ArticleJournal of chemical theory and computation2025
Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca
Article in Journal of chemical theory and computation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
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Authors and funding
7 authors.
Funding
Abstract
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
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