ACS Publications. Most Trusted. Most Cited. Most Read

OR SEARCH CITATIONS

My Activity
Publications
CONTENT TYPES

Figure 1Loading Img
Download Hi-Res ImageDownload to MS-PowerPointCite This:J. Am. Chem. Soc. 2018, 140, 7, 2386-2396
RETURN TO ISSUEPREVPerspectiveNEXT

Markov State Models: From an Art to a Science

View Author Information
Department of Chemistry, Stanford University, Stanford, California 94305, United States
*[email protected]
Cite this: J. Am. Chem. Soc. 2018, 140, 7, 2386–2396
Publication Date (Web):January 11, 2018
https://doi.org/10.1021/jacs.7b12191
Copyright © 2018 American Chemical Society
Request reuse permissions

    Article Views

    23472

    Altmetric

    -

    Citations

    495
    LEARN ABOUT THESE METRICS
    Other access options
    Get e-Alerts
    SUBJECTS:

    Abstract

    Abstract Image

    Markov state models (MSMs) are a powerful framework for analyzing dynamical systems, such as molecular dynamics (MD) simulations, that have gained widespread use over the past several decades. This perspective offers an overview of the MSM field to date, presented for a general audience as a timeline of key developments in the field. We sequentially address early studies that motivated the method, canonical papers that established the use of MSMs for MD analysis, and subsequent advances in software and analysis protocols. The derivation of a variational principle for MSMs in 2013 signified a turning point from expertise-driving MSM building to a systematic, objective protocol. The variational approach, combined with best practices for model selection and open-source software, enabled a wide range of MSM analysis for applications such as protein folding and allostery, ligand binding, and protein–protein association. To conclude, the current frontiers of methods development are highlighted, as well as exciting applications in experimental design and drug discovery.

    Read this article

    To access this article, please review the available access options below.

    Get instant access

    Purchase Access

    Read this article for 48 hours. Check out below using your ACS ID or as a guest.

    Recommended

    Access through Your Institution

    You may have access to this article through your institution.

    Your institution does not have access to this content. You can change your affiliated institution below.

    Cited By

    This article is cited by 495 publications.

    1. Sunita Muduli, Soumyajit Karmakar, Sabyashachi Mishra. Conformational Dynamics in Corynebacterium glutamicum Diaminopimelate Epimerase: Insights from Ligand Parameterization, Atomistic Simulation, and Markov State Modeling. Journal of Chemical Information and Modeling 2024, Article ASAP.
    2. Ofir Blumer, Shlomi Reuveni, Barak Hirshberg. Short-Time Infrequent Metadynamics for Improved Kinetics Inference. Journal of Chemical Theory and Computation 2024, Article ASAP.
    3. Masao Inoue, Toru Ekimoto, Tsutomu Yamane, Mitsunori Ikeguchi. Computational Analysis of Activation of Dimerized Epidermal Growth Factor Receptor Kinase Using the String Method and Markov State Model. Journal of Chemical Information and Modeling 2024, Article ASAP.
    4. Shinji Ikizawa, Tatsuki Hori, Tegar Nurwahyu Wijaya, Hiroshi Kono, Zhen Bai, Tatsuhiro Kimizono, Wenbo Lu, Duy Phuoc Tran, Akio Kitao. PaCS-Toolkit: Optimized Software Utilities for Parallel Cascade Selection Molecular Dynamics (PaCS-MD) Simulations and Subsequent Analyses. The Journal of Physical Chemistry B 2024, 128 (15) , 3631-3642. https://doi.org/10.1021/acs.jpcb.4c01271
    5. Yongna Yuan, Xuqi Mao, Xiaohang Pan, Ruisheng Zhang, Wei Su. Kinetic Ensemble of Tau Protein through the Markov State Model and Deep Learning Analysis. Journal of Chemical Theory and Computation 2024, 20 (7) , 2947-2958. https://doi.org/10.1021/acs.jctc.3c01211
    6. Tiantian Xu, Yongfang Li, Xin Gao, Lu Zhang. Understanding the Fast-Triggering Unfolding Dynamics of FK-11 upon Photoexcitation of Azobenzene. The Journal of Physical Chemistry Letters 2024, 15 (13) , 3531-3540. https://doi.org/10.1021/acs.jpclett.4c00091
    7. Song Yang, Chen Song. Switching Go̅-Martini for Investigating Protein Conformational Transitions and Associated Protein–Lipid Interactions. Journal of Chemical Theory and Computation 2024, 20 (6) , 2618-2629. https://doi.org/10.1021/acs.jctc.3c01222
    8. Thomas L. Beck, Paolo Carloni, Dilipkumar N. Asthagiri. All-Atom Biomolecular Simulation in the Exascale Era. Journal of Chemical Theory and Computation 2024, 20 (5) , 1777-1782. https://doi.org/10.1021/acs.jctc.3c01276
    9. Gao Tu, Tingting Fu, Guoxun Zheng, Binbin Xu, Rongpei Gou, Ding Luo, Panpan Wang, Weiwei Xue. Computational Chemistry in Structure-Based Solute Carrier Transporter Drug Design: Recent Advances and Future Perspectives. Journal of Chemical Information and Modeling 2024, 64 (5) , 1433-1455. https://doi.org/10.1021/acs.jcim.3c01736
    10. Hong Ha Nguyen, Jim Tufts, David D. L. Minh. On Inactivation of the Coronavirus Main Protease. Journal of Chemical Information and Modeling 2024, 64 (5) , 1644-1656. https://doi.org/10.1021/acs.jcim.3c01518
    11. Mengrong Li, Xiaoxiao Zhang, Shu Li, Jingjing Guo. Unraveling the Interplay of Extracellular Domain Conformational Changes and Parathyroid Hormone Type 1 Receptor Activation in Class B1 G Protein-Coupled Receptors: Integrating Enhanced Sampling Molecular Dynamics Simulations and Markov State Models. ACS Chemical Neuroscience 2024, 15 (4) , 844-853. https://doi.org/10.1021/acschemneuro.3c00747
    12. Silvia Rinaldi, Elisabetta Moroni, Riccardo Rozza, Alessandra Magistrato. Frontiers and Challenges of Computing ncRNAs Biogenesis, Function and Modulation. Journal of Chemical Theory and Computation 2024, 20 (3) , 993-1018. https://doi.org/10.1021/acs.jctc.3c01239
    13. Robert E. Arbon, Yanchen Zhu, Antonia S. J. S. Mey. Markov State Models: To Optimize or Not to Optimize. Journal of Chemical Theory and Computation 2024, 20 (2) , 977-988. https://doi.org/10.1021/acs.jctc.3c01134
    14. Tsuyoshi Ishizone, Yasuhiro Matsunaga, Sotaro Fuchigami, Kazuyuki Nakamura. Representation of Protein Dynamics Disentangled by Time-Structure-Based Prior. Journal of Chemical Theory and Computation 2024, 20 (1) , 436-450. https://doi.org/10.1021/acs.jctc.3c01025
    15. Diego E. Kleiman, Hassan Nadeem, Diwakar Shukla. Adaptive Sampling Methods for Molecular Dynamics in the Era of Machine Learning. The Journal of Physical Chemistry B 2023, 127 (50) , 10669-10681. https://doi.org/10.1021/acs.jpcb.3c04843
    16. Divya Rai, Dulal Mondal, Srabani Taraphder. pH-Dependent Structure and Dynamics of the Catalytic Domains of Human Carbonic Anhydrase II and IX. The Journal of Physical Chemistry B 2023, 127 (48) , 10279-10294. https://doi.org/10.1021/acs.jpcb.3c04721
    17. Haohao Fu, Han Liu, Jingya Xing, Tong Zhao, Xueguang Shao, Wensheng Cai. Deep-Learning-Assisted Enhanced Sampling for Exploring Molecular Conformational Changes. The Journal of Physical Chemistry B 2023, 127 (46) , 9926-9935. https://doi.org/10.1021/acs.jpcb.3c05284
    18. Samik Bose, Samuel D. Lotz, Indrajit Deb, Megan Shuck, Kin Sing Stephen Lee, Alex Dickson. How Robust Is the Ligand Binding Transition State?. Journal of the American Chemical Society 2023, 145 (46) , 25318-25331. https://doi.org/10.1021/jacs.3c08940
    19. Xiaowei Wang, Tiantian Xu, Yuan Yao, Peter Pak-Hang Cheung, Xin Gao, Lu Zhang. SARS-CoV-2 RNA-Dependent RNA Polymerase Follows Asynchronous Translocation Pathway for Viral Transcription and Replication. The Journal of Physical Chemistry Letters 2023, 14 (45) , 10119-10128. https://doi.org/10.1021/acs.jpclett.3c01249
    20. Carles Navarro, Maciej Majewski, Gianni De Fabritiis. Top-Down Machine Learning of Coarse-Grained Protein Force Fields. Journal of Chemical Theory and Computation 2023, 19 (21) , 7518-7526. https://doi.org/10.1021/acs.jctc.3c00638
    21. Tianjiao Li, Chenxi Tian, Atieh Moridi, Jingjie Yeo. Elucidating Interfacial Dynamics of Ti–Al Systems Using Molecular Dynamics Simulation and Markov State Modeling. ACS Applied Materials & Interfaces 2023, 15 (43) , 50489-50498. https://doi.org/10.1021/acsami.3c09868
    22. Ru Wang, Hao Wang, Wenjian Liu, Ron Elber. Approximating First Hitting Point Distribution in Milestoning for Rare Event Kinetics. Journal of Chemical Theory and Computation 2023, 19 (19) , 6816-6826. https://doi.org/10.1021/acs.jctc.3c00315
    23. Valerio Rizzi, Simone Aureli, Narjes Ansari, Francesco Luigi Gervasio. OneOPES, a Combined Enhanced Sampling Method to Rule Them All. Journal of Chemical Theory and Computation 2023, 19 (17) , 5731-5742. https://doi.org/10.1021/acs.jctc.3c00254
    24. Dhiman Ray, Michele Parrinello. Kinetics from Metadynamics: Principles, Applications, and Outlook. Journal of Chemical Theory and Computation 2023, 19 (17) , 5649-5670. https://doi.org/10.1021/acs.jctc.3c00660
    25. Tegar N. Wijaya, Akio Kitao. Energetic and Kinetic Origins of CALB Interfacial Activation Revealed by PaCS-MD/MSM. The Journal of Physical Chemistry B 2023, 127 (34) , 7431-7441. https://doi.org/10.1021/acs.jpcb.3c02041
    26. Nicolai Kozlowski, Helmut Grubmüller. Uncertainties in Markov State Models of Small Proteins. Journal of Chemical Theory and Computation 2023, 19 (16) , 5516-5524. https://doi.org/10.1021/acs.jctc.3c00372
    27. Mahdi Ghorbani, Bernard R. Brooks, Jeffery B. Klauda. Conformational Fluctuations in β2-Microglubulin Using Markov State Modeling and Molecular Dynamics. The Journal of Physical Chemistry B 2023, 127 (31) , 6887-6895. https://doi.org/10.1021/acs.jpcb.3c02473
    28. Daniel Nagel, Sofia Sartore, Gerhard Stock. Toward a Benchmark for Markov State Models: The Folding of HP35. The Journal of Physical Chemistry Letters 2023, 14 (31) , 6956-6967. https://doi.org/10.1021/acs.jpclett.3c01561
    29. Artur Meller, Soumendranath Bhakat, Shahlo Solieva, Gregory R. Bowman. Accelerating Cryptic Pocket Discovery Using AlphaFold. Journal of Chemical Theory and Computation 2023, 19 (14) , 4355-4363. https://doi.org/10.1021/acs.jctc.2c01189
    30. Suguru Kato, Shoji Takada, Sotaro Fuchigami. Particle Smoother to Assimilate Asynchronous Movie Data of High-Speed AFM with MD Simulations. Journal of Chemical Theory and Computation 2023, 19 (14) , 4678-4688. https://doi.org/10.1021/acs.jctc.2c01268
    31. Diego E. Kleiman, Diwakar Shukla. Active Learning of the Conformational Ensemble of Proteins Using Maximum Entropy VAMPNets. Journal of Chemical Theory and Computation 2023, 19 (14) , 4377-4388. https://doi.org/10.1021/acs.jctc.3c00040
    32. Michael S. Jones, Zachary A. McDargh, Rafal P. Wiewiora, Jesus A. Izaguirre, Huafeng Xu, Andrew L. Ferguson. Molecular Latent Space Simulators for Distributed and Multimolecular Trajectories. The Journal of Physical Chemistry A 2023, 127 (25) , 5470-5490. https://doi.org/10.1021/acs.jpca.3c01362
    33. Vinayak Bhat, Connor P. Callaway, Chad Risko. Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials. Chemical Reviews 2023, 123 (12) , 7498-7547. https://doi.org/10.1021/acs.chemrev.2c00704
    34. Bhupendra R. Dandekar, Navjeet Ahalawat, Suman Sinha, Jagannath Mondal. Markov State Models Reconcile Conformational Plasticity of GTPase with Its Substrate Binding Event. JACS Au 2023, 3 (6) , 1728-1741. https://doi.org/10.1021/jacsau.3c00151
    35. Yohei Munei, Kowit Hengphasatporn, Yuta Hori, Ryuhei Harada, Yasuteru Shigeta. Determination of the Association between Mesotrione Sensitivity and Conformational Change of 4-Hydroxyphenylpyruvate Dioxygenase via Free-Energy Analyses. Journal of Agricultural and Food Chemistry 2023, 71 (24) , 9528-9537. https://doi.org/10.1021/acs.jafc.3c01253
    36. W. G. Noid. Perspective: Advances, Challenges, and Insight for Predictive Coarse-Grained Models. The Journal of Physical Chemistry B 2023, 127 (19) , 4174-4207. https://doi.org/10.1021/acs.jpcb.2c08731
    37. Tarak Karmakar, Aaron R. Finney, Matteo Salvalaglio, A. Ozgur Yazaydin, Claudio Perego. Non-Equilibrium Modeling of Concentration-Driven processes with Constant Chemical Potential Molecular Dynamics Simulations. Accounts of Chemical Research 2023, 56 (10) , 1156-1167. https://doi.org/10.1021/acs.accounts.2c00811
    38. Sneha Menon, Jagannath Mondal. Conformational Plasticity in α-Synuclein and How Crowded Environment Modulates It. The Journal of Physical Chemistry B 2023, 127 (18) , 4032-4049. https://doi.org/10.1021/acs.jpcb.3c00982
    39. Anthony J. Dominic III, Siqin Cao, Andrés Montoya-Castillo, Xuhui Huang. Memory Unlocks the Future of Biomolecular Dynamics: Transformative Tools to Uncover Physical Insights Accurately and Efficiently. Journal of the American Chemical Society 2023, 145 (18) , 9916-9927. https://doi.org/10.1021/jacs.3c01095
    40. Navjeet Ahalawat, Mohammad Sahil, Jagannath Mondal. Resolving Protein Conformational Plasticity and Substrate Binding via Machine Learning. Journal of Chemical Theory and Computation 2023, 19 (9) , 2644-2657. https://doi.org/10.1021/acs.jctc.2c00932
    41. Kirill Shmilovich, Andrew L. Ferguson. Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-Fly Data-Driven Discovery of and Enhanced Sampling in Slow Collective Variables. The Journal of Physical Chemistry A 2023, 127 (15) , 3497-3517. https://doi.org/10.1021/acs.jpca.3c00505
    42. Anupam Anand Ojha, Saumya Thakur, Surl-Hee Ahn, Rommie E. Amaro. DeepWEST: Deep Learning of Kinetic Models with the Weighted Ensemble Simulation Toolkit for Enhanced Sampling. Journal of Chemical Theory and Computation 2023, 19 (4) , 1342-1359. https://doi.org/10.1021/acs.jctc.2c00282
    43. Jonas Köhler, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, Frank Noé. Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics without Forces. Journal of Chemical Theory and Computation 2023, 19 (3) , 942-952. https://doi.org/10.1021/acs.jctc.3c00016
    44. Friederike Schmid. Understanding and Modeling Polymers: The Challenge of Multiple Scales. ACS Polymers Au 2023, 3 (1) , 28-58. https://doi.org/10.1021/acspolymersau.2c00049
    45. Xiaokun Hong, Kaiyuan Song, Mueed Ur Rahman, Ting Wei, Yan Zhang, Lin-Tai Da, Hai-Feng Chen. Phosphorylation Regulation Mechanism of β2 Integrin for the Binding of Filamin Revealed by Markov State Model. Journal of Chemical Information and Modeling 2023, 63 (2) , 605-618. https://doi.org/10.1021/acs.jcim.2c01177
    46. Lorenzo Casalino, Christian Seitz, Julia Lederhofer, Yaroslav Tsybovsky, Ian A. Wilson, Masaru Kanekiyo, Rommie E. Amaro. Breathing and Tilting: Mesoscale Simulations Illuminate Influenza Glycoprotein Vulnerabilities. ACS Central Science 2022, 8 (12) , 1646-1663. https://doi.org/10.1021/acscentsci.2c00981
    47. Marius T. Wenz, Miriam Bertazzon, Jana Sticht, Stevan Aleksić, Daniela Gjorgjevikj, Christian Freund, Bettina G. Keller. Target Recognition in Tandem WW Domains: Complex Structures for Parallel and Antiparallel Ligand Orientation in h-FBP21 Tandem WW. Journal of Chemical Information and Modeling 2022, 62 (24) , 6586-6601. https://doi.org/10.1021/acs.jcim.1c01426
    48. Sharon Emily Stone, Dhiman Ray, Ioan Andricioaei. Force-Field-Dependent DNA Breathing Dynamics: A Case Study of Hoogsteen Base Pairing in A6-DNA. Journal of Chemical Information and Modeling 2022, 62 (24) , 6749-6761. https://doi.org/10.1021/acs.jcim.2c00519
    49. Joseph R. Persichetti, Yang Jiang, Phillip S. Hudson, Edward P. O’Brien. Modeling Ensembles of Enzyme Reaction Pathways with Hi-MSM Reveals the Importance of Accounting for Pathway Diversity. The Journal of Physical Chemistry B 2022, 126 (47) , 9748-9758. https://doi.org/10.1021/acs.jpcb.2c04496
    50. Talant Ruzmetov, Ruben Montes, Jianan Sun, Si-Han Chen, Zhiye Tang, Chia-en A. Chang. Binding Kinetics Toolkit for Analyzing Transient Molecular Conformations and Computing Free Energy Landscapes. The Journal of Physical Chemistry A 2022, 126 (46) , 8761-8770. https://doi.org/10.1021/acs.jpca.2c05499
    51. Shiqi Gong, Xinheng He, Qi Meng, Zhiming Ma, Bin Shao, Tong Wang, Tie-Yan Liu. Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics. The Journal of Physical Chemistry B 2022, 126 (46) , 9465-9475. https://doi.org/10.1021/acs.jpcb.2c03711
    52. Vangelis Daskalakis. Deciphering the QR Code of the CRISPR-Cas9 System: Synergy between Gln768 (Q) and Arg976 (R). ACS Physical Chemistry Au 2022, 2 (6) , 496-505. https://doi.org/10.1021/acsphyschemau.2c00041
    53. Yan Xie, Shelley D. Minteer, Scott Banta, Scott Calabrese Barton. Markov State Study of Electrostatic Channeling within the Tricarboxylic Acid Cycle Supercomplex. ACS Nanoscience Au 2022, 2 (5) , 414-421. https://doi.org/10.1021/acsnanoscienceau.2c00011
    54. Jaehyeok Jin, Alexander J. Pak, Aleksander E. P. Durumeric, Timothy D. Loose, Gregory A. Voth. Bottom-up Coarse-Graining: Principles and Perspectives. Journal of Chemical Theory and Computation 2022, 18 (10) , 5759-5791. https://doi.org/10.1021/acs.jctc.2c00643
    55. Divya Rai, Satyajit Khatua, Srabani Taraphder. Structure and Dynamics of the Isozymes II and IX of Human Carbonic Anhydrase. ACS Omega 2022, 7 (35) , 31149-31166. https://doi.org/10.1021/acsomega.2c03356
    56. Dylan Novack, Lei Qian, Gwyneth Acker, Vincent A. Voelz, Richard H. G. Baxter. Oncogenic Mutations in the DNA-Binding Domain of FOXO1 that Disrupt Folding: Quantitative Insights from Experiments and Molecular Simulations. Biochemistry 2022, 61 (16) , 1669-1682. https://doi.org/10.1021/acs.biochem.2c00224
    57. David J Wales. Dynamical Signatures of Multifunnel Energy Landscapes. The Journal of Physical Chemistry Letters 2022, 13 (27) , 6349-6358. https://doi.org/10.1021/acs.jpclett.2c01258
    58. Marharyta Blazhynska, Emma Goulard Coderc de Lacam, Haochuan Chen, Benoît Roux, Christophe Chipot. Hazardous Shortcuts in Standard Binding Free Energy Calculations. The Journal of Physical Chemistry Letters 2022, 13 (27) , 6250-6258. https://doi.org/10.1021/acs.jpclett.2c01490
    59. Benjamin Pampel, Omar Valsson. Improving the Efficiency of Variationally Enhanced Sampling with Wavelet-Based Bias Potentials. Journal of Chemical Theory and Computation 2022, 18 (7) , 4127-4141. https://doi.org/10.1021/acs.jctc.2c00197
    60. Yao Li, Haipeng Gong. Identifying a Feasible Transition Pathway between Two Conformational States for a Protein. Journal of Chemical Theory and Computation 2022, 18 (7) , 4529-4543. https://doi.org/10.1021/acs.jctc.2c00390
    61. Lingyan Wang, Kun Xi, Lizhe Zhu, Lin-Tai Da. DNA Deformation Exerted by Regulatory DNA-Binding Motifs in Human Alkyladenine DNA Glycosylase Promotes Base Flipping. Journal of Chemical Information and Modeling 2022, 62 (13) , 3213-3226. https://doi.org/10.1021/acs.jcim.2c00091
    62. Alexis W. Mills, Joshua J. Goings, David Beck, Chao Yang, Xiaosong Li. Exploring Potential Energy Surfaces Using Reinforcement Machine Learning. Journal of Chemical Information and Modeling 2022, 62 (13) , 3169-3179. https://doi.org/10.1021/acs.jcim.2c00373
    63. Thomas Löhr, Kai Kohlhoff, Gabriella T. Heller, Carlo Camilloni, Michele Vendruscolo. A Small Molecule Stabilizes the Disordered Native State of the Alzheimer’s Aβ Peptide. ACS Chemical Neuroscience 2022, 13 (12) , 1738-1745. https://doi.org/10.1021/acschemneuro.2c00116
    64. Jacob M. Remington, Jonathon B. Ferrell, Severin T. Schneebeli, Jianing Li. Concerted Rolling and Penetration of Peptides during Membrane Binding. Journal of Chemical Theory and Computation 2022, 18 (6) , 3921-3929. https://doi.org/10.1021/acs.jctc.2c00014
    65. Matthew C. Chan, Erik Procko, Diwakar Shukla. Structural Rearrangement of the Serotonin Transporter Intracellular Gate Induced by Thr276 Phosphorylation. ACS Chemical Neuroscience 2022, 13 (7) , 933-945. https://doi.org/10.1021/acschemneuro.1c00714
    66. Briana L. Sobecks, Jiming Chen, Diwakar Shukla. Dual Role of Strigolactone Receptor Signaling Partner in Inhibiting Substrate Hydrolysis. The Journal of Physical Chemistry B 2022, 126 (11) , 2188-2195. https://doi.org/10.1021/acs.jpcb.1c10663
    67. Marcin Sobieraj, Piotr Setny. Granger Causality Analysis of Chignolin Folding. Journal of Chemical Theory and Computation 2022, 18 (3) , 1936-1944. https://doi.org/10.1021/acs.jctc.1c00945
    68. Stefano Motta, Lara Callea, Laura Bonati, Alessandro Pandini. PathDetect-SOM: A Neural Network Approach for the Identification of Pathways in Ligand Binding Simulations. Journal of Chemical Theory and Computation 2022, 18 (3) , 1957-1968. https://doi.org/10.1021/acs.jctc.1c01163
    69. Soumendranath Bhakat, Pär Söderhjelm. Flap Dynamics in Pepsin-Like Aspartic Proteases: A Computational Perspective Using Plasmepsin-II and BACE-1 as Model Systems. Journal of Chemical Information and Modeling 2022, 62 (4) , 914-926. https://doi.org/10.1021/acs.jcim.1c00840
    70. Soumajit Dutta, Balaji Selvam, Diwakar Shukla. Distinct Binding Mechanisms for Allosteric Sodium Ion in Cannabinoid Receptors. ACS Chemical Neuroscience 2022, 13 (3) , 379-389. https://doi.org/10.1021/acschemneuro.1c00760
    71. Zineb Belkacemi, Paraskevi Gkeka, Tony Lelièvre, Gabriel Stoltz. Chasing Collective Variables Using Autoencoders and Biased Trajectories. Journal of Chemical Theory and Computation 2022, 18 (1) , 59-78. https://doi.org/10.1021/acs.jctc.1c00415
    72. S. Kashif Sadiq, Abraham Muñiz Chicharro, Patrick Friedrich, Rebecca C. Wade. Multiscale Approach for Computing Gated Ligand Binding from Molecular Dynamics and Brownian Dynamics Simulations. Journal of Chemical Theory and Computation 2021, 17 (12) , 7912-7929. https://doi.org/10.1021/acs.jctc.1c00673
    73. Adam K. Nijhawan, Arnold M. Chan, Darren J. Hsu, Lin X. Chen, Kevin L. Kohlstedt. Resolving Dynamics in the Ensemble: Finding Paths through Intermediate States and Disordered Protein Structures. The Journal of Physical Chemistry B 2021, 125 (45) , 12401-12412. https://doi.org/10.1021/acs.jpcb.1c05820
    74. Sun-Ting Tsai, Zachary Smith, Pratyush Tiwary. SGOOP-d: Estimating Kinetic Distances and Reaction Coordinate Dimensionality for Rare Event Systems from Biased/Unbiased Simulations. Journal of Chemical Theory and Computation 2021, 17 (11) , 6757-6765. https://doi.org/10.1021/acs.jctc.1c00431
