Distance-Restraint-Guided Diffusion Models for Sampling Protein Conformational Changes and Ligand Dissociation Pathways
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Le résumé fourni par la source
Protein conformational dynamics and ligand binding processes are fundamental to biological function, yet their systematic sampling and thermodynamic characterization remain challenging. Here, we present a distance-restraint-guided inference method that extends AlphaFold3-like diffusion model frameworks to predict protein structures at specified conformational states. By restraining intergroup distances─defined as geometric centroid distances between atom groups─during the reverse diffusion process, our method enables systematic sampling along reaction coordinates without model retraining. We implemented this approach in Boltz-2 and demonstrated its effectiveness on three model proteins that undergo open-closed conformational transitions, as well as on a protein-peptide dissociation pathway. Compared with conventional approaches that induce conformational diversity by manipulating the input multiple sequence alignments, our method achieved more uniform coverage of conformational space while maintaining high structural quality as assessed by both learning-based confidence metrics and stereochemistry-based validation. By combining distance-restrained sampling with molecular dynamics simulations, we constructed free energy landscapes and quantitatively estimated binding free energies. Altogether, our approach bridges deep learning-based structure prediction and physics-based simulations, providing an efficient strategy for characterizing the dynamics of biomolecules.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Distance-Restraint-Guided Diffusion Models for Sampling Protein Conformational Changes and Ligand Dissociation Pathways
- Date Crossref
- 07/05/2026
- Éditeur
- American Chemical Society (ACS)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Tokyo Medical and Dental University pays non établi dans la noticeUniversité ou école supérieure
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The University of Tokyo pays non établi dans la noticeUniversité ou école supérieure
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Medical Research Laboratory Department of Computational Drug Discovery and Design pays non établi dans la noticeStructure de recherche
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Graduate School of Agricultural and Life Sciences pays non établi dans la noticeUniversité ou école supérieure
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Graduate School of Science Department of Biological Sciences pays non établi dans la noticeUniversité ou école supérieure
Tokyo Medical and Dental University, The University of Tokyo et Department of Computational Drug Discovery and Design — Medical Research Laboratory, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.