Antibody dynamic structure property analysis device, antibody dynamic structure property analysis method, and program

Abstract

An antibody dynamic structural characterization analysis device is provided that enables analysis without averaging the motion of residues. In the antibody dynamic structural characterization analysis device 1, the molecular motion calculation processing unit 29 obtains multiple snapshots of the antibody to be analyzed through, for example, molecular dynamics calculations. The R-plotting processing unit 31 generates a Laplace plot of the nth residue of the antibody to be analyzed, and obtains the dihedral angle and ψ of the nth residue in each snapshot. The set variable calculation processing unit 35 calculates the dihedral angle and ψ of the nth residue in each snapshot and compares it with the dihedral angle reference value of the nth residue. 0 n and ψ 0 n The value of the set variable is generated based on the distance. If the proportion of set variables whose values ​​are in the region exceeding the running analysis baseline value among the set variables of the nth residue in all snapshots exceeds the proportional baseline value, the running analysis processing unit 39 determines that the nth residue is a deviated residue.

Description

Technical Field

[0001] This invention relates to an antibody dynamic structural characterization analysis device, an antibody dynamic structural characterization analysis method and program, etc., which uses data for a specific analyte antibody stored in an analyte storage unit to analyze the dynamic structural characterization of that antibody. Background Technology

[0002] Figure 13 This is a diagram used to illustrate antibodies. Antibodies are biological macromolecules of the immune system that function by specifically binding to antigens of foreign substances (such as pathogens) and eliminating them.

[0003] exist Figure 13 In (a), antibody 101 represents IgG (150 kDa), an immunoglobulin. Antibodies can bind to specific antigens. Antibody diversity arises from the combination of heavy chain variable regions 103 and 113 with light chain variable regions 107 and 117. The variable regions of antigen receptors combine both heavy chain variable regions 103 and 113 with light chain variable regions 107 and 117.

[0004] The complementarity-determining region (CDR) is the region within the variable regions 103, 107, 113, and 117 that actually comes into contact with the antigen. It is a crucial region determining antigen specificity and exhibits high sequence diversity. Figure 13 In (a), symbols 121, 123 and 125 represent CDR1, CDR2 and CDR3 in variable region 113, respectively.

[0005] The framework region (FR) is the region in variable regions 103, 107, 113, and 117, excluding CDR1, CDR2, and CDR3. FRs exhibit high sequence homology between antibodies, providing the antibody's backbone structure and stability, thus supporting its function. Figure 13 In (a), symbols 127, 129, 131 and 133 represent FR1, FR2, FR3 and FR4 in variable region 113, respectively.

[0006] Figure 13 (b) Show the relationship between CDR1, CDR2, and CDR3 and FR1, FR2, FR3, and FR4. CDR1, CDR2, and CDR3 exist between FR1, FR2, FR3, and FR4, respectively.

[0007] Figure 14 It is a diagram used to illustrate the denaturation process of proteins. Figure 14 (a) Proteins in their native state. Figure 14 (b) Displays the state where a locally deformed region (dashed line) 141 exists (locally deformed state). Then, as... Figure 14 As shown in (c), the protein as a whole is in a denatured state. The results are as follows: Figure 14 (d) becomes a cohesive group.

[0008] By Figure 14 (b) The locally denatured region 141 is stabilized, thereby improving protein stability. Locally denatured regions can be identified using RMSF (Root Mean Square Fluctuation) values. RMSF values ​​are an indicator of fluctuation obtained through molecular dynamics methods. The inventors proposed in Non-Patent Literature 1 that RMSF values ​​can be used as an indicator to identify locally denatured regions, thereby enabling the design of stable antibodies.

[0009] Existing technical documents

[0010] Non-patent literature

[0011] Non-patent literature 1: Okazaki K et al., Molecular Dynamics-Based Design and Biophysical Evaluation of Thermostable Single-Chain Fv Antibody Mutants Derived from Pharmaceutical Antibodies. ACS Omega. 2023;8:22945-22954. Summary of the Invention

[0012] The problem that the invention aims to solve

[0013] However, analyses using RMSF values ​​are manual and therefore limited in the number of variants that can be explored. Furthermore, the motion of residues in RMSF values ​​is averaged, making it difficult to evaluate rare or small-amplitude motions.

[0014] Therefore, the present invention aims to provide an antibody dynamic structural characterization analysis device that can analyze the movement of residues without averaging the movement of residues.

