A sequence-based elastin protein signature prediction method and system

Abstract

The application discloses a sequence-based elastomer protein feature prediction method and system, and belongs to the technical field of biological information. The prediction method comprises the following steps: obtaining an amino acid sequence of a protein to be analyzed; performing motif identification on the amino acid sequence, and calculating parameters such as a spring constant and a phase transition critical temperature of the protein to be analyzed. The application integrates motif identification, phase transition temperature calculation and elastic mechanical property calculation in the same sequence analysis process starting from the amino acid sequence, solves problems such as lack of a targeted analysis tool, high cost and long time consumption of experimental determination of physicochemical properties faced by elastin researchers, and improves the research efficiency of elastomer proteins and reduces the research and development cost.

Description

Technical Field

[0001] This invention belongs to the field of bioinformatics technology, specifically relating to a sequence-based method and system for predicting the features of elastomer proteins. Background Technology

[0002] Elastomer proteins are a class of biomolecular materials with significant application value, including natural elastin, proelastin, and elastin-like peptides. Natural elastin is widely found in tissues such as skin, blood vessels, lungs, and ligaments, providing them with excellent elasticity and flexibility. Elastin-like peptides are typically composed of repetitive sequences derived from the hydrophobic domains of elastin, such as VPGXG pentapeptide repeat units, and generally exhibit temperature responsiveness, self-assembly, and reversible phase transition properties in aqueous solutions. Due to their good biocompatibility, designability, and environmental responsiveness, these materials have high application potential in drug delivery, tissue engineering, bioseparation and purification, thermosensitive hydrogels, biosensor materials, and regenerative medicine.

[0003] Currently, there is a lot of research on temperature-responsive elastomer protein materials. For example, the existing technology CN119331077A discloses an elastin-like polypeptide, its preparation method and application, and the amino acid sequence of the elastin-like polypeptide is (VPGXG). n Existing technology CN115724916A also discloses an elastin-like polypeptide (VPTGIG). 25 Its preparation method and applications, and the amino acid sequence of its elastin-like polypeptide is (VPTGIG). 25 Both exhibit reversible phase transition behavior. However, the function of elastin proteins mainly depends on specific sequences. For new candidate sequences, a large number of experiments are usually required to determine their phase transition temperature and elastic coefficient. McDaniel JR (A unified model for de novo design of elastin-like polypeptides with tunable inverse transition temperatures. Biomacromolecules. 2013 Aug 12;14(8):2866-2872) disclosed a simple predictive model for elastin proteins, which uses the intrinsic phase transition temperature T tc Used to predict the critical temperature T of phase transition tThe current prediction model only considers the influence of amino acid residues A and V in elastin (VPGXG)n. However, the phase transition temperature of elastin is not only affected by the content of amino acid residues A and V, and the amino acid sequence of elastin is not composed solely of the repeating sequence of (VPGXG)n. Therefore, this model has limited application scenarios and is difficult to apply in the research and development of elastin. Existing technology CN116102172A discloses a high-throughput computation-based method for predicting elastin, which can predict whether a protein belongs to elastin based on an algorithm. However, it can only achieve a qualitative judgment on whether a protein belongs to elastin based on its amino acid sequence. It still cannot make a meaningful prediction of key performance parameters such as the phase transition temperature and elastic constant of elastin. When facing the problem of screening elastins that meet the performance requirements, extensive experimental verification is still needed, which still consumes a lot of manpower and resources.

[0004] Therefore, there is an urgent need to develop a tool-based method that can directly identify motifs based on amino acid sequences and quantitatively calculate functional property parameters such as phase transition temperature and elastic constant during the candidate sequence design stage of elastomeric proteins, thereby providing a quantitative basis for the sequence design and high-throughput screening of elastomeric materials. Summary of the Invention

[0005] In order to overcome the above-mentioned shortcomings of the existing technology, the purpose of this invention is to provide a sequence-based method and system for predicting the features of elastomeric proteins.

[0006] The objective of this invention is achieved by at least one of the following technical solutions.

[0007] In a first aspect, the present invention provides a method for predicting the characteristics of elastomeric proteins based on sequences, the method comprising:

[0008] S1. Obtain the amino acid sequence of the protein to be analyzed;

[0009] S2. Motif identification is performed on the amino acid sequence to obtain motif identification results, which include repeat unit features and / or guest residue features;

[0010] S3. Calculate one or more of the following parameters of the protein to be analyzed: amino acid composition parameters, sequence characteristic parameters, physicochemical property parameters, spatial structure parameters, and functional property parameters;

[0011] The functional property parameters include the single-molecule phase transition critical temperature and / or spring constant; wherein the spring constant includes the entropic elastic spring constant and the hydrophobic elastic spring constant.

[0012] The single-molecule phase transition critical temperature T c Predict using the following steps:

[0013] S311. Count the total number of residues N and the absolute frequency count of residue type i in the amino acid sequence. i ;

[0014] S312. Obtain the contact free energy temperature response parameters of residue type i, and calculate the fundamental contact free energy ΔG between residues of residue type i under temperature variable T using the contact free energy temperature response parameters of residue type i. e (T) i ;

[0015] S313. Calculate the all-atom interaction energy G of the protein to be analyzed under temperature variable T. int_raw (T):

[0016] ,

[0017] Where, count i : Absolute frequency of residue type i; n c : Average coordination number, representing the average number of neighboring residues that are in physical contact with other residues in a fully collapsed, dense sphere state; N: Total number of residues in the amino acid sequence;

[0018] S314. Calculate the conformational entropy resistance G of the protein to be analyzed under temperature variable T. conf (T):

[0019] ,

[0020] Among them, R gas α: Molar gas constant; α: Chain expansion / compressibility factor. , where R coil and R ext These are the radii of the collapsed state and the radii of the highly swollen and expanded state, respectively.

[0021] S315. Establish the single-chain collapse free energy function G with respect to the temperature variable T. total (T):

[0022] ,

[0023] Where, n offset : Pre-set calibration offset;

[0024] S316. Solve for the single-chain collapse free energy function G. total (T) The temperature at which the zero-point condition is met, and this temperature is determined as the single-molecule phase transition critical temperature T of the protein to be analyzed. c The zero-point condition is as follows:

[0025] .

[0026] Furthermore, the elastomeric proteins include natural elastomeric proteins, extrin, elastomeric polypeptides, natural elastomeric fragments or variants, extrin fragments or variants, elastomeric polypeptide variants, and fusion proteins containing elastomeric repeat sequences.

[0027] Furthermore, step S1 also includes standardizing the amino acid sequence to generate a standardized sequence.

[0028] Furthermore, in step S2, the motif identification is performed based on a preset elastin motif library or a motif database accessed via a remote interface.

[0029] Furthermore, in step S312, the contact free energy temperature response parameter of residue type i includes the fundamental contact free energy ΔG between residues of residue type i at the reference temperature T0. e (T0) i Hydration free energy u h (T0) i and linear coupling coefficient (slope) k i ;

[0030] The fundamental contact free energy ΔG between residues of residue type i at a temperature variable T e (T) i Calculated using the following formula:

[0031] ,

[0032] Wherein, ΔG e (T) i : Fundamental contact free energy between residues of residue type i; ΔG e (T0) i The fundamental contact free energy between residues of residue type i at a reference temperature T0; u h (T) i : The solvation (hydration) free energy of residue type i at a temperature variable T; u h (T0) i : The fundamental solvation (hydration) free energy of residue type i at the reference temperature T0; k i The linear coupling coefficient (slope) of the residues.

