Brian J. Bennion

Adverse drug reaction prediction using scores produced by large-scale drug-protein target docking on high-performance computing machines.

Late-stage or post-market identification of adverse drug reactions (ADRs) is a significant public health issue and a source of major economic liability for drug development. Thus, reliable in silico screening of drug candidates for possible ADRs would be advantageous. In this work, we introduce a computational approach that predicts ADRs by combining the results of molecular docking and leverages known ADR information from DrugBank and SIDER. We employed a recently parallelized version of AutoDock Vina (VinaLC) to dock 906 small molecule drugs to a virtual panel of 409 DrugBank protein targets. L1-regularized logistic regression models were trained on the resulting docking scores of a 560 compound subset from the initial 906 compounds to predict 85 side effects, grouped into 10 ADR phenotype groups. Only 21% (87 out of 409) of the drug-protein binding features involve known targets of the drug subset, providing a significant probe of off-target effects. As a control, associations of this drug subset with the 555 annotated targets of these compounds, as reported in DrugBank, were used as features to train a separate group of models. The Vina off-target models and the DrugBank on-target models yielded comparable median area-under-the-receiver-operating-characteristic-curves (AUCs) during 10-fold cross-validation (0.60–0.69 and 0.61–0.74, respectively). Evidence was found in the PubMed literature to support several putative ADR-protein associations identified by our analysis. Among them, several associations between neoplasm-related ADRs and known tumor suppressor and tumor invasiveness marker proteins were found. A dual role for interstitial collagenase in both neoplasms and aneurysm formation was also identified. These associations all involve off-target proteins and could not have been found using available drug/on-target interaction data. This study illustrates a path forward to comprehensive ADR virtual screening that can potentially scale with increasing number of CPUs to tens of thousands of protein targets and millions of potential drug candidates.

Computational Enzymology and Organophosphorus Degrading Enzymes: Promising Approaches Toward Remediation Technologies of Warfare Agents and Pesticides

The re-emergence of chemical weapons as a global threat in hands of terrorist groups, together with an increasing number of pesticides intoxications and environmental contaminations worldwide, has called the attention of the scientific community for the need of improvement in the technologies for detoxification of organophosphorus (OP) compounds. A compelling strategy is the use of bioremediation by enzymes that are able to hydrolyze these molecules to harmless chemical species. Several enzymes have been studied and engineered for this purpose. However, their mechanisms of action are not well understood. Theoretical investigations may help elucidate important aspects of these mechanisms and help in the development of more efficient bio-remediators. In this review, we point out the major contributions of computational methodologies applied to enzyme based detoxification of OPs. Furthermore, we highlight the use of PTE, PON, DFP, and BuChE as enzymes used in OP detoxification process and how computational tools such as molecular docking, molecular dynamics simulations and combined quantum mechanical/molecular mechanics have and will continue to contribute to this very important area of research.

Oximes: inhibitors of human recombinant acetylcholinesterase. A structure-activity relationship (SAR) study.

Acetylcholinesterase (AChE) reactivators were developed for the treatment of organophosphate intoxication. Standard care involves the use of anticonvulsants (e.g., diazepam), parasympatolytics (e.g., atropine) and oximes that restore AChE activity. However, oximes also bind to the active site of AChE, simultaneously acting as reversible inhibitors. The goal of the present study is to determine how oxime structure influences the inhibition of human recombinant AChE (hrAChE). Therefore, 24 structurally different oximes were tested and the results compared to the previous eel AChE (EeAChE) experiments. Structural factors that were tested included the number of pyridinium rings, the length and structural features of the linker, and the number and position of the oxime group on the pyridinium ring.

A wrench in the works of human acetylcholinesterase: soman induced conformational changes revealed by molecular dynamics simulations.

Irreversible inactivation of human acetylcholinesterase (hAChE) by organophosphorous pesticides (OPs) and chemical weapon agents (CWA) has severe morbidity and mortality consequences. We present data from quantum mechanics/molecular mechanics (QM/MM) and 80 classical molecular dynamics (MD) simulations of the apo and soman-adducted forms of hAChE to investigate the effects on the dynamics and protein structure when the catalytic Serine 203 is phosphonylated. We find that the soman phosphonylation of the active site Ser203 follows a water assisted addition-elimination mechanism with the elimination of the fluoride ion being the highest energy barrier at 6.5 kcal/mole. We observe soman-dependent changes in backbone and sidechain motions compared to the apo form of the protein. These alterations restrict the soman-adducted hAChE to a structural state that is primed for the soman adduct to be cleaved and removed from the active site. The altered motions and resulting structures provide alternative pathways into and out of the hAChE active site. In the soman-adducted protein both side and back door pathways are viable for soman adduct access. Correlation analysis of the apo and soman adducted MD trajectories shows that the correlation of gorge entrance and back door motion is disrupted when hAChE is adducted. This supports the hypothesis that substrate and product can use two different pathways as entry and exit sites in the apo form of the protein. These alternative pathways have important implications for the rational design of medical countermeasures.

Predicting a Drug’s Membrane Permeability: A Computational Model Validated With in Vitro Permeability Assay Data

Membrane permeability is a key property to consider during the drug design process, and particularly vital when dealing with small molecules that have intracellular targets as their efficacy highly depends on their ability to cross the membrane. In this work, we describe the use of umbrella sampling molecular dynamics (MD) computational modeling to comprehensively assess the passive permeability profile of a range of compounds through a lipid bilayer. The model was initially calibrated through in vitro validation studies employing a parallel artificial membrane permeability assay (PAMPA). The model was subsequently evaluated for its quantitative prediction of permeability profiles for a series of custom synthesized and closely related compounds. The results exhibited substantially improved agreement with the PAMPA data, relative to alternative existing methods. Our work introduces a computational model that underwent progressive molding and fine-tuning as a result of its synergistic collaboration with numerous in vitro PAMPA permeability assays. The presented computational model introduces itself as a useful, predictive tool for permeability prediction.

Quantitative assessment of electrostatic embedding in Density Functional Theory calculations of biomolecular systems

We evaluate the accuracy of density functional theory quantum calculations of biomolecular subsystems using a simple electrostatic embedding scheme. Our scheme is based on dividing the system of interest into a primary and secondary subsystem. A finite difference discretization of the Kohn-Sham equations is used for the primary subsystem, while its electrostatic environment is modeled with a simple one-electron potential. Force-field atomic partial charges are used to generate smeared Gaussian charge densities and to model the secondary subsystem. We illustrate the utility of this approach with calculations of truncated dipeptide chains. We analyze quantitatively the accuracy of this approach by calculating atomic forces and comparing results with full QM calculations. The impact of the choice made in terminating dangling bonds at the frontier of the QM region is also investigated.

Large-Scale First-Principles Molecular Dynamics Simulations with Electrostatic Embedding: Application to Acetylcholinesterase Catalysis

Enzymes are complicated solvated systems that typically require many atoms to simulate their function with any degree of accuracy. We have recently developed numerical techniques for large scale first-principles molecular dynamics simulations and applied them to the study of the enzymatic reaction catalyzed by acetylcholinesterase. We carried out density functional theory calculations for a quantum-mechanical (QM) subsystem consisting of 612 atoms with an O(N) complexity finite-difference approach. The QM subsystem is embedded inside an external potential field representing the electrostatic effect due to the environment. We obtained finite-temperature sampling by first-principles molecular dynamics for the acylation reaction of acetylcholine catalyzed by acetylcholinesterase. Our calculations show two energy barriers along the reaction coordinate for the enzyme-catalyzed acylation of acetylcholine. The second barrier (8.5 kcal/mol) is rate-limiting for the acylation reaction and in good agreement with experiment.