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Department of Chemistry projects

Studying metastable states in biomolecules by analysing rare events (2018) - Robert Jack

Biological molecules such as peptides and proteins have complex dynamics associated with folding, unfolding, and misfolding. These are difficult to analyse by conventional molecular simulation, because the associated time scales are very long, so one needs to simulate the molecules for very long times. Moreover, there may be conformations of the molecule that are rare, but still have important effects on their behaviour. We propose a new method for identifying and analysing rare (metastable molecular states, based on the mathematical theory of large deviations. The idea is that the most important metastable states have long lifetimes -- they can be identified by studying rare dynamical events in which the system gets trapped in an unusual state for an long period of time. The main parts of the project would be: adapt and apply rare-event sampling methods for this specific problem; use it to analyse metastable states in small peptides; compare with general the mathematical theory of large deviations and rare events.

Self-assembly of mesoscopic structures and nanodevices (2018) - David Wales

This project will focus on self-assembly of mesoscopic structures using computer simulation. Theory and methodology development will involve a framework for fitting coarse-grained potentials to describe protein-protein interactions and analysis of global optimisation strategies. One particular avenue for exploration will be the use of continuous symmetry measures and further exploitation of the principle of maximum symmetry. An interpolated grid-based scheme may be evaluated for parsing the energies of possible arrangements before minimisation.  More general building blocks will be considered for the design of targeted mesoscale structures. Here the building blocks are likely to be colloids or supramolecular systems. There are a couple of long-range targets: hierarchical self-assembly, and nanodevices. The hierarchical problem may be treated using analytical models and theory before attempting to translate this into an interparticle potential and exploring the energy landscape. An existing model for helix inversion will be adapted to couple the pathway to source of energy. The simplest way to do this is probably to design a chiral ligand that discriminates between the left- and right-handed helices. Once some insight is gained into the optimal conditions for driving this system the project will attempt to identify some alternative pathways for systems inspired by experimental nanomachines.

Free energy landscapes and observation time scales (2018) - David Wales

The analysis of energy landscapes is a powerful tool in modern theoretical chemistry, of particular use in structure prediction and the calculation of global thermodynamic and kinetic properties for systems of chemical, physical or biological interest. In order to compare the results of this theory with experiment, we should consider the effect of the time scale on which the experimental method views the system. The equilibrium free energy landscape that we usually study is not necessarily the same as the landscape probed by experimental measurements, because the system may only interconvert between locally stable conformations on a timescale longer than the temporal resolution of the experiment. The proposed project would be to investigate in detail the effects of incorporating this blurring of resolution into theoretical calculations. This approach promises to be insightful for comparison with experiments, but will also explore some interesting fundamental theoretical concepts, such as the connections between ergodicity (which may be broken on short observation timescales), symmetry and equilibrium. We will begin by studying the effect of timescales on the landscapes and rearrangement kinetics of colloidal clusters, for comparison with a new generation of optical microscopy experiments. The temporal and spatial resolution and data from such experiments is much higher than can typically be obtained for structural glass formers, where the question of time scale underpins much of the complex phenomenology. Hence the new insight gained into clusters of colloidal particles, with direct comparison of thermodynamic properties with experiment, should help to guide subsequent efforts to understand the glass transition.

Structure prediction and machine learning for variable pathogens (2018) - David Wales

The hemagglutinin (HA) glycoprotein of influenza is known to be a major factor in determining virus species specificity (avian, swine or human) and pathogenicity.  Mutations in HA arise frequently during genome replication, allowing the virus to escape the host's adaptive immune response and to infect the same individual multiple times.  The number of amino acids in the HA binding region and diversity of mutations observed make it difficult to test all the possible changes using current experimental screening methods. A method that could predict the receptor binding behaviour of potential future strains would be of tremendous benefit to virology and human health. This is highlighted by the current global pandemic situation. Extensive studies of HA have already been conducted using molecular dynamics simulations, and we propose using basin-hopping global optimisation to identify novel binding conformations and minimise the time required to model and quantitatively assess a particular protein-ligand combination.  Investigations have been conducted into the interactions of various human, swine and avian HAs with (2,3) and (2,6) linked 3-ring SLeX ligands. The initial phase of the proposed research would be to expand the set of ligands modelled to reflect glycan diversity in different species and different sections of the human respiratory tract. The work will involve collaboration with experimental groups involved in influenza research and influenza surveillance efforts worldwide. Whilst the immediate focus of the research will be upon predicting developments in the pandemic H1N1 strain, due to the change in the prevalence of different influenza strains, it is difficult to predict which strains may be of interest in the longer term.