Search, Edit, and Fold: LLM-Guided MSA Optimization for Protein Conformation Prediction

Accurately identifying alternative protein conformations remains a fundamental challenge, particularly when functionally relevant states are encoded by sparse evolutionary signals within large multiple sequence alignments (MSAs). In this work, protein conformation prediction is formulated as a combinatorial search problem in MSA space, shifting the focus from structure divergence to evolutionary information discovery. We introduce MSA-Evolver, an optimization framework that enables LLM with direct manipulation and iterative exploration of MSAs. With unified action space and feedback-guided multi-step reasoning strategy, our framework efficiently identifies informative sub-MSAs under limited folding budgets and substantially improves the prediction accuracy of alternative conformations, including open-closed, inward-outward, apo-holo, fold-switching, and intrinsically disordered proteins.