Potential of mean force for protein–protein interaction studies

Abstract

Calculating protein-protein interaction energies is crucial for understanding protein-protein associations. On the basis of the methodology of mean-field potential, we have developed an empirical approach to estimate binding free energy for protein-protein interactions. This knowledge-based approach has been used to derive distance-dependent free energies of protein complexes from a nonredundant training set in the Protein Data Bank (PDB), with a careful treatment of homology. We calculate atom pair potentials for 16 pair interactions, which can reflect the importance of hydrophobic interactions and specific hydrogen-bonding interactions. The derived potentials for hydrogen-bonding interactions show a valley of favorable interactions at a distance of approximately 3 A, corresponding to that of an established hydrogen bond. For the test set of 28 protein complexes, the calculated energies have a correlation coefficient of 0.75 compared with experimental binding free energies. The performance of the method in ranking the binding energies of different protein-protein complexes shows that the energy estimation can be applied to value binding free energies for protein-protein associations.

Rational optimization of tumor epitopes using in silico analysis‐assisted substitution of TCR contact residues

“…However, the prediction of the interaction energy between TCR and pMHC is much more difficult than calculating peptide and MHC interaction energies. Only a few theoretical approaches such as the free energy perturbation (FEP) method, regression method, and the statistical mechanics method have been developed to predict protein-protein binding affinity [18][19][20]. In a FEP method-based study performed by Michielin and Karplus [18], the computed free energy difference in the binding of a particular TCR (A6) with an HLA-A2-restricted WT peptide (Tax) and a mutant peptide (Tax P6A) was shown in good agreement with the experimental value.…”

“…Although this study has shed new light on the application of a molecular simulation approach to guide peptide modifications for alteration of TCR-ligand binding, the large computational burden made the application of the FEP method only applicable to established facility. In a recent study, Lai and coworkers applied a statistical mechanics method termed PMFScore, which is based on the potential of mean force (PMF), to calculate protein-protein interaction energies precisely and efficiently [19,21] and here we aimed to test the feasibility of the application of PMFbased in silico approach in order to develop a more applicable approach for peptide modifications.…”

“…Although this study has shed new light on the application of a molecular simulation approach to guide peptide modifications for alteration of TCR-ligand binding, the large computational burden made the application of the FEP method only applicable to established facility. In a recent study, Lai and coworkers applied a statistical mechanics method termed PMFScore, which is based on the potential of mean force (PMF), to calculate protein-protein interaction energies precisely and efficiently [19,21] and here we aimed to test the feasibility of the application of PMFbased in silico approach in order to develop a more applicable approach for peptide modifications.NY-ESO-1 is a testis cancer antigen, expressed in various types of tumors, including melanoma, breast, lung, and bladder cancers. This HLA-A Ã 0201 restricted T-cell epitope NY-ESO-1 157-165 has been indicated as a promising candidate for T-cellbased tumor vaccination strategies [22,23]; however, several studies have revealed its defects in stability and bioavailability and its frequent failure to elicit robust anti-tumor CTL response.…”

“…The method used in our computational study is an empirical approach based on the methodology of PMF, which is a powerful bioinformatics tool for in silico prediction of protein-protein interaction energies. In a recent study, Lai et al developed the PMFScore by using the non-redundant training set containing a wide range of protein-protein complex structures [19]. The computational binding free energies have a correlation coefficient of 0.75 compared with experimental results.…”

“…The following four atom types were used in the PMFScore calculation: hydrogen bond donor, hydrogen bond acceptor, both donor and acceptor, and neutral atom (neither donor nor acceptor). The details of the specific definitions have been described [19]. Briefly, the PMFScore is defined using following formula:…”