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Advancing Loop Prediction to Ultra-High Resolution Sampling
Homology modeling is integral to structure-based drug discovery. Robust homology modeling to atomic-level accuracy requires in the general case successful prediction of protein loops containing small segments of secondary structure. For loops identified to possess α-helical segments, an alternative dihedral library is employed composed of (phi,psi) angles commonly found in helices. Even with imperfect knowledge coming from sequence-based secondary structure, helix or hairpin embedded loops, up to 17 residues in length, are successfully predicted to median sub-angstrom RMSD. Having demonstrated success with these cases, performance costs for these and other similar long loop predictions will be discussed. Dramatic improvements in both speed and accuracy are possible through the development of a Cβ-based scoring function, applicable to hydrophobic residues, that can be applied as early as half-loop buildup. With this scoring function, up to a 30-fold reduction in the cost to produce competitive sub-2 A loops are observed. Through the use of this scoring function, an efficient method will be presented to achieve ultra-high resolution buildup that restrains combinatorial explosion and offers an alternative to the current approach to full-loop buildup. This novel method is designed to be inherently suitable for homology model refinement
G6PD structure and activity: Potential for development of novel low-cost assays for field detection of G6PD deficiency for malaria management
Glucose-6-Phosphate Dehydrogenase (G6PD) is a cytoplasmic protein involved in the first step of the pentose phosphate pathway, that is the oxidation of glucose-6-phosphate to 6-phosphogluconolactone with reduction of NADP+. Deficiency in G6PD activity may result in the formation of different pathologies such as severe forms of anaemia and respiratory distress. In individuals with \emph{Plasmodium vivax} malaria infection, a depression in G6PD activity greatly increases the toxicity of the drugs used in malaria treatment. Tests to detect G6PD deficiencies already exist, but are relatively costly, difficult to perform, and therefore unlikely to be used in endemic areas. The overall aim of this project is the structural analysis of G6PD variants to provide information that could be used in the development of low-cost and simple-to-use immunological field tests for G6PD. Ideally it would have been possible to identify regions that are markers to reduced, or normal activity, to be selected as antibody targets. Initially the SAAP family of tools were used to find G6PD variants that are associated with structural effects at a phenotype level. The selected variants were then studied with all-atom Molecular Dynamics (MD) experiments and looked for shared behaviours among mutants. The difficulty of collecting extensive simulation data made the interpretation of the results challenging, and incomplete, so a united-atom force field (UNRES) was used both to improve the sampling and to increase the numbers of mutants studied. The collected data suggested that the reduced activity in the mutants is not the result of complete unfolding, but it is more likely the result of a very local disruptions in the protein structure the effects of which influence the overall stability and function of the enzyme. To understand the mechanisms of action of the mutations better, a network analysis using the software \emph{wordom} was performed. The idea was to outline key residues (hubs) of G6PD and observe if and how the mutations were capable of altering the communication pathways between hubs. If a mutation, instead of damaging the structure of G6PD, alters the interaction between two or more hubs in the network, it could explain the linkage between the mutation and the reduced activity in G6PD. The final attempt to characterise G6PD behaviours better was the performance of metadynamics simulations with a focus on the role played by Proline 172. This residue has a critical role in allowing the correct positioning of both the substrate and the co-enzyme