Robust Biomolecular Complex Design Across Protein Conformational Landscapes
This study addresses the failure of single-conformation protein design across multiple native conformations by proposing FlexEvo, a framework that enhances compatibility with unseen conformations through inference-time evolutionary adaptation without retraining or conformational ensembles. Core innovations include a model-agnostic evolutionary algorithm, FlexBox geometric partitioning, all-atom representations, and a Pareto selection mechanism enabling dual-objective optimization of geometric constraints from mono-state to multi-state regimes. Experimental results demonstrate that FlexEvo substantially reduces cross-conformation performance degradation from 47.8% to 4.4%, while introducing only 1.4 to 3.1 minutes of additional computational overhead per sample, thereby achieving both high efficiency and strong generalizability.