. In short, several residues within the framework and CDR regions of nPD-Ll were mutated to the cycloaddition product between AzK and DBCO and evaluated based on two major criteria: the root-mean-square deviation (RMSD) of the carbon backbone (A) and the change in folding free energy (AAG). Although the output of these metrics is not directly translatable to kcal / mol without iterative experimental validation, they can provide intuitive guidance on ncAA site selection based on consistent principles of thermodynamic favorability and the structure-function relationship dogma of biology. From the candidate sites identified, R45 and K75 were selected to mutate due to their structural similarity to AzK and distal positioning from each other and the CDRs (FIG. 28 A). As with the other mutants, nPD-LlR45AzKand nPD-LlK75Azkproduced highly homogeneous nanobodies with correct ncAA incorporation,characterized by ESI-MS (FIG. 28B). It is worth noting that yields for nPD-LlQ1AzKand nPD- LlR45AzKwereconsistently higher, compared to those for nPD-LlK75AzKand nPD-Llsl l6AzK.

[0123] To verify that the new AzK incorporation sites did not significantly impact the binding capacity of nPD-Ll, each variant was labeled with AF 488 DBCO (or a lysine-reactive Alexa Fluor 488 in the case of nPD-LlWT) to confirm binding to MDA-MB-231 cells. Following this verification, the panel was updated to include isoforms (5)-(8) (FIG. 29), which displayed similar reaction efficiency to (l)-(4) and were subsequently purified and characterized via ESIMS and visualized via SDS PAGE (FIG. 30A).

[0124] The adaptation of GCE technology to a high-throughput platform for creating novel bispecific therapeutics is thus shown. The modularity of the system is demonstrated by rapidly creating eight unique isoforms of a nanobody based anti-PD-Ll / CTLA-4 therapeutic without the need to generate a new plasmid for each. The use of ncAA-based conjugation offers the freedom to quickly study the potential impact of physicochemical properties like conjugate linker length, flexibility, and individual domain orientation on therapeutic outcomes without the need to create new expression plasmids.EXAMPLE 3In Vitro Characterization of Fusions Using Nanobody Building Blocks

[0125] To verify the bispecific molecules of the present disclosure retain the ability to target extracellular CTLA-4 and PD-L1 post conjugation, MDA-MB-231 cells overexpressing PD-L1 and Jurkat cells overexpressing CTLA-4 / PD1 labeled with CytoTrace Red and CytoTrace Green, respectively, may be co-cultured with (l)-(4) of the genetic fusions (250 nM) of FIG. 26 for 90 minutes before being imaged using confocal microscopy and analyzed using flow cytometry, as depicted in FIG. 27C.

[0126] Further, to understand if equivalent binding performance between isoforms translated to comparable levels of T-cell mediated cytolytic activity, MDA-MB-231-luc cells may be cocultured, which stably express firefly luciferase to produce bioluminescence when exposed to luciferin, with activated CD3+ T-cells isolated from primary peripheral blood mononuclear cells (PBMCs) for 6 hours at a target to effector ratio (R:T) of 1 :5 and dosages ranging from 0.69 to 500 nM. Co-localization of MDA-MB-231 cells with CD3+ T-cells isolated from PBMCs may be analyzed using flow cytometry (FIG. 30B).EMBODIMENTS

[0127] Some additional, non-limiting, example embodiments are provided below.

[0128] Embodiment 1. A bioorthogonal conjugation system, comprising at least one non- canonical amino acid (ncAA) configured for participating in an orthogonal click reaction, and at least one plasmid comprising a gene for one or more of: an aminoacyl-tRNA synthetase (aaRS), a tRNA, and a nanobody having at least one suppression stop codon that defines a location for conjugation of the at least one ncAA.

[0129] Embodiment 2. The bioorthogonal conjugation system according to Embodiment 1, wherein the at least one ncAA is a phenylalanine derived azide containing amino acid or a tetrazine containing amino acid.

[0130] Embodiment 3. The bioorthogonal conjugation system of Embodiments 1 or 2, wherein the phenylalanine derived azide containing amino acid is 4-Azido-L-phenylalanine (pAzF).

[0131] Embodiment 4. The bioorthogonal conjugation system of Embodiments 1 or 2, wherein the tetrazine containing amino acid is 3-(6-methyl-s-tetrazin-3-yl)phenylalanine (Tet- 3.0-Me) or (S)-2-amino-3-(6-phenyl-l,2,4,5-tetrazin-3-yl)propanoic acid (Tet-4.0-Ph).

[0132] Embodiment 5. The bioorthogonal conjugation system of Embodiments 1, 2, 3, or 4, wherein the at least one ncAA is capable of participating in an orthogonal click chemistry reaction.

[0133] Embodiment 6. The bioorthogonal conjugation system of any of Embodiments 1-5, wherein the at least one ncAA is capable of participating in spontaneous copper-free Diels- Alder cycloaddition reactions with trans-cyclooctene (TCO) and dibenzo cyclooctyne (DBCO) groups.

