Iman Deznabi1,2, Busra Arabaci1, Mehmet Koyutürk3,4, Oznur Tastan5. 1. Computer Engineering Department, Bilkent University, Ankara 06800, Turkey. 2. College of Information and Computer Sciences, University of Massachusetts, Amherst, MA 01003, USA. 3. Department of Computer and Data Sciences. 4. Center for Proteomics & Bioinformatics, Case Western Reserve University, Cleveland, OH 44106, USA. 5. Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul 34956, Turkey.
Abstract
MOTIVATION: Protein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target-specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase-specific predictions, yet for a large body of kinases, only a few or no target sites are reported. RESULTS: We present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase-specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/Tastanlab/DeepKinZero. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
MOTIVATION: Protein phosphorylation is a key regulator of protein function in signal transduction pathways. Kinases are the enzymes that catalyze the phosphorylation of other proteins in a target-specific manner. The dysregulation of phosphorylation is associated with many diseases including cancer. Although the advances in phosphoproteomics enable the identification of phosphosites at the proteome level, most of the phosphoproteome is still in the dark: more than 95% of the reported human phosphosites have no known kinases. Determining which kinase is responsible for phosphorylating a site remains an experimental challenge. Existing computational methods require several examples of known targets of a kinase to make accurate kinase-specific predictions, yet for a large body of kinases, only a few or no target sites are reported. RESULTS: We present DeepKinZero, the first zero-shot learning approach to predict the kinase acting on a phosphosite for kinases with no known phosphosite information. DeepKinZero transfers knowledge from kinases with many known target phosphosites to those kinases with no known sites through a zero-shot learning model. The kinase-specific positional amino acid preferences are learned using a bidirectional recurrent neural network. We show that DeepKinZero achieves significant improvement in accuracy for kinases with no known phosphosites in comparison to the baseline model and other methods available. By expanding our knowledge on understudied kinases, DeepKinZero can help to chart the phosphoproteome atlas. AVAILABILITY AND IMPLEMENTATION: The source codes are available at https://github.com/Tastanlab/DeepKinZero. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Authors: Peter V Hornbeck; Bin Zhang; Beth Murray; Jon M Kornhauser; Vaughan Latham; Elzbieta Skrzypek Journal: Nucleic Acids Res Date: 2014-12-16 Impact factor: 16.971
Authors: Denis V Petrovsky; Arthur T Kopylov; Vladimir R Rudnev; Alexander A Stepanov; Liudmila I Kulikova; Kristina A Malsagova; Anna L Kaysheva Journal: J Pers Med Date: 2021-12-03