※ Computational resources of protein phosphorylation:

Introduction:

Protein phosphorylation is the most ubiquitous post-translational modification (PTM), and plays important roles in most of biological processes in prokaryotes. Besides experimental approaches, prediction of potential candidates with computational methods has also attracted great attention for its convenience and fast-speed. In this review, we present a comprehensive but brief summarization of computational resources of prokaryotic protein phosphorylation, including prokaryotic phosphorylation databases, prokaryotic phosphorylation prediction tools, and other tools.

We appreciate all help from databases and tools therefore we present references about them. We are grateful for users feedback. Please inform Dr. Yu Xue or Chi Zhang to add, remove or update one or multiple web links below.

Index:

<1> Public databases containing prokaryotic phosphorylation sites

<2> Prediction of phosphorylation sites in prokaryotes

<3> Sequence and structure information

<4> Implemented Tools

<5> Taxonomy annotation

<6> Miscellaneous tools

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<1> Public databases containing prokaryotic phosphorylation sites:

1. dbPSP 2.0: An updated database of protein phosphorylation sites in prokaryotes. (Shi Y, et al., 2020).

2. dbPTM: An integrated resource for protein post-translational modifications (Huang, et al., 2019).

3. UniProt: A worldwide hub of protein knowledge (UniProt Consortium, 2019).

4. SysPTM: A systematic resource for proteomic research on post-translational modifications (Li, et al., 2014).

5. PHOSIDA: A phosphorylation site database, integrates thousands of high-confidence in vivo phosphosites identified by mass spectrometry-based proteomics in various species (Gnad, et al., 2011).

6. Phosphorylation Site Database: A guide to the serine-, threonine-, and/or tyrosine-phosphorylated proteins in prokaryotic organisms (Wurgler-Murphy, et al., 2004).

<2> Prediction of phosphorylation sites in prokaryotes:

1. MPSite: produces neural network predictions for serine, threonine and tyrosine phosphorylation sites in eukaryotic proteins (Hasan MM, et al., 2019).

2. NetPhosBac: NetPhosBac - a predictor for Ser/Thr phosphorylation sites in bacterial proteins (Miller ML, et al., 2009).

3. cPhosBac: Prediction of serine/threonine phosphorylation sites in bacteria proteins (Li Z, et al., 2015).

4. RotPhoPred:Accurately predicting microbial phosphorylation sites using evolutionary and structural features (Ahmed F, et al., 2023).

5. prkC-PSP: Prediction of prkC-mediated protein serine/threonine phosphorylation sites for bacteria (Zhang QB et al. 2018). The tool is not available.

6. EcapsP: A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites (Wang S, et al., 2022; ). The tool is not available.

7. PROSPECT : PROSPECT: A web server for predicting protein histidine phosphorylation sites (Chen Z, et al., 2020). The tool is not available.

8. pHisPred : pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties(Zhao J, et al., 2022)

<3> Sequence and structure information:

1. Compute pI/Mw: A tool which allows the computation of the theoretical pI (isoelectric point) and Mw (molecular weight) for a list of entered sequences (Wilkins, et al., 1999).

2. MMDB: 3D structures and macromolecular interactions (Madej, et al., 2012).

3. DisProt: A database of protein disorder (Vucetic, et al., 2005).

4. RCSB PDB: A database of biological macromolecular structures (Burley, et al., 2019).

5. IUPred2A: A web interface that allows to identify disordered protein regions using IUPred2 and disordered binding regions using ANCHOR2 (Mészáros, et al., 2018).

<4> Implemented Tools:

1. Echarts: An Open Source JavaScript Visualization Library.

2. IUPred: The web server takes a single amino acid sequence as an input and calculates the pairwise energy profile along the sequence (Dosztányi,et al., 2021).

3. 3Dmol.js: A modern, object-oriented JavaScript library for visualizing molecular data.

4. NetSurfP 1.1: A generic method for assignment of reliability scores applied to solvent accessibility predictions (Petersen B,et al., 2009).

<5> Taxonomy annotation:

1. BacDive: A comprehensive resource for structured data on the taxonomy, morphology, physiology, cultivation, isolation and molecular data of prokaryotes (Reimer, et al., 2019).

2. LPSN: List of prokaryotic names with standing in nomenclature (Parte, et al., 2018).

3. MicrobeWiki: A free wiki resource on microbes and microbiology, authored by students at many colleges and universities.

4. NCBI Taxonomy: A standard nomenclature and classification repository for the International Nucleotide Sequence Database Collaboration (INSDC), comprising the GenBank, ENA (EMBL) and DDBJ databases (Federhen, et al., 2012).

5. PATRIC: the all-bacterial bioinformatics database and analysis resource center (Wattam, et al., 2017).

6. proGenomes: A resource for consistent functional and taxonomic annotations of prokaryotic genomes (Mende, et al., 2017).

7. fusionDB: A databse assessing microbial diversity and environmental preferences via functional similarity networks (Zhu, et al., 2018).

<6> Miscellaneous tools:

1. GPS 6.0 GPS: An update on the prediction of kinase-specific phosphorylation sites in proteins (Chen, et al., 2023).

2. HemI 2.0 GPS: An online service for heatmap illustration (Ning, et al., 2022).