[0033] Furthermore, the contact free energy temperature response parameter of residue type i also includes the enthalpy of hydration ΔH. h (T0) i Hydration isobaric heat capacity change (ΔC) p ) i ;

[0034] Under a temperature variable T, the solvation (hydration) free energy u of residue type i h (T) i Calculated using the following formula:

[0035] ,

[0036] Where, ΔH h (T0) i At the reference temperature T0, the basic enthalpy of hydration of residue type i; (ΔC) p ) i : Hydration isobaric thermal capacity change of residue type i.

[0037] Furthermore, in step S315, the pre-set calibration offset is determined by having a known single-chain collapse critical temperature T. ref The baseline sequence is determined; Among them, G int_raw (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref All-atomic interaction energy; G conf (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref Configurational entropy resistance.

[0038] Furthermore, the reference sequence is a VGVPG sequence repeated 20 times, denoted as (VGVPG). 20 The baseline sequence (VGVPG) 20 The known single-chain collapse critical temperature T ref It is 305 K.

[0039] Furthermore, in step S316, the single-chain collapse free energy function G is solved. total (T) The temperature corresponding to the zero point condition is obtained by using the bisection method, secant method, Newton's iteration method or spline interpolation method.

[0040] Furthermore, in step S3, the spring constant k ent Predicted using the entropy spring effect model;

[0041] ,

[0042] Where, k B Boltzmann constant; T: absolute temperature; Rg: mean square radius of gyration; Ree: mean square end-to-end distance.

[0043] Furthermore, in step S3, the spring constant k hyd Predicting rebound using a hydrophobic interaction-driven rebound model:

[0044] ,

[0045] Wherein, γ: surface tension constant; H: average hydrophobicity score of the sequence; SASAmax: maximum solvent-accessible surface area in the stretched state; Rg: mean square radius of gyration; N: total length of the polypeptide chain; Ree: mean square end-to-end distance.

[0046] Secondly, the present invention also provides a prediction system for sequence-based elastomer protein features, the system comprising:

[0047] The acquisition module is used to acquire the amino acid sequence information of the protein to be analyzed.

[0048] A motif identification module is used to identify the motif of the amino acid sequence and obtain a motif identification result, wherein the motif identification result includes repeat unit features and / or guest residue features.

[0049] The calculation module is used to calculate one or more of the following parameters of the protein to be analyzed: amino acid composition parameters, sequence characteristic parameters, physicochemical property parameters, spatial structure parameters, and functional property parameters; the functional property parameters include spring constant and / or single-molecule phase transition critical temperature.

[0050] The single-molecule phase transition critical temperature T c Predict using the following steps:

[0051] S311. Count the total number of residues N and the absolute frequency count of residue type i in the amino acid sequence. i ;

[0052] S312. Obtain the contact free energy temperature response parameters of residue type i, and calculate the fundamental contact free energy ΔG between residues of residue type i under temperature variable T using the contact free energy temperature response parameters of residue type i. e (T) i ;

[0053] S313. Calculate the all-atom interaction energy G of the protein to be analyzed under temperature variable T. int_raw (T):

[0054] ,

[0055] Where, count i : Absolute frequency of residue type i; n c : Average coordination number; N: Total number of residues in the amino acid sequence;

[0056] S314. Calculate the conformational entropy resistance G of the protein to be analyzed under temperature variable T. conf (T):

[0057] ,

[0058] Among them, R gas α: Molar gas constant; α: Chain expansion / compressibility factor. , where R coil and R ext These are the radii of the collapsed state and the radii of the highly swollen and expanded state, respectively.

[0059] S315. Establish the single-chain collapse free energy function G with respect to the temperature variable T. total (T):

[0060] ,

[0061] Where, n offset : A pre-set calibration offset; the pre-set calibration offset is determined by a known single-chain collapse critical temperature T. ref The baseline sequence is determined;

[0062] ,

[0063] G int_raw (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref All-atomic interaction energy; G conf (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref Configurational entropy resistance;

[0064] S316. Solve for the single-chain collapse free energy function G. total (T) is the temperature at which the zero-point condition is met, and this temperature is determined as the phase transition critical temperature T of the protein to be analyzed. c The zero-point condition is as follows:

[0065] .

[0066] Thirdly, the present invention also provides an apparatus comprising at least one storage medium and at least one processor, the at least one storage medium being configured to store computer instructions, and the at least one processor being configured to execute the computer instructions to implement the sequence-based elastomeric protein feature prediction method as described in the first aspect.

[0067] Fourthly, the present invention also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the sequence-based elastomeric protein feature prediction method as described in the first aspect.

[0068] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0069] (1) Starting from the amino acid sequence, this invention automatically identifies the motifs related to elastomeric proteins and further calculates the critical temperature of single-molecule phase transition and spring parameters. It integrates motif identification, single-molecule phase transition temperature calculation and mechanical property calculation into the same sequence analysis process, which can provide multi-dimensional evaluation for the design of elastomeric proteins.

[0070] (2) Current technology does not yet have the phase transition temperature T of elastin. t Reasonable prediction methods are difficult to implement due to the numerous influencing factors (such as protein concentration). This invention innovatively and directly uses the amino acid sequence of elastin to predict the single-molecule phase transition critical temperature T of elastin. c To make a prediction, T c The predicted value and T t The (minimum values) are similar, therefore T c The predicted value can be accurately used as T t By using a reference value (the minimum value), a large amount of experimental work can be avoided, and the phase transition temperature T can be predicted directly using the amino acid sequence of elastin. t This invention shortens the research and development cycle. It is suitable for high-throughput sequence design and virtual screening, reducing experiments such as expression, purification, and characterization of elastin, lowering research and development costs, improving screening efficiency, and enhancing the feasibility of subsequent research and development projects.

[0071] 3) This invention can predict the spring constant of elastin, providing a prediction range for the spring constant of elastin for the first time. The prediction range can accurately cover the true spring constant of elastin, which can significantly reduce the experimental cost in the research and development stage and thus improve the research and development efficiency. Attached Figure Description

[0072] Figure 1 This is a flowchart illustrating the prediction method of the present invention.

[0073] Figure 2 This is a local hydrophobicity scan result of the input sequence in Example 1.

[0074] Figure 3 This is a graph showing the amino acid composition fraction of the input sequence in Example 1.

[0075] Figure 4 The figure shows the statistical results of amino acid sequence analysis of human elastin in Example 2 (domain 26A-domain 30 on the left side of the figure represent different structural domains in human elastin).

[0076] Figure 5 For example, the duration L of Example 3 p Comparison chart (based on entropy, elastic spring constant k)ent Hydrophobic elastic spring constant k hyd The predicted value is converted into the duration L. p The following diagram is drawn, where L is the number of digits. p,exp L is the experimental value for the duration. p，ent For predicting the duration of entropy elasticity, L p，hyd (Predicting the duration of hydrophobic elasticity).