[0134] Embodiment 7. The bioorthogonal conjugation system of any of Embodiments 1-5, wherein the at least one ncAA is capable of participating in spontaneous copper-free azidealkyne cycloaddition reactions with bicyclo[6.1.0]non-4-yne (BCN) and dibenzo cyclooctyne (DBCO) groups.

[0135] Embodiment 8. The bioorthogonal conjugation system of any of Embodiments 1-7, wherein the location for conjugation of the at least one ncAA is surface-exposed on an expressed nanobody of the bioorthogonal conjugation system.

[0136] Embodiment 9. The bioorthogonal conjugation system of any of Embodiments 1-8, comprising a first plasmid having genes for a first aaRS and a first tRNA, and a second plasmid having a gene for the nanobody with a first suppression stop codon.

[0137] Embodiment 10. The biorthogonal conjugation system of any of Embodiments 1-9, wherein the first tRNA is configured to incorporate a first ncAA at the first suppression stop codon.

[0138] Embodiment 11. The biorthogonal conjugation system of any of Embodiments 1-10, wherein the nanobody further includes a second suppression stop codon, and further comprising a third plasmid having genes for a second aaRS and a second tRNA.

[0139] Embodiment 12. The biorthogonal conjugation system of any of Embodiments 1-11, wherein the second tRNA is configured to incorporate a second ncAA at the second suppression stop codon, such that an expressed nanobody includes dual, bioorthogonal conjugation sites at the first ncAA and second ncAA.

[0140] Embodiment 13. The bioorthogonal conjugation system of any of Embodiments 1-7, comprising one plasmid having genes for the aaRS, the tRNA, and the nanobody with the suppression stop codon.

[0141] Embodiment 14. The bioorthogonal conjugation system of any of Embodiments 1-7 or 13, wherein the tRNA is configured to incorporate the at least one ncAA at the suppression stop codon such that an expressed nanobody includes a biorthogonal conjugation site at the ncAA.

[0142] Embodiment 15. A modified protein for modular conjugation, the modified protein having at least one bioorthogonal conjugation site where a ncAA is located, the ncAA capable of participating in an orthogonal click chemistry reaction.

[0143] Embodiment 16. The modified protein according to Embodiment 15, having at least two bioorthogonal conjugation sites, each where a different ncAA is located.

[0144] Embodiment 17. The modified protein of Embodiments 15 or 16, wherein each bioorthogonal conjugation site is configured to conjugate a different cargo.

[0145] Embodiment 18. A protein assembly comprising one or more modified protein of Embodiments 15, 16, or 17.

[0146] Embodiment 19. The protein assembly according to Embodiment 18, wherein each bioorthogonal conjugation site is configured to conjugate a different cargo.

[0147] Embodiment 20. The protein assembly of Embodiments 17 or 18, wherein the one or modified proteins are assembled with a linker selected from the group consisting of TCO-PEG- TCO, DBCO-PEG-DBCO, and TCO-PEG-DBCO.

[0148] Embodiment 21. A method expressing a nanobody with one or more bioorthogonal conjugation sites, comprising transforming into a bacterial host at least one plasmid comprising a gene for one or more of: an aaRS, a tRNA, and a nanobody having at least one suppression stop codon that defines a location for conjugation of the at least one ncAA, and expressing the nanobody with a bioorthogonal conjugation site at each incorporated ncAA.

[0149] Embodiment 22. The method according to Embodiment 21, wherein the bacterial host is transformed with one plasmid having genes for the aaRS, the tRNA, and the nanobody with the suppression stop codon.

[0150] Embodiment 23. The method according to Embodiment 21, wherein the bacterial host is co-transformed with a first plasmid having genes for a first aaRS and a first tRNA, a second plasmid having genes for a second aaRS and a second tRNA, and a third plasmid having a gene for the nanobody with a first suppression stop codon and a second suppression stop codon.

[0151] Embodiment 24. A method of treating or preventing a condition in a subject in need thereof, the method comprising: administering to the subject in need thereof a therapeutically effective amount of a modified protein, the modified protein having at least one cargo conjugated at a bioorthogonal conjugation site of the modified protein where a noncanonical amino acid (ncAA) is located.

[0152] Embodiment 25. The method according to Embodiment 24, wherein the condition is cancer or an autoimmune disease.

[0153] Embodiment 26. The method of Embodiments 24 or 25, wherein the modified protein penetrates the blood-brain barrier.

[0154] Embodiment 27. The method of Embodiments 24, 25, or 26, wherein the at least one cargo is conjugated via orthogonal click chemistry reaction.

[0155] Embodiment 28. The method of Embodiments 24, 25, 26, or 27, wherein the at least one cargo is selected from the group consisting of small molecules, oligonucleotides, and peptides.

[0156] Embodiment 29. The method of any of Embodiments 24-28, wherein the modified protein has at least two bioorthogonal conjugation sites, each where a different ncAA is located.

[0157] Embodiment 30. The method of any of Embodiments 24-29, wherein a different cargo is located at each bioorthogonal conjugation site.APPENDIX A

[0158] Title: Sasa.txt import csv import numpy as np import pandas as pd# Create an empty list to store residue SASA values sasa data = [] output = cmd.get_sasa_relative() array = str(output).split(',') array. pop(O) array = np.reshape(array, (122, 4)) df = pd.DataFrame(array) df.to_csv('sasa_relative_EGFR.csv')Purpose:This script is run through PyMol, and is used to pull the surface exposure of every single residue. These are then loaded into a CSV fde to be used later with Python. import csv from pymol import cmd# Select alpha carbons selection = "name CA"# Create a list to store atom information (resi, atom name, x, y, z) calpha data = []# Iterate over the selected atoms and append the coordinates along with residue and atom name cmd.iterate_state(l, selection, 'calpha_data.append([model, resi, name, x, y, z])', space=globals()) with open("EGFR_calpha_coordinates.csv", "w", newline- '") as csvfde: csvwriter = csv.writer(csvfile) csvwriter.writerow(["Model", "Residue", "Atom", "X", "Y", "Z"]) for row in calpha data:csvwriter, writerow(row)Purpose:This script is used to collect positions of all residues found within the protein. Each residue’s location is dependent based on the alpha carbon location. These are then loaded into a CSV file for later use.Title: exposure.py import pandas as pd import numpy as np import pyrosetta from pyrosetta import * init() def csv_reader( / / / ): df = pd.read_csv(file) df = df = df.drop(['0', T, '2'], axis=V) return df def organizer^ / / ): print(df) matrix = pd.DataFrame.to numpy(df) newmatrix = [] for i in range(len(matrix)): splitmatrix = str(matrix[i] ). split('V) :') newmatrix. append(splitmatrix) print(newmatrix) alldatapoints= np. array (newmatrix)# matrix = matrix[matrix[:, l].argsort()]# top20AA = matrix[-20::]# top20AA = str(matrix[l ::]).split("")[l]# top20AA = pd.DataFrame(matrix[-20::])# top20AA.to_excel('top20AA_EGFR.xlsx')# matrix = np.array(top20AA) retum(pd.DataFrame(alldatapoints)) if name == " main " : file = ‘path to file’ df = csv_reader(file)Top20Sites = organizer(df)Top20Sites.to csv('EGFR Exposure. csv') print(Top20 Sites)Purpose:PyMol preprocesses the data before further analysis.Optionally, can uncomment code to just receive the top 20 residues aloneTitle: CDR and nCDR checkerFunction:Pulls the list of coordinates and residues. Iterates through the list and checks the residue number. If the residue number is in the list of CDR residue list, add the row to the CDR list. If not, add it to the non CDR list and pass onInputs:Coordinates of all alpha carbon atoms and the sequence positions of the CDR residuesOutputs:2 Lists - A) CDR residue coordinates B) Non-CDR residue coordinates cdrs = np.empty((0, coordinates.shape[l])) # Initialize an empty array with same columns as coordinates cdr free coordinates = np.empty((0, coordinates. shapefl])) for i in range(len(coordinates)): if i in cdr_residues: cdrs = np.vstack([cdrs, coordinates!)]]) # Append the row to cdrs else: cdr_free_coordinates = np.vstack([cdr_free_coordinates, coordinates!)]]) # Append the row to cdr free coordinates return cdrs, cdr free coordinatesPurpose:Iterates through a list of all the CDR points and checks whether the indexing number classifies it as a CDR or nonCDR point. CDR points are then stored into a separate list and nonCDR points are stored into another.Title: Distance def di stancef / ' coordinates, cdr Jr ee coordinates).Function:Calculate the pairwise distanes between all CDR alpha carbons to every other non-CDR alpha carbon.Inputs:2 Lists - A) CDR Coordinates B) Non-CDR CoordinatesOutputs:A matrix with all the pairwise distances, denoting the distance between CDR point i and non-CDR point j cdrs = torch, from numpy(cdr coordinates) non cdrs = torch. from numpy(cdr free coordinates) distances = torch. cdist(cdrs, non cdrs, / ? = 2) return distancesPurpose:Calculates the pairwise Euclidian distance between every nonCDR point and every CDR point.Title: Distance RankerFunction:Takes the outputted tensors from the distance function to create a ranking of the top 10 positions based on distance from cdrs. Does so through weighing out the average eucleadian distnace between the pointsInputs:1 list of distances, in the form of a torch tensorOutputs:A list of both the position of the residue as well as the average distance of the point to all other cdr points in cartesian coordinates average_di stances = torch. mean(torch.transpose(distances, 0,l),l,True) values, indices = torch. sort(average_di stances, -2) values = values. numpy() indices = indices. numpy() matrix = np.concatenate((indices, values), axis = 1) top 10 = matrix[-10::] return top 10Description:Ranks the best nonbinding residues based on distance to the CDR residues. def exposure jrepper(ex / 2o.s7 / re ^ cdr residues)' '.