Predicting the distance between antibody's interface residue and antigen to recognize antigen types by support vector machine
In this paper, a machine learning approach, known as support vector machine (SVM) is employed to predict the distance between antibody's interface residue and antigen in antigen-antibody complex. The heavy chains, light chains and the corresponding antigens of 37 antibodies are extracted from the antibody-antigen complexes in protein data bank. According to different distance ranges, sequence patch sizes and antigen classes, a number of, computational experiments are conducted to describe the distance between antibody's interface residue and antigen with antibody sequence information. The high prediction accuracy of both self-consistent and cross-validation tests indicates that the sequential discovered information from antibody structure characterizes much in predicting the distance between antibody's interface residue and antigen. Furthermore, the antigen class is predicted from residue composition information that belongs to different distance range by SVM, which shows some potential significance.
support vector machine antibody-antigen complexes protein protein data bank cross-validation bioinformatics