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Recruiting Machine Learning Methods for Molecular Simulations of Proteins

Illinois
Shriyaa Mittal and Diwakar Shukla
In review, 2017.

Computational Biophysics and the Plant Kinome

Illinois
Alexander S. Moffett and Diwakar Shukla
In review, 2017.

Predicting Optimal DEER Label Positions to Study Protein Conformational Heterogeneity.

Illinois
Shriyaa Mittal and Diwakar Shukla
Journal of Physical Chemistry B, In press, 2017.

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

Illinois
Alexander S. Moffett, Kyle W. Bender, Steven C. Huber, and Diwakar Shukla
Biophysical Journal, In press, 2017.

Universality of Sodium Ion Binding Mechanism in G-Protein-Coupled Receptors

Illinois
Balaji Pannerselvam, Zahra Shamsi and Diwakar Shukla
In review, 2017.

Free energy landscape of the complete transport cycle in a key bacterial transporter

Illinois
Balaji Pannerselvam*, Shriyaa Mittal* and Diwakar Shukla
In review, 2017.

Enhanced unbiased sampling of protein dynamics using evolutionary coupling information.

Illinois
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.

Illinois
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

Illinois
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)Stanford
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

Stanford
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 ChapterIllinoisStanford
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

Stanford
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

Stanford
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.

Stanford
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).

OpenMM 4.0: A Reusable, Extensible, Hardware Independent Library for High Performance Molecular Simulation.

Stanford
P. Eastman, M. S. Friedrichs, J. D. Chodera, R. J. Radmer, C. M. Bruns, J. P. Ku, K. A. Beauchamp, T. J. Lane, L.-P. Wang, D. Shukla, T. Tye, M. Houston, T. Stich, C. Klein, M. R. Shirts, & V. S. Pande
Journal of Chemical Theory & Computation, 9, 1, 461-469, (2013).

Understanding the Role of Arginine and Citrate as Eluents in Affinity Chromatography.

Book ChapterMIT
D. Shukla and B. L. Trout
Developments in Biotechnology and Bioprocessing, ACS Symposium Series, Vol 1125, 67-86, 2013

Effects of Solute-Solute Interactions on Protein Stability Studied Using Various Counterions and Dendrimers.

MIT
D. Shukla*, C. P. Schneider* and B. L. Trout
PLoS One, Vol.6, No. 11, e27665, 2011

Complex Interactions between Molecular Ions in Solution and Their Effect on Protein Stability.

MIT
D. Shukla, C. P. Schneider and B. L. Trout
Journal of American Chemical Society, 133, 46, 18713-18718, 2011.

Understanding the Synergistic Effect of Arginine and Glutamic Acid Mixtures on Protein Solubility.

MIT
D. Shukla and B. L. Trout
Journal of Physical Chemistry B, 115, 41, 11831-11839, 2011.

Effect of PAMAM Dendrimer Salts on Protein Stability.

MIT
D. Shukla, C. P. Schneider and B. L. Trout
Journal of Physical Chemistry Letters, 2, 14, 1782-1788, 2011.

Molecular Level Insight Into Intra-Solvent Interaction Effects on Protein Stability and Aggregation.

MIT
D. Shukla, C. P. Schneider and B. L. Trout
Advanced Drug Delivery Reviews, 63, 1074-1085 , 2011.

Arginine and the Hofmeister Series: The Role of Ion-Ion Interactions in Protein Aggregation Suppression.

MIT
D. Shukla*, C. P. Schneider* and B. L. Trout
Journal of Physical Chemistry B, 115, 22, 7447-7458, 2011.

Understanding The Role of Arginine as an Eluent in Affinity Chromatography via Molecular Computations.

MIT
D. Shukla, L. Zamolo, C. Cavallotti, and B. L. Trout
Journal of Physical Chemistry B, 115, 11, 2645-2654, 2011.

Preferential Interaction Coefficients of Proteins in Aqueous Arginine Solutions and its Molecular Origins.

MIT
D. Shukla and B. L. Trout
Journal of Physical Chemistry B, 115, 5, 1243-1253, 2011.

Interaction of Arginine with Proteins and the Mechanism by Which it Inhibits Aggregation.

MIT
D. Shukla and B. L. Trout
Journal of Physical Chemistry B, 114, 42, 13426-13438, 2010.

Molecular Computations of Preferential Interaction Coefficients of Proteins.

MIT
D. Shukla, C. Shinde and B. L. Trout
Journal of Physical Chemistry B, 113, 37, 12456-12554, 2009.

Modeling of the Formation of Nanoparticles in Reverse Micellar Systems.

IIT Bombay
D. Shukla, and A. Mehra
Proceeding of the 17th International Congress of Chemical and Process Engineering, Prague, Czech Republic, 2006.

CaCO3 Nanoparticle Synthesis by Carbonation of Lime Solution in Microemulsion Systems

IIT Bombay
A. K. Sugih, D. Shukla, H. J. Heeres and A. Mehra
Nanotechnology, 18, 035607, 2007

A Monte Carlo Model for the Formation of Core-Shell Nanocrystals in Reverse Micellar Systems.

IIT Bombay
R. Jain, D. Shukla and A. Mehra
Industrial & Engineering Chemistry Research, 45, 1, 2249-2254, 2006.

Coagulation of Nanoparticles in Reverse Micellar Systems: A Monte Carlo Model.

IIT Bombay
R. Jain, D. Shukla and A. Mehra
Langmuir, 21, 1, 11528-11533, 2005.