A method for predicting in vivo kinase-substrate relationships, that augments intrinsic specificities of kinases with cellular context for kinases and phosphoproteins. Based on the latest human phosphoproteome from the Phospho.ELM and PhosphoSite databases, NetworKIN offers insight into phosphorylation-modulated interaction networks. Via the web interface users can query the database of precomputed kinase-substrate relations or obtain predictions on novel phosphoproteins.
Searches for motifs within proteins that are likely to be phosphorylated by specific protein kinases or bind to domains. Scansite is an application to predict short linear sequence motif sites. It uses position-specific scoring matrices (PSSMs) to predict interaction sites that are important in cellular signaling. This application can also be used to show all potential sites in a given protein or all proteins in a database that contains sites for one or more motifs.
A computational workflow that aims to help non-bioinformaticians perform biological meaningful analyses of global phospho-proteomics datasets easily and to guide the design of downstream experiments to uncover the mechanistic details of signal transduction in their system. SELPHI uses correlation analysis of phospho-sites to extract kinase/phosphatase and phospho-peptide associations, and highlights the potential flow of signaling in the system under study.
A strategy based on functional protein microarrays and bioinformatics to experimentally identify substrates for 289 unique kinases, resulting in 3656 high-quality kinase-substrate relationships (KSRs). The value of this data set is demonstrated through the discovery of a new role for PKA downstream of Btk (Bruton's tyrosine kinase) during B-cell receptor signaling.
A bioinformatical platform to characterize the structures of phosphorylation site-oriented PPI networks. From the quantitative phosphoproteome data on EGF-stimulated glioblastoma stem cells, our PTMapper-based network analysis unveiled p70S6K-related signaling as one of the most significantly regulated sub-networks, which was not observed in the conventional PPI network. As some previous studies demonstrated that p70S6K was correlated with survival and stemness maintenance of glioblastoma stem cells, the strategy based on phosphorylation site-oriented network analysis proved to be effective to unbiasedly extract crucial signaling pathways from the complex signaling networks.
Merges the information encoded in the kinase with the information encoded in the target peptide. AKID is a program able to identify protein kinases in a given proteome, their key residues for specificities, termed Determinants of Specificity (DoS) and ultimately their target kinase-specific phosphorylation sites (KsP). Moreover, this tool is available through a web interface or a local version.
Predicts kinase-substrate relationships based on protein domains with the assumption that kinase-substrate interactions are accomplished with kinase-domain interactions. PhosD identifies the kinases that phosphorylate corresponding proteins by further considering protein-protein interactions. Some predictions were validated by signaling pathways in which they were involved.
Retrieves kinase-substrate predictions from NetworKIN algorithm and contains various statistical modules for further analysis. PhosphoSiteAnalyzer is a bioinformatical tool for analyzing (quantitative) phosphoproteome datasets. It has a modular design where each module represents a statistical analysis method for extracting various biological features from the phosphoproteomic data set. It also is a computational tool created with the aim of facilitating complex kinase−substrate network analysis in a user-friendly and user-tailored way.
A protein kinase identification web server is presented for the identification of the protein kinases responsible for experimentally verified P-sites at high specificity, which incorporates the composition of monomer spectrum (CMS) encoding strategy and support vector machines (SVMs).
Allows users to pinpoint protein kinases based on multiple kernel learning (MKL). ksrMKL is a standalone software that uses both sequence and functional information, thanks to a combination of multiple kernels, to build a predictive model. The application can be used to investigate potential protein kinases for experimentally verified phosphorylation sites and assist in experimental verification.
A software package for the prediction of in vivo site-specific kinase-substrate relations mainly from the phosphoproteomic data. This work contributes to the understanding of phosphorylation mechanisms at the systemic level, and provides a powerful methodology for the general analysis of in vivo post-translational modifications regulating sub-proteomes.
Predicts the substrate specificity of kinase domains based on the protein sequences. DeepSignal translates a kinase sequence to its binding substrate sequence profile via encoder network and decoder network components. This software exploits information extracted from protein sequence to determine if each residue should be enhanced or silenced. It can also predict the substrate specificity on SH2 domain proteins.
Predicts kinase-substrate associations (KSA). CoPhosK leans co-phosphorylation patterns derived from mass spectrometry mass spectrometry (MS) data to perform its prediction. It consists of two methods: (i) CophosK, dedicated to KSAs global predictions as well as to realize context-specific determination of cophosphorylation networks and; (ii) CophosK+ that delivers reproducible predictions by considering several elements such as cophosphorylation of substrates of kinases.
Integrates various functional analysis methods to assist researchers in analyzing and interpreting their large-scale time-series phosphoproteomics data. DynaPho is an integrative web tool for analysis of time-series phosphoproteomics data, which contain information about signaling dynamics. It provides six analytical modules: 1) data summary; 2) profile clustering; 3) function enrichment; 4) dynamic network; 5) kinase activity; and 6) correlation analysis.