Literature DB >> 28682641

pSuc-PseRat: Predicting Lysine Succinylation in Proteins by Exploiting the Ratios of Sequence Coupling and Properties.

Haixin Ai1,2, Runlin Wu3, Li Zhang1,2, Xuewei Wu1, Junchao Ma3, Huan Hu1, Liangchao Huang3, Wen Chen3, Jian Zhao1, Hongsheng Liu1,2.   

Abstract

Lysine succinylation is an extremely important protein post-translational modification that plays a fundamental role in regulating various biological reactions, and dysfunction of this process is associated with a number of diseases. Thus, determining which Lys residues in an uncharacterized protein sequence are succinylated underpins both basic research and drug development endeavors. To solve this problem, we have developed a predictor called pSuc-PseRat. The features of the pSuc-PseRat predictor are derived from two aspects: (1) the binary encoding from succinylated sites and non-succinylated sites; (2) the sequence-coupling effects between succinylated sites and non-succinylated sites. Eleven gradient boosting machine classifiers were trained with these features to build the predictor. The pSuc-PseRat predictor achieved an average ACU (area under the receiver operating characteristic curve) score of 0.805 in the fivefold cross-validation set and performed better than existing predictors on two comprehensive independent test sets. A freely available web server has been developed for pSuc-PseRat.

Entities:  

Keywords:  binary encoding; gradient boosting machine; lysine succinylation.

Mesh:

Substances:

Year:  2017        PMID: 28682641     DOI: 10.1089/cmb.2016.0206

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  5 in total

1.  Mal-Light: Enhancing Lysine Malonylation Sites Prediction Problem Using Evolutionary-based Features.

Authors:  Wakil Ahmad; Easin Arafat; Ghazaleh Taherzadeh; Alok Sharma; Shubhashis Roy Dipta; Abdollah Dehzangi; Swakkhar Shatabda
Journal:  IEEE Access       Date:  2020-04-22       Impact factor: 3.367

Review 2.  Large-Scale Assessment of Bioinformatics Tools for Lysine Succinylation Sites.

Authors:  Md Mehedi Hasan; Mst Shamima Khatun; Hiroyuki Kurata
Journal:  Cells       Date:  2019-01-28       Impact factor: 6.600

3.  A Transfer Learning-Based Approach for Lysine Propionylation Prediction.

Authors:  Ang Li; Yingwei Deng; Yan Tan; Min Chen
Journal:  Front Physiol       Date:  2021-04-21       Impact factor: 4.566

4.  LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

Authors:  Guohua Huang; Qingfeng Shen; Guiyang Zhang; Pan Wang; Zu-Guo Yu
Journal:  Biomed Res Int       Date:  2021-05-28       Impact factor: 3.411

5.  HybridSucc: A Hybrid-learning Architecture for General and Species-specific Succinylation Site Prediction.

Authors:  Wanshan Ning; Haodong Xu; Peiran Jiang; Han Cheng; Wankun Deng; Yaping Guo; Yu Xue
Journal:  Genomics Proteomics Bioinformatics       Date:  2020-08-28       Impact factor: 7.691

  5 in total

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