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