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DAPPLE
Predicts phosphorylation sites in an organism of interest. DAPPLE represents an alternative method to machine-learning approaches and is based on a direct homology-based approach. It employs experimentally-determined phosphorylation sites in organisms to predict phosphorylation. This tool uses BLAST to search for similar 15-mer peptides in the proteome of the target organism. It can report descriptions of the query and matching proteins or the locations of the putative phosphorylation sites in the protein sequences.
PhosphoSVM
Employs Support Vector Machine (SVM) to combine protein secondary structure information and seven other one-dimensional structural properties. PhosphoSVM is a non-kinase specific protein phosphorylation site prediction method. This method achieves high AUC values for serine (S), threonine (T), and tyrosine (Y) phosphorylation sites in animals with a tenfold cross-validation. In structural properties, it also includes Shannon entropy, relative entropy, predicted protein disorder information, predicted solvent accessible area, amino acid overlapping properties, averaged cumulative hydrophobicity, and subsequence k-nearest neighbor profiles.
IKAP / Inference of Kinase Activities from Phosphoproteomics
Estimates the activities of all kinases that are known to phosphorylate at least one phosphosite in a phosphoproteomics dataset. IKAP takes information from the PhosphoSitePlus (PSP) database as input and uses a nonlinear optimization routine to minimize a cost function that relates kinase activities and affinities to phosphosite measurements. The predicted activities directly display the engagement of signal transduction pathways such as cAMP/PKA, Ca2+/PKC, mTOR or Akt kinase pathways. By comparing the estimated kinase activity profiles to the measured phosphosite profiles it is furthermore possible to derive the kinases that are most likely to phosphorylate the respective phosphosite.
iPhos-PseEn
Identifies the phosphorylation sites in proteins. iPhos-PseEn is a predictor developed by fusing four different pseudo component approaches (amino acids' disorder scores, nearest neighbors, occurrence frequencies, and position weights) into an ensemble classifier via a voting system to identify human phosphorylation sites. To obtain the predicted result with the anticipated success rate, the entire sequence of the query protein rather than its fragment should be used as an input.
ArMone
Simplifies the processing of phosphoproteome data. ArMone contains modules allowing the preprocessing of the mass spectra, the parsing of the search results, the validation of the peptide, and protein identification. The software also implements an O-glycan heterogeneity analysis strategy. It is applicable to process search results obtained by most of the commonly used database search algorithms. Moreover, this tool allows the export of results in formats required for publication.
PTMProber
A homology-based pipeline that allows identification of potential modification sites for most of the proteomes lacking post-translational modifications (PTMs) data. PTMProber provides a unique functionality for constructing customized models (such as organism-specific and modification-specific models) from user-provided data sets. Cross-promotion E-value (CPE) as stringent benchmark has been used in PTMProber to evaluate homology to known modification sites. Independent-validation tests show that PTMProber achieves over 58.8% recall with high precision by CPE benchmark. Comparisons with other machine-learning tools show that PTMProber pipeline performs better on general predictions.
NetPhosBac
Predicts bacteria-specific protein phosphorylation. NetPhosBac was tested on the predictions of phosphorylation sites in E. coli proteins on protein and site-specific levels. It shows the advantage of taxa-specific predictors and provides a useful asset to the microbiological community. The tool significantly outperformed all benchmark predictors. It can be applied to any bacterial system, since there are bacteria such as Myxococcus xanthus and Mycobacterium tuberculosis that contain numerous eukaryal-type kinases.
KinView / Kinome Viewer
Places eukaryotic protein kinase cancer variants in the context of natural sequence variation and experimentally determined post-translational modifications, which play central roles in the regulation of cellular signaling pathways. KinView can identify differential phosphorylation patterns between tyrosine and serine/threonine kinases in the activation segment, a major kinase regulatory region that is often mutated in proliferative diseases. Kinome Viewer can be used as a comparative tool to identify differences and similarities in natural variation, cancer variants and post-translational modifications between kinase groups, families and subfamilies.
PPRED / Phosphorylation PREDictor
Provides an efficient way to identify phosphorylation sites in a given protein primary sequence that would be a valuable information for the molecular biologists working on protein phosphorylation sites and for bioinformaticians developing generalized prediction systems for the post translational modifications like phosphorylation or glycosylation. PPRED ignores the kinase information and only uses the evolutionary information of proteins for classifying phosphorylation sites.
PyTMs
A plugin implemented with the commonly used visualization software PyMOL. PyTMs enables users to introduce a set of common post-translational modifications (PTMs) into protein/peptide models and can be used to address research questions related to PTMs. Ten types of modification are currently supported, including acetylation, carbamylation, citrullination, cysteine oxidation, malondialdehyde adducts, methionine oxidation, methylation, nitration, proline hydroxylation and phosphorylation.
Multi-iPPseEvo
Detects the phosphorylation proteins. Multi-iPPseEvo is a predictor that incorporates the protein sequence evolutionary information into the Chou's general pseudo amino acid composition (PseAAC) via the grey system theory. It also balances out the skewed training datasets by the asymmetric bootstrap approach, and constructs an ensemble predictor by fusing an array of individual random forest classifiers through a voting system. This approach represents a strategy to deal with the multi-label biological problems.
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