Benchmarking TriadAb using targets from the second antibody modeling assessment

Protein Eng Des Sel. 2023 Jan 21:36:gzad013. doi: 10.1093/protein/gzad013.

Abstract

Computational modeling and design of antibodies has become an integral part of today's research and development in antibody therapeutics. Here we describe the Triad Antibody Homology Modeling (TriadAb) package, a functionality of the Triad protein design platform that predicts the structure of any heavy and light chain sequences of an antibody Fv domain using template-based modeling. To gauge the performance of TriadAb, we benchmarked against the results of the Second Antibody Modeling Assessment (AMA-II). On average, TriadAb produced main-chain carbonyl root-mean-square deviations between models and experimentally determined structures at 1.10 Å, 1.45 Å, 1.41 Å, 3.04 Å, 1.47 Å, 1.27 Å, 1.63 Å in the framework and the six complementarity-determining regions (H1, H2, H3, L1, L2, L3), respectively. The inaugural results are comparable to those reported in AMA-II, corroborating with our internal bench-based experiences that models generated using TriadAb are sufficiently accurate and useful for antibody engineering using the sequence design capabilities provided by Triad.

Keywords: antibody homology modeling; benchmarking; protein model quality.

MeSH terms

  • Antibodies / chemistry
  • Antibodies / genetics
  • Benchmarking*
  • Complementarity Determining Regions / chemistry
  • Immunoglobulin Variable Region* / chemistry
  • Molecular Dynamics Simulation
  • Protein Conformation

Substances

  • Immunoglobulin Variable Region
  • Complementarity Determining Regions
  • Antibodies

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