[0077] Figure 6 L in Example 3 p Predicted value and L p Comparison chart of experimental value trends (based on entropy, elastic spring constant k) ent Hydrophobic elastic spring constant k hyd The predicted value is converted into the duration L. p The following diagram is drawn, where L is the number of digits. p,exp L is the experimental value for the duration. p，hyd Predict the duration of hydrophobic elasticity; Figure 6 A represents trend synchronization analysis; Figure 6 B is a bivariate linear correlation analysis.

[0078] Figure 7 Phase transition critical temperature prediction diagram (T) c_pre T represents c (Predicted value). Detailed Implementation

[0079] The specific implementation of the present invention will be further described below with reference to the accompanying drawings and examples, but the implementation and protection of the present invention are not limited thereto. It should be noted that any processes not specifically described in detail below are those that can be implemented or understood by those skilled in the art by referring to the prior art. Reagents or instruments whose manufacturers are not specified are considered to be conventional products that can be purchased commercially.

[0080] In this invention, "elastin-like polypeptide" refers to an engineered, recombinant, synthetic, or semi-synthetic polypeptide comprising one or more amino acid repeating sequence units derived from, mimicking, or modified from natural elastin or protoelastin. The elastin-like polypeptide includes, but is not limited to, elastin-like repeating polypeptides, elastin-like block polypeptides, silk-elastin-like polypeptides, collagen-elastin-like fusion polypeptides, elastin-like polypeptides containing cell adhesion sequences, elastin-like polypeptides containing enzyme cleavage sites, elastin-like polypeptides containing cross-linking sites, as well as fragments, mutants, variants, derivatives, fusions, covalently modified products, or cross-linked products of the above polypeptides. The elastin-like polypeptide preferably possesses at least one elastin-like characteristic, including elastic recovery, flexibility, reversible phase transition, temperature responsiveness, self-assembly, cross-linkability, biocompatibility, the ability to form an elastic network, or a combination thereof.

[0081] In this invention, "motif identification" refers to the process of matching, scanning, comparing, and statistically analyzing the amino acid sequence of the protein to be analyzed based on preset motif rules or a motif database to determine whether an elastin motif exists in the amino acid sequence, and to obtain the type, quantity, location, repetition, significance parameters, matching score, and confidence level of the elastin motif. Elastin motifs include, but are not limited to, hydrophobic motifs, receptor-binding motifs, cross-linking motifs, cell adhesion motifs, and structural organization motifs.

[0082] In this invention, "guest residue" refers to a variable amino acid residue in the repeating unit of an elastomeric protein, such as the X1 amino acid residue in VPGX1G.

[0083] In this invention, "solvent-accessible surface area" refers to the total surface area of ​​a protein molecule that can be contacted by water molecules or solvent molecules.

[0084] In this invention, "hydrophobic solvent-accessible surface area" refers to the surface area of ​​a protein surface molecule that is accessible to a solvent, contributed by hydrophobic amino acid residues.

[0085] In this invention, "surface charge distribution" refers to the spatial distribution of positive charge, negative charge, and neutral regions on the surface of a protein's three-dimensional structure, characterized by net surface charge, positive charge surface area, negative charge surface area, the proportion of positive charge surface area, the proportion of negative charge surface area, surface electrostatic potential, positive charge patch area, negative charge patch area, and / or the number of charge patches.

[0086] In this invention, the absolute frequency count of a specific type of amino acid (i) is used. i , which is dimensionless, is used to calculate the proportion of a specific amino acid in a sequence. This is used to calculate the weighted average of the contact energy.

[0087] In this invention, the average coordination number n c Unitless and dimensionless, it represents the average number of neighbors that each amino acid residue has in physical contact with other residues in a completely collapsed, dense sphere state.

[0088] In this invention, the total length N of the polypeptide chain (i.e., the total number of amino acid residues) is dimensionless and has no unit.

[0089] In this invention, ΔG e The fundamental contact free energy between residues, measured in kcal / mol, represents the difference in free energy between two amino acid residues in direct contact in a dense phase (collapsed phase) and those surrounded by water in a dilute phase (solvent-exposed state) at a given temperature.

[0090] In this invention, ΔH h (T0) is the basic enthalpy of hydration at 300 K, in kcal / mol; (ΔC) p ) i : Hydration isobaric thermal capacity change of residue type i, kcal / (mol·K).

[0091] In this invention, the molar gas constant R gas The value is 0.001987, and the unit is kcal / (mol·K).

[0092] In this invention, the chain expansion / compression factor α, being dimensionless and unitless, is used to quantify the degree of deformation of a polymer chain when it is "compressed" from an ideal swollen state to an extremely dense spherical state. The calculation method is as follows: , where R coil and R ext These are the radii of the collapsed state and the highly swollen expanded state, respectively, both in nm.

[0093] In this invention, the spring constant k (pN / nm) includes the entropic elastic spring constant and the hydrophobic elastic spring constant, wherein... ;k B Boltzmann constant, in pN·nm·K -1 k B ≈1.3806×10 −23 J / K = 0.013806 pN·nm·K -1 It is a proportionality constant that relates the average thermal kinetic energy of microscopic particles to macroscopic absolute temperature; T: absolute temperature, unit is K; L c Outline length, in nm, is the maximum theoretical physical length (total geometric length of the backbone chain) of a polypeptide chain when fully stretched without breaking covalent bonds. This invention uses L... c =0.38N, where N is the total number of residues in the amino acid sequence; L p : Duration length, in nm, is the physical characteristic length that characterizes the bending stiffness (flexibility) of a polypeptide chain, i.e., the characteristic distance at which the correlation along the chain tangent decreases exponentially with distance.

[0094] In this invention, the root-mean-square radius of gyration (Rg), measured in nm, is the root-mean-square distance from the mass element of each residue to the center of mass of the chain in all conformations of the polypeptide chain, used to measure the compactness of the polymer spatial scale. In this invention, it is calculated directly from the input amino acid sequence using the AFRC (Analytical FRC) engine.

[0095] In this invention, the mean square end-to-end distance (Ree), measured in nm, is the root mean square distance of the straight line from the start point (N-terminus) to the end point (C-terminus) of the polymer chain in three-dimensional space, reflecting the degree of chain extension in the relaxed state. It is also calculated directly by the AFRC engine through the analysis of the input amino acid sequence.

[0096] In this invention, γ: surface tension constant, in pN / nm, is the free energy cost required to generate a unit area of ​​water-nonpolar solute interface. The interfacial tension between water and typical nonpolar organic liquids (such as alkanes) is typically 20-30 mJ / m. 2 In this invention, γ=25pN / nm is a constant.

[0097] In this invention, H is the average hydrophobicity score of the sequence, which is dimensionless and has a value between 0 and 1.

[0098] In this invention, SASAmax: the maximum solvent-accessible surface area in the stretched state, in nm. 2 The maximum solvent-accessible surface area (SASA) of a polypeptide chain in its fully unfolded and stretched conformation is theoretically the largest that can be exposed to water molecules.

