Towards simple kinetic models of functional dynamics for a kinase subfamily
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Abstract
Kinases are ubiquitous enzymes involved in the regulation of critical cellular pathways. However, in silico modelling of the conformational ensembles of these enzymes is difficult due to inherent limitations and the cost of computational approaches. Recent algorithmic advances combined with homology modelling and parallel simulations have enabled researchers to address this computational sampling bottleneck. Here, we present the results of molecular dynamics studies for seven Src family kinase (SFK) members: Fyn, Lyn, Lck, Hck, Fgr, Yes and Blk. We present a sequence invariant extension to Markov state models, which allows us to quantitatively compare the structural ensembles of the seven kinases. Our findings indicate that in the absence of their regulatory partners, SFK members have similar in silico dynamics with active state populations ranging from 4 to 40% and activation timescales in the hundreds of microseconds. Furthermore, we observe several potentially druggable intermediate states, including a pocket next to the adenosine triphosphate binding site that could potentially be targeted via a small-molecule inhibitor.
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Cites
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
VMD: visual molecular dynamics
Humphrey, W., Dalke, A., & Schulten, K. (1996) · Journal of Molecular Graphics · doi:10.1016/0263-7855(96)00018-5 · Cited by 66,952 · see citing works
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249 · see citing works
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 38,335 · see citing works
Particle mesh Ewald: An N⋅log(N) method for Ewald sums in large systems
Darden, T., York, D., & Pedersen, L. (1993) · The Journal of Chemical Physics · doi:10.1063/1.464397 · Cited by 29,588 · see citing works
Kabsch, W., & Sander, C. (1983) · Biopolymers · doi:10.1002/bip.360221211 · Cited by 15,320 · see citing works
The protein kinase complement of the human genome
Manning, G., Whyte, D. B., Martinez, R., Hunter, T., & Sudarsanam, S. (2002) · Science · doi:10.1126/science.1075762 · Cited by 8,145 · see citing works
Improved 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 6,037 · see citing works
Comparative Protein Structure Modeling Using Modeller
Eswar, N., Webb, B., Martı́-Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M., Pieper, U., & Šali, A. (2006) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi0506s15 · Cited by 4,564 · see citing works
IPython: A System for Interactive Scientific Computing
Pérez, F., & Granger, B. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.53 · Cited by 3,927 · see citing works
Salomón–Ferrer, R., Götz, A. W., Poole, D., LeGrand, S., & Walker, R. C. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400314y · Cited by 3,373 · see citing works
Comparative Protein Structure Modeling Using MODELLER
Eswar, N., Webb, B., Martı́-Renom, M. A., Madhusudhan, M. S., Eramian, D., Shen, M., Pieper, U., & Šali, A. (2007) · Current Protocols in Protein Science · doi:10.1002/0471140864.ps0209s50 · Cited by 2,862 · 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 2,358 · see citing works
Self-citationRoutine 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
Hydrogen bonding in globular proteins
Baker, E. N., & Hubbard, R. E. (1984) · Progress in Biophysics and Molecular Biology · doi:10.1016/0079-6107(84)90007-5 · Cited by 1,880 · see citing works
The Conformational Plasticity of Protein Kinases
Huse, M., & Kuriyan, J. (2002) · Cell · doi:10.1016/s0092-8674(02)00741-9 · Cited by 1,760 · 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
Three-dimensional structure of the tyrosine kinase c-Src
Xu, W., Harrison, S. C., & Eck, M. J. (1997) · Nature · doi:10.1038/385595a0 · Cited by 1,428 · see citing works
Active and Inactive Protein Kinases: Structural Basis for Regulation
Johnson, L. N., Noble, M. E. M., & Owen, D. J. (1996) · Cell · doi:10.1016/s0092-8674(00)81092-2 · Cited by 1,396 · 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. O., Dirks, R. M., Grossman, J. P., Xu, H., & Shaw, D. E. (2012) · Annual Review of Biophysics · doi:10.1146/annurev-biophys-042910-155245 · Cited by 1,196 · see citing works
Identification of slow molecular order parameters for Markov model construction
Pérez-Hernández, G., Paul, F., Giorgino, T., De Fabritiis, G., & Noé, F. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4811489 · Cited by 990 · see citing works
Quorum sensing triggers the stochastic escape of individual cells from Pseudomonas putida biofilms
Cárcamo‐Oyarce, G., Lumjiaktase, P., Kümmerli, R., & Eberl, L. (2015) · Nature Communications · doi:10.1038/ncomms6945 · Cited by 959 · see citing works
Src family kinases, key regulators of signal transduction
Parsons, S. J., & Parsons, J. T. (2004) · Oncogene · doi:10.1038/sj.onc.1208160 · Cited by 942 · see citing works
Protein kinases: evolution of dynamic regulatory proteins
Taylor, S. S., & Kornev, A. P. (2010) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2010.09.006 · Cited by 940 · see citing works
Structural Basis for the Autoinhibition of c-Abl Tyrosine Kinase
Nagar, B., Hantschel, O. D., Young, M. A., Scheffzek, K., Veach, D. R., Bornmann, W. G., Clarkson, B., Superti‐Furga, G., & Kuriyan, J. (2003) · Cell · doi:10.1016/s0092-8674(03)00194-6 · Cited by 880 · see citing works
Crystal Structures of c-Src Reveal Features of Its Autoinhibitory Mechanism
Xu, W., Doshi, A. H., Lei, M., Eck, M. J., & Harrison, S. C. (1999) · Molecular Cell · doi:10.1016/s1097-2765(00)80356-1 · Cited by 871 · see citing works
Markov 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 · see citing works
Kornev, A. P., Haste, N. M., Taylor, S. S., & Eyck, L. F. T. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0607656103 · Cited by 766 · see citing works
Development of polyphosphate parameters for use with the AMBER force field
Meagher, K. L., Redman, L. T., & Carlson, H. A. (2003) · Journal of Computational Chemistry · doi:10.1002/jcc.10262 · Cited by 715 · see citing works
Everything 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-citationEastman, P., Friedrichs, M., Chodera, J., Radmer, R. J., Bruns, C. M., Ku, J. P., Beauchamp, K., Lane, T., Wang, L.-P., Shukla, D., et al. (2012) · Journal of Chemical Theory and Computation · doi:10.1021/ct300857j · Cited by 678 · see citing works
Self-citationTransition-Path Theory and Path-Finding Algorithms for the Study of Rare Events
Weinan, E., & Vanden‐Eijnden, E. (2008) · Annual Review of Physical Chemistry · doi:10.1146/annurev.physchem.040808.090412 · Cited by 635 · see citing works
Schwantes, C. R., & Pande, V. S. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct300878a · Cited by 597 · see citing works
Self-citationAn Introduction to Markov State Models and Their Application to Long Timescale Molecular Simulation
Bowman, G., Pande, V., & Noé, F. (2014) · Advances in Experimental Medicine and Biology · doi:10.1007/978-94-007-7606-7 · Cited by 585 · see citing works
Self-citationScreen 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-citationRegulation of the c-Abl and Bcr–Abl tyrosine kinases
Hantschel, O. D., & Superti‐Furga, G. (2004) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm1280 · Cited by 491 · see citing works
Transition Path Theory for Markov Jump Processes
Metzner, P., Schütte, C., & Vanden-Eijnden, E. (2009) · Multiscale Modeling & simulation · doi:10.1137/070699500 · Cited by 484 · see citing works
Role of ABL family kinases in cancer: from leukaemia to solid tumours
Greuber, E. K., Smith-Pearson, P. S., Wang, J., & Pendergast, A. M. (2013) · Nature reviews. Cancer · doi:10.1038/nrc3563 · Cited by 457 · see citing works
Abrams, C. F., & Bussi, G. (2013) · Entropy · doi:10.3390/e16010163 · Cited by 429 · see citing works
The Structural Basis for Control of Eukaryotic Protein Kinases
Endicott, J., Noble, M. E. M., & Johnson, L. N. (2012) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-052410-090317 · Cited by 428 · 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 411 · see citing works
Self-citationTo milliseconds and beyond: challenges in the simulation of protein folding
Lane, T. J., Shukla, D., Beauchamp, K. A., & Pande, V. S. (2013) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2012.11.002 · Cited by 409 · see citing works
Self-citationStructures of Src-family tyrosine kinases
Sicheri, F., & Kuriyan, J. (1997) · Current Opinion in Structural Biology · doi:10.1016/s0959-440x(97)80146-7 · Cited by 381 · see citing works
Finding transition pathways using the string method with swarms of trajectories.
Pan, A. C., Sezer, D., & Roux, B. (2008) · Journal of Physical Chemistry B · doi:10.1021/jp0777059 · Cited by 364 · see citing works
Cowan‐Jacob, S. W., Fendrich, G., Manley, P. W., Jahnke, W., Fabbro, D., Liebetanz, J., & Meyer, T. (2005) · Structure · doi:10.1016/j.str.2005.03.012 · 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-citationA Src-Like Inactive Conformation in the Abl Tyrosine Kinase Domain
Levinson, N. M., Kuchment, O., Shen, K., Young, M. A., Koldobskiy, M. A., Karplus, M., Cole, P. A., & Kuriyan, J. (2006) · PLoS Biology · doi:10.1371/journal.pbio.0040144 · Cited by 309 · see citing works
The Abl family kinases: Mechanisms of regulation and signaling
Pendergast, A. M. (2002) · Advances in cancer research · doi:10.1016/s0065-230x(02)85003-5 · Cited by 296 · see citing works
MSMBuilder: Statistical Models for Biomolecular Dynamics
Harrigan, M. P., Sultan, M. M., Hernández, C. X., Husic, B. E., Eastman, P., Schwantes, C. R., Beauchamp, K. A., McGibbon, R. T., & Pande, V. S. (2017) · Biophysical Journal · doi:10.1016/j.bpj.2016.10.042 · Cited by 277 · see citing works
Self-citationNowakowski, J., Cronin, C. N., McRee, D. E., Knuth, M. W., Nelson, C. G., Pavletich, N. P., Rogers, J., Sang, B., Scheibe, D. N., Swanson, R. V., et al. (2002) · Structure · doi:10.1016/s0969-2126(02)00907-3 · Cited by 225 · see citing works
Transitions to catalytically inactive conformations in EGFR kinase
Shan, Y., Arkhipov, A. S., Kim, E. T., Pan, A. C., & Shaw, D. E. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1220843110 · Cited by 217 · see citing works
Kinetic Distance and Kinetic Maps from Molecular Dynamics Simulation
Noé, F., & Clementi, C. (2015) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.5b00553 · Cited by 214 · see citing works
Computing smooth molecular surfaces
Varshney, A., Brooks, F. P., & Wright, W. V. (1994) · IEEE Computer Graphics and Applications · doi:10.1109/38.310720 · Cited by 201 · see citing works
Variational cross-validation of slow dynamical modes in molecular kinetics.
McGibbon, R., & Pande, V. (2014) · Journal of Chemical Physics · doi:10.1063/1.4916292 · Cited by 195 · see citing works
Self-citationHarnessing Allostery: A Novel Approach to Drug Discovery
Lu, S., Li, S., & Zhang, J. (2014) · Medicinal Research Reviews · doi:10.1002/med.21317 · Cited by 147 · see citing works
Fyn, a Src family tyrosine kinase
Resh, M. D. (1998) · The International Journal of Biochemistry & Cell Biology · doi:10.1016/s1357-2725(98)00089-2 · Cited by 145 · see citing works
Skóra, L., Mestan, J., Fabbro, D., Jahnke, W., & Grzesiek, S. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1314712110 · Cited by 138 · see citing works
Structural Basis for the Recognition of c-Src by Its Inactivator Csk
Levinson, N. M., Seeliger, M. A., Cole, P. A., & Kuriyan, J. (2008) · Cell · doi:10.1016/j.cell.2008.05.051 · Cited by 132 · see citing works
Insights into Src kinase functions: structural comparisons
Williams, J. C., Wierenga, R. K., & Saraste, M. (1998) · Trends in Biochemical Sciences · doi:10.1016/s0968-0004(98)01202-x · Cited by 126 · see citing works
The Small Molecules AZD0530 and Dasatinib Inhibit Dengue Virus RNA Replication via Fyn Kinase
de Wispelaere, M., LaCroix, A. J., & Yang, P. L. (2013) · Journal of Virology · doi:10.1128/jvi.00632-13 · Cited by 122 · see citing works
A dynamically coupled allosteric network underlies binding cooperativity in Src kinase
