A Novel Antibody Humanization Method Based on Epitopes Scanning and Molecular Dynamics Simulation

作者：Ding Zhang, Caifeng Chen, Binbin Zhao, Lulu Gong, Wenjing Jin, Jingjun Liu, Jingfei Wang, Tiantian Wang, Xiaohui Yuan, You-Wen He · 发表于：PLoS ONE · 年份：2013 · DOI：10.1371/journal.pone.0080636 · 被引用次数：32 · 研究领域：Monoclonal and Polyclonal Antibodies Research、Glycosylation and Glycoproteins Research、Immunotherapy and Immune Responses

1-17-2 is a rat anti-human DEC-205 monoclonal antibody that induces internalization and delivers antigen to dendritic cells (DCs). The potentially clinical application of this antibody is limited by its murine origin. Traditional humanization method such as complementarity determining regions (CDRs) graft often leads to a decreased or even lost affinity. Here we have developed a novel antibody humanization method based on computer modeling and bioinformatics analysis. First, we used homology modeling technology to build the precise model of Fab. A novel epitope scanning algorithm was designed to identify antigenic residues in the framework regions (FRs) that need to be mutated to human counterpart in the humanization process. Then virtual mutation and molecular dynamics (MD) simulation were used to assess the conformational impact imposed by all the mutations. By comparing the root-mean-square deviations (RMSDs) of CDRs, we found five key residues whose mutations would destroy the original conformation of CDRs. These residues need to be back-mutated to rescue the antibody binding affinity. Finally we constructed the antibodies in vitro and compared their binding affinity by flow cytometry and surface plasmon resonance (SPR) assay. The binding affinity of the refined humanized antibody was similar to that of the original rat antibody. Our results have established a novel method based on epitopes scanning and MD simulation for antibody humanization.