[0099] This invention provides a method for predicting the characteristics of elastomer proteins based on sequences, the process of which is as follows: Figure 1 As shown, this prediction method can be deployed on servers, personal computers, cloud computing platforms, laboratory information management systems, protein design platforms, or local workstations and executed by one or more processors.

[0100] The prediction method includes:

[0101] S1. Obtain the amino acid sequence of the protein to be analyzed;

[0102] S2. Motif identification is performed on the amino acid sequence to obtain motif identification results, which include repeat unit features and / or guest residue features (Peter JA Cock, et al., Biopython: freely available Python tools for computational molecular biology and bioinformatics, Bioinformatics, Volume 25, Issue 11, June 2009, Pages 1422–1423).

[0103] S3. Calculate one or more of the following parameters of the protein to be analyzed: amino acid composition parameters, sequence characteristic parameters, physicochemical property parameters, spatial structure parameters, and functional property parameters;

[0104] The functional property parameters include the spring constant and / or the single-molecule phase transition critical temperature (T). c );

[0105] The single-molecule phase transition critical temperature T c Predict using the following steps:

[0106] S311. Count the total number of residues N and the absolute frequency count of residue type i in the amino acid sequence. i ;

[0107] S312. Obtain the contact free energy temperature response parameters of residue type i, and calculate the fundamental contact free energy ΔG between residues of residue type i under temperature variable T using the contact free energy temperature response parameters of residue type i. e (T) i ;

[0108] S313. Calculate the all-atom interaction energy G of the protein to be analyzed under temperature variable T. int_raw (T):

[0109] ,

[0110] Among them, the all-atom interaction energy G int_raw (T) is in kcal / mol; count i : Absolute frequency of residue type i; n c : Average coordination number; N: Total length of polypeptide chain (i.e., total number of amino acid residues).

[0111] S314. Calculate the conformational entropy resistance G of the protein to be analyzed under temperature variable T. conf (T):

[0112] ,

[0113] Among them, configuration entropy drag G conf (T) is in kcal / mol; R gas : Molar gas constant, valued at 0.001987, unit is kcal / (mol·K); α: Chain expansion / compressibility factor, dimensionless, calculated as follows: , where R coil and R ext These are the radii of the collapsed state and the radii of the highly swollen expanded state, respectively, both in nm;

[0114] S315. Establish the single-chain collapse free energy function G with respect to the temperature variable T. total (T):

[0115] ,

[0116] Where, n offset : Pre-set calibration offset;

[0117] S316. Solve for the single-chain collapse free energy function G. total (T) is the temperature at which the zero-point condition is met, and this temperature is determined as the phase transition critical temperature T of the protein to be analyzed. c The zero-point condition is as follows:

[0118] .

[0119] In predicting the phase transition temperature of elastin, protein-protein interaction energies are typically obtained through all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, residue pair interaction matrix calculations, or empirical regression methods based on hydrophobicity indices. While molecular dynamics simulations and residue pair interaction calculations can describe residue interactions in detail, they require computationally intensive molecular dynamics sampling over long periods or the construction of complete protein conformations and enumeration of residue contact pairs. On the other hand, empirical regression methods based solely on hydrophobicity indices fail to reflect the impact of temperature changes on residue interactions and hydration states.

[0120] This invention employs a simplified computational method that avoids direct full-atom simulation of the protein under analysis and the enumeration of interactions between all residue pairs in the sequence. It transforms the problem of calculating a large number of different residue contact pairs into a low-dimensional computational problem based on amino acid type, the proportion of specific amino acid types in the protein sequence, temperature-dependent basic contact free energy between residues, and the average coordination number of the collapsed state. This significantly reduces computational complexity while preserving key physical factors affecting elastomeric proteins. The simplified computational method of this application significantly improves the computational efficiency of phase transition temperature prediction, and the calculation results show good consistency with full-residue contact models / high-precision molecular dynamics simulations.

[0121] In some embodiments, the elastomeric proteins include natural elastomeric proteins, extrin, elastomeric polypeptides, fragments or variants of natural elastomeric proteins, fragments or variants of extrin, variants of elastomeric polypeptides, and fusion proteins containing repeating sequences of elastomeric proteins.

[0122] In some implementations, in step S1, the amino acid sequence can be input by the user through a graphical interface, or imported from fa files, faSTA files, txt files, csv files, database records, API interfaces, protein design software output files, or batch sequence libraries.

[0123] In some embodiments, step S1 further includes standardizing the amino acid sequence to generate a standardized sequence. The standardization process includes, but is not limited to, one or more of the following methods, and each method can be used alone or in combination:

[0124] (1) Delete spaces, tabs, newlines, serial numbers, comment symbols, and non-amino acid characters;

[0125] (2) Generate an unknown residue mask for unknown residues (e.g., B, J, O, U, X, or Z);

[0126] (3) Identify Fasta header information, connectors, repeat count markers, and fusion protein tags;

[0127] (4) Generate annotations for obvious signal peptides, purification tags, or non-target regions;

[0128] (5) Perform block processing on sequences that exceed the preset length.

[0129] In some preferred embodiments, the standardized sequence is denoted as S = a1 a2…ai…aL, where ai is the i-th amino acid residue and L is the sequence length. If the input sequence contains unknown residues, the system simultaneously outputs the standard amino acid length, the number of unknown residues, and the effective length ratio.

[0130] In some implementations, step S1 further includes inputting condition parameters; these parameters include protein concentration, pH, salt type, salt concentration, buffer solution, crosslinking agent type, crosslinking agent concentration, degree of crosslinking, water content, temperature scan rate, material forming method, test temperature, frequency, strain amplitude, and compression rate. When the user does not input condition parameters, the system uses default parameters. For example, the default protein concentration is 25 μmol or 1 mg / mL, the default pH is 7.4, the default salt concentration is 150 mmol NaCl, the default water content is 90%, and the default is no crosslinking. The system labels the corresponding condition parameters in the output results to avoid mixing calculated values ​​from different experimental conditions.

[0131] In some implementations, step S1 also includes inputting a reference threshold to facilitate a visual comparison between the protein feature values ​​to be analyzed and the reference values.

[0132] Furthermore, the reference values ​​include a P / G ratio reference threshold, a hydrophobicity reference threshold, and R... g One or more of the / N reference threshold and the global information entropy reference threshold. Where R... g / N represents the mean square gyroradius divided by the chain length.

[0133] Furthermore, the P / G ratio reference threshold is 0.4135.

[0134] Furthermore, the hydrophobicity reference threshold is 0.6256.

[0135] Furthermore, the R g The / N reference threshold is 0.0615.

[0136] Furthermore, the global information entropy reference threshold is 3.0467.

[0137] In some implementations, in step S2, the motif identification is performed based on a preset elastin motif library or a motif database accessed via a remote interface.

