Antibody calculation optimization method based on genetic algorithm

Abstract

The invention provides an antibody calculation optimization method based on a genetic algorithm. The method covers algorithms such as peptide chain processing, epitope recognition, sequence annotation, CDR H3 sequence design, antibody modeling, molecular docking, antibody property evaluation and the like, and has a full-process automatic antibody design function. Based on known antibody sequence data, a variant antibody sequence formed by combining random sites and random residues is iteratively generated and evaluated by utilizing a genetic algorithm aiming at a heavy-chain highly variable H3section (CDR H3) of the antibody and is subjected to comprehensive scoring comparison with an original antibody, so that an optimized antibody is obtained or a low-quality antibody is removed, and finally, a candidate antibody sequence library is generated, and the biophysical property of the candidate antibody is predicted. According to the invention, basic elements of an antibody calculation optimization process are integrated, and automation of the process is realized on the same platform.

Description

Technical field[0001]The invention relates to the technical field of bioinformatics, in particular to protein molecular structure design, and in particular to a genetic algorithm-based antibody calculation optimization method.Background technique[0002]In recent years, with the continuous increase of bioinformatics and structural biology data of antibodies and their targets, as well as the iterative development of computational tools, the technology of computational antibody design optimization for specific antigens or epitopes has been rapidly developed. The computational antibody design method can target the design of antibodies with epitope specificity and affinity. The advantage of computational antibody design is that the algorithm model based on artificial intelligence can construct a large-scale computational antibody mutation library, and the solubility, surface hydrophobicity, local surface charge, aggregation tendency and other key characteristics of the antibody can be obt...

AI Technical Summary

Problems solved by technology

[0004] Antibody computational optimization design involves multiple steps, including but not limited to sequence annotation, sequence design, antibody modeling, H3 loop modeling, molecular docking, and predictability of development, etc. The realization of the above functions usually requires multiple different tools. These tools Developed by different researchers, there are uneven algorithm performance, different writing languages, and complex code writing, which greatly limits the ability of researchers to use these tools for antibody design

[0005] Therefore, the existing antibody computational optimization design method needs to learn and operate a variety of different algorithms, tools, and software; and it needs to rely on expert experience to implement specific mutations on individual sites of the antibody to predict whether the binding performance of the antibody is improved; The generated antibody sequence is obviously constrained by the existing antibody sequence, and it is impossible to explore more possible optimal solutions for antibodies

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