ArticleScientific reports2019
Computational identification of microbial phosphorylation sites by the enhanced characteristics of sequence information.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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19 citing papers in PubMed, 40 citations in OpenAlex.
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- Experimental measurement and computational prediction of bacterial Hanks-type Ser/Thr signaling system regulatory targets.Molecular microbiology · 2024Review
- A Review of Machine Learning and Algorithmic Methods for Protein Phosphorylation Site Prediction.Genomics, proteomics & bioinformatics · 2023Review
- A Novel Capsule Network with Attention Routing to Identify Prokaryote Phosphorylation Sites.Biomolecules · 2022Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.International journal of molecular sciences · 2021Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
- PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations.International journal of molecular sciences · 2021Article
- Comparative Phosphoproteomics of Classical Bordetellae Elucidates the Potential Role of Serine, Threonine and Tyrosine Phosphorylation inFrontiers in cellular and infection microbiology · 2021Article
- Article
- ProIn-Fuse: improved and robust prediction of proinflammatory peptides by fusing of multiple feature representations.Journal of computer-aided molecular design · 2020Article
- iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique.Current genomics · 2020Article
- Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction.Current genomics · 2020Review
- dbPSP 2.0, an updated database of protein phosphorylation sites in prokaryotes.Scientific data · 2020Article
- i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature representation.Plant molecular biology · 2020Article
- Recent Development of Machine Learning Methods in Microbial Phosphorylation Sites.Current genomics · 2020Review
- i4mC-Mouse: Improved identification of DNA N4-methylcytosine sites in the mouse genome using multiple encoding schemes.Computational and structural biotechnology journal · 2020Article
- AtbPpred: A Robust Sequence-Based Prediction of Anti-Tubercular Peptides Using Extremely Randomized Trees.Computational and structural biotechnology journal · 2019Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Protein phosphorylation on serine (S) and threonine (T) has emerged as a key device in the control of many biological processes. Recently phosphorylation in microbial organisms has attracted much attention for its critical roles in various cellular processes such as cell growth and cell division. Here a novel machine learning predictor, MPSite (Microbial Phosphorylation Site predictor), was developed to identify microbial phosphorylation sites using the enhanced characteristics of sequence features. The final feature vectors optimized via a Wilcoxon rank sum test. A random forest classifier was then trained using the optimum features to build the predictor. Benchmarking investigation using the 5-fold cross-validation and independent datasets test showed that the MPSite is able to achieve robust performance on the S- and T-phosphorylation site prediction. It also outperformed other existing methods on the comprehensive independent datasets. We anticipate that the MPSite is a powerful tool for proteome-wide prediction of microbial phosphorylation sites and facilitates hypothesis-driven functional interrogation of phosphorylation proteins. A web application with the curated datasets is freely available at http://kurata14.bio.kyutech.ac.jp/MPSite/ .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.