[0138] In some preferred embodiments, in step S2, the preset elastin motif library includes, but is not limited to, the following rules:

[0139] (1) VPGX1G, where X1 is selected from any amino acid except proline;

[0140] (2) X2GX3X4PG, where X2, X3 and X4 are independently selected from any amino acid;

[0141] (3) X5GX6PGX7GX8G, wherein X5, X6, X7 and X8 are independently selected from any amino acid;

[0142] (4) GVGVP, PGVGV, VGVP, GVGV, PGVG or GVP;

[0143] (5) VAPGVG, APGVGV, GVAPGV or GVGVAP;

[0144] (6) KA, KP, KAAK, AAAKAA, AKAAAKA or AKAAAKAAAKA;

[0145] (7) RKRK or GRKRK.

[0146] Furthermore, X1 = V, A, I, K, F, M, S, T, E, D, Q, or N.

[0147] Furthermore, X2 = V or I, X3 = V, A or L, and X4 = A, M or S.

[0148] Furthermore, X5 = V or A, X6 = V or I, X7 = L or F, and X8 = V or A.

[0149] In some preferred embodiments, the sequence database accessed by the remote interface includes MEME Suite.

[0150] Furthermore, the amino acid sequence is used to identify motifs using the MEME tool in MEME Suite to obtain candidate motifs. These candidate motifs are then filtered based on their E-value, retaining those with an E-value less than 1 × 10⁻⁶. -5 Candidate motifs. The E-value is used to characterize the statistical significance of candidate motifs, representing the number of motifs with the same or higher significance as the candidate motifs expected to appear in a random sequence under a predefined background model; the smaller the E-value, the higher the statistical significance of the candidate motif.

[0151] In some implementations, in step S2, the repeating unit features include one or more of the following: repeating unit type, repeating unit number, repeating unit start and end positions, longest continuous repeating segment length, and continuous repeating unit segments.

[0152] In some embodiments, in step S2, the guest residue characteristics include one or more of the following: guest residue type, number of guest residues, location of guest residues, proportion of hydrophobic guest residues, proportion of hydrophilic guest residues, proportion of charged guest residues, and proportion of aromatic guest residues.

[0153] In some implementations, step S2 may include one or more of the following: motif matching score, confidence level, domain, and functional annotation.

[0154] In some implementations, the motif identification includes the following steps:

[0155] S21, the amino acid sequence is scanned by calling a preset elastin motif library or a motif database accessed via a remote interface based on one or more of the following: full matching, regular expression matching, position weight matrix matching, and hidden Markov model matching.

[0156] S22, obtaining candidate elastin motifs;

[0157] S23, the candidate elastin motifs are screened based on the matching scores;

[0158] S24. The selected elastin motifs are deduplicated and their positions are corrected to obtain the motif identification results.

[0159] Furthermore, the position weight matrix matching includes: calling the position weight matrix of the elastin motif; scanning the amino acid sequence with a sliding window corresponding to the length of the elastin motif; calculating a matching score based on the weight value of each amino acid residue at the corresponding position in the sliding window; and identifying the sequence fragment corresponding to the sliding window as the elastin motif when the matching score reaches a preset threshold.

[0160] Furthermore, the Hidden Markov Model (HMM) matching includes: calling the HMM corresponding to the elastin motif; using the HMM to scan and compare the amino acid sequence to be analyzed to determine whether an elastin motif exists in the sequence. The HMM includes matching, insertion, and deletion states, and contains state transition probabilities and amino acid emission probabilities; by calculating the matching probability of the sequence fragment to be analyzed relative to the HMM, it is determined whether the sequence fragment is an elastin motif.

[0161] In some embodiments, in step S3, the amino acid composition parameters include one or more of the following: the content of one or more amino acid residues, the total content of two or more amino acid residues, the content of hydrophobic amino acid residues, the content of polar amino acid residues, the content of charged amino acid residues, and the content of aromatic amino acid residues.

[0162] Furthermore, the amino acid composition parameters include the combined content of proline (P) and glycine (G) in the amino acid sequence.

[0163] Furthermore, the amino acid composition parameters include the total content of proline (P) and glycine (G) in different segments or different sliding windows.

[0164] In some implementations, in step S3, the sequence feature parameters include one or more of the following: the amino acid sequence length of the protein to be analyzed, the global information entropy, the length of consecutive hydrophobic fragments, the length of consecutive hydrophilic fragments, the number of low-complexity structural domains, and the proportion of low-complexity structural domains.

[0165] In some preferred embodiments, the proportion of low-complexity structural domains is calculated using the SEG algorithm.

[0166] In some preferred embodiments, the global information entropy is calculated based on the occurrence frequency of various amino acid residues in the amino acid sequence, and is used to characterize the uncertainty or complexity of the overall amino acid composition of the amino acid sequence to be analyzed.

[0167] Furthermore,

[0168] ,

[0169] Among them, H global Global information entropy, A: set of amino acid residues, p i: The frequency of occurrence of amino acid residue i in the amino acid sequence to be analyzed (Khandelwal M, Rout RK. PRMxAI: protein arginine methylationsites prediction based on amino acid spatial distribution using explainable artificial intelligence. BMC Bioinformatics. 2023 Oct 4;24(1):376.).

[0170] In some embodiments, in step S3, the physicochemical properties parameters include one or more of the following: molecular weight, isoelectric point, net charge, hydrophobicity, hydrophilicity, aromaticity index, aliphatic index, flexibility index, disorder tendency, solubility, and aggregation tendency of the protein to be analyzed.

[0171] In some preferred embodiments, in step S3, the hydrophobicity includes the sequence average hydrophobicity score and / or local hydrophobicity characteristics of the protein to be analyzed.

[0172] Furthermore, the average hydrophobic score of the sequence is the average value of the hydrophobicity parameters of each amino acid residue in the amino acid sequence of the protein to be analyzed.

[0173] Furthermore, the average hydrophobicity score of the sequence is characterized by the Kyte-Doolittle hydrophobicity index:

[0174] ,

[0175] Where H: average hydrophobicity score of the protein sequence (refer to Table 1), N: total number of amino acid residues (i.e., total length of the polypeptide chain), h o : The Kyte-Doolittle hydrophobic coefficient value corresponding to the oth amino acid of the protein to be analyzed (Kyte J, A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982 May 5;157(1):105-132).

[0176] Table 1. Hydrophobicity Score of Amino Acids (h) o )

[0177]

[0178] In some embodiments, in step S3, the spatial structure parameters include one or more of the following: secondary structure ratio, disordered structure ratio, solvent-accessible surface area, hydrophobic solvent-accessible surface area, surface hydrophobic residue ratio, surface hydrophobic patch area, surface hydrophobic patch number, surface charge distribution, and cyclotron radius.

[0179] In some implementations, residue type refers to a type of amino acid residue in the amino acid sequence of the protein to be analyzed, and the symbol i is used to identify different residue types. The amino acid residue type includes native amino acid residues and / or modified amino acid residues.

[0180] In some preferred embodiments, residue type i is selected from any one of twenty natural amino acid residue types, including Ala, Arg, Asn, Asp, Cys, Gln, Glu, Gly, His, Ile, Leu, Lys, Met, Phe, Pro, Ser, Thr, Trp, Tyr, and Val.

