TY - JOUR T1 - Rationalization and Design of the Complementarity Determining Region Sequences in an Antibody-Antigen Recognition Interface A1 - Yu, Chung-Ming A1 - Peng, Hung-Pin A1 - Chen, Ing-Chien A1 - Lee, Yu-Ching A1 - Chen, Jun-Bo A1 - Tsai, Keng-Chang A1 - Chen, Ching-Tai A1 - Chang, Jeng-Yih A1 - Yang, Ei-Wen A1 - Hsu, Po-Chiang A1 - Jian, Jhih-Wei A1 - Hsu, Hung-Ju A1 - Chang, Hung-Ju A1 - Hsu, Wen-Lian A1 - Huang, Kai-Fa A1 - Ma, Alex Che A1 - Yang, An-Suei Y1 - 2012/03/22 N2 - Protein-protein interactions are critical determinants in biological systems. Engineered proteins binding to specific areas on protein surfaces could lead to therapeutics or diagnostics for treating diseases in humans. But designing epitope-specific protein-protein interactions with computational atomistic interaction free energy remains a difficult challenge. Here we show that, with the antibody-VEGF (vascular endothelial growth factor) interaction as a model system, the experimentally observed amino acid preferences in the antibody-antigen interface can be rationalized with 3-dimensional distributions of interacting atoms derived from the database of protein structures. Machine learning models established on the rationalization can be generalized to design amino acid preferences in antibody-antigen interfaces, for which the experimental validations are tractable with current high throughput synthetic antibody display technologies. Leave-one-out cross validation on the benchmark system yielded the accuracy, precision, recall (sensitivity) and specificity of the overall binary predictions to be 0.69, 0.45, 0.63, and 0.71 respectively, and the overall Matthews correlation coefficient of the 20 amino acid types in the 24 interface CDR positions was 0.312. The structure-based computational antibody design methodology was further tested with other antibodies binding to VEGF. The results indicate that the methodology could provide alternatives to the current antibody technologies based on animal immune systems in engineering therapeutic and diagnostic antibodies against predetermined antigen epitopes. JF - PLOS ONE JA - PLOS ONE VL - 7 IS - 3 UR - https://doi.org/10.1371/journal.pone.0033340 SP - e33340 EP - PB - Public Library of Science M3 - doi:10.1371/journal.pone.0033340 ER -