A computational approach for studying antibody-antigen interactions without prior structural information: the anti-testosterone binding antibody as a case study.

Given the increasing exploitation of antibodies in different contexts such as molecular diagnostics and therapeutics, it would be beneficial to unravel the atomistic level properties of antibody-antigen complexes with the help of computational modeling. Thus, here we have studied the feasibility of computational tools to gather atomic scale information regarding the antibody-antigen complexes solely starting from an amino acid sequence. First, we constructed a homology model for the anti-testosterone binding antibody based on the knowledge based classification of complementary determining regions (CDRs) and implicit solvent molecular dynamics simulations. To further examine whether the generated homology model is suitable for studying antibody-antigen interactions, docking calculations were carried out followed by binding free-energy simulations. Our results indicate that with the antibody modeling approach presented here it is possible to construct accurate homology models for antibodies which correctly describes the antibody-antigen interactions, and produces absolute binding free-energies that are comparable with experimental values. In addition, our simulations suggest that the conformations of complementary determining regions (CDRs) may considerably change from the X-ray configuration upon solvation. In conclusion, here we have introduced an antibody modeling workflow that can be used in studying the interactions between antibody and antigen solely based on an amino acid sequence, which in turn provides novel opportunities to tune the properties of antibodies in different applications. Proteins 2017; 85:322-331. (c) 2016 Wiley Periodicals, Inc.