EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering

arXiv cs.LG / 3/13/2026

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Key Points

EvoFlows is a variable-length sequence-to-sequence protein modeling approach designed for protein engineering, enabling a limited, controllable number of insertions, deletions, and substitutions on a template protein sequence.
Unlike autoregressive and masked language models, EvoFlows predict both which mutation to perform and where it should occur by learning mutational trajectories between evolutionarily related sequences using edit flows.
In silico evaluations on diverse protein communities from UNIREF and OAS show EvoFlows capture protein sequence distributions with quality comparable to leading masked language models while better generating non-trivial yet natural-like mutants from a given template.
The work points to a more controllable, trajectory-aware approach to protein design that could influence future workflows and tooling in protein engineering.

Abstract

We introduce EvoFlows, a variable-length sequence-to-sequence protein modeling approach uniquely suited to protein engineering. Unlike autoregressive and masked language models, EvoFlows perform a limited, controllable number of insertions, deletions, and substitutions on a template protein sequence. In other words, EvoFlows predict not only _which_ mutation to perform, but also _where_ it should occur. Our approach leverages edit flows to learn mutational trajectories between evolutionarily-related protein sequences, simultaneously modeling distributions of related natural proteins and the mutational paths connecting them. Through extensive _in silico_ evaluation on diverse protein communities from UNIREF and OAS, we demonstrate that EvoFlows capture protein sequence distributions with a quality comparable to leading masked language models commonly used in protein engineering, while showing improved ability to generate non-trivial yet natural-like mutants from a given template protein.