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Current Genomics

Author(s): Mst. Shamima Khatun, Watshara Shoombuatong, Md. Mehedi Hasan* and Hiroyuki Kurata*

DOI: 10.2174/1389202921999200625103936

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Evolution of Sequence-based Bioinformatics Tools for Protein-protein Interaction Prediction

Page: [454 - 463] Pages: 10

  • * (Excluding Mailing and Handling)

Abstract

Protein-protein interactions (PPIs) are the physical connections between two or more proteins via electrostatic forces or hydrophobic effects. Identification of the PPIs is pivotal, which contributes to many biological processes including protein function, disease incidence, and therapy design. The experimental identification of PPIs via high-throughput technology is time-consuming and expensive. Bioinformatics approaches are expected to solve such restrictions. In this review, our main goal is to provide an inclusive view of the existing sequence-based computational prediction of PPIs. Initially, we briefly introduce the currently available PPI databases and then review the state-of-the-art bioinformatics approaches, working principles, and their performances. Finally, we discuss the caveats and future perspective of the next generation algorithms for the prediction of PPIs.

Keywords: Protein-protein interactions, PPIs database, sequence features, feature selection, machine learning, bioinformatics.

Graphical Abstract

[1]
De Las Rivas, J.; Fontanillo, C. Protein-protein interactions essentials: key concepts to building and analyzing interactome networks. PLOS Comput. Biol., 2010, 6(6): e1000807.
[http://dx.doi.org/10.1371/journal.pcbi.1000807] [PMID: 20589078]
[2]
Liu, X.; Yang, Z.; Sang, S.; Lin, H.; Wang, J.; Xu, B. Detection of protein complexes from multiple protein interaction networks using graph embedding. Artif. Intell. Med., 2019, 96, 107-115.
[http://dx.doi.org/10.1016/j.artmed.2019.04.001] [PMID: 31164203]
[3]
Dos Santos Vasconcelos, C.R.; de Lima Campos, T.; Rezende, A.M. Building protein-protein interaction networks for Leishmania species through protein structural information. BMC Bioinformatics, 2018, 19(1), 85.
[http://dx.doi.org/10.1186/s12859-018-2105-6] [PMID: 29510668]
[4]
Caterino, M.; Ruoppolo, M.; Mandola, A.; Costanzo, M.; Orrù, S.; Imperlini, E. Protein-protein interaction networks as a new perspective to evaluate distinct functional roles of voltage-dependent anion channel isoforms. Mol. Biosyst., 2017, 13(12), 2466-2476.
[http://dx.doi.org/10.1039/C7MB00434F] [PMID: 29028058]
[5]
Xiao, H.; Yang, L.; Liu, J.; Jiao, Y.; Lu, L.; Zhao, H. Protein-protein interaction analysis to identify biomarker networks for endometriosis. Exp. Ther. Med., 2017, 14(5), 4647-4654.
[http://dx.doi.org/10.3892/etm.2017.5185] [PMID: 29201163]
[6]
Planas-Iglesias, J.; Marin-Lopez, M.A.; Bonet, J.; Garcia-Garcia, J.; Oliva, B. iLoops: a protein-protein interaction prediction server based on structural features. Bioinformatics, 2013, 29(18), 2360-2362.
[http://dx.doi.org/10.1093/bioinformatics/btt401] [PMID: 23842807]
[7]
Ammari, MG; Gresham, CR; McCarthy, FM; Nanduri, B HPIDB 2.0: a curated database for host-pathogen interactions. Database: J. Biol. Databases Curation, 2016.baw103.
[8]
Ohue, M.; Matsuzaki, Y.; Uchikoga, N.; Ishida, T.; Akiyama, Y. MEGADOCK: an all-to-all protein-protein interaction prediction system using tertiary structure data. Protein Pept. Lett., 2014, 21(8), 766-778.
[http://dx.doi.org/10.2174/09298665113209990050] [PMID: 23855673]
[9]
Goel, R.; Harsha, H.C.; Pandey, A.; Prasad, T.S. Human Protein Reference Database and Human Proteinpedia as resources for phosphoproteome analysis. Mol. Biosyst., 2012, 8(2), 453-463.
[http://dx.doi.org/10.1039/C1MB05340J] [PMID: 22159132]
[10]
Lian, X.; Yang, S.; Li, H.; Fu, C.; Zhang, Z. Machine-learning-based predictor of human-bacteria protein-protein interactions by incorporating comprehensive host-network properties. J. Proteome Res., 2019, 18(5), 2195-2205.
[http://dx.doi.org/10.1021/acs.jproteome.9b00074] [PMID: 30983371]
[11]
Liu, C.; Liu, L.; Zhou, C.; Zhuang, J.; Wang, L.; Sun, Y.; Sun, C. Protein-protein interaction networks and different clustering analysis in Burkitt’s lymphoma. Hematology, 2018, 23(7), 391-398.
[http://dx.doi.org/10.1080/10245332.2017.1409947] [PMID: 29189103]
[12]
Hanna, E.M.; Zaki, N.; Amin, A. Detecting protein complexes in protein interaction networks modeled as gene expression biclusters. PLoS One, 2015, 10(12): e0144163.
[http://dx.doi.org/10.1371/journal.pone.0144163] [PMID: 26641660]
[13]
Giurgiu, M.; Reinhard, J.; Brauner, B.; Dunger-Kaltenbach, I.; Fobo, G.; Frishman, G.; Montrone, C.; Ruepp, A. CORUM: the comprehensive resource of mammalian protein complexes-2019. Nucleic Acids Res., 2019, 47(D1), D559-D563.
[http://dx.doi.org/10.1093/nar/gky973] [PMID: 30357367]
[14]
Ruepp, A.; Waegele, B.; Lechner, M.; Brauner, B.; Dunger-Kaltenbach, I.; Fobo, G.; Frishman, G.; Montrone, C.; Mewes, H.W. CORUM: the comprehensive resource of mammalian protein complexes--2009. Nucleic Acids Res., 2010, 38(Database issue), D497-D501.
[http://dx.doi.org/10.1093/nar/gkp914] [PMID: 19884131]
[15]
Kaake, R.M.; Wang, X.; Huang, L. Profiling of protein interaction networks of protein complexes using affinity purification and quantitative mass spectrometry. Mol. Cell. Proteomics, 2010, 9(8), 1650-1665.
[http://dx.doi.org/10.1074/mcp.R110.000265] [PMID: 20445003]
[16]
Ochoa, D.; García-Gutiérrez, P.; Juan, D.; Valencia, A.; Pazos, F. Incorporating information on predicted solvent accessibility to the co-evolution-based study of protein interactions. Mol. Biosyst., 2013, 9(1), 70-76.
[http://dx.doi.org/10.1039/C2MB25325A] [PMID: 23104128]
[17]
Marsh, J.A.; Teichmann, S.A. Structure, dynamics, assembly, and evolution of protein complexes. Annu. Rev. Biochem., 2015, 84, 551-575.
[http://dx.doi.org/10.1146/annurev-biochem-060614-034142] [PMID: 25494300]
[18]
Yeh, F.L.; Tung, L.; Chang, T.H. Detection of protein-protein interaction within an RNA-protein complex via unnatural-amino-acid-mediated photochemical crosslinking. Methods Mol. Biol., 2016, 1421, 175-189.
[http://dx.doi.org/10.1007/978-1-4939-3591-8_15] [PMID: 26965266]
[19]
Pham, C.D. Detection of protein-protein interaction using bimolecular fluorescence complementation assay. Methods Mol. Biol., 2015, 1278, 483-495.
[http://dx.doi.org/10.1007/978-1-4939-2425-7_32] [PMID: 25859971]
[20]
Lavallee-Adam, M.; Coulombe, B.; Blanchette, M. Detection of locally over-represented GO terms in protein-protein interaction networks. J. Computational. Biol., 2010, 17(3), 443-457.
[http://dx.doi.org/10.1089/cmb.2009.0165]
[21]
Yang, S.; Fu, C.; Lian, X.; Dong, X.; Zhang, Z. Understanding human-virus protein-protein interactions using a human protein complex-based analysis framework. mSystems, 2019, 4(2), e00303-18.
[http://dx.doi.org/10.1128/mSystems.00303-18] [PMID: 30984872]
[22]
Saha, S.; Prasad, A.; Chatterjee, P.; Basu, S.; Nasipuri, M. Protein function prediction from protein-protein interaction network using gene ontology based neighborhood analysis and physico-chemical features. J. Bioinform. Comput. Biol., 2018, 16(6): 1850025.
[http://dx.doi.org/10.1142/S0219720018500257] [PMID: 30400756]
[23]
Zhai, J.X.; Cao, T.J.; An, J.Y.; Bian, Y.T. Highly accurate predic-tion of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC. J. Theor. Biol.,. 2017, 432, 80-86.
[http://dx.doi.org/10.1016/j.jtbi.2017.08.009] [PMID: 28802824]
[24]
Teng, WJ; Zhou, C; Liu, LJ; Cao, XJ; Zhuang, J; Liu, GX; Sun, C.G. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes. Ge-net. Molecul. Res., 2016, 15(2), 1-9.
[http://dx.doi.org/10.4238/gmr.15028365]
[25]
Aumentado-Armstrong, T.T.; Istrate, B.; Murgita, R.A. Algorith-mic approaches to protein-protein interaction site prediction. Algo-rithms Mol. Biol., 2015, 10, 7.
[http://dx.doi.org/10.1186/s13015-015-0033-9] [PMID: 25713596]
[26]
Taghipour, S.; Zarrineh, P.; Ganjtabesh, M.; Nowzari-Dalini, A. Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources. BMC Bioinforma-tics 2017, 18(1), 10.
[http://dx.doi.org/10.1186/s12859-016-1422-x] [PMID: 28049415]
[27]
Keane, H.; Ryan, B.J.; Jackson, B.; Whitmore, A.; Wade-Martins, R. Protein-protein interaction networks identify targets which res-cue the MPP+ cellular model of Parkinson’s disease. Sci. Rep., 2015, 5, 17004.
[http://dx.doi.org/10.1038/srep17004] [PMID: 26608097]
[28]
Ji, C.; Cao, X.; Yao, C.; Xue, S.; Xiu, Z. Protein-protein interaction network of the marine microalga Tetraselmis subcordiformis: pre-diction and application for starch metabolism analysis. J. Ind. Microbiol. Biotechnol., 2014, 41(8), 1287-1296.
[http://dx.doi.org/10.1007/s10295-014-1462-z] [PMID: 24879479]
[29]
Wang, L.; Tam, J.P.; Liu, D.X. Biochemical and functional charac-terization of Epstein-Barr virus-encoded BARF1 protein: interac-tion with human hTid1 protein facilitates its maturation and secre-tion. Oncogene, 2006, 25(31), 4320-4331.
[http://dx.doi.org/10.1038/sj.onc.1209458] [PMID: 16518412]
[30]
Amoutzias, G.D.; Robertson, D.L.; Bornberg-Bauer, E. The evolu-tion of protein interaction networks in regulatory proteins. Comp. Funct. Genomics, 2004, 5(1), 79-84.
[http://dx.doi.org/10.1002/cfg.365] [PMID: 18629034]
[31]
Ivanic, J.; Yu, X.; Wallqvist, A.; Reifman, J. Influence of protein abundance on high-throughput protein-protein interaction detec-tion. PLoS One 2009, 4(6): e5815.
[http://dx.doi.org/10.1371/journal.pone.0005815] [PMID: 19503833]
[32]
Hurst, R.; Hook, B.; Slater, M.R.; Hartnett, J.; Storts, D.R.; Nath, N. Protein-protein interaction studies on protein arrays: effect of detection strategies on signal-to-background ratios. Anal. Biochem., 2009, 392(1), 45-53.
[http://dx.doi.org/10.1016/j.ab.2009.05.028] [PMID: 19464993]
[33]
Park, H.; Kang, H.; Ko, W.; Lee, W.; Jo, K.; Lee, H.S. FRET-based analysis of protein-nucleic acid interactions by genetically incorpo-rating a fluorescent amino acid. Amino Acids, 2015, 47(4), 729-734.
[http://dx.doi.org/10.1007/s00726-014-1900-2] [PMID: 25540052]
[34]
Xu, B.; Guan, J.; Wang, Y.; Wang, Z. Essential protein detection by random walk on weighted protein-protein interaction networks, IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2019, 16(2), 377-387.
[http://dx.doi.org/10.1109/TCBB.2017.2701824] [PMID: 28504946]
[35]
Zaki, N.; Alashwal, H. Improving the Detection of Protein Com-plexes by Predicting Novel Missing Interactome Links in the Pro-tein-Protein Interaction Network Conference proceedings : Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual Conference 2018 , 2018; pp. 5041-5044.
[http://dx.doi.org/10.1109/embc.2018.8513476]
[36]
Liu, W.; Ma, L.; Jeon, B.; Chen, L.; Chen, B. A Network Hie-rarchy-Based method for functional module detection in protein-protein interaction networks, J. Theor. Biol., 2018, 455, 26-38.
[http://dx.doi.org/10.1016/j.jtbi.2018.06.026] [PMID: 29981337]
[37]
Liu, TY; Chou, WC; Chen, WY; Chu, CY; Dai, CY; Wu, PY De-tection of membrane protein-protein interaction in planta based on dual-intein-coupled tripartite split-GFP association, Plant J., 2018, 94(3), 426-438.
[http://dx.doi.org/10.1111/tpj.13874]
[38]
Song, B.; Wang, F.; Guo, Y.; Sang, Q.; Liu, M.; Li, D.; Fang, W.; Zhang, D. Protein-protein interaction network-based detection of functionally similar proteins within, species Proteins, 2012, 80(7), 1736-1743.
[http://dx.doi.org/10.1002/prot.24066] [PMID: 22411607]
[39]
Subramanian, C.; Xu, Y.; Johnson, C.H.; von Arnim, A.G. In vivodetection of protein-protein interaction in plant cells using BRET, Methods Mol. Biol., 2004, 284, 271-286.
[http://dx.doi.org/10.1385/1-59259-816-1:271] [PMID: 15173623]
[40]
Pang, E.; Lin, K. Yeast protein-protein interaction binding sites: prediction from the motif-motif, motif-domain and domain-domain levels, Mol. Biosyst., 2010, 6(11), 2164-2173.
[http://dx.doi.org/10.1039/c0mb00038h] [PMID: 20714642]
[41]
Singhal, M.; Resat, H. A domain-based approach to predict protein-protein interactions, BMC Bioinformatics, 2007, 8, 199.
[http://dx.doi.org/10.1186/1471-2105-8-199] [PMID: 17567909]
[42]
Dyer, M.D.; Murali, T.M.; Sobral, B.W. Computational prediction of host-pathogen protein-protein interactions, Bioinformatics, 2007, 23(13), i159-i166.
[http://dx.doi.org/10.1093/bioinformatics/btm208] [PMID: 17646292]
[43]
Burgoyne, N.J.; Jackson, R.M. Predicting protein interaction sites: binding hot-spots in protein-protein and protein-ligand interfaces, Bioinformatics, 2006, 22(11), 1335-1342.
[http://dx.doi.org/10.1093/bioinformatics/btl079] [PMID: 16522669]
[44]
Tachiki, H.; Kato, R.; Kuramitsu, S. DNA binding and protein-protein interaction sites in MutS, a mismatched DNA recognition protein from Thermus thermophilus HB8, J. Biol. Chem., 2000, 275(52), 40703-40709.
[http://dx.doi.org/10.1074/jbc.M007124200] [PMID: 11024056]
[45]
Yu, H.; Luscombe, N.M.; Lu, H.X.; Zhu, X.; Xia, Y.; Han, J.D.; Bertin, N.; Chung, S.; Vidal, M.; Gerstein, M. Annotation transfer between genomes: protein-protein interologs and protein-DNA re-gulogs, Genome Res., 2004, 14(6), 1107-1118.
[http://dx.doi.org/10.1101/gr.1774904] [PMID: 15173116]
[46]
Khatun, M.S.; Hasan, M.M.; Mollah, M.N.H.; Kurata, H. SIPMA: A Systematic Identification of Protein-Protein Interactions in Zea mays Using Autocorrelation Features in a Machine-Learning Framework 2018. IEEE 18th International Conference on Bioin-formatics and Bioengineering (BIBE), Taichung, Taiwan, , 2018; pp. 122-125.
[47]
Romero-Molina, S.; Ruiz-Blanco, Y.B.; Harms, M.; Münch, J.; Sanchez-Garcia, E. PPI-Detect: A support vector machine model for sequence-based prediction of protein-protein interactions, J. Comput. Chem., 2019, 40(11), 1233-1242.
[http://dx.doi.org/10.1002/jcc.25780] [PMID: 30768790]
[48]
An, J.Y.; You, Z.H.; Zhou, Y.; Wang, D.F. Sequence-based predic-tion of protein-protein interactions using gray wolf optimizer-based relevance vector machine, Evol. Bioinform. Online, 2019, 151176934319844522
[http://dx.doi.org/10.1177/1176934319844522] [PMID: 31080346]
[49]
Sun, T.; Zhou, B.; Lai, L.; Pei, J. Sequence-based prediction of protein protein interaction using a deep-learning algorithm, BMC Bioinformatics, 2017, 18(1), 277.
[http://dx.doi.org/10.1186/s12859-017-1700-2] [PMID: 28545462]
[50]
Xia, J.F.; Han, K.; Huang, D.S. Sequence-based prediction of pro-tein-protein interactions by means of rotation forest and autocorre-lation descriptor, Protein Pept. Lett., 2010, 17(1), 137-145.
[http://dx.doi.org/10.2174/092986610789909403] [PMID: 20214637]
[51]
Huang, Y.A.; You, Z.H.; Chen, X.; Chan, K.; Luo, X. Sequence-based prediction of protein-protein interactions using weighted sparse representation model combined with global encoding, BMC Bioinformatics, 2016, 17(1), 184.
[http://dx.doi.org/10.1186/s12859-016-1035-4] [PMID: 27112932]
[52]
Eid, F.E.; ElHefnawi, M.; Heath, L.S. DeNovo: virus-host se-quence-based protein-protein interaction prediction, Bioinformatics, 2016, 32(8), 1144-1150.
[http://dx.doi.org/10.1093/bioinformatics/btv737] [PMID: 26677965]
[53]
Hamp, T.; Rost, B. Evolutionary profiles improve protein-protein interaction prediction from sequence, Bioinformatics, 2015, 31(12), 1945-1950.
[http://dx.doi.org/10.1093/bioinformatics/btv077] [PMID: 25657331]
[54]
Zahiri, J.; Yaghoubi, O.; Mohammad-Noori, M.; Ebrahimpour, R.; Masoudi-Nejad, A. PPIevo: protein-protein interaction prediction from PSSM based evolutionary information, Genomics, 2013, 102(4), 237-242.
[http://dx.doi.org/10.1016/j.ygeno.2013.05.006] [PMID: 23747746]
[55]
Zahiri, J.; Mohammad-Noori, M.; Ebrahimpour, R.; Saadat, S.; Bozorgmehr, J.H.; Goldberg, T.; Masoudi-Nejad, A. LocFuse: hu-man protein-protein interaction prediction via classifier fusion u-sing protein localization information, Genomics, 2014, 104(6 Pt B), 496-503.
[http://dx.doi.org/10.1016/j.ygeno.2014.10.006] [PMID: 25458812]
[56]
Neuvirth, H.; Raz, R.; Schreiber, G. ProMate: a structure based prediction program to identify the location of protein-protein bin-ding sites, J. Mol. Biol., 2004, 338(1), 181-199.
[http://dx.doi.org/10.1016/j.jmb.2004.02.040] [PMID: 15050833]
[57]
Yang, S.; Li, H.; He, H.; Zhou, Y.; Zhang, Z. Critical assessment and performance improvement of plant-pathogen protein-protein interaction prediction methods, Brief. Bioinform., 2019, 20(1), 274-287.
[http://dx.doi.org/10.1093/bib/bbx123] [PMID: 29028906]
[58]
Alonso-Lopez, D; Campos-Laborie, FJ; Gutierrez, MA; Lambourne, L; Calderwood, MA; Vidal, M.; De Las Rivas, J. APID database: redefining protein-protein interaction experimental evidences and binary interactomes, Database , 2019.
[http://dx.doi.org/10.1093/database/baz005]
[59]
Poole, R.L. The TAIR database, Methods Mol. Biol., 2007, 406, 179-212.
[PMID: 18287693]
[60]
Alanis-Lobato, G.; Andrade-Navarro, M.A.; Schaefer, M.H. HIP-PIE v2.0: enhancing meaningfulness and reliability of protein-protein interaction networks, Nucleic Acids Res., 2017, 45(D1), D408-D414.
[http://dx.doi.org/10.1093/nar/gkw985] [PMID: 27794551]
[61]
Zhu, G.; Wu, A.; Xu, X.J.; Xiao, P.P.; Lu, L.; Liu, J.; Cao, Y.; Chen, L.; Wu, J.; Zhao, X.M. PPIM: a protein-protein interaction database for maize, Plant Physiol., 2016, 170(2), 618-626.
[http://dx.doi.org/10.1104/pp.15.01821] [PMID: 26620522]
[62]
Oughtred, R.; Stark, C.; Breitkreutz, B.J.; Rust, J.; Boucher, L.; Chang, C.; Kolas, N.; O’Donnell, L.; Leung, G.; McAdam, R.; Zhang, F.; Dolma, S.; Willems, A.; Coulombe-Huntington, J.; Chatr-Aryamontri, A.; Dolinski, K.; Tyers, M. The BioGRID inter-action database: 2019 update, Nucleic Acids Res., 2019, 47(D1), D529-D541.
[http://dx.doi.org/10.1093/nar/gky1079] [PMID: 30476227]
[63]
Chatr-Aryamontri, A.; Oughtred, R.; Boucher, L.; Rust, J.; Chang, C.; Kolas, N.K.; O’Donnell, L.; Oster, S.; Theesfeld, C.; Sellam, A.; Stark, C.; Breitkreutz, B.J.; Dolinski, K.; Tyers, M. The Bio-GRID interaction database: 2017 update, Nucleic Acids Res., 2017, 45(D1), D369-D379.
[http://dx.doi.org/10.1093/nar/gkw1102] [PMID: 27980099]
[64]
Xenarios, I.; Salwínski, L.; Duan, X.J.; Higney, P.; Kim, S.M.; Eisenberg, D. DIP, the Database of Interacting Proteins: a research tool for studying cellular networks of protein interactions, Nucleic Acids Res., 2002, 30(1), 303-305.
[http://dx.doi.org/10.1093/nar/30.1.303] [PMID: 11752321]
[65]
Hashemifar, S.; Neyshabur, B.; Khan, A.A.; Xu, J. Predicting pro-tein-protein interactions through sequence-based deep learning, Bioinformatics, 2018, 34(17), i802-i810.
[http://dx.doi.org/10.1093/bioinformatics/bty573] [PMID: 30423091]
[66]
Fu, L.; Niu, B.; Zhu, Z.; Wu, S.; Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics, 2012, 28(23), 3150-3152.
[http://dx.doi.org/10.1093/bioinformatics/bts565] [PMID: 23060610]
[67]
Chen, K.H.; Wang, T.F.; Hu, Y.J. Protein-protein interaction pre-diction using a hybrid feature representation and a stacked genera-lization scheme, BMC Bioinformatics, 2019, 20(1), 308.
[http://dx.doi.org/10.1186/s12859-019-2907-1] [PMID: 31182027]
[68]
Ray, S.; Alberuni, S.; Maulik, U. Computational prediction of HCV-human protein-protein interaction via topological analysis of HCV infected PPI modules, IEEE Trans. Nanobioscience, 2018, 17(1), 55-61.
[http://dx.doi.org/10.1109/TNB.2018.2797696] [PMID: 29570075]
[69]
Sze-To, A.; Fung, S.; Lee, E.A.; Wong, A.K.C. Prediction of pro-tein-protein interaction via co-occurring aligned pattern clusters, Methods, 2016, 110, 26-34.
[http://dx.doi.org/10.1016/j.ymeth.2016.07.018] [PMID: 27476008]
[70]
Zhang, L.; Yu, G.; Guo, M.; Wang, J. Predicting protein-protein interactions using high-quality non-interacting pairs, BMC Bioinformatics, 2018, 19(Suppl. 19), 525.
[http://dx.doi.org/10.1186/s12859-018-2525-3] [PMID: 30598096]
[71]
Li, Y.; Ilie, L. SPRINT: ultrafast protein-protein interaction predic-tion of the entire human interactome, BMC Bioinformatics, 2017, 18(1), 485.
[http://dx.doi.org/10.1186/s12859-017-1871-x] [PMID: 29141584]
[72]
Guo, Y.; Li, M.; Pu, X.; Li, G.; Guang, X.; Xiong, W.; Li, J. PRED_PPI: a server for predicting protein-protein interactions ba-sed on sequence data with probability assignment, BMC Res. Notes, 2010, 3, 145.
[http://dx.doi.org/10.1186/1756-0500-3-145] [PMID: 20500905]
[73]
Quan, L.; Wu, H.; Lyu, Q.; Zhang, Y. DAMpred: recognizing disease-associated nsSNPs through bayes-guided neural-network model built on low-resolution structure prediction of proteins and protein-protein interactions, J. Mol. Biol., 2019, 431(13), 2449-2459.
[http://dx.doi.org/10.1016/j.jmb.2019.02.017] [PMID: 30796987]
[74]
Kong, M.; Zhang, Y.; Xu, D.; Chen, W.; Dehmer, M. FCTP-WSRC: protein-protein interactions prediction via weighted sparse representation based classification Front. Genet., 2020, 11, 18.
[http://dx.doi.org/10.3389/fgene.2020.00018] [PMID: 32117437]
[75]
Murakami, Y.; Mizuguchi, K. Homology-based prediction of inter-actions between proteins using Averaged One-Dependence Estima-tors BMC Bioinformatics, 2014, 15, 213.
[http://dx.doi.org/10.1186/1471-2105-15-213] [PMID: 24953126]
[76]
Islam, M.M.; Alam, M.J.; Ahmed, F.F.; Hasan, M.M.; Mollah, M.N.H. Improved prediction of protein-protein interaction mapping on Homo sapiens by using amino acid sequence features in a su-pervised learning framework, Protein Pept. Lett., 2020.
[http://dx.doi.org/10.2174/0929866527666200610141258] [PMID: 32520672]
[77]
Mosharaf, M.P.; Hassan, M.M.; Ahmed, F.F.; Khatun, M.S.; Moni, M.A.; Mollah, M.N.H. Computational prediction of protein ubiquitination sites mapping on Arabidopsis thaliana, Comput. Biol. Chem., 2020, 85: 107238.
[http://dx.doi.org/10.1016/j.compbiolchem.2020.107238] [PMID: 32114285]
[78]
Altschul, S.F.; Madden, T.L.; Schäffer, A.A.; Zhang, J.; Zhang, Z.; Miller, W.; Lipman, D.J. Gapped BLAST and PSI-BLAST: a new generation of protein database search programs, Nucleic Acids Res., 1997, 25(17), 3389-3402.
[http://dx.doi.org/10.1093/nar/25.17.3389] [PMID: 9254694]
[79]
Hasan, M.M.; Khatun, M.S. Recent progress and challenges for protein pupylation sites prediction, EC Proteomics and Bioinformatics, 2017, 2(1), 36-45.
[80]
Murakami, Y.; Mizuguchi, K. PSOPIA: Toward more reliable protein-protein interaction prediction from sequence information. 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, 2017, pp. 255-261.
[http://dx.doi.org/10.1109/ICIIBMS.2017.8279749]
[81]
Li, Z.W.; You, Z.H.; Chen, X.; Gui, J.; Nie, R. Highly accurate prediction of protein-protein interactions via incorporating evoluti-onary information and physicochemical characteristics, Int. J. Mol. Sci., 2016, 17(9), E1396.
[http://dx.doi.org/10.3390/ijms17091396] [PMID: 27571061]
[82]
Zhu-Hong You, ; MengChu Zhou, ; Xin Luo, ; Shuai, L. Highly efficient framework for predicting interactions between proteins, IEEE Trans. Cybern., 2017, 47(3), 731-743.
[http://dx.doi.org/10.1109/TCYB.2016.2524994] [PMID: 28113829]
[83]
Kawashima, S.; Pokarowski, P.; Pokarowska, M.; Kolinski, A.; Katayama, T.; Kanehisa, M. AAindex: amino acid index database, progress report 2008, Nucleic Acids Res., . 2008, 36(Database issue), D202-D205.
[PMID: 17998252]
[84]
Kawashima, S.; Kanehisa, M. AAindex: amino acid index data-base, Nucleic Acids Res., 2000, 28(1), 374.
[http://dx.doi.org/10.1093/nar/28.1.374] [PMID: 10592278]
[85]
Narykov, O.; Bogatov, D.; Korkin, D. DISPOT: a simple know-ledge-based protein domain interaction statistical potential Bioinformatics, 2019, 35(24), 5374-5378.
[http://dx.doi.org/10.1093/bioinformatics/btz587] [PMID: 31350874]
[86]
Li, X.; Yang, L.; Zhang, X.; Jiao, X. Prediction of protein-protein interactions based on domain., Comput. Math. Methods Med., 2019, 2019, 5238406.
[http://dx.doi.org/10.1155/2019/5238406] [PMID: 31531123]
[87]
Wojcik, J.; Schächter, V. Protein-protein interaction map inference using interacting domain profile pairs, Bioinformatics, 2001, 17(Suppl. 1), S296-S305.
[http://dx.doi.org/10.1093/bioinformatics/17.suppl_1.S296] [PMID: 11473021]
[88]
Kim, W.K.; Park, J.; Suh, J.K. Large scale statistical prediction of protein-protein interaction by potentially interacting domain (PID) pair; ; Genome Informatics, 2002, pp. 22-50.
