Prediction of Peptide Binding to Major Histocompatibility II Receptors with Molecular Mechanics and Semi-Empirical Quantum Mechanics Methods

Methods for prediction of the binding of peptides to major histocompatibility complex (MHC) II receptors are examined, using literature values of IC50 as a benchmark. Two sets of IC50 data for closely structurally related peptides based on hen egg lysozyme (HEL) and myelin basic protein (MBP) are reported first. This shows that methods based on both molecular mechanics and semi-empirical quantum mechanics can predict binding with good-to-reasonable accuracy, as long as a suitable method for estimation of solvation effects is included. A more diverse 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 analysis shows that the MM/GBVI method performs particularly well, as does the AMBER94 forcefield with Born solvation model. Indeed, MM/GBVI can be used as an alternative to sequence based methods in generating binding poses, leading to still better accuracy

Theoretical prediction of the interaction between peptides and major histocompatibility Complex II Receptor

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.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Theoretical prediction of the interaction between peptides and major histocompatibility Complex II Receptor

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

Sequences of the selected peptides, their codes, number of sequences and binding affinity from docking in kcal/mol.

Sequences of the selected peptides, their codes, number of sequences and binding affinity from docking in kcal/mol.</p

The structural of the <i>Lymnaea stagnalis</i> Acetylcholine-Binding Protein Q55R mutant complex (PDB ID 3WTH).

The structural of the Lymnaea stagnalis Acetylcholine-Binding Protein Q55R mutant complex (PDB ID 3WTH).</p

The average free energies result in kcal/mol for the solvated systems, details of the energy contributions for the different complexes and the dissociation constant ^a.

The average free energies result in kcal/mol for the solvated systems, details of the energy contributions for the different complexes and the dissociation constant a.</p

Fig 4 -

A) Chemical structure of the three neonicotinoid pesticides and B) Plots of root mean square deviation (RMSD) and the average free energy determined for the selectivity of WQA34 peptide (the error bars are obtained from three different trajectories). Figure (A) shows the 2D chemical structure of the three neonicotinoid pesticides IMI: imidacloprid, ACE: acetamiprid, CLT: Clothianidin and Figure (B) shows the plots of root mean square deviation (RMSD) in Å (grey color) and MM-PBSA results in kcal/mol with the error bars (green color) determined for the selectivity of WQA34 peptide.</p

3D structures for IMI and the four peptides YSM21, PSM22, PSW31, and WQA34.

In the middle is the structure of the IMI compound surrounded by four peptides YSM21, PSM22, PSW31, and WQA34 which have been obtained from the combination of the initial ones.</p

Fig 2 -

Interactions between IMI and a) YSM21, b) PSM22, c) PSW31 and d) WQA34 form the molecular docking results. The non-covalent interactions established between the IMI compound and the four peptides as the results of the molecular docking generated by BIOVIA Discovery Studio Visualizer software.</p