Projects within this area have focused on different aspects of modelling biochemical systems. For example: What methods are most appropriate for modelling biochemical systems? What types of information are obtainable from theory? How can theory lead to a better understanding of the physics that control the chemistry and biology of these large systems?
We have also applied these methods (and others) to rationalise the formation of hydrogels based on carbohydrate amphiphiles (CS cover). In related projects on peptide self-assembly we employ coarse-grained methods topredict peptide self-assembly – and in this context have promoted the use of a new metric the peptide’s Aggregation Propensity (AP score) as a convenient method for scoring a peptide’s ability to aggregate and as such to self-assemble.
In a study of the enzymatic activity of 4-Oxalocrotonate tautomerase (4-OT) and its mutant analogs, molecular mechanics (MM) MD simulations were used in conjunction with QM/MM optimisations to determine the effect of mutating residues in the wild-type binding site of 4-OT on the catalytic ability of the enzyme. This led to new insights into the cause of the decreased catalytic ability of the mutants, which could be correlated to the experimental decrease in Kcat and the increase in KM. In addition to studying enzymatic activity the group has also investigated the use of such methods to understand the binding of Latrunculin A and its analogs to G-actin. In this collaboration with the experimental group of Alois Fürstner, we revealed that when the inhibitors were complexed with G-actin there were differences in the H-bond network, which in turn could be related to the experimentally observed differences in the biological activity of the ligands. The ability of hybrid QM/MM approaches to explain not only the chemical reactivity in biochemical systems, but also to extract the delicate balance of forces involved in the binding of substrates was further demonstrated in the docking and triggering of Dynemicin A and Calicheamicin γ1I in DNA. This project focused on the design of novel enediyne based antitumor leads.