[0015] Methods for solving problems

[0016] The first aspect of this invention is an antibody dynamic structural property analysis apparatus that uses data for a specific analyte antibody stored in an analyte storage unit to analyze the dynamic structural properties of that antibody. It comprises: a molecular motion calculation processing unit that calculates the motion of atoms and / or molecules to obtain multiple snapshots for the analyte antibody; and a Laplace diagram of the nth residue (n is a natural number from 1 to N) out of N residues of the analyte antibody to obtain the dihedral angle of the nth residue in each snapshot. The R plotting unit for ψ; for the dihedral angle of the nth residue in each snapshot. And ψ, calculate the dihedral reference value with respect to the nth residue. 0 n and ψ 0 n The set variable calculation processing unit generates the value of the set variable based on the distance; and the operation analysis processing unit determines that the nth residue is a deviated residue when the proportion of set variables whose values ​​are in the region exceeding the operation analysis benchmark value in the set variables of the nth residue of all snapshots exceeds the proportion benchmark value.

[0017] The second aspect of this invention is the antibody dynamic structural characteristic analysis device of the first aspect, comprising: a mutation processing unit that stores data for a specific new analyte antibody in the analyte storage unit, wherein the new analyte antibody is obtained by introducing mutations into the analyte antibody.

[0018] The third aspect of this invention is an antibody dynamic structural characteristic analysis device based on the first or second aspect, comprising: an R-plotting analysis benchmark value determination unit; a molecular motion calculation and processing unit that calculates the motion of atoms and / or molecules for multiple benchmark antibodies to obtain multiple snapshots; and an R-plotting processing unit that generates a Laplace plot of the nth residue for the nth residue of the benchmark antibody to obtain the dihedral angle of the nth residue in each snapshot. And ψ, the R plotting analysis benchmark value determination unit uses the Laplace plot of the nth residue of the benchmark antibody to determine the dihedral benchmark value of the nth residue. 0 n and ψ 0 n .

[0019] The fourth aspect of this invention is an antibody dynamic structural characteristic analysis method that uses data for a specific analyte antibody stored in an analyte storage unit to analyze the dynamic structural characteristics of that antibody. This method includes: a molecular motion calculation processing unit provided by an information processing device, which calculates the motion of atoms and / or molecules for the analyte antibody to obtain multiple snapshots; and an R-plotting processing unit provided by the information processing device, which constructs a Laplace diagram for each nth residue (n is a natural number from 1 to N) of the N residues of the analyte antibody to obtain dihedral angles. The R-plotting process for ψ; the set variable calculation processing unit of the information processing device, for the dihedral angle of the nth residue in each snapshot. And ψ, calculate the reference value of the dihedral angle. 0 n and ψ 0 n The set variable calculation step that generates the value of the set variable based on the distance; and the operation analysis processing unit provided by the information processing device that determines that the nth residue is a deviated residue when the proportion of set variables whose values ​​are in the region exceeding the operation analysis reference value among the set variables of the nth residue in all snapshots exceeds the proportion reference value.

[0020] The fifth aspect of this invention is the antibody dynamic structural characteristic analysis method of the fourth aspect, which includes: a mutation processing step in which a mutation processing unit provided by an information processing device stores data for a specific new analytical target antibody in the analytical target storage unit, wherein the new analytical target antibody is obtained by introducing mutations into the analytical target antibody.

[0021] The sixth aspect of this invention is a method for analyzing the dynamic structural characteristics of antibodies from the fourth or fifth aspect, comprising: a step of obtaining multiple snapshots by calculating the movement of atoms and / or molecules for multiple reference antibodies using the molecular motion calculation processing unit; and a step of obtaining the dihedral angle of the nth residue in each snapshot by generating a Laplace plot of the nth residue for the nth residue using the R plotting processing unit. The steps are as follows: the R-plotting analysis reference value determination unit of the information processing device determines the dihedral reference value of the nth residue using the Laplace plot of the nth residue of the reference antibody. 0 n and ψ 0 n The steps.

[0022] The seventh aspect of this invention is a program for enabling a computer to function as an antibody dynamic structural characteristic analysis device for any of the first to third aspects. Alternatively, this invention can be implemented using a computer-readable recording medium that records the program for the seventh aspect.

[0023] Furthermore, the nth residue can be, for example, a residue in the frame region. That is, the present invention can also be implemented in a way that, for each of the N (N is a natural number) residues that are part or all of the residues located in the frame region, it is determined whether the nth residue is a deviated residue.

[0024] Alternatively, in the 3rd or 6th side, it can also be implemented as follows: the set variable calculus processing unit calculates the dihedral angle of the nth residue in each snapshot. Calculation of ψ and dihedral reference value 0 n and ψ 0 n The value of the set variable is generated based on the distance, and the analysis and processing unit performs processing to use the nth run analysis baseline value as the baseline value for determining the nth residue as a common movement between antibodies.

