The DOI matches its Crossref record; 94 of the 98 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
The cited source says what the claim attributes to it.
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J., Impey, R., & Klein, M. (1983) · The Journal of Chemical Physics · doi:10.1063/1.445869 · Cited by 38,397 · cited 1×
“MKClconcentration(matchingtheexperimentalionicstrength). ThesystemwasthenparametrizedwiththeAMBERff99SB-ILDNforcefield [49];waterandionsweremodeledrespectivelywiththeTIP3PandJoung-Cheathamparametersets [38,37]. Thesystemthuspreparedwasfirstequilibratedfor24nsintheconstant-pressureensemble, during which it stabilized at a…”
BackgroundTefas, A., & Pitas, I. (2003) · Encyclopedia of Machine Learning · doi:10.1007/978-0-387-30164-8_665 · Cited by 29,409
Least squares quantization in PCM
Lloyd, S. (1982) · IEEE Transactions on Information Theory · doi:10.1109/tit.1982.1056489 · Cited by 15,836 · cited 1×
“thetrajectory isexploredandnrepresentativepointsofthecoordinatespaceareselectedas 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…”
MethodIndependent Component Analysis
Hyvärinen, A., Karhunen, J., & Oja, E. (2001) · Encyclopedia of Biometrics · doi:10.1007/978-0-387-73003-5_305 · Cited by 10,459 · cited 1×
“We assume that the molecular dynamics implementation is 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…”
BackgroundImproved side-chain torsion potentials for the Amber ff99SB protein force field
Lindorff-Larsen, K., Piana, S., Palmo, K., Maragakis, P., Klepeis, J., Dror, R., & Shaw, D. (2010) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.22711 · Cited by 6,056 · cited 1×
“The system was then parametrized with the AMBER ff99SB-ILDN forcefield [49]; water and ions were modeled respectively with the TIP3P and Joung-Cheatham parameter sets [38, 37].”
MethodIndependent Component Analysis
Hyvärinen, A., Karhunen, J., & Erkki Oja, A. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,003
Joung, I. S., & Cheatham, T. E. (2008) · The Journal of Physical Chemistry B · doi:10.1021/jp8001614 · Cited by 3,504
Essential dynamics of proteins
Amadei, A., Linssen, A. B. M., & Berendsen, H. J. C. (1993) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340170408 · Cited by 3,487 · cited 2×
“KID peptide analyzed below). Principal component analysis (PCA) is a frequently-used method to reducethedimensionofanorderparameterspacebyprojectingitonitslinearsubspaceofthelargestamplitude motions [4]. PCA has also been used successfully in Markov model construction [67, 65], however is suffers from the similar limit…”
BackgroundMethodA solution for the best rotation to relate two sets of vectors
Kabsch, W. (1976) · Acta Crystallographica Section A · doi:10.1107/s0567739476001873 · Cited by 3,462 · cited 2×
“A metric often used in (2) is the normalized Euclidean metric after rigid-body translation and rotation has been removed, in short the minimal RMSD (or least RMSD) metric [39].”
BackgroundMethodHow fast-folding proteins fold
Lindorff-Larsen, K., Piana, S., Dror, R. O., & Shaw, D. E. (2011) · Science · doi:10.1126/science.1208351 · Cited by 1,980 · 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].”
Background
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
- 2009, Proc. Natl. Acad. Sci. U.S.A., 106, 19011, 10.1073/pnas.0905466106 · DOI · Paper page
- 2003, Energy Landscapes
- 1909, J. Reine Angew. Math., 135, 1
- 1990, Circuits Syst., 3, 1784
- 2008, J. Phys. Chem. B, 112, 11155, 10.1021/jp801391t · DOI
Cited by
Sorted by citations
Items 501–600 of 1,004
Zhang, I., Rufa, D. A., Pulido, I., Henry, M. M., Rosen, L. E., Hauser, K., Singh, S., & Chodera, J. D. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.3c00333 · Cited by 23
Simulation of ligand dissociation kinetics from the protein kinase PYK2
Spiriti, J. M., Noé, F., & Wong, C. F. (2022) · Journal of Computational Chemistry · doi:10.1002/jcc.26991 · Cited by 14
Self-citationDjoković, N., Ružić, D., Rahnasto-Rilla, M., Srdić-Rajić, T., Lahtela-Kakkonen, M., & Nikolic, K. M. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c00241 · Cited by 13
Kraus, J., Russell, R. W., Kudryashova, E., Xu, C.-Y., Katyal, N., Perilla, J., Kudryashov, D. S., & Polenova, T. (2022) · Nature Communications · doi:10.1038/s41467-022-29595-9 · Cited by 13
Multidimensional minimum-work control of a 2D Ising model.
