ArticleScientific data2024
mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed.
- Biasing Conformational Sampling in AlphaFold 3 and Boltz-2 via Pair Representation Scaling.Journal of chemical information and modeling · 2026Article
- ESMDynamic: Fast and accurate prediction of protein dynamic contact maps from single sequences.Nature communications · 2026Article
- Structure-aware artificial intelligence for next-generation drug discovery: from protein-ligand modeling to generative biomolecular design.Briefings in bioinformatics · 2026Review
- AI-Physics-Experiment Trinity for Integrated Protein Dynamics Modeling.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- A dataset of small protein conformational ensembles from all-atom molecular dynamics simulations.Scientific data · 2026Article
- AI-based methods for simulating, sampling, and predicting protein ensembles.Current opinion in structural biology · 2026Review
- Molecular Modeling of the Pathogenetic Mechanisms of Neuropsychiatric Disorders.International journal of molecular sciences · 2026Review
- Fast prediction of protein flexibility.Bioinformatics (Oxford, England) · 2026Article
- Unlocking hidden biomolecular conformational landscapes in diffusion models at inference time.ArXiv · 2026Article
- Protein language models trained on biophysical dynamics inform mutation effects.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Enhanced Sampling for Efficient Learning of Coarse-Grained Machine Learning Potentials.Journal of chemical theory and computation · 2026Article
- Learning physical interactions to compose biological large language models.Communications chemistry · 2026Review
- Deep generative modeling of temperature-dependent structural ensembles of proteins.Communications chemistry · 2025Article
- SLAE: Strictly Local All-atom Environment for Protein Representation.bioRxiv : the preprint server for biology · 2025Article
- Designing high-affinity 3D drug molecules via geometric spatial perception diffusion model.Briefings in bioinformatics · 2025Article
- Conditional Protein Structure Generation with Protpardelle-1c.bioRxiv : the preprint server for biology · 2025Article
- PEGASUS: Prediction of MD-derived protein flexibility from sequence.Protein science : a publication of the Protein Society · 2025Article
- Generation of protein dynamics by machine learning.Current opinion in structural biology · 2025Review
- Beyond static structures: protein dynamic conformations modeling in the post-AlphaFold era.Briefings in bioinformatics · 2025Review
- Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction.bioRxiv : the preprint server for biology · 2025Article
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Abstract
Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains. This dataset comprises all-atom systems for 5,398 domains, modeled with a state-of-the-art classical force field, and simulated in five replicates each at five temperatures from 320 K to 450 K. The mdCATH dataset records coordinates and forces every 1 ns, for over 62 ms of accumulated simulation time, effectively capturing the dynamics of the various classes of domains and providing a unique resource for proteome-wide statistical analyses of protein unfolding thermodynamics and kinetics. We outline the dataset structure and showcase its potential through four easily reproducible case studies, highlighting its capabilities in advancing protein science.
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