Milind Misra

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

MOTIVATION Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. RESULTS Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.

Toward quantitative structure-property relationships for charge transfer rates of polycyclic aromatic hydrocarbons

Quantitative structure-property relationships (QSPRs) have been developed and assessed for predicting the reorganization energy of polycyclic aromatic hydrocarbons (PAHs). Preliminary QSPR models, based on a combination of molecular signature and electronic eigenvalue difference descriptors, have been trained using more than 200 PAHs. Monte Carlo cross-validation systematically improves the performance of the models through progressive reduction of the training set and selection of best performing training subsets. The final biased QSPR model yields correlation coefficients q(2) and r(2) of 0.7 and 0.8, respectively, and an estimated error in predicting reorganization energy of ±0.014 eV.

Conformational analysis of piperazine and piperidine analogs of GBR 12909: stochastic approach to evaluating the effects of force fields and solvent.

Analogs of the flexible dopamine reuptake inhibitor, GBR 12909 (1), may have potential utility in the treatment of cocaine abuse. As a first step in the 3D-QSAR modeling of the dopamine transporter (DAT)/serotonin transporter (SERT) selectivity of these compounds, we carried out conformational analyses of two analogs of 1: a piperazine (2) and a related piperidine (3). Ensembles of conformers consisting of local minima on the potential energy surface of the molecule were generated in the vacuum phase and in implicit solvent by random search conformational analysis using the Tripos and MMFF94 force fields. Some differences were noted in the conformer populations due to differences in the treatment of the tertiary amine nitrogen and ether oxygen atom types by the force fields. The force fields also differed in their descriptions of internal rotation around the C(sp3)–O(sp3) bond proximal to the bisphenyl moiety. Molecular orbital calculations at the HF/6-31G(d) and B3LYP/6-31G(d) levels of C–O internal rotation in model compound (5), designed to model the effect of the proximity of the bisphenyl group on C-O internal rotation, showed a broad region of low energy between −60° to 60° with minima at both −60° and 30° and a low rotational barrier at 0°, in closer agreement with the MMFF94 results than the Tripos results. Molecular mechanics calculations on model compound (6) showed that the MMFF94 force field was much more sensitive than the Tripos force field to the effects of the bisphenyl moiety on C–O internal rotation.

Understanding virulence mechanisms in M. tuberculosis infection via A circuit-based simulation framework

Tuberculosis (TB), caused by the bacterium Mycobacterium tuberculosis (Mtb), is a growing international health crisis. Mtb is able to persist in host tissues in a non-replicating persistent (NRP) or latent state. This presents a challenge in the treatment of TB. Latent TB can re-activate in 10% of individuals with normal immune systems, higher for those with compromised immune systems. A quantitative understanding of latency-associated virulence mechanisms may help researchers develop more effective methods to battle the spread and reduce TB associated fatalities. Leveraging BioXyces ability to simulate whole-cell and multi-cellular systems we are developing a circuit-based framework to investigate the impact of pathogenicity-associated pathways on the latency/reactivation phase of tuberculosis infection. We discuss efforts to simulate metabolic pathways that potentially impact the ability of Mtb to persist within host immune cells. We demonstrate how simulation studies can provide insight regarding the efficacy of potential anti-TB agents on biological networks critical to Mtb pathogenicity using a systems chemical biology approach.