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Identification of slow molecular order parameters for Markov model construction
Open accessAbstract
A goal in the kinetic characterization of a macromolecular system is the description of its slow relaxation processes via (i) identification of the structural changes involved in these processes and (ii) estimation of the rates or timescales at which these slow processes occur. Most of the approaches to this task, including Markov models, master-equation models, and kinetic network models, start by discretizing the high-dimensional state space and then characterize relaxation processes in terms of the eigenvectors and eigenvalues of a discrete transition matrix. The practical success of such an approach depends very much on the ability to finely discretize the slow order parameters. How can this task be achieved in a high-dimensional configuration space without relying on subjective guesses of the slow order parameters? In this paper, we use the variational principle of conformation dynamics to derive an optimal way of identifying the “slow subspace” of a large set of prior order parameters – either generic internal coordinates or a user-defined set of parameters. Using a variational formulation of conformational dynamics, it is shown that an existing method—the time-lagged independent component analysis—provides the optional solution to this problem. In addition, optimal indicators—order parameters indicating the progress of the slow transitions and thus may serve as reaction coordinates—are readily identified. We demonstrate that the slow subspace is well suited to construct accurate kinetic models of two sets of molecular dynamics simulations, the 6-residue fluorescent peptide MR121-GSGSW and the 30-residue intrinsically disordered peptide kinase inducible domain (KID). The identified optimal indicators reveal the structural changes associated with the slow processes of the molecular system under analysis.
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“which was found to adopt a conformation that may precede its KIX-bound geometry even in the absence of its binding partner [21].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [21]
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“This approach can also be shown to solve the variational problem for the specific choice of basis functions identical to the input coordinates [21].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [21]
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“This approach, independently rediscovered by two groups [20, 21], linearly transforms the input coordinates (e.g. torsions, distances, contacts) into collective coordinates sorted by “slowness”, thus providing an excellent dimension reduction method for MSM construction.”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [21]
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“This can be understood as a generalization of principal component analysis, as it solves a generalized eigenvalue problem with the instantaneous and time-lagged correlation matrices of the input coordinates [20, 21].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [21]
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“This proved critical in enabling the construction of MSMs for the intrinsically disordered KID peptide”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [21]
In the citing paper
“the main drawback being the large amount of simulation data that is necessary to reliably estimate the autocorrelation function.”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [81]
In the citing paper
“The time-lagged independent component analysis method (TICA) 80,81 identifies linear combinations of features from a time-dependent trajectory that not only have the largest variance, but that also have the longest autocorrelation time”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [81]
In the citing paper
“Dimensionality reduction algorithms as TICA 80,81 or SGOOP 82 , that incorporate information on the system's dynamics, implicitly ensure that the order parameter(s) perform well as descriptors of the slowest molecular motion.”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [81]
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Citations
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MethodIndependent Component Analysis
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Kabsch, W. (1976) · Acta Crystallographica Section A · doi:10.1107/s0567739476001873 · Cited by 3,462 · cited 2×
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Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980 · cited 1×
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BackgroundAtomic-level characterization of the structural dynamics of proteins
Shaw, D. E., Maragakis, P., Lindorff-Larsen, K., Piana, S., Dror, R. O., Eastwood, M. P., Bank, J. A., Jumper, J. M., Salmon, J. K., Shan, Y., et al. (2010) · Science · doi:10.1126/science.1187409 · Cited by 1,923 · cited 1×
“…models of molecular kinetics: Generation and validation The Journal of Chemical Physics 134, 174105 (2011); 10.1063/1.3565032 Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions The Journal of Chemical Physics 134,…”
MethodInfluentialCoifman, R. R., Lafon, S., Lee, A. B., Maggioni, M., Nadler, B., Warner, F., & Zucker, S. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0500334102 · Cited by 1,675 · cited 1×
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundMarkov models of molecular kinetics: Generation and validation
Prinz, J.-H., Wu, H., Sarich, M., Keller, B., Senne, M., Held, M., Chodera, J. D., Schütte, C., & Noé, F. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3565032 · Cited by 1,275
Self-citationMechanism of coupled folding and binding of an intrinsically disordered protein
Sugase, K., Dyson, H. J., & Wright, P. E. (2007) · Nature · doi:10.1038/nature05858 · Cited by 1,071 · cited 4×
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“Identification of slow molecular order parameters for Markov model construction Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé Citation: The Journal of Chemical Physics 139, 015102 (2013); View online: https://doi.org/10.1063/1.4811489 View Table of Contents: http://aip.scita…”
BackgroundInfluentialSpeeding molecular recognition by using the folding funnel: The fly-casting mechanism
Shoemaker, B. A., Portman, J. J., & Wolynes, P. G. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.160259697 · Cited by 992 · cited 1×
“…much interest (e.g. for the druggability of protein-protein interactions) to investigate whether the presence of pre-formed elements causes folded conformations to be selected from the ensemble (conformational selection) [54], or whether the binding rather occurs through induced-fit mechanics [85].”
BackgroundACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale
Harvey, M. J., Giupponi, G., & Fabritiis, G. D. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9000685 · Cited by 916 · cited 1×
“We have performed 7706 all-atom explicit-solvent simulations of 24 ns each using the ACEMD software [29] on the GPUGRID distributed computing platform [12], yielding a total 168 μs simulation data.”
MethodSeparation of a mixture of independent signals using time delayed correlations
Molgedey, L., & Schuster, H. G. (1994) · Physical Review Letters · doi:10.1103/physrevlett.72.3634 · Cited by 882 · cited 4×
“Like PCA, TICA [55] uses a linear transform to map the original order parameters r ( t ) to a new set of order parameters z ( t ) — the independent components (ICs).”
BackgroundMethodInfluentialAnalysis of Molecular Recognition Features (MoRFs)
Mohan, A., Oldfield, C. J., Predrag Radivojac, A., Vacic, V., Cortese, M. S., Dunker, A. K., & Uversky, V. N. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.07.087 · Cited by 769
Improving efficiency of large time‐scale molecular dynamics simulations of hydrogen‐rich systems
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“All of the simulations were performed with a time step of 4 fs, enabled by the hydrogen mass repartitioning scheme [25]; long-range electrostatic forces were computed with the particle-mesh Ewald summation method.”
Über eine neue Methode zur Lösung gewisser Variationsprobleme der mathematischen Physik.
Ritz, W. (1909) · crll · doi:10.1515/crll.1909.135.1 · Cited by 753 · cited 2×
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ResultComplete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations
Buch, I., Giorgino, T., & De Fabritiis, G. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1103547108 · Cited by 655 · cited 2×
“Rare conformational transitions can be explicitly traced by either single-molecule experiments [70, 88, 17, 44] or by high-throughput molecular dynamics simulations, either realized with few long trajectories [84, 48] or with many shorter trajectories [92, 11, 67, 13, 77, 12].”
BackgroundSelf-citationChodera, J. D., Singhal, N., Pande, V. S., Dill, K. A., & Swope, W. C. (2007) · The Journal of Chemical Physics · doi:10.1063/1.2714538 · Cited by 633 · cited 2×
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundSchwantes, C. R., & Pande, V. S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300878a · Cited by 597
Molecular simulation of ab initio protein folding for a millisecond folder NTL9(1–39)
Voelz, V. A., Bowman, G., Beauchamp, K., & Pande, V. (2010) · Journal of the American Chemical Society · doi:10.1021/ja9090353 · Cited by 529 · cited 1×
“…and validation The Journal of Chemical Physics 134, 174105 (2011); 10.1063/1.3565032 Slow dynamics in protein fluctuations revealed by time-structure based independent component analysis: The case of domain motions The Journal of Chemical Physics 134, 065101 (2011);…”
MethodInfluentialRobust Perron cluster analysis in conformation dynamics
Deuflhard, P., & Weber, M. (2005) · Linear Algebra and its Applications · doi:10.1016/j.laa.2004.10.026 · Cited by 506 · cited 1×
“• The metastable states (often referred to as “free energy basins”—although we will avoid this term as it would imply the projection onto some pre-defined coordinate set), can be computed from the sign structure of the leading eigenfunctions [81, 22].”
BackgroundTransition Path Theory for Markov Jump Processes
Metzner, P., Schütte, C., & Vanden-Eijnden, E. (2009) · Multiscale Modeling & simulation · doi:10.1137/070699500 · Cited by 484 · cited 1×
“From this propagator, many properties can be calculated, such as transition pathways between two sets of configurations [51, 7, 67].”
