Predicting Peptide Binding Affinities to MHC Molecules Using a Modified Semi-Empirical Scoring Function

The Major Histocompatibility Complex (MHC) plays an important role in the human immune system. The MHC is involved in the antigen presentation system assisting T cells to identify foreign or pathogenic proteins. However, an MHC molecule binding a self-peptide may incorrectly trigger an immune response and cause an autoimmune disease, such as multiple sclerosis. Understanding the molecular mechanism of this process will greatly assist in determining the aetiology of various diseases and in the design of effective drugs. In the present study, we have used the Fresno semi-empirical scoring function and modify the approach to the prediction of peptide-MHC binding by using open-source and public domain software. We apply the method to HLA class II alleles DR15, DR1, and DR4, and the HLA class I allele HLA A2. Our analysis shows that using a large set of binding data and multiple crystal structures improves the predictive capability of the method. The performance of the method is also shown to be correlated to the structural similarity of the crystal structures used. We have exposed some of the obstacles faced by structure-based prediction methods and proposed possible solutions to those obstacles. It is envisaged that these obstacles need to be addressed before the performance of structure-based methods can be on par with the sequence-based methods

Structure of a human autoimmune TCR bound to a myelin basic protein self-peptide and a multiple sclerosis-associated MHC class II molecule

Multiple sclerosis is mediated by T-cell responses to central nervous system antigens such as myelin basic protein (MBP). To investigate self-peptide/major histocompatibility complex (MHC) recognition and T-cell receptor (TCR) degeneracy, we determined the crystal structure, at 2.8 Å resolution, of an autoimmune TCR (3A6) bound to an MBP self-peptide and the multiple sclerosis-associated MHC class II molecule, human leukocyte antigen (HLA)-DR2a. The complex reveals that 3A6 primarily recognizes the N-terminal portion of MBP, in contrast with antimicrobial and alloreactive TCRs, which focus on the peptide center. Moreover, this binding mode, which may be frequent among autoimmune TCRs, is compatible with a wide range of orientation angles of TCR to peptide/MHC. The interface is characterized by a scarcity of hydrogen bonds between TCR and peptide, and TCR-induced conformational changes in MBP/HLA-DR2a, which likely explain the low observed affinity. Degeneracy of 3A6, manifested by recognition of superagonist peptides bearing substitutions at nearly all TCR-contacting positions, results from the few specific interactions between 3A6 and MBP, allowing optimization of interface complementarity through variations in the peptide

Arbitrary protein−protein docking targets biologically relevant interfaces

<p>Abstract</p> <p>Background</p> <p>Protein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations.</p> <p>Results</p> <p>In this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners.</p> <p>Conclusions</p> <p>An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.</p