Login Register Cart 0
Generic placeholder image

Current Bioinformatics

Editor-in-Chief

ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

Back
Journal Home About Journal Editorial Board Journal Insight Current Issue Volumes/Issues
Subscribe
Research Article

Multivariate Information Fusion for Identifying Antifungal Peptides with Hilbert-Schmidt Independence Criterion

Author(s): Haohao Zhou, Hao Wang, Yijie Ding* and Jijun Tang*

Volume 17, Issue 1, 2022

Published on: 27 July, 2021

Page: [89 - 100] Pages: 12

DOI: 10.2174/1574893616666210727161003

Price: $65

Abstract

Background: Antifungal Peptides (AFP) have been found to be effective against many fungal infections.

Objective: However, it is difficult to identify AFP. Therefore, it is great practical significance to identify AFP via machine learning methods (with sequence information).

Methods: In this study, a Multi-Kernel Support Vector Machine (MKSVM) with Hilbert-Schmidt Independence Criterion (HSIC) is proposed. Proteins are encoded with five types of features (188-bit, AAC, ASDC, CKSAAP, DPC), and then construct kernels using Gaussian kernel function. HSIC are used to combine kernels and multi-kernel SVM model is built.

Results: Our model performed well on three AFPs datasets and the performance is better than or comparable to other state-of-art predictive models.

Conclusion: Our method will be a useful tool for identifying antifungal peptides.

Keywords: Antifungal peptides, feature representation, amino acid composition, multiple kernel learning, hilbert-schmidt independence criterion, support vector machine.

