Use of single-molecule time-series data for refining conformational dynamics in molecular simulations.

[1]  F. Jiang,et al.  Developments and Applications of Coil-Library-Based Residue-Specific Force Fields for Molecular Dynamics Simulations of Peptides and Proteins. , 2019, Journal of chemical theory and computation.

[2]  T. Komatsuzaki,et al.  Multibasin Dynamics in Off-Lattice Minimalist Protein Landscapes † , 2002 .

[3]  Jianqing Fan Nonlinear Time Series , 2003 .

[4]  W. Eaton,et al.  Characterizing the unfolded states of proteins using single-molecule FRET spectroscopy and molecular simulations , 2007, Proceedings of the National Academy of Sciences.

[5]  Kresten Lindorff-Larsen,et al.  Combining Experiments and Simulations Using the Maximum Entropy Principle , 2014, PLoS Comput. Biol..

[6]  R. Best,et al.  Balanced Protein–Water Interactions Improve Properties of Disordered Proteins and Non-Specific Protein Association , 2014, Journal of chemical theory and computation.

[7]  Dimitar V. Pachov,et al.  The energy landscape of adenylate kinase during catalysis , 2014, Nature Structural &Molecular Biology.

[8]  Vincent A. Voelz,et al.  Bridging Microscopic and Macroscopic Mechanisms of p53-MDM2 Binding with Kinetic Network Models. , 2017, Biophysical journal.

[9]  Amanda L. Jonsson,et al.  Φ-Analysis at the Experimental Limits: Mechanism of β-Hairpin Formation , 2006 .

[10]  Toma E Tomov,et al.  Photon-by-Photon Hidden Markov Model Analysis for Microsecond Single-Molecule FRET Kinetics. , 2016, The journal of physical chemistry. B.

[11]  A. Szabó,et al.  Decoding the pattern of photon colors in single-molecule FRET. , 2009, The journal of physical chemistry. B.

[12]  W F van Gunsteren,et al.  Calculation of NMR-relaxation parameters for flexible molecules from molecular dynamics simulations , 2001, Journal of biomolecular NMR.

[13]  G. Hummer,et al.  SAXS ensemble refinement of ESCRT-III CHMP3 conformational transitions. , 2011, Structure.

[14]  W. Eaton,et al.  Protein folding studied by single-molecule FRET. , 2008, Current opinion in structural biology.

[15]  Jane R. Allison Using simulation to interpret experimental data in terms of protein conformational ensembles. , 2017, Current opinion in structural biology.

[16]  V. Pande,et al.  Markov State Models: From an Art to a Science. , 2018, Journal of the American Chemical Society.

[17]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[18]  Hao Wu,et al.  Combining experimental and simulation data of molecular processes via augmented Markov models , 2017, Proceedings of the National Academy of Sciences.

[19]  Lucas P. Watkins,et al.  Detection of intensity change points in time-resolved single-molecule measurements. , 2005, The journal of physical chemistry. B.

[20]  Xiaojin Zhu,et al.  Introduction to Semi-Supervised Learning , 2009, Synthesis Lectures on Artificial Intelligence and Machine Learning.

[21]  Hidekazu Hiroaki,et al.  High-resolution multi-dimensional NMR spectroscopy of proteins in human cells , 2009, Nature.

[22]  Kevin J. McHale,et al.  Single-Molecule Fluorescence Experiments Determine Protein Folding Transition Path Times , 2012, Science.

[23]  Ken A Dill,et al.  Caliber Corrected Markov Modeling (C2M2): Correcting Equilibrium Markov Models. , 2017, Journal of chemical theory and computation.

[24]  R. Best,et al.  Residue-specific α-helix propensities from molecular simulation. , 2012, Biophysical journal.

[25]  H. Chan,et al.  Phase Separation and Single-Chain Compactness of Charged Disordered Proteins Are Strongly Correlated. , 2017, Biophysical journal.

