ArticleNature communications2026
Hidden structural states of proteins revealed by conformer selection.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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
8 citing papers in PubMed.
- The Advantages of AI for Computational Protein Studies and Looking Ahead at the Next Challenges: Single Structures Are Not Enough.Journal of molecular biology · 2026Review
- The latest AI breakthroughs in structural biology: protein binder design and conformational state prediction.Communications biology · 2026Article
- Modernizing biomolecular NMR: The POKY suite.The Journal of biological chemistry · 2026Review
- The Ensemble Basis of Allostery and Function: Insights from Models of Local Unfolding.Journal of molecular biology · 2025Review
- AlphaFold modeling uncovers global structural features of class I and class II fungal hydrophobins.Protein science : a publication of the Protein Society · 2025Article
- Resurgence of magnetic resonance techniques in the era of AlphaFold.Biophysical reviews · 2025Review
- Memorization Bias Impacts Modeling of Alternative Conformational States of Symmetric Solute Carrier Membrane Proteins with Methods from Deep Learning.bioRxiv : the preprint server for biology · 2025Article
- Integrative Modeling of Protein-Polypeptide Complexes by Bayesian Model Selection using AlphaFold and NMR Chemical Shift Perturbation Data.bioRxiv : the preprint server for biology · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
We introduce AISAR (AI SAmpling with NMR Recall selection), a computational-experimental framework for identifying alternative conformational states from NMR data. Unlike conventional NMR methods that rely on spatial restraints, AISAR combines AI-driven conformational sampling of realistic models with Bayesian-like scoring against NOESY and other NMR observables. Applied to Gaussia luciferase, AISAR reveals two interconverting states involving large rearrangements of two lids, binding pockets, and cryptic surface cavities. AISAR also identifies two distinct conformational states of the human tumor suppressor Cyclin-Dependent Kinase 2-Associated Protein 1, demonstrating its utility across diverse protein scaffolds. Validation using the NOESY Double Recall method shows that these multistate models account for NOESY peaks that are not explained by single-state models, supporting the presence of fast-exchanging structural states in dynamic equilibrium. AISAR enables detection and evaluation of conformational heterogeneity and cryptic pockets not resolved by conventional single-state NMR analysis, providing insights into protein structural dynamics and function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.