« Previous Next »
Graphical Abstract

[1]
Brown GD, Denning DW, Gow NAR, Levitz SM, Netea MG, White TC. Hidden killers: Human fungal infections. Sci Transl Med 2012; 4: 165.
[2]
Gamaletsou MN, Walsh TJ, Sipsas NV. Invasive fungal infections in patients with hematological malignancies: Emergence of resistant pathogens and new antifungal therapies. Turk J Haematol 2018; 35(1): 1-11.
[http://dx.doi.org/10.4274/tjh.2018.0007] [PMID: 29391334]
[3]
Faruck MO, Yusof F, Chowdhury S. An overview of antifungal peptides derived from insect. Peptides 2016; 80: 80-8.
[http://dx.doi.org/10.1016/j.peptides.2015.06.001] [PMID: 26093218]
[4]
Debono M, Gordee RS. Antibiotics that inhibit fungal cell wall development. Annu Rev Microbiol 1994; 48(1): 471-97.
[http://dx.doi.org/10.1146/annurev.mi.48.100194.002351] [PMID: 7826015]
[5]
Di Luca M, Maccari G, Nifosì R. Treatment of microbial biofilms in the post-antibiotic era: Prophylactic and therapeutic use of antimicrobial peptides and their design by bioinformatics tools. Pathog Dis 2014; 70(3): 257-70.
[http://dx.doi.org/10.1111/2049-632X.12151] [PMID: 24515391]
[6]
Fjell CD, Hiss JA, Hancock REW, Schneider G. Designing antimicrobial peptides: Form follows function. Nat Rev Drug Discov 2011; 11(1): 37-51.
[http://dx.doi.org/10.1038/nrd3591] [PMID: 22173434]
[7]
Lakshminarayanan R, Liu S, Li J, et al. Synthetic multivalent antifungal peptides effective against fungi. PLoS One 2014; 9(2)e87730
[http://dx.doi.org/10.1371/journal.pone.0087730] [PMID: 24498363]
[8]
Ding Y, Tang J, Guo F. Identification of drug–target interactions via dual laplacian regularized least squares with multiple kernel fusion. Knowl Base Syst 2020; 204106254
[http://dx.doi.org/10.1016/j.knosys.2020.106254]
[9]
Zou Y, Wu H, Guo X, et al. MK-FSVM-SVDD: a multiple Kernelbased fuzzy SVM model for predicting DNA-binding proteins via support vector data description. Curr Bioinform 2021; 16(2): 274-83.
[http://dx.doi.org/10.2174/1574893615999200607173829]
[10]
Ding Y, Tang J, Guo F. Protein crystallization identification via fuzzy model on linear neighborhood representationIEEE/ACM Trans Comput Biol Bioinform 2019; 1
[http://dx.doi.org/10.1109/TCBB.2019.2954826]
[11]
Ding Y, Tang J, Guo F. Identification of drug-side effect association via semisupervised model and multiple kernel learning. IEEE J Biomed Health Inform 2019; 23(6): 2619-32.
[http://dx.doi.org/10.1109/JBHI.2018.2883834] [PMID: 30507518]
[12]
Ding Y, Tang J, Guo F. Identification of drug-side effect association via multiple information integration with centered kernel alignment. Neurocomputing 2019; 325: 211-24.
[http://dx.doi.org/10.1016/j.neucom.2018.10.028]
[13]
Ding Y, Tang J, Guo F. Identification of drug-target interactions via multiple information integration. Inf Sci 2017; 418-419: 546-60.
[http://dx.doi.org/10.1016/j.ins.2017.08.045]
[14]
Zhang J, Zhang Z, Pu L, Tang J, Guo F. AIEpred: An ensemble predictive model of classifier chain to identify anti-inflammatory peptides. IEEE/ACM transactions on computational biology and bioinformatics 2020; 10-1.
[http://dx.doi.org/10.1109/TCBB.2020.2968419]
[15]
Li J, Pu Y, Tang J, Zou Q, Guo F. DeepAVP: A dual-channel deep neural network for identifying variable-length antiviral peptides. IEEE J Biomed Health Inform 2020; 24(10): 3012-9.
[http://dx.doi.org/10.1109/JBHI.2020.2977091] [PMID: 32142462]
[16]
Zhao Q, Yang Y, Ren G, Ge E, Fan C. Integrating bipartite network projection and katz measure to identify novel circrna-disease associations. IEEE Trans Nanobioscience 2019; 18(4): 578-84.
[http://dx.doi.org/10.1109/TNB.2019.2922214] [PMID: 31199265]
[17]
Huang Y, Yuan K, Tang M, et al. Melatonin inhibiting the survival of human gastric cancer cells under er stress involving autophagy and ras-raf-mapk signalling. J Cell Mol Med 2021; 25(3): 1480-92.
[http://dx.doi.org/10.1111/jcmm.16237] [PMID: 33369155]
[18]
