Pareto-optimal sampling for multi-objective protein sequence design.

ABSTRACT: Supervised machine learning (ML) has significantly advanced sequence-based protein property prediction. However, its inverse application, designing protein sequences with desired properties, remains under-explored. The challenges in sequence design stem from the vast search space and the rugged protein fitness landscape. In this work, we present MosPro, an efficient ML algorithm for property-guided protein sequence design. We frame sequence design as a discrete sampling problem. Utilizing a pre-trained differentiable ML model that predicts properties of sequences, MosPro shapes a distribution that assigns high probability mass to regions for high-property sequences. To generate designs, MosPro efficiently samples sequences from this constructed distribution. We further develop a Pareto opt

SUBMITTER: Luo J 

PROVIDER: S-EPMC11952807 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature