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Using Molecular Simulation to Explore the Nanoscale Dynamics of the Plant Kinome.

IllinoisPlant Biology
Alexander S. Moffett and Diwakar Shukla
Biochemical Journal, Vol. 475, Issue 5, Pages 905-921, 2018.

Recruiting Machine Learning Methods for Molecular Simulations of Proteins

IllinoisMethods
Shriyaa Mittal and Diwakar Shukla
Molecular Simulation, Vol. 44, Issue 11, Pages 891-904, 2018.

Predicting Optimal DEER Label Positions to Study Protein Conformational Heterogeneity.

IllinoisMethods
Shriyaa Mittal and Diwakar Shukla
Journal of Physical Chemistry B, Volume 121, Issue 42, Pages 9761–9770, 2017.

Allosteric control of a plant receptor kinase through S-glutathionylation.

IllinoisPlant Biology
Alexander S. Moffett, Kyle W. Bender, Steven C. Huber, and Diwakar Shukla
Biophysical Journal, Volume 113, Issue 11, Pages 2354-2363, 2017.

Enhanced unbiased sampling of protein dynamics using evolutionary coupling information.

IllinoisMethods
Zahra Shamsi*, Alexander S. Moffett* and Diwakar Shukla. * indicates co-first author.
Scientific Reports, 7, Article number: 12700, 2017. doi:10.1038/s41598-017-12874-7

Molecular dynamics simulations reveal the conformational dynamics of Arabidopsis thaliana BRI1 and BAK1 receptor-like kinases.

IllinoisPlant Biology
Alexander S. Moffett, Kyle W. Bender, Steven C. Huber and Diwakar Shukla
Journal of Biological Chemistry, 292, 30, 12643–12652, 2017.

Crops in silico: A prospectus from the Plants in silico symposium and workshop

IllinoisPlant Biology
Amy Marshall-Colon, Stephen P. Long, Douglas K. Allen, Gabrielle Allen, Daniel A. Beard, Bedrich Benes, Susanne von Caemmerer, AJ Christensen, Donna J. Cox, John C. Hart, Peter M. Hirst, Kavya Kannan, Daniel S. Katz, Jonathan P. Lynch, Andrew J. Millar, Balaji Panneerselvam, Nathan D. Price, David Raila, Rachel G. Shekar, Stuti Shrivastava, Diwakar Shukla, Venkatraman Srinivasan, Mark Stitt, Eberhard O. Voit, Yu Wang, Xinyou Yin, Xin-Guang Zhu
Frontiers in Plant Science, Vol. 8, Article 786, 2017. doi: 10.3389/fpls.2017.00786

Dynamic-Template-Directed Multiscale Assembly for Large-Area Coating of Highly-Aligned Conjugated Polymer Thin Films

Illinois
Erfan Mohammadi, Chuankai Zhao, Y. Meng, Fengjiao Zhang, Ge Qu, X. Zhao, Jianguo Mei, J. M. Zuo, Diwakar Shukla, Ying Diao.
Nature Communications, Vol. 8, Article number: 16070, 2017. doi:10.1038/ncomms16070

Markov State Model Reveals Slow Folding Phase of NuG2.

Stanford
C. Schwantes, D. Shukla & V. S. Pande,
Biophysical Journal, Volume 110, Issue 8, p1716–1719, 2016.

A Transition Path Theory Analysis of The Activating Transition in c-Src Kinase Domain

Faculty of 1000 (F1000)MethodsStanford
Yilin Meng, Diwakar Shukla, Vijay Pande and Benoît Roux
Proceedings of the National Academy of Sciences, Vol. 113, No. 33, 9193–9198, 2016.

Application of Hidden Markov Models in Biomolecular Simulations

Book ChapterIllinois
Saurabh Shukla, Zahra Shamsi, Alexander S. Moffett, Balaji Selvam and Diwakar Shukla*
Methods in Molecular Biology, Hidden Markov Models, pp 29-41, 2017.

Conformational Heterogeneity of the Calmodulin Binding Interface.

IllinoisStanford
Diwakar Shukla*, Ariana Peck* and Vijay S. Pande
Nature Communications, 7, Article number: 10910, 2016. doi:10.1038/ncomms10910

Heat Dissipation Guides Activation in Signaling Proteins

MethodsStanford
Jeffrey K. Weber, Diwakar Shukla, and Vijay S. Pande
Proceedings of National Academy of Sciences USA, 112, 33, 10377–10382, 2015.

A Network of Molecular Switches Control the Activation of Key Bacterial Signaling Protein

Stanford
Dan K. Vanatta, Diwakar Shukla, Morgan Lawrenz & Vijay S. Pande
Nature Communications, 6, Article number: 7283, 2015. doi:10.1038/ncomms8283

Elucidating Ligand-Modulated Conformational Landscape of GPCRs Using Cloud-computing Approaches.

Book ChapterIllinoisMethodsStanford
Diwakar Shukla*, Morgan Lawrenz and Vijay S. Pande
Methods in Enzymology, 557, 551-572, 2015.

Markov State Models Provide Insights into Dynamic Modulation of Protein Function.

Stanford
Diwakar Shukla, Carlos X. Hernandez, Jeffrey K. Weber and Vijay S. Pande
Accounts of Chemical Research, 48 (2), 414–422, 2015.

Cloud computing approaches for predicting ligand-binding poses and pathways

MethodsStanford
Morgan Lawrenz, Diwakar Shukla and Vijay S. Pande
Scientific Reports, 5, Article number: 7918, 2015. doi:10.1038/srep07918

Conserve Water: A Method for the Analysis of Solvent in Molecular Dynamics

MethodsStanford
Matthew P. Harrigan, Diwakar Shukla and Vijay S. Pande
Journal of Chemical Theory and Computation, 11 (3), 1094–1101, 2015.

Activation pathway of Src kinase reveals intermediate states as targets for drug design.

Faculty of 1000 (F1000)Stanford
Diwakar Shukla, Yilin Meng, Benoit Roux and Vijay S. Pande
Nature Communications, 5, Article number: 3397 (2014) doi:10.1038/ncomms4397

Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways.

Faculty of 1000 (F1000)Stanford
K. J. Kohlhoff*, D. Shukla*, M. Lawrenz*, G. R. Bowman, D. E. Konerding, D. Belov, R. B. Altman & V. S. Pande
Nature Chemistry 6, 15–21 (2014) doi:10.1038/nchem.1821 *denotes co-first author

Data-driven drug discovery: integration of multiple information sources to generate kinase inhibitor candidates

Stanford
Lili Peng, Morgan Lawrenz, Diwakar Shukla, Grace Tang, Vijay S. Pande and Russ B. Altman
bioRxiv, 2016

Automatic Order Parameters Selection In Markov State Models for Atomistic Understanding of Molecular Dynamics Data.

MethodsStanford
Mohammad M. Sultan, Gert Kiss, Diwakar Shukla & Vijay S. Pande
Journal of Chemical Theory and Computation, 12, 10, 5217-5223, 2014.

Complex Pathways in Folding of Protein G Explored by Simulation and Experiment.

Stanford
Lisa J. Lapidus, Srabasti Acharya, Christian R. Schwantes, Ling Wu, Diwakar Shukla, Michael King, Stephen J. DeCamp & Vijay S. Pande
Biophysical Journal, 107, 4, 947-955, 2014.

To Milliseconds and Beyond: Challenges in the Simulation of Protein Folding.

Stanford
T. J. Lane, D. Shukla, K. A. Beauchamp & V. S. Pande
Current Opinion in Structural Biology, 23, 1, 58-65, (2013).