Latest news for the Scientific Computing Research Unit
Congratulations to Dr Tharindu Senapathi for obtaining his PhD at the July 2021 graduation ceremony. Dr Senapathi’s doctoral thesis titled ‘Development of a Computational Platform for System Based High Throughput Drug Discovery Illustrated on Pneumococcal Sialidases’ focused on combining computational chemistry with bioinformatics methodologies in aid of furthering drug discovery and development.
Tharindu performed his doctoral research in the Computational and Modelling laboratory, where he developed his expertise in computational Glycoenzymology. His research revealed how sialic acid processing enzymes (sialyltransferases and sialidases) work. Using the FEARCF method, he discovered reaction mechanisms of Neuraminidases. These mechanisms made possible the rational design of Transition State Analogue inhibitor models. To discover inhibitors from existing large molecular databases, he developed a computational platform that links computational chemistry with computational biology making the designing of novel inhibitors using rigorous and accurate methods such as free energy methods easily accessible and useable.
Earlier this year, the Journal of Computational Chemistry published the paper reporting the first GPU accelerated coarse-grain numerical potential algorithm authored by Ananya Gangopadhyay, Simon Winberg and Kevin J. Naidoo. The paper titled ‘Anisotropic numerical potentials for coarse-grained modelling from high-speed multidimensional lookup table and interpolation algorithms’ reports on a new method for increasing the accuracy in classical molecular dynamics simulations of anisotropic systems that opens the way to produce accurate coarse grain potentials for MD simulations. This accuracy is made possible by a high-speed numerical potential which delivers computational performance comparable with complex coarse-grained analytic potentials which, in turn, enables improved physical and chemical accuracy.
One of the main problems in predicting protein-ligand binding is the complexity of performing reproducible protein conformational analysis and ligand binding calculations, using vetted methods and protocols. Protein-ligand binding prediction is central to the drug-discovery process.
Upon reading the paper reporting the open-source BRIDGE platform the editors of Bio Protocol, a journal that aims to make scientific discoveries open and reproducible (https://bio-protocol.org/) invited Kevin Naidoo to write a paper describing the protocol for free energy computations.
When faced with the investigation of the preferential binding of a series of ligands against a known target, the solution is not always evident from single structure analysis. An ensemble of structures generated from computer simulations is valuable; however, visual analysis of the extensive structural data can be overwhelming. Rapid analysis of trajectory data, with tools available in the Galaxy platform, can be used to understand key features and compare differences that inform the preferential ligand structure that favors binding. We illustrate this informatics approach by investigating the in-silico binding of a peptide and glycopeptide epitope of the glycoprotein Mucin 1 (MUC1) binding with the antibody AR20.5
Recently published in the Journal of Chemical information and Modelling from the SCRU Lab is BRIDGE see citation : An Open Platform for Reproducible High-Throughput Free Energy Simulations. This research was undertaken by Tharindu Senepathi as a part of his PhD project supervised by Prof. Kevin J. Naidoo and co-supervised by Dr. Christopher Barnett.
The paper presents BRIDGE (or the Biomolecular Reaction and Interaction Dynamics Global Environment), an open-source web platform developed with the aim to provide an environment for the design of reliable methods to conduct reproducible biomolecular simulations. Built on the Galaxy bioinformatics platform, BRIDGE is able to centralize components of workflow, including protocols for experimentation. This construction improves the accessibility, shareability, and reproducibility of computational methods for molecular simulations.
SCRU, in collaboration with researchers from the UK (from universities including the University of Southampton and Cambridge) have developed a Python library which automates relative protein−ligand binding free energy calculations in GROMACS. These free energy calculations are an increasingly promising approach for facilitating drug discovery.
An international effort aiming to alert the public to the prevalence of breast cancer. Breast cancer is, in South Africa as well as worldwide, one of the most common types of cancer and is the most commonly occurring cancer amongst South African women. According to statistics sourced from the National Cancer Registry, breast cancer mainly affects older women, with an estimated lifetime risk of 1 in 27. Breast cancer also affects men, although the lifetime risk is significantly lower affecting 1 in 763 men.
SARS-CoV-2, more commonly known as COVID-19 or simply the coronavirus, has consumed the medical and scientific research community for the majority of 2020. In particular, the foci have been a search for a vaccine to prevent illness and therapeutics to treat the critically ill. A recent article in GEN makes the point that the secret to a vaccine for COVID-19 or therapeutic may lie with glycans.
Congratulations to Matthew Coulson, who graduated in March with a MSc in Computational Science with distinction, his thesis entitled “Machine Learning Algorithm Development for Separating Cancer Sub-Classes”.
Researchers from across South Africa gathered in Johannesburg for the Centre for High Performance Computing (CHPC) Conference. The conference was held from the 1-5 of December 2019 and attracted researchers from South Africa’s top research institutions, including Wits University, University of Kwazulu Natal and Stellenbosch University.
The Scientific Computing Research Unit will be a hosting the Biomolecular Reaction and Interaction Dynamics Global Environment (BRIDGE) workshop on Thursday 5th December 2019. Professor Kevin Naidoo will open the workshop which will be presented by Dr Chris Barnett and Mr Tharindu Senapathi (PhD student). Dr Gerhard Venter presented a talk entitled "Predicting Thermodynamic Properties of Ionic Liquids — from Molecular Simulation to Machine Learning". Emre Kaya, Tharindu Senapathi, Tomás Bruce-Chwatt, Lenard Carroll and Tayla Wilson all presented posters at the conference. Lenard Carroll won 1st prize for the poster presentation.
The Scientific Computing Research Unit at the University of Cape Town is hosting its 10th annual Scientific Computing International Lecturer Series (SCILS).
The 2019 SCILS will be in the form of an interactive Workshop entitled “The link between Machine Learning and Machines” including some applications of Deep Learning & AI which will conducted by industry leaders from IBM OpenPower, Mellanox and NVIDIA.