Foda, Z. H., Shan, Y., Kim, E. T., Shaw, D. E., & Seeliger, M. A. (2015) · Nature Communications · doi:10.1038/ncomms6939 · Cited by 117 · see citing works
Yang, S., Banavali, N. K., & Roux, B. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808261106 · Cited by 115 · see citing works
The ABC of protein kinase conformations
Möbitz, H. (2015) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2015.03.009 · Cited by 105 · see citing works
Signaling through dynamic linkers as revealed by PKA
Akimoto, M., Selvaratnam, R., McNicholl, E. T., Verma, G., Taylor, S. S., & Melacini, G. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1312644110 · Cited by 104 · see citing works
Src Kinase Conformational Activation: Thermodynamics, Pathways, and Mechanisms
Yang, S., & Roux, B. (2008) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000047 · Cited by 98 · see citing works
Shaltiel, S., Cox, S. E., & Taylor, S. S. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.2.484 · Cited by 77 · see citing works
Fyn Kinase in Brain Diseases and Cancer: The Search for Inhibitors
Schenone, S., Brullo, C., Musumeci, F., Biava, M., Falchi, F., & Botta, M. (2011) · Current Medicinal Chemistry · doi:10.2174/092986711796150531 · Cited by 74 · see citing works
Phosphorylation releases constraints to domain motion in ERK2
Xiao, Y., Lee, T., Latham, M. P., Warner, L., Tanimoto, A., Pardi, A., & Ahn, N. G. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1318899111 · Cited by 69 · see citing works
Structure of human Fyn kinase domain complexed with staurosporine
Kinoshita, T., Matsubara, M., Ishiguro, H., Okita, K., & Tada, T. (2006) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2006.05.212 · Cited by 66 · see citing works
An electrostatic network and long‐range regulation of Src kinases
Özkırımlı, E., Yadav, S. S., Miller, W. T., & Post, C. B. (2008) · Protein Science · doi:10.1110/ps.037457.108 · Cited by 58 · see citing works
Bernadó, P., Pérez, Y., Svergun, D. I., & Pons, M. (2007) · Journal of Molecular Biology · doi:10.1016/j.jmb.2007.11.066 · Cited by 58 · see citing works
αC Helix as a Switch in the Conformational Transition of Src/CDK-like Kinase Domains
Huang, H., Zhao, R., Dickson, B. M., Skeel, R. D., & Post, C. B. (2012) · The Journal of Physical Chemistry B · doi:10.1021/jp301628r · Cited by 54 · see citing works
Kuglstatter, A., Wong, A., Tsing, S., Lee, S. W., Lou, Y. C., Villaseñor, A. G., Bradshaw, J. M., Shaw, D. E., Barnett, J. W., & Browner, M. F. (2010) · Protein Science · doi:10.1002/pro.575 · Cited by 54 · see citing works
Sultan, M. M., Denny, R., Unwalla, R., Lovering, F., & Pande, V. (2017) · Scientific Reports · doi:10.1038/s41598-017-10697-0 · Cited by 50 · see citing works
Self-citationPucheta-Martínez, E., Saladino, G., Morando, M. A., Martinez-Torrecuadrada, J. L., Lelli, M., Sutto, L., D’Amelio, N., & Gervasio, F. L. (2016) · Scientific Reports · doi:10.1038/srep24235 · Cited by 50 · see citing works
Sun, G., Ramdas, L., Wang, W., Vinci, J., McMurray, J., & Budde, R. J. A. (2002) · Archives of Biochemistry and Biophysics · doi:10.1006/abbi.2001.2627 · Cited by 49 · see citing works
Differential Sensitivity of Src-Family Kinases to Activation by SH3 Domain Displacement
Moroco, J. A., Craigo, J. K., Iacob, R. E., Wales, T. E., Engen, J. R., & Smithgall, T. E. (2014) · PLoS ONE · doi:10.1371/journal.pone.0105629 · Cited by 46 · see citing works
A Maximum-Caliber Approach to Predicting Perturbed Folding Kinetics Due to Mutations.
