DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Overview

Journal Front Pharmacol

Date 2021 Dec 17

PMID 34916947

Citations 8

Authors

Yuxin Gong

Bo Liao

Peng Wang

Quan Zou

Affiliations

Soon will be listed here.

Abstract

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

Citing Articles

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Empirical comparison and analysis of machine learning-based approaches for druggable protein identification.

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References

Zheng L, Huang S, Mu N, Zhang H, Zhang J, Chang Y . RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule. Database (Oxford). 2019; 2019. PMC: 6893003. DOI: 10.1093/database/baz131. View

Russ A, Lampel S . The druggable genome: an update. Drug Discov Today. 2005; 10(23-24):1607-10. DOI: 10.1016/S1359-6446(05)03666-4. View

Zhu Y, Li F, Xiang D, Akutsu T, Song J, Jia C . Computational identification of eukaryotic promoters based on cascaded deep capsule neural networks. Brief Bioinform. 2020; 22(4). PMC: 8522485. DOI: 10.1093/bib/bbaa299. View

Ding Y, Tang J, Guo F . Identification of Drug-Side Effect Association via Semisupervised Model and Multiple Kernel Learning. IEEE J Biomed Health Inform. 2018; 23(6):2619-2632. DOI: 10.1109/JBHI.2018.2883834. View

Jamali A, Ferdousi R, Razzaghi S, Li J, Safdari R, Ebrahimie E . DrugMiner: comparative analysis of machine learning algorithms for prediction of potential druggable proteins. Drug Discov Today. 2016; 21(5):718-24. DOI: 10.1016/j.drudis.2016.01.007. View

Wei L, Xing P, Zeng J, Chen J, Su R, Guo F . Improved prediction of protein-protein interactions using novel negative samples, features, and an ensemble classifier. Artif Intell Med. 2017; 83:67-74. DOI: 10.1016/j.artmed.2017.03.001. View

Wang Z, Liu D, Xu B, Tian R, Zuo Y . Modular arrangements of sequence motifs determine the functional diversity of KDM proteins. Brief Bioinform. 2020; 22(3). DOI: 10.1093/bib/bbaa215. View

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

Meng C, Guo F, Zou Q . CWLy-SVM: A support vector machine-based tool for identifying cell wall lytic enzymes. Comput Biol Chem. 2020; 87:107304. DOI: 10.1016/j.compbiolchem.2020.107304. View

10.

Ru X, Wang L, Li L, Ding H, Ye X, Zou Q . Exploration of the correlation between GPCRs and drugs based on a learning to rank algorithm. Comput Biol Med. 2020; 119:103660. DOI: 10.1016/j.compbiomed.2020.103660. View

11.

Wu X, Yu L . EPSOL: sequence-based protein solubility prediction using multidimensional embedding. Bioinformatics. 2021; 37(23):4314-4320. DOI: 10.1093/bioinformatics/btab463. View

12.

Zuo Y, Li Y, Chen Y, Li G, Yan Z, Yang L . PseKRAAC: a flexible web server for generating pseudo K-tuple reduced amino acids composition. Bioinformatics. 2016; 33(1):122-124. DOI: 10.1093/bioinformatics/btw564. View

13.

Huang Y, Zhou D, Wang Y, Zhang X, Su M, Wang C . Prediction of transcription factors binding events based on epigenetic modifications in different human cells. Epigenomics. 2020; 12(16):1443-1456. DOI: 10.2217/epi-2019-0321. View

14.

Dudoit S, Fridlyand J . Bagging to improve the accuracy of a clustering procedure. Bioinformatics. 2003; 19(9):1090-9. DOI: 10.1093/bioinformatics/btg038. View

15.

Zou Q, Xing P, Wei L, Liu B . Gene2vec: gene subsequence embedding for prediction of mammalian -methyladenosine sites from mRNA. RNA. 2018; 25(2):205-218. PMC: 6348985. DOI: 10.1261/rna.069112.118. View

16.

Zeng X, Zhong Y, Lin W, Zou Q . Predicting disease-associated circular RNAs using deep forests combined with positive-unlabeled learning methods. Brief Bioinform. 2019; 21(4):1425-1436. DOI: 10.1093/bib/bbz080. View

17.

Ji X, Freudenberg J, Agarwal P . Integrating Biological Networks for Drug Target Prediction and Prioritization. Methods Mol Biol. 2018; 1903:203-218. DOI: 10.1007/978-1-4939-8955-3_12. View

18.

Lv H, Zhang Z, Li S, Tan J, Chen W, Lin H . Evaluation of different computational methods on 5-methylcytosine sites identification. Brief Bioinform. 2019; 21(3):982-995. DOI: 10.1093/bib/bbz048. View

19.

Xu L, Liang G, Shi S, Liao C . SeqSVM: A Sequence-Based Support Vector Machine Method for Identifying Antioxidant Proteins. Int J Mol Sci. 2018; 19(6). PMC: 6032279. DOI: 10.3390/ijms19061773. View

20.

Zhang L, Xiao X, Xu Z . iPromoter-5mC: A Novel Fusion Decision Predictor for the Identification of 5-Methylcytosine Sites in Genome-Wide DNA Promoters. Front Cell Dev Biol. 2020; 8:614. PMC: 7399635. DOI: 10.3389/fcell.2020.00614. View