Monte Carlo Thompson sampling-guided design for antibody engineering.

ABSTRACT: Antibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions and exhaustively explored their sequence space. However, it is practically unfeasible to evaluate all possible combinations of mutations owing to combinatorial explosion when multiple amino acid residues are incorporated. It was recently reported that a machine-learning guided protein engineering approach such as Thompson sampling (TS) has been used to efficiently explore sequence space in the framework of Bayesian optimization. For TS, over-exploration occurs when the initial data are biasedly distributed in the vicinity of the lead antibody. We handle a large-scale virtu

SUBMITTER: Kakuzaki T 

PROVIDER: S-EPMC10446805 | biostudies-literature | 2023 Jan-Dec

REPOSITORIES: biostudies-literature