Computational Modelling Group

Centre for Doctoral Training in Next Generation Computational Modelling

Hans Fangohr, Ian Hawke, Peter Horak (Investigators), Susanne Ufermann Fangohr, Thorsten Wittemeier, Kieran Selvon, Alvaro Perez-Diaz, David Lusher, Ashley Setter, Emanuele Zappia, Hossam Ragheb, Ryan Pepper, Stephen Gow, Jan Kamenik, Paul Chambers, Robert Entwistle, Rory Brown, Joshua Greenhalgh, James Harrison, Jonathon Waters, Ioannis Begleris, Craig Rafter

The £10million Centre for Doctoral Training was launched in November 2013 and is jointly funded by EPSRC, the University of Southampton, and its partners.

The NGCM brings together world-class simulation modelling research activities from across the University of Southampton and hosts a 4-year doctoral training programme that is the first of its kind in the UK.

Control and Prediction of the Organic Solid State

Richard Boardman

This project aims to produce a computer technology for the prediction of the crystal structure(s) of an organic molecule, that could be used even prior to the synthesis of the compound.

Such a computational study could be done relatively quickly to predict the dangers and opportunities of the solid phases of a molecule under development. Our project will develop the methods of experimental screening for polymorphs and their characterisation, and hence the combination will provide a major new technology for aiding industrial formulation.

How far can we stretch the MARTINI?

Syma Khalid (Investigator), Ric Gillams

To date, coarse-grained lipid models have generally been parameterised to ensure the correct prediction of structural properties of membranes, such as the area per lipid and the bilayer thickness. The work described here explores the extent to which coarse-grained models are able to predict correctly bulk properties of lipids (phase behaviour) as well as the mechanical properties, such as lateral pressure profiles and stored elastic stress in bilayers. Such an evaluation is crucial for understanding the predictive capabilities of coarse-grained models.

Immunotherapy Research: Modelling MHC Class I Complex Assembly

Timothy Elliott, Jorn Werner (Investigators), Alistair Bailey

This project uses mathematical modelling and simulation to investigate mechanisms by which our cells process and present biological information that is used by our immune system to distinguish between healthy and diseased cells.

Integrated in silico prediction of protein-protein interaction motifs

Richard Edwards (Investigator), Nicolas Palopoli, Kieren Lythgow

Many vital protein-protein interactions are mediated by Short Linear Motifs (SLiMs) which are short proteins typically 5-15 amino acids long containing only a few positions crucial to function. This project integrates a number of leading computational techniques to predict novel SLiMs and add crucial detail to protein-protein interaction networks.

Multiscale modelling of biological membranes

Jonathan Essex (Investigator), Mario Orsi

Biological membranes are complex and fascinating systems, characterised by proteins floating in a sea of lipids. Biomembranes, besides being the fundamental structures employed by nature to encapsulate cells, play crucial roles in many phenomena indispensable for life, such as growth, energy storage, and in general information transduction via neural activity. In this project, we develop and apply multiscale computational models to simulate biological membranes and obtain molecular-level insights into fundamental structures and phenomena.

The application and critical assessment of protein-ligand binding affinities

Jonathan Essex (Investigator), Ioannis Haldoupis

A method that can accurately predict the binding affinity of small molecules to a protein target would be imperative to pharmaceutical development due to the time and resources that could be saved. A head-to-head comparison of such methodology, ranging from approximate methods to more rigorous methods, is performed in order to assess their accuracy and utility across a range of targets.

The autotransporter ? domain: insights into structure and function through multi scale molecular dynamics simulations

Syma Khalid (Investigator), Daniel Holdbrook, Thomas Piggot

We are performing a series of molecular dynamics simulations involving all autotransporters with known structure. We aim to identify key structural and dynamic properties in this family of proteins.

Using Molecular Dynamics to Understand the Antibacterial Mechanisms of Daptomycin & Chlorhexidine to Target the Bacterial Membrane

This project aims to use molecular dynamics techniques to understand how antimicrobial peptides, daptomycin and chlorhexidine, disrupt both gram positive and negative cell membranes on an atomic level.

Using Molecular Dynamics to Understand the Antibacterial Mechanisms of Daptomycin & Chlorhexidine to Target the Bacterial Membrane

This project aims to use molecular dynamics techniques to understand how antimicrobial peptides, daptomycin and chlorhexidine, disrupt both gram positive and negative cell membranes on an atomic level.

Using Molecular Dynamics to Understand the Antibacterial Mechanisms of Daptomycin & Chlorhexidine to Target the Bacterial Membrane

Syma Khalid (Investigator), Eilish McBurnie

This project aims to use molecular dynamics techniques to understand how antimicrobial peptides, daptomycin and chlorhexidine, disrupt both gram positive and negative cell membranes on an atomic level.