[0025] Alternatively, the invention of this application can be implemented as follows: the running analysis processing unit determines that the nth residue is a deviated residue when the proportion of the set variables whose values ​​are in the region exceeding the running analysis reference value (the region where the value of the set variable is greater than the running analysis reference value) in the set variables of the nth residue of all snapshots exceeds the proportional reference value (the case where it is greater than the proportional reference value), and does not determine that the nth residue is a deviated residue when it does not exceed the proportional reference value (the case where it is less than the proportional reference value).

[0026] The effects of the invention

[0027] According to the invention of this application, a snapshot obtained by calculating the motion of atoms and / or molecules is used to determine the dihedral angles in the Laplace diagram. By calculating two-dimensional values ​​like ψ as one-dimensional values ​​like set variables for analysis, it is possible to achieve analysis without averaging the motion of residues. Therefore, localized denaturation states that cannot be detected using RMSF values ​​can be discovered. Furthermore, multiple antibodies can be compared simultaneously. Therefore, according to the present invention, it is possible to rapidly design antibodies with particularly high evaluation performance and high thermal stability. Furthermore, it may be possible to produce more stable antibodies. Attached Figure Description

[0028] Figure 1 This is a block diagram showing an example of the configuration of an antibody dynamic structural characteristic analysis device 1, which is an embodiment of the invention of this application.

[0029] Figure 2 It is a display Figure 1 A flowchart illustrating an example of the operation of the antibody dynamic structural characterization analysis device 1.

[0030] Figure 3 It is a display Figure 1 A flowchart illustrating an example of the operation of the antibody dynamic structural characterization analysis device 1.

[0031] Figure 4 This is an example of a set of variables that shows the proportion of set variables in the region exceeding the benchmark value of the nth run analysis, where the proportion does not exceed the benchmark value.

[0032] Figure 5 Figure 1 is an example of an experiment conducted by the inventors using trastuzumab antibodies.

[0033] Figure 6 Figure 2 is an example of an experiment conducted by the inventors using trastuzumab antibodies.

[0034] Figure 7 Figure 3 is an example of an experiment conducted by the inventors using trastuzumab antibodies.

[0035] Figure 8 Figure 4 is an example of an experiment conducted by the inventors using trastuzumab antibodies.

[0036] Figure 9 Figure 1 is an example of an experiment conducted by the inventors using the OKT3 antibody.

[0037] Figure 10 Figure 2 is an example of an experiment conducted by the inventors using the OKT3 antibody.

[0038] Figure 11 Figure 3 is an example of an experiment conducted by the inventors using the OKT3 antibody.

[0039] Figure 12 Figure 4 is an example of an experiment conducted by the inventors using the OKT3 antibody.

[0040] Figure 13 This is a diagram used to illustrate antibodies.

[0041] Figure 14 It is a diagram used to illustrate the denaturation process of proteins. Detailed Implementation

[0042] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, the present invention is not limited to these embodiments.

[0043] Figure 1 This is a block diagram showing an example of the configuration of an antibody dynamic structural characteristic analysis device 1, which is an embodiment of the invention of this application.

[0044] The antibody dynamic structural characteristic analysis device 1 is, for example, an information processing device. The antibody dynamic structural characteristic analysis device 1 includes an input / output processing device 3, a storage device 5, and a processing device 7.

[0045] The input / output processing device 3 is used for inputting and outputting data with external people and devices. The input / output processing device 3 may be, for example, a keyboard, mouse, monitor, or touch panel, and is a device for the user to input data into the antibody dynamic structural characteristic analysis device 1 and output data to the user. Additionally, the input / output processing device 3 may be a device for communicating with other devices, such as obtaining data from external information processing devices or databases, and transmitting data to external information processing devices.

[0046] The storage device 5 is, for example, a memory, a hard disk, or a device for storing data. The storage device 5 includes an analysis object storage unit 11, a snapshot storage unit 13, an R plot storage unit 15, an R plot analysis reference value storage unit 17, a set variable storage unit 19, a set variable plot storage unit 21, and a runtime analysis storage unit 23.

[0047] The processing unit 7, such as a central processing unit (CPU), is a data processing device. The processing unit 7 includes a control processing unit 25, a variation processing unit 27, a molecular motion calculation processing unit 29, an R-plotting processing unit 31, an R-plotting analysis benchmark value determination unit 33, a set variable calculation processing unit 35, a set variable plotting processing unit 37, and a runtime analysis processing unit 39. Each of the units included in the processing unit 7 can be implemented, for example, by operating the processing unit 7 under program control.

