Mass spectrometry-based prokaryote gene annotation.

MS combined with database searching has become the preferred method for identifying proteins present in cell or tissue samples. The technique enables us to execute large-scale proteome analyses of species whose genomes have already been sequenced. Searching mass spectrometric data against protein databases composed of annotated genes has been widely conducted. However, there are some issues with this technique; wrong annotations in protein databases cause deterioration in the accuracy of protein identification, and only proteins that have already been annotated can be identified. We propose a new framework that can detect correct ORFs by integrating an MS/MS proteomic data mapping and a knowledge-based system regarding the translation initiation sites. This technique can provide correction of predicted coding sequences, together with the possibility of identifying novel genes. We have developed a computational system; it should first conduct the probabilistic peptide-matching against all possible translational frames using MS/MS data, then search for discriminative DNA patterns around the detected peptides, and lastly integrate the facts using empirical knowledge stored in knowledge bases to obtain correct ORFs. We used photosynthetic bacteria Synechocystis sp. PCC6803 as a sample prokaryote, resulting in the finding of 14 N-terminus annotation errors and several new candidate genes.