Quantitative peptide binding motifs for 19 human and mouse MHC class I molecules derived using positional scanning combinatorial peptide libraries.

John Sidney,Sandy Ngo,Erika Assarsson,Carrie Moore,Bjoern Peters,Alessandro Sette,Clemencia Pinilla

doi:10.1186/1745-7580-4-2

Abstract

BackgroundIt has been previously shown that combinatorial peptide libraries are a useful tool to characterize the binding specificity of class I MHC molecules. Compared to other methodologies, such as pool sequencing or measuring the affinities of individual peptides, utilizing positional scanning combinatorial libraries provides a baseline characterization of MHC molecular specificity that is cost effective, quantitative and unbiased.ResultsHere, we present a large-scale application of this technology to 19 different human and mouse class I alleles. These include very well characterized alleles (e.g. HLA A*0201), alleles with little previous data available (e.g. HLA A*3201), and alleles with conflicting previous reports on specificity (e.g. HLA A*3001). For all alleles, the positional scanning combinatorial libraries were able to elucidate distinct binding patterns defined with a uniform approach, which we make available here. We introduce a heuristic method to translate this data into classical definitions of main and secondary anchor positions and their preferred residues. Finally, we validate that these matrices can be used to identify candidate MHC binding peptides and T cell epitopes in the vaccinia virus and influenza virus systems, respectively.ConclusionThese data confirm, on a large scale, including 15 human and 4 mouse class I alleles, the efficacy of the positional scanning combinatorial library approach for describing MHC class I binding specificity and identifying high affinity binding peptides. These libraries were shown to be useful for identifying specific primary and secondary anchor positions, and thereby simpler motifs, analogous to those described by other approaches. The present study also provides matrices useful for predicting high affinity binders for several alleles for which detailed quantitative descriptions of binding specificity were previously unavailable, including A*3001, A*3201, B*0801, B*1501 and B*1503.

Highlights

It has been previously shown that combinatorial peptide libraries are a useful tool to characterize the binding specificity of class I major histocompatibility complex (MHC) molecules
Evaluation of the positional scanning combinatorial library approach for predicting human leukocyte antigen (HLA) A*0201 binding peptides Previous studies in other laboratories have demonstrated that the combinatorial approach performs well in predicting binders to several murine MHC class I molecules
The present analysis has provided sets of high affinity binders derived from vaccinia Western Reserve (WR) for 5 relatively common HLA class I alleles

Summary

Introduction

It has been previously shown that combinatorial peptide libraries are a useful tool to characterize the binding specificity of class I MHC molecules. Compared to other methodologies, such as pool sequencing or measuring the affinities of individual peptides, utilizing positional scanning combinatorial libraries provides a baseline characterization of MHC molecular specificity that is cost effective, quantitative and unbiased. T cells recognize a complex formed between a major histocompatibility complex (MHC) molecule and an antigenic peptide, or epitope. A large body of literature relates to the definition of MHC binding motifs for class I molecules of several different species, including humans, mice, chimpanzees and macaques (see, e.g., [1], for review). Class I MHC molecules recognize peptides of 9 to 10 residues in length and carrying residues with similar physiochemical specificity at main anchor positions. The main anchors are found in position 2 and at the C-terminus of the peptide ligand, other anchor arrangements have been described for several alleles

Methods

Results

Discussion

Conclusion