Exhaustive Proteome Mining for Functional MHC-I Ligands

• DOI: 10.1021/cb400252t
• Publication Date: 2013-06-17T14:37:25Z
• Authors: Christian P. Koch; Anna M. Perna; Sabrina Weissmüller; Stefanie Bauer; Max Pillong; Renato B. Baleeiro; Michael Reutlinger; Gerd Folkers; Peter Walden; Paul Wrede; Jan A. Hiss; Zoe Waibler; Gisbert Schneider
• Keywords: Proteome

We present the development and application of a new machine-learning approach to exhaustively reliably identify major histocompatibility complex class I (MHC-I) ligands among all 208 octapeptides in genome-derived proteomes Mus musculus, influenza A H3N8, vesicular stomatitis virus (VSV). Focusing on murine H-2Kb, we identified potent exhibiting direct MHC-I binding stabilization surface TAP-deficient RMA-S cells. Computationally VSV-derived peptides induced CD8+ T-cell proliferation after VSV-infection mice. The study demonstrates that high-level models provide unique access rationally designed promising toward "reverse vaccinology".