#pull only the exposure values exposures = exposuredff'l'] exposures = exposures. to_numpy()#Effectively cleaning the exposures for i in range(len(exposures)): exposuresfi] = / r(exposures[i]) exposures[i] = exposures [i].strip(']exposuresfi] = 7orz / (exposures[i])#recombine them with their indicies indicies = range(len(exposures))#connect them back together exposure_indici_matrix = np.transpose(np.vstack([indicies, exposures])) cdr_exposures, ncdr_exposures = cdr_checker(exposure_indici_matrix, cdr_residues) ncdr_exposures = ncdr_exposures[:,l] return (np.transpose(ncdr_exposures))Purpose:Simply cleans the outputs given by PyMol to be used later in the code. This cleans the overall data frame, allowing us to iterate through it more efficiently.<: / < / ' PC A_function( / zct / / ' distances, ncdr exposures)'. print(ncdr_di stances, shape) print(ncdr_exposures. shape) scaler = StandardScaler() temp = ncdr_distances.numpy() temp = pd.DataFrame(ncdr distances) seal ed_di stances = scaler.fit_transform(temp) scaled_distances *= .01 temp = scaled distances combined_array = np.vstack((temp, ncdr_exposures)) combined_array = np.transpose(combined_array) combined df = pd.DataFrame(combined array)# scaled_data = scaler.fit_transform(combined_df) pea = PC A( / / components=2) pc array = pca.fit transform(combined df) prinicipledf = pd.DataFrame(pc_array, columns = ['PCI', 'PC2']) return prinicipledfPurpose:Reexamines an appended matrix of the pairwise distances and surface exposures as a fingerprint to each of the nonbinding residues. This fingerprint is then broken down into its principalcomponents, which is then used to find “extreme” points. These points will be tested experimentally, and the data resolved form them will be used to interpolate other values found within the abstract space provided by PCA.This allows us to greatly reduce the amount of testing needed, as data can be generalized to some combination of principle components.Title: AlphaCarbons.txt import csv from pymol import cmd# Select alpha carbons selection = "name CA"# Create a list to store atom information (resi, atom name, x, y, z) calpha data = []# Iterate over the selected atoms and append the coordinates along with residue and atom name cmd.iterate_state(l, selection, 'calpha_data.append([model, resi, name, x, y, z])', space=globals())# Write the data to a CSV file with open("EGFR_calpha_coordinates.csv", "w", newline- '") as csvfile: csvwriter = csv.writer(csvfile)# Write header csvwriter.writerow(["Model", "Residue", "Atom", "X", "Y", "Z"])# Write atom data (resi, atom name, x, y, z) for row in calpha data: csvwriter, writerow(row) print(" Coordinates saved to calpha_coordinates.csv")Title: Sasa.txt import csv import numpy as np import pandas as pd# Create an empty list to store residue SAS A values sasa data = [] output = cmd.get_sasa_relative()array = str(output).split(',') array. pop(O) array = np.reshape(array, (122, 4)) df = pd.DataFrame(array) df.to_csv('sasa_relative_EGFR.csv')Title: Exposure.py'"The purpose of this code is to screen through the relative sasa information recieved by PyMol and look for valid candidates based on surface exposure'" import pandas as pd import numpy as np import pyrosetta from pyrosetta import * init() def csv_reader( / / e): df = pd.read_csv(file) df = df = df.drop(['0', T', '2'], axis=l)# array = np.reshape(array, (115, 4))# df = pd.DataFrame(array) return df def organizer(t / / ): print(df) matrix = pd.DataFrame.to numpy(df) newmatrix = [] for i in range(len(matrix)): splitmatrix = s / r(matrix[i]).split('\'):') newm atrix . append(spl itmatrix) print(newmatrix) alldatapoints= np. array (newmatrix)# matrix = matrix[matrix[:, l].argsort()]# top20AA = matrix[-20::]# top20AA = str(matrix[l ::]).split("")[l]# top20AA = pd.DataFrame(matrix[-20::])# top20AA.to_excel('top20AA_EGFR.xlsx')# matrix = np.array(top20AA) retum(pd.DataFrame(alldatapoints)) if name == " main file = path to file df = csv_reader(file)Top20Sites = organizer(df)Top20Sites.to_csv('EGFR_Exposure.csv') print(Top20 Sites)Title: Coordinates.py import pandas as pd import numpy as np import matplotlib.pyplot as pit import torchFunction:Pulls the list of coordinates and residues. Iterates through the list and checks the residue number. If the residue number is in the list ofCDR residue list, add the row to the CDR list. If not, add it to the nonCDR list and pass onInputs:Coordinates of all alpha carbon atoms and the sequence positions of the CDR residuesOutputs:2 Lists - A) CDR residue coordinates B) Non-CDR residue coordinates cdrs = np.empty((0, coordinates.shape[l])) # Initialize an empty array with same columns as coordinates cdr_free_coordinates = np.empty((0, coordinates. shape[l])) for i in range(len(coordinates)): if i in cdr_residues: cdrs = np.vstack([cdrs, coordinates!)]]) # Append the row to cdrs else: cdr_free_coordinates = np.vstack([cdr_free_coordinates, coordinates!)]]) # Append the row to cdr free coordinates return cdrs, cdr free coordinatesFunction:Calculate the pairwise distanes between all CDR alpha carbons to every other non-CDR alpha carbon.Inputs:2 Lists - A) CDR Coordinates B) Non-CDR CoordinatesOutputs:A matrix with all the pairwise distances, denoting the distance between CDR point i and non-CDR point j cdrs = torch. from numpy(cdr coordinates) non cdrs = torch. from numpy(cdr free coordinates) distances = torch. cdist(cdrs, non cdrs. / ? = 2) return distances def di stan ce ran k ^distances) :Function:Takes the outputted tensors from the distance function to create a ranking of the top 10 positions based on distance from cdrs. Does so through weighing out the average eucleadian distnace between the pointsInputs:1 list of distances, in the form of a torch tensorOutputs:A list of both the position of the residue as well as the average distance of the point to all other cdr points in cartesian coordinates average_di stances = torch. mean(torch.transpose(distances, 0,l),l,True) values, indices = torch. sort(average_di stances, -2) values = values. numpy() indices = indices. numpy() matrix = np.concatenate((indices, values), axis = 1) top 10 = matrix[-10::] return top 10 def plotfunctionfcz / r coordinates, cdr free coordinates, topl0 .Function:Simply plots the two lists in a scatter format, also looks for and highlights the top point based on distance to the other cdr pointsInputs:3 Lists - A) CDR Coordinates B) Non-CDR Coordinates C) top 10 residues and locations based on distance to CDROutputs:None (plotted later in the self check function) x = cdr_free_coordinates[:,l] y = cdr free coordinates):, 2] z = cdr_free_coordinates[:,3] xp = cdr_coordinates[:,l] yp = cdr_coordinates[:,2] zp = cdr_coordinates[:,3] top = top 10[-l : :] print(cdr_free_coordinates[:,0]) for i in range(len(cdr_free_coordinates[:,0])): print(i) if i == top[0,0]: xtop = cdr_free_coordinates[i,l] ytop = cdr_free_coordinates[i,2] ztop = cdr_free_coordinates[i,3] cdr_free_coordinates = np.delete(cdr_free_coordinates, i) break print(xtop, ytop, ztop) fig = plt.figure() ax = fig.add_subplot(p7' / ec / / on='3d') ax.scatter(x,y,z, c = 'k', label = 'non-Binding aCarbon') ax.scatter(xp,yp,zp, c = 'r', label = 'Binding aCarbon') ax. scatter(xtop, ytop, ztop, c = 'y', label = 'Top CDR', marker = '*', 5 = 500) if name ==1main ': df = pd.read_csv( path to file) coordinates = df[['Residue', 'X', 'Y', 'Z']] coordinates = pd.DataFrame.to numpy(coordinates) cdr_residues = np.concatenate([np.arange(20, 36), np.arange(39, 48), np.arange(88, 103)]) cdr coordinates, cdr free coordinates = cdr_checker(coordinates, cdr residues) distance = distance(cdr_coordinates, cdr free coordinates) print(cdr free coordinates) plotfunction(cdr_coordinates, cdr free coordinates, distance ranker(distance)) print(di stance_ranker(di stance)) plt.legend( / oc = Q,frameon = 0) plt.showQTitle: fingerprinting.py import pandas as pd import numpy as np import matpl otlib.pyplot as pitimport torch from mpl_toolkits.mplot3d import Axes3D from ski earn. decomposition import PCA from ski earn. preprocessing import StandardScaler def cdr checkedcoord