[89]
Hayashida, M.; Kamada, M.; Song, J.; Akutsu, T. Conditional random field approach to prediction of protein-protein interactions using domain information BMC Syst. Biol., 2011, 5(Suppl. 1), S8.
[http://dx.doi.org/10.1186/1752-0509-5-S1-S8] [PMID: 21689483]
[90]
Ghadie, M.A.; Lambourne, L.; Vidal, M.; Xia, Y. Domain-based prediction of the human isoform interactome provides insights into the functional impact of alternative splicing, PLOS Comput. Biol., 2017, 13(8), e1005717.
[http://dx.doi.org/10.1371/journal.pcbi.1005717] [PMID: 28846689]
[91]
Hasan, M.M.; Zhou, Y.; Lu, X.; Li, J.; Song, J.; Zhang, Z. Compu-tational identification of protein pupylation sites by using profile-based composition of k-spaced amino acid pairs, PLoS One, 2015, 10(6), e0129635.
[http://dx.doi.org/10.1371/journal.pone.0129635] [PMID: 26080082]
[92]
Hasan, M.M.; Kurata, H. iLMS, Computational Identification of Lysine-Malonylation Sites by Combining Multiple Sequence Fea-tures. 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE), Taichung, Taiwan, , 2018; pp. 356-359.
[93]
Liaw, A. Wiener: Classification and regression by random forest, R News, 2002, 2, 18-22.
[94]
Su, R.; Hu, J.; Zou, Q.; Manavalan, B.; Wei, L. Empirical compari-son and analysis of web-based cell-penetrating peptide prediction tools, Brief. Bioinform., 2019, 21(2), 408-420.
[http://dx.doi.org/10.1093/bib/bby124] [PMID: 30649170]
[95]
Shoombuatong, W.; Schaduangrat, N.; Pratiwi, R.; Nantasenamat, C. THPep: a machine learning-based approach for predicting tumor homing peptides, Comput. Biol. Chem., 2019, 80, 441-451.
[http://dx.doi.org/10.1016/j.compbiolchem.2019.05.008] [PMID: 31151025]
[96]
Schaduangrat, N.; Nantasenamat, C.; Prachayasittikul, V.; Shoombuatong, W. Meta-iAVP: a sequence-based meta-predictor for im-proving the prediction of antiviral peptides using effective feature representation. Int. J. Mol. Sci., 2019, 20(22), E5743.
[http://dx.doi.org/10.3390/ijms20225743] [PMID: 31731751]
[97]
Win, T.S.; Malik, A.A.; Prachayasittikul, V.S; Wikberg, J.E.; Nantasenamat, C.; Shoombuatong, W. HemoPred: a web server for predicting the hemolytic activity of peptides Future Med. Chem., 2017, 9(3), 275-291.
[http://dx.doi.org/10.4155/fmc-2016-0188] [PMID: 28211294]
[98]
Manavalan, B.; Subramaniyam, S.; Shin, T.H.; Kim, M.O.; Lee, G. Machine-learning-based prediction of cell-penetrating peptides and their uptake efficiency with improved accuracy. J. Proteome Res., 2018, 17(8), 2715-2726.
[http://dx.doi.org/10.1021/acs.jproteome.8b00148] [PMID: 29893128]
[99]
Manavalan, B.; Shin, T.H.; Kim, M.O.; Lee, G. PIP-EL: a new ensemble learning method for improved proinflammatory peptide predictions, Front. Immunol., 2018, 9, 1783.
[http://dx.doi.org/10.3389/fimmu.2018.01783] [PMID: 30108593]
[100]
Boopathi, V.; Subramaniyam, S.; Malik, A.; Lee, G.; Manavalan, B.; Yang, D.C. mACPpred: a support vector machine-based meta-predictor for identification of anticancer peptides. Int. J. Mol. Sci., 2019, 20(8), E1964.
[http://dx.doi.org/10.3390/ijms20081964] [PMID: 31013619]
[101]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. mAHT-Pred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation. Bioinformatics, 2018, 35(16), 2757-2765.
[PMID: 30590410]
[102]
Hasan, M.M.; Khatun, M.S.; Mollah, M.N.H.; Yong, C.; Guo, D. A systematic identification of species-specific protein succinylation sites using joint element features information. Int. J. Nanomedicine, 2017, 12, 6303-6315.
[http://dx.doi.org/10.2147/IJN.S140875] [PMID: 28894368]
[103]
Hasan, M.M.; Yang, S.; Zhou, Y.; Mollah, M.N. SuccinSite: a computational tool for the prediction of protein succinylation sites by exploiting the amino acid patterns and properties, Mol. Biosyst., 2016, 12(3), 786-795.
[http://dx.doi.org/10.1039/C5MB00853K] [PMID: 26739209]
[104]
Hasan, M.M.; Schaduangrat, N.; Basith, S.; Lee, G.; Shoombuatong, W.; Manavalan, B. HLPpred-Fuse: improved and robust pre-diction of hemolytic peptide and its activity by fusing multiple fea-ture representation, Bioinformatics, 2020, 36(11), 3350-3356.