BackgroundCoarse master equations for peptide folding dynamics
Buchete, N.-V., & Hummer, G. (2008) · The Journal of Physical Chemistry B · doi:10.1021/jp0761665 · Cited by 477 · cited 3×
“…The Journal of Chemical Physics 126, 155101 (2007); 10.1063/1.2714538 Identification of slow molecular order parameters for Markov model construction Guillermo Pérez-Hernández,1 Fabian Paul,2,a),b) Toni Giorgino,3,a) Gianni De Fabritiis,4,c) and Frank Noé1,c) 1Department of Mathematics and…”
BackgroundInfluentialIntrinsically disordered proteins from A to Z
Uversky, V. N. (2011) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/j.biocel.2011.04.001 · Cited by 457
Describing Protein Folding Kinetics by Molecular Dynamics Simulations. 1. Theory
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Transition networks for modeling the kinetics of conformational change in macromolecules.
Noé, F., & Fischer, S. (2008) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2008.01.008 · Cited by 450
Self-citationBowman, G. R., Beauchamp, K. A., Boxer, G., & Pande, V. S. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3216567 · Cited by 396 · cited 1×
“The TICA problem can easily be solved by subsequently solving two simple eigenvalue problems.”
BackgroundA Direct Approach to Conformational Dynamics Based on Hybrid Monte Carlo
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Chung, H.-S., McHale, K. J., Louis, J., & Eaton, W. (2012) · Science · doi:10.1126/science.1215768 · Cited by 392 · cited 1×
“Rare conformational transitions can be explicitly traced by either single-molecule experiments [70, 88, 17, 44] or by high-throughput molecular dynamics simulations, either realized with few long trajectories [84, 48] or with many shorter trajectories [92, 11, 67, 13, 77, 12].”
BackgroundNoé, F., Horenko, I., Schütte, C., & Smith, J. (2007) · Journal of Chemical Physics · doi:10.1063/1.2714539 · Cited by 377 · cited 3×
“Other frequently used coordinate sets are intramolecular coordinates such as dihedral angles [62] or inter-atomic distances.”
BackgroundSelf-citationThe Complex Folding Network of Single Calmodulin Molecules
Stigler, J., Ziegler, F., Gieseke, A., Gebhardt, J. C. M., & Rief, M. (2011) · Science · doi:10.1126/science.1207598 · Cited by 372 · cited 1×
“Rare conformational transitions can be explicitly traced by either single-molecule experiments [70, 88, 17, 44] or by high-throughput molecular dynamics simulations, either realized with few long trajectories [84, 48] or with many shorter trajectories [92, 11, 67, 13, 77, 12].”
BackgroundMSMBuilder2: Modeling Conformational Dynamics on the Picosecond to Millisecond Scale
Beauchamp, K. A., Bowman, G. R., Lane, T. J., Maibaum, L., Haque, I. S., & Pande, V. S. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct200463m · Cited by 372 · cited 1×
“For clustering and Markov model construction of molecular dynamics data, the packages EMMA [83], MSMbuilder [5], Wordom [82] and METAGUI [8] are currently available.”
MethodObservation of a power-law memory kernel for fluctuations within a single protein molecule.
Min, W., Luo, G., Cherayil, B., Kou, S. C., & Xie, X. S. (2005) · Physical Review Letters · doi:10.1103/physrevlett.94.198302 · Cited by 371 · cited 1×
“Conformational transitions between long-lived, or “metastable” states are essential to the function of biomolecules [27, 58, 52, 24, 78, 28, 95].”
BackgroundLigand binding and conformational motions in myoglobin
Ostermann, A., Waschipky, R., Parak, F., & Nienhaus, G. U. (2000) · Nature · doi:10.1038/35004622 · Cited by 368
Hidden complexity of free energy surfaces for peptide (protein) folding
Krivov, S. V., & Karplus, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0406234101 · Cited by 362 · cited 2×
“gle reference structure, the radius of gyration, or pre-selected distances or angles are often not good indicators of the slow eigenfunctions, and thus bear the danger of disguising the slow kinetics [45, 61, 56]. In order to avoid this, Markov model construction has focused in the last years focused on the other extr…”
BackgroundRao, F. V., & Caflisch, A. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.06.063 · Cited by 357 · cited 2×
“Kinetic models usually first partition the conformation space into discrete states [93, 61, 40, 33, 94, 14, 74, 56, 21, 80, 69].”
BackgroundA Variational Approach to Modeling Slow Processes in Stochastic Dynamical Systems
Noé, F., & Nüske, F. (2013) · Multiscale Modeling & Simulation · doi:10.1137/110858616 · Cited by 327
Self-citationAMUSE: a new blind identification algorithm
Tong, L., Soon, V., Huang, Y.-F., & Liu, R. (1990) · IEEE International Symposium on Circuits and Systems · doi:10.1109/iscas.1990.111981 · Cited by 314 · cited 2×
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“…Markovian in (i.e., the time step to xt+τ is computed based on the current value of xt only), and gives rise to a unique stationary density μ(x), usually the Boltzmann density: μ(x) = Z−1e−βH (x), where H is the Hamiltonian, Z is the partition function, and β = (kBT)−1 is the inverse temperature.”
