RosettaAntibodyDesign (RAbD): A general framework for computational antibody design

By Jared Adolf-Bryfogle, Oleksandr Kalyuzhniy, Michael Kubitz, Brian D. Weitzner, Xiaozhen Hu, Yumiko Adachi, William R. Schief, Roland L. Dunbrack. 2017

233 citations recorded by the source corpus.

A structural-bioinformatics-based computational methodology has been developed for the design of antibodies to targets of interest. RosettaAntibodyDesign (RAbD) samples the diverse sequence, structure, and binding space of an antibody to an antigen in highly customizable protocols for the design of antibodies in a broad range of applications. The program samples antibody sequences and structures by grafting structures from updated canonical clusters of CDRs (North et al., J. Mol. Biol., 406:228-256, 2011), performs sequence design according to amino acid sequence profiles of each cluster, and samples CDR backbones using a flexible-backbone design protocol incorporating cluster-based CDR constraints. We computationally benchmarked RAbD on a set of ten diverse antibody–antigen complexes, using two novel metrics, the design risk ratio and the antigen risk ratio, for measuring success in computational protein design. These metrics provide a measure of statistical significance typically absent in protein design benchmarking. Risk ratios for all CDRs were above 1.0 for cluster recovery in the presence of the antigen when compared against simulations in the absence of the antigen. We tested RAbD experimentally on both a lambda and kappa antibody–antigen complex, successfully improving their affinities 10 to 50 fold by replacing individual CDRs of the native antibody with new CDR lengths and clusters.

Subfields: Education, Sociology and Political Science

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1. Sheng Wang
2. Jianzhu Ma

Citations in the corpus

• Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies (2022, background): “Accurate modeling of these CDR loops provides insights into these binding mechanisms and promises 8 to enable rational design of specific antibodies (4).”
• Multistate design of influenza antibodies improves affinity and breadth against seasonal viruses (2019, background): “Such a problem has been approached computationally by several groups (11, 12), and we expect that our multistate design approach could be applied in the future to a de novo engineered antibody to further increase the power of the method.”
• Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures (2022, methodology): “2 Related Work Computational Antibody Design Conventional computational approaches are mostly based on sampling algorithms over hand-crafted and statistical energy functions and iteratively modify protein sequences and structures [1, 29, 49, 36, 38].”