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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Languages: English
Types: Doctoral thesis
Subjects: QD
Ab initio, density functional (DFT), semi-empirical and force field methods are used\ud to predict non-covalent interactions between peptides and major histocompatibility\ud complex (MHC) class II receptors. Two ab initio methods are shown to be in good\ud agreement for pairwise interaction of amino-acids for myelin basic protein (MBP)-\ud MHC II complex. These data are then used to benchmark more approximate DFT and\ud semi-empirical approaches, which are shown to be significantly in error. However, in\ud some cases significant improvement is apparent on inclusion of an empirical\ud dispersion correction. Most promising among these cases is RM1 with the dispersion\ud correction. This approach is used to predict binding for progressively larger model\ud systems, up to binding of the peptide with the entire MHC receptor, and is then\ud applied to snapshots taken from molecular dynamics simulation. These methods were\ud then compared to literature values of IC50 as a benchmark for three datasets, two sets\ud of IC50 data for closely structurally related peptides based on hen egg lysozyme\ud (HEL) and myelin basic protein (MBP) and more diverse set of 22 peptides bound to\ud HLA-DR1. The set of 22 peptides bound to HLA-DR1 provides a tougher test of such\ud methods, especially since no crystal structure is available for these peptide-MHC\ud complexes. We therefore use sequence based methods such as SYFPEITHI and\ud SVMHC to generate possible binding poses, using a consensus approach to determine\ud the most likely anchor residues, which are then mapped onto the crystal structure of\ud an unrelated peptide bound to the same receptor. This shows that methods based on\ud molecular mechanics and semi-empirical quantum mechanics can predict binding\ud with reasonable accuracy, as long as a suitable method for estimation of solvation\ud effects is included. The analysis also shows that the MM/GBVI method performs\ud particularly well, as does the AMBER94 forcefield with Born solvation. Indeed,\ud MM/GBVI can be used as an alternative to sequence based methods in generating\ud binding poses, leading to still better accuracy. Finally, we investigated the influence\ud of motion in implicit and explicit solvents for a set of 22 peptides. Binding free\ud energies were calculated by Molecular Mechanics Generalized -Born Surface Area\ud (MM/GBSA) method, but it was found that the results are worse than MM/GBVI on\ud MOE, which show that the MM/GBVI approach can deliver reasonable predictions of\ud peptide-MHC binding in a matter of a few seconds on a desktop computer.

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