BackgroundDetermination of reaction coordinates via locally scaled diffusion map
Rohrdanz, M. A., Zheng, W., Maggioni, M., & Clementi, C. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3569857 · Cited by 292 · cited 1×
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BackgroundPerformance guarantees for hierarchical clustering
Dasgupta, S., & Long, P. M. (2002) · Journal of computer and system sciences (Print) · doi:10.1016/j.jcss.2004.10.006 · Cited by 286 · cited 1×
“Various algorithms have been proposed in the literature, including k-means [50], k-centers [20], k-medoids [42], regular spatial clustering[83], regular temporal clustering[83] and Ward clustering[6].”
MethodBowman, G. R., & Geissler, P. L. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1209309109 · Cited by 279 · cited 1×
“The TICA problem can easily be solved by subsequently solving two simple eigenvalue problems.”
BackgroundSeeber, M., Felline, A., Raimondi, F., Muff, S., Friedman, R., Rao, F. V., Caflisch, A., & Fanelli, F. (2010) · Journal of Computational Chemistry · doi:10.1002/jcc.21688 · Cited by 255 · cited 1×
“For clustering and Markov model construction of molecular dynamics data, the packages EMMA [83], MSMbuilder [5], Wordom [82] and METAGUI [8] are currently available.”
MethodConformational transitions in DNA polymerase I revealed by single-molecule FRET
Santoso, Y., Joyce, C. M., Potapova, O., Reste, L. L., Hohlbein, J., Torella, J. P., Grindley, N. D. F., & Kapanidis, A. N. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0910909107 · Cited by 248 · cited 1×
“Conformational transitions between long-lived, or “metastable” states are essential to the function of biomolecules [27, 58, 52, 24, 78, 28, 95].”
BackgroundAltis, A., Otten, M., Nguyen, P. H., Hegger, R., & Stock, G. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2945165 · Cited by 227 · cited 1×
“of only ˝ = 2 ns [67]. Stock and co-workers have exploredthepossibilityofusingdihedralanglesasaninputtoPCA(dPCA).Asangularcoordinates, theycannotbeaveragedinthesamewaylikenonperiodiccoordinates. Ref. [3]thussuggeststouse the sine and cosine of backbone dihedral angles as input coordinates. It is found for hepta-alanin…”
BackgroundStructure-function-folding relationship in a WW domain.
Jäger, M., Zhang, Y., Bieschke, J., Nguyen, H., Dendle, M. T. A., Bowman, M. E., Noel, J., Gruebele, M., & Kelly, J. (2006) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0600511103 · Cited by 214 · cited 1×
“These rare transitions are ubiquitously found in biomolecular processes, including folding [34, 44], complex conformational rearrangements between native protein substates [26, 63], and ligand binding [68].”
BackgroundKasper, L. H., Fukuyama, T., Biesen, M. A., Boussouar, F., Tong, C., Pauw, A. D., Murray, P. J., van Deursen, J. M. A., & Brindle, P. K. (2006) · Molecular and Cellular Biology · doi:10.1128/mcb.26.3.789-809.2006 · Cited by 214
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“Kinetic models usually first partition the conformation space into discrete states [93, 61, 40, 33, 94, 14, 74, 56, 21, 80, 69].”
BackgroundFull distance-resolved folding energy landscape of one single protein molecule
Gebhardt, J., Bornschlögl, T., & Rief, M. (2010) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0909854107 · Cited by 209 · cited 1×
“pf.edu *frank.noe@fu-berlin.de 1 arXiv:1302.6614v1 [physics.chem-ph] 26 Feb 2013 1 Introduction Conformationaltransitionsbetweenlong-lived,or“metastable” statesareessentialtothefunctionof biomolecules[27,58,52,24,78,28,95]. Theseraretransitionsareubiquitouslyfoundinbiomolecularprocesses,includingfolding[34,44], comple…”
BackgroundNucleosome disassembly intermediates characterized by single-molecule FRET
Gansen, A., Valeri, A., Hauger, F., Felekyan, S., Kalinin, S., Toth, K., Langowski, J., & Seidel, C. (2009) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0903005106 · Cited by 187 · cited 1×
“Conformational transitions between long-lived, or “metastable” states are essential to the function of biomolecules [27, 58, 52, 24, 78, 28, 95].”