Wan, H., Zhou, G., & Voelz, V. A. (2016) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.6b00938 · Cited by 42 · see citing works
Dynamics of Protein Kinases: Insights from Nuclear Magnetic Resonance
Xiao, Y., Liddle, J. C., Pardi, A., & Ahn, N. G. (2015) · Accounts of Chemical Research · doi:10.1021/acs.accounts.5b00001 · Cited by 38 · see citing works
Weijland, A., Neubauer, G., Courtneidge, S. A., Mann, M., Wierenga, R. K., & Superti‐Furga, G. (1996) · European Journal of Biochemistry · doi:10.1111/j.1432-1033.1996.0756h.x · Cited by 36 · see citing works
Pan, A. C., Weinreich, T. M., Shan, Y., Scarpazza, D. P., & Shaw, D. E. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500223p · Cited by 33 · see citing works
Surprisal Metrics for Quantifying Perturbed Conformational Dynamics in Markov State Models
Voelz, V. A., Elman, B., Razavi, A. M., & Zhou, G. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500827g · Cited by 24 · see citing works
Sultan, M. M., Kiss, G., Shukla, D., & Pande, V. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct500353m · Cited by 23 · see citing works
Self-citationBacterial Expression and Characterization of Catalytic Loop Mutants of Src Protein Tyrosine Kinase
Kemble, D. J., Wang, Y.-H., & Sun, G. (2006) · Biochemistry · doi:10.1021/bi061664+ · Cited by 21 · see citing works
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Unsupervised Learning Methods for Molecular Simulation Data
Glielmo, A., Husic, B. E., Rodríguez, Á., Clementi, C., Noé, F., & Laio, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01195 · Cited by 408 · see citing works
Investigating Cryptic Binding Sites by Molecular Dynamics Simulations
Kuzmanic, A., Bowman, G. R., Juárez‐Jiménez, J., Michel, J., & Gervasio, F. L. (2020) · Accounts of Chemical Research · doi:10.1021/acs.accounts.9b00613 · Cited by 195 · see citing works
Allosteric Modulator Discovery: From Serendipity to Structure-Based Design
Lu, S., He, X., Ni, D., & Zhang, J. (2019) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.8b01749 · Cited by 176 · see citing works
Automated design of collective variables using supervised machine learning
Sultan, M. M., & Pande, V. S. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5029972 · Cited by 137 · see citing works
Self-citationAssessing Lysine and Cysteine Reactivities for Designing Targeted Covalent Kinase Inhibitors
Liu, R., Yue, Z., Tsai, C.-C., & Shen, J. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.8b13248 · Cited by 137 · see citing works
Ni, D., Wei, J., He, X., Rehman, A. U., Li, X., Qiu, Y., Pu, J., Lu, S., & Zhang, J. (2020) · Chemical Science · doi:10.1039/d0sc05131d · Cited by 137 · see citing works
Harnessing Conformational Plasticity to Generate Designer Enzymes
Crean, R., Gardner, J. M., & Kamerlin, S. C. L. (2020) · Journal of the American Chemical Society · doi:10.1021/jacs.0c04924 · Cited by 129 · see citing works
Loop Motion in Triosephosphate Isomerase Is Not a Simple Open and Shut Case
Liao, Q., Kulkarni, Y., Sengupta, U., Petrović, D., Mulholland, A. J., van der Kamp, M. W., Strodel, B., & Kamerlin, S. C. L. (2018) · Journal of the American Chemical Society · doi:10.1021/jacs.8b09378 · Cited by 95 · see citing works
Martin-Fernandez, M. L., Clarke, D. T., Roberts, S. K., Zanetti-Domingues, L. C., & Gervasio, F. L. (2019) · Cells · doi:10.3390/cells8040316 · Cited by 94 · see citing works
Along the allostery stream: Recent advances in computational methods for allosteric drug discovery
Ni, D., Chai, Z., Wang, Y., Li, M., Yu, Z., Liu, Y., Lu, S., & Zhang, J. (2021) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.1585 · Cited by 67 · 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-citationQiu, Y., Yin, X., Li, X., Wang, Y., Fu, Q., Huang, R., & Lu, S. (2021) · Pharmaceutics · doi:10.3390/pharmaceutics13050747 · Cited by 54 · see citing works
Mechanistic origin of partial agonism of tetrahydrocannabinol for cannabinoid receptors
Dutta, S., Selvam, B., Das, A., & Shukla, D. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.101764 · Cited by 55 · see citing works
