用变分方法优化蛋白质序列，高效寻找高适应性新蛋白
A Variational Perspective on Generative Protein Fitness Optimization
- 将蛋白序列映射到连续潜空间，实现高效采样
- 结合流动匹配先验与适应度预测器，提升优化效率
- 框架灵活可扩展，适用于多种蛋白设计任务
蛋白质适应度优化的目标是发现具有更高功能适应性的新蛋白变体。由于搜索空间巨大、适应度分布稀疏且蛋白序列为离散结构，传统方法难以计算适应度梯度。本文提出变分潜变量生成蛋白优化（VLGPO），将蛋白序列嵌入连续潜空间，实现对适应度分布的高效采样，并结合学习得到的序列突变流匹配先验与适应度预测器，引导优化向高适应度序列逼近。在两个不同复杂度的蛋白基准测试中，VLGPO均达到当前最优性能。其显式的先验与似然函数设计，提供了灵活可插拔的框架，便于适配各类蛋白设计任务。
原文摘要 · Abstract (English)
The goal of protein fitness optimization is to discover new protein variants with enhanced fitness for a given use. The vast search space and the sparsely populated fitness landscape, along with the discrete nature of protein sequences, pose significant challenges when trying to determine the gradient towards configurations with higher fitness. We introduce Variational Latent Generative Protein Optimization (VLGPO), a variational perspective on fitness optimization. Our method embeds protein sequences in a continuous latent space to enable efficient sampling from the fitness distribution and combines a (learned) flow matching prior over sequence mutations with a fitness predictor to guide optimization towards sequences with high fitness. VLGPO achieves state-of-the-art results on two different protein benchmarks of varying complexity. Moreover, the variational design with explicit prior and likelihood functions offers a flexible plug-and-play framework that can be easily customized to suit various protein design tasks.
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