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

John Sidney¹ , Erika Assarsson¹ , Carrie Moore¹ , Sandy Ngo¹ , Clemencia Pinilla² , Alessandro Sette¹ and Bjoern Peters¹

¹La Jolla Institute for Allergy and Immunology, 9420 Athena Circle, La Jolla, CA 92037, USA

²Torrey Pines Institute for Molecular Studies, 3550 General Atomics Court, 2-129, San Diego, CA 92121, USA

author email corresponding author email

Immunome Research 2008, 4:2doi:10.1186/1745-7580-4-2


Published:	25 January 2008

Abstract

Background

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.

Results

Here, 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.

Conclusion

These 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.