Computational de novo design of antibodies binding to a peptide with high affinity. Issue 6 (2nd February 2017)

• Record Type: Journal Article
• Authors: Poosarla, Venkata Giridhar; Li, Tong; Goh, Boon Chong; Schulten, Klaus; Wood, Thomas K.; Maranas, Costas D.
• Is Part Of: Biotechnology and bioengineering. Volume 114:Issue 6(2017)
• Page Start: 1331
• Page End: 1342
• Publication Date: 2017-02-02
• Subjects: OptMAVEn -- computational antibody design -- de novo design -- antibody structure prediction -- single‐chain antibodies -- biolayer interferometry; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.26244
• Languages: English
• ISSNs: 0006-3592

Abstract

ABSTRACT: Antibody drugs play a critical role in infectious diseases, cancer, autoimmune diseases, and inflammation. However, experimental methods for the generation of therapeutic antibodies such as using immunized mice or directed evolution remain time consuming and cannot target a specific antigen epitope. Here, we describe the application of a computational framework called OptMAVEn combined with molecular dynamics to de novo design antibodies. Our reference system is antibody 2D10, a single‐chain antibody (scFv) that recognizes the dodecapeptide DVFYPYPYASGS, a peptide mimic of mannose‐containing carbohydrates. Five de novo designed scFvs sharing less than 75% sequence similarity to all existing natural antibody sequences were generated using OptMAVEn and their binding to the dodecapeptide was experimentally characterized by biolayer interferometry and isothermal titration calorimetry. Among them, three scFvs show binding affinity to the dodecapeptide at the nM level. Critically, these de novo designed scFvs exhibit considerably diverse modeled binding modes with the dodecapeptide. The results demonstrate the potential of OptMAVEn for the de novo design of thermally and conformationally stable antibodies with high binding affinity to antigens and encourage the targeting of other antigen targets in the future. Biotechnol. Bioeng. 2017;114: 1331–1342. © 2017 Wiley Periodicals, Inc. Abstract : The authors (Li and coworkers) have previously demonstrated that OptMAVEn (Optimal Method for Antibody Variable region Engineering) simulates in silico the in vivo steps of antibody generation and evolution, and is capable of capturing the critical structural features responsible for affinity maturation of antibodies with optimized binding affinity to antigens. Here, the authors (Poosarla and coworkers) further demonstrated the successful application of OptMAVEn combined with molecular dynamics to de novo design antibodies against specific antigen‐peptide target displaying nanomolar binding affinities.