Liu H, Ren G, Chen H, Liu Q, Yang Y, Zhao Q. Predicting lncrna-mirna interactions based on logistic matrix factorization with neighborhood regularized. Knowl Base Syst 2020; 191105261
[http://dx.doi.org/10.1016/j.knosys.2019.105261]
[19]
Joseph S, Karnik S, Nilawe P, Jayaraman VK, Idicula-Thomas S. ClassAMP: A prediction tool for classification of antimicrobial peptides. IEEE/ACM Trans Comput Biol Bioinformatics 2012; 9(5): 1535-8.
[http://dx.doi.org/10.1109/TCBB.2012.89] [PMID: 22732690]
[20]
Xiao X, Wang P, Lin WZ, Jia JH, Chou KC. iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 2013; 436(2): 168-77.
[http://dx.doi.org/10.1016/j.ab.2013.01.019] [PMID: 23395824]
[21]
Agrawal P, Bhalla S, Chaudhary K, Kumar R, Sharma M, Raghava GPS. In silico approach for prediction of antifungal peptides. Front Microbiol 2018; 9: 323.
[http://dx.doi.org/10.3389/fmicb.2018.00323] [PMID: 29535692]
[22]
Fang C, Moriwaki Y, Li C, Shimizu K. Prediction of antifungal peptides by deep learning with character embedding. IPSJ Transactions on Bioinformatics 2019; 12: 21-9.
[http://dx.doi.org/10.2197/ipsjtbio.12.21]
[23]
Bach FR, Lanckriet G. Multiple kernel learningConic duality, and the SMO 2004; 2211-68
[24]
Nascimento ACA, Prudêncio RBC, Costa IG. A multiple kernel learning algorithm for drug-target interaction prediction. BMC Bioinformatics 2016; 17(1): 46.
[http://dx.doi.org/10.1186/s12859-016-0890-3] [PMID: 26801218]
[25]
Yang Z, Tang N, Zhang X, Lin H, Li Y, Yang Z. Multiple kernel learning in protein-protein interaction extraction from biomedical literature. Artif Intell Med 2011; 51(3): 163-73.
[http://dx.doi.org/10.1016/j.artmed.2010.12.002] [PMID: 21208788]
[26]
Hasan MAM, Ahmad S, Molla MKI. iMulti-humphos: A multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines. Mol Biosyst 2017; 13(8): 1608-18.
[http://dx.doi.org/10.1039/C7MB00180K] [PMID: 28682387]
[27]
Shen Y, Tang J, Guo F. Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC. J Theor Biol 2019; 462: 230-9.
[http://dx.doi.org/10.1016/j.jtbi.2018.11.012] [PMID: 30452958]
[28]
Ding Y, Tang J, Guo F. Human protein subcellular localization identification via fuzzy model on Kernelized Neighborhood Representation. Appl Soft Comput 2020; 96106596
[http://dx.doi.org/10.1016/j.asoc.2020.106596]
[29]
Wang H, Ding Y, Tang J, Guo F. Identification of membrane protein types via multivariate information fusion with hilbert–schmidt independence criterion. Neurocomputing 2020; 383: 257-69.
[http://dx.doi.org/10.1016/j.neucom.2019.11.103]
[30]
Fan L, Sun J, Zhou M, et al. DRAMP: A comprehensive data repository of antimicrobial peptides. Sci Rep 2016; 6: 24482.
[http://dx.doi.org/10.1038/srep24482] [PMID: 27075512]
[31]
Bateman A. UniProt: A worldwide hub of protein knowledge. Nucleic Acids Res 2019; 47(D1): D506-15.
[http://dx.doi.org/10.1093/nar/gky1049] [PMID: 30395287]
[32]
Agrawal P, Bhalla S, Usmani SS, et al. CPPsite 2.0: A repository of experimentally validated cell-penetrating peptides. Nucleic Acids Res 2016; 44(D1): D1098-103.
[http://dx.doi.org/10.1093/nar/gkv1266] [PMID: 26586798]
[33]
Bhasin M, Raghava GPS. Classification of nuclear receptors based on amino acid composition and dipeptide composition. J Biol Chem 2004; 279(22): 23262-6.
[http://dx.doi.org/10.1074/jbc.M401932200] [PMID: 15039428]
[34]
Wei L, Hu J, Li F, Song J, Su R, Zou Q. Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Brief Bioinform 2018; 21(1): 106-19.
[http://dx.doi.org/10.1093/bib/bby107] [PMID: 30383239]
[35]
Govindan G, Nair A. Composition, transition and distribution (ctd) — a dynamic feature for predictions based on hierarchical structure of cellular sorting Proceedings - 2011 Annual IEEE India Conference: Engineering sustainable solutions, INDICON-2011.
[36]
Lin C, Zou Y, Qin J, et al. Hierarchical classification of protein folds using a novel ensemble classifier. PLoS One 2013; 8(2)e56499
[http://dx.doi.org/10.1371/journal.pone.0056499] [PMID: 23437146]
[37]
Zou Q, Wang Z, Guan X, Liu B, Wu Y, Lin Z. An approach for identifying cytokines based on a novel ensemble classifier. BioMed Res Int 2013; 2013686090
[http://dx.doi.org/10.1155/2013/686090] [PMID: 24027761]
[38]
Wei L, Tang J, Zou Q. SkipCPP-Pred: AZn improved and promising sequence-based predictor for predicting cell-penetrating peptides. BMC Genomics 2017; 18(Suppl. 7): 742.
[http://dx.doi.org/10.1186/s12864-017-4128-1] [PMID: 29513192]
[39]
Wei L, Xing P, Su R, Shi G, Ma ZS, Zou Q. CPPred-RF: A sequence-based predictor for identifying cell-penetrating peptides and their uptake efficiency. J Proteome Res 2017; 16(5): 2044-53.
[http://dx.doi.org/10.1021/acs.jproteome.7b00019] [PMID: 28436664]
[40]
Wei L, Zhou C, Chen H, Song J, Su R. ACPred-FL: A sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics 2018; 34(23): 4007-16.
[http://dx.doi.org/10.1093/bioinformatics/bty451] [PMID: 29868903]
[41]
Chen K, Jiang Y, Du L, Kurgan L. Prediction of integral membrane protein type by collocated hydrophobic amino acid pairs. J Comput Chem 2009; 30(1): 163-72.
[http://dx.doi.org/10.1002/jcc.21053] [PMID: 18567007]
[42]
Chen K, Kurgan L, Rahbari M. Prediction of protein crystallization using collocation of amino acid pairs. Biochem Biophys Res Commun 2007; 355(3): 764-9.
[http://dx.doi.org/10.1016/j.bbrc.2007.02.040] [PMID: 17316561]
[43]
Chen K, Kurgan LA, Ruan J. Prediction of flexible/rigid regions from protein sequences using k-spaced amino acid pairs. BMC Struct Biol 2007; 7: 25.
[http://dx.doi.org/10.1186/1472-6807-7-25] [PMID: 17437643]
[44]
Chen K, Kurgan LA, Ruan J. Prediction of protein structural class using novel evolutionary collocation-based sequence representation. J Comput Chem 2008; 29(10): 1596-604.
[http://dx.doi.org/10.1002/jcc.20918] [PMID: 18293306]
[45]
Saravanan V, Gautham N. Harnessing computational biology for exact linear B-cell epitope prediction: A novel amino acid composition-based feature descriptor. OMICS 2015; 19(10): 648-58.
[http://dx.doi.org/10.1089/omi.2015.0095] [PMID: 26406767]
[46]
Chen Z, Zhao P, Li F, et al. iFeature: A Python package and web server for features extraction and selection from protein and peptide sequences. Bioinformatics 2018; 34(14): 2499-502.
[http://dx.doi.org/10.1093/bioinformatics/bty140] [PMID: 29528364]
[47]
Cortes C, Vapnik V. Support-vector networks. Mach Learn 1995; 20(3): 273-97.
[http://dx.doi.org/10.1007/BF00994018]
[48]
Pedregosa F, Michel V, Varoquaux G, et al. Machine learning in python. J Mach Learn Res 2011; 12: 2825-30.
[49]
Chang CC, Lin CJ. LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2011; 2(3): 1-27.
[http://dx.doi.org/10.1145/1961189.1961199]
[50]
Gretton A, Bousquet O, Smola A, Schölkopf B. Measuring statistical dependence with hilbert-schmidt norms.Algorithmic learning theory: 16th international conference, ALT 2005, . 63-78.
[51]
Gangeh MJ, Bedawi SMA, Ghodsi A, Karray F. Semi-supervised dictionary learning based on hilbert-schmidt independence criterionLecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics) 2016; 9730: 12-9.
[52]
Wang T, Li W, He X. Kernel learning with Hilbert-Schmidt independence criterion. Commun Comput Inf Sci 2016; 720-30.
[http://dx.doi.org/10.1007/978-981-10-3002-4_58]
[53]
Wilson CM, Li K, Yu X, Kuan PF, Wang X. Multiple-kernel learning for genomic data mining and prediction. BMC Bioinformatics 2019; 20(1): 426.
[http://dx.doi.org/10.1186/s12859-019-2992-1] [PMID: 31416413]
[54]
Vacic V, Iakoucheva LM, Radivojac P. Two Sample Logo: A graphical representation of the differences between two sets of sequence alignments. Bioinformatics 2006; 22(12): 1536-7.
[http://dx.doi.org/10.1093/bioinformatics/btl151] [PMID: 16632492]
[55]
Available from. https://github.com/guofei-tju/Identify-AFP-with-HSIC

Rights & Permissions Print Cite
Article Metrics
37
5
1
1
© 2024 Bentham Science Publishers | Privacy Policy