BackgroundHigh-Throughput All-Atom Molecular Dynamics Simulations Using Distributed Computing
Buch, I., Harvey, M., Giorgino, T., Anderson, D. P., & de Fabritiis, G. (2010) · Journal of Chemical Information and Modeling · doi:10.1021/ci900455r · Cited by 183 · cited 3×
“Rare conformational transitions can be explicitly traced by either single-molecule experiments [70, 88, 17, 44] or by high-throughput molecular dynamics simulations, either realized with few long trajectories [84, 48] or with many shorter trajectories [92, 11, 67, 13, 77, 12].”
BackgroundMethodSelf-citationOn the Approximation Quality of Markov State Models
Sarich, M., Noé, F., & Schütte, C. (2010) · Multiscale Modeling & Simulation · doi:10.1137/090764049 · Cited by 182
Self-citationSingle-molecule fluorescence spectroscopy maps the folding landscape of a large protein
Pirchi, M., Ziv, G., Riven, I., Cohen, S. S., Zohar, N., Barak, Y., & Haran, G. (2011) · Nature Communications · doi:10.1038/ncomms1504 · Cited by 179 · cited 1×
“…Published by the American Institute of Physics Articles you may be interested in Markov models of molecular kinetics: Generation and validation The Journal of Chemical Physics 134, 174105 (2011); 10.1063/1.3565032 Slow dynamics in protein fluctuations revealed by…”
BackgroundInfluentialStructural mechanism of the recovery stroke in the myosin molecular motor.
Fischer, S., Windshügel, B., Horák, D., Holmes, K., & Smith, J. C. (2005) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0408784102 · Cited by 176 · cited 1×
“These rare transitions are ubiquitously found in biomolecular processes, including folding [34, 44], complex conformational rearrangements between native protein substates [26, 63], and ligand binding [68].”
BackgroundError analysis and efficient sampling in Markovian state models for molecular dynamics.
Singhal, N., & Pande, V. (2005) · Journal of Chemical Physics · doi:10.1063/1.2116947 · Cited by 175 · cited 1×
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundReactive flux and folding pathways in network models of coarse-grained protein dynamics
Berezhkovskii, A. M., Hummer, G., & Szabó, A. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3139063 · Cited by 169 · cited 1×
“From this propagator, many properties can be calculated, such as transition pathways between two sets of configurations [51, 7, 67].”
BackgroundBuilding Markov state models along pathways to determine free energies and rates of transitions
Pan, A. C., & Roux, B. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2959573 · Cited by 163 · cited 2×
“Identification of slow molecular order parameters for Markov model construction Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé Citation: The Journal of Chemical Physics 139, 015102 (2013); View online: https://doi.org/10.1063/1.4811489 View Table of Contents: http://aip.scita…”
BackgroundInfluentialSimple few-state models reveal hidden complexity in protein folding
Beauchamp, K. A., McGibbon, R., Lin, Y.-S., & Pande, V. S. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1201810109 · Cited by 161 · cited 2×
“n many examples, especially protein folding (see [9, 72] and references therein). Recent applications include folding of MR121-GSGS-W peptide[72], folding of FiP35 WW domain, GTT, NTL9, and protein G [6] and discovery of cryptic allosteric sites in -lactamase, interleukin-2, and RNase H [10]. However, minimal RMSD ten…”
BackgroundMethodEMMA: A Software Package for Markov Model Building and Analysis
Senne, M., Trendelkamp-Schroer, B., Mey, A. S. J. S., Schütte, C., & Noé, F. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300274u · Cited by 148 · cited 2×
“inatespaceareselectedas cluster centers witha 8 clustering method. Various algorithms have been proposed in the literature, including k-means [50], k-centers[20],k-medoids[42],regularspatialclustering[83],regulartemporalclustering[83]andWard clustering[6]. The k-meansalgorithmrequiresthecoordinatespacetobeavectorspace…”
MethodInfluentialSelf-citationAtomistic folding simulations of the five helix bundle protein λ6–85
Bowman, G., Voelz, V. A., & Pande, V. (2011) · Journal of the American Chemical Society · doi:10.1021/ja106936n · Cited by 139
Probability distributions of molecular observables computed from Markov models
Noé, F. (2008) · The Journal of Chemical Physics · doi:10.1063/1.2916718 · Cited by 135 · cited 1×
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundSelf-citationCoarse Master Equation from Bayesian Analysis of Replica Molecular Dynamics Simulations
Sriraman, S., Kevrekidis, I. G., & Hummer, G. (2005) · The Journal of Physical Chemistry B · doi:10.1021/jp046448u · Cited by 127 · cited 1×
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundHinrichs, N., & Pande, V. (2007) · Journal of Chemical Physics · doi:10.1063/1.2740261 · Cited by 123
A coarse graining method for the identification of transition rates between molecular conformations.