Activation loop dynamics are controlled by conformation-selective inhibitors of ERK2
Pegram, L. M., Liddle, J. C., Xiao, Y., Hoh, M., Rudolph, J., Iverson, D. B., Vigers, G. P. A., Smith, D. J., Zhang, H., Wang, W., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1906824116 · Cited by 46 · see citing works
How Electrostatic Coupling Enables Conformational Plasticity in a Tyrosine Kinase
Tsai, C.-C., Yue, Z., & Shen, J. (2019) · Journal of the American Chemical Society · doi:10.1021/jacs.9b06064 · Cited by 46 · see citing works
Harnessing Reversed Allosteric Communication: A Novel Strategy for Allosteric Drug Discovery
Fan, J., Liu, Y., Kong, R., Ni, D., Yu, Z., Lu, S., & Zhang, J. (2021) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.1c01695 · Cited by 41 · see citing works
KinaMetrix: a web resource to investigate kinase conformations and inhibitor space
Rahman, R., Ung, P. M. U., & Schlessinger, A. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky916 · Cited by 36 · see citing works
Reduced efficacy of a Src kinase inhibitor in crowded protein solution
Kasahara, K., Re, S., Nawrocki, G., Oshima, H., Mishima-Tsumagari, C., Miyata-Yabuki, Y., Kukimoto‐Niino, M., Yu, I., Shirouzu, M., Feig, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24349-5 · Cited by 36 · see citing works
Narayan, B., Fathizadeh, A., Templeton, C., He, P., Arasteh, S., Elber, R., Buchete, N., & Levy, R. M. (2019) · Biochimica et Biophysica Acta (BBA) - General Subjects · doi:10.1016/j.bbagen.2019.129508 · Cited by 35 · see citing works
Discovery of a hidden transient state in all bromodomain families
Raich, L., Meier, K., Günther, J., Christ, C. D., Noé, F., & Olsson, S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2017427118 · Cited by 34 · see citing works
Distinct Binding Mechanisms for Allosteric Sodium Ion in Cannabinoid Receptors
Dutta, S., Selvam, B., & Shukla, D. (2022) · ACS Chemical Neuroscience · doi:10.1021/acschemneuro.1c00760 · Cited by 31 · see citing works
Activation Loop Dynamics Are Coupled to Core Motions in Extracellular Signal-Regulated Kinase-2
Iverson, D. B., Xiao, Y., Jones, D. N. M., Eisenmesser, E. Z., & Ahn, N. G. (2020) · Biochemistry · doi:10.1021/acs.biochem.0c00485 · Cited by 28 · see citing works
Shao, Q., & Zhu, W. (2019) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b05226 · Cited by 24 · see citing works
Independent Markov decomposition: Toward modeling kinetics of biomolecular complexes
Hempel, T., del Razo, M. J., Lee, C. T., Taylor, B. C., Amaro, R. E., & Noé, F. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2105230118 · Cited by 22 · see citing works
Weigle, A. T., & Shukla, D. (2024) · Communications Biology · doi:10.1038/s42003-024-06291-6 · Cited by 22 · see citing works
Liang, K., Dong, W., Gao, J., Liu, Z., Zhou, R., Shu, Z., & Duan, M. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.3c00194 · Cited by 21 · see citing works
Distinct activation mechanisms regulate subtype selectivity of Cannabinoid receptors
Dutta, S., & Shukla, D. (2022) · bioRxiv · doi:10.1038/s42003-023-04868-1 · Cited by 21 · see citing works
Manifold learning in atomistic simulations: a conceptual review
Rydzewski, J., Chen, M., & Valsson, Ó. (2023) · Machine Learning Science and Technology · doi:10.1088/2632-2153/ace81a · Cited by 21 · see citing works
Meller, A., Kelly, D. G., Smith, L. G., & Bowman, G. R. (2024) · Protein Science · doi:10.1002/pro.4902 · Cited by 17 · see citing works
Hidden allosteric sites and De-Novo drug design
Rehman, A. U., Lu, S., Khan, A. A., Khurshid, B., Rasheed, S., Wadood, A., & Zhang, J. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2022.2017876 · Cited by 17 · see citing works
Mi, X., Desormeaux, E. K., Le, T., van der Donk, W. A., & Shukla, D. (2023) · Chemical Science · doi:10.1039/d2sc06546k · Cited by 16 · see citing works
Du, Y., & Wang, R. (2020) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.0c00342 · Cited by 15 · see citing works
