PTM-ssMP: A Web Server for Predicting Different Types of Post-translational Modification Sites Using Novel Site-specific Modification Profile

Abstract

Protein post-translational modifications (PTMs) are chemical modifications of a protein after its translation. Owing to its play an important role in deep understanding of various biological processes and the development of effective drugs, PTM site prediction have become a hot topic in bioinformatics. Recently, many online tools are developed to prediction various types of PTM sites, most of which are based on local sequence and some biological information. However, few of existing tools consider the relations between different PTMs for their prediction task. Here, we develop a web server called PTM-ssMP to predict PTM site, which adopts site-specific modification profile (ssMP) to efficiently extract and encode the information of both proximal PTMs and local sequence simultaneously. In PTM-ssMP we provide efficient prediction of multiple types of PTM site including phosphorylation, lysine acetylation, ubiquitination, sumoylation, methylation, O-GalNAc, O-GlcNAc, sulfation and proteolytic cleavage. To assess the performance of PTM-ssMP, a large number of experimentally verified PTM sites are collected from several sources and used to train and test the prediction models. Our results suggest that ssMP consistently contributes to remarkable improvement of prediction performance. In addition, results of independent tests demonstrate that PTM-ssMP compares favorably with other existing tools for different PTM types. PTM-ssMP is implemented as an online web server with user-friendly interface, which is freely available at http://bioinformatics.ustc.edu.cn/PTM-ssMP/index/.

Truncated human angiotensin converting enzyme 2; a potential inhibitor of SARS-CoV-2 spike glycoprotein and potent COVID-19 therapeutic agent

“…In order to determine post translation modifications (PTMs) in ACE2, the protein sequence was submitted to PTM-ssMP server, which combines the submitted sequence and site specific modification profile to predict PTM sites in mammalian protein (Liu et al, 2018). Since, glycosylation is the most abundant and diverse posttranslational modification of proteins, therefore, we further determined the O-glycosylation sites in ACE2 using NetOGlyc 4.0 Server which specifically predict the GalNAc-type O-glycosylation site, unique to Ser and Thr (Steentoft et al, 2013).…”

“…Since, experimental methods are high-cost and time-consuming, therefore, it is necessary to theoretically predict PTMs site on protein to be expressed heterologously. PTM-ssMP, which predict PTMs sites on human protein based on local sequence and site specific modification profile (Liu et al, 2018). ACE2 analysis through PTM-ssMP server predicted ubiquitination at position 74 and 304, phosphorylation at 606 and O-glycosylation at 720 residue position.…”

Ubiquitin-Like Modifiers: Emerging Regulators of Protozoan Parasites

“…Where experimental data are lacking, the predictive power of bioinformatics can be harnessed to guide experimental research. A variety of models for predicting PTM sites on protein substrates have been developed [ 36 ], some of which were applicable to parasitic protozoa. In particular, algorithms based on amino acid sequence motifs [ 36 , 37 ], protein secondary structure [ 38 , 39 ], or a combination of both [ 40 ], have been devised to determine SUMOylation sites.…”

“…A variety of models for predicting PTM sites on protein substrates have been developed [ 36 ], some of which were applicable to parasitic protozoa. In particular, algorithms based on amino acid sequence motifs [ 36 , 37 ], protein secondary structure [ 38 , 39 ], or a combination of both [ 40 ], have been devised to determine SUMOylation sites. Enabled by the release of protozoan proteomic and genomic datasets over the last two decades [ 41 , 42 , 43 ], computational approaches have been instrumental in identifying putative Ubls and enzymes mediating Ubl cascades in these organisms based on sequence and structural similarity to relevant conserved domains [ 19 , 20 , 21 , 22 ].…”

Special issue on Computational Resources and Methods in Biological Sciences

“…Accordingly, methods for bioinformatics were developed to predict PTM sites in proteins. Liu et al developed a powerful webserver called PTM-ssMP to identify PTM sites by using protein sequence information 14 . The webserver provides nine kinds of PTM prediction and is freely available at http://bioinformatics.ustc.edu.cn/PTM-ssMP/index/ .…”