Computational simulation plays an important role in the early stages of the development of new drugs by identifying molecules (potential drugs) which can bind to biomolecular targets (e.g. sites in a protein) with high affinity and selectivity. This process involves several stages. Crude but computationally inexpensive methods (e.g. docking) are initially used to scan huge libraries of molecules and reduce the number of candidates. Eventually a small number of the “best” leads can be refined with computationally more demanding but also more accurate approaches based on classical and statistical mechanics in order to compute relative free energies of binding. This is a particularly challenging area as these methods require generating and sampling a large number of biomolecular configurations, in order to capture the entropic contribution to the binding of the drug to the target. For each of these configurations we need to have a very accurate evaluation of its energy. However, commonly used approaches depend on the use of empirical classical mechanics force fields for the generation of configurations and their energies. These have limited accuracy, as they cannot capture explicitly important energy contributions such as the electronic polarisation and charge transfer that occur in a biomolecular association event. The limitations of force fields are more severe when the molecules considered are different from the parameterisation of the force field, which is often the case when searching for new drugs.
The goal of this project is to overcome the force field limitations in biomolecular free energy calculations by employing large-scale first principles quantum chemistry calculations. To achieve this goal we will develop hybrid free energy methods which start with force fields to compute free energies of different ligands but then correct errors by computing the free energy of mutation from the classical to the quantum description. This work will build on our previous experience in this area [1,2] and will use the ONETEP linear-scaling DFT program , which we develop in our group. Particular challenges in this project will be the development of free energy methods that have high configurational overlap between the classical and the quantum description and produce correct ensembles of structures. Energy Decomposition Analysis (EDA)  will be used on the DFT calculations to dissect the protein-drug interaction in terms of energy components (such as electrostatic, exchange, polarisation, charge transfer) and into particular chemical functional groups, providing information for subsequent chemical modifications to improve the activity.
The new methods will be validated in actual protein-ligand targets of relevance to the pharmaceutical industry.
 S. J. Fox, J. Dziedzic, T. Fox, C. S. Tautermann, and C.-K. Skylaris, Proteins 82 (2014) 3335.
 C. Sampson, T. Fox, C. S. Tautermann, C. J. Woods, and C.-K. Skylaris, J. Phys. Chem. B 119 (2015) 7030-7040.
 C.-K. Skylaris, P. D. Haynes, A. A. Mostofi and M. C. Payne, J. Chem. Phys. 122 (2005) 084119.
 M. J. S. Phipps, T. Fox, C. S. Tautermann and C.-K. Skylaris, Chem. Soc. Rev. 44(2015) 3177
If you wish to discuss any details of the project informally, please contact Professor Chris-Kriton Skylaris, Email: c.skylaris[email protected]
, Tel: +44 (0) 2380 59 9381.
This project is run through participation in the EPSRC Centre for Doctoral Training in Next Generation Computational Modelling (http://ngcm.soton.ac.uk). For details of our 4 Year PhD programme, please see http://www.findaphd.com/search/PhDDetails.aspx?CAID=331&LID=2652
For a details of available projects click here http://www.ngcm.soton.ac.uk/projects/index.html