mates, cdr residues)'.Function:Pulls the list of coordinates and residues. Iterates through the list and checks the residue number. If the residue number is in the list ofCDR residue list, add the row to the CDR list. If not, add it to the nonCDR list and pass onInputs:Coordinates of all alpha carbon atoms and the sequence positions of the CDR residuesOutputs:2 Lists - A) CDR residue coordinates B) Non-CDR residue coordinates cdrs = np.empty((0, coordinates. shapefl])) # Initialize an empty array with same columns as coordinates cdr_free_coordinates = np.empty((0, coordinates. shapefl])) for i in range(len(coordinates)): if i in cdr_residues: cdrs = np.vstack([cdrs, coordinatesfi]]) # Append the row to cdrs else: cdr_free_coordinates = np.vstack([cdr_free_coordinates, coordinatesfi]]) # Append the row to cdr free coordinates return cdrs, cdr free coordinatesFunction:Calculate the pairwise distanes between all CDR alpha carbons to every other non-CDR alpha carbon.Inputs:2 Lists - A) CDR Coordinates B) Non-CDR CoordinatesOutputs:A matrix with all the pairwise distances, denoting the distance between CDR point i and non-CDR point] cdrs = torch. from numpy(cdr coordinates) non_cdrs = torch. from_numpy(cdr_free_coordinates) distances = torch. cdist(cdrs, non cdrs, p = 2)return distances def di stance rank er(c / / '.s7r / / 7cc.s) :Function:Takes the outputted tensors from the distance function to create a ranking of the top 10 positions based on distance from cdrs. Does so through weighing out the average eucleadian distnace between the pointsInputs:1 list of distances, in the form of a torch tensorOutputs:A list of both the position of the residue as well as the average distance of the point to all other cdr points in cartesian coordinates average_di stances = torch. mean(torch.transpose(distances, 0,l),l ,True) values, indices = torch. sort(average_di stances, -2) values = values. numpy() indices = indices. numpy() matrix = np.concatenate((indices, values), axis = 1) top 10 = matrix[-10::] return top 10 def Q^oswe_^VQ^Qv(exposuredf cdr_residues)'.#pull only the exposure values exposures = exposuredffT'] exposures = exposures. to_numpy()#Effectively cleaning the exposures for i in range(len(exposures)): exposures! ] = ,s7 / '(exposures[i]) exposures[i] = exposures^]. strip ] "})')') exposuresfi] =float( exposures!)])#recombine them with their indicies indicies = range(len(exposures))#connect them back together exposure indici matrix = np.transpose(np.vstack([indicies, exposures])) cdr_exposures, ncdr_exposures = cdr_checker(exposure_indici_matrix, cdr_residues) ncdr_exposures = ncdr_exposures[:,l] return (np.transpose(ncdr_exposures))def PCA_function( / ct / / ' distances, ncdr exposures . print(ncdr_di stances, shape) print(ncdr exposures. shape) scaler = StandardScaler() temp = ncdr_distances.numpy() temp = pd.DataFrame(ncdr distances) seal ed_di stances = scaler.fit_transform(temp) scaled_distances *= .01 temp = scaled distances combined array = np.vstack((temp, nedr exposures)) combined_array = np.transpose(combined_array) combined df = pd.DataFrame(combined array)# scaled_data = scaler.fit_transform(combined_df) pea = PC A( / components=2) pc array = pca.fit transform(combined df) prinicipledf = pd.DataFrame(pc_array, columns = ['PCI', 'PC2']) return prinicipledf plt.scatter(prinicipledf['PCl'], prinicipledf['PC2']) plt.title('PC A Decomposition of nCDR Points of PDL1 with .01 Multiplyer on distance') plt.showQ# ncdr_exposures = torch. from numpy(ncdr exposures)# combined tensor = torch. cat((ncdr distances, ncdr exposures), 0) print(combined_array. shape)# print(exposures. shape) if name == ' main ' :#Import the two datasets into dataframes df = pd.read_csv(path to file) exposuredf = pd.read_csv(path to file) coordinates = df[['Residue', 'X', 'Y', 'Z']] coordinates = pd.DataFrame.to numpy(coordinates)# cdr_residues = np.concatenate([np.arange(25, 32), np.arange(50, 57), np.arange(95, 105)]) cdr_residues = np.concatenate([np.arange(20, 36), np.arange(39, 48), np.arange(88, 103)]) nedr exposures = exposure_prepper(exposuredf, edr residues) edr eoordinates, nedr eoords = cdr_checker(coordinates, edr residues) nedr di stances = distance(cdr_coordinates, nedr eoords)# PCA_function(ncdr_distances, ncdr_exposures)PC A functi on(ncdr_di stance