EvoFlows通过编辑流实现可控制的蛋白序列变异，支持插入删除替换。
EvoFlows: Evolutionary Edit-Based Flow-Matching for Protein Engineering
- 基于进化相关序列学习编辑流，实现可调控的突变轨迹。
- 在多个蛋白家族中生成更远离模板的变异，且保持自然家族一致性。
- 适合需要灵活设计蛋白序列的工程应用，如药物研发。
我们提出EvoFlows，一种用于蛋白质工程的可变长序列到序列建模方法。现有蛋白质语言模型不适用于优化任务：自回归模型需完整生成序列，掩码语言模型和离散扩散模型依赖预设突变位置，且无现有方法能自然支持相对于模板序列的插入和删除。EvoFlows通过编辑流学习进化相关蛋白质序列间的突变轨迹，可在模板序列上执行可控数量的突变（插入、删除、替换），不仅预测突变类型，还确定突变位置。通过对UniRef和OAS中多样化蛋白质家族的大量体外评估，结果显示EvoFlows生成的变体与自然蛋白质家族一致，同时比领先基线更远离模板序列。
原文摘要 · Abstract (English)
We introduce EvoFlows, a variable-length protein sequence-to-sequence modeling approach designed for protein engineering. Existing protein language models are poorly suited for optimization tasks: autoregressive models require full sequence generation, masked language and discrete diffusion models rely on pre-specified mutation locations, and no existing methods naturally support insertions and deletions relative to a template sequence. EvoFlows learns mutational trajectories between evolutionarily related protein sequences via edit flows, allowing it to perform a controllable number of mutations (insertions, deletions, and substitutions) on a template sequence, predicting not only _which_ mutation to perform, but also _where_ it should occur. Through extensive _in silico_ evaluation on diverse protein families from UniRef and OAS, we show that EvoFlows generates variants that remain consistent with natural protein families while exploring farther from template sequences than leading baselines.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。