Kube, S., & Weber, M. (2007) · Journal of Chemical Physics · doi:10.1063/1.2404953 · Cited by 123 · cited 1×
“• From the largest m Eigenvalues and their associated Eigenfunctions, a rank-m propagator can be assembled that can describe the dynamics slower than timescale t m [46].”
BackgroundExperimental evidence for a frustrated energy landscape in a 3-helix bundle protein family
Wensley, B., Batey, S., Bone, F. A. C., Chan, Z.-M., Tumelty, N. R., Steward, A., Kwa, L., Borgia, A., & Clarke, J. (2010) · Nature · doi:10.1038/nature08743 · Cited by 122 · cited 1×
“Conformational transitions between long-lived, or “metastable” states are essential to the function of biomolecules [27, 58, 52, 24, 78, 28, 95].”
Backgroundde Groot, B. L., Daura, X., Mark, A., & Grubmüller, H. (2001) · Journal of Molecular Biology · doi:10.1006/jmbi.2001.4655 · Cited by 121 · cited 1×
“…Chemical Physics 126, 155101 (2007); 10.1063/1.2714538 Identification of slow molecular order parameters for Markov model construction Guillermo Pérez-Hernández,1 Fabian Paul,2,a),b) Toni Giorgino,3,a) Gianni De Fabritiis,4,c) and Frank Noé1,c) 1Department of Mathematics and Computer Science,…”
BackgroundInfluentialNeuweiler, H., Löllmann, M., Doose, S., & Sauer, M. (2006) · Journal of Molecular Biology · doi:10.1016/j.jmb.2006.10.021 · Cited by 118 · cited 1×
“MR121-GSGSW is a well-studied fluorescent peptide that has been extensively characterized by experiments [59], simulations [19] and also Markov models [60, 65].”
BackgroundNoé, F., Doose, S., Daidone, I., Löllmann, M., Sauer, M., Chodera, J. D., & Smith, J. C. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1004646108 · Cited by 116 · cited 6×
“PCA has also been used successfully in Markov model construction [67, 65], however is suffers from the similar limitations like minimal RMSD, as there is no general guarantee that large-amplitude motions are associated with slow transitions.”
BackgroundMethodInfluentialSelf-citationThe Free Energy Landscape of Small Molecule Unbinding
Huang, D., & Caflisch, A. (2011) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1002002 · Cited by 116 · cited 1×
“The resulting models are often called transition networks [74, 62, 32], Diffusion maps [18, 76], Master equation models [87, 14], Markov models [64] or Markov state models [86, 16] (MSM), where “Markovianity” means that the kinetics are modeled by a memoryless jump process between states.”
BackgroundKinetic characterization of the critical step in HIV-1 protease maturation
Sadiq, S. K., Noé, F., & De Fabritiis, G. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1210983109 · Cited by 102 · cited 1×
“Rare conformational transitions can be explicitly traced by either single-molecule experiments [70, 88, 17, 44] or by high-throughput molecular dynamics simulations, either realized with few long trajectories [84, 48] or with many shorter trajectories [92, 11, 67, 13, 77, 12].”
BackgroundSelf-citationNoé, F., Krachtus, D., Smith, J. C., & Fischer, S. (2006) · Journal of Chemical Theory and Computation · doi:10.1021/ct050162r · Cited by 93 · cited 1×
“These rare transitions are ubiquitously found in biomolecular processes, including folding [34, 44], complex conformational rearrangements between native protein substates [26, 63], and ligand binding [68].”
BackgroundSelf-citationMETAGUI. A VMD interface for analyzing metadynamics and molecular dynamics simulations
Biarnés, X., Pietrucci, F., Marinelli, F., & Laio, A. (2012) · Computer Physics Communications · doi:10.1016/j.cpc.2011.08.020 · Cited by 92 · cited 1×
“For clustering and Markov model construction of molecular dynamics data, the packages EMMA [83], MSMbuilder [5], Wordom [82] and METAGUI [8] are currently available.”
