A computational method for assessing peptide- identification reliability in tandem mass spectrometry analysis with SEQUEST.

High-throughput protein identification in mass spectrometry is predominantly achieved by first identifying tryptic peptides by a database search and then by combining the peptide hits for protein identification. One of the popular tools used for the database search is SEQUEST. Peptide identification is carried out by selecting SEQUEST hits above a specified threshold, the value of which is typically chosen empirically in an attempt to separate true identifications from false ones. These SEQUEST scores are not normalized with respect to the composition, length and other parameters of the peptides. Furthermore, there is no rigorous reliability estimate assigned to the protein identifications derived from these scores. Hence, the interpretation of SEQUEST hits generally requires human involvement, making it difficult to scale up the identification process for genome-scale applications. To overcome these limitations, we have developed a method, which combines a neural network and a statistical model, for normalizing SEQUEST scores, and also for providing a reliability estimate for each SEQUEST hit. This method improves the sensitivity and specificity of peptide identification compared to the standard filtering procedure used in the SEQUEST package, and provides a basis for estimating the reliability of protein identifications.