[0181] In some embodiments, in step S312, the contact free energy temperature response parameter of residue type i includes the fundamental contact free energy ΔG between residues of residue type i at a reference temperature T0. e (T0) i Hydration free energy u h (T0) i and linear coupling coefficient (slope) k i (See Table 2);

[0182] The fundamental contact free energy ΔG between residues of residue type i at a temperature variable T e (T) i Calculated using the following formula:

[0183]

[0184] Wherein, ΔG e (T) i : The fundamental contact free energy between residues of residue type i, measured in kcal / mol, represents the difference in free energy between two amino acid residues in direct contact in a dense phase (collapsed phase) compared to when they are surrounded by water in a dilute phase (solvent-exposed state) at a given temperature; ΔG e (T0) i The fundamental contact free energy between residues of residue type i at a reference temperature T0, expressed in kcal / mol; u h (T) i : The solvation (hydration) free energy of residue type i at a temperature variable T, in kcal / mol; u h (T0) iThe fundamental solvation (hydration) free energy of residue type i at a reference temperature T0, expressed in kcal / mol; k i The linear coupling coefficient (slope) describes the modulation intensity of the direct contact energy between residues due to changes in the solvation environment, where k is the linear coupling coefficient for residue type i. i The values ​​are shown in Table 2, refer to (Chen FK, Simulating Thermoresponsive Behavior of Disordered Proteins with Temperature-Dependent Coarse-Grained PotentialsDerived from Hydration Free Energies).

[0185] In some preferred embodiments, in step S311, the reference temperature T0 = 300 K.

[0186] Table 2 Critical Temperature T of Unimolecular Phase Transition c The prediction method involves the selection of parameter values ​​(T0=300 K).

[0187]

[0188] In some preferred embodiments, the contact free energy temperature response parameter of residue type i further includes enthalpy of hydration.

[0189] ΔH h (T0) i Hydration isobaric heat capacity change (ΔC) p ) i ;

[0190] Under a temperature variable T, the solvation (hydration) free energy u of residue type i h (T) i Calculated using the following formula:

[0191]

[0192] Where, ΔH h (T0) i The basic enthalpy of hydration of residue type i at the reference temperature T0, in kcal / mol;

[0193] (ΔC p ) i : Hydration isobaric thermal capacity change of residue type i, in units of cal / (mol·K), which is converted to kcal / (mol·K) by dividing by 1000 in the formula.

[0194] In step S313, the average coordination number n cIt is the effective average coordination number after double counting correction, and its value is derived from the average contact number of the collapsed protein or coarse-grained bead model. In some embodiments, the average coordination number n c Calculated through user input, preset default values, or collapsed state conformation models.

[0195] In some preferred embodiments, the average coordination number n c The average coordination number n was calculated using a collapsed conformation model. This model involved the protein to be analyzed generating one or more collapsed conformations. The number of non-bonded neighbors of each residue within the contact cutoff distance in each conformation was counted, and the average was taken for all residues and all collapsed conformations. c (Tunyasuvunakool,K., Adler, J., Wu, Z. et al. Highly accurate protein structure prediction for the human proteome. Nature 596, 590-596 (2021).).

[0196] In some preferred embodiments, the average coordination number n c The default value range is 2-6.

[0197] In some preferred embodiments, the average coordination number n c The default value is 4.

[0198] In some embodiments of the present invention, the collapse radius R coil The calculation method is as follows:

[0199] ,

[0200] Where, count i : Absolute frequency of residue type i in the protein being analyzed; V i,vdW : Van der Waals volume of residue type i.

[0201] First, calculate the volume V of the sphere in the final collapsed and compacted state. compact Then, according to the formula for the volume of a sphere The collapsed state radius R is obtained. coil Among them, the volume V of the sphere in the final collapsed and compacted state. compact Obtained through van der Waals accumulation of amino acid residues (assuming the collapsed compact state is a sphere, therefore the radius of the collapsed state is calculated):

[0202] ,

[0203] In this invention, the van der Waals volume V of residue type i i,vdWThese are parameters well-known in the art. In specific calculations, this invention constructs a van der Waals volume mapping dictionary for amino acid residue types (see Table 2).

[0204] In some implementations, the radius R of the highly swollen expanded state ext The calculation method is as follows:

[0205]

[0206] For the radius R of the highly swollen expanded state ext This invention is based on the empirical scaling law of self-avoiding random walk (Dobay A, Dubochet J, Millett K, Sottas PE, Stasiak A. Scaling behavior of random knots. Proc Natl Acad Sci USA. 2003 May 13;100(10):5611-5615), using the recognized physical constant in condensed matter physics (ν≈0.588) (i.e., the Flory exponent) and the empirical scaling law of peptides to calibrate R0 (studies have shown that the equivalent characteristic step size factor R0 in typical disordered chains is generally 1.92 Å) as the radius R of the highly swollen unfolded state. ext The parameters used in the calculation.

[0207] In some implementations, in step S315, the preset calibration offset is determined by having a known single-chain collapse critical temperature T. ref The baseline sequence is determined;

[0208] ,

[0209] Among them, G int_raw (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref All-atomic interaction energy;

[0210] G conf (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref Configurational entropy resistance.

[0211] In some preferred embodiments, the reference sequence is a VGVPG sequence repeated 20 times, denoted as (VGVPG). 20 The baseline sequence (VGVPG) 20 The known single-chain collapse critical temperature T ref It is 305 K.

[0212] In some implementations, step S316 involves solving the single-chain collapse free energy function G. total (T) is the temperature at which the zero-point condition is met. Roots are found using the bisection method, secant method, Newton's iteration method, or spline interpolation method.

[0213] In some implementations, in step S3, the spring constant is predicted using an entropy spring effect model;

[0214] Entropy elastic spring constant k ent (pN / nm) is approximated based on the worm-like chain (WLC) model:

[0215] ,

[0216] Where, k B Boltzmann constant, in pN·nm·K -1 kB≈0.013806 pN·nm·K -1 T: Absolute temperature, in K; Rg: Mean square radius of gyration, in nm; Ree: Mean square end-to-end distance, in nm.

[0217] In some preferred embodiments, the mean square radius of gyration Rg and the mean square end distance Ree are calculated using the AFRC (Analytical FRC) engine through amino acid sequence analysis (Alston JJ, Ginell GM, Soranno A, Holehouse AS. The Analytical Flory Random Coil Is a Simple-to-Use Reference Model for Unfolded and Disordered Proteins. Journal of Physical Chemistry B, 2023, 127(21): 4746–4760).

[0218] In some implementations, in step S3, the spring constant is predicted by the rebound model through hydrophobic interaction:

[0219] Hydrophobic elastic spring constant k hyd (pN / nm):

[0220]

[0221] Where γ is the surface tension constant, measured in pN / nm, which is the free energy cost required to generate a unit area of ​​water-nonpolar solute interface. The interfacial tension between water and typical nonpolar organic liquids (such as alkanes) is typically 20-30 mJ / m. 2H: Sequence average hydrophobicity score (see Table 1), unitless and dimensionless, value between 0 and 1. SASAmax: Maximum solvent-accessible surface area in the stretched state, in nm. 2 Summing and accumulating the entire sequence. Rg: mean square radius of gyration, in nm; N: total number of amino acid residues (i.e., total length of the polypeptide chain); Ree: mean square end-to-end distance, in nm.

