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Vijay S Pande

Publications and source records attributed to Vijay S Pande.

3 recordsLinked to original sources

Application of new informatics tools for identifying allosteric lead ligands of the c-Src kinase

Recent molecular dynamics (MD) simulations of the catalytic domain of the c-Src kinase revealed intermediate conformations with a potentially druggable allosteric pocket adjacent to the C-helix, bound by 8-anilino-1-naphthalene sulfonate. Towards confirming the existence of this pocket, we have developed a novel lead enrichment protocol using new target and lead enrichment software to identify sixteen allosteric lead ligands of the c-Src kinase. First, Markov State Models analysis was used to identify the most statistically significant c-Src target conformations from all MD-simulated conformations. The most statistically relevant candidate MSM targets were then prioritized by assessing how well each reproduced binding poses of ligands specific to the ATP-competitive and allosteric pockets. The top-performing MSM targets, identified by receiver-operating curve analysis, were then used to screen the ZINC library of 13 million clean, drug-like ligands, all of which prioritized based on their empirical scoring function, binding pose consistency across MSM targets, and strong hydrogen bonding and hydrophobic interactions with Src residues. The FragFEATURE knowledgebase of fragment-protein pocket interactions was then used to identify fragments specific to the ATP-competitive and allosteric pockets. This information was used to identify seven Type II and nine Type III lead ligands with binding poses supported by fragment predictions. Of these, Type II lead ligands, ZINC13037947 and ZINC09672647, and Type III lead ligands, ZINC12530852 and ZINC30012975, exhibited the most favorable fragment profiles and are recommended for further experimental testing for the existence of the allosteric pocket in Src.

Bioinformatics

MDTraj: a modern, open library for the analysis of molecular dynamics trajectories

Summary: MDTraj is a modern, lightweight and efficient software package for analyzing molecular dynamics simulations. MDTraj reads trajectory data from a wide variety of commonly used formats. It provides a large number of trajectory analysis capabilities including RMSD, DSSP secondary structure assignment and the extraction of common order parameters. The package has a strong focus on interoperability with the wider scientific Python ecosystem, bridging the gap between molecular dynamics data and the rapidly-growing collection of industry-standard statistical analysis and visualization tools in Python. Availability: Package downloads, detailed examples and full documentation are available at http://mdtraj.org. The source code is distributed under the GNU Lesser General Public License at https://github.com/simtk/mdtraj.

Bioinformatics

A Fast 3 x N Matrix Multiply Routine for Calculation of Protein RMSD

The bottleneck for the rapid calculation of the root-mean-square deviation in atomic coordinates (RMSD) between pairs of protein structures for large numbers of conformations is the evaluation of a 3 x N x N x 3 matrix product over conformation pairs. Here we describe two matrix multiply routines specialized for the 3 x N case that are able to significantly outperform (by up to 3X) off-the-shelf high-performance linear algebra libraries for this computation, reaching machine limits on performance. The routines are implemented in C and Python libraries, and are available at https://github.com/simtk/IRMSD.

Biophysics