ReviewJACS Au2021
Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning.
Review in JACS Au, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
55 citing papers in PubMed.
- Role of Hsp70 chaperone in client-protein folding elucidated by Markov state modeling and NMR restraint-assisted molecular dynamics simulations.Biophysical journal · 2026Article
- Substrate binding reorganizes the energetic landscape of Plasmodium falciparum hexose transporter PfHT1.bioRxiv : the preprint server for biology · 2026Article
- A continuous-space analytical framework for committor functions from molecular dynamics.The Journal of chemical physics · 2026Article
- Adaptive Normal Mode Sampling (aMDeNM) Enhances Exploration of Protein Conformational Space and Reveals the Functional Role of Frequency Coupling.Journal of chemical theory and computation · 2026Article
- Metastable Protein-Protein Interactions as a Design Principle for PROTACs: Insights from the RIPK1-VHL System.JACS Au · 2026Article
- Molecular Modeling of the Pathogenetic Mechanisms of Neuropsychiatric Disorders.International journal of molecular sciences · 2026Review
- Sequence constraints predispose Class D GPCRs to follow an atypical activation mechanism.bioRxiv : the preprint server for biology · 2026Article
- Nucleosome condensate and linker DNA alter chromatin folding pathways and rates.Biophysical journal · 2026Article
- Unlocking the undruggable spliceosome: generative AI and structural dynamics in cancer therapy.Frontiers in cell and developmental biology · 2026Review
- Exploring the distinct activation mechanisms of neuromedin B receptor through multiple replica molecular dynamics simulations and Markov state modeling.Acta pharmacologica Sinica · 2025Article
- A Practical Guide to Transition State Analysis in Biomolecular Simulations with TS-DAR.The journal of physical chemistry. B · 2025Article
- How minor sequence changes enable mechanistic diversity in MFS transporters? An atomic-level rationale for symport emergence in NarU.bioRxiv : the preprint server for biology · 2025Article
- Hierarchical incremental learning deciphers molecular arrangements in multi-component materials.Nature communications · 2025Article
- CADENCE: Clustering Algorithm─Density-Based Exploration and Novelty Clustering with Efficiency.Journal of chemical information and modeling · 2025Article
- Unfolding of the Villin Headpiece Domain: Revealing Structural Heterogeneity with Time-Resolved X-Ray Solution Scattering and Markov State Modeling.Chemphyschem : a European journal of chemical physics and physical chemistry · 2025Article
- Extended Quality (eQual): Radial Threshold Clustering Based onJournal of chemical information and modeling · 2025Article
- Robustness in biomolecular simulations: Addressing challenges in data generation, analysis, and curation.Cell reports. Physical science · 2025Article
- High-Throughput Ligand Dissociation Kinetics Predictions Using Site Identification by Ligand Competitive Saturation.Journal of chemical theory and computation · 2025Article
- AMUSET-TICA: A Tensor-Based Approach for Identifying Slow Collective Variables in Biomolecular Dynamics.Journal of chemical theory and computation · 2025Article
- Molecular Modelling in Bioactive Peptide Discovery and Characterisation.Biomolecules · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Markov state models (MSMs) based on molecular dynamics (MD) simulations are routinely employed to study protein folding, however, their application to functional conformational changes of biomolecules is still limited. In the past few years, the field of computational chemistry has experienced a surge of advancements stemming from machine learning algorithms, and MSMs have not been left out. Unlike global processes, such as protein folding, the application of MSMs to functional conformational changes is challenging because they mostly consist of localized structural transitions. Therefore, it is critical to properly select a subset of structural features that can describe the slowest dynamics of these functional conformational changes. To address this challenge, we recommend several automatic feature selection methods such as Spectral-OASIS. To identify states in MSMs, the chosen features can be subject to dimensionality reduction methods such as TICA or deep learning based VAMPNets to project MD conformations onto a few collective variables for subsequent clustering. Another challenge for the application of MSMs to the study of functional conformational changes is the ability to comprehend their biophysical mechanisms, as MSMs built for these processes often require a large number of states. We recommend the recently developed quasi-MSMs (qMSMs) to address this issue. Compared to MSMs, qMSMs encode the non-Markovian dynamics via the generalized master equation and can significantly reduce the number of states. As a result, qMSMs can be built with a handful of states to facilitate the interpretation of functional conformational changes. In the wake of machine learning, we believe that the rapid advancement in the MSM methodology will lead to their wider application in studying functional conformational changes of biomolecules.
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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.