Louwerse, M. D., & Sivak, D. A. (2022) · Journal of Chemical Physics · doi:10.1063/5.0086079 · Cited by 13
Conformational Landscapes of Halohydrin Dehalogenases and Their Accessible Active Site Tunnels
Estévez‐Gay, M., Iglesias‐Fernández, J., & Osuna, S. (2020) · Catalysts · doi:10.3390/catal10121403 · Cited by 16
“Subsequently, the time-lagged Independent Component Analysis (t-ICA) [35], with a lag time τ set to obtain the minimum number of reduced dimensions, was applied to reduce the dimensionality of the initial MD features.”
MethodInfluentialZhang, L., Wu, S., Feng, Y., Wang, D., Jia, X., Liu, Z., Liu, J., & Wang, W. (2020) · Communications Biology · doi:10.1038/s42003-020-01149-z · Cited by 21
Hempel, T., Plattner, N., & Noé, F. (2020) · bioRxiv · doi:10.1101/2020.02.25.964353 · Cited by 13
Self-citationMorra, G., Meli, M., & Colombo, G. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00195 · Cited by 13
Fast-Folding Pathways of the Thrombin-Binding Aptamer G-Quadruplex Revealed by a Markov State Model
Bian, Y., Song, F., Cao, Z., Zhao, L., Yu, J., Guo, X., & Wang, J. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.02.021 · Cited by 13
Global Langevin model of multidimensional biomolecular dynamics
Schaudinnus, N., Lickert, B., Biswas, M., & Stock, G. (2016) · The Journal of Chemical Physics · doi:10.1063/1.4967341 · Cited by 17
Unveiling the Conformational Dynamics of the Histone Tails Using Markov State Modeling
Patel, R., & Loverde, S. M. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00196 · Cited by 13
Bonneau, K., Pasos-Trejo, A. S., Plainer, M., Sagresti, L., Venturin, J., Zaporozhets, I., Caruso, A., Rolando, E., Guljas, A., Klein, L., et al. (2026) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70064 · Cited by 9
Markov State Models Reveal How Folding Kinetics Influence Absorption Spectra of Foldamers
Wang, Y., Li, C., & Zheng, X. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00202 · Cited by 14
Deep-Learning-Assisted Enhanced Sampling for Exploring Molecular Conformational Changes
Fu, H., Liu, H., Xing, J., Zhao, T. Z., Shao, X., & Cai, W. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c05284 · Cited by 15
A variational approach to assess reaction coordinates for two-step crystallization
Finney, A. R., & Salvalaglio, M. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0139842 · Cited by 13
Morra, G., Razavi, A. M., Menon, A. K., & Khelashvili, G. (2022) · Structure · doi:10.1016/j.str.2022.05.010 · Cited by 14
Progress in deep Markov state modeling: Coarse graining and experimental data restraints
Mardt, A., & Noé, F. (2021) · The Journal of Chemical Physics · doi:10.1063/5.0064668 · Cited by 12
Self-citationTräger, S., Tamò, G., Aydin, D., Fonti, G., Audagnotto, M., & Peraro, M. D. (2020) · Bioinform. · doi:10.1093/bioinformatics/btaa742 · Cited by 12
Blind Analysis of Molecular Dynamics.
