Hydroxylation site detection software tools | Post-translational modification data analysis
Hydroxylation is an important post-translational modification and closely related to various diseases. Besides the biotechnology experiments, in silico prediction methods are alternative ways to identify the potential hydroxylation sites.
Predicts the identifying protein hydroxylation sites. iHyd-PseCp is a predictor that incorporates the sequence-coupled information into the general pseudo amino acid composition (PseAAC). It can become a useful high throughput tool for both basic research and drug development in the areas relevant to the protein hydroxylation. 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.
A predictor is proposed by incorporating the dipeptide position-specific propensity into the general form of pseudo amino acid composition. It was demonstrated by rigorous cross-validation tests on stringent benchmark datasets that iHyd-PseAAC is quite promising and may become a useful high throughput tool in this area.
Identifies putative hydroxylysine and hydroxyproline residues in proteins. RF-Hydroxysite was tested with the jack-knife cross validation method and an independent test set. It is able to annotate potential hydroxylation sites within a protein with high confidence. The tool uses only the primary amino acid sequence as input to identify putative hydroxylation sites. It contains features such as cumulative hydrophobicity and position-specific entropy.
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.
Predicts protein hydroxylation sites. OH-PRED is based on the adapted normal distribution bi-profile Bayes (ANBPB) feature extraction and physicochemical property indexes of the amino acids (AAPPI). It helps to reveal protein hydroxylation sites from exact molecular mechanisms in physiological and pathological processes. The tool uses a support vector machine (SVM) method which was trained with the LIBSVM package (a library of SVM).
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