[0048] Figure 2 and Figure 3 It is a display Figure 1 A flowchart illustrating an example of the operation of the antibody dynamic structural characterization analysis device 1.

[0049] Reference Figure 2 The processing of the reference antibody for analysis will be described. In this example, a medical antibody is used as the reference antibody. The input / output processing device 3 obtains data for a specific medical antibody from a protein stereostructure database and stores it in the analysis object storage unit 11. The molecular motion calculation processing unit 29 calculates the motion of atoms and / or molecules of the medical antibody stored in the analysis object storage unit 11, for example, through molecular dynamics calculations and obtains multiple snapshots (step STA1). The snapshot storage unit 13 stores the obtained snapshots.

[0050] The protein stereostructure database lists 17 types of medical antibodies. The following explanation uses the case where calculations were performed on all 17 medical antibodies as an example; however, in this invention, calculations may have been performed on a subset of them, or calculations may have been performed on newly added medical antibodies. Furthermore, the explanation uses the case of obtaining 3000 snapshots for each antibody as an example; however, different numbers of snapshots may be obtained in this invention.

[0051] The number of residues in the frame region is taken as N (N is a natural number). The R plotting processing unit 31 generates a Laplace plot for each of the N residues in the frame region, and merges the data for all antibodies (step STA2). The R plotting storage unit 15 stores the generated data. As an example, for a total of 17 antibodies, the case where 3000 snapshots are obtained for each antibody will be described, but the Laplace plot for each residue consists of 3000 frames × 17 antibodies = 51000 data points. Furthermore, in this invention, N can be either a portion or all of the residues in the frame region.

[0052] Amino acids are linked by peptide bonds. Peptide bonds (CN) have partial double bond properties, exist in the peptide plane, and cannot rotate freely. However, they are linked to the α-carbon (C)... α The bonds between (carbon atoms located on the main chain side adjacent to the functional group being considered) (NC) α Key and C α The -C bond is not rigid; it is only restricted by the size and properties of the side chain (R group) and can rotate freely. NC α The rotation angle around the key is called (phi), C α The rotation angle around the -C bond is called ψ (psi). ψ is also known as the torsion angle, dihedral angle, etc. This dihedral angle... ψ largely determines the 3D shape of the polypeptide backbone of a protein.

[0053] A Ramachandran plot is a plot of amino acid residues relative to the structure of a protein. The ψ plot is generated by drawing a diagram. Numerous software programs for generating Laplace plots are available in the prior art. The R plotting unit 31 can use such software to generate a Laplace plot for each residue in the framework region.

[0054] The R plotting processing unit 31 merges all data for medical antibodies. That is, it integrates them according to predetermined rules. In each snapshot, the dihedral angle is determined for each residue. The combination of ψ and ψ. Therefore, for a total of 17 medical antibodies, in the example of obtaining 3000 snapshots for each antibody, the Laplace plot of each residue contains 3000 frames × 17 antibodies = 51000 dihedrals. The combination of ψ and ψ.

[0055] The control processing unit 25 sets the initial value of variable n to 1 (step STA3).

[0056] The R-plotting analysis reference value determination unit 33 uses the Laplace plot of the nth residue to determine the dihedral reference value. 0 n and ψ 0 n (Step STA4). R drawing analysis reference value storage unit 17 stores dihedral reference values. 0 n and ψ 0 n Dihedral reference value 0 n and ψ 0 n For example, the most frequent value of the Laplace plot for the nth residue. As a method for discovering dense data clusters and performing clustering from a dataset, DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is known. The most frequent value of the Laplace plot can be obtained using, for example, DBSCAN. Furthermore, in this invention, the dihedral reference value... 0 n and ψ 0 n It can be a value other than the most frequent value.

[0057] The set variable calculus processing unit 35 calculates the dihedral angle of the nth residue in each snapshot. The dihedral reference value of ψ with respect to the nth residue is calculated. 0 n and ψ 0 n The value of the set variable is generated based on the distance (step STA5). The set variable storage unit 19 stores the calculated set variable. The dihedral angle contained in the snapshot. and ψ and the dihedral reference value 0 n and ψ 0 nThe distance is considered small if the difference is small, and large if the difference is large. This applies to the snapshot of the nth residue and the dihedral reference value. 0 n and ψ 0 n The distance can be represented by the dihedral angle of the nth residue in each snapshot. and the dihedral reference value of ψ and the nth residue 0 n and ψ 0 n The distance is calculated. The set variable approaches zero when antibodies exhibit highly shared dynamic structural characteristics, and its value increases as they become more detached. Therefore, the dihedral angle based on the Laplace diagram can be used to calculate the value. The two-dimensional data of ψ are treated as one-dimensional data based on set variables.

