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 which compute relative free energies of binding. The most rigorous methods for calculation of relative free energies of binding are based on statistical mechanics and are an active field of research. 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 free energy. Each of these configurations needs to be a correct (in terms of statistical probabilities) representative of the thermodynamic state and we need to have a very accurate evaluation of its energy. Finally, the accurate description of the solvent and its interactions with the solutes is also crucial as the ligand de-solvation free energy is required in free energy thermodynamic cycles. Commonly used rigorous approaches for free energy calculations depend on the use of empirical force fields for the generation of configurations and their energies. While force fields are computationally more tractable than ab initio quantum chemistry calculations they have limited accuracy and transferability, 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 scope of 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 ab initio calculations. To achieve this goal we will develop hybrid free energy methods which start with force fields to compute free energy differences between different ligands but then compute the free energy of mutation from the classical to the quantum description, as free energies are thermodynamic state functions and such a transition is well-defined. 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 in both descriptions. The project will also involve developments towards quantum methods that provide the most accurate description of biomolecular interactions (such as new generations of DFT approaches) while reducing the computational demands, and the calibration of explicit and implicit models for the solvent. The project will involve development of new theory and code within ONETEP and in stand-alone free energy methods programs. 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 thus 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: [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
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