Latest news for the Scientific Computing Research Unit
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.
Dr Rizqah Kamies joined the Scientific Computing Research Unit in 2017 as a postdoctoral fellow. Rizqah managed the Cancer Translation Laboratory and worked on testing computational designs of key cancer targets inhibitors.
Dr Chris Barnett attended the Galaxy Community Conference 2019 in Freiburg, Germany. He presented on "Galaxy Computational Chemistry" and overviewed his collaborative research on creating computational tools, the BRIDGE platform (see the publication) and the Galaxy computational chemistry community. Applications for the tools include investigation of molecular conformation, protein dynamics and bio-orthogonal sugars. Tools for analysis of bio-orthogonal sugars and post analysis of ab initio MD simulations are in further development. Many of the tools are currently available at https://cheminformatics.usegalaxy.eu in collaboration with Björn Grüning, Simon Bray and the University of Freiburg.
Congratulations to Ananya Gangopadhyay, who graduated in April with a Masters Degree in Computational Science, with distinction, her thesis titled Accelerator-based Look-up Table for Coarse-grained simulations.