Project Ref: NGCM-0114
Supervisor: Prof Jonathan Essex; Prof Jeremy Frey
Research Area: Computational Engineering
Project Description: Enhanced sampling simulations of biological systemsBiological systems and processes are notoriously complex and difficult to understand. They span distance scales across nine orders of magnitude ranging from individual small molecules (1 nm) to whole organisms (1 m), and times scales of 21 orders of magnitude from individual bond breaking in an enzyme (1 ps) to the human lifetime (2 Gs). While there are many computer modelling approaches available to understand a particular timescale, extending these methods beyond their usual time domain remains a challenge. This PhD project will work to address this problem at the molecular level, with a particular emphasis on solving real biological problems.In this project we are trying to address the fundamental problem of enhancing the timescales accessible to our simulations through the development and optimisation of enhanced sampling methods. These approaches fall into two general categories, namely those that modify the underlying energy surface, removing barriers to sampling, and those that enhance the rate of sampling on the existing energy surface. For the latter, we have found hybrid Monte Carlo approaches to be particularly productive, whereby biased sampling is induced during a molecular dynamics simulation, which is subsequently corrected through the application of an appropriate Monte Carlo test. Thus far, we have tested this approach in the context of using velocity distributions biased in such a way as to enhance low-frequency motion through the application of a digital filter. However, it is clear that this broad approach of hybrid Monte Carlo (HMC) may be extended in a number of ways, to address, for example, the sampling of different protonation states in the course of a simulation. Furthermore, the combination of HMC with enhanced sampling methods that modify the underlying potential energy surface have not been explored, and offer considerable promise.The types of problem we are seeking to address include modelling large-scale self-assembly of biological systems, how changes in protein conformation regulate protein function, particularly in the context of protein-protein interactions, and how seemingly simple changes in pH or ionic concentration affect membranes, proteins and DNA. We have long-standing collaborations with the pharmaceutical industry, and linking drug discovery to solving these problems is a big target for us.In this PhD project you will further develop and extend these and related methods, to target specific biological problems.Applicants should have a good undergraduate degree in chemistry, physics or biochemistry, and a keen interest in developing and applying computational methods to biological problems. If you wish to discuss the project informally, please contact Jon Essex at email@example.com
If you wish to discuss any details of the project informally, please contact Prof Jonathan Essex, Email: J.W.Essex@soton.ac.uk, Tel: +44 (0) 2380 592794
Keywords: Applied Mathematics, Applied Physics, Biochemistry, Computer Science, Materials Science, Structural Biology
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