SulfoTyr-PseAAC: A Machine Learning Framework to Identify Sulfotyrosine Sites

Tyrosine is a type of protein which is a chain of amino acid residues. It is utilized to synthesize proteins by eukaryotic. It is post-Translational modified (PTM) by its side chain hydeoxyl group by sulfation, phosphorylation, and nitration. Sulfotyrosine participate in protein-protein interaction. Sulfotyrosine is irreversible because still there is no enzymes that removes sulfate from tyrosine residue. Sulfotyrosines are identified in human diseases such as diabetes and Age-related Macular degeneration (AMD). It is very important to investigate the occurrence of sulfation in Tyrosine. There are many methods available to accurately predict Sulfotyrosines but most of them need more time and expert team. Therefore, a model is developed to predict sulfotyrosine accurately and efficiently and it is possible through machine learning algorithms which reduce cast and time. This study created a Machine through Logistic Regression which learnt statistical moments and can predict accurately. The prediction accuracy of this proposed model is 100%. The methodology of this study depends upon Chou’s 5-step rule.