Jeong, H., Shin, J. Y., Lee, K., Lee, S. J., Chong, H., Jeong, H., Jeon, Y.-E., Shin, D., Jang, S., Kim, K. H., et al. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21197160 · Cited by 14 · see citing works
Wang, D., Wang, W., Song, M., Xie, Y., Kuang, W., & Yang, P. (2024) · European Journal of Medicinal Chemistry · doi:10.1016/j.ejmech.2024.116774 · Cited by 14 · see citing works
TYROSINE KINASES: COMPLEX MOLECULAR SYSTEMS CHALLENGING COMPUTATIONAL METHODOLOGIES
Thomas, T., & Roux, B. (2021) · European Physical Journal B : Condensed Matter Physics · doi:10.1140/epjb/s10051-021-00207-7 · Cited by 9 · see citing works
Kakarala, K. K., & Jamil, K. (2021) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2021.1891140 · Cited by 13 · see citing works
Structural basis for the hyperthermostability of an archaeal enzyme induced by succinimide formation
Dongre, A., Das, S. K., Bellur, A., Kumar, S., Chandrashekarmath, A., Karmakar, T., Balaram, P., Balasubramanian, S., & Balaram, H. (2021) · Biophysical Journal · doi:10.1016/j.bpj.2021.07.014 · Cited by 11 · see citing works
Naresh, G. K. R. S., & Guruprasad, L. (2020) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2020.1730968 · Cited by 11 · see citing works
Mechanism of dimer selectivity and binding cooperativity of BRAF inhibitors
Clayton, J., Romany, A., Matenoglou, E., Gavathiotis, E., Poulikakos, P. I., & Shen, J. (2025) · eLife · doi:10.7554/elife.95334.4 · Cited by 9 · see citing works
Pressure-Temperature Analysis of the Stability of the CTL9 Domain Reveals Hidden Intermediates
Zhang, S., Zhang, Y., Stenzoski, N. E., Zou, J., Peran, I., McCallum, S. A., Raleigh, D. P., & Royer, C. A. (2019) · Biophysical Journal · doi:10.1016/j.bpj.2019.01.002 · Cited by 9 · see citing works
Sk, M. F., & Kar, P. (2022) · SAR and QSAR in environmental research · doi:10.1080/1062936x.2022.2145352 · Cited by 8 · see citing works
Wu, H., Huang, H., & Post, C. (2020) · Journal of Chemical Physics · doi:10.1063/5.0021603 · Cited by 8 · see citing works
Song, J., Sun, Q., Skirtach, A. G., & Parakhonskiy, B. V. (2025) · Advances in Colloid and Interface Science · doi:10.1016/j.cis.2025.103732 · Cited by 6 · see citing works
TAB1 binding induced p38α conformation change: an accelerated molecular dynamics simulation study
Zang, Y., Wang, H., Kang, Y. R., Zhang, J., Li, X., Zhang, L., Yang, Z., & Zhang, S. (2022) · Physical Chemistry Chemical Physics · doi:10.1039/d2cp00144f · Cited by 6 · see citing works
Hong, X.-K., Song, K.-Y., Rahman, M., Wei, T., Zhang, Y., Da, L.-T., & Chen, H.-F. (2023) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.2c01177 · Cited by 5 · see citing works
Bao, Y., Xu, R., & Guo, J. (2024) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2024.108224 · Cited by 5 · see citing works
Identifying a Feasible Transition Pathway between Two Conformational States for a Protein.
Li, Y., & Gong, H.-P. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.2c00390 · Cited by 7 · see citing works
Kong, F., Zhu, Y., Xu, J., Ling, B., Wang, C., Ji, J., Yang, Q., Liu, X., Shao, L., Zhou, X., et al. (2025) · International Immunopharmacology · doi:10.1016/j.intimp.2025.114194 · Cited by 4 · see citing works
Naresh, G. K. R. S., & Guruprasad, L. (2023) · Journal of Biomolecular Structure and Dynamics · doi:10.1080/07391102.2022.2162128 · Cited by 4 · see citing works
AtSWEET13 transporter discriminates sugars by selective facial and positional substrate recognitionPreprint
Weigle, A. T., & Shukla, D. (2022) · bioRxiv · doi:10.1101/2022.10.12.511964 · Cited by 6 · see citing works
Sk, M. F., Samanta, S., Poddar, S., & Kar, P. (2024) · Journal of Computer-Aided Molecular Design · doi:10.1007/s10822-023-00548-8 · Cited by 3 · see citing works
Mahapatra, S., & Kar, P. (2026) · Physical Chemistry Chemical Physics · doi:10.1039/d5cp04248h · Cited by 2 · see citing works
Molecular Modeling of Protein Kinases: Current Status and Challenges
Poso, A. (2020) · Topics in medicinal chemistry · doi:10.1007/7355_2020_106 · Cited by 2 · see citing works
Machine Learning in Molecular Dynamics Simulations of Biomolecular Systems
Kolloff, C., & Olsson, S. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-821978-2.00089-1 · Cited by 2 · see citing works
Aljoundi, A., Agoni, C., Olotu, F. A., & Soliman, M. E. S. (2019) · Future Medicinal Chemistry · doi:10.4155/fmc-2018-0354 · Cited by 2 · see citing works
Mechanism of dimer selectivity and binding cooperativity of BRAF inhibitorsPreprint
Clayton, J., Romany, A., Matenoglou, E., Gavathiotis, E., Poulikakos, P. I., & Shen, J. (2024) · eLife · doi:10.7554/elife.95334 · Cited by 2 · see citing works
Weigle, A. T., & Shukla, D. (2024) · bioRxiv · doi:10.1101/2024.06.12.598708 · Cited by 1 · see citing works
Dynamic mechanism for subtype selectivity of endocannabinoids
Dutta, S., Zhao, L., & Shukla, D. (2026) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2026.111434
Mechanism of Dimer Selectivity and Binding Cooperativity of BRAF InhibitorsPreprint
Clayton, J., Romany, A., Matenoglou, E., Gavathiotis, E., Poulikakos, P. I., & Shen, J. (2025) · eLife · doi:10.7554/elife.95334.3 · Cited by 1 · see citing works
Why does Δ 9 -Tetrahydrocannabinol act as a partial agonist of cannabinoid receptors?Preprint
Dutta, S., Selvam, B., Das, A., & Shukla, D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441987
The impact of inhibitor size and flexibility on the binding pathways to c-Src kinasePreprint
Shinobu, A., Re, S., & Sugita, Y. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.10.25.513784
Sequence Controlled Secondary Structure Determines Site-selectivity of LanthipeptidesPreprint
Mi, X., Desormeaux, E. K., Le, T., van der Donk, W. A., & Shukla, D. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.28.518241
Mechanism of Dimer Selectivity and Binding Cooperativity of BRAF inhibitorsPreprint
Clayton, J., Romany, A., Matenoglou, E., Gavathiotis, E., Poulikakos, P. I., & Shen, J. (2024) · eLife · doi:10.7554/elife.95334.1
Early Aggregation Mechanism of SOD128–38 Based on Force Field Parameter of 5-Cyano-Tryptophan
Rahman, M. U., Bano, S., Hong, X., Gu, R., & Chen, H. (2024) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c00289
Mechanism of Dimer Selectivity and Binding Cooperativity of BRAF InhibitorsPreprint
Clayton, J., Romany, A., Matenoglou, E., Gavathiotis, E., Poulikakos, P. I., & Shen, J. (2024) · eLife · doi:10.7554/elife.95334.2
Jin, L., Qian, C., Wei, Z., Zhang, D., Xi, J., Sun, D., Fu, L., Liu, X., & Zhang, X. (2024) · Arabian Journal of Chemistry · doi:10.1016/j.arabjc.2024.105979
Impact of Inhibitor Size and Flexibility on the Binding Pathways to c-Src Kinase
Shinobu, A., Re, S., Oshima, H., & Sugita, Y. (2026) · The Journal of Physical Chemistry Letters · doi:10.1021/acs.jpclett.6c02620
BindingDB (2021) · UC San Diego · doi:10.7270/q2c53q9m
- Authors
- Sultan, M. M., Kiss, G., & Pande, V. S.
- Affiliations
- Stanford University (US)
- Revolution Medicines (United States) (US)
- Corresponding author
- Pande, Vijay S.
- Venue
- Nature Chemistry
- Published
- 2018-07-09
- Volume
- 10
- Issue
- 9
- Pages
- 903-909
- ISSN
- 1755-4330 (ISSN-L)
- 1755-4349
- OpenAlex
- W2950292741
- Cited by
- 68
- References
- 85
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 11 · 2023: 8 · 2022: 8 · 2021: 12 · 2020: 9 · 2019: 9 · 2018: 3
- FWCI
- 3.02
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Chemistry (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (0954714)
- National Institutes of Health (1s10rr026647-01)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Structure and Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Kinase Regulation and GTPase Signaling
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- Src family kinases, kinase activation, conformational dynamics, Markov state models, ATP binding site, small-molecule kinase inhibitors, Fyn, Lyn kinase, Lck, Hck, Fgr, Yes, BLK, enzyme kinetics
- MeSH terms
- Adenosine Triphosphate, Binding Sites, Kinetics, Markov Chains, Models, Biological, Protein Structure, Tertiary, src-Family Kinases, Amino Acid Motifs, Protein Kinase Inhibitors, Small Molecule Libraries, Molecular Dynamics Simulation
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