Introducing Thetis: a comprehensive suite for event detection in molecular dynamics

A suite of computer programs has been developed under the general name Thetis, for monitoring structural changes during molecular dynamics (MD) simulations on proteins. Conformational analysis includes estimation of structural similarities during the simulation and analysis of the secondary structure with emphasis on helices. In contrast to available freeware dealing with MD snapshots, Thetis can be used on a series of consecutive MD structures, thus allowing a detailed conformational analysis over the time course of the simulation

Molecular simulations and visualization: introduction and overview

Here we provide an introduction and overview of current progress in the field of molecular simulation and visualization, touching on the following topics: (1) virtual and augmented reality for immersive molecular simulations; (2) advanced visualization and visual analytic techniques; (3) new developments in high performance computing; and (4) applications and model building

Haptic-assisted interactive molecular docking incorporating receptor flexibility

Haptic-assisted interactive docking tools immerse the user in an environment where intuition and knowledge can be used to help guide the docking process. Here we present such a tool where the user “holds” a rigid ligand via a haptic device through which they feel interaction forces with a flexible receptor biomolecule. To ensure forces transmitted through the haptic device are smooth and stable, they must be updated at a rate greater than 500 Hz. Due to this time constraint, the majority of haptic docking tools do not attempt to model the conformational changes that would occur when molecules interact during binding. Our haptic-assisted docking tool, “Haptimol Flexidock”, models a receptor’s conformational response to forces of interaction with a ligand whilst maintaining the required haptic refresh rate. In order to model receptor flexibility we use the method of linear response for which we determine the variance-covariance matrix of atomic fluctuations from the trajectory of an explicit-solvent Molecular Dynamics simulation of the ligand-free receptor molecule. Key to satisfying the time constraint is an eigenvector decomposition of the variance-covariance matrix which enables a good approximation to the conformational response of the receptor to be calculated rapidly. This exploits a feature of protein dynamics whereby most fluctuation occurs within a relatively small subspace. The method is demonstrated on Glutamine Binding Protein in interaction with glutamine, and Maltose Binding Protein in interaction with maltose. For both proteins, the movement that occurs when the ligand is docked near to its binding site matches the experimentally determined movement well. It is thought that this tool will be particularly useful for structure-based drug design

Identification of structural requirements of estrogen receptor modulators using pharmacoinformatics techniques for application to estrogen therapy

An attempt was made in the present study to explore the structural requirements of known estrogen receptor (ER) modulators for biological activity using pharmacoinformatics approaches to elucidate critical functionalities for new, potent and less toxic chemical agents for successful application in estrogen therapy. For this purpose a group of non-steroidal ligands, 7-thiabicyclo[2.2.1]hept-2-ene-7- oxide derivatives were collected from the literature to perform quantitative structure-activity relationship (QSAR), pharmacophore and molecular docking studies. The 2D QSAR models (R2 α = 0.857, seα = 0.370, Q2 α = 0.848, R2 pred-α = 0.675, spα = 0.537; R2 β = 0.874, seβ = 0.261, Q2 β = 0.859, R2 pred-β = 0.659, spβ = 0.408) explained that hydrophobicity and molar refractivity were crucial for binding affinity in both α- and β-subtypes. The space modeling study (R2 α = 0.955, seα = 1.311, Q2 α = 0.932, R2 pred-α = 0.737, spα = 0.497; R2 β = 0.885, seβ = 1.328, Q2 β = 0.878, R2 pred-β = 0.769, spβ = 0.336) revealed the importance of HB donor and hydrophobic features for both subtypes, whereas, HB acceptor and aromatic ring were critical for α- and β-subtypes respectively. The functionalities developed in the QSAR and pharmacophore studies were substantiated by molecular docking which provided the preferred orientation of ligands for effective interaction at the active site cavity.MA Islam and TS Pillay were funded by the University of Pretoria Vice Chancellor’s post-doctoral fellowship and National Research Foundation (NRF), South Africa Innovation Post-doctoral fellowship schemes.http://link.springer.com/journal/442017-03-31hb2016Chemical Patholog

High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets

Computer-Aided Drug Design of Neuraminidase Inhibitors and MCL-1 Specific Drugs

Ph.DDOCTOR OF PHILOSOPH

The development of sialidase inhibitors using structure-based drug design

The sialidases/neuraminidases represent a family of enzymes whose function is important in the pathogenicity of bacteria and the virulence of influenza. Relenza and Tamiflu represent two drugs that were developed using structure-based drug design (SBDD) and computational-assisted drug design (CADD). These drugs target the active site of the influenza neuraminidase A and B (GH-34 family). Sialidases in the GH-33 family could represent novel drug targets for the treatment of bacterial or parasitic infection. SBDD was employed to develop chemical tools of two GH-33 sialidases, NanB and TcTS. NanB is a potential drug target for S. pneumoniae. The chemical tool developed for NanB follows on from work within the Taylor and Westwood research groups, in which a molecule of CHES and a glycerol were found serendipitously bound within a water channel at an allosteric site. Using this information as a basis for SBDD an allosteric inhibitor of NanB, Optactin was developed. Within this work, synthesis of this inhibitor was achieved and optimised. Optactin was then modified to improve potency. This proceeded through an amide analogue and addition of an arene resulting in a mid- micromolar inhibitor (IC₅₀: 55.4±2.5 µM). Addition of polar substituents improved potency further resulting in a low micromolar inhibitor of NanB, Optactamide (IC₅₀: 3.0±1.7 µM). Application of this tool in vitro demonstrated that NanB and NanA have a role in invasion of S. pneumoniae into lung epithelial cells. TcTS is a potential drug target for the treatment of Chagas disease. A CADD approach using a fragment library was unsuccessful at identifying an allosteric inhibitor of TcTS despite structural similarity with NanB. A re-task of the CADD approach towards the active site was successful in identifying an inhibitor of TcTS and a fragment useful for further development. This work sets the groundwork for the development of a chemical tool targeting TcTS