Chakrapani Kalyanaraman

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Ligand binding affinity prediction is one of the most important applications of computational chemistry. However, accurately ranking compounds with respect to their estimated binding affinities to a biomolecular target remains highly challenging. We provide an overview of recent work using molecular mechanics energy functions to address this challenge. We(More)
We report an atomistic physical model for the passive membrane permeability of cyclic peptides. The computational modeling was performed in advance of the experiments and did not involve the use of "training data". The model explicitly treats the conformational flexibility of the peptides by extensive conformational sampling in low (membrane) and high(More)
The protein databases contain many proteins with unknown function. A computational approach for predicting ligand specificity that requires only the sequence of the unknown protein would be valuable for directing experiment-based assignment of function. We focused on a family of unknown proteins in the mechanistically diverse enolase superfamily and used(More)
This review describes studies of particular enzymatically catalyzed reactions to investigate the possibility that catalysis is mediated by protein dynamics. That is, evolution has crafted the protein backbone of the enzyme to direct vibrations in such a fashion to speed reaction. The review presents the theoretical approach we have used to investigate this(More)
We demonstrate that using an all-atom molecular mechanics force field combined with an implicit solvent model for scoring protein-ligand complexes is a promising approach for improving inhibitor enrichment in the virtual screening of large compound databases. The rescoring method is evaluated by the extent to which known binders for nine diverse,(More)
The structure of an uncharacterized member of the enolase superfamily from Oceanobacillus iheyensis (GI 23100298, IMG locus tag Ob2843, PDB entry 2OQY ) was determined by the New York SGX Research Center for Structural Genomics (NYSGXRC). The structure contained two Mg(2+) ions located 10.4 A from one another, with one located in the canonical position in(More)
We have developed a computational approach to aid the assignment of enzymatic function for uncharacterized proteins that uses homology modeling to predict the structure of the binding site and in silico docking to identify potential substrates. We apply this method to proteins in the functionally diverse enolase superfamily that are homologous to the(More)
The rapid advance in genome sequencing presents substantial challenges for protein functional assignment, with half or more of new protein sequences inferred from these genomes having uncertain assignments. The assignment of enzyme function in functionally diverse superfamilies represents a particular challenge, which we address through a combination of(More)
We have developed a virtual ligand screening method designed to help assign enzymatic function for alpha-beta barrel proteins. We dock a library of approximately 19,000 known metabolites against the active site and attempt to identify the relevant substrate based on predicted relative binding free energies. These energies are computed using a physics-based(More)
The identification of novel metabolites and the characterization of their biological functions are major challenges in biology. X-ray crystallography can reveal unanticipated ligands that persist through purification and crystallization. These adventitious protein-ligand complexes provide insights into new activities, pathways and regulatory mechanisms. We(More)