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Activation pathway of Src kinase reveals intermediate states as targets for drug design
Shukla, Diwakar[ca. July 2012 - February 2013]This dataset contains following items: 1) MD Simulation trajectories (~23000 Trajectories for a total simulation of ~450 microseconds with frames stores at 100 ps) containing only the protein atoms in lh5 format. The lh5 format files could be converted into any other MD trajectory format using MDTraj program (http://mdtraj.org). 2) 2000 State MSM model of c-src tyrosine kinase built from the MD simulation data. 3) 100 microseconds long trajectory with 20000 frames at an interval of 5 ns generated using the MSM.
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Conformational Heterogeneity of the Calmodulin Binding Interface
Shukla, Diwakar[ca. 2014]Calmodulin is a ubiquitous Ca2+ sensor and a crucial signaling hub in many pathways aberrantly activated in disease. However, the mechanistic basis of its ability to bind diverse signaling molecules including GPCRs, ion channels, and kinases remains poorly understood. Here we harness the high resolution of molecular dynamics simulations and the analytical power of Markov state models to dissect the molecular underpinnings of calmodulin binding diversity. Our computational model indicates that in the absence of Ca2+, substates in the folded ensemble of calmodulin’s C-terminal domain present chemically and sterically distinct topologies that may facilitate conformational selection. Furthermore, we find that local unfolding is off-pathway for the exchange process relevant for peptide binding, in contrast to prior hypotheses that unfolding might account for binding diversity. Finally, our model predicts a novel binding interface that is well-populated in the Ca2+-bound regime and thus a candidate for pharmacological intervention.
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Markov State Model of B2 Adrenergic Receptor analyzed in "Cloud-based simulations on Google Exacycle reveal ligand modulation of GPCR activation pathways."
Kohlhoff, Kai[ca. 2011 - 2013]Simulations can provide tremendous insight into the atomistic details of biological mechanisms, but micro- to millisecond timescales are historically only accessible on dedicated supercomputers. We demonstrate that cloud computing is a viable alternative that brings long-timescale processes within reach of a broader community. We used Google's Exacycle cloud-computing platform to simulate two milliseconds of dynamics of a major drug target, the G-protein-coupled receptor β2AR. Markov state models aggregate independent simulations into a single statistical model that is validated by previous computational and experimental results. Moreover, our models provide an atomistic description of the activation of a G-protein-coupled receptor and reveal multiple activation pathways. Agonists and inverse agonists interact differentially with these pathways, with profound implications for drug design.
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