ILSES: Identification lysine succinylation-sites with ensemble classification

Lysine succinylation is one of most important types in protein post-translational modification, which is involved in many cellular processes and serious diseases. However, effective recognition of such sites with traditional experiment methods may seem to be treated as time-consuming and laborious. Those methods can hardly meet the need of efficient identification a great deal of succinylated sites at speed. In this work, several physicochemical properties of succinylated sites have been extracted, such as the physicochemical property of the amino acids. Flexible neural tree, which is employed as the classification model, was utilized to integrate above mentioned features for generating a novel lysine succinylation prediction framework named ILSES (identification lysine succinylation-sites with ensemble features classification). Such method owns the ability to combining diverse features to predict lysine succinylation with high accuracy and real time.

[1]  De-Shuang Huang,et al.  ChIP-PIT: Enhancing the Analysis of ChIP-Seq Data Using Convex-Relaxed Pair-Wise Interaction Tensor Decomposition , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  De-Shuang Huang,et al.  A Two-Stage Geometric Method for Pruning Unreliable Links in Protein-Protein Networks , 2015, IEEE Transactions on NanoBioscience.

[3]  Minoru Kanehisa,et al.  AAindex: amino acid index database, progress report 2008 , 2007, Nucleic Acids Res..

[4]  De-Shuang Huang,et al.  Normalized Feature Vectors: A Novel Alignment-Free Sequence Comparison Method Based on the Numbers of Adjacent Amino Acids , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[5]  Yingming Zhao,et al.  Metabolic Regulation by Lysine Malonylation, Succinylation, and Glutarylation* , 2015, Molecular & Cellular Proteomics.

[6]  Yuehui Chen,et al.  Reverse engineering of gene regulatory networks using flexible neural tree models , 2013, Neurocomputing.

[7]  Zhiqiang Ma,et al.  Accurate in silico identification of protein succinylation sites using an iterative semi-supervised learning technique. , 2015, Journal of theoretical biology.

[8]  K. Chou,et al.  Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.

[9]  S. Knapp,et al.  Targeting bromodomains: epigenetic readers of lysine acetylation , 2014, Nature Reviews Drug Discovery.

[10]  Wenzheng Bao,et al.  Prediction of protein structure classes with flexible neural tree. , 2014, Bio-medical materials and engineering.

[11]  Dariusz Plewczynski,et al.  AutoMotif server: prediction of single residue post-translational modifications in proteins , 2005, Bioinform..

[12]  Hiroyuki Ogata,et al.  AAindex: Amino Acid Index Database , 1999, Nucleic Acids Res..

[13]  Yu Shyr,et al.  Improved prediction of lysine acetylation by support vector machines. , 2009, Protein and peptide letters.

[14]  Zong Dai,et al.  Identification of protein methylation sites by coupling improved ant colony optimization algorithm and support vector machine. , 2011, Analytica chimica acta.

[15]  Kuo-Chen Chou,et al.  pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. , 2016, Journal of theoretical biology.

[16]  Lei Zhang,et al.  Prediction of protein-protein interactions based on protein-protein correlation using least squares regression. , 2014, Current protein & peptide science.

[17]  Dekel Tsur,et al.  Identification of post-translational modifications via blind search of mass-spectra , 2005, 2005 IEEE Computational Systems Bioinformatics Conference (CSB'05).

[18]  Z. R. Li,et al.  Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence , 2006, Nucleic Acids Res..

[19]  De-Shuang Huang,et al.  Predicting Hub Genes Associated with Cervical Cancer through Gene Co-Expression Networks , 2016, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[20]  Vasant Honavar,et al.  Glycosylation site prediction using ensembles of Support Vector Machine classifiers , 2007, BMC Bioinformatics.

[21]  De-Shuang Huang,et al.  A Constructive Hybrid Structure Optimization Methodology for Radial Basis Probabilistic Neural Networks , 2008, IEEE Transactions on Neural Networks.

[22]  Mark Gerstein,et al.  Genome-wide sequence-based prediction of peripheral proteins using a novel semi-supervised learning technique , 2010, BMC Bioinformatics.

[23]  Zhihong Zhang,et al.  Identification of lysine succinylation as a new post-translational modification. , 2011, Nature chemical biology.

[24]  Zhen Wang,et al.  SFAPS: An R package for structure/function analysis of protein sequences based on informational spectrum method , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[25]  Jean Armengaud,et al.  Proteogenomics and systems biology: quest for the ultimate missing parts , 2010, Expert review of proteomics.

[26]  Sebastian A. Wagner,et al.  Lysine succinylation is a frequently occurring modification in prokaryotes and eukaryotes and extensively overlaps with acetylation. , 2013, Cell reports.

[27]  D.-S. Huang,et al.  Radial Basis Probabilistic Neural Networks: Model and Application , 1999, Int. J. Pattern Recognit. Artif. Intell..