MultiRTA: A simple yet reliable method for predicting peptide binding affinities for multiple class II MHC allotypes

Abstract

BackgroundThe binding of peptide fragments of antigens to class II MHC is a crucial step in initiating a helper T cell immune response. The identification of such peptide epitopes has potential applications in vaccine design and in better understanding autoimmune diseases and allergies. However, comprehensive experimental determination of peptide-MHC binding affinities is infeasible due to MHC diversity and the large number of possible peptide sequences. Computational methods trained on the limited experimental binding data can address this challenge. We present the MultiRTA method, an extension of our previous single-type RTA prediction method, which allows the prediction of peptide binding affinities for multiple MHC allotypes not used to train the model. Thus predictions can be made for many MHC allotypes for which experimental binding data is unavailable.ResultsWe fit MultiRTA models for both HLA-DR and HLA-DP using large experimental binding data sets. The performance in predicting binding affinities for novel MHC allotypes, not in the training set, was tested in two different ways. First, we performed leave-one-allele-out cross-validation, in which predictions are made for one allotype using a model fit to binding data for the remaining MHC allotypes. Comparison of the HLA-DR results with those of two other prediction methods applied to the same data sets showed that MultiRTA achieved performance comparable to NetMHCIIpan and better than the earlier TEPITOPE method. We also directly tested model transferability by making leave-one-allele-out predictions for additional experimentally characterized sets of overlapping peptide epitopes binding to multiple MHC allotypes. In addition, we determined the applicability of prediction methods like MultiRTA to other MHC allotypes by examining the degree of MHC variation accounted for in the training set. An examination of predictions for the promiscuous binding CLIP peptide revealed variations in binding affinity among alleles as well as potentially distinct binding registers for HLA-DR and HLA-DP. Finally, we analyzed the optimal MultiRTA parameters to discover the most important peptide residues for promiscuous and allele-specific binding to HLA-DR and HLA-DP allotypes.ConclusionsThe MultiRTA method yields competitive performance but with a significantly simpler and physically interpretable model compared with previous prediction methods. A MultiRTA prediction webserver is available at http://bordnerlab.org/MultiRTA.

Learning Sequence Determinants of Protein:Protein Interaction Specificity with Sparse Graphical Models

“…Several large-scale studies have been conducted using such techniques for particular families of interacting proteins, including PDZ domains and their peptide ligands (Chen et al, 2008;Tonikian et al, 2008), and human basic-region leucine zippers (bZIPs) and their coiled-coil partners (Fong et al, 2004;Grigoryan et al, 2009). In lieu of large-scale studies, the aggregation of a large number of smaller-scale experiments can also yield extensive amounts of detailed binding data, for example, for major histocompability complex (MHC) and ligands (Peters et al, 2005;Nielsen et al, 2007;Wang et al, 2008;Bordner and Mittelmann, 2010;Zhang et al, 2012), and serine proteases and inhibitors (Lu et al, 2001;Li et al, 2005).…”

Learning Sequence Determinants of Protein: Protein Interaction Specificity with Sparse Graphical Models

“…Several large-scale studies have been conducted using such techniques for particular families of interacting proteins, including PDZ domains and their peptide ligands [4, 40], and human basic-region leucine zippers (bZIPs) and their coiled-coil partners [6, 8]. In lieu of large-scale studies, the aggregation of a large number of smaller-scale experiments can also yield extensive amounts of detailed binding data, e.g., for major histocompability complex (MHC) and ligands [29, 27, 41, 2, 43], and serine proteases and inhibitors [21, 18]. …”

Dana-Farber repository for machine learning in immunology

“…Scaling and comparison transformations were also applied to integrate data from different sources. DFRMLI has already been used in a number of reported studies (Lin et al ., 2008a; Lin et al ., 2008b; Singh and Mishra, 2008; Bordner and Mittelman, 2010). We plan to expand DFRMLI with other annotated data from the immunological domain.…”