Exhaustive proteome mining for functional MHC-I ligands.

Abstract

We present the development and application of a new machine-learning approach to exhaustively and reliably identify major histocompatibility complex class I (MHC-I) ligands among all 20(8) octapeptides and in genome-derived proteomes of Mus musculus , influenza A H3N8, and vesicular stomatitis virus (VSV). Focusing on murine H-2K(b), we identified potent octapeptides exhibiting direct MHC-I binding and stabilization on the surface of TAP-deficient RMA-S cells. Computationally identified VSV-derived peptides induced CD8(+) T-cell proliferation after VSV-infection of mice. The study demonstrates that high-level machine-learning models provide a unique access to rationally designed peptides and a promising approach toward "reverse vaccinology".

DOI: 10.1021/cb400252t

Cite this paper

@article{Koch2013ExhaustivePM, title={Exhaustive proteome mining for functional MHC-I ligands.}, author={Christian P. Koch and Anna Maria Perna and Sabrina Weissm{\"{u}ller and Stefanie Bauer and Max Pillong and Renato Brito Baleeiro and Michael Reutlinger and Gerd Folkers and Peter Walden and Paul Wrede and Jan A. Hiss and Zoe Waibler and Gisbert Schneider}, journal={ACS chemical biology}, year={2013}, volume={8 9}, pages={1876-81} }