Tyrosine nitration site detection software tools | Post-translational modification data analysis
The last decade has witnessed rapid progress in the identification of protein tyrosine nitration (PTN), which is an essential and ubiquitous post-translational modification (PTM) that plays a variety of important roles in both physiological and pathological processes, such as the immune response, cell death, aging and neurodegeneration. Identification of site-specific nitrated substrates is fundamental for understanding the molecular mechanisms and biological functions of PTN.
Detects proteins tyrosine nitration (PTNs) sites. GPS-YNO2 aims to assist users in investigating relationship between PTN and S-nitrosylation. The application can be queried through a web platform for basic research with sequences up to 1000 or as a local application for further analysis. Users can set different levels of threshold or query multiple protein sequences by using a batch prediction mode.
A predictor was developed by incorporating the position-specific dipeptide propensity into the general pseudo amino acid composition for discriminating the nitrotyrosine sites from non-nitrotyrosine sites in proteins. It was demonstrated via the rigorous jackknife tests that iNitro-Tyr not only can yield higher success rate but also is much more stable and less noisy.
Performs prediction of nitrotyrosine sites. NTyroSite is an in-silico predictor that uses sequence evolutionary information. Users can submit query sequence by browsing or pasting their data. It first produces tyrosine fragments of all the putative nitrotyrosine sites, secondly it generates position-specific scoring matrix (PSSM) and calculates possible k-space amino acid pair/ It then classifies sequence similarity and finally returns the result on the output webpage.
Serves for prediction of nitration, sulfation, and phosphorylation of tyrosine residues in a system. TyrPred is able to determine tyrosine post-translational modification (PTM) sites. It produces a list of features containing the protein name, the position of site, flanking amino acids, and support vector machine (SVM) probability. This tool maps the input samples into a higher dimensional space using the kernel function radial basis function.
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