News
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.
Pages
