Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca²⁺-Binding Proteins

Protein function is driven by transitions between metastable conformations, many of which are not conserved across homologues, offering opportunities for selective drug design. Accurately modeling both backbone and side chain metastability, and generating structures suitable for rigid docking in high-throughput virtual screening, is thus desirable yet challenging. Here, we present a hierarchical AF2RAVE pipeline that integrates AlphaFold2 with machine learning-based enhanced sampling to systematically explore the free energy landscape and metastability of protein systems, particularly at both backbone and side chain levels. Applied to the calcium-binding S100 protein family, this approach enables the generation of diverse holo-like conformations, starting from sequence. Retrospective docking and enrichment testing with a new <i>Ca</i>²⁺-S100B inhibitor data set demonstrates that AF2RAVE-generated structures outperform standard AlphaFold2 and even outperform experimentally resolved X-ray structures in enrichment testing. Our results highlight the potential of AF2RAVE for high-throughput virtual screening and selective inhibitor discovery, particularly for challenging targets such as the <i>Ca</i>²⁺-S100 family.