[0058] The set variable drawing processing unit 37 draws the set variable obtained for the nth residue (step STA6). For example, if 3000 snapshots are obtained for each of the 17 medical antibodies, the set variable for each residue becomes 3000 frames × 17 antibodies = 51000 values. The set variable drawing storage unit 21 stores the drawing of the set variable. For example, in Figure 5 Line L in graphs (a) and (b) 11 and L 13 It is a plot of the set of variables obtained from medical antibodies.

[0059] The runtime analysis storage unit 23 stores the nth runtime analysis baseline value. For the nth residue, the movement of the region where the aggregate variable falls below the nth runtime analysis baseline value is defined as a common movement among antibodies. The runtime analysis processing unit 39 performs processing (step STA7) as needed to use the nth runtime analysis baseline value as a baseline value for determining the movement of the nth residue as common among antibodies. Furthermore, the nth runtime analysis baseline value can be a value common to all residues (e.g., 50), or it can be a value that varies depending on the distribution of the aggregate variable. For example, the runtime analysis processing unit can adjust the calculation formula of the aggregate variable for the nth residue in a way that adapts to the distribution of the value of the nth runtime analysis baseline value, or it can determine the value of the nth runtime analysis baseline value based on the distribution of the aggregate variable. Furthermore, this processing can be omitted if unnecessary.

[0060] The control processing unit 25 increments n by 1 (step STA8). The control processing unit 25 determines whether n is greater than N (step STA9). If n is greater than N, the process ends. Figure 2 If n is not greater than N, then return to step STA4.

[0061] Reference Figure 3 The processing methods used for analyzing analyte antibodies (AOIs) are explained.

[0062] The input / output processing unit 3 obtains data for a specific analyte antibody (AOI) in a non-mutated state from an analysis device that analyzes actual antibodies and obtains data for that specific antibody, or a simulation device that generates data for a specific antibody through simulation, and stores it in the analyte storage unit 11. The molecular motion calculation processing unit 29 calculates the motion of atoms and / or molecules for the analyte antibody stored in the analyte storage unit 11 using, for example, molecular dynamics calculations and obtains multiple snapshots (step STB1). The snapshot storage unit 13 stores the obtained snapshots. For example, 3000 snapshots are obtained for the AOI.

[0063] The R drawing processing unit 31 constructs a Laplace diagram for each of the N residues in the framework region of the AOI and obtains the dihedral angles. And ψ (step STB2). The R drawing storage unit 15 stores the obtained Laplace plot. For example, in the case where 3000 frames of snapshots were obtained for AOI, the Laplace plot of each residue contains 3000 dihedral angles. The combination of ψ and ψ.

[0064] The control processing unit 25 sets the initial value of variable n to 1 and the proportional reference value to 20% (step STB3).

[0065] Set variable calculation processing unit 35 calculates the dihedral angle of the nth residue with respect to the nth residue in each snapshot. The dihedral reference value of ψ with respect to the nth residue is calculated. 0 n and ψ 0 n The values ​​of the set variables are generated based on the distance (step STB4). For example, if 3000 frame snapshots are obtained for the AOI, the set variables for each residue become 3000 values. The set variable storage unit 19 stores the calculated values ​​of the set variables.

[0066] The set variable drawing processing unit 37 draws a graph of the set variable obtained for the nth residue (step STB5). The set variable drawing storage unit 21 stores the graph of the set variable. For example, in Figure 5 In diagram (a), line L 12 This is obtained by plotting the set variables in the antibody before the mutation. Additionally, in Figure 5 In diagram (b), line L 14 It is obtained by plotting the set variables in the antibody after importing the mutation.

[0067] If the proportion of regions in the set variable of residue n that exceed the nth operational analysis benchmark value exceeds the proportional benchmark value, the operational analysis processing unit 39 determines that residue n is a deviated residue (step STB6). If the proportion of regions in the set variable of residue n that exceed the nth operational analysis benchmark value does not exceed the proportional benchmark value, residue n is not determined to be a deviated residue. The operational analysis storage unit 23 stores whether residue n is determined to be a deviated residue. Figure 4 This is an example where the proportion of regions in the set variable for residue n that exceed the baseline value for the nth run analysis does not exceed the proportional baseline value. The horizontal axis represents the value of the set variable, and the vertical axis represents the density (the value obtained by dividing the number of snapshots belonging to the values ​​of each set variable by the total number of snapshots).

[0068] The control processing unit 25 increments n by 1 (step STB7). The control processing unit 25 determines whether n is greater than N (step STB8). If n is not greater than N, it returns to step STB4.

