SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins

Overview

Journal Int J Mol Sci

Publisher MDPI

Specialties Biochemistry
Chemistry
Molecular Biology

Date 2018 Jun 20

PMID 29914044

Citations 35

Authors

Lei Xu

Guangmin Liang

Shuhua Shi

Changrui Liao

Affiliations

Soon will be listed here.

Abstract

Antioxidant proteins can be beneficial in disease prevention. More attention has been paid to the functionality of antioxidant proteins. Therefore, identifying antioxidant proteins is important for the study. In our work, we propose a computational method, called SeqSVM, for predicting antioxidant proteins based on their primary sequence features. The features are removed to reduce the redundancy by max relevance max distance method. Finally, the antioxidant proteins are identified by support vector machine (SVM). The experimental results demonstrated that our method performs better than existing methods, with the overall accuracy of 89.46%. Although a proposed computational method can attain an encouraging classification result, the experimental results are verified based on the biochemical approaches, such as wet biochemistry and molecular biology techniques.

Citing Articles

StackedEnC-AOP: prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning.

Rukh G, Akbar S, Rehman G, Alarfaj F, Zou Q BMC Bioinformatics. 2024; 25(1):256.

PMID: 39098908 PMC: 11298090. DOI: 10.1186/s12859-024-05884-6.

Special Protein or RNA Molecules Computational Identification.

Qi R, Zou Q Int J Mol Sci. 2023; 24(14).

PMID: 37511072 PMC: 10379736. DOI: 10.3390/ijms241411312.

Predicting promoters based on novel feature descriptor and feature selection technique.

Li H, Zhang J, Zhao Y, Yang W Front Microbiol. 2023; 14:1141227.

PMID: 36937275 PMC: 10018189. DOI: 10.3389/fmicb.2023.1141227.

Sequence-Based Prediction of Plant Allergenic Proteins: Machine Learning Classification Approach.

Nedyalkova M, Vasighi M, Azmoon A, Naneva L, Simeonov V ACS Omega. 2023; 8(4):3698-3704.

PMID: 36743013 PMC: 9893444. DOI: 10.1021/acsomega.2c02842.

The Interplay of Oxidative Stress and ROS Scavenging: Antioxidants as a Therapeutic Potential in Sepsis.

Kumar S, Saxena J, Srivastava V, Kaushik S, Singh H, Abo-El-Sooud K Vaccines (Basel). 2022; 10(10).

PMID: 36298439 PMC: 9609850. DOI: 10.3390/vaccines10101575.

References

Zou Q, Wan S, Ju Y, Tang J, Zeng X . Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy. BMC Syst Biol. 2017; 10(Suppl 4):114. PMC: 5259984. DOI: 10.1186/s12918-016-0353-5. View

Liu B . BioSeq-Analysis: a platform for DNA, RNA and protein sequence analysis based on machine learning approaches. Brief Bioinform. 2017; 20(4):1280-1294. DOI: 10.1093/bib/bbx165. View

Pan Y, Wang Z, Zhan W, Deng L . Computational identification of binding energy hot spots in protein-RNA complexes using an ensemble approach. Bioinformatics. 2017; 34(9):1473-1480. DOI: 10.1093/bioinformatics/btx822. View

Feng P, Lin H, Chen W . Identification of antioxidants from sequence information using naïve Bayes. Comput Math Methods Med. 2013; 2013:567529. PMC: 3766563. DOI: 10.1155/2013/567529. View

Zhang C, Chou K . An optimization approach to predicting protein structural class from amino acid composition. Protein Sci. 1992; 1(3):401-8. PMC: 2142205. DOI: 10.1002/pro.5560010312. View

Chou K . Some remarks on protein attribute prediction and pseudo amino acid composition. J Theor Biol. 2010; 273(1):236-47. PMC: 7125570. DOI: 10.1016/j.jtbi.2010.12.024. View

Esmaeili M, Mohabatkar H, Mohsenzadeh S . Using the concept of Chou's pseudo amino acid composition for risk type prediction of human papillomaviruses. J Theor Biol. 2009; 263(2):203-9. DOI: 10.1016/j.jtbi.2009.11.016. View

Gao J, Tao X, Zhao J, Feng Y, Cai Y, Zhang N . Computational Prediction of Protein Epsilon Lysine Acetylation Sites Based on a Feature Selection Method. Comb Chem High Throughput Screen. 2017; 20(7):629-637. DOI: 10.2174/1386207320666170314093216. View

Chen X, Tang H, Li W, Wu H, Chen W, Ding H . Identification of Bacterial Cell Wall Lyases via Pseudo Amino Acid Composition. Biomed Res Int. 2016; 2016:1654623. PMC: 4942628. DOI: 10.1155/2016/1654623. View

10.

Fu L, Niu B, Zhu Z, Wu S, Li W . CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012; 28(23):3150-2. PMC: 3516142. DOI: 10.1093/bioinformatics/bts565. View

11.

Chen W, Feng P, Lin H, Chou K . iSS-PseDNC: identifying splicing sites using pseudo dinucleotide composition. Biomed Res Int. 2014; 2014:623149. PMC: 4055483. DOI: 10.1155/2014/623149. View

12.

Wang X, Zhang W, Zhang Q, Li G . MultiP-SChlo: multi-label protein subchloroplast localization prediction with Chou's pseudo amino acid composition and a novel multi-label classifier. Bioinformatics. 2015; 31(16):2639-45. DOI: 10.1093/bioinformatics/btv212. View

13.

Feng P, Chen W, Lin H . Identifying Antioxidant Proteins by Using Optimal Dipeptide Compositions. Interdiscip Sci. 2015; 8(2):186-191. DOI: 10.1007/s12539-015-0124-9. View

14.

Lin H, Deng E, Ding H, Chen W, Chou K . iPro54-PseKNC: a sequence-based predictor for identifying sigma-54 promoters in prokaryote with pseudo k-tuple nucleotide composition. Nucleic Acids Res. 2014; 42(21):12961-72. PMC: 4245931. DOI: 10.1093/nar/gku1019. View

15.

Feng P, Ding H, Yang H, Chen W, Lin H, Chou K . iRNA-PseColl: Identifying the Occurrence Sites of Different RNA Modifications by Incorporating Collective Effects of Nucleotides into PseKNC. Mol Ther Nucleic Acids. 2017; 7():155-163. PMC: 5415964. DOI: 10.1016/j.omtn.2017.03.006. View

16.

Cai C, Han L, Ji Z, Chen X, Chen Y . SVM-Prot: Web-based support vector machine software for functional classification of a protein from its primary sequence. Nucleic Acids Res. 2003; 31(13):3692-7. PMC: 169006. DOI: 10.1093/nar/gkg600. View

17.

Schwarz J, Cooper D, Schuelke M, Seelow D . MutationTaster2: mutation prediction for the deep-sequencing age. Nat Methods. 2014; 11(4):361-2. DOI: 10.1038/nmeth.2890. View

18.

Chen W, Tang H, Ye J, Lin H, Chou K . iRNA-PseU: Identifying RNA pseudouridine sites. Mol Ther Nucleic Acids. 2017; 5:e332. PMC: 5330936. DOI: 10.1038/mtna.2016.37. View

19.

Shah A, Channon K . Free radicals and redox signalling in cardiovascular disease. Heart. 2004; 90(5):486-7. PMC: 1768240. DOI: 10.1136/hrt.2003.029389. View

20.

Zhou X, Chen C, Li Z, Zou X . Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes. J Theor Biol. 2007; 248(3):546-51. DOI: 10.1016/j.jtbi.2007.06.001. View