s, ncdr_exposures).to_csv('PDLl_postPCA_standard_distance.csv')# print(distance)# plotfunction(cdr_coordinates, ncdr_coords, di stance_ranker(di stance))# plt.legend(font = 20)# plt.showQTitle: PCAplotting.py import matplotlib.pyplot as pit from mpl_toolkits.mplot3d import Axes3D import pandas as pd def ploting(t7<ato): nPDLl , nEGFR = data plt.figure() plt.scatter(nPDLl['PCr], nPDLl ['PC2'], label - 'nPDLl', color = 'b') plt.scatter(nEGFR['PCl'], nEGFR['PC2'], label = 'nEGFR', color = 'r') plt.title('PCA Decomposition of nonbinding residues of nEGFR and nPDLl with no Scaling on Distance') if name ==1main ' : file ! = path to file file2 = path to file nPDLl = pd.read csv(filel) nEGFR = pd.read_csv(file2) data = (nPDLl, nEGFR) plotting(data) plt.legend() plt.showQREFERENCES(1) Ma, Y.; Xue, J.; Zhao, Y.; Zhang, Y.; Huang, Y.; Yang, Y.; Fang, W .; Guo, Y.; Li, Q.; Ge, X.; Sun, J.; Zhang, B.; Zhang, Y.; Xiao, J.; Zhang, L.; Zhao, H. Phase I Trial of KN046, a Novel Bispecific Antibody Targeting PD-L1 and CTLA-4 in Patients with Advanced Solid Tumors. J. Imrmmother. Cancer 2023, 11 (6), e006654. doi.org / 10.1136 / jitc-2022-006654.(2) Van Fossen, E. M.; Bednar, R. M.; Jana, S.; Franklin, R.; Beckman, J.; Karplus, P. A.; Mehl, R. A. Nanobody Assemblies with Fully Flexible Topology Enabled by Genetically Encoded Tetrazine Amino Acids. Set. Adv. 2022, 8 (18), eabm6909. doi.org / 10.1126 / sciadv.abm6909.(3) Yong, K. W .; Yuen, D.; Chen, M. Z.; Porter, C. J. H.; Johnston, A. P. R. Pointing in the Right Direction: Controlling the Orientation of Proteins on Nanoparticles Improves Targeting Efficiency. Nano Lett. 2019, 19 (3), 1827-1831. doi.org / 10.1021 / acs.nanolett.8b04916.(4) Lee, K. J.; Kang, D.; Park, H.-S. Site-Specific Labeling of Proteins Using Unnatural Amino Acids. Mol. Cells 2019, 42 (5), 386-396. doi.org / 10.14348 / molcells.2019.0078.(5) L. Oliveira, B.; Guo, Z.; L. Bernardes, G. J. Inverse Electron Demand Diels-Alder Reactions in Chemical Biology. Chem. Soc. Rev. 2017, 46 (16), 4895-4950. doi.org / 10.1039 / C7CS00184C.(6) Jana, S.; Evans, E. G. B.; Jang, H. S.; Zhang, S.; Zhang, H.; Rajca, A.; Gordon, S. E.; Zagotta, W. N.; Stoll, S.; Mehl, R. A. Ultrafast Bioorthogonal Spin-Labeling and Distance Measurements in Mammalian Cells Using Small, Genetically Encoded Tetrazine Amino Acids.J. Am. Chem. Soc. 2023, 145 (27), 14608-14620. doi.org / 10.1021 / jacs.3c00967.(7) Jang, H. S.; Jana, S.; Blizzard, R. J.; Meeuwsen, J. C.; Mehl, R. A. Access to Faster Eukaryotic Cell Labeling with Encoded Tetrazine Amino Acids. J. Am. Chem. Soc. 2020, 142 (16), 7245-7249. doi.org / 10.1021 / jacs.9bl 1520.(8) Bednar, R. M.; Jana, S.; Kuppa, S.; Franklin, R ; Beckman, J.; Antony, E.; Cooley, R. B.; Mehl, R. A. Genetic Incorporation of Two Mutually Orthogonal Bioorthogonal Amino Acids That Enable Efficient Protein Dual-Labeling in Cells. ACS Chem. Biol. 2021, 16 (11), 2612- 2622. doi.org / 10.1021 / acschembio. lc00649.(9) Dunkelmann, D. L.; Oehm, S. B.; Beattie, A. T.; Chin, J. W. A 68-Codon Genetic Code to Incorporate Four Distinct Non-Canonical Amino Acids Enabled by Automated Orthogonal mRNA Design. Nat. Chem. 2021, 13 (11), 1110-1117. doi.org / 10.1038 / s41557-021- 00764-5.(10) Wang, K.; Neumann, H.; Peak-Chew, S. Y.; Chin, J. W. Evolved Orthogonal Ribosomes Enhance the Efficiency of Synthetic Genetic Code Expansion. Nat. Biotechnol. 2007, 25 (7), 770-777. doi.org / 10.1038 / nbtl314.(11) Galindo Casas, M.; Stargardt, P.; Mairhofer, J.; Wiltschi, B. Decoupling Protein Production from Cell Growth Enhances the Site-Specific Incorporation of Noncanonical Amino Acids in E. Coli. ACS Synth. Biol. 2020, 9 (11), 3052-3066. doi.org / 10.1021 / acssynbio.0c00298.(12) Chatteijee, A.; Sun, S. B.; Furman, J. L.; Xiao, H.; Schultz, P. G. A Versatile Platform for Single- and Multiple-Unnatural Amino Acid Mutagenesis in Escherichia Coli. Biochemistry 2Q13, 52 (10), 1828-1837. doi.org / 10.1021 / bi4000244.(13) Lafranchi, L.; Schlesinger, D.; Kimler, K. J.; Elsasser, S. J. Universal Single-Residue Terminal Labels for Fluorescent Live Cell Imaging of Microproteins. J. Am. Chem. Soc. 2020, 142 (47), 20080-20087. doi.org / 10.1021 / jacs.0c09574.(14) Kalstrup, T.; Blunck, R. Reinitiation at Non-Canonical Start Codons Leads to Leak Expression When Incorporating Unnatural Amino Acids. Sci. Rep. 2015, 5 (1), 11866. doi . org / 10.1038 / srep 11866.