[26]  Takashi Kameshima,et al.  A three-dimensional movie of structural changes in bacteriorhodopsin , 2016, Science.

[27]  H. Grubmüller,et al.  Structural Heterogeneity and Quantitative FRET Efficiency Distributions of Polyprolines through a Hybrid Atomistic Simulation and Monte Carlo Approach , 2011, PloS one.

[28]  S. McKinney,et al.  Analysis of single-molecule FRET trajectories using hidden Markov modeling. , 2006, Biophysical journal.

[29]  Yasuhiro Matsunaga,et al.  Linking time-series of single-molecule experiments with molecular dynamics simulations by machine learning , 2018, eLife.

[30]  Joseph F. Rudzinski,et al.  Communication: Consistent interpretation of molecular simulation kinetics using Markov state models biased with external information. , 2016, The Journal of chemical physics.

[31]  C. Schütte,et al.  Supplementary Information for “ Constructing the Equilibrium Ensemble of Folding Pathways from Short Off-Equilibrium Simulations ” , 2009 .

[32]  W. F. Gunsteren,et al.  Time-dependent distance restraints in molecular dynamics simulations , 1989 .

[33]  Richard Lavery,et al.  Significance of Molecular Dynamics Simulations for Life Sciences , 2014 .

[34]  Andrew L. Ferguson,et al.  Recovery of Protein Folding Funnels from Single-Molecule Time Series by Delay Embeddings and Manifold Learning. , 2018, The journal of physical chemistry. B.

[35]  Gerhard Hummer,et al.  Bayesian ensemble refinement by replica simulations and reweighting. , 2015, The Journal of chemical physics.

[36]  Chris H Wiggins,et al.  Learning rates and states from biophysical time series: a Bayesian approach to model selection and single-molecule FRET data. , 2009, Biophysical journal.

[37]  Ken A. Dill,et al.  Inferring Microscopic Kinetic Rates from Stationary State Distributions , 2014, Journal of chemical theory and computation.

[38]  Haw Yang,et al.  Expectation-maximization of the potential of mean force and diffusion coefficient in Langevin dynamics from single molecule FRET data photon by photon. , 2013, The journal of physical chemistry. B.

[39]  O. Krichevsky,et al.  Fluorescence correlation spectroscopy: the technique and its applications , 2002 .

[40]  Eric Vanden-Eijnden,et al.  Transition Path Theory for Markov Jump Processes , 2009, Multiscale Model. Simul..

[41]  G. Evensen Data Assimilation: The Ensemble Kalman Filter , 2006 .

[42]  K. Dill,et al.  Inferring Transition Rates of Networks from Populations in Continuous-Time Markov Processes. , 2015, Journal of chemical theory and computation.

[43]  Yasushi Sako,et al.  Variational Bayes analysis of a photon-based hidden Markov model for single-molecule FRET trajectories. , 2012, Biophysical journal.

[44]  John D Chodera,et al.  On the Use of Experimental Observations to Bias Simulated Ensembles. , 2012, Journal of chemical theory and computation.

[45]  F. Jiang,et al.  Folding of fourteen small proteins with a residue-specific force field and replica-exchange molecular dynamics. , 2014, Journal of the American Chemical Society.

[46]  Zaida Luthey-Schulten,et al.  CryoEM-based hybrid modeling approaches for structure determination. , 2018, Current opinion in microbiology.

[47]  Jane R. Allison,et al.  A method to explore protein side chain conformational variability using experimental data. , 2009, Chemphyschem : a European journal of chemical physics and physical chemistry.

[48]  G. Hummer,et al.  Optimized molecular dynamics force fields applied to the helix-coil transition of polypeptides. , 2009, The journal of physical chemistry. B.

[49]  Helmut Grubmüller,et al.  Maximum likelihood trajectories from single molecule fluorescence resonance energy transfer experiments , 2003 .

[50]  L. Rabiner,et al.  An introduction to hidden Markov models , 1986, IEEE ASSP Magazine.

