ArticleJournal of chemical theory and computation2022
Size-and-Shape Space Gaussian Mixture Models for Structural Clustering of Molecular Dynamics Trajectories.
Article in Journal of chemical theory and computation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- The impact of glycosylation on the conformational ensembles of β-, δ-, and γ-sarcoglycans.Biophysical journal · 2026Article
- Mechanochemical Decoupling of ATP Hydrolysis and RNA Translocation in SARS-CoV-2 nsp13 by the L405D Mutation.bioRxiv : the preprint server for biology · 2026Article
- Divide and Cluster: The DIVINE Framework for Deterministic Top-Down Analysis of Molecular Dynamics Trajectories.Journal of chemical information and modeling · 2026Article
- Random Functions as Data Compressors for Machine Learning of Molecular Processes.Journal of chemical theory and computation · 2026Article
- The Impact of Glycosylation on the Conformational Ensembles ofbioRxiv : the preprint server for biology · 2026Article
- Tutorial on quantifying and sampling biomolecular ensembles with ShapeGMM.The Journal of chemical physics · 2025Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Improved Data-Driven Collective Variables for Biased Sampling through Iteration on Biased Data.The journal of physical chemistry. B · 2025Article
- Hierarchical Extended Linkage Method (HELM)'s Deep Dive into Hybrid Clustering Strategies.Journal of chemical information and modeling · 2025Article
- A direct computational assessment of vinculin-actin unbinding kinetics reveals catch-bonding behavior.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Exploring transition states of protein conformational changes via out-of-distribution detection in the hyperspherical latent space.Nature communications · 2025Article
- Alzheimer's Disease Immunotherapy and Mimetic Peptide Design for Drug Development: Mutation Screening, Molecular Dynamics, and a Quantum Biochemistry Approach Focusing on Aducanumab::Aβ2-7 Binding Affinity.ACS chemical neuroscience · 2024Article
- Clustering Molecules at a Large Scale: Integrating Spectral Geometry with Deep Learning.Molecules (Basel, Switzerland) · 2024Article
- Molecular simulation approaches to probing the effects of mechanical forces in the actin cytoskeleton.Cytoskeleton (Hoboken, N.J.) · 2024Article
- Good Rates From Bad Coordinates: The Exponential Average Time-dependent Rate Approach.Journal of chemical theory and computation · 2024Article
- Motif-VI loop acts as a nucleotide valve in the West Nile Virus NS3 Helicase.Nucleic acids research · 2024Article
- k-Means NANI: An Improved Clustering Algorithm for Molecular Dynamics Simulations.Journal of chemical theory and computation · 2024Article
- Quantifying Unbiased Conformational Ensembles from Biased Simulations Using ShapeGMM.Journal of chemical theory and computation · 2024Article
- An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors.Journal of chemical information and modeling · 2024Article
- Tutorial on how to build non-Markovian dynamic models from molecular dynamics simulations for studying protein conformational changes.The Journal of chemical physics · 2024Article
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3 authors.
Funding
Abstract
Determining the optimal number and identity of structural clusters from an ensemble of molecular configurations continues to be a challenge. Recent structural clustering methods have focused on the use of internal coordinates due to the innate rotational and translational invariance of these features. The vast number of possible internal coordinates necessitates a feature space supervision step to make clustering tractable but yields a protocol that can be system type-specific. Particle positions offer an appealing alternative to internal coordinates but suffer from a lack of rotational and translational invariance, as well as a perceived insensitivity to regions of structural dissimilarity. Here, we present a method, denoted shape-GMM, that overcomes the shortcomings of particle positions using a weighted maximum likelihood alignment procedure. This alignment strategy is then built into an expectation maximization Gaussian mixture model (GMM) procedure to capture metastable states in the free-energy landscape. The resulting algorithm distinguishes between a variety of different structures, including those indistinguishable by root-mean-square displacement and pairwise distances, as demonstrated on several model systems. Shape-GMM results on an extensive simulation of the fast-folding HP35 Nle/Nle mutant protein support a four-state folding/unfolding mechanism, which is consistent with previous experimental results and provides kinetic details comparable to previous state-of-the art clustering approaches, as measured by the VAMP-2 score. Currently, training of shape-GMMs is recommended for systems (or subsystems) that can be represented by ≲200 particles and ≲100k configurations to estimate high-dimensional covariance matrices and balance computational expense. Once a shape-GMM is trained, it can be used to predict the cluster identities of millions of configurations.
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