[0069] If n is greater than N, the control processing unit 25 displays the residues determined to be deviated from the input / output processing device 3, and determines whether to end the processing (step STB9). For example, in the case where the antibody has been stabilized and the input / output processing device 3 is instructed to end the processing, the process ends. Figure 3 The processing.

[0070] If the user continues processing without ending the input / output processing unit 3, data for introducing mutations (e.g., data specifying the amino acids to be mutated and what kind of mutation to introduce) is input to the input / output processing unit 3. The frame region exhibits high conservation of amino acid residues and structure between antibodies, and similar dynamic structural characteristics. There are structural distortions around regions that have deviated from common motion; these distortions can be corrected by introducing mutations, thereby aiming to produce antibodies with high thermal stability. Candidate mutation sites exist around spatially deviated residues. Therefore, to stabilize the antibody, the user introduces mutations around residues identified as deviated. Identifying the deviated residues in step STB6 is useful for determining the site for introducing the mutation. The mutation processing unit 27 changes to introducing the mutated antibody into the analyte storage unit 11, storing it in the analyte storage unit 11 (step STB10), and returns to step STB1.

[0071] Reference Figures 5-8 This illustrates an example of experiments conducted by the inventors using trastuzumab antibodies. In the figures, the horizontal axis represents the values ​​of the set variables, and the vertical axis represents the density.

[0072] Figure 5 (a) Figure 6 (a) Figure 7 (a)~(e) Figure 8 (a) to (d) show plots of the set of variables obtained from the antibody before the mutation (wild type). Figure 5 (a) shows residue 14. Figure 6 (a) shows residue 18. Figure 7 (a)–(e) show residues 8–12. Figure 8 (a) to (d) show residues 13, 15, 16 and 17.

[0073] Figure 5 (b) Figure 6 (b) Figure 7 (f)~(j), Figure 8 (e)~(h) show plots of the set variables obtained by importing the mutated antibody. Figure 5 (b) shows residue 14. Figure 6 (b) shows residue 18. Figure 7 (f) to (j) show residues 8 to 12. Figure 8 (e)~(h) show residues 13, 15, 16 and 17.

[0074] Reference Figure 5 (a) For residue 14, line L 11 It is a plot of the set of variables obtained from medical antibodies, line L. 12 It is a plot of the set of variables obtained from the wild-type antibody before the mutation.

[0075] Line L 12 With line L 11 In comparison, there are more points with larger values ​​for the set variables. The analysis processing unit 39 determines that residue 14 is a deviated residue because the proportion of regions in the set variables of residue 14 that exceed the 14th run analysis baseline value exceeds the proportional baseline value. The user then introduces mutations around residue 14.

[0076] Reference Figure 5 (b), line L 13 It is a plot of the set of variables obtained from medical antibodies, and... Figure 5 Line L in (a) 11 Same. Line L 14 It is plotted by drawing a set of variables obtained from the antibody after the mutation was introduced around residue 14.

[0077] Line L 14 With line L 13 It is basically the same as the peak (dashed line). Figure 5In (b), the running analysis processing unit 39 may be in a state of not judging that the 14th residue is a deviated residue because the proportion of the region in the set variable of the 14th residue that is above the 14th running analysis reference value does not exceed the proportion reference value.

[0078] Reference Figure 6 (a) For residue 18, line L 21 It is a plot of the set of variables obtained from medical antibodies, line L. 22 This is a plot of the set of variables obtained from the wild-type antibody before the mutation. Line L 22 With line L 21 In contrast, there are states where there are more sets of variables with large values.

[0079] Reference Figure 6 (b), line L 23 It is a plot of the set of variables obtained from medical antibodies, line L. 24 This is a plot of the set of variables obtained by introducing the mutated antibody around residue 14. (By...) Figure 6 (b) It was confirmed that the mutation was introduced around residue 14 and stabilized for residue 18.

[0080] Will Figure 7 (a)~(e) and Figure 8 (a)~(d), and Figure 7 (f)~(j) and Figure 8 The comparison between (e) and (h) confirms that the distribution of the aggregate variables did not change significantly before and after the introduction of these antibodies.

[0081] Reference Figures 9-12 This illustrates an example of the experiments conducted by the inventors using the OKT3 antibody. In the figures, the horizontal axis represents the values ​​of the set variables, and the vertical axis represents the density.

