MDTraj: A Modern Open Library for the Analysis of Molecular Dynamics Trajectories

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

Fast Parallel Algorithms for Short-Range Molecular Dynamics

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Matplotlib: a 2D graphics environment

Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249

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

Dictionary of protein secondary structure: Pattern recognition of hydrogen‐bonded and geometrical features

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Best Practices for Scientific Computing

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To 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

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

EMMA: 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

Valence and patterning of aromatic residues determine the phase behavior of prion-like domains

Martin, E. W., Holehouse, A. S., Peran, I., Farag, M., Incicco, J. J., Bremer, A., Grace, C. R., Soranno, A., Pappu, R. V., & Mittag, T. (2020) · Science · doi:10.1126/science.aaw8653 · Cited by 1,147

Circulating SARS-CoV-2 spike N439K variants maintain fitness while evading antibody-mediated immunity

Thomson, E. C., Rosen, L. E., Shepherd, J. G., Spreafico, R., da Silva Filipe, A., Wojcechowskyj, J. A., Davis, C., Piccoli, L., Pascall, D. J., Dillen, J., et al. (2021) · Cell · doi:10.1016/j.cell.2021.01.037 · Cited by 606

Predicting multiple conformations via sequence clustering and AlphaFold2

Wayment-Steele, H. K., Ojoawo, A., Otten, R., Apitz, J. M., Pitsawong, W., Hömberger, M., Ovchinnikov, S., Colwell, L., & Kern, D. (2024) · Nature · doi:10.1038/s41586-023-06832-9 · Cited by 545

Structure and dynamics of the active human parathyroid hormone receptor-1

Zhao, L.-H., Ma, S., Sutkeviciute, I., Shen, D.-D., Zhou, X. E., de Waal, P. W., Li, C.-Y., Kang, Y., Clark, L. J., Jean-Alphonse, F. G., et al. (2019) · Science · doi:10.1126/science.aav7942 · Cited by 256

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

Sivaraman, G., Krishnamoorthy, A. N., Baur, M., Holm, C., Stan, M., Csányi, G., Benmore, C., & Vázquez-Mayagoitia, Á. (2020) · npj Computational Materials · doi:10.1038/s41524-020-00367-7 · Cited by 194

A molecular mechanism for transthyretin amyloidogenesis

Yee, A. W., Aldeghi, M., Blakeley, M. P., Ostermann, A., Mas, P. J., Moulin, M., de Sanctis, D., Bowler, M. W., Mueller-Dieckmann, C., Mitchell, E. P., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-08609-z · Cited by 139

Conformational buffering underlies functional selection in intrinsically disordered protein regions

González-Foutel, N. S., Glavina, J., Borcherds, W. M., Safranchik, M., Barrera-Vilarmau, S., Sagar, A., Estaña, A., Barozet, A., Garrone, N. A., Fernandez-Ballester, G., et al. (2022) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00811-w · Cited by 118

Potential neutralizing antibodies discovered for novel corona virus using machine learning

Magar, R., Yadav, P., & Barati Farimani, A. (2021) · Scientific Reports · doi:10.1038/s41598-021-84637-4 · Cited by 114

PepNN: a deep attention model for the identification of peptide binding sites

Abdin, O., Nim, S., Wen, H., & Kim, P. M. (2022) · Communications Biology · doi:10.1038/s42003-022-03445-2 · Cited by 104

Molecular Insights from Conformational Ensembles via Machine Learning

Fleetwood, O., Kasimova, M. A., Westerlund, A. M., & Delemotte, L. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2019.12.016 · Cited by 93

Protein–protein binding pathways and calculations of rate constants using fully-continuous, explicit-solvent simulations

Saglam, A. S., & Chong, L. T. (2019) · Chemical Science · doi:10.1039/c8sc04811h · Cited by 90

SARS-CoV-2 Nsp16 activation mechanism and a cryptic pocket with pan-coronavirus antiviral potential

Vithani, N., Ward, M. D., Zimmerman, M. I., Novak, B., Borowsky, J. H., Singh, S., & Bowman, G. R. (2021) · Biophysical Journal · doi:10.1016/j.bpj.2021.03.024 · Cited by 84

Sequence-to-conformation relationships of disordered regions tethered to folded domains of proteins

Mittal, A., Holehouse, A. S., Cohan, M. C., & Pappu, R. V. (2018) · Journal of Molecular Biology · doi:10.1016/j.jmb.2018.05.012 · Cited by 72

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

APOGEE 2: multi-layer machine-learning model for the interpretable prediction of mitochondrial missense variants

Bianco, S. D., Parca, L., Petrizzelli, F., Biagini, T., Giovannetti, A., Liorni, N., Napoli, A., Carella, M., Procaccio, V., Lott, M. T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40797-7 · Cited by 44

Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms

Lambourne, L., Mattioli, K., Santoso, C., Sheynkman, G., Inukai, S., Kaundal, B., Berenson, A., Spirohn-Fitzgerald, K., Bhattacharjee, A., Rothman, E., et al. (2025) · Molecular Cell · doi:10.1016/j.molcel.2025.03.004 · Cited by 42

Glimps: A machine learning approach to resolution transformation for multiscale modeling

Louison, K. A., Dryden, I. L., & Laughton, C. A. (2021) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c00735 · Cited by 35

Un-gating and allosteric modulation of a pentameric ligand-gated ion channel captured by molecular dynamics

Martin, N. E., Malik, S., Calimet, N., Changeux, J.-P., & Cecchini, M. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005784 · Cited by 38

Protein representations: Encoding biological information for machine learning in biocatalysis

Harding-Larsen, D., Funk, J., Madsen, N. G., Gharabli, H., Acevedo-Rocha, C. G., Mazurenko, S., & Welner, D. H. (2024) · Biotechnology Advances · doi:10.1016/j.biotechadv.2024.108459 · Cited by 38

Protein p K a Prediction by Tree-Based Machine Learning

Chen, A. Y., Lee, J., Damjanovic, A., & Brooks, B. R. (2022) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.1c01257 · Cited by 37

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

KIF—Key Interactions Finder: A program to identify the key molecular interactions that regulate protein conformational changes

Crean, R. M., Slusky, J. S. G., Kasson, P. M., & Kamerlin, S. C. L. (2023) · The Journal of Chemical Physics · doi:10.1063/5.0140882 · Cited by 30

Molecular Mechanism of Brassinosteroid Perception by the Plant Growth Receptor BRI1

Aldukhi, F., Deb, A., Zhao, C., Moffett, A. S., & Shukla, D. (2020) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.9b09377 · Cited by 29

Dynamic design: manipulation of millisecond timescale motions on the energy landscape of cyclophilin A

(2020) · doi:10.1039/c9sc04696h · Cited by 29

The role of metabolism in shaping enzyme structures over 400 million years

(2025) · doi:10.1038/s41586-025-09205-6 · Cited by 28

ExEnDiff: An Experiment-Guided Diffusion Model for Protein Conformational Ensemble Generation

Liu, Y., Yu, Z., Lindsay, R. J., Lin, G., Chen, M., Sahoo, A., & Hanson, S. M. (2025) · PRX Life · doi:10.1103/prxlife.3.023013 · Cited by 28

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Conev, A., Rigo, M. M., Devaurs, D., Fonseca, A. F., Kalavadwala, H., de Freitas, M. V., Clementi, C., Zanatta, G., Antunes, D. A., & Kavraki, L. E. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad242 · Cited by 28

Cyanobacterial α-carboxysome carbonic anhydrase is allosterically regulated by the Rubisco substrate RuBP

Pulsford, S. B., Outram, M. A., Förster, B., Rhodes, T., Williams, S. J., Badger, M. R., Price, G. D., Jackson, C. J., & Long, B. M. (2024) · Science Advances · doi:10.1126/sciadv.adk7283 · Cited by 25

Structural delineation and computational design of SARS-CoV-2-neutralizing antibodies against Omicron subvariants

Moriyama, S., Anraku, Y., Taminishi, S., Adachi, Y., Kuroda, D., Kita, S., Higuchi, Y., Kirita, Y., Kotaki, R., Tonouchi, K., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-39890-8 · Cited by 22

AlphaFold-predicted protein structures and small-angle X-ray scattering: insights from an extended examination of selected data in the Small-Angle Scattering Biological Data Bank

Brookes, E., Rocco, M., Vachette, P., & Trewhella, J. (2023) · Journal of Applied Crystallography · doi:10.1107/s1600576723005344 · Cited by 21

Markov modeling reveals novel intracellular modulation of the human TREK-2 selectivity filter

Harrigan, M. P., McKiernan, K. A., Shanmugasundaram, V., Denny, R. A., & Pande, V. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00256-y · Cited by 19

The TPR domain of PgaA is a multifunctional scaffold that binds PNAG and modulates PgaB-dependent polymer processing

Pfoh, R., Subramanian, A. S., Huang, J., Little, D. J., Forman, A., DiFrancesco, B. R., Balouchestani-Asli, N., Kitova, E. N., Klassen, J. S., Pomès, R., et al. (2022) · PLOS Pathogens · doi:10.1371/journal.ppat.1010750 · Cited by 17

Accurate Predictions of Molecular Properties of Proteins via Graph Neural Networks and Transfer Learning

Wozniak, S., Janson, G., & Feig, M. (2025) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c01682 · Cited by 14

A Putative Binding Model of Nitazene Derivatives at the μ-Opioid Receptor

Clayton, J., Shi, L., Robertson, M. J., Skiniotis, G., Michaelides, M., Stavitskaya, L., & Shen, J. (2025) · Neuropharmacology · doi:10.1016/j.neuropharm.2025.110437 · Cited by 12

Dynamic ensembles of SARS-CoV-2 N-protein reveal head-to-head coiled-coil-driven oligomerization and phase separation

Hernandez, G., Martins, M. L., Fernandes, N. P., Veloso, T., Lopes, J., Gomes, T., & Cordeiro, T. N. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf502 · Cited by 12

Amino acid sequence encodes protein abundance shaped by protein stability at reduced synthesis cost

(2024) · doi:10.1002/pro.5239 · Cited by 12

Thermo Link: Bridging disulfide bonds and enzyme thermostability through database construction and machine learning prediction

Xu, R., Pan, Q., Zhu, G., Ye, Y., Xin, M., Wang, Z., Wang, S., Li, W., Wei, Y., Guo, J., et al. (2024) · Protein Science · doi:10.1002/pro.5097 · Cited by 12

Interfacial water confers transcription factors with dinucleotide specificity

(2025) · doi:10.1038/s41594-024-01449-6 · Cited by 11

SOP-MULTI: A Self-Organized Polymer-Based Coarse-Grained Model for Multidomain and Intrinsically Disordered Proteins with Conformation Ensemble Consistent with Experimental Scattering Data

Baratam, K., & Srivastava, A. (2024) · Journal of Chemical Theory and Computation · doi:10.1021/acs.jctc.4c00579 · Cited by 9

AlphaFold2 SLiM screen for LC3-LIR interactions in autophagy

Stuke, J. F. M., & Hummer, G. (2025) · Autophagy · doi:10.1080/15548627.2025.2493999 · Cited by 8

Nuclear Receptor Interdomain Communication is Mediated by the Hinge with Ligand Specificity

Hazarika, S., Yu, T., Biswas, A., Dube, N., Villalona, P., & Okafor, C. D. (2024) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168805 · Cited by 7

Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)

Deganutti, G., Pipito, L., Rujan, R. M., Weizmann, T., Griffin, P., Ciancetta, A., Moro, S., & Reynolds, C. A. (2025) · eLife · doi:10.7554/elife.96513 · Cited by 7

An allosteric network governs Tom70 conformational dynamics to coordinate mitochondrial import

Bachochin, M. J., McGuire, K. L., Cook, B. D., Ye, Q., Silletti, S., Corbett, K. D., Komives, E. A., & Herzik, M. A. (2026) · Structure · doi:10.1016/j.str.2025.11.011 · Cited by 7

Computational generation of proteins with predetermined three-dimensional shapes using ProteinSolver

Strokach, A., Becerra, D., Corbi-Verge, C., Perez-Riba, A., & Kim, P. M. (2021) · STAR Protocols · doi:10.1016/j.xpro.2021.100505 · Cited by 6

Molecular insights from conformational ensembles via machine learning

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drMD: Molecular Dynamics for Experimentalists

Shrimpton-Phoenix, E., Notari, E., Kluonis, T., & Wood, C. W. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2024.168918 · Cited by 6

Conditional diffusion with locality-aware modal alignment for generating diverse protein conformational ensembles

Wang, B., Wang, C., Chen, J., Liu, D., Sun, C., Zhang, J., Zhang, K., & Li, H. (2026) · Nature Machine Intelligence · doi:10.1038/s42256-026-01198-9 · Cited by 5

Structure of the malaria vaccine candidate Pfs48/45 and its recognition by transmission blocking antibodies

Ko, K.-T., Lennartz, F., Mekhaiel, D., Guloglu, B., Marini, A., Deuker, D. J., Long, C. A., Jore, M. M., Miura, K., Biswas, S., et al. (2022) · doi:10.1101/2022.05.24.493318 · Cited by 4

Molecular exaptation by the integrin αI domain

Hollis, J. A., Chan, M. C., Malik, H. S., & Campbell, M. G. (2025) · Science Advances · doi:10.1126/sciadv.adx9567 · Cited by 4

Conformational dynamics of a histidine molecular switch in a cation/proton antiporter

(2025) · doi:10.1016/j.bbabio.2025.149563 · Cited by 4

Across (Conformational) Space and (Relaxation) Time: Using Coarse-Grain Simulations to Probe the Intra- and Interdomain Dynamics of the Tau Protein

Marien, J., Prévost, C., & Sacquin-Mora, S. (2025) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.5c06282 · Cited by 4

SWEET Family Transporters Act as Water-Conducting Carrier Proteins in Plants

Selvam, B., Paul, A., Yu, Y.-C., Chen, L.-Q., & Shukla, D. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.5c00110 · Cited by 3

AlphaFold2 SLiM screen for LC3-LIR interactions in autophagy

(2024) · doi:10.1101/2024.09.06.611604 · Cited by 3

Anillin-related Mid1 as an adaptive and multimodal contractile ring anchoring protein: A simulation study

Hall, A. R., Choi, Y. K., Im, W., & Vavylonis, D. (2024) · Structure · doi:10.1016/j.str.2023.11.010 · Cited by 3

WatCon: A Python Tool for Analysis of Conserved Water Networks Across Protein Families

Brownless, A.-L. R., Harrison-Rawn, T., & Kamerlin, S. C. L. (2025) · JACS Au · doi:10.1021/jacsau.5c00447 · Cited by 2

Xport-A functions as a chaperone by stabilizing the first five transmembrane domains of rhodopsin-1

(2023) · doi:10.1016/j.isci.2023.108309 · Cited by 2

Deciphering the Cofilin Oligomers via Intermolecular Disulfide Bond Formation: A Coarse-Grained Molecular Dynamics Approach to Understanding Cofilin’s Regulation on Actin Filaments

Li, C., Wei, T.-Y., Cheung, M. S., & Tsai, M.-Y. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.3c07938 · Cited by 1

The TPR domain of PgaA is a multifunctional scaffold that binds PNAG and modulates PgaB-dependent polymer processing

(2023) · doi:10.32920/24746796 · Cited by 1

Conformational distributions of isolated myosin motor domains encode their mechanochemical properties

Porter, J. R., Meller, A., Zimmerman, M. I., Greenberg, M. J., & Bowman, G. R. (2019) · doi:10.1101/2019.12.16.878264

Structural dynamics of IRE1 and its interaction with unfolded peptides

Spinetti, E., Ścibisz, G., Karagöz, G. E., & Covino, R. (2026) · eLife · doi:10.7554/elife.106716

SMC complex unidirectionally translocates DNA by coupling segment capture with an asymmetric kleisin path

Yamauchi, M., Brandani, G. B., Terakawa, T., & Takada, S. (2026) · eLife · doi:10.7554/elife.106752