[0222] In some preferred embodiments, the surface tension constant γ is obtained through user input, preset default values, calculations based on protein structure, calculations based on molecular simulation, or machine learning.

[0223] Furthermore, the default value for the surface tension constant γ is 25 pN / nm.

[0224] Example 1

[0225] The input elastin sequence is:

[0226] The system identified one valid sequence with a length of 697 aa, a global proline and glycine content (global P+G content) of 42.18%, and a total computation time of 0.71 s. The local hydrophobicity characteristics of the input sequence are as follows: Figure 2 As shown; the percentage content of various amino acids in the input sequence is as follows: Figure 3 As shown.

[0227] The motif identification results are shown in Table 3.

[0228] Table 3. Motif recognition results of the input sequence in Example 1

[0229]

[0230] Example 2

[0231] The amino acid composition parameters, sequence characteristic parameters, physicochemical properties parameters, and spatial structure parameters of human elastin were calculated, and the statistical results are as follows: Figure 4 As shown (only partial data is displayed, where domain26A, etc., represent different structural domains of human elastin), the above results demonstrate that this invention can accurately and rapidly analyze the amino acid composition parameters, sequence characteristic parameters, physicochemical property parameters, and spatial structure parameters of elastin, providing fundamental data for advancing elastin research.

[0232] Example 3

[0233] The spring constants of elastins ELP120, ELP1-180, ELP4-120, ELP2-64, and Tropoelastin-698, and mucin-mini1, mucin-mini2, and mucin-mini1plus2 were calculated and predicted. Mucin-mini1, mucin-mini2, and mucin-mini1plus2 are mucins and can be used as controls for elastin (disordered but hydrophilic, while elastin is disordered and hydrophobic). The spring constant k (pN / nm) includes the entropic elastic spring constant k. ent and the hydrophobic elastic spring constant k hyd ,according to The predicted spring constant is converted into the duration L. p Plot the graph, where k ent Convert to entropy elasticity prediction duration L p，ent k hyd Converted to hydrophobic elastic prediction of duration L p，hyd The results are shown Figure 5 , Figure 6 .

[0234] according to Figure 5 It can be seen that the experimental value of duration L p，exp The fact that the duration of the predicted entropic elasticity lies between the predicted duration of the hydrophobic elasticity indicates that the true elastic spring constant of elastin (i.e., the experimental values ​​of ELP120, ELP1-180, ELP4-120, ELP2-64, and Tropoelastin-698) lies between the predicted duration of the entropic elasticity and the predicted duration of the hydrophobic elasticity. ent and the hydrophobic elastic spring constant k hyd Between the two, and the predicted value k ent and predicted value k hydThe difference range is relatively narrow, which can provide a more accurate reference range for the elasticity prediction of elastin, and can significantly improve R&D efficiency; however, the present invention cannot accurately predict the elastic spring constant of non-elastin (mucin-mini1, mucin-mini2, mucin-mini1plus2), which shows that the method described in the present invention can specifically and accurately predict the spring constant of elastin.

[0235] according to Figure 6 It can be seen that the experimental values ​​L of elastin (ELP120, ELP1-180, ELP4-120, ELP2-64 and Tropoelastin-698) are... p，exp Compared with the predicted value L p,hyd The trend of change remains basically consistent. Figure 6 A) The trend lines nearly overlap; further bivariate correlation analysis shows r = 0.9389, indicating a good linear relationship. This demonstrates that the method described in this invention can predict the spring constants of different elastins, providing reliable predictive data support for elastin research and development.

[0236] Example 4

[0237] This embodiment focuses on the single-molecule phase transition critical temperature T of elastin. c A prediction was made, and the predicted value obtained is T. c_pre and the predicted value T c_pre Compared with the experimental value of intrinsic phase transition temperature (T) tc Correlation analysis was performed (see) Figure 7 This embodiment investigated the single-molecule phase transition critical temperature T of elastin composed of VPGXG repeat sequences. c The prediction is that X is amino acid A or V, and the content ratio of residues involving A and V includes 1.0:0, 0.9:0.1, 0.8:0.2, 0.7:0.3, 0.6:0.4, 0.5:0.5, 0.2:0.8, and 0:1.0. The chain length of elastin (i.e., the total number of amino acid residues) includes 50, 500, 1000, and 2500.

[0238] according to Figure 7 It can be seen that, under the condition that the content ratio of A and V residues is the same, the longer the elastin chain, the higher its T content. c_pre Compared with the experimental value of intrinsic phase transition temperature (T) tc The closer the predicted value is to the value of elastin chain length N, the better; for elastin chain length N, the predicted value T is between 50-2500. c_pre Compared with the experimental value of intrinsic phase transition temperature (T) tc The relationship exhibits a good linear relationship (coefficient of determination R). 2 =0.8831). This indicates that the critical temperature T for the unimolecular phase transition in this invention is 0.8831.c The predicted value can accurately reflect the phase transition temperature (T) of elastin. t (Minimum value)

[0239] Example 5

[0240] This embodiment predicts the single-molecule phase transition critical temperature T for elastin with different repeating sequence compositions, such as FPGVG and FPGV. c And the critical temperature T of the unimolecular phase transition c Predicted value vs. experimentally determined phase transition temperature (T) t A comparison was made (see Table 3).

[0241] Sequence 4: MHHHHHADEQEEKAKVRTELIQELAQGLGGFEKKNFPTLGDEDLDHTYMTKLLTYLQEREQAENSWRKRLLKGIQDHALDLVPRGFPGFGVGVGGIPGVAGVPGVGGVPGVGGVPGVGISPEAQAAAAAKAAKYGAAGAGVLGGLVPGAPGAVPGVPGTGGVPGVGTP AAAAAKAAAKAAQFGLVPGVGVAPGVGVAPGVGVAPGVGLAPGVGVAPGVGVAPGVGVAPGIGEAQAAAAAKAAKYGAAGAGVLGGLVPGAPGAVPGVPGTGGVPGVGTPAAAAAKAAAKAAQFGLVPGVGVAPGVGVAPGVGVAPGVGLAPGVGVAPGVGVAPGVGVAPGIG.

[0242] Table 3. Phase transition temperatures T of the same sequence t With the critical temperature of unimolecular phase transition T c Compare

[0243] As shown in Table 3, for the same elastin sequence, the higher the concentration, the higher the experimentally determined phase transition temperature T. t The lower the concentration, the higher the experimentally determined phase transition temperature T. t The higher the temperature, the higher the phase transition temperature T of the elastin described in this embodiment. t It shows a negative correlation with its concentration, which is consistent with the characteristics of elastin. In this embodiment, the phase transition temperature T was determined experimentally using sequences 1-4. t The lowest values ​​were 293.7 K, 291.1 K, 290.2 K, and 307.2 K, respectively, and the critical temperature T for the unimolecular phase transition was... cThe predicted values ​​are 283.2 K, 282.8 K, 282.6 K, and 304.4 K, respectively, indicating that the critical temperature T for the single-molecule phase transition is... c Predicted value vs. experimentally determined phase transition temperature T t The lowest values ​​are very close, indicating that the present invention is based on the sequence-predicted single-molecule phase transition critical temperature T. c The value can serve as a reference for the phase transition temperature of elastin. Although the repeating sequences and amino acid chain lengths contained in sequences 1-4 differ, this invention can provide a reliable single-molecule phase transition critical temperature T0. c Predicted value, and can be used as phase transition temperature T t The reference values ​​indicate that the method described in this invention is suitable for the phase transition critical temperature T of elastin with different chain lengths and sequence compositions. t The prediction.

