An efficient ACS algorithm for classification-based peptide identification

Peptide sequence assignment is the central task in protein identification with MS/MS-based strategies. Sequence database searching routinely generate a large number of peptide spectrum matches (PSMs). Due to either the poor quality of the experimental MS/MS data or unexpected amino acid modifications, there are a large number of incorrect target PSMs. CRanker has shown its efficiency and accuracy in discrimination between correct and incorrect PSMs. However, it costs CRanker too much time on large PSM datasets as a built-in matlab optimization solver needs to be called for training the model. In this work, we exploit the bi-convex structure of the CRanker model and develop an alternate convex search (ACS) algorithm to reduce its total running time. At each iteration, ACS alternately solves one part of the problem when the other part of variables fixed. Compared with Matlab optimization tools, ACS is one order of magnitude faster on most datasets.