Benchmarking TriadAb using targets from the second antibody modeling assessment

Frederick S Lee, Amos G Anderson, Barry D. Olafson

Protein Engineering Design and Selection · 2023 · 12 citations · 38 references

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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.

References

38