Identifying Human Kinase-Specific Protein Phosphorylation Sites by Integrating Heterogeneous Information from Various Sources

Abstract

Phosphorylation is an important type of protein post-translational modification. Identification of possible phosphorylation sites of a protein is important for understanding its functions. Unbiased screening for phosphorylation sites by in vitro or in vivo experiments is time consuming and expensive; in silico prediction can provide functional candidates and help narrow down the experimental efforts. Most of the existing prediction algorithms take only the polypeptide sequence around the phosphorylation sites into consideration. However, protein phosphorylation is a very complex biological process in vivo. The polypeptide sequences around the potential sites are not sufficient to determine the phosphorylation status of those residues. In the current work, we integrated various data sources such as protein functional domains, protein subcellular location and protein-protein interactions, along with the polypeptide sequences to predict protein phosphorylation sites. The heterogeneous information significantly boosted the prediction accuracy for some kinase families. To demonstrate potential application of our method, we scanned a set of human proteins and predicted putative phosphorylation sites for Cyclin-dependent kinases, Casein kinase 2, Glycogen synthase kinase 3, Mitogen-activated protein kinases, protein kinase A, and protein kinase C families (avaiable at http://cmbi.bjmu.edu.cn/huphospho). The predicted phosphorylation sites can serve as candidates for further experimental validation. Our strategy may also be applicable for the in silico identification of other post-translational modification substrates.

Characterizing Gene and Protein Crosstalks in Subjects at Risk of Developing Alzheimer’s Disease: A New Computational Approach

“…To create a potential pathway crosstalk reference map, we used cellular pathway data from the KEGG database [38][39][40]. We obtained evidence for human chemical interaction, genetic interaction, and synthetic lethal gene pairs from BioGRID [41], domain interaction from GeneMania [42], transcription factors from the FANTOM database [43,44], and protein phosphorylation [45]. We obtained SNPs associated with genes that were manually curated to be associated with AD from the Comparative Toxicogenomics Database [46], and we obtained a compilation of genes from the literature that have been identified as likely risk factors of AD from SNPedia [47].…”

Deciphering signaling specificity with interpretable deep neural networks

“…Second, majority of these methods have narrow coverage and coarse resolution. For example, PhosphoPredict (Song et al, 2017) and (Li et al, 2010) can predict phosphosites for only 12 and 8 kinase families, respectively. They were trained at the kinase family level and cannot make accurate predictions for individual kinases or mutated kinases.…”

Prediction of human disease-specific phosphorylation sites with combined feature selection approach and support vector machine

“…To remedy this shortcoming, recently several phosphorylation site prediction approaches incorporating different feature selection methods were proposed. Li et al [21] developed a method to choose only over-represented or underrepresented features for phosphorylation site identification. Fan et al [22] developed an mRMR (minimum-redundancymaximum-relevance)-based feature selection to pick out PPI and Go features that are useful in kinase-specific phosphorylation site prediction.…”