Background: The amino acid residues, in protein, undergo post-translation modification (PTM) during protein synthesis, a process of chemical and physical change in an amino acid that in turn alters behavioral properties of proteins. Tyrosine sulfation is a ubiquitous posttranslational modification which is known to be associated with regulation of various biological functions and pathological pro-cesses. Thus its identification is necessary to understand its mechanism. Experimental determination through site-directed mutagenesis and high throughput mass spectrometry is a costly and time taking process, thus, the reliable computational model is required for identification of sulfotyrosine sites.
Methodology: In this paper, we present a computational model for the prediction of the sulfotyrosine sites named iSulfoTyr-PseAAC in which feature vectors are constructed using statistical moments of protein amino acid sequences and various position/composition relative features. These features are in-corporated into PseAAC. The model is validated by jackknife, cross-validation, self-consistency and in-dependent testing.
Results: Accuracy determined through validation was 93.93% for jackknife test, 95.16% for cross-validation, 94.3% for self-consistency and 94.3% for independent testing.
Conclusion: The proposed model has better performance as compared to the existing predictors, how-ever, the accuracy can be improved further, in future, due to increasing number of sulfotyrosine sites in proteins.
Keywords: 5-step rule; PseAAC; Sulfation; pseudo components; statistical moments; sulfotyrosine.
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