MethodMuff, S., & Caflisch, A. (2007) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.21565 · Cited by 91 · cited 3×
“Identification of slow molecular order parameters for Markov model construction Guillermo Pérez-Hernández, Fabian Paul, Toni Giorgino, Gianni De Fabritiis, and Frank Noé Citation: The Journal of Chemical Physics 139, 015102 (2013); View online: https://doi.org/10.1063/1.4811489 View Table of Contents: http://aip.scita…”
BackgroundInfluentialUnderstanding ensemble protein folding at atomic detail
Hubner, I. A., Deeds, E., & Shakhnovich, E. (2006) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0605580103 · Cited by 82 · cited 1×
“Kinetic models usually first partition the conformation space into discrete states [93, 61, 40, 33, 94, 14, 74, 56, 21, 80, 69].”
BackgroundMg2+-dependent folding of a Diels-Alderase ribozyme probed by single-molecule FRET analysis
Kobitski, A., Nierth, A., Helm, M., Jäschke, A., & Nienhaus, G. U. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm072 · Cited by 81 · cited 2×
“Rare conformational transitions can be explicitly traced by either single-molecule experiments [70, 88, 17, 44] or by high-throughput molecular dynamics simulations, either realized with few long trajectories [84, 48] or with many shorter trajectories [92, 11, 67, 13, 77, 12].”
BackgroundMechanisms of protein–ligand association and its modulation by protein mutations
Held, M., Metzner, P., Prinz, J.-H., & Noé, F. (2011) · Biophysical Journal · doi:10.1016/j.bpj.2010.12.3699 · Cited by 77 · cited 1×
“[30, 13], the binding of a small ligand was analyzed by using the three-dimensional positions of the ligand with respect to the protein.”
BackgroundSelf-citationNeubauer, H., Gaiko, N., Berger, S., Schaffer, J., Eggeling, C., Tuma, J., Verdier, L., Seidel, C. A. M., Griesinger, C., & Volkmer, A. (2007) · Journal of the American Chemical Society · doi:10.1021/ja0722574 · Cited by 69 · cited 1×
“Conformational transitions between long-lived, or “metastable” states are essential to the function of biomolecules [27, 58, 52, 24, 78, 28, 95].”
BackgroundEstimating the Eigenvalue Error of Markov State Models
Djurdjevac, N., Sarich, M., & Schütte, C. (2012) · Multiscale Modeling & Simulation · doi:10.1137/100798910 · Cited by 63 · cited 2×
“The approximation error of all of the above quantities can be cast in terms of the approximation error of the eigenvalues and eigenfunctions [79, 23, 72, 71].”
BackgroundMarkov models and dynamical fingerprints: Unraveling the complexity of molecular kinetics
Keller, B. G., Prinz, J.-H., & Noé, F. (2012) · Chemical Physics · doi:10.1016/j.chemphys.2011.08.021 · Cited by 59 · cited 1×
“…correlation functions (e.g. fluorescence correlation, intermediate scattering function in dynamic neutron or X-ray scattering) can be computed as a sum of single-exponential relaxations with timescales computed from λ i and amplitudes from the φ i and the experimental observable [65, 43, 15].”
BackgroundSelf-citationBuchner, G. S., Murphy, R. D., Buchete, N., & Kubelka, J. (2010) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2010.09.013 · Cited by 58 · cited 1×
“The TICA problem can easily be solved by subsequently solving two simple eigenvalue problems.”
BackgroundHydrogen-Bond Driven Loop-Closure Kinetics in Unfolded Polypeptide Chains
Daidone, I., Neuweiler, H., Doose, S., Sauer, M., & Smith, J. C. (2010) · PLoS Comput. Biol. · doi:10.1371/journal.pcbi.1000645 · Cited by 48 · cited 1×
“MR121-GSGSW is a well-studied fluorescent peptide that has been extensively characterized by experiments [59], simulations [19] and also Markov models [60, 65].”
BackgroundRelaxation mode analysis of a peptide system: comparison with principal component analysis.
Mitsutake, A., Iijima, H., & Takano, H. (2011) · Journal of Chemical Physics · doi:10.1063/1.3652959 · Cited by 45 · cited 1×
“Mitsutake et al [53] used relaxation mode analysis, a related technique, to analyze the dynamics of Met-enkephalin.”
MethodExtracting Markov Models of Peptide Conformational Dynamics from Simulation Data.
