基于LLM智能体的评估者参与式蒙特卡洛树搜索用于蛋白质设计中的基序支架构建
Evaluator-in-the-Loop Monte Carlo Tree Search via LLM Agents for Motif Scaffolding in Protein Design
- Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出ELMS框架，将结构评估反馈融入蒙特卡洛树搜索，通过批评与策略智能体指导基序支架构建，在GeomMotif和MotifBench上显著超越现有基线。
AI中文摘要：
基序支架构建系统通常遵循“先生成后过滤”的范式，即候选蛋白质被独立生成，结构评估主要用于最终筛选或排序。这种范式未能充分利用评估：失败的预测包含关于设计是否需要修复基序几何结构、整体可折叠性或其他结构约束的状态特定证据。我们提出ELMS（基于证据的LLM引导蒙特卡洛搜索），一种用于基序支架构建的评估者参与式搜索框架，将此类评估者反馈转化为有针对性的设计操作。有效复用结构反馈并非易事，因为不同的支架状态表现出不同的失败模式，而反复细化单一轨迹可能过早地将计算投入序列空间中无成效的区域。因此，ELMS将已评估的支架保留为持久搜索状态：批评智能体诊断状态局部的结构失败，策略智能体选择带有执行参数的有针对性操作，基序锁定操作实现合法的序列修改，而MCTS决定哪些历史状态应获得进一步的设计努力。在标准GeomMotif协议下（每任务100个候选），ELMS在单基序任务上达到86.41%的成功率，在配对基序任务上达到84.57%的成功率，分别超过最强先前基线19.3和21.9个百分点。在MotifBench上，在匹配的100个候选搜索预算下，它平均解决30个任务中的26.7个（88.89%的任务成功率），而最强基线解决16.0个任务（53.33%）。这些结果确立了ELMS作为将结构评估从终端过滤器转化为迭代基序支架构建的可操作指导的有效方法。
英文摘要：
Motif-scaffolding systems commonly follow a generate-then-filter paradigm, in which candidate proteins are generated independently and structural evaluation is used primarily for terminal screening or ranking. This paradigm underuses evaluation: failed predictions contain state-specific evidence about whether a design requires repair of motif geometry, global foldability, or other structural constraints. We introduce \textbf{ELMS} (Evidence-based LLM-guided Monte Carlo Search), an evaluator-in-the-loop search framework for motif scaffolding that turns such evaluator feedback into targeted design actions. Effective reuse of structural feedback is nontrivial because different scaffold states exhibit different failure modes, and repeatedly refining a single trajectory can prematurely commit computation to an unproductive region of sequence space. ELMS therefore retains evaluated scaffolds as persistent search states: a Critic Agent diagnoses state-local structural failures, a Policy Agent selects targeted operators with execution parameters, motif-locked operators realize legal sequence modifications, and MCTS determines which historical states should receive further design effort. Under the standard GeomMotif protocol (100 candidates per task), ELMS achieves Successful rates of 86.41\% on single-motif tasks and 84.57\% on paired-motif tasks, exceeding the strongest prior baseline by 19.3 and 21.9 percentage points, respectively. On MotifBench, under a matched 100-candidate search budget, it solves 26.7 of 30 tasks on average (88.89\% Task Success), compared with 16.0 tasks (53.33\%) for the strongest baseline. These results establish ELMS as an effective approach for converting structural evaluation from a terminal filter into actionable guidance for iterative motif scaffolding.
