Constructing Markov State Models to elucidate the functional conformational changes of complex biomolecules
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Summary
A general protocol of constructing Markov State Models to investigate functional conformational changes, which integrates the state‐of‐the‐art techniques for building and optimizing initial pathways, performing adaptive sampling and constructing MSMs is proposed.
- Type
- article
- Published
- 2018-01-01
- Cited by
- 134
- References
- 136
- OpenAlex
- https://openalex.org/W2760982347
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:126116309
Keywords
Computer science, Markov chain, Conformational ensembles, Protocol (science), Molecular dynamics
References
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- Structural Model of RNA Polymerase II Elongation Complex with Complete Transcription Bubble Reveals NTP Entry Routes
- Allostery through the computational microscope: cAMP activation of a canonical signaling domain
- Markov State Models Reveal a Two-Step Mechanism of miRNA Loading into the Human Argonaute Protein: Selective Binding followed by Structural Re-arrangement
- Nonequilibrium statistical mechanics
- Three-Dimensional Electron Microscopy of Macromolecular Assemblies
- Cryo-EM: A Unique Tool for the Visualization of Macromolecular Complexity
- Protein structure determination in solution by NMR spectroscopy.
- Targeted molecular dynamics: a new approach for searching pathways of conformational transitions.
- Bayesian comparison of Markov models of molecular dynamics with detailed balance constraint.
- Clustering to Minimize the Maximum Intercluster Distance
- OpenMM 4: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation
- Perspective: Markov models for long-timescale biomolecular dynamics.
- Finding transition pathways using the string method with swarms of trajectories.
- Markov state models based on milestoning.
- Building Markov state models along pathways to determine free energies and rates of transitions.
Cited by
- Molecular mechanisms of RNA polymerase II transcription elongation elucidated by kinetic network models.
- Computational structure‐based drug design: Predicting target flexibility
- Choice of adaptive sampling strategy impacts state discovery, transition probabilities, and the apparent mechanism of conformational changes
- Combined approaches from physics, statistics, and computer science for ab initio protein structure prediction: ex unitate vires (unity is strength)?
- An efficient Bayesian kinetic lumping algorithm to identify metastable conformational states via Gibbs sampling.
- Exploring Configuration Space and Path Space of Biomolecules Using Enhanced Sampling Techniques—Searching for Mechanism and Kinetics of Biomolecular Functions
- Perspective: Identification of collective variables and metastable states of protein dynamics.
- Loop Motion in Triosephosphate Isomerase Is Not a Simple Open and Shut Case.
- A Data‐Driven Accelerated Sampling Method for Searching Functional States of Proteins
- Adaptive Partitioning QM/MM for Molecular Dynamics Simulations: 6. Proton Transport through a Biological Channel.
- Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems
- On approximating a weak Markovian process as Markovian: Are we justified when discarding longtime correlations.
- Dynamical coring of Markov state models.
- TAPS: A traveling-salesman based automated path searching method for functional conformational changes of biological macromolecules.
- A Viral T7 RNA Polymerase Ratcheting Along DNA With Fidelity Control
- Overcoming the Bottleneck of the Enzymatic Cycle by Steric Frustration.
- Galerkin approximation of dynamical quantities using trajectory data.
- Deactivation Pathway of Ras GTPase Underlies Conformational Substates as Targets for Drug Design
- Encounter complexes and hidden poses of kinase-inhibitor binding on the free-energy landscape
- Identifying mechanistically distinct pathways in kinetic transition networks.
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