基于因子图的进化感知多序列比对抽样推理
Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs
- School of Information Science and Technology, ShanghaiTech University（信息科学与技术学院，上海科技大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
研究针对MSA抽样在有限预算下难以控制进化信号的问题，提出将其作为优化问题，引入基于亲和传播的因子图方法AP-REASONER，通过特定因子和控制旋钮进行推理，实验表明该方法在下游任务中表现优异，能可控恢复蛋白质构象。
AI中文摘要：
多序列比对（MSA）为蛋白质语言模型提供了明确的进化背景，但在有限的令牌预算下，其深度大使得抽样不可避免。现有策略对保留在子集中的进化信号控制有限。本文将MSA抽样重塑为一个明确的优化问题，将关键进化指标视为可控目标。在此基础上，引入了基于亲和传播的因子图方法AP-REASONER。通过进化感知一元因子、范例一致性因子和两个控制旋钮，AP-REASONER通过消息传递进行因子图推理，以推断固定预算的MSA子集。实验表明，AP-REASONER在结构敏感的下游任务上优于基线抽样器，并能可控地恢复替代蛋白质构象。这些结果凸显了将MSA抽样建模为可控优化问题的价值，其中因子图推理为启发式选择提供了有效替代方案。
英文摘要：
Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets. Existing strategies, including random selection, identity-based filtering, and diversity-driven sampling, are effective heuristics, yet provide limited control over the evolutionary signals retained in the subset. In this work, we recast MSA subsampling as an explicit optimization problem, where key evolutionary measures, including query identity and diversity, are treated as controllable objectives. Building on this view, we introduce AP-REASONER, an Affinity-Propagation-based factor-graph approach. With evolution-aware unary factors, exemplar-consistency factors, and two control knobs, AP-REASONER performs factor-graph reasoning through message passing to infer a fixed-budget MSA subset. Experiments on long-range contact prediction and conformational ensemble prediction show that AP-REASONER outperforms baseline subsamplers on structure-sensitive downstream tasks and enables controllable recovery of alternative protein conformations. These results highlight the value of modeling MSA subsampling as a controllable optimization problem, where factor-graph reasoning offers an effective alternative to heuristic selection.
