Summary: Molecular dynamics (MD) determines the physical motions of atoms of a biological macromolecule in a cell-like environment and is an important method in structural bioinformatics. Traditionally, measurements such as root mean square deviation, root mean square fluctuation, radius of gyration, and various energy measures have been used to analyze MD simulations. Here, we present MD-TASK, a novel software suite that employs graph theory techniques, perturbation response scanning, and dynamic cross-correlation to provide unique ways for analyzing MD trajectories. Availability and implementation: MD-TASK has been open-sourced and is available for download from https://github.com/RUBi-ZA/MD-TASK, implemented in Python and supported on Linux/Unix.

Understanding molecular mechanisms underlying the complexity of allosteric regulation in proteins has attracted considerable attention in drug discovery due to the benefits and versatility of allosteric modulators in providing desirable selectivity against protein targets while minimizing toxicity and other side effects. The proliferation of novel computational approaches for predicting ligand–protein interactions and binding using dynamic and network-centric perspectives has led to new insights into allosteric mechanisms and facilitated computer-based discovery of allosteric drugs. Although no absolute method of experimental and in silico allosteric drug/site discovery exists, current methods are still being improved. As such, the critical analysis and integration of established approaches into robust, reproducible, and customizable computational pipelines with experimental feedback could make allosteric drug discovery more efficient and reliable. In this article, we review computational approaches for allosteric drug discovery and discuss how these tools can be utilized to develop consensus workflows for in silico identification of allosteric sites and modulators with some applications to pathogen resistance and precision medicine. The emerging realization that allosteric modulators can exploit distinct regulatory mechanisms and can provide access to targeted modulation of protein activities could open opportunities for probing biological processes and in silico design of drug combinations with improved therapeutic indices and a broad range of activities.

MODE-TASK, a novel and versatile software suite, comprises Principal Component Analysis, Multidimensional Scaling, and t-Distributed Stochastic Neighbor Embedding techniques using Molecular Dynamics trajectories. MODE-TASK also includes a Normal Mode Analysis tool based on Anisotropic Network Model so as to provide a variety of ways to analyse and compare large-scale motions of protein complexes for which long MD simulations are prohibitive. Beside the command line function, a GUI has been developed as a PyMOL plugin. MODE-TASK is open source, and available for download from https://github.com/RUBi-ZA/MODE-TASK. It is implemented in Python and C++. It is compatible with Python 2.x and Python 3.x and can be installed by Conda. Supplementary data are available at Bioinformatics online.

Drug resistance in HIV treatment is still a worldwide problem. Predicting resistance to antiretrovirals (ARVs) before starting any treatment is important. Prediction accuracy is essential, as low-accuracy predictions increase the risk of prescribing sub-optimal drug regimens leading to patients developing resistance sooner. Artificial Neural Networks (ANNs) are a powerful tool that would be able to assist in drug resistance prediction. In this study, we constrained the dataset to subtype B, sacrificing generalizability for a higher predictive performance, and demonstrated that the predictive quality of the ANN regression models have definite improvement for most ARVs. Trained regression ANNs were optimized for eight protease inhibitors, six nucleoside reverse transcriptase (RT) inhibitors and four non-nucleoside RT inhibitors by experimenting combinations of rare variant filtering (none versus 1 residue occurrence) and ANN topologies (1–3 hidden layers with 2, 4, 6, 8 and 10 nodes per layer). Single hidden layers (5–20 nodes) were used for training where overfitting was detected. 5-fold cross-validation produced mean R2 values over 0.95 and standard deviations lower than 0.04 for all but two antiretrovirals. Overall, higher accuracies and lower variances (compared to results published in 2016) were obtained by experimenting with various preprocessing methods, while focusing on the most prevalent subtype in the raw dataset (subtype B).We thus highlight the need to develop and make available subtype-specific datasets for developing higher accuracy in drug-resistance prediction methods.

Background: The renin- angiotensin system (RAS) plays an important role in regulating blood pressure and controlling sodium levels in the blood. Hyperactivity of this system has been linked to numerous conditions including hypertension, kidney disease, and congestive heart failure. Three classes of drugs have been developed to inhibit RAS. In this study, we provide a structure-based analysis of the effect of single nucleotide variants (SNVs) on the interaction between renin and angiotensinogen with the aim of revealing important residues and potentially damaging variants for further inhibitor design purposes. Objectives: To identify SNVs that have functional and potentially damaging effects on the renin-angiotensinogen complex and to use computational approaches to investigate how SNVs might have damaging effects. Methods: A comprehensive set of all known SNVs in the renin and angiotensinogen proteins was extracted from the HUMA database. This dataset was filtered by removing synonymous and missense variants and using the VAPOR pipeline to predict which variants were likely to be deleterious. Variants in the filtered dataset were modeled into the renin-angiotensinogen complex using MODELLER and subjected to molecular dynamics simulations using GROMACS. The residue interaction networks of the resultant trajectories were analyzed using graph theory. Conclusions: This research identified important SNVs in the interface of RAS and showed how they might affect the function of the proteins. For instance, the mutant complex containing the variant P40L in angiotensinogen caused instability in the complex, indicating that this mutation plays an important role in disrupting the interaction between renin and angiotensinogen. The mutant complex containing the SNV A188V in renin was shown to have significantly increased fluctuation in the residue interaction networks. D104N in renin, associated with renal tubular dysgenesis, caused increased rigidity in the protein complex comparison to the wild type, which probably in turn negatively affects the function of RAS.