In silico proof of principle of machine learning-based antibody design at unconstrained scale.

ABSTRACT: Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing arbitrarily large numbers of antibody sequences for their most critical design parameters: paratope, epitope, affinity, and developability. To address this challenge, we leveraged a lattice-based antibody-antigen binding simulation framework, which incorporates a wide range of physiological antibody-binding parameters. The simulation framework enables the computation of synthetic antibody-antigen 3D-structures, and it functions as an oracle for unrestricted prospective evaluation and benchmarking of antibody design parameters of ML-generated an

SUBMITTER: Akbar R 

PROVIDER: S-EPMC8986205 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature