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Identifying Dipeptidyl Peptidase-IV Inhibitory Peptides Based on Correlation Information of Physicochemical Properties

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Abstract

Dipeptidyl peptidase-IV (DPP-IV) inhibitory peptides play a crucial role in drug development and the treatment of diabetes. Thus, it is an urgent task to fast and precise distinguishing DPP-IV inhibitory peptides from non-DPP-IV inhibitory peptides. This study developed a support vector machine (SVM) based model to accurately identify DPP-IV inhibitory peptides. Specifically, the peptide sequences were firstly encoded by fifty kinds of physicochemical properties, and dynamic time warping algorithm was introduced to capture the correlation information of distinct physicochemical properties of amino acids. To further remove the effect of noise, orthogonal minimum spanning tree algorithm was proposed. Finally, the features were inputted into SVM to discriminate DPP-IV from non-DPP-IV inhibitory peptides. In the jackknife test, our proposed method achieved 86.28% and 87.97% classification accuracies on benchmark and independent datasets, respectively. The experimental results showed that the proposed method achieved significant improvement in classification performance, as compared with the existing method. The datasets and code are publicly available at https://figshare.com/articles/online_resource/iDPPIV/14769174.

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Data Availability

The datasets and source code of this study can be downloaded via https://figshare.com/articles/online_resource/iDPPIV/14769174.

References

  • Abna B, Spa B, Pm C, Sm C, Fga B (2018) Identification of novel dipeptidyl peptidase IV (DPP-IV) inhibitory peptides in camel milk protein hydrolysates. Food Chem 244:340–348

    Article  CAS  Google Scholar 

  • Amori RE, Lau J, Pittas AG (2007) Efficacy and safety of incretin therapy in type 2 diabetes: systematic review and meta-analysis. JAMA 298(2):194–206

    Article  CAS  PubMed  Google Scholar 

  • Bin Y, Zhang W, Tang W, Dai R, Li M, Zhu Q, Xia J (2020) Prediction of neuropeptides from sequence information using ensemble classifier and hybrid features. J Proteome Res 19(9):3732–3740

    Article  CAS  PubMed  Google Scholar 

  • Chang CC (2001) LIBSVM: a library for support vector machines, Software. http://wwwcsie.ntu.edu.tw. Accessed 24 Aug 2021

  • Charoenkwan P, Kanthawong S, Nantasenamat C, Hasan MM, Shoombuatong W (2020) iDPPIV-SCM: a sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method. J Proteome Res 19(10):4125–4136

    Article  CAS  PubMed  Google Scholar 

  • Charoenkwan P, Yana J, Schaduangrat N, Nantasenamat C, Hasan MM, Shoombuatong W (2020) iBitter-SCM: identification and characterization of bitter peptides using a scoring card method with propensity scores of dipeptides. Genomics 112(4):2813–2822

    Article  CAS  PubMed  Google Scholar 

  • Charoenkwan P, Kanthawong S, Schaduangrat N, Yana J, Shoombuatong W (2020) PVPred-SCM: improved prediction and analysis of phage virion proteins using a scoring card method. Cells 9(2):353

    Article  CAS  PubMed Central  Google Scholar 

  • Charoenkwan P, Nantasenamat C, Hasan MM, Shoombuatong W (2020) iTTCA-Hybrid: Improved and robust identification of tumor T cell antigens by utilizing hybrid feature representation. Anal Biochem 599:113747

    Article  CAS  PubMed  Google Scholar 

  • Charoenkwan P, Nantasenamat C, Hasan MM, Shoombuatong W (2020) Meta-iPVP: a sequence-based meta-predictor for improving the prediction of phage virion proteins using effective feature representation. J Comput-Aided Mol Des 34:1–12

    Article  CAS  Google Scholar 

  • Charoenkwan P, Chiangjong W, Nantasenamat C, Hasan MM, Manavalan B, Shoombuatong W (2021) StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides. Brief Bioinform. https://doi.org/10.1093/bib/bbab172

    Article  PubMed  Google Scholar 

  • Chen W, Feng P, Nie F (2019) iATP: a sequence based method for identifying anti-tubercular peptides. Med Chem 16(5):620–625

    Article  CAS  Google Scholar 

  • Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  Google Scholar 

  • Dai R, Zhang W, Tang W, Wynendaele E, Zhu Q, Bin Y, De Spiegeleer B, Xia J (2021) BBPpred: sequence-based prediction of blood-brain barrier peptides with feature representation learning and logistic regression. J Chem Inf Model 61(1):525–534

    Article  CAS  PubMed  Google Scholar 

  • Dai C, Feng P, Cui L, Su R, Chen W, Wei L (2020) Iterative feature representation algorithm to improve the predictive performance of N7-methylguanosine sites. Brief Bioinform. https://doi.org/10.1093/bib/bbaa278

    Article  PubMed  PubMed Central  Google Scholar 

  • Dhall A, Patiyal S, Sharma N, Usmani SS, Raghava GP (2021) Computer-aided prediction and design of IL-6 inducing peptides: IL-6 plays a crucial role in COVID-19. Brief Bioinform 22(2):936–945

    Article  CAS  PubMed  Google Scholar 

  • Duez H, Cariou B, Staels B (2012) DPP-4 inhibitors in the treatment of type 2 diabetes. Biochem Pharmacol 83(7):823–832

    Article  CAS  PubMed  Google Scholar 

  • I Federation (2017) IDF diabetes atlas, 8th edn. International Diabetes Federation, Brussels, pp 905–911

    Google Scholar 

  • Hasan MM, Schaduangrat N, Basith S, Lee G, Shoombuatong W, Manavalan B (2020) HLPpred-Fuse: improved and robust prediction of hemolytic peptide and its activity by fusing multiple feature representation. Bioinformatics 36(11):3350–3356

    Article  CAS  PubMed  Google Scholar 

  • Iwaniak A, Hrynkiewicz M, Bucholska J, Darewicz M, Minkiewicz P (2018) Structural characteristics of food protein-originating di-and tripeptides using principal component analysis. Eur Food Res Technol 244(10):1751–1758

    Article  CAS  Google Scholar 

  • Jia C, He W (2016) EnhancerPred: a predictor for discovering enhancers based on the combination and selection of multiple features. Sci Rep 6:38741

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Jin D, Li R, Xu J (2019) Multiscale community detection in functional brain networks constructed using dynamic time warping. IEEE Trans Neural Syst Rehabil Eng 28(1):52–61

    Article  PubMed  Google Scholar 

  • Kawashima S, Ogata H, Kanehisa M (1999) AAindex: amino acid index database. Nucleic Acids Res 27(1):368–369

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Kawashima S, Pokarowski P, Pokarowska M, Kolinski A, Katayama T, Kanehisa M (2007) AAindex: amino acid index database, progress report 2008. Nucleic Acids Res 36(suppl_1):D202–D205

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  • Kruskal BJ (1956) On the shortest spanning subtree of a graph and the traveling salesman problem. Proc Am Math Soc 7(1):48–50

    Article  Google Scholar 

  • Lacroix I, Li-Chan E (2013) Inhibition of dipeptidyl peptidase (DPP)-IV and α-glucosidase activities by pepsin-treated whey proteins. J Agric Food Chem 61(31):7500–7506

    Article  CAS  PubMed  Google Scholar 

  • Lin J, Chen H, Li S, Liu Y, Li X, Yu B (2019) Accurate prediction of potential druggable proteins based on genetic algorithm and bagging-SVM ensemble classifier. Artif Intell Med 98:35–47

    Article  PubMed  Google Scholar 

  • Liu K, Chen W (2020) iMRM: a platform for simultaneously identifying multiple kinds of RNA modifications. Bioinformatics 36(11):3336–3342

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Fang L, Long R, Lan X, Chou K-C (2016) iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition. Bioinformatics 32(3):362–369

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Li K, Huang D-S, Chou K-C (2018) iEnhancer-EL: identifying enhancers and their strength with ensemble learning approach. Bioinformatics 34(22):3835–3842

    Article  CAS  PubMed  Google Scholar 

  • Liu B, Weng F, Huang D-S, Chou K-C (2018) iRO-3wPseKNC: identify DNA replication origins by three-window-based PseKNC. Bioinformatics 34(18):3086–3093

    Article  CAS  PubMed  Google Scholar 

  • Meszlényi RJ, Hermann P, Buza K, Gál V, Vidnyánszky Z (2017) Resting state fMRI functional connectivity analysis using dynamic time warping. Front Neurosci 11:75

    Article  PubMed  PubMed Central  Google Scholar 

  • Min J-L, Xiao X, Chou K-C (2013) iEzy-Drug: a web server for identifying the interaction between enzymes and drugs in cellular networking. BioMed Res Int 2013:1–13

    Google Scholar 

  • Minkiewicz P, Dziuba J, Iwaniak A, Dziuba M, Darewicz M (2008) BIOPEP database and other programs for processing bioactive peptide sequences. J AOAC Int 91(4):965–980

    Article  CAS  PubMed  Google Scholar 

  • Minkiewicz P, Iwaniak A, Darewicz M (2019) BIOPEP-UWM database of bioactive peptides: current opportunities. Int J Mol Sci 20(23):5978

    Article  CAS  PubMed Central  Google Scholar 

  • Nongonierma AB, FitzGerald RJ (2014) An in silico model to predict the potential of dietary proteins as sources of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides. Food Chem 165:489–498

    Article  CAS  PubMed  Google Scholar 

  • Nongonierma AB, FitzGerald RJ (2016) Structure activity relationship modelling of milk protein-derived peptides with dipeptidyl peptidase IV (DPP-IV) inhibitory activity. Peptides 79:1–7