Schultheis, V., Hirschberger, T., Carstens, H., & Tavan, P. (2005) · Journal of Chemical Theory and Computation · doi:10.1021/ct050020x · Cited by 44 · cited 2×
“Kinetic models usually first partition the conformation space into discrete states [93, 61, 40, 33, 94, 14, 74, 56, 21, 80, 69].”
BackgroundJain, A., Hegger, R., & Stock, G. (2010) · The Journal of Physical Chemistry Letters · doi:10.1021/jz101069e · Cited by 28 · cited 1×
“In subsequent work [35], the approach was extended to the “dihedral PCA by parts” method.”
MethodSpectral rate theory for two-state kinetics
Prinz, J.-H., Chodera, J. D., & Noé, F. (2014) · Physical Review X · doi:10.1103/physrevx.4.011020 · Cited by 21
Self-citationBerezovska, G., Prada-Gracia, D., Mostarda, S., & Rao, F. (2012) · Journal of Chemical Physics · doi:10.1063/1.4764868 · Cited by 17
Noé, F., Daidone, I., Smith, J. C., Nola, A. D., & Amadei, A. (2008) · Journal of Physical Chemistry B · doi:10.1021/jp801391t.s001 · Cited by 10 · cited 1×
“MR121-GSGSW is a well-studied fluorescent peptide that has been extensively characterized by experiments [59], simulations [19] and also Markov models [60, 65].”
BackgroundSelf-citationA variational approach to modeling slow processes in stochastic dynamical systems
Noé, F., & Nüske, F. (2012) · arXiv · doi:10.48550/arxiv.1211.7103
Self-citation
Flagged first, then by citations
4 of the 98 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
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Sorted by citations
PyEMMA 2: A Software Package for Estimation, Validation, and Analysis of Markov Models
Scherer, M. K., Trendelkamp-Schroer, B., Paul, F., Pérez‐Hernández, G., Hoffmann, M., Plattner, N., Wehmeyer, C., Prinz, J.-H., & Noé, F. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00743 · Cited by 1,160
Self-citationBoltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Noé, F., Olsson, S., Köhler, J., & Wu, H. (2019) · Science · doi:10.1126/science.aaw1147 · Cited by 863
“To promote simultaneous sampling of high-and low-probability configurations, we included an RC loss using two slow collective coordinates that have been used earlier in the analysis of this BPTI simulation (supplementary materials) ( 29 , 30 ).”
MethodSelf-citationMachine Learning for Molecular Simulation
Noé, F., Tkatchenko, A., Müller, K.-R., & Clementi, C. (2020) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-042018-052331 · Cited by 956
Self-citationMarkov state models of biomolecular conformational dynamics
Chodera, J. D., & Noé, F. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.04.002 · Cited by 776
“[21 ].”
BackgroundMethodInfluentialSelf-citationVAMPnets for deep learning of molecular kinetics
Mardt, A., Pasquali, L., Wu, H., & Noé, F. (2017) · Nature Communications · doi:10.1038/s41467-017-02388-1 · Cited by 691
Self-citation
Metadata
- Authors
- Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F.
- Affiliations
- Freie Universität Berlin (DE)
- Max Planck Institute of Colloids and Interfaces (DE)
- National Research Council (IT)
- GRIB 4 , Barcelona Biomedical Research Park (PRBB), C/Dr. Aiguader 88, 08003 Barcelona, Spain
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2013-07-03
- Volume
- 139
- Issue
- 1
- Pages
- 015102-015102
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2143592395
- Cited by
- 990
- References
- 98
- Citations by year
- 2026: 92 · 2025: 89 · 2024: 95 · 2023: 106 · 2022: 103 · 2021: 102 · 2020: 80 · 2019: 87 · 2018: 89 · 2017: 61 · 2016: 42 · 2015: 27 · 2014: 14 · 2013: 2
- FWCI
- 12.69
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Chemical Physics (publishedVersion)
- PubMed (publishedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Refubium (Universitätsbibliothek der Freien Universität Berlin) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Deutsche Forschungsgemeinschaft
- Generalitat de Catalunya
- Max-Planck-Gesellschaft
- Ministerio de Ciencia e Innovación (BIO2011/27450)
- Agència de Gestió d'Ajuts Universitaris i de Recerca
- FP7 Ideas: European Research Council (307494)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- Markov model, conformational dynamics, variational principle, time-lagged independent component analysis, reaction coordinate, master equation, peptide dynamics, intrinsically disordered proteins
- MeSH terms
- Algorithms, Kinetics, Markov Chains, Molecular Conformation, Phosphotransferases, Temperature, Protein Structure, Tertiary, Molecular Dynamics Simulation
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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