[0244] The above embodiments are merely preferred embodiments of the present invention and are only used to explain the present invention, not to limit the present invention. Any changes, substitutions, modifications, etc., made by those skilled in the art without departing from the spirit and essence of the present invention should be within the protection scope of the present invention.

Claims

1. A method for predicting the characteristics of elastomer proteins based on sequence, characterized in that, The prediction method includes: S1. Obtain the amino acid sequence of the protein to be analyzed; S2. Perform motif identification on the amino acid sequence to obtain the motif identification results; S3. Calculate the amino acid composition parameters, sequence characteristic parameters, physicochemical property parameters, spatial structure parameters, and functional property parameters of the protein to be analyzed; The functional property parameters include the single-molecule phase transition critical temperature and / or spring constant; wherein, the spring constant includes the entropic elastic spring constant and the hydrophobic elastic spring constant.

2. The prediction method according to claim 1, characterized in that, The single-molecule phase transition critical temperature T c Predict using the following steps: S311. Count the total number of residues N and the absolute frequency count of residue type i in the amino acid sequence. i ; S312. Obtain the contact free energy temperature response parameters of residue type i, and calculate the fundamental contact free energy ΔG between residues of residue type i under temperature variable T using the contact free energy temperature response parameters of residue type i. e (T) i ; S313. Calculate the all-atom interaction energy G of the protein to be analyzed under temperature variable T. int_raw (T): ， Where, count i : Absolute frequency of residue type i; n c : Average coordination number; N: Total number of residues in the amino acid sequence; S314. Calculate the conformational entropy resistance G of the protein to be analyzed under temperature variable T. conf (T): ， Among them, R gas α: Molar gas constant; α: Chain expansion / compressibility factor. , where R coil and R ext These are the radii of the collapsed state and the radii of the highly swollen and expanded state, respectively. S315. Establish the single-chain collapse free energy function G with respect to the temperature variable T. total (T): ， Where, n offset : A pre-set calibration offset; the pre-set calibration offset is determined by having a known single-chain collapse critical temperature T. ref The baseline sequence is determined; , G int_raw (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref All-atomic interaction energy; G conf (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref Configurational entropy resistance; S316. Solve for the single-chain collapse free energy function G. total (T) The temperature at which the zero-point condition is met, and this temperature is determined as the single-molecule phase transition critical temperature T of the protein to be analyzed. c The zero-point condition is as follows: 。 3. The prediction method according to claim 1, characterized in that, The elastin proteins include natural elastin, extrin, elastin-like peptides, natural elastin fragments or variants, extrin fragments or variants, elastin-like peptide variants, and fusion proteins containing elastin-like repeat sequences.

4. The prediction method according to claim 1, characterized in that, In step S312, the contact free energy temperature response parameter of residue type i includes the fundamental contact free energy ΔG between residues of residue type i at a reference temperature T0 = 300 K. e (T0) i Hydration free energy u h (T0) i and linear coupling coefficient k i ; The fundamental contact free energy ΔG between residues of residue type i at a temperature variable T e (T) i Calculated using the following formula: , Among them, u h (T) i : The solvation free energy of residue type i at a temperature variable T; Under temperature variable T, the solvation free energy u of residue type i h (T) i Calculated using the following formula: ΔH h (T0) i Enthalpy of hydration, (ΔC) p ) i The isobaric thermal capacity change of the residues is due to hydration.

5. The prediction method according to claim 1, characterized in that, In step S3, the entropy elastic spring constant k ent Predicted using the entropy spring effect model: ， Where, k B Boltzmann constant; T: absolute temperature; Rg: mean square radius of gyration; Ree: mean square end-to-end distance.

6. The prediction method according to claim 1, characterized in that, In step S3, the hydrophobic elastic spring constant k hyd Predicting rebound using a hydrophobic interaction-driven rebound model: ， Wherein, γ: surface tension constant; H: average hydrophobicity score of the sequence; SASAmax: maximum solvent-accessible surface area in the stretched state; Rg: mean square radius of gyration; N: total number of residues in the amino acid sequence; Ree: mean square end-to-end distance.

7. A prediction system for elastomeric protein features based on sequence, characterized in that, The system includes: The acquisition module is used to acquire the amino acid sequence information of the protein to be analyzed. A motif identification module is used to identify the motif of the amino acid sequence and obtain a motif identification result, wherein the motif identification result includes repeat unit features and / or guest residue features. The calculation module is used to calculate the amino acid composition parameters, sequence characteristic parameters, physicochemical property parameters, spatial structure parameters, and functional property parameters of the protein to be analyzed; the functional property parameters include the spring constant and / or the single-molecule phase transition critical temperature. The critical temperature of the single-molecule phase transition is predicted through the following steps: S311. Count the total number of residues N and the absolute frequency count of residue type i in the amino acid sequence. i ; S312. Obtain the contact free energy temperature response parameters of residue type i, and calculate the fundamental contact free energy ΔG between residues of residue type i under temperature variable T using the contact free energy temperature response parameters of residue type i. e (T) i ; S313. Calculate the all-atom interaction energy G of the protein to be analyzed under temperature variable T. int_raw (T): ， Where, count i : Absolute frequency of residue type i; n c : Average coordination number; N: Total number of residues in the amino acid sequence; S314. Calculate the conformational entropy resistance G of the protein to be analyzed under temperature variable T. conf (T): ， Among them, R gas α: Molar gas constant; α: Chain expansion / compressibility factor. , where R coil and R ext These are the radii of the collapsed state and the radii of the highly swollen and expanded state, respectively. S315. Establish the single-chain collapse free energy function G with respect to the temperature variable T. total (T): ， Where, n offset : A pre-set calibration offset; the pre-set calibration offset is determined by having a known single-chain collapse critical temperature T. ref The baseline sequence is determined; , G int_raw (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref All-atomic interaction energy; G conf (T) ref The baseline sequence is at the known single-chain collapse critical temperature T. ref Configurational entropy resistance; S316. Solve for the single-chain collapse free energy function G. total (T) The temperature at which the zero-point condition is met, and this temperature is determined as the single-molecule phase transition critical temperature T of the protein to be analyzed. c The zero-point condition is as follows: 。 8. An apparatus, characterized in that, The apparatus includes at least one storage medium and at least one processor, the at least one storage medium being used to store computer instructions, and the at least one processor being used to execute the computer instructions to implement the sequence-based elastomeric protein feature prediction method as described in any one of claims 1-7.

9. A computer-readable storage medium, characterized in that, The storage medium stores computer instructions that, when executed by a processor, implement the sequence-based elastomeric protein feature prediction method as described in any one of claims 1-7.

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