ArticleNature communications2025
Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 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
- Pathway representationDigital discovery · 2026Article
- A continuous-space analytical framework for committor functions from molecular dynamics.The Journal of chemical physics · 2026Article
- Minimum-Excess-Work Guidance: Score-Based Sampling with Experimental Data or Sparse Restraints.Journal of chemical theory and computation · 2026Article
- Pathway Representation via Intrinsic Structural Medoids (PRISM): A Structural Mapping Approach to Clustering Molecular Pathways.bioRxiv : the preprint server for biology · 2026Article
- De novo Folding Mechanisms of Lasso Peptides.bioRxiv : the preprint server for biology · 2026Article
- Unveiling hidden intermediate states in protein folding with AI-based conditional transition clustering.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Efficient sampling of large-scale transition pathways and intermediate conformations in sub-mesoscopic protein complexes.Nature communications · 2026Article
- Efficient Sampling of Short Protein Trajectories with Conditional Diffusion Models.Journal of chemical theory and computation · 2026Article
- All roads lead to Rome: Emergence of Mechanistic Divergence Shapes Activation of Class B1 G-Protein Coupled Receptors.bioRxiv : the preprint server for biology · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- A Practical Guide to Transition State Analysis in Biomolecular Simulations with TS-DAR.The journal of physical chemistry. B · 2025Article
- Calculation of minimum energy pathways in transport proteins.Communications chemistry · 2025Article
- Unbiased Enhanced Sampling in Molecular Dynamics via Simultaneously Accelerating Diffusion in Multiple Low-Dimensional Collective Variable Spaces.Journal of chemical theory and computation · 2025Article
- Memory kernel minimization-based neural networks for discovering slow collective variables of biomolecular dynamics.Nature computational science · 2025Article
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Targeting Bacterial RNA Polymerase: Harnessing Simulations and Machine Learning to Design Inhibitors for Drug-Resistant Pathogens.Biochemistry · 2025Review
- Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space.Nature communications · 2025Article
- AI alignment is all your need for future drug discovery.Frontiers in artificial intelligence · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Identifying transitional states is crucial for understanding protein conformational changes that underlie numerous biological processes. Markov state models (MSMs), built from Molecular Dynamics (MD) simulations, capture these dynamics through transitions among metastable conformational states, and have demonstrated success in studying protein conformational changes. However, MSMs face challenges in identifying transition states, as they partition MD conformations into discrete metastable states (or free energy minima), lacking description of transition states located at the free energy barriers. Here, we introduce Transition State identification via Dispersion and vAriational principle Regularized neural networks (TS-DAR), a deep learning framework inspired by out-of-distribution (OOD) detection in trustworthy artificial intelligence (AI). TS-DAR offers an end-to-end pipeline that can simultaneously detect all transition states between multiple free minima from MD simulations using the regularized hyperspherical embeddings in latent space. The key insight of TS-DAR lies in treating transition state structures as OOD data, recognizing that they are sparsely populated and exhibit a distributional shift from metastable states. We demonstrate the power of TS-DAR by applying it to a 2D potential, alanine dipeptide, and the translocation of a DNA motor protein on DNA, where it outperforms previous methods in identifying transition states.
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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 Socratic graph.