[51]  Paul Robustelli,et al.  Water dispersion interactions strongly influence simulated structural properties of disordered protein states. , 2015, The journal of physical chemistry. B.

[52]  F. Noé,et al.  Complex RNA Folding Kinetics Revealed by Single-Molecule FRET and Hidden Markov Models , 2014, Journal of the American Chemical Society.

[53]  T. Ando,et al.  A high-speed atomic force microscope for studying biological macromolecules , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Gunnar F Schröder,et al.  Hybrid methods for macromolecular structure determination: experiment with expectations. , 2015, Current opinion in structural biology.

[55]  Paul Robustelli,et al.  Developing a molecular dynamics force field for both folded and disordered protein states , 2018, Proceedings of the National Academy of Sciences.

[56]  Stefano Piana,et al.  Assessing the accuracy of physical models used in protein-folding simulations: quantitative evidence from long molecular dynamics simulations. , 2014, Current opinion in structural biology.

[57]  A. Fersht,et al.  Protein Folding and Unfolding at Atomic Resolution , 2002, Cell.

[58]  Y. Sugita,et al.  Slow-Down in Diffusion in Crowded Protein Solutions Correlates with Transient Cluster Formation. , 2017, The journal of physical chemistry. B.

[59]  Hongbin Wan,et al.  A Maximum-Caliber Approach to Predicting Perturbed Folding Kinetics Due to Mutations. , 2016, Journal of chemical theory and computation.

[60]  K. Lindorff-Larsen,et al.  How robust are protein folding simulations with respect to force field parameterization? , 2011, Biophysical journal.

[61]  C. Toyoshima,et al.  Structural aspects of ion pumping by Ca2+-ATPase of sarcoplasmic reticulum. , 2008, Archives of biochemistry and biophysics.

[62]  Benoît Roux,et al.  On the statistical equivalence of restrained-ensemble simulations with the maximum entropy method. , 2013, The Journal of chemical physics.

[63]  J. Fraser,et al.  Integrative, dynamic structural biology at atomic resolution—it's about time , 2015, Nature Methods.

[64]  B. L. de Groot,et al.  CHARMM36m: an improved force field for folded and intrinsically disordered proteins , 2016, Nature Methods.

[65]  Carlo Camilloni,et al.  Molecular dynamics simulations with replica-averaged structural restraints generate structural ensembles according to the maximum entropy principle. , 2013, The Journal of chemical physics.

[66]  F. Noé,et al.  Dynamic properties of force fields. , 2015, The Journal of chemical physics.

[67]  R. Dror,et al.  Improved side-chain torsion potentials for the Amber ff99SB protein force field , 2010, Proteins.

[68]  Y Matsunaga,et al.  Refining Markov state models for conformational dynamics using ensemble-averaged data and time-series trajectories. , 2018, The Journal of chemical physics.

[69]  Massimiliano Bonomi,et al.  Principles of protein structural ensemble determination. , 2017, Current opinion in structural biology.

[70]  V. Hornak,et al.  Comparison of multiple Amber force fields and development of improved protein backbone parameters , 2006, Proteins.

[71]  Chun-Biu Li,et al.  Multiscale complex network of protein conformational fluctuations in single-molecule time series , 2008, Proceedings of the National Academy of Sciences.

[72]  John D. Chodera,et al.  Bayesian hidden Markov model analysis of single-molecule force spectroscopy: Characterizing kinetics under measurement uncertainty , 2011, 1108.1430.

[73]  Robert B Best,et al.  Effect of flexibility and cis residues in single-molecule FRET studies of polyproline , 2007, Proceedings of the National Academy of Sciences.

[74]  Nicolas L. Fawzi,et al.  Protein Phase Separation: A New Phase in Cell Biology. , 2018, Trends in cell biology.

[75]  R. Best,et al.  Balancing Force Field Protein–Lipid Interactions To Capture Transmembrane Helix–Helix Association , 2018, Journal of chemical theory and computation.