[http://dx.doi.org/10.1093/bioinformatics/btaa160] [PMID: 32145017]
[105]
Hasan, M.M.; Manavalan, B.; Shoombuatong, W.; Khatun, M.S.; Kurata, H. i6mA-Fuse: improved and robust prediction of DNA 6 mA sites in the Rosaceae genome by fusing multiple feature re-presentation. Plant Mol. Biol., 2020, 103(1-2), 225-234.
[http://dx.doi.org/10.1007/s11103-020-00988-y] [PMID: 32140819]
[106]
Hasan, M.M.; Manavalan, B.; Khatun, M.S.; Kurata, H. i4mC-ROSE, a bioinformatics tool for the identification of DNA N4-methylcytosine sites in the Rosaceae genome, Int. J. Biol. Macromol., 2019, 157, 752-758.
[PMID: 31805335]
[107]
Khatun, S.; Hasan, M.; Kurata, H. Efficient computational model for identification of antitubercular peptides by integrating amino acid patterns and properties. FEBS Lett., 2019, 593(21), 3029-3039.
[http://dx.doi.org/10.1002/1873-3468.13536] [PMID: 31297788]
[108]
Khatun, M.S.; Hasan, M.M.; Kurata, H. PreAIP: computational prediction of anti-inflammatory peptides by integrating multiple complementary features, Front. Genet., 2019, 10, 129.
[http://dx.doi.org/10.3389/fgene.2019.00129] [PMID: 30891059]
[109]
Hasan, M.M.; Rashid, M.M.; Khatun, M.S.; Kurata, H. Computati-onal identification of microbial phosphorylation sites by the enhan-ced characteristics of sequence information, Sci. Rep., 2019, 9(1), 8258.
[http://dx.doi.org/10.1038/s41598-019-44548-x] [PMID: 31164681]
[110]
Hasan, M.M.; Khatun, M.S.; Kurata, H. Large-scale assessment of bioinformatics tools for lysine succinylation sites, Cells, 2019, 8(2)E95
[http://dx.doi.org/10.3390/cells8020095] [PMID: 30696115]
[111]
Hasan, M.M.; Khatun, M.S.; Mollah, M.N.H.; Yong, C.; Dianjing, G. NTyroSite: computational identification of protein nitrotyrosine sites using sequence evolutionary features, Molecules, 2018, 23(7)E1667
[http://dx.doi.org/10.3390/molecules23071667] [PMID: 29987232]
[112]
Hasan, M.M.; Guo, D.; Kurata, H. Computational identification of protein S-sulfenylation sites by incorporating the multiple sequence features information, Mol. Biosyst., 2017, 13(12), 2545-2550.
[http://dx.doi.org/10.1039/C7MB00491E] [PMID: 28990628]
[113]
Hasan, M.M.; Khatun, M.S.; Kurata, H. A comprehensive review of in silico analysis for protein S-sulfenylation sites, Protein Pept. Lett., 2018, 25(9), 815-821.
[http://dx.doi.org/10.2174/0929866525666180905110619] [PMID: 30182830]
[114]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. mAHT-Pred: a sequence-based meta-predictor for improving the prediction of anti-hypertensive peptides using effective feature representation, Bioinformatics, 2019, 35(16), 2757-2765.
[http://dx.doi.org/10.1093/bioinformatics/bty1047] [PMID: 30590410]
[115]
Hasan, MM; Manavalan, B.; Khatun, MS; Kurata, H. Prediction of S-nitrosylation sites by integrating support vector machines and random forest, Molecular Omics, 2019, 15(6), 451-458.
[116]
Hasan, M.M.; Kurata, H. GPSuc: Global Prediction of Generic and Species-specific Succinylation Sites by aggregating multiple se-quence features PLoS One, 2018, 13(10), e0200283.
[http://dx.doi.org/10.1371/journal.pone.0200283] [PMID: 30312302]
[117]
Win, T.S.; Schaduangrat, N.; Prachayasittikul, V.; Nantasenamat, C.; Shoombuatong, W. PAAP: a web server for predicting antihy-Evolution of Sequence-based Bioinformatics Tools Current Genomics, 2020, Vol. 21, No. 6 463pertensive activity of peptides, Future Med. Chem., 2018, 10(15), 1749-1767.
[http://dx.doi.org/10.4155/fmc-2017-0300] [PMID: 30039980]
[118]
Simeon, S.; Shoombuatong, W.; Anuwongcharoen, N.; Preeyanon, L.; Prachayasittikul, V.; Wikberg, J.E.; Nantasenamat, C. osFP: a web server for predicting the oligomeric states of fluorescent pro-teins, J. Cheminform., 2016, 8, 72.
[http://dx.doi.org/10.1186/s13321-016-0185-8] [PMID: 28053671]
[119]
Shoombuatong, W.; Prachayasittikul, V.; Anuwongcharoen, N.; Songtawee, N.; Monnor, T.; Prachayasittikul, S.; Prachayasittikul, V.; Nantasenamat, C. Navigating the chemical space of dipeptidyl peptidase-4 inhibitors, Drug Des. Devel. Ther., 2015, 9, