Biomedical subjects

Chakrapani Kalyanaraman

Publications and source records attributed to Chakrapani Kalyanaraman.

5 recordsLinked to original sources

PubMed2006

Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides.

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 (water) dielectric environments. The passive membrane permeabilities of 11 cyclic peptides were obtained experimentally using a parallel artificial membrane permeability assay (PAMPA) and showed a linear correlation with the computational results with R(2) = 0.96. In general, the results support the hypothesis, already well established in the literature, that the ability to form internal hydrogen bonds is critical for passive membrane permeability and can be the distinguishing factor among closely related compounds, such as those studied here. However, we have found that the number of internal hydrogen bonds that can form in the membrane and the solvent-exposed polar surface area correlate more poorly with PAMPA permeability than our model, which quantitatively estimates the solvation free energy losses upon moving from high-dielectric water to the low-dielectric interior of a membrane.

Amino Acid Sequence↗

PubMed2006

Molecular mechanics methods for predicting protein-ligand binding.

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 briefly review methods that use molecular dynamics and Monte Carlo simulations to predict absolute and relative ligand binding free energies, as well as our own work in which we have developed a physics-based scoring method that can be applied to hundreds of thousands of compounds by invoking a number of simplifying approximations. In our previous studies, we have demonstrated that our scoring method is a promising approach for improving the discrimination between ligands that are known to bind and those that are presumed not to, in virtual screening of large compound databases. In new results presented here, we explore several improvements to our computational method including modifying the dielectric constant used for the protein and ligand interiors, and empirically scaling energy terms to compensate for deficiencies in the energy model. Future directions for further improving our physics-based scoring method are also discussed.

Algorithms↗

PubMed2005

Virtual ligand screening against Escherichia coli dihydrofolate reductase: improving docking enrichment using physics-based methods.

Motivated by their participation in the McMaster Data-Mining and Docking Competition, the authors developed 2 new computational technologies and applied them to docking against Escherichia coli dihydrofolate reductase: a receptor preparation procedure that incorporates rotamer optimization of side chains and a physics-based rescoring procedure for estimating relative binding affinities of the protein-ligand complexes. Both methods use the same energy function, consisting of the all-atom OPLS-AA force field and a generalized Born solvent model, which treats the protein receptor and small-molecule ligands in a consistent manner. Thus, the energy function is similar to that used in more sophisticated approaches, such as free-energy perturbation and the molecular mechanics Poisson-Boltzmann/surface area, but sampling during the rescoring procedure is limited to simple energy minimization of the ligand. The use of a highly efficient minimization algorithm permitted the authors to apply this rescoring procedure to hundreds of thousands of protein-ligand complexes during the competition, using a modest Linux cluster. To test these methods, they used the 12 competitive inhibitors identified in the training set, plus methotrexate, as positive controls in enrichment studies with both the training and test sets, each containing 50,000 compounds. The key conclusion is that combining the receptor preparation and rescoring methods makes it possible to identify most of the positive controls within the top few tenths of a percent of the rank-ordered training and test set libraries.

Computational Biology↗

PubMed2005

Virtual screening against highly charged active sites: identifying substrates of alpha-beta barrel enzymes.

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 energy function based on an all-atom force field (OPLS-AA) and a generalized Born implicit solvent model. We evaluate the ability of this method to identify the known substrates of several members of the enolase superfamily of enzymes, including both holo and apo structures (11 total). The active sites of these enzymes contain numerous charged groups (lysines, carboxylates, histidines, and one or more metal ions) and thus provide a challenge for most docking scoring functions, which treat electrostatics and solvation in a highly approximate manner. Using the physics-based scoring procedure, the known substrate is ranked within the top 6% of the database in all cases, and in 8 of 11 cases, it is ranked within the top 1%. Moreover, the top-ranked ligands are strongly enriched in compounds with high chemical similarity to the substrate (e.g., different substitution patterns on a similar scaffold). These results suggest that our method can be used, in conjunction with other information including genomic context and known metabolic pathways, to suggest possible substrates or classes of substrates for experimental testing. More broadly, the physics-based scoring method performs well on highly charged binding sites and is likely to be useful in inhibitor docking against polar binding sites as well. The method is fast (<1 min per ligand), due largely to an efficient minimization algorithm based on the truncated Newton method, and thus, it can be applied to thousands of ligands within a few hours on a small Linux cluster.

Alanine Racemase↗

PubMedDate not supplied

Physics-based scoring of protein-ligand complexes: enrichment of known inhibitors in large-scale virtual screening.

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, therapeutically relevant enzymes are enriched against a background of approximately 100,000 drug-like decoys. The improvement in enrichment is most robust and dramatic within the top 1% of the ranked database, that is, the first thousand compounds; below the first few percent of the ranked database, there is little overall improvement. The improved early enrichment is likely due to the more realistic treatment of ligand and receptor desolvation in the rescoring procedure. We also present anecdotal but encouraging results assessing the ability of the rescoring method to predict specificity of inhibitors for structurally related proteins.

Biophysical Phenomena↗