How to make the most of your masked language model for protein engineering
Current antibody engineering lacks systematic approaches for efficiently optimizing specific biological functions of proteins. This work proposes a flexible sampling strategy based on stochastic beam search, which leverages a masked language model to evaluate the pseudo-perplexity of single-point mutation neighborhoods and reframes the sequence generation process as a full-sequence multi-objective optimization problem. For the first time, large-scale in vitro experiments validate that the choice of sampling strategy exerts an influence on optimization performance comparable to that of the underlying model itself, thereby highlighting the critical role of sampling design in protein engineering.
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