Abstract
Posttranslational modifications (PTMs) of proteins are responsible for sensing and transducing signals to regulate various cellular functions and signaling events. S-nitrosylation (SNO) is one of the most important and universal PTMs. With the avalanche of protein sequences generated in the post-genomic age, it is highly desired to develop computational methods for timely identifying the exact SNO sites in proteins because this kind of information is very useful for both basic research and drug development. Here, a new predictor, called iSNO-PseAAC, was developed for identifying the SNO sites in proteins by incorporating the position-specific amino acid propensity (PSAAP) into the general form of pseudo amino acid composition (PseAAC). The predictor was implemented using the conditional random field (CRF) algorithm. As a demonstration, a benchmark dataset was constructed that contains 731 SNO sites and 810 non-SNO sites. To reduce the homology bias, none of these sites were derived from the proteins that had pairwise sequence identity to any other. It was observed that the overall cross-validation success rate achieved by iSNO-PseAAC in identifying nitrosylated proteins on an independent dataset was over 90%, indicating that the new predictor is quite promising. Furthermore, a user-friendly web-server for iSNO-PseAAC was established at http://app.aporc.org/iSNO-PseAAC/, by which users can easily obtain the desired results without the need to follow the mathematical equations involved during the process of developing the prediction method. It is anticipated that iSNO-PseAAC may become a useful high throughput tool for identifying the SNO sites, or at the very least play a complementary role to the existing methods in this area.
Highlights
The post-translational modifications (PTMs) play a key role in providing proteins with structural and functional diversity, as well as in regulating cellular plasticity and dynamics
Recent reports have indicated that SNO can modulate protein stability and activities [2,3], as well as play an important role in a variety of biological processes, including cell signaling, transcriptional regulation, apoptosis, and chromatin remodeling [4]
Increasing evidences have indicated that SNO plays an important role in various major diseases [5], such as cancer [6], Parkinson’s [7,8], Alzheimer’s [9], and Amyotrophic Lateral Sclerosis (ALS) [10]
Summary
The post-translational modifications (PTMs) play a key role in providing proteins with structural and functional diversity, as well as in regulating cellular plasticity and dynamics. As summarized in [19] and demonstrated in a series of recent publications (see, e.g., [20,21,22,23]), to establish a really useful statistical predictor for a protein or DNA system based on the sequence information, we usually need to consider the following procedures: (i) construct or select a valid benchmark dataset to train and test the predictor; (ii) formulate the protein or DNA sequence samples with a feature vector that can truly reflect the intrinsic correlation with the target to be predicted; (iii) introduce or develop a powerful algorithm (or engine) to operate the prediction; (iv) properly perform cross-validation tests to objectively evaluate the anticipated prediction accuracy; (v) establish a user-friendly web-server for the predictor that is accessible to the public. Let us describe how to deal with these procedures one by one
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