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

• Published in: Briefings in bioinformatics Vol. 21; no. 5; pp. 1549 - 1567
• Main Authors: Norman, Richard A, Ambrosetti, Francesco, Bonvin, Alexandre M J J, Colwell, Lucy J, Kelm, Sebastian, Kumar, Sandeep, Krawczyk, Konrad
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 25.09.2020; Oxford Publishing Limited (England)
• Subjects: Adaptive immunity; Antibodies; Antibodies, Monoclonal - chemistry; Antibodies, Monoclonal - immunology; Antibodies, Monoclonal - therapeutic use; Autoimmune diseases; Computational Biology - methods; Computer applications; Databases, Protein; Drug development; Immune response; Immunosuppressive agents; Molecular Docking Simulation; Monoclonal antibodies; Nanobodies; Next-generation sequencing; Protein Conformation; Proteins; Review; Trends; homology modelling; docking; databases; therapeutic antibodies; antibody–antigen complexes
• ISSN: 1467-5463; 1477-4054; 1477-4054
• 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.