Computational approaches to therapeutic antibody design: established methods and emerging trends

Brief Bioinform. 2020 Sep 25;21(5):1549-1567. doi: 10.1093/bib/bbz095.

Abstract

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.

Keywords: antibody–antigen complexes; databases; docking; homology modelling; therapeutic antibodies.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Antibodies, Monoclonal / chemistry*
  • Antibodies, Monoclonal / immunology
  • Antibodies, Monoclonal / therapeutic use
  • Computational Biology / methods
  • Databases, Protein
  • Molecular Docking Simulation
  • Protein Conformation

Substances

  • Antibodies, Monoclonal