Journal ArticleDOI
Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification
Susanna Röblitz,Marcus Weber +1 more
TLDR
It is demonstrated in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states.Abstract:
Given a row-stochastic matrix describing pairwise similarities between data objects, spectral clustering makes use of the eigenvectors of this matrix to perform dimensionality reduction for clustering in fewer dimensions. One example from this class of algorithms is the Robust Perron Cluster Analysis (PCCA+), which delivers a fuzzy clustering. Originally developed for clustering the state space of Markov chains, the method became popular as a versatile tool for general data classification problems. The robustness of PCCA+, however, cannot be explained by previous perturbation results, because the matrices in typical applications do not comply with the two main requirements: reversibility and nearly decomposability. We therefore demonstrate in this paper that PCCA+ always delivers an optimal fuzzy clustering for nearly uncoupled, not necessarily reversible, Markov chains with transition states.read more
Citations
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Journal ArticleDOI
PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models.
Martin K. Scherer,Benjamin Trendelkamp-Schroer,Fabian Paul,Guillermo Pérez-Hernández,Moritz Hoffmann,Nuria Plattner,Christoph Wehmeyer,Jan-Hendrik Prinz,Frank Noé +8 more
TL;DR: The open-source Python package PyEMMA is presented, derived a systematic and accurate way to coarse-grain MSMs to few states and to illustrate the structures of the metastable states of the system.
Journal ArticleDOI
VAMPnets for deep learning of molecular kinetics.
TL;DR: A deep learning framework that automates construction of Markov state models from MD simulation data is introduced that performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.
Journal ArticleDOI
Machine Learning for Molecular Simulation.
Frank Noé,Frank Noé,Alexandre Tkatchenko,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller,Cecilia Clementi,Cecilia Clementi +7 more
TL;DR: Recent ML methods for molecular simulation are reviewed, with particular focus on (deep) neural networks for the prediction of quantum-mechanical energies and forces, on coarse-grained molecular dynamics, on the extraction of free energy surfaces and kinetics, and on generative network approaches to sample molecular equilibrium structures and compute thermodynamics.
Journal ArticleDOI
RNA Structural Dynamics As Captured by Molecular Simulations: A Comprehensive Overview.
Jiří Šponer,Giovanni Bussi,Miroslav Krepl,Miroslav Krepl,Pavel Banáš,Sandro Bottaro,Richard A. Cunha,Alejandro Gil-Ley,Giovanni Pinamonti,Simón Poblete,Petr Jurečka,Nils G. Walter,Michal Otyepka +12 more
TL;DR: An in-depth, evaluatory coverage of the most fundamental methodological challenges that set the basis for the future development of the field, in particular, the current developments and inherent physical limitations of the atomistic force fields and the recent advances in a broad spectrum of enhanced sampling methods are covered.
Journal ArticleDOI
Protein conformational plasticity and complex ligand-binding kinetics explored by atomistic simulations and Markov models
Nuria Plattner,Frank Noé +1 more
TL;DR: These wild-type simulations explore a space of conformations that can be individually stabilized by adding ligands or making suitable changes in protein sequence, and provide direct evidence of conformational plasticity in receptors.
References
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Book
Nonnegative Matrices and Applications
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Journal ArticleDOI
Robust Perron cluster analysis in conformation dynamics
Peter Deuflhard,Marcus Weber +1 more
TL;DR: In this article, an improved Perron cluster analysis algorithm is presented, which is more robust than earlier suggestions, for the detection of metastable conformations in molecular conformation dynamics.