Hierarchical AF2RAVE for multi-conformation virtual screening targeting S100 Ca$^{2+}$-binding proteins

Protein function is driven by transitions between metastable conformations, many of which are not conserved across homologs, offering opportunities for selective drug design. Accurately modeling both backbone and sidechain 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 sidechain 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 $Ca^{2+}$-S100B inhibitor dataset 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 $Ca^{2+}$-S100 family.