ArticleJournal of chemical theory and computation2016
Uncovering Large-Scale Conformational Change in Molecular Dynamics without Prior Knowledge.
Article in Journal of chemical theory and computation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
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- 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
- SuperSalt: equivariant neural network force fields for multicomponent molten salts system.Nature communications · 2025Article
- Extended Quality (eQual): Radial Threshold Clustering Based onJournal of chemical information and modeling · 2025Article
- Global Proteomics Indicates Subcellular-Specific Anti-Ferroptotic Responses to Ionizing Radiation.Molecular & cellular proteomics : MCP · 2025Article
- k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations.Journal of chemical theory and computation · 2024Article
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- k-Means NANI: an improved clustering algorithm for Molecular Dynamics simulations.bioRxiv : the preprint server for biology · 2024Article
- Revealing clinically relevant specific IgE sensitization patterns in Hymenoptera venom allergy with dimension reduction and clustering.The World Allergy Organization journal · 2023Article
- Embedding Nonrigid Solutes in an Averaged Environment: A Case Study on Rhodopsins.Journal of chemical theory and computation · 2023Article
- Unraveling the Role of Hydrogen Bonds in Thrombin via Two Machine Learning Methods.Journal of chemical information and modeling · 2023Article
- Characterisation of HOIP RBR E3 ligase conformational dynamics using integrative modelling.Scientific reports · 2022Article
- Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories.Journal of chemical theory and computation · 2022Article
- Simulations suggest double sodium binding induces unexpected conformational changes in thrombin.Journal of molecular modeling · 2022Article
- Unsupervised Learning Methods for Molecular Simulation Data.Chemical reviews · 2021Article
- Light Chain Mutation Effects on the Dynamics of Thrombin.Journal of chemical information and modeling · 2021Article
- Effect of mutations on binding of ligands to guanine riboswitch probed by free energy perturbation and molecular dynamics simulations.Nucleic acids research · 2019Article
- Probing light chain mutation effects on thrombin via molecular dynamics simulations and machine learning.Journal of biomolecular structure & dynamics · 2019Article
- Visualizing correlated motion with HDBSCAN clustering.Protein science : a publication of the Protein Society · 2018Article
- Mechanistic insights into thrombin's switch between "slow" and "fast" forms.Physical chemistry chemical physics : PCCP · 2017Article
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Authors and funding
6 authors.
Funding
Abstract
As the length of molecular dynamics (MD) trajectories grows with increasing computational power, so does the importance of clustering methods for partitioning trajectories into conformational bins. Of the methods available, the vast majority require users to either have some a priori knowledge about the system to be clustered or to tune clustering parameters through trial and error. Here we present non-parametric uses of two modern clustering techniques suitable for first-pass investigation of an MD trajectory. Being non-parametric, these methods require neither prior knowledge nor parameter tuning. The first method, HDBSCAN, is fast-relative to other popular clustering methods-and is able to group unstructured or intrinsically disordered systems (such as intrinsically disordered proteins, or IDPs) into bins that represent global conformational shifts. HDBSCAN is also useful for determining the overall stability of a system-as it tends to group stable systems into one or two bins-and identifying transition events between metastable states. The second method, iMWK-Means, with explicit rescaling followed by K-Means, while slower than HDBSCAN, performs well with stable, structured systems such as folded proteins and is able to identify higher resolution details such as changes in relative position of secondary structural elements. Used in conjunction, these clustering methods allow a user to discern quickly and without prior knowledge the stability of a simulated system and identify both local and global conformational changes.
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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.