Krivov, S. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c01277 · Cited by 12
Sodium-induced population shift drives activation of thrombin
Kahler, U., Kamenik, A. S., Kraml, J., & Liedl, K. R. (2020) · Scientific Reports · doi:10.1038/s41598-020-57822-0 · Cited by 12
Lim, N., Osato, M., Warren, G., & Mobley, D. (2019) · Journal of Chemical Theory and Computation · doi:10.26434/chemrxiv.10032218.v1 · Cited by 12
Generative Stochastic Modeling of Strongly Nonlinear Flows with Non-Gaussian Statistics
Arbabi, H., & Sapsis, T. (2019) · SIAM/ASA J. Uncertain. Quantification · doi:10.1137/20m1359833 · Cited by 12
“…reproduces both the statistics and dynamical behavior, it is not computationally scalable to moderate and high-dimensional systems, and for systems like complex flows one needs to use extreme coarse-graining of the dynamics e.g. by clustering data into a few discrete representative states [31, 68].”
BackgroundYang, W., Riley, B., Lei, X.-Y., Porebski, B., Kass, I., Buckle, A., & McGowan, S. (2018) · ChemMedChem · doi:10.1002/cmdc.201800563 · Cited by 12
Computer Simulations Predict High Structural Heterogeneity of Functional State of NMDA Receptors
Sinitskiy, A. V., & Pande, V. (2017) · bioRxiv · doi:10.1101/180091 · Cited by 12
“tICA (51,52) was shown to be an efficient technique to automatically”
MethodGe, Y., Kier, B. L., Andersen, N. H., & Voelz, V. A. (2017) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.7b00132 · Cited by 11
Pan, X., & Schwartz, S. D. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b05119 · Cited by 12
High throughput molecular dynamics for drug discovery
Stanley, N., & Fabritiis, G. D. (2015) · In Silico Pharmacology · doi:10.1186/s40203-015-0007-0 · Cited by 16
“A projection method known as time-sensitive Independent Component Analysis (tICA) (Schwantes and Pande 2013; Pérez-Hernández et al 2013) was therefore incorporated into the process before clustering in order to alleviate this problem. tICA projects the data along its slowest varying coordinates,…”
MethodMcKiernan, K. A., Koster, A. K., Maduke, M., & Pande, V. S. (2020) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1007530 · Cited by 12
MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models
Kapuśniak, K., Gabellini, C., Bronstein, M. M., Tossou, P., & Giovanni, F. D. (2025) · arXiv.org · doi:10.48550/arxiv.2509.24779 · Cited by 11
“Noticeably, a large improvement is achieved on the Macrostate MAE, highlighting that M AR S-FM can better sample from rare meta-stable states of the underlying TICA space of unseen peptides.”
InfluentialSelf-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
Turri, G., Bonati, L., Zhu, K., Pontil, M., & Novelli, P. (2025) · arXiv.org · doi:10.48550/arxiv.2505.18671 · Cited by 11
“These ideas lie at the core of the celebrated Time-lagged Independent Component Analysis [17, 21, 22], and Dynamical Mode Decomposition [23, 24].”
UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules
Yu, Z., Huang, W.-B., & Liu, Y. (2025) · International Conference on Machine Learning · doi:10.48550/arxiv.2506.03157 · Cited by 11
“Here we use the relative distances between all heavy atom pairs as the projected feature to compute TIC and TIC-2D metrics.”