Claims

1. CLAIMSWhat is claimed:

1. A bioorthogonal conjugation system, comprising: at least one non-canonical amino acid (ncAA) configured for participating in an orthogonal click reaction; and at least one plasmid comprising a gene for one or more of: an aminoacyl-tRNA synthetase (aaRS), a tRNA, and a nanobody having at least one suppression stop codon that defines a location for conjugation of the at least one ncAA.

2. The bioorthogonal conjugation system of claim 1, wherein the at least one ncAA is a phenylalanine derived azide containing amino acid or a tetrazine containing amino acid.

3. The bioorthogonal conjugation system of claim 2, wherein the phenylalanine derived azide containing amino acid is 4-Azido-L-phenylalanine (pAzF).

4. The bioorthogonal conjugation system of claim 2, wherein the tetrazine containing amino acid is 3-(6-methyl-s-tetrazin-3-yl)phenylalanine (Tet-3.0-Me) or (S)-2-amino-3-(6-phenyl- l,2,4,5-tetrazin-3-yl)propanoic acid (Tet-4.0-Ph).

5. The bioorthogonal conjugation system of claim 1, wherein the at least one ncAA is capable of participating in an orthogonal click chemistry reaction.

6. The bioorthogonal conjugation system of claim 5, wherein the at least one ncAA is capable of participating in spontaneous copper-free Diels- Alder cycloaddition reactions with trans-cyclooctene (TCO) and dibenzo cyclooctyne (DBCO) groups.

7. The bioorthogonal conjugation system of claim 5, wherein the at least one ncAA is capable of participating in spontaneous copper-free azide-alkyne cycloaddition reactions with bicyclo[6.1.0]non-4-yne (BCN) and dibenzo cyclooctyne (DBCO) groups.

8. The bioorthogonal conjugation system of claim 1 , wherein the location for conjugation of the at least one ncAA is surface-exposed on an expressed nanobody of the bioorthogonal conjugation system.

9. The bioorthogonal conjugation system of claim 1, comprising a first plasmid having genes for a first aaRS and a first tRNA, and a second plasmid having a gene for the nanobody with a first suppression stop codon.

10. The biorthogonal conjugation system of claim 9, wherein the first tRNA is configured to incorporate a first ncAA at the first suppression stop codon.

11. The biorthogonal conjugation system of claim 10, wherein the nanobody further includes a second suppression stop codon, and further comprising a third plasmid having genes for a second aaRS and a second tRNA.

12. The biorthogonal conjugation system of claim 11, wherein the second tRNA is configured to incorporate a second ncAA at the second suppression stop codon, such that an expressed nanobody includes dual, bioorthogonal conjugation sites at the first ncAA and second ncAA.

13. The bioorthogonal conjugation system of claim 1, comprising one plasmid having genes for the aaRS, the tRNA, and the nanobody with the suppression stop codon.

14. The bioorthogonal conjugation system of claim 13, wherein the tRNA is configured to incorporate the at least one ncAA at the suppression stop codon such that an expressed nanobody includes a biorthogonal conjugation site at the ncAA.

15. A modified protein for modular conjugation, the modified protein having at least one bioorthogonal conjugation site where a noncanonical amino acid (ncAA) is located, the ncAA capable of participating in an orthogonal click chemistry reaction.

16. The modified protein of claim 15, having at least two bioorthogonal conjugation sites, each where a different ncAA is located.

17. The modified protein of claim 16, wherein each bioorthogonal conjugation site is configured to conjugate a different cargo.

18. A protein assembly comprising one or more modified protein of claim 15.

19. The protein assembly of claim 18, wherein each bioorthogonal conjugation site is configured to conjugate a different cargo.

20. The protein assembly of claim 18, wherein the one or modified proteins are assembled with a linker selected from the group consisting of TCO-PEG-TCO, DBCO-PEG-DBCO, and TCO-PEG-DBCO.

21. A method expressing a nanobody with one or more bioorthogonal conjugation sites, comprising: transforming into a bacterial host at least one plasmid comprising a gene for one or more of: an aminoacyl-tRNA synthetase (aaRS), a tRNA, and a nanobody having at least one suppression stop codon that defines a location for conjugation of the at least one ncAA; and expressing the nanobody with a bioorthogonal conjugation site at each incorporated ncAA.

22. The method of claim 21, wherein the bacterial host is transformed with one plasmid having genes for the aaRS, the tRNA, and the nanobody with the suppression stop codon.

23. The method of claim 21, wherein the bacterial host is co-transformed with a first plasmid having genes for a first aaRS and a first tRNA, a second plasmid having genes for a second aaRS and a second tRNA, and a third plasmid having a gene for the nanobody with a first suppression stop codon and a second suppression stop codon.

24. A method of treating or preventing a condition in a subject in need thereof, the method comprising: administering to the subject in need thereof a therapeutically effective amount of a modified protein, the modified protein having at least one cargo conjugated at a bioorthogonal conjugation site of the modified protein where a noncanonical amino acid (ncAA) is located.

25. The method of claim 24, wherein the condition is cancer or an autoimmune disease.

26. The method of claim 24, wherein the modified protein penetrates the blood-brain barrier.

27. The method of claim 24, wherein the at least one cargo is conjugated via orthogonal click chemistry reaction.

28. The method of claim 24, wherein the at least one cargo is selected from the group consisting of small molecules, oligonucleotides, and peptides.

29. The method of claim 24, wherein the modified protein has at least two bioorthogonal conjugation sites, each where a different ncAA is located.

30. The method of claim 29, wherein a different cargo is located at each bioorthogonal conjugation site.

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