Identification, isoform classification, ligand binding, and database construction of the protein-tyrosine sulfotransferase family in metazoans

Abstract

Protein tyrosine sulfonation (PTS) influences various crucial physiological and pathological processes in animals. Protein-tyrosine sulfotransferase (TPST) serves as a pivotal enzyme in this process. Research on TPST is still in its early stages, and current identification methods have not yet effectively differentiated TPST from other type II sulfotransferases. Furthermore, this study has revealed that TPST in animals is highly conserved and exhibits significant differences when compared to other sulfotransferases and TPSTs in non-animal species. However, precise and efficient methods for identifying TPST, conducting subfamily classification, performing functional and sequence analyses, and accessing corresponding databases and analytical platforms for the entire TPST family of metazoan species are lacking. These findings provide a foundation for more in-depth research on TPST in animals and are crucial for advancing the understanding of PTS and its broader impacts.

In this study, a Hidden Markov Model (TPST-HMM) was formulated based on the conserved motifs binding to the substrate PAPS and the ligand tyrosine in metazoan TPSTs. TPST-HMM successfully identified more than 91.8 % of metazoan TPSTs in UniProt (e-value < 1e-5). When the threshold was adjusted to 1e-20, the identification rate of TPST was 83.9 % in metazoans and approximately 0 % in other species (fungi, bacteria, etc.). Subsequently, 5638 TPSTs were identified from 1311 metazoan genomes, and these TPSTs were classified into three subfamilies. The classification of the TPST1 and TPST2 subtypes, which were initially annotated in mammals, was extended across vertebrates. Additionally, a novel subtype, TPST3, belonging to a distinct subfamily, was discovered in invertebrates. We proposed a molecular docking prediction method for TPST and tyrosine ligands based on the observation that TPST-tyrosine binding recognition and binding in metazoans were primarily driven by electrostatic interactions.

Finally, a database website for animal TPST sequences was established (http://sz.bjfskj.com/). The website included an online tool for identifying TPST protein sequences, enabling annotation and visualization of functional motifs and active amino acids. Its design aimed to assist users in studying TPST in animals.