pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties

Zhao, Jian; Zhuang, Minhui; Liu, Jingjing; Zhang, Meng; Zeng, Cong; Jiang, Bin; Wu, Jing; Song, Xiaofeng

doi:10.1186/s12859-022-04938-x

Cited by 9 publications

(5 citation statements)

References 52 publications

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“…Previously, three predictors, iPhosH-PseAAC [32], PROSPECT [31], and pHisPred [30], were developed for the prediction of protein pHis sites. PROSPECT is an Escherichia coli -specific pHis predictor, iPhosH-PseAAC is a general pHis predictor.…”

Section: Resultsmentioning

confidence: 99%

“…Some N-/O -phosphorylation predictors optimize model performance by selecting different peptide lengths [20]. For example, in pHisPred, different machine learning algorithms have different sensitivities to peptide lengths [30]. Therefore, the selection of the optimized sequence length for model construction is expected in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, two protein histidine kinases (NME1 and NME2) and several protein histidine phosphatases (such as PHPT1 and LHPP), as well as a handful of substrates, were identified in mammals [29]. In recent years, three models (pHisPred [30], PROSPECT [31], and iPhosH-PseAAC [32]) were trained for the large-scale prediction of pHis sites based on 487, 242, and 602 pHis sites. However, because eukaryotes and prokaryotes might not share common mechanisms of N -phosphorylation, implementation of these models for eukaryote pHis prediction is questionable.…”

Section: Introductionmentioning

confidence: 99%

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Nphos: Database and Predictor of ProteinN-phosphorylation

Zhao,

Ding,

Chen

et al. 2023

Preprint

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ProteinN-phosphorylation widely present in nature and participates in various biological functions. However, current knowledge onN-phosphorylation is extremely limited compared to that onO-phosphorylation. In this study, we collected 11,710 experimentally verifiedN-phosphosites of 7344 proteins from 39 species and subsequently constructed the database Nphos to share up-to-date information on proteinN-phosphorylation. Upon these substantial data, we characterized the sequential and structural features of proteinN-phosphorylation. Moreover, after comparing of hundreds of learning models, we chose and optimized gradient boosting decision tree (GBDT) models to predict three types of humanN-phosphorylation, achieving mean areas under the receiver operating characteristic curve (AUC) of 90.56%, 91.24%, and 92.01% for pHis, pLys, and pArg, respectively. Meanwhile, we discovered 488,825 distinctN-phosphosites in the human proteome. The models were also deployed in Nphos for interactiveN-phosphosite prediction. In summary, this work provides new insights and points for both flexible and focused investigations ofN-phosphorylation. It will also facilitate a deeper and more systematic understanding of proteinN-phosphorylation modification by providing a data and technical foundation. Nphos is freely available athttp://www.bio-add.organdhttp://ppodd.org.cn/Nphos/.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Nphos: Database and Predictor of ProteinN-phosphorylation

Zhao,

Ding,

Chen

et al. 2023

Preprint

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“…SVM is one of the most applicable machine learning methods for binary problems and has high accuracy and also high performance. This method utilizes an optimized hyperplane to distinguish classes and it is widely used for the prediction of PTM [25], [40] and phosphorylation [41].…”

Section: Svmmentioning

confidence: 99%

DF-Phos: Prediction of Protein Phosphorylation Sites by Deep Forest

Zahiri,

Mehrshad,

Mehrshad

2023

The Journal of Biochemistry

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Phosphorylation is the most important and studied post-translational modification (PTM), which plays a crucial role in protein function studies and experimental design. Many significant studies have been performed to predict phosphorylation sites using various machine-learning methods. Recently, several studies have claimed that deep learning-based methods are the best way to predict the phosphorylation sites because deep learning as an advanced machine learning method can automatically detect complex representations of phosphorylation patterns from raw sequences and thus offers a powerful tool to improve phosphorylation site prediction. In this study, we report DF-Phos, a new phosphosite predictor based on the Deep Forest to predict phosphorylation sites. In DF-Phos, the feature vector taken from the CkSAApair method is as input for a Deep Forest framework for predicting phosphorylation sites. The results of 10-fold cross-validation show that the Deep Forest method has the highest performance among other available methods. We implemented a Python program of DF-Phos, which is freely available for non-commercial use at https://github.com/zahiriz/DF-Phos Moreover, users can use it for various PTM predictions.

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“…The computational characterizations of His functions, so far, were reported for single modifications only. For example, histidine phosphorylation sites were predicted using a convoluted neural network (CNN) -based model, PROSPECT [7], and support vector machine-based model, pHisPred [8]. Transition metal-binding sites for Cys and His were predicted by exploiting position-specific evolutionary profiles using support vector machines and neural networks [9].…”

Section: Introductionmentioning

confidence: 99%

Hist-i-fy: Multiple histidine function prediction based on protein sequences using deep neural network

Bandyopadhyay,

Jalan,

Diptiman

et al. 2023

Preprint

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Histidine (His) is the most reactive amino acid at enzyme active sites. Multiple post-translational modifications (functions) are reported for His side chains. The high-throughput sequencing techniques produce a large number of protein sequences without functional annotations at the amino acid level. Experimental characterization of His functions in proteins is laborious and time-consuming. Computational characterization based on protein sequences may complement the need. There are only a handful of Histidine function prediction tools available and those annotate only a single function. Here we curated a dataset of active Histidine with known functions based on protein sequences obtained from UniProt database (sample size n=1584) and trained against four machine learning methods. The convolution neural network (CNN) model (“ Hist-i-fy”) performed the best with 75% overall accuracy. The external validation of Hist-i-fy on phosphorylated histidine data (sample size 34) showed 94.1% prediction accuracy. For the first time, we report multiple His function prediction, based on protein sequences using deep neural networks. The inputs to the model are i) protein sequence containing His, and ii) the His residue number. The model predicts one out of the eight histidine functions, namely, acetylation, ribosylation, glycosylation, hydroxylation, methylation, oxidation, phosphorylation, and protein splicing. The novelty of the work is, it predicts maximum number of histidine functions at a time with optimal performance. There is a scope of improvement in the model upon availability of a larger dataset. The model is available as a web application ([https://histify.streamlit.app/](https://histify.streamlit.app/)) and a stand-alone code [https://github.com/dibyansu24-maker/Histify](https://github.com/dibyansu24-maker/Histify)).

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pHisPred: a tool for the identification of histidine phosphorylation sites by integrating amino acid patterns and properties

Cited by 9 publications

References 52 publications

Nphos: Database and Predictor of ProteinN-phosphorylation

Nphos: Database and Predictor of ProteinN-phosphorylation

DF-Phos: Prediction of Protein Phosphorylation Sites by Deep Forest

Hist-i-fy: Multiple histidine function prediction based on protein sequences using deep neural network

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