Sequence tag-based peptide search is a critical technology in proteomics for the characterization of proteins from tandem mass spectrometry data. However, the main reason for hindering the full application of such an approach lies that accurately extracting sequence tags responsible for each experimental spectrum. Towards that end, we propose GameTag, a novel cooperative game framework for sequence tag generation, which includes a tag generator and a tag discriminator to collaboratively generate sequence tags. Specifically, the tag generator works to extract as many correct tag candidates as possible and the tag discriminator serves to determine the correctness of tag candidates and reduce the total number of output tags simultaneously. Through the dynamic two-player game, the number of extracted tags is decreased while the number of correct tags gets boosted. We also investigate the performance of our proposed method under various hyperparameter and structure settings. Extensive experiments on a wide variety of data sets from different species demonstrate that GameTag outperforms previous state-of-the-art methods, InsPecT, PepNovo+, DirecTag, and the existing tag-extraction method in Open-pFind, increasing by at least 10% the number of spectra extracted more than one correct tag. This article is protected by copyright. All rights reserved.
Fei Zheng-Cong, Wang Kaifei, Chi Hao
cooperative game, deep learning, proteomics, sequence tag generation