InfluentialCyclopamine modulates smoothened receptor activity in a binding position dependent manner
Kim, K., Bansal, P. D., & Shukla, D. (2024) · Communications Biology · doi:10.1038/s42003-024-06906-y · Cited by 17
AMARO: All Heavy-Atom Transferable Neural Network Potentials of Protein Thermodynamics
Mirarchi, A., Peláez, R. P., Simeon, G., & Fabritiis, G. D. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01239 · Cited by 8
Zhang, M., Wu, H., & Wang, Y. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00764 · Cited by 12
Flow Matching for Optimal Reaction Coordinates of Biomolecular Systems
Zhang, M., Zhang, Z., Wu, H., & Wang, Y. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01139 · Cited by 10
“Various VAC-based RC machine learning algorithms such as time-lagged independent component analysis (TICA) 8 and its variants Koopman-reweighted TICA, 18 kernel TICA, 19 state reversible VAMPnets (SRV), 7 etc. have been proposed to numerically approximate these eigenfunctions from data by seeking a…”
F3low: Frame-to-Frame Coarse-grained Molecular Dynamics with SE(3) Guided Flow Matching
Li, S.-N., Wang, Y., Li, M., Zhang, J., Shao, B., Zheng, N.-N., & Tang, J. (2024) · arXiv.org · doi:10.48550/arxiv.2405.00751 · Cited by 11
Sinha, K., Basu, I., Shah, Z., Shah, S., & Chakrabarty, S. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00294 · Cited by 15
Structural biology in the age of X-ray free-electron lasers and exascale computing
Mous, S. T., Poitevin, F., Hunter, M. S., Asthagiri, D. N., & Beck, T. L. (2024) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2024.102808 · Cited by 13
Water will Find Its Way: Transport through Narrow Tunnels in Hydrolases
Sequeiros-Borja, C., Surpeta, B., Thirunavukarasu, A., Foumthuim, C. J. D., Marchlewski, I., & Brezovsky, J. (2024) · bioRxiv · doi:10.1021/acs.jcim.4c00094 · Cited by 11
Effective data-driven collective variables for free energy calculations from metadynamics of paths
Müllender, L., Rizzi, A., Parrinello, M., Carloni, P., & Mandelli, D. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgae159 · Cited by 11
“However, learning dynamical information from the trajectory data (18, 19, 44) is another very appealing avenue, as the sampled paths carry information on the unbiased dynamics of the system.”
BackgroundLatent Representation and Simulation of Markov Processes via Time-Lagged Information BottleneckPreprint
Federici, M., Forré, P. D., Tomioka, R., & Veeling, B. S. (2023) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.2309.07200 · Cited by 9
“A prominent strategy in dynamical system representation revolves around maximizing linear autocorrelation, with application in various fields including MRI analysis (9), molecular dynamics (49), and temporal sensor activities (70).”
BackgroundDavila-Calderon, J., Li, M.-L., Penumutchu, S. R., Haddad, C., Malcolm, L. M., King, J., Hargrove, A., Brewer, G., & Tolbert, B. (2023) · bioRxiv · doi:10.1101/2023.02.20.529064 · Cited by 11
“An important aspect of the MSM process is the TICA for reducing dimensionality and k-centers clustering ( 36 – 38 ).”
MethodGαs slow conformational transition upon GTP binding and a novel Gαs regulator
Ahn, D., Provasi, D., Duc, N. M., Xu, J. Z., Salas-Estrada, L. A., Spasic, A., Yun, M. W., Kang, J., Gim, D., Lee, J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106603 · Cited by 13
“…we developed a novel integrative modeling framework that combines the notions of Bayesian inference (Rout and Sali, 2019), the MaxCal principle (Dixit and Dill, 2014; Dixit et al., 2015), MSM (Perez-Hernandez et al., 2013; Schwantes and Pande, 2013), and TPT (E and Vanden-Eijnden, 2010).”
MethodInfluentialRashidian, A., Mustonen, E.-K., Kronenberger, T., Schwab, M., Burk, O., Laufer, S. A., & Pantsar, T. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.020 · Cited by 11
“Time-lagged independent component analysis (TICA) was used for dimension reduction [72], using 10 ns as a lag time, and two dimensions.”
MethodZariquiey, F. S., Jacoby, E., Vos, A., van Vlijmen, H. V., Tresadern, G., & Harvey, J. N. (2022) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c01164 · Cited by 11
Optimal Reaction Coordinates: Variational Characterization and Sparse Computation
Bittracher, A., Mollenhauer, M., Koltai, P., & Schütte, C. (2023) · Multiscale Modeling and Simulation · doi:10.1137/21m1448367 · Cited by 17
“The TICA (time-lagged independent component analysis) method constructs RCs as those linear combinations of the original degrees of freedom with the highest autocorrelation [39, 45].”
BackgroundMethodGe, Y.-H., Zhang, S., Erdélyi, M., & Voelz, V. A. (2020) · Journal of Chemical Information and Modeling · doi:10.26434/chemrxiv.13500765 · Cited by 11
Zhang, J., & Gong, H. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00064 · Cited by 16
Capturing the Continuous Complexity of Behavior in C. elegansPreprint
Ahamed, T., Costa, A. C., & Stephens, G. (2019) · bioRxiv · doi:10.1101/827535 · Cited by 11
“[106] G.”
MethodXiao, J., & Salsbury, F. R. (2019) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp07293k · Cited by 17
Intrinsically Disordered Landscapes for Human CD4 Receptor Peptide
Joseph, J. A., & Wales, D. J. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b08371 · Cited by 13
Examining a Thermodynamic Order Parameter of Protein Folding
Chong, S., & Ham, S. (2018) · Scientific Reports · doi:10.1038/s41598-018-25406-8 · Cited by 11
Virtual screening for inhibitors of the human TSLP:TSLPR interaction
Rompaey, D. V., Verstraete, K., Peelman, F., Savvides, S. N., Augustyns, K., der Veken, P. V., & Winter, H. L. D. (2017) · Scientific Reports · doi:10.1038/s41598-017-17620-7 · Cited by 12
Kinetic Machine Learning Unravels Ligand-Directed Conformational Change of μ Opioid Receptor
Feinberg, E. N., Farimani, A., Hernández, C. X., & Pande, V. (2017) · bioRxiv · doi:10.1101/170886 · Cited by 11
Kitao, A., & Takemura, K. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2016.10.014 · Cited by 11
Force-matching Coarse-Graining without Forces
Köhler, J., Chen, Y.-Y., Krämer, A., Clementi, C., & No'e, F. (2022) · arXiv.org · doi:10.48550/arxiv.2203.11167 · Cited by 11
Elucidating Ligand-Modulated Conformational Landscape of GPCRs Using Cloud-Computing Approaches
Shukla, D., Lawrenz, M., & Pande, V. S. (2015) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2014.12.007 · Cited by 15
Morozova, T. I., García, N. A., Matsarskaia, O., Roosen-Runge, F., & Barrat, J.-L. (2022) · doi:10.1101/2022.09.25.509363 · Cited by 11
Accelerating Protein Molecular Dynamics Simulation with DeepJump
Costa, A. D. S., Ponnapati, M., Rubin, D., Smidt, T., & Jacobson, J. (2025) · arXiv.org · doi:10.48550/arxiv.2509.13294 · Cited by 10
“For that, we employ Time-lagged Independent Component Analysis (TICA) [Molgedey and Schuster, 1994, Pérez-Hernández et al., 2013] and find clusters that represent macrostates in the reduced dimensional space.”
Model Selection Using Replica Averaging with Bayesian Inference of Conformational Populations.
Raddi, R. M., Marshall, T., Ge, Y.-H., & Voelz, V. A. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5c00044 · Cited by 10
Kihn, K. C., Purdy, O., Lowe, V. M., Šlachtová, L., Smith, A. K., Shapiro, P., & Deredge, D. J. (2024) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.4c00202 · Cited by 14
Haloi, N., Karlsson, E., Delarue, M., Howard, R., & Lindahl, E. (2024) · bioRxiv · doi:10.1101/2023.11.13.566806 · Cited by 10
Haloi, N., Lidbrink, S. E., Howard, R. J., & Lindahl, E. (2024) · bioRxiv · doi:10.1101/2024.05.03.592340 · Cited by 10
“The free energy was projected on the first two time-lagged independent components (tICs), which capture the slowest transitions of the system [24].”
Meraz, V. J., Zou, Z., & Tiwary, P. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c02740 · Cited by 7
Nabi, F., Ahmad, O., Khan, A., Hassan, M. N., Hisamuddin, M., Malik, S., Chaari, A., & Khan, R. H. (2024) · Proteins: Structure, Function, and Bioinformatics · doi:10.1002/prot.26682 · Cited by 10
“59,60 It was feasible to construct models that exhibited markovian behavior, characterized by a lag time of ≥ 50, with the selection of this lag time being based on the convergence of implied timescales.”
Tian, J., Dong, X. W., Wu, T., Wen, P., Liu, X., Zhang, M., An, X., & Shi, D. (2023) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2023.128405 · Cited by 8
Computational Probing of the Folding Mechanism of Human Telomeric G-Quadruplex DNA
Kim, H., Kim, E., & Pak, Y. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c01257 · Cited by 10
Liu, K., Guo, F., Ma, Y., Yu, X., Fu, X.-Q., Li, W.-N., & Han, W. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241914471 · Cited by 10
“Next, the dimension of the space was reduced to two collective coordinates using time-lagged independent component analysis (TICA) [42].”
Caíno-Lores, S., Cuendet, M., Marquez, J. D., Kots, E., Estrada, T., Deelman, E., Weinstein, H., & Taufer, M. (2023) · Platform for Advanced Scientific Computing Conference · doi:10.1145/3592979.3593420 · Cited by 10
“These are not single observation methods but are derived from the full simulation post hoc [3, 35, 41, 11].”
MethodEnhanced Sampling with Machine Learning: A Review
Mehdi, S., Smith, Z., Herron, L., Zou, Z., & Tiwary, P. (2023) · Annual review of physical chemistry (Print) · doi:10.48550/arxiv.2306.09111 · Cited by 10
“The separation of states can be done using geometric criteria, kinetic criteria, or even by discretizing CVs (36, 37).”
BackgroundMethodMolecular Latent Space Simulators for Distributed and Multimolecular Trajectories
Jones, M. S., McDargh, Z. A., Wiewiora, R., Izaguirre, J. A., Xu, H., & Ferguson, A. L. (2023) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.3c01362 · Cited by 9
Efficient Purification of Cowpea Chlorotic Mottle Virus by a Novel Peptide Aptamer
Tscheuschner, G., Ponader, M., Raab, C., Weider, P. S., Hartfiel, R., Kaufmann, J. O., Völzke, J. L., Bosc-Bierne, G., Prinz, C., Schwaar, T., et al. (2023) · Viruses · doi:10.3390/v15030697 · Cited by 10
Tran, D. P., Taira, Y., Ogawa, T., Misu, R., Miyazawa, Y., & Kitao, A. (2021) · Scientific Reports · doi:10.1038/s41598-022-07792-2 · Cited by 10
Maximally predictive ensemble dynamics from dataPreprint
Costa, A. C., Ahamed, T., Jordan, D. J., & Stephens, G. (2021) · bioRxiv · doi:10.1101/2021.05.26.445816 · Cited by 10
“Previous work in molecular and neural dynamics [29, 92] have also implicitly expanded states in time in order to improve the accuracy of Markov models.”
BackgroundMethodZhao, L., Ouyang, Y., Li, Q., & Zhang, Z. (2019) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2019.1637784 · Cited by 11
Wang, F., Zhou, H., Wang, X., & Tao, P. (2019) · Entropy · doi:10.3390/e21111130 · Cited by 9
Beyerle, E. R., & Guenza, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5123513 · Cited by 11
Directed kinetic transition network model
Zhou, H.-Y., Wang, F., Bennett, D. I. G., & Tao, P. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5110896 · Cited by 8
Molecular Mechanisms of Macular Degeneration Associated with the Complement Factor H Y402H Mutation
Harrison, R. E. S., & Morikis, D. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.12.007 · Cited by 11
“Time-lagged independent component analysis (48) was used to decompose the SCR7 and SCR7 torsional feature sets into projections along the five slowest eigenvectors for each isoform.”