[0082] Figure 9 (a) Figure 10 (a) Figure 11 (a)~(e) Figure 12 (a) to (e) show plots of the set of variables obtained from the antibody before the mutation (wild type). Figure 9 (a) shows residue 9. Figure 10 (a) shows residue 27. Figure 11 (a)–(e) show residues 24, 25, 26, 28, and 29. Figure 12 (a) to (e) show residues 30 to 34.

[0083] Figure 9 (b) Figure 10 (b) Figure 11 (f)~(j), Figure 12 (f) to (j) show plots of the set of variables obtained by importing the mutated antibody. Figure 9 (b) shows residue 9. Figure 10 (b) shows residue 27. Figure 11 (f) to (j) show residues 24, 25, 26, 28, and 29. Figure 12 (f) to (j) show residues 30 to 34.

[0084] Reference Figure 9 (a) For residue 9, line L 31 It is a plot of the set of variables obtained from medical antibodies, line L. 32 It is a plot of the set of variables obtained from the wild-type antibody before the mutation.

[0085] Line L 32 With line L 31 In contrast, there are more points with large values ​​for the set variables. The analysis processing unit 39 determines that residue 9 is a deviated residue because the proportion of regions in the set variables of residue 9 that exceed the proportional reference value exceeds the proportional reference value. The user then introduces mutations around residue 9.

[0086] Reference Figure 9 (b), line L 33 It is a plot of the set of variables obtained from medical antibodies, line L. 34 It is plotted by drawing a set of variables obtained from the antibody after the mutation was introduced around residue 9.

[0087] Line L 34 With line L 33 In comparison, the peak values ​​(dashed lines) are basically the same. Figure 9 In (b), the running analysis processing unit 39 is able to remain in a state where it does not determine that the 9th residue is a deviated residue because the proportion of the region in the set variable of the 9th residue that is above the 9th running analysis reference value is above the reference value.

[0088] Reference Figure 10 (a) For residue 27, line L 41 It is a plot of the set of variables obtained from medical antibodies, line L. 42 This is a plot of the set of variables obtained from the wild-type antibody before the mutation. Line L 42 With line L 41 In contrast, there are states where there are more sets of variables with large values.

[0089] Reference Figure 10 (b), line L43 It is a plot of the set of variables obtained from medical antibodies, line L. 44 This was plotted using aggregate variables obtained from antibodies with mutations introduced around residue 9. (Through...) Figure 10 (b) It was confirmed that the mutation was introduced around residue 9 and stabilized for residue 27.

[0090] Will Figure 11 (a)~(e) and Figure 12 (a)~(e), and Figure 11 (f)~(j) and Figure 12 Comparing (f) to (j), it can be seen that the distribution of the aggregate variables did not change significantly before and after the introduction of these antibodies.

[0091] Antibodies are used in pharmaceuticals, diagnostic reagents, and sensor components, but they are susceptible to physical stress (heat, friction, vibration, etc.), necessitating the fabrication of stable antibodies for practical application. In analyses utilizing RMSF values, the analysis is manual, thus limiting the number of variants that can be explored. Furthermore, because residue motion is averaged in RMSF values, it is difficult to evaluate rare or small-amplitude movements.

[0092] The framework region exhibits high conservation of amino acid residues and structure among antibodies, and their dynamic structural properties are similar. Considering the structural distortions around regions deviating from standard dynamic structural properties, it was presumed that this would reduce stability. The inventors used molecular dynamics, a method for simulating protein motion on a computer, to perform molecular dynamics calculations on 17 medical antibodies registered in a protein stereostructure database. Cluster analysis was performed on the results to extract common dynamic structural properties among the antibodies. Based on this data, a set variable was defined, where the value approaches zero when exhibiting highly common dynamic structural properties among antibodies, and the value increases with deviation. Frames with values ​​above a threshold were defined as normal values ​​if the proportion was below a baseline value, and as outliers if the proportion was above the baseline value. Variants were imported onto the computer around regions showing outliers, and molecular dynamics calculations were performed again to recalculate the set variable. The results confirmed that outliers disappeared in variants with improved thermal stability. The inventors studied multiple antibodies and confirmed that this method is universal.

[0093] This invention enables analysis that is not averaged out and can detect localized denaturation regions that cannot be found using RMSF values. Furthermore, it allows for the simultaneous comparison of multiple antibodies. According to this invention, it is possible to rapidly design antibodies with exceptionally high performance and thermal stability. According to this invention, the time required to obtain stable antibodies can be reduced from several months to approximately one year to about two weeks. Furthermore, it may be possible to produce even more stable antibodies.

