To milliseconds and beyond: challenges in the simulation of protein folding
Open accessAbstract
Quantitatively accurate all-atom molecular dynamics (MD) simulations of protein folding have long been considered a holy grail of computational biology. Due to the large system sizes and long timescales involved, such a pursuit was for many years computationally intractable. Further, sufficiently accurate forcefields needed to be developed in order to realistically model folding. This decade, however, saw the first reports of folding simulations describing kinetics on the order of milliseconds, placing many proteins firmly within reach of these methods. Progress in sampling and forcefield accuracy, however, presents a new challenge: how to turn huge MD datasets into scientific understanding. Here, we review recent progress in MD simulation techniques and show how the vast datasets generated by such techniques present new challenges for analysis. We critically discuss the state of the art, including reaction coordinate and Markov state model (MSM) methods, and provide a perspective for the future.
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The timescales relevant to biomolecular folding and function (often microseconds to seconds) are enormously long compared to the timesteps required for stable integration (generally femtoseconds).
“the timescales relevant to biomolecular folding and function (often microseconds to seconds) are enormously long compared to the timesteps required for stable integration (generally femtoseconds) [1]”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [1]
The Introduction (page unavailable) says classical simulations use femtosecond timesteps and require about 10¹² steps to reach millisecond folding timescales, supporting the large timescale gap. It also discusses microsecond folding times, but the passages do not establish that folding and function often extend to seconds. (LLM, conf=0.84)
Experimental — accuracy not yet measuredNumerous challenges have traditionally stood in the way of simulations reaching experimentally relevant timescales and gathering sufficient statistics to make definitive statements about protein folding mechanism.
“However, numerous challenges have traditionally stood in the way of simulations reaching experimentally relevant timescales and gathering sufficient statistics to make definitive statements about protein folding mechanism [1].”
From Markov state models of biomolecular conformational dynamics (2014) · cites this paper as [1]
MSMs can predict kinetic quantities on long timescales (e.g., milliseconds) using shorter MD simulations.
“MSMs can predict kinetic quantities on long timescales (e.g. milliseconds) using a set of much shorter MD simulations.395–396”
From Protein Ensembles: How Does Nature Harness Thermodynamic Fluctuations for Life? The Diverse Functional Roles of Conformational Ensembles in the Cell (2016) · cites this paper as [396]
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Cites
Scalable molecular dynamics with NAMD
Phillips, J. C., Braun, R., Wang, W., Gumbart, J., Tajkhorshid, E., Villa, E., Chipot, C., Skeel, R. D., Kalé, L., & Schulten, K. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20289 · Cited by 16,914 · see citing works
GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
Hess, B., Kutzner, C., van der Spoel, D., & Lindahl, E. R. (2008) · Journal of Chemical Theory and Computation · doi:10.1021/ct700301q · Cited by 15,934 · see citing works
The Amber biomolecular simulation programs
Case, D. A., Cheatham, T. E., Darden, T., Gohlke, H., Luo, R., Merz, K. M., Onufriev, A., Simmerling, C., Wang, B., & Woods, R. J. (2005) · Journal of Computational Chemistry · doi:10.1002/jcc.20290 · Cited by 9,535 · see citing works
Principles that Govern the Folding of Protein Chains
Anfinsen, C. B. (1973) · Science · doi:10.1126/science.181.4096.223 · Cited by 7,096 · see citing works
Replica-exchange molecular dynamics method for protein folding
Sugita, Y., & Okamoto, Y. (1999) · Chemical Physics Letters · doi:10.1016/s0009-2614(99)01123-9 · Cited by 4,499 · see citing works
Molecular dynamics simulations of biomolecules
Karplus, M., & McCammon, J. (2002) · Nature Structural Biology · doi:10.1038/nsb0902-646 · Cited by 3,401 · see citing works
From Levinthal to pathways to funnels
Dill, K. A., & Chan, H. S. (1997) · Nature Structural & Molecular Biology · doi:10.1038/nsb0197-10 · Cited by 2,293 · see citing works
Routine Microsecond Molecular Dynamics Simulations with AMBER on GPUs. 1. Generalized Born
Götz, A. W., Williamson, M. J., Xu, D., Poole, D., LeGrand, S., & Walker, R. C. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct200909j · Cited by 2,057 · see citing works
How 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 · see citing works
McCammon, J., Gelin, B., & Karplus, M. (1977) · Nature · doi:10.1038/267585a0 · Cited by 1,972 · see citing works
Atomic-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 · see citing works
Force Fields for Protein Simulations
Ponder, J. W., & Case, D. A. (2003) · Advances in protein chemistry · doi:10.1016/s0065-3233(03)66002-x · Cited by 1,914 · see citing works
Spin glasses and the statistical mechanics of protein folding.
Bryngelson, J. D., & Wolynes, P. G. (1987) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.84.21.7524 · Cited by 1,647 · see citing works
Accelerated molecular dynamics: A promising and efficient simulation method for biomolecules
Hamelberg, D., Mongan, J., & McCammon, J. A. (2004) · The Journal of Chemical Physics · doi:10.1063/1.1755656 · Cited by 1,536 · see citing works
Duan, Y., & Kollman, P. A. (1998) · Science · doi:10.1126/science.282.5389.740 · Cited by 1,341 · see citing works
Onuchic, J. N., & Wolynes, P. G. (2004) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2004.01.009 · Cited by 1,298 · see citing works
Markov 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 · see citing works
Biomolecular simulation: a computational microscope for molecular biology.
Dror, R., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. (2012) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-042910-155245 · Cited by 1,176 · see citing works
Šali, A., Shakhnovich, E. I., & Karplus, M. (1994) · Nature · doi:10.1038/369248a0 · Cited by 944 · see citing works
ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale.
Harvey, M., Giupponi, G., & de Fabritiis, G. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct9000685 · Cited by 887 · see citing works
Constructing the equilibrium ensemble of folding pathways from short off-equilibrium simulations
Noé, F., Schütte, C., Vanden-Eijnden, E., Reich, L., & Weikl, T. R. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905466106 · Cited by 820 · see citing works
Accelerating molecular modeling applications with graphics processors
Stone, J. E., Phillips, J. C., Freddolino, L., Hardy, D. J., Trabuco, L. G., & Schulten, K. J. (2007) · Journal of Computational Chemistry · doi:10.1002/jcc.20829 · Cited by 739 · see citing works
Absolute comparison of simulated and experimental protein-folding dynamics
Snow, C. D., Nguyen, H., Pande, V. S., & Gruebele, M. (2002) · Nature · doi:10.1038/nature01160 · Cited by 701 · see citing works
Self-citationEverything you wanted to know about Markov State Models but were afraid to ask
Pande, V. S., Beauchamp, K., & Bowman, G. R. (2010) · Methods · doi:10.1016/j.ymeth.2010.06.002 · Cited by 698 · see citing works
Self-citationTransition-path theory and path-finding algorithms for the study of rare events.
E, W., & Vanden-Eijnden, E. (2010) · Annual review of physical chemistry (Print) · doi:10.1146/annurev.physchem.040808.090412 · Cited by 682 · see citing works
Systematic Validation of Protein Force Fields against Experimental Data
Lindorff‐Larsen, K., Maragakis, P., Piana, S., Eastwood, M. P., Dror, R. O., & Shaw, D. E. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032131 · Cited by 648 · see citing works
Computational solutions to large‐scale data management and analysis
Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2857 · Cited by 631 · see citing works
A Bias-Exchange Approach to Protein Folding
Piana, S., & Laio, A. (2007) · The Journal of Physical Chemistry B · doi:10.1021/jp067873l · Cited by 600 · see citing works
Anton, a special-purpose machine for molecular dynamics simulation
Shaw, D., Deneroff, M. M., Dror, R., Kuskin, J., Larson, R. H., Salmon, J., Young, C., Batson, B., Bowers, K., Chao, J. C., et al. (2008) · CACM · doi:10.1145/1364782.1364802 · Cited by 588 · see citing works
Screen Savers of the World Unite!
Shirts, M., & Pande, V. (2000) · Science · doi:10.1126/science.290.5498.1903 · Cited by 550 · see citing works
Self-citationMolecular 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 · see citing works
Self-citationAccelerating molecular dynamic simulation on graphics processing units
Friedrichs, M. S., Eastman, P. K., Vaidyanathan, V., Houston, M., LeGrand, S., Beberg, A. L., Ensign, D. L., Bruns, C. M., & Pande, V. S. (2009) · Journal of Computational Chemistry · doi:10.1002/jcc.21209 · Cited by 529 · see citing works
Self-citationReaction coordinates and rates from transition paths
Best, R. B., & Hummer, G. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0408098102 · Cited by 512 · see citing works
On the transition coordinate for protein folding
Du, R., Pande, V. S., Grosberg, A. Y., Tanaka, T., & Shakhnovich, E. I. (1998) · The Journal of Chemical Physics · doi:10.1063/1.475393 · Cited by 486 · see citing works
Self-citationCoarse 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 · see citing works
Are Current Molecular Dynamics Force Fields too Helical?
Best, R. B., Buchete, N., & Hummer, G. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.132696 · Cited by 454 · see citing works
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 · see citing works
From transition paths to transition states and rate coefficients
Hummer, G. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1630572 · Cited by 417 · see citing works
GPU-accelerated molecular modeling coming of age
Stone, J. E., Hardy, D. J., Ufimtsev, I. S., & Schulten, K. J. (2010) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2010.06.010 · Cited by 411 · see citing works
An Implementation of the Smooth Particle Mesh Ewald Method on GPU Hardware
Harvey, M. J., & Fabritiis, G. D. (2009) · Journal of Chemical Theory and Computation · doi:10.1021/ct900275y · Cited by 411 · see citing works
Bowman, G. R., Beauchamp, K. A., Boxer, G., & Pande, V. S. (2009) · The Journal of Chemical Physics · doi:10.1063/1.3216567 · Cited by 396 · see citing works
Self-citationAre Protein Force Fields Getting Better? A Systematic Benchmark on 524 Diverse NMR Measurements
Beauchamp, K., Lin, Y.-S., Das, R., & Pande, V. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct2007814 · Cited by 394 · see citing works
Self-citationSingle molecule fluorescence experiments determine protein folding transition path times
Chung, H.-S., McHale, K. J., Louis, J., & Eaton, W. (2012) · Science · doi:10.1126/science.1215768 · Cited by 392 · see citing works
MSMBuilder2: 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 · see citing works
Self-citationPande, V. S., Baker, I., Chapman, J., Elmer, S. P., Khaliq, S., Larson, S. M., Rhee, Y. M., Shirts, M. R., Snow, C. D., Sorin, E. J., et al. (2002) · Biopolymers · doi:10.1002/bip.10219 · Cited by 370 · see citing works
Self-citationPlaxco, K. W., Simons, K. T., Ruczinski, I., & Baker, D. A. (2000) · Biochemistry · doi:10.1021/bi000200n · Cited by 365 · see citing works
Rao, F. V., & Caflisch, A. (2004) · Journal of Molecular Biology · doi:10.1016/j.jmb.2004.06.063 · Cited by 358 · see citing works
Ten-Microsecond Molecular Dynamics Simulation of a Fast-Folding WW Domain
Freddolino, L., Liu, F., Gruebele, M., & Schulten, K. J. (2008) · Biophysical Journal · doi:10.1529/biophysj.108.131565 · Cited by 336 · see citing works
The nature of protein folding pathways: The classical versus the new view
Baldwin, R. L. (1995) · Journal of Biomolecular NMR · doi:10.1007/bf00208801 · Cited by 307 · see citing works
Using generalized ensemble simulations and Markov state models to identify conformational states
Bowman, G. R., Huang, X., & Pande, V. S. (2009) · Methods · doi:10.1016/j.ymeth.2009.04.013 · Cited by 304 · see citing works
Self-citationCoordinate-dependent diffusion in protein folding
Best, R., & Hummer, G. (2009) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.0910390107 · Cited by 292 · see citing works
Žagrović, B., Snow, C. D., Shirts, M. R., & Pande, V. S. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00997-x · Cited by 286 · see citing works
Self-citationKollman, P. A. (1996) · Accounts of Chemical Research · doi:10.1021/ar9500675 · Cited by 264 · see citing works
Enhanced Modeling via Network Theory: Adaptive Sampling of Markov State Models
Bowman, G. R., Ensign, D. L., & Pande, V. S. (2010) · Journal of Chemical Theory and Computation · doi:10.1021/ct900620b · Cited by 245 · see citing works
Self-citationThe Trp Cage: Folding Kinetics and Unfolded State Topology via Molecular Dynamics Simulations
Snow, C. D., Žagrović, B., & Pande, V. S. (2002) · Journal of the American Chemical Society · doi:10.1021/ja028604l · Cited by 237 · see citing works
Self-citationMarkov state models based on milestoning
Schütte, C., Noé, F., Lu, J., Sarich, M., & Vanden-Eijnden, E. (2011) · The Journal of Chemical Physics · doi:10.1063/1.3590108 · Cited by 228 · see citing works
OpenMM: A Hardware Independent Framework for Molecular Simulations
Eastman, P., & Pande, V. (2010) · Computing in science & engineering (Print) · doi:10.1109/mcse.2010.27 · Cited by 222 · see citing works
Self-citationProtein folded states are kinetic hubs
Bowman, G., & Pande, V. (2010) · Proceedings of the National Academy of Sciences of the United States of America · doi:10.1073/pnas.1003962107 · Cited by 216 · see citing works
Self-citationProtein folding and misfolding: mechanism and principles
Englander, S. W., Mayne, L. C., & Mallela, K. M. G. (2007) · Quarterly Reviews of Biophysics · doi:10.1017/s0033583508004654 · Cited by 209 · see citing works
Force Field Bias in Protein Folding Simulations
Freddolino, L., Park, S., Roux, B., & Schulten, K. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.02.033 · Cited by 197 · see citing works
Simulations of the role of water in the protein-folding mechanism
Rhee, Y. M., Sorin, E. J., Jayachandran, G., Lindahl, E. R., & Pande, V. S. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307898101 · Cited by 196 · see citing works
Self-citationTrp zipper folding kinetics by molecular dynamics and temperature-jump spectroscopy
Snow, C. D., Qiu, L., Du, D., Gai, F., Hagen, S. J., & Pande, V. S. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0305260101 · Cited by 184 · see citing works
Self-citationError 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 · see citing works
Self-citationEnsign, D. L., Kasson, P., & Pande, V. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.09.069 · Cited by 173 · see citing works
Self-citationMarkov State Model Reveals Folding and Functional Dynamics in Ultra-Long MD Trajectories
Lane, T., Bowman, G., Beauchamp, K., Voelz, V. A., & Pande, V. (2011) · Journal of the American Chemical Society · doi:10.1021/ja207470h · Cited by 171 · see citing works
Self-citationNerenberg, P. S., & Head-Gordon, T. L. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct2000183 · Cited by 162 · see citing works
Simple 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 · see citing works
Self-citationEMMA: 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 · see citing works
Common Structural Transitions in Explicit-Solvent Simulations of Villin Headpiece Folding
Freddolino, L., & Schulten, K. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.08.012 · Cited by 142 · see citing works
Voelz, V. A., Jäger, M., Yao, S., Chen, Y., Zhu, L., Waldauer, S. A., Bowman, G. R., Friedrichs, M., Bakajin, O., Lapidus, L. J., et al. (2012) · Journal of the American Chemical Society · doi:10.1021/ja302528z · Cited by 140 · see citing works
Self-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 · see citing works
Self-citationPrinz, J.-H., Keller, B., & Noé, F. (2011) · Physical Chemistry, Chemical Physics - PCCP · doi:10.1039/c1cp21258c · Cited by 122 · see citing works
Desmond Performance on a Cluster of Multicore Processors
Chow, E., Rendleman, C. A., Bowers, K. J., Dror, R. O., Gullingsrud, J., Sacerdoti, F. D., & Shaw, D. E. (2008) · Cited by 122 · see citing works
Noé, 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 · see citing works
Paschek, D., Nymeyer, H., & Garcı́a, A. E. (2006) · Journal of Structural Biology · doi:10.1016/j.jsb.2006.10.031 · Cited by 113 · see citing works
Computational Design and Experimental Testing of the Fastest-Folding β-Sheet Protein
Piana, S., Sarkar, K., Lindorff‐Larsen, K., Guo, M., Gruebele, M., & Shaw, D. E. (2010) · Journal of Molecular Biology · doi:10.1016/j.jmb.2010.10.023 · Cited by 110 · see citing works
Modeling Conformational Ensembles of Slow Functional Motions in Pin1-WW
Morcos, F., Chatterjee, S., McClendon, C. L., Brenner, P. R., López-Rendón, R., Zintsmaster, J. S., Ercsey-Ravasz, M., Sweet, C. R., Jacobson, M. P., Peng, J. W., et al. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1001015 · Cited by 89 · see citing works
Unfolded-State Dynamics and Structure of Protein L Characterized by Simulation and Experiment
Voelz, V. A., Singh, V. R., Wedemeyer, W. J., Lapidus, L. J., & Pande, V. S. (2010) · Journal of the American Chemical Society · doi:10.1021/ja908369h · Cited by 85 · see citing works
Self-citationDiffusion models of protein folding
Best, R. B., & Hummer, G. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21541h · Cited by 80 · see citing works
The Ultimate Speed Limit to Protein Folding Is Conformational Searching
Ghosh, K., Ozkan, S. B., & Dill, K. A. (2007) · Journal of the American Chemical Society · doi:10.1021/ja066785b · Cited by 74 · see citing works
Characterization and Rapid Sampling of Protein Folding Markov State Model Topologies
Weber, J. K., & Pande, V. S. (2011) · Journal of Chemical Theory and Computation · doi:10.1021/ct2004484 · Cited by 68 · see citing works
Self-citationEnsign, D. L., & Pande, V. S. (2009) · Biophysical Journal · doi:10.1016/j.bpj.2009.01.024 · Cited by 67 · see citing works
Self-citationBeauchamp, K. A., Ensign, D. L., Das, R., & Pande, V. S. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1010880108 · Cited by 66 · see citing works
Self-citationThe Free Energy Landscape Analysis of Protein (FIP35) Folding Dynamics
Krivov, S. V. (2011) · The Journal of Physical Chemistry B · doi:10.1021/jp208585r · Cited by 65 · see citing works
Splitting Probabilities as a Test of Reaction Coordinate Choice in Single-Molecule Experiments
Chodera, J. D., & Pande, V. S. (2011) · Physical Review Letters · doi:10.1103/physrevlett.107.098102 · Cited by 52 · see citing works
Self-citationβ-Sheet folding mechanisms from perturbation energetics
Deechongkit, S., NGUYEN, H., JAGER, M., Powers, E. T., Gruebele, M., & Kelly, J. W. (2006) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2006.01.014 · Cited by 38 · see citing works
Structural Characterization of λ-Repressor Folding from All-Atom Molecular Dynamics Simulations
Liu, Y., Strümpfer, J., Freddolino, L., Gruebele, M., & Schulten, K. J. (2012) · The Journal of Physical Chemistry Letters · doi:10.1021/jz300017c · Cited by 37 · see citing works
The Fast and the Slow: Folding and Trapping of λ6–85
Prigozhin, M. B., & Gruebele, M. (2011) · Journal of the American Chemical Society · doi:10.1021/ja209073z · Cited by 25 · see citing works
Immersive Out-of-Core Visualization of Large-Size and Long-Timescale Molecular Dynamics Trajectories
Stone, J. E., Vandivort, K. L., & Schulten, K. J. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-24031-7_1 · Cited by 19 · see citing works
Sosnick, T. R., & Hinshaw, J. R. (2011) · Science · doi:10.1126/science.1214018 · Cited by 18 · see citing works
Probing biomolecular machines with graphics processors
Phillips, J. C., & Stone, J. E. (2009) · Communications of the ACM · doi:10.1145/1562764.1562780 · Cited by 18 · see citing works
A Simple Model Predicts Experimental Folding Rates and a Hub-Like Topology
Lane, T. J., & Pande, V. S. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp212332c · Cited by 13 · see citing works
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Allen, M. P., & Tildesley, D. J. (2017) · doi:10.1093/oso/9780198803195.001.0001 · Cited by 20,945 · see citing works
MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories
McGibbon, R. T., Beauchamp, K. A., Harrigan, M. P., Klein, C., Swails, J., Hernández, C. X., Schwantes, C. R., Wang, L., Lane, T. J., & Pande, V. S. (2015) · Biophysical Journal · doi:10.1016/j.bpj.2015.08.015 · Cited by 2,400 · see citing works
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Vivo, M. D., Masetti, M., Bottegoni, G., & Cavalli, A. (2016) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5b01684 · Cited by 1,330 · see citing works
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Chodera, J. D., & Noé, F. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2014.04.002 · Cited by 776 · see citing works
Gaussian Accelerated Molecular Dynamics: Unconstrained Enhanced Sampling and Free Energy Calculation
Miao, Y., Feher, V. A., & McCammon, J. A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00436 · Cited by 845 · see citing works
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Best, R. B., Hummer, G., & Eaton, W. A. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311599110 · Cited by 596 · see citing works
Molecular dynamics simulations and novel drug discovery
Liu, X., Shi, D., Zhou, S., Liu, H., Liu, H., & Yao, X. (2017) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2018.1403419 · Cited by 520 · see citing works
Piana, S., Klepeis, J. L., & Shaw, D. E. (2014) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2013.12.006 · Cited by 513 · see citing works
Wei, G., Xi, W., Nussinov, R., & Ma, B. (2016) · Chemical Reviews · doi:10.1021/acs.chemrev.5b00562 · Cited by 436 · see citing works
Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways
Kohlhoff, K., Shukla, D., Lawrenz, M., Bowman, G. R., Konerding, D. E., Belov, D., Altman, R. B., & Pande, V. S. (2013) · Nature Chemistry · doi:10.1038/nchem.1821 · Cited by 417 · see citing works
Self-citationA brief history of bioinformatics
Gauthier, J., Vincent, A. T., Charette, S. J., & Derôme, N. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bby063 · Cited by 388 · see citing works
Scalable emulation of protein equilibrium ensembles with generative deep learning
Lewis, S., Hempel, T., Jiménez-Luna, J., Gastegger, M., Xie, Y., Foong, A. Y. K., Satorras, V. G., Abdin, O., Veeling, B. S., Zaporozhets, I., et al. (2025) · Science · doi:10.1126/science.adv9817 · Cited by 335 · see citing works
Activation pathway of Src kinase reveals intermediate states as targets for drug design
Shukla, D., Meng, Y., Roux, B., & Pande, V. S. (2014) · Nature Communications · doi:10.1038/ncomms4397 · Cited by 333 · see citing works
Self-citationGaussian accelerated molecular dynamics: Principles and applications
Wang, J., Arantes, P. R., Bhattarai, A., Hsu, R. V., Pawnikar, S. P., Huang, Y. M., Palermo, G., & Miao, Y. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1521 · Cited by 298 · see citing works
RNA force field with accuracy comparable to state-of-the-art protein force fields
Tan, D., Piana, S., Dirks, R. M., & Shaw, D. E. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1713027115 · Cited by 280 · see citing works
Chemical Kinetics and Mechanisms of Complex Systems: A Perspective on Recent Theoretical Advances
Klippenstein, S. J., Pande, V. S., & Truhlar, D. G. (2013) · Journal of the American Chemical Society · doi:10.1021/ja408723a · Cited by 264 · see citing works
Self-citationMarkov State Models Provide Insights into Dynamic Modulation of Protein Function
Shukla, D., Hernández, C. X., Weber, J. K., & Pande, V. S. (2015) · Accounts of Chemical Research · doi:10.1021/ar5002999 · Cited by 257 · see citing works
Self-citationReaction Coordinates and Mechanistic Hypothesis Tests.
Peters, B. (2016) · Annual review of physical chemistry (Print) · doi:10.1146/annurev-physchem-040215-112215 · Cited by 184 · see citing works
Boldon, L., Laliberte, F., & Liu, E. L. (2015) · Nano Reviews · doi:10.3402/nano.v6.25661 · Cited by 203 · see citing works
Coarse graining molecular dynamics with graph neural networks
Husic, B. E., Charron, N. E., Lemm, D., Wang, J., Pérez, A., Majewski, M., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G. D., et al. (2020) · The Journal of Chemical Physics · doi:10.1063/5.0026133 · Cited by 206 · see citing works
Dynamic Docking: A Paradigm Shift in Computational Drug Discovery
Gioia, D., Bertazzo, M., Recanatini, M., Masetti, M., & Cavalli, A. (2017) · Molecules · doi:10.3390/molecules22112029 · Cited by 200 · see citing works
Parallelization of CPPTRAJ enables large scale analysis of molecular dynamics trajectory data
Roe, D. R., & Cheatham, T. E. (2018) · Journal of Computational Chemistry · doi:10.1002/jcc.25382 · Cited by 197 · see citing works
Stepwise protein folding at near amino acid resolution by hydrogen exchange and mass spectrometry
Hu, W., Walters, B. T., Kan, Z.-Y., Mayne, L. C., Rosen, L. E., Marqusee, S., & Englander, S. W. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1305887110 · Cited by 172 · see citing works
Accelerated molecular dynamics simulations of protein folding
Miao, Y., Feixas, F., Eun, C., & McCammon, J. A. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23964 · Cited by 202 · see citing works
Computational ‘microscopy’ of cellular membranes
Ingólfsson, H. I., Arnarez, C., Périole, X., & Marrink, . J. (2016) · Journal of Cell Science · doi:10.1242/jcs.176040 · Cited by 157 · see citing works
Modeling Molecular Kinetics with tICA and the Kernel Trick
Schwantes, C. R., & Pande, V. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/ct5007357 · Cited by 157 · see citing works
Self-citationCoarse graining molecular dynamics with graph neural networks
Husic, B. E., Charron, N. E., Lemm, D., Wang, J., Pérez, A., Majewski, M., Krämer, A., Chen, Y., Olsson, S., Fabritiis, G. D., et al. (2020) · Refubium (Universitätsbibliothek der Freien Universität Berlin) · doi:10.17169/refubium-29706 · Cited by 151 · see citing works
Gaussian Accelerated Molecular Dynamics: Theory, Implementation, and Applications
Miao, Y., & McCammon, J. A. (2017) · Annual reports in computational chemistry · doi:10.1016/bs.arcc.2017.06.005 · Cited by 145 · see citing works
Wang, W., Cao, S.-Q., Zhu, L.-Z., & Huang, X.-H. (2018) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1343 · Cited by 137 · see citing works
The case for defined protein folding pathways
Englander, S. W., & Mayne, L. C. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1706196114 · Cited by 138 · see citing works
Naritomi, Y., & Fuchigami, S. (2013) · Journal of Chemical Physics · doi:10.1063/1.4834695 · Cited by 109 · see citing works
Finding Our Way in the Dark Proteome
Bhowmick, A., Brookes, D. H., Yost, S. R., Dyson, H. J., Forman-Kay, J. D., Gunter, D., Head-Gordon, M., Hura, G. L., Pande, V. S., Wemmer, D. E., et al. (2016) · Journal of the American Chemical Society · doi:10.1021/jacs.6b06543 · Cited by 128 · see citing works
Self-citationShamsi, Z., Cheng, K. J., & Shukla, D. (2018) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.8b06521 · Cited by 123 · see citing works
Self-citationProtein folding transition path times from single molecule FRET
Chung, H. S., & Eaton, W. A. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.10.007 · Cited by 122 · see citing works
Residue-Specific Force Field Based on Protein Coil Library. RSFF2: Modification of AMBER ff99SB
Zhou, C., Jiang, F., & Wu, Y. (2014) · The Journal of Physical Chemistry B · doi:10.1021/jp5064676 · Cited by 120 · see citing works
How do metal ions direct ribozyme folding?
Denesyuk, N. A., & Thirumalai, D. (2015) · Nature Chemistry · doi:10.1038/nchem.2330 · Cited by 117 · see citing works
Šponer, J., Bussi, G., Stadlbauer, P., Kührová, P., Banáš, P., Islam, B., Haider, S. M., Neidle, S., & Otyepka, M. (2016) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2016.12.008 · Cited by 117 · see citing works
Biomolecular modeling thrives in the age of technology
Tamar Schlick, A., & Portillo‐Ledesma, S. (2021) · Nature Computational Science · doi:10.1038/s43588-021-00060-9 · Cited by 110 · see citing works
Amyloid aggregation simulations: challenges, advances and perspectives
Strodel, B. (2020) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2020.10.019 · Cited by 109 · see citing works
Global optimization of parameters in the reactive force field ReaxFF for SiOH
Larsson, H. R., van Duin, A. C. T., & Hartke, B. (2013) · Journal of Computational Chemistry · doi:10.1002/jcc.23382 · Cited by 103 · see citing works
Calero‐Rubio, C., Saluja, A., & Roberts, C. J. (2016) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.6b04907 · Cited by 95 · see citing works
Da, L., Pardo‐Ávila, F., Xu, L., Silva, D., Zhang, L., Gao, X., Wang, D., & Huang, X. (2016) · Nature Communications · doi:10.1038/ncomms11244 · Cited by 91 · see citing works
Identification of simple reaction coordinates from complex dynamics
McGibbon, R. T., Husic, B. E., & Pande, V. S. (2017) · The Journal of Chemical Physics · doi:10.1063/1.4974306 · Cited by 90 · see citing works
Self-citationNguyen, T. H. H., Pérez, A., Bermeo, S., & Simmerling, C. L. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00271 · Cited by 91 · see citing works
Perspective: Markov models for long-timescale biomolecular dynamics.
Schwantes, C. R., McGibbon, R., & Pande, V. (2014) · Journal of Chemical Physics · doi:10.1063/1.4895044 · Cited by 88 · see citing works
Self-citationAppadurai, R., Nagesh, J., & Srivastava, A. K. (2021) · Nature Communications · doi:10.1038/s41467-021-21105-7 · Cited by 87 · see citing works
Statistical Mechanics of the Denatured State of a Protein Using Replica-Averaged Metadynamics
Camilloni, C., & Vendruscolo, M. (2014) · Journal of the American Chemical Society · doi:10.1021/ja5027584 · Cited by 77 · see citing works
Computational protein structure refinement: almost there, yet still so far to go
Feig, M. (2017) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1307 · Cited by 83 · see citing works
Pradeepkiran, J. A., & Reddy, P. H. (2019) · Cells · doi:10.3390/cells8030260 · Cited by 82 · see citing works
Knotted proteins: A tangled tale of Structural Biology
Faísca, P. F. N. (2015) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2015.08.003 · Cited by 82 · see citing works
Whole-Cell Models and Simulations in Molecular Detail
Feig, M., & Sugita, Y. (2019) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev-cellbio-100617-062542 · Cited by 77 · see citing works
Predicting Biomolecular Binding Kinetics: A Review
Wang, J., Do, H. N., Koirala, K., & Miao, Y. (2023) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c01085 · Cited by 77 · see citing works
Žoldák, G., Stigler, J., Pelz, B., Li, H., & Rief, M. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1311495110 · Cited by 76 · see citing works
Molecular Dynamics Simulation in Drug Discovery: Opportunities and Challenges
Shukla, R., & Tripathi, T. (2021) · Springer eBooks · doi:10.1007/978-981-15-8936-2_12 · Cited by 77 · see citing works
Energy Landscapes for Proteins: From Single Funnels to Multifunctional Systems
Röder, K., Joseph, J. A., Husic, B. E., & Wales, D. J. (2019) · Advanced Theory and Simulations · doi:10.1002/adts.201800175 · Cited by 74 · see citing works
Benchmarking all-atom simulations using hydrogen exchange
Skinner, J. J., Yu, W., Gichana, E., Baxa, M. C., Hinshaw, J. R., Freed, K. F., & Sosnick, T. R. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1404213111 · Cited by 73 · see citing works
Transition path theory analysis of c-Src kinase activation
Meng, Y., Shukla, D., Pande, V. S., & Roux, B. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602790113 · Cited by 73 · see citing works
Self-citationTime Integrators for Molecular Dynamics
Bou-Rabee, N. M. (2013) · Entropy · doi:10.3390/e16010138 · Cited by 72 · see citing works
Jiang, F., & Wu, Y. (2014) · Journal of the American Chemical Society · doi:10.1021/ja502735c · Cited by 72 · see citing works
Recent advances in QM/MM free energy calculations using reference potentials
Duarte, F., Amrein, B. A., Blaha-Nelson, D., & Kamerlin, S. C. L. (2014) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2014.07.008 · Cited by 70 · see citing works
Frequency adaptive metadynamics for the calculation of rare-event kinetics
Wang, Y., Valsson, Ó., Tiwary, P., Parrinello, M., & Lindorff‐Larsen, K. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5024679 · Cited by 69 · see citing works
Towards simple kinetic models of functional dynamics for a kinase subfamily
Sultan, M. M., Kiss, G., & Pande, V. S. (2018) · Nature Chemistry · doi:10.1038/s41557-018-0077-9 · Cited by 68 · see citing works
Self-citationLiu, S.-C., Ying, Y., Li, W., Wan, Y., & Long, Y. (2021) · Chemical Science · doi:10.1039/d0sc06106a · Cited by 67 · see citing works
How to understand atomistic molecular dynamics simulations of RNA and protein– RNA complexes?
Šponer, J., Krepl, M., Banáš, P., Kührová, P., Zgarbová, M., Jurečka, P., Havrila, M., & Otyepka, M. (2016) · Wiley Interdisciplinary Reviews - RNA · doi:10.1002/wrna.1405 · Cited by 67 · see citing works
Gu, Y., Li, D., & Brüschweiler, R. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500181v · Cited by 65 · see citing works
Folding@home: Achievements from over 20 years of citizen science herald the exascale era
Voelz, V. A., Pande, V. S., & Bowman, G. R. (2023) · Biophysical Journal · doi:10.1016/j.bpj.2023.03.028 · Cited by 63 · see citing works
Self-citationOne bead per residue can describe all-atom protein structures
Heo, L., & Feig, M. (2023) · Structure · doi:10.1016/j.str.2023.10.013 · Cited by 70 · see citing works
Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning
Kleiman, D. E., Nadeem, H., & Shukla, D. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c04843 · Cited by 62 · see citing works
Self-citationA General Mechanism for the Propagation of Mutational Effects in Proteins
Rajasekaran, N., Suresh, S., Gopi, S., Raman, K., & Naganathan, A. N. (2016) · Biochemistry · doi:10.1021/acs.biochem.6b00798 · Cited by 62 · see citing works
Cooperative Changes in Solvent Exposure Identify Cryptic Pockets, Switches, and Allosteric Coupling.
Porter, J., Moeder, K. E., Sibbald, C., Zimmerman, M. I., Hart, K., Greenberg, M. J., & Bowman, G. (2019) · Biophysical Journal · doi:10.1016/j.bpj.2018.11.3144 · Cited by 68 · see citing works
Do, H. N., Wang, J., Bhattarai, A., & Miao, Y. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01055 · Cited by 60 · see citing works
Challenges in computational studies of enzyme structure, function and dynamics
Carvalho, A. T. P., Barrozo, A. H., Doron, D., Vardi‐Kilshtain, A., Major, D. T., & Kamerlin, S. C. L. (2014) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2014.09.003 · Cited by 60 · see citing works
Conformational heterogeneity of the calmodulin binding interface
Shukla, D., Peck, A., & Pande, V. (2016) · Nature Communications · doi:10.1038/ncomms10910 · Cited by 59 · see citing works
Self-citationAdvanced Methods for Accessing Protein Shape-Shifting Present New Therapeutic Opportunities
Knoverek, C. R., Amarasinghe, G. K., & Bowman, G. R. (2018) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2018.11.007 · Cited by 55 · see citing works
Rakers, C., Bermúdez, M., Keller, B. G., Mortier, J., & Wolber, G. (2015) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1222 · Cited by 55 · see citing works
Enspara : Modeling molecular ensembles with scalable data structures and parallel computing
Porter, J. R., Zimmerman, M. I., & Bowman, G. R. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5063794 · Cited by 55 · see citing works
Common Features of Extraordinary Rate Theories
Peters, B. (2015) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5b02547 · Cited by 54 · see citing works
Stadlbauer, P., Mazzanti, L., Cragnolini, T., Wales, D. J., Derreumaux, P., Pasquali, S., & Šponer, J. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00667 · Cited by 54 · see citing works
Looking at the Disordered Proteins through the Computational Microscope
Das, P., Matysiak, S., & Mittal, J. (2018) · ACS Central Science · doi:10.1021/acscentsci.7b00626 · Cited by 53 · see citing works
Schubert, F., Rossi, M., Baldauf, C., Pagel, K., Warnke, S., von Helden, G., Filsinger, F., Kupser, P., Meijer, G., Salwiczek, M., et al. (2015) · Physical Chemistry Chemical Physics · doi:10.1039/c4cp05541a · Cited by 53 · see citing works
Benchmark assessment of molecular geometries and energies from small molecule force fields
Lim, V. T., Hahn, D. F., Tresadern, G., Bayly, C. I., & Mobley, D. L. (2020) · F1000Research · doi:10.12688/f1000research.27141.1 · Cited by 53 · see citing works
Xia, J., Flynn, W. F., Gallicchio, E., Zhang, B. W., He, P., Tan, Z., & Levy, R. M. (2015) · Journal of Computational Chemistry · doi:10.1002/jcc.23996 · Cited by 52 · see citing works
α-helix to β-hairpin transition of human amylin monomer.
Singh, S., Chiu, C.-C., Reddy, A. S., & de Pablo, J. D. (2013) · Journal of Chemical Physics · doi:10.1063/1.4798460 · Cited by 48 · see citing works
Homologous ligands accommodated by discrete conformations of a buried cavity
Merski, M., Fischer, M. M., Balius, T. E., Eidam, O., & Shoichet, B. K. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1500806112 · Cited by 52 · see citing works
Amyloid assembly is dominated by misregistered kinetic traps on an unbiased energy landscape
Jia, Z., Schmit, J. D., & Chen, J. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1911153117 · Cited by 51 · see citing works
Current and emerging opportunities for molecular simulations in structure-based drug design
Michel, J. (2014) · Physical Chemistry Chemical Physics · doi:10.1039/c3cp54164a · Cited by 50 · see citing works
Townsend, P. A. (2024) · ACS Omega · doi:10.1021/acsomega.3c07488 · Cited by 48 · see citing works
Going clean: structure and dynamics of peptides in the gas phase and paths to solvation
Baldauf, C., & Rossi, M. (2015) · Journal of Physics Condensed Matter · doi:10.1088/0953-8984/27/49/493002 · Cited by 45 · see citing works
A network of molecular switches controls the activation of the two-component response regulator NtrC
Vanatta, D. K., Shukla, D., Lawrenz, M., & Pande, V. S. (2015) · Nature Communications · doi:10.1038/ncomms8283 · Cited by 47 · see citing works
Self-citationAtomic Detail of Protein Folding Revealed by an Ab Initio Reappraisal of Circular Dichroism
Ianeselli, A., Orioli, S., Spagnolli, G., Faccioli, P., Cupellini, L., Jurinovich, S., & Mennucci, B. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.7b12399 · Cited by 47 · see citing works
Molecular simulations and visualization: introduction and overview
Hirst, J. D., Glowacki, D. R., & Baaden, M. (2014) · Faraday Discussions · doi:10.1039/c4fd90024c · Cited by 46 · see citing works
Harada, R., & Kitao, A. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00723 · Cited by 45 · see citing works
Research Update: Computational materials discovery in soft matter
Bereau, T., Andrienko, D., & Kremer, K. (2016) · APL Materials · doi:10.1063/1.4943287 · Cited by 41 · see citing works
Das, A. K., Demerdash, O., & Head-Gordon, T. L. (2018) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.8b00978 · Cited by 41 · see citing works
Unarta, I. C., Cao, S., Kubo, S., Wang, W., Cheung, P. P., Gao, X., Takada, S., & Huang, X. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024324118 · Cited by 39 · see citing works
Protein Folding: An Introduction
Gomes, C. M., & Faísca, P. F. N. (2019) · Springer briefs in molecular science · doi:10.1007/978-3-319-00882-0_1 · Cited by 40 · see citing works
FF12MC: A revised AMBER forcefield and new protein simulation protocol
Pang, Y. (2016) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.25094 · Cited by 40 · see citing works
Computational Studies of Smell and Taste Receptors
Pizio, A. D., & Niv, M. Y. (2014) · Israel Journal of Chemistry · doi:10.1002/ijch.201400027 · Cited by 39 · see citing works
- Authors
- Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Corresponding author
- Pande, Vijay S
- Venue
- Current Opinion in Structural Biology
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 23
- Issue
- 1
- Pages
- 58-65
- ISSN
- 0959-440X (ISSN-L)
- PMC
- PMC3673555
- OpenAlex
- W2039369142
- Cited by
- 409
- References
- 92
- Citations by year
- 2026: 13 · 2025: 21 · 2024: 11 · 2023: 22 · 2022: 21 · 2021: 23 · 2020: 26 · 2019: 26 · 2018: 38 · 2017: 70 · 2016: 38 · 2015: 44 · 2014: 36 · 2013: 20
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Current Opinion in Structural Biology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Simbios NIH Center for Biomedical Computation (GM072970)
- NIH (R01GM62828)
- NSF GRF
- Stanford Graduate Fellowship
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
- Physical Sciences › Chemistry › Physical and Theoretical Chemistry › Advanced Physical and Chemical Molecular Interactions
- Keywords
- protein folding, all-atom molecular dynamics, folding kinetics, force fields, reaction coordinate, Markov state models, sampling efficiency
- MeSH terms
- Kinetics, Proteins, Protein Folding, Molecular Dynamics Simulation
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