    Article  CAS  PubMed  Google Scholar 

  • Nongonierma AB, FitzGerald RJ (2016) Learnings from quantitative structure–activity relationship (QSAR) studies with respect to food protein-derived bioactive peptides: a review. RSC Adv 6(79):75400–75413

    Article  CAS  Google Scholar 

  • Nongonierma AB, Mooney C, Shields DC, FitzGerald RJ (2014) In silico approaches to predict the potential of milk protein-derived peptides as dipeptidyl peptidase IV (DPP-IV) inhibitors. Peptides 57:43–51

    Article  CAS  PubMed  Google Scholar 

  • Nongonierma AB, Paolella S, Mudgil P, Maqsood S, FitzGerald RJ (2017) Dipeptidyl peptidase IV (DPP-IV) inhibitory properties of camel milk protein hydrolysates generated with trypsin. J Funct Foods 34:49–58

    Article  CAS  Google Scholar 

  • Prajapat R, Bhattacharya I (2016) In-silico structure modeling and docking studies using dipeptidyl peptidase 4 (DPP4) inhibitors against diabetes type-2. Adv Diabetes Metab 4:73–84

    Article  CAS  Google Scholar 

  • Rivero-Pino F, Espejo-Carpio FJ, Guadix EM (2020) Production and identification of dipeptidyl peptidase IV (DPP-IV) inhibitory peptides from discarded Sardine pilchardus protein. Food Chem 328:127096

    Article  CAS  PubMed  Google Scholar 

  • Sakoe H, Chiba S (1978) Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans Acoust Speech Signal Process 26(1):43–49

    Article  Google Scholar 

  • Su R, Hu J, Zou Q, Manavalan B, Wei L (2020) Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools. Brief Bioinform 21(2):408–420

    Article  PubMed  CAS  Google Scholar 

  • Tewarie P, van Dellen E, Hillebrand A, Stam CJ (2015) The minimum spanning tree: an unbiased method for brain network analysis. Neuroimage 104:177–188

    Article  CAS  PubMed  Google Scholar 

  • Wei L, Zhou C, Chen H, Song J, Su R (2018) ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides. Bioinformatics 34(23):4007–4016

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Wei L, Hu J, Li F, Song J, Su R, Zou Q (2020) Comparative analysis and prediction of quorum-sensing peptides using feature representation learning and machine learning algorithms. Brief Bioinform 21(1):106–119

    CAS  Google Scholar 

  • W.H. Organization (2016), Global report on diabetes: executive summary, World Health Organization

  • Xiao X, Min J-L, Wang P, Chou K-C (2013) Predict drug-protein interaction in cellular networking. Curr Top Med Chem 13(14):1707–1712

    Article  CAS  PubMed  Google Scholar 

  • Xiao X, Wang P, Lin WZ, Jia JH, Chou KC (2013) iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. Anal Biochem 436(2):168–177

    Article  CAS  PubMed  Google Scholar 

  • Xiao X, Wang P, Chou K (2012) iNR-PhysChem: a sequence-based predictor for identifying nuclear receptors and their subfamilies via physical-chemical property matrix. PLoS One 7(2):e30869

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xiao X, Min JL, Pu W, Kuo-Chen C, Seema S (2013) iGPCR-drug: a web server for predicting interaction between GPCRs and drugs in cellular networking. PLoS One 8(8):e72234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Xiao X, Min JL, Wang P, Chou KC (2013) iCDI-PseFpt: identify the channel–drug interaction in cellular networking with PseAAC and molecular fingerprints. J Theor Biol 337:71–79

    Article  CAS  PubMed  Google Scholar 

  • Zhang Z-Y, Yang Y-H, Ding H, Wang D, Chen W, Lin H (2021) Design powerful predictor for mRNA subcellular location prediction in Homo sapiens. Brief Bioinform 22(1):526–535

    Article  CAS  PubMed  Google Scholar 

  • Zheng L, Xu Q, Lin L, Zeng X-A, Sun B, Zhao M (2019) In vitro metabolic stability of a casein-derived dipeptidyl peptidase-IV (DPP-IV) inhibitory peptide VPYPQ and its controlled release from casein by enzymatic hydrolysis. J Agric Food Chem 67(38):10604–10613

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by the National Nature Scientific Foundation of China (No. 62061019).

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Authors and Affiliations

  1. School of Communications and Electronics, Jiangxi Science and Technology Normal University, Nanchang, 330003, China

    Hongliang Zou & Zhijian Yin

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  1. Hongliang Zou
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  2. Zhijian Yin
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Contributions

HZ: conceptualization, methodology, data curation, writing-original draft, preparation, visualization, investigation, validation, writing-review and editing. ZY: supervision, funding acquisition.

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Correspondence to Hongliang Zou.

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Zou, H., Yin, Z. Identifying Dipeptidyl Peptidase-IV Inhibitory Peptides Based on Correlation Information of Physicochemical Properties. Int J Pept Res Ther 27, 2651–2659 (2021). https://doi.org/10.1007/s10989-021-10280-2

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