ArticleFrontiers in molecular biosciences2019
Using Dimensionality Reduction to Analyze Protein Trajectories.
Article in Frontiers in molecular biosciences, 2019. 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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27 citing papers in PubMed, 81 citations in OpenAlex.
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- Random Functions as Data Compressors for Machine Learning of Molecular Processes.Journal of chemical theory and computation · 2026Article
- Clustering and Analyzing Ensembles of Residue Interaction Networks from Molecular Dynamics Simulations.Journal of chemical information and modeling · 2025Article
- Energy Landscapes and Structural Plasticity of Intrinsically Disordered Histones.Journal of chemical information and modeling · 2025Article
- NeuralTSNE: A Python Package for the Dimensionality Reduction of Molecular Dynamics Data Using Neural Networks.Journal of chemical information and modeling · 2025Article
- Relevant, Hidden, and Frustrated Information in High-Dimensional Analyses of Complex Dynamical Systems with Internal Noise.Journal of chemical theory and computation · 2025Article
- Extended Quality (eQual): Radial Threshold Clustering Based onJournal of chemical information and modeling · 2025Article
- PLUMED Tutorials: A collaborative, community-driven learning ecosystem.The Journal of chemical physics · 2025Article
- Molecular similarity: Theory, applications, and perspectives.Artificial intelligence chemistry · 2024Article
- Streamlining NMR Chemical Shift Predictions for Intrinsically Disordered Proteins: Design of Ensembles with Dimensionality Reduction and Clustering.Journal of chemical information and modeling · 2024Article
- Computational Approaches to Predict Protein-Protein Interactions in Crowded Cellular Environments.Chemical reviews · 2024Review
- Acceleration of Molecular Simulations by Parametric Time-Lagged tSNE Metadynamics.The journal of physical chemistry. B · 2024Article
- A Retrospective on the Development of Methods for the Analysis of Protein Conformational Ensembles.The protein journal · 2023Review
- Visualizing the Residue Interaction Landscape of Proteins by Temporal Network Embedding.Journal of chemical theory and computation · 2023Article
- Machine Learning Generation of Dynamic Protein Conformational Ensembles.Molecules (Basel, Switzerland) · 2023Review
- Interpretable artificial intelligence and exascale molecular dynamics simulations to reveal kinetics: Applications to Alzheimer's disease.Current opinion in structural biology · 2022Review
- From Data to Knowledge: Systematic Review of Tools for Automatic Analysis of Molecular Dynamics Output.Frontiers in pharmacology · 2022Review
- Dissecting Multiple Pathways in the Relaxation Dynamics of Helix <==> Coil Transitions with Optimum Dimensionality Reduction.Biomolecules · 2021Review
- Multiscale Reweighted Stochastic Embedding: Deep Learning of Collective Variables for Enhanced Sampling.The journal of physical chemistry. A · 2021Article
- UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study.The journal of physical chemistry. B · 2021Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years the analysis of molecular dynamics trajectories using dimensionality reduction algorithms has become commonplace. These algorithms seek to find a low-dimensional representation of a trajectory that is, according to a well-defined criterion, optimal. A number of different strategies for generating projections of trajectories have been proposed but little has been done to systematically compare how these various approaches fare when it comes to analysing trajectories for biomolecules in explicit solvent. In the following paper, we have thus analyzed a molecular dynamics trajectory of the C-terminal fragment of the immunoglobulin binding domain B1 of protein G of Streptococcus modeled in explicit solvent using a range of different dimensionality reduction algorithms. We have then tried to systematically compare the projections generated using each of these algorithms by using a clustering algorithm to find the positions and extents of the basins in the high-dimensional energy landscape. We find that no algorithm outshines all the other in terms of the quality of the projection it generates. Instead, all the algorithms do a reasonable job when it comes to building a projection that separates some of the configurations that lie in different basins. Having said that, however, all the algorithms struggle to project the basins because they all have a large intrinsic dimensionality.
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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 OpenQuestion graph.