MethodSerrano‐Hervás, E., Casadevall, G., Garcia‐Borràs, M., Feixas, F., & Osuna, S. (2018) · Chemistry - A European Journal · doi:10.1002/chem.201801068 · Cited by 11
Harnessing complexity in molecular self-assembly using computer simulations
Zeng, X., Zhu, L., Zheng, X., Cecchini, M., & Huang, X. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c7cp06181a · Cited by 22
Binding Pathway of Opiates to μ-Opioid Receptors Revealed by Machine Learning
Farimani, A. B., Feinberg, E. N., & Pande, V. S. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2017.11.390 · Cited by 13
Transition Paths of Met-Enkephalin from Markov State Modeling of a Molecular Dynamics Trajectory
Banerjee, R., & Cukier, R. I. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp412130d · Cited by 10
Nucleosome condensate and linker DNA alter chromatin folding pathways and rates
Qiu, Y., Liu, S., Lin, X., Unarta, I. C., Huang, X., & Zhang, B. (2024) · doi:10.1101/2024.11.15.623891 · Cited by 10
Accelerated sampling of protein dynamics using BioEmu augmented molecular simulationPreprint
Bhakat, S., & Strauch, E.-M. (2026) · bioRxiv · doi:10.64898/2026.01.07.698041 · Cited by 9
Protein Language Model Embeddings Improve Generalization of Implicit Transfer Operators
Antoniadis, P., Pavesi, B., Olsson, S., & Winther, O. (2026) · arXiv.org · doi:10.48550/arxiv.2602.11216 · Cited by 9
“For each system, we estimate a Time-lagged Independent Component Analysis (TICA) model (P´erez-Hern´andez et al., 2013) on pairwise C α -”
Bera, P., & Mondal, J. (2025) · Chemical Science · doi:10.1039/d5sc00108k · Cited by 10
Bhattacharya, S., & Chakrabarty, S. (2025) · Biophysical Chemistry · doi:10.1016/j.bpc.2025.107389 · Cited by 10
Xie, P.-C., Qiu, Y.-R., & Weinan, E. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00729 · Cited by 9
“Some CV identification algorithms, such as time-lagged independent component analysis [36, 37] or the variational approach for Markov processes [38], can isolate slow collective motions while filtering out fast dynamics, making them potential candidates for constructing CVs with reduced memory…”
MethodDecoding the dynamics of BCL9 triazole stapled peptide
Gaikwad, V., Choudhury, A. R., & Chakrabarti, R. (2024) · Biophysical Chemistry · doi:10.1016/j.bpc.2024.107197 · Cited by 10
Wang, X., Xu, T., Yao, Y., Cheung, P. P., Gao, X., & Zhang, L. (2023) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.3c01249 · Cited by 10
Zhang, W., & Schütte, C. (2023) · PAMM · doi:10.1002/pamm.202300189 · Cited by 10
Zhang, I., Rufa, D. A., Pulido, I., Henry, M., Rosen, L. E., Hauser, K., Singh, S., & Chodera, J. (2023) · bioRxiv · doi:10.1101/2023.03.07.530278 · Cited by 9
“We emphasize that the slow convergence of charge-changing mutations only occurs in the complex (and not apo) phase (Figure 3H), indicating that introduction of a counterion to accommodate charge-changes does not significantly contribute to slow convergence.”
Höllring, K., Baer, A., Vučemilović‐Alagić, N., Smith, D. M., & Smith, A. (2023) · Journal of Colloid and Interface Science · doi:10.1016/j.jcis.2023.07.088 · Cited by 11
McGinnis, R. J., Brambley, C. A., Stamey, B., Green, W. C., Gragg, K. N., Cafferty, E. R., Terwilliger, T. C., Hammel, M., Hollis, T., Miller, J. M., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31678-6 · Cited by 13
Employing Artificial Neural Networks to Identify Reaction Coordinates and Pathways for Self-Assembly
Appeldorn, J. H., Lemcke, S., Speck, T., & Nikoubashman, A. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c02232 · Cited by 10
Koneru, J. K., Sinha, S., & Mondal, J. (2021) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2021.101271 · Cited by 12
Tyrosine kinases: complex molecular systems challenging computational methodologies
Thomas, T., & Roux, B. (2021) · The European Physical Journal B · doi:10.1140/epjb/s10051-021-00207-7 · Cited by 13
“…analysis, dimensionality reduction of the full configurational space was carried out via the time-lagged independent component analysis (TICA) [15,16], applied to pre-identified structural regions displaying high conformational variability, e.g., the A-loop, the α C-helix, the DFG motif, and the…”
MethodYang, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-64034-z · Cited by 11
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
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
