Software Development for Life Sciences
Quantum Chemistry on GPUs (QSL) | Multidimensional Free Energy (FEARCF) | GPU Numerical Potential Coarse Grain MD (FEFI)
Reaction Dynamics and Inhibitor Design
Role of Glycosyltransferases in Cancer | Role of Neuraminidases in Respiratory Diseases | Glycomimetic Inhibitor Design
Visualisation and Data Analytics
Virtual HTS Software(BRIDGE) | Differential Gene Expression Analytics | Cancer Class Discovery Software (DASOM)
Translational Science
Breast Cancer Biomarker Development | Breast Cancer Biomarker Clinical Trails
Glycobiomedical Therapeutics from Mechanisms
Glycomimetic TSA synthesis | Glycoenzyme Expression and Structure | TSA Inhibitor HTS | Cellular Assays


Friday, 28 January 2022
PhD Awarded

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.

Publication Date:
Thu, 09 Sep 2021 - 16:15
Anisotropic numerical potentials for coarse-grained modeling from high-speed multidimensional lookup table and interpolation algorithms

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.


Publication Date:
Tue, 20 Oct 2020 - 15:00
BRIDGE: An Open Platform for Reproducible Protein-Ligand Simulations and Free Energy of Binding Calculations

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 ( invited Kevin Naidoo to write a paper describing the protocol for free energy computations.


Publication Date:
Tue, 20 Oct 2020 - 15:00
Comparative ligand structural analytics illustrated on variably glycosylated MUC1 antigen–antibody binding

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

Publication Date:
Tue, 20 Oct 2020 - 15:00