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Publication Abstract from PubMed

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.

Rationalization and design of the complementarity determining region sequences in an antibody-antigen recognition interface.,Yu CM, Peng HP, Chen IC, Lee YC, Chen JB, Tsai KC, Chen CT, Chang JY, Yang EW, Hsu PC, Jian JW, Hsu HJ, Chang HJ, Hsu WL, Huang KF, Ma AC, Yang AS PLoS One. 2012;7(3):e33340. Epub 2012 Mar 22. PMID:22457753^[1]

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.