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

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 optimization algorithm to propose sequences that are simultaneously optimized for multiple properties. Evaluations on experimental fitness landscapes demonstrated that MosPro generates sequences that optimally trade off multiple desiderata. Our results suggested an unparalleled potential of generative ML for efficient and controllable design for functional proteins.