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Proximal Exploration for Model-guided Protein Sequence Design
Zhizhou Ren · Jiahan Li · Fan Ding · Yuan Zhou · Jianzhu Ma · Jian Peng

Wed Jul 20 08:35 AM -- 08:40 AM (PDT) @ Hall G

Designing protein sequences with a particular biological function is a long-lasting challenge for protein engineering. Recent advances in machine-learning-guided approaches focus on building a surrogate sequence-function model to reduce the burden of expensive in-lab experiments. In this paper, we study the exploration mechanism of model-guided sequence design. We leverage a natural property of protein fitness landscape that a concise set of mutations upon the wild-type sequence are usually sufficient to enhance the desired function. By utilizing this property, we propose Proximal Exploration (PEX) algorithm that prioritizes the evolutionary search for high-fitness mutants with low mutation counts. In addition, we develop a specialized model architecture, called Mutation Factorization Network (MuFacNet), to predict low-order mutational effects, which further improves the sample efficiency of model-guided evolution. In experiments, we extensively evaluate our method on a suite of in-silico protein sequence design tasks and demonstrate substantial improvement over baseline algorithms.

Author Information

Zhizhou Ren (University of Illinois at Urbana-Champaign)
Jiahan Li (Peking University)
Fan Ding (Purdue University)
Yuan Zhou (UIUC)
Jianzhu Ma (Institute for Artificial Intelligence, Peking University)
Jian Peng (UIUC)

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