[0094] Explanation of symbols

[0095] 1. Antibody Dynamic Structural Characterization Device

[0096] 3 Input / output processing device

[0097] 5. Storage devices

[0098] 7. Processing device

[0099] 11. Analysis of Object Storage Department

[0100] 13 Snapshot Storage Department

[0101] 15 R Drawing Storage Department

[0102] 17 R Plotting Analysis Baseline Value Storage Unit

[0103] 19. Collection Variable Storage Department

[0104] 21. Set Variable Drawing Storage Department

[0105] 23. Operational Analysis Storage Department

[0106] 25 Control Processing Unit

[0107] 27. Mutation Processing Department

[0108] 29. Molecular Motion Calculation Processing Unit

[0109] 31 R Drawing Processing Department

[0110] 33 R Drawing Analysis Baseline Determination Section

[0111] 35 Set Variable Calculus Processing Department

[0112] 37. Set Variable Drawing Processing Department

[0113] 39. Operation Analysis and Processing Department

Claims

1. An antibody dynamic structural characterization analysis device, which analyzes the dynamic structural characterization of an antibody using data for a specific analyte antibody stored in an analyte storage unit, comprising: For the antibody being analyzed, a molecular motion calculation processing unit is used to obtain multiple snapshots by calculating the motion of atoms and / or molecules; For the nth residue among the N residues of the antibody being analyzed, a Laplace diagram of the nth residue is constructed to obtain the dihedral angle of the nth residue in each snapshot. and ψ's R drawing processing unit, where, N is a natural number, and n is a natural number from 1 to N; For the dihedral angle of the nth residue in each snapshot And ψ, calculate the dihedral reference value with respect to the nth residue. 0 n and ψ 0 n The set variable calculus processing unit generates the values ​​of set variables based on their distance; and If the proportion of set variables whose values ​​are in regions exceeding the operational analysis baseline value among all snapshots of residue n exceeds the proportional baseline value, the operational analysis processing unit determines that residue n is a deviated residue.

2. The antibody dynamic structural characteristic analysis apparatus according to claim 1, comprising: a mutation processing unit that stores data for a specific new analyte antibody in the analyte storage unit, wherein, The new analyte antibody is obtained by introducing mutations into the analyte antibody.

3. The antibody dynamic structural characteristic analysis device according to claim 1, comprising: an R plotting analysis benchmark value determination unit, The molecular motion calculation and processing unit calculates the motion of atoms and / or molecules for multiple benchmark antibodies and obtains multiple snapshots. The R plotting unit generates a Laplace plot of the nth residue for the reference antibody to obtain the dihedral angle of the nth residue in each snapshot. and ψ, The R-plotting analysis benchmark determination unit uses the Laplace plot of the nth residue of the benchmark antibody to determine the dihedral benchmark value of the nth residue. 0 n and ψ 0 n .

4. A method for analyzing the dynamic structural characteristics of an antibody, comprising: using data on an antibody for a specific analyte stored in an analyte storage unit to analyze the dynamic structural characteristics of the antibody; and including: The molecular motion calculation processing unit of the information processing device calculates the motion of atoms and / or molecules to obtain multiple snapshots for the antibody being analyzed; The R plotting unit of the information processing device generates a Laplace diagram for each of the nth residues of the N residues of the antibody to be analyzed, thereby obtaining the dihedral angle. The R plotting process for ψ, where N is a natural number and n is a natural number from 1 to N; The set variable calculation processing unit of the information processing device calculates the dihedral angle of the nth residue in each snapshot. And ψ, calculate the reference value of the dihedral angle. 0 n and ψ 0 n The set variable calculus steps that generate the value of a set variable based on the distance; and The information processing unit determines that the nth residue is a deviated residue when the proportion of set variables whose values ​​are in regions exceeding the operational analysis benchmark value among the set variables of the nth residue in all snapshots exceeds the proportional benchmark value.

5. The method for analyzing the dynamic structural characteristics of antibodies according to claim 4, comprising: The mutation processing unit of the information processing device stores data for a specific new analyte antibody in the analyte storage unit, wherein the new analyte antibody is obtained by introducing mutations into the analyte antibody.

6. The method for analyzing the dynamic structural characteristics of antibodies according to claim 4, comprising: The step of obtaining multiple snapshots by the molecular motion calculation and processing unit for calculating the motion of atoms and / or molecules for multiple reference antibodies; The R plotting unit generates a Laplace plot of the nth residue for the reference antibody to obtain the dihedral angle of the nth residue in each snapshot. The steps are as follows: the R-plotting analysis reference value determination unit of the information processing device determines the dihedral reference value of the nth residue using the Laplace plot of the nth residue of the reference antibody. 0 n and ψ 0 n The steps.

7. A program for enabling a computer to function as the antibody dynamic structural characterization device as described in claim 1.

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