AMBER

Amber, an acronym for Assisted Model Building with Energy Refinement, is a family of force fields for molecular dynamics of biomolecules originally developed by the late Peter Kollman's group at the University of California, San Francisco. AMBER is also the name for the molecular dynamics software package that simulates these force fields. It is maintained by an active collaboration between David Case at Rutgers University, Tom Cheatham at the University of Utah, Tom Darden at NIEHS, Ken Merz at Florida, Carlos Simmerling at Stony Brook University, Ray Luo at UC Irvine, and Junmei Wang at Encysive Pharmaceuticals. (Read more on Wikipedia).

For queries about this topic, contact Hans Fangohr.

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Projects

Can we calculate the pKa of new drugs, based on their structure alone?

Chris-Kriton Skylaris (Investigator), Chris Pittock, Jacek Dziedzic

The pKa of an active compound in a pharmaceutical drug affects how it is absorbed and distributed around the human body. While there are various computational methods to predict pKa using only molecular structure data, these tend to be specialised to only one class of drug - we aim to generate a more generalised prediction method using quantum mechanics.

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.

Electrostatic embedded energy calculations of proteins, using the ONETEP DFT code

Chris-Kriton Skylaris (Investigator), Stephen Fox, Chris Pittock

Calculating the energy of a biomolecule in solvent, using quantum mechanics (QM) is possible, but extremely challenging, even with linear-scaling QM methods like ONETEP. Using electrostatic embedding, a novel twist on the existing QM/MM method is used to calculate the binding energy of a small ligand to a solvated protein, increasing the accuracy and realism of our general project work.

Hybrid quantum and classical free energy methods in computational drug optimisation

Jonathan Essex, Chris-Kriton Skylaris (Investigators), Christopher Cave-Ayland

This work is based around the application of thermodynamics and quantum mechanics to the field of computational drug design and optimisation. Through the application of these theories the calculation of the physical properties of drug-like molecules is possible and hence some predictive power for their pharmaceutical activity in vivo can be obtained.

Molecular Fragments in Inhibitor Design

Jonathan Essex (Investigator), Michael Bodnarchuk

Fragment-Based Drug Discovery (FBDD) has emerged as an important tool in the drug discovery process. Instead of screening entire drug molecules, FBDD screens molecular fragments; constituents which make up drug molecules. A computational approach to identifying fragment binding is currently being sought which also yield binding free energy estimation.

Sustainable domain-specific software generation tools for extremely parallel particle-based simulations

Chris-Kriton Skylaris (Investigator)

A range of particle based methods (PBM) are currently used to simulate materials in chemistry, engineering, physics and biophysics. The 4 types of PBM considered directly in the proposed are molecular dynamics (MD), the ONETEP quantum mechanics-based program, discrete element modelling (DEM), and smoothed particle hydrodynamics (SPH).
The overall research objective is to develop a sustainable tool that will deliver, in the future, cutting edge research applicable to applications ranging from dam engineering to atomistic drug design.

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.

Water molecules in drug development: can we predict drug affinity when water molecules are involved?

Jonathan Essex (Investigator), Hannah Bruce Macdonald, Christopher Cave-Ayland

Water molecules are often found to be involved in drug-protein binding and can influence the effectiveness of a drug. We aim to aid drug design by calculating the energies involved with complexes of drugs, proteins and water molecules to predict the affinities of drug molecules.

Water Molecules in Protein Binding Sites

Jonathan Essex (Investigator), Michael Bodnarchuk

Water molecules are commonplace in protein binding sites, although the true location of them can often be hard to predict from crystallographic methods. We are developing tools which enable the location and affinity of water molecules to be found.