    75. Adam Antoszewski, Chatipat Lorpaiboon, John Strahan, Aaron R. Dinner. Kinetics of Phenol Escape from the Insulin R6 Hexamer. The Journal of Physical Chemistry B 2021, 125 (42) , 11637-11649. https://doi.org/10.1021/acs.jpcb.1c06544
    76. Michael S. Jones, Brennan Ashwood, Andrei Tokmakoff, Andrew L. Ferguson. Determining Sequence-Dependent DNA Oligonucleotide Hybridization and Dehybridization Mechanisms Using Coarse-Grained Molecular Simulation, Markov State Models, and Infrared Spectroscopy. Journal of the American Chemical Society 2021, 143 (42) , 17395-17411. https://doi.org/10.1021/jacs.1c05219
    77. Henrique F. Carvalho, Valerio Ferrario, Jürgen Pleiss. Molecular Mechanism of Methanol Inhibition in CALB-Catalyzed Alcoholysis: Analyzing Molecular Dynamics Simulations by a Markov State Model. Journal of Chemical Theory and Computation 2021, 17 (10) , 6570-6582. https://doi.org/10.1021/acs.jctc.1c00559
    78. Kirill A. Konovalov, Ilona Christy Unarta, Siqin Cao, Eshani C. Goonetilleke, Xuhui Huang. Markov State Models to Study the Functional Dynamics of Proteins in the Wake of Machine Learning. JACS Au 2021, 1 (9) , 1330-1341. https://doi.org/10.1021/jacsau.1c00254
    79. Steffen Schultze, Helmut Grubmüller. Time-Lagged Independent Component Analysis of Random Walks and Protein Dynamics. Journal of Chemical Theory and Computation 2021, 17 (9) , 5766-5776. https://doi.org/10.1021/acs.jctc.1c00273
    80. Aldo Glielmo, Brooke E. Husic, Alex Rodriguez, Cecilia Clementi, Frank Noé, Alessandro Laio. Unsupervised Learning Methods for Molecular Simulation Data. Chemical Reviews 2021, 121 (16) , 9722-9758. https://doi.org/10.1021/acs.chemrev.0c01195
    81. Kun Xi, Zhenquan Hu, Qiang Wu, Meihan Wei, Runtong Qian, Lizhe Zhu. Assessing the Performance of Traveling-salesman based Automated Path Searching (TAPS) on Complex Biomolecular Systems. Journal of Chemical Theory and Computation 2021, 17 (8) , 5301-5311. https://doi.org/10.1021/acs.jctc.1c00182
    82. Robin J. Shannon, Emilio Martínez-Núñez, Dmitrii V. Shalashilin, David R. Glowacki. ChemDyME: Kinetically Steered, Automated Mechanism Generation through Combined Molecular Dynamics and Master Equation Calculations. Journal of Chemical Theory and Computation 2021, 17 (8) , 4901-4912. https://doi.org/10.1021/acs.jctc.1c00335
    83. Toru Ekimoto, Takafumi Kudo, Tsutomu Yamane, Mitsunori Ikeguchi. Mechanism of Vitamin D Receptor Ligand-Binding Domain Regulation Studied by gREST Simulations. Journal of Chemical Information and Modeling 2021, 61 (7) , 3625-3637. https://doi.org/10.1021/acs.jcim.1c00534
    84. Francesco Trozzi, Xinlei Wang, Peng Tao. UMAP as a Dimensionality Reduction Tool for Molecular Dynamics Simulations of Biomacromolecules: A Comparison Study. The Journal of Physical Chemistry B 2021, 125 (19) , 5022-5034. https://doi.org/10.1021/acs.jpcb.1c02081
    85. Chi-Jui Feng, Anton Sinitskiy, Vijay Pande, Andrei Tokmakoff. Computational IR Spectroscopy of Insulin Dimer Structure and Conformational Heterogeneity. The Journal of Physical Chemistry B 2021, 125 (18) , 4620-4633. https://doi.org/10.1021/acs.jpcb.1c00399
    86. John Strahan, Adam Antoszewski, Chatipat Lorpaiboon, Bodhi P. Vani, Jonathan Weare, Aaron R. Dinner. Long-Time-Scale Predictions from Short-Trajectory Data: A Benchmark Analysis of the Trp-Cage Miniprotein. Journal of Chemical Theory and Computation 2021, 17 (5) , 2948-2963. https://doi.org/10.1021/acs.jctc.0c00933
    87. Ernesto Suárez, Rafal P. Wiewiora, Chris Wehmeyer, Frank Noé, John D. Chodera, Daniel M. Zuckerman. What Markov State Models Can and Cannot Do: Correlation versus Path-Based Observables in Protein-Folding Models. Journal of Chemical Theory and Computation 2021, 17 (5) , 3119-3133. https://doi.org/10.1021/acs.jctc.0c01154
    88. Kiyoto Aramis Tanemura, Susanta Das, Kenneth M. Merz Jr.. AutoGraph: Autonomous Graph-Based Clustering of Small-Molecule Conformations. Journal of Chemical Information and Modeling 2021, 61 (4) , 1647-1656. https://doi.org/10.1021/acs.jcim.0c01492
    89. François Sicard, Vladimir Koskin, Alessia Annibale, Edina Rosta. Position-Dependent Diffusion from Biased Simulations and Markov State Model Analysis. Journal of Chemical Theory and Computation 2021, 17 (4) , 2022-2033. https://doi.org/10.1021/acs.jctc.0c01151
    90. Maohua Yang, Yegen Tang, Jingwei Weng, Zhijun Liu, Wenning Wang. The Role of Calcium in Regulating the Conformational Dynamics of d-Galactose/d-Glucose-Binding Protein Revealed by Markov State Model Analysis. Journal of Chemical Information and Modeling 2021, 61 (2) , 891-900. https://doi.org/10.1021/acs.jcim.0c01119
    91. Neda Ojaghlou, Justin Airas, Lauren M. McRae, Cooper A. Taylor, Bill R. Miller, III, Carol A. Parish. Understanding the Structure and Apo Dynamics of the Functionally Active JIP1 Fragment. Journal of Chemical Information and Modeling 2021, 61 (1) , 324-334. https://doi.org/10.1021/acs.jcim.0c01008
    92. Navjeet Ahalawat, Jagannath Mondal. An Appraisal of Computer Simulation Approaches in Elucidating Biomolecular Recognition Pathways. The Journal of Physical Chemistry Letters 2021, 12 (1) , 633-641. https://doi.org/10.1021/acs.jpclett.0c02785
    93. Maxim Ziatdinov, Shuai Zhang, Orion Dollar, Jim Pfaendtner, Christopher J. Mundy, Xin Li, Harley Pyles, David Baker, James J. De Yoreo, Sergei V. Kalinin. Quantifying the Dynamics of Protein Self-Organization Using Deep Learning Analysis of Atomic Force Microscopy Data. Nano Letters 2021, 21 (1) , 158-165. https://doi.org/10.1021/acs.nanolett.0c03447
    94. Jingwei Weng, Maohua Yang, Wenning Wang, Xin Xu, Zhongqun Tian. Revealing Thermodynamics and Kinetics of Lipid Self-Assembly by Markov State Model Analysis. Journal of the American Chemical Society 2020, 142 (51) , 21344-21352. https://doi.org/10.1021/jacs.0c09343
    95. Sergio Decherchi, Andrea Cavalli. Thermodynamics and Kinetics of Drug-Target Binding by Molecular Simulation. Chemical Reviews 2020, 120 (23) , 12788-12833. https://doi.org/10.1021/acs.chemrev.0c00534
    96. Alexander-Maurice Illig, Birgit Strodel. Performance of Markov State Models and Transition Networks on Characterizing Amyloid Aggregation Pathways from MD Data. Journal of Chemical Theory and Computation 2020, 16 (12) , 7825-7839. https://doi.org/10.1021/acs.jctc.0c00727
    97. Daniel Nagel, Anna Weber, Gerhard Stock. MSMPathfinder: Identification of Pathways in Markov State Models. Journal of Chemical Theory and Computation 2020, 16 (12) , 7874-7882. https://doi.org/10.1021/acs.jctc.0c00774
    98. Eugen Hruska, Vivekanandan Balasubramanian, Hyungro Lee, Shantenu Jha, Cecilia Clementi. Extensible and Scalable Adaptive Sampling on Supercomputers. Journal of Chemical Theory and Computation 2020, 16 (12) , 7915-7925. https://doi.org/10.1021/acs.jctc.0c00991
    99. Joan F. Gilabert, Oriol Gracia Carmona, Anders Hogner, Victor Guallar. Combining Monte Carlo and Molecular Dynamics Simulations for Enhanced Binding Free Energy Estimation through Markov State Models. Journal of Chemical Information and Modeling 2020, 60 (11) , 5529-5539. https://doi.org/10.1021/acs.jcim.0c00406
    100. Janani Sampath, Sarah Alamdari, Jim Pfaendtner. Closing the Gap Between Modeling and Experiments in the Self-Assembly of Biomolecules at Interfaces and in Solution. Chemistry of Materials 2020, 32 (19) , 8043-8059. https://doi.org/10.1021/acs.chemmater.0c01891
    Load more citations
    Download PDF

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    Pair your accounts.

    Export articles to Mendeley

    Get article recommendations from ACS based on references in your Mendeley library.

    You’ve supercharged your research process with ACS and Mendeley!

    STEP 1:
    Click to create an ACS ID

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    Please note: If you switch to a different device, you may be asked to login again with only your ACS ID.

    MENDELEY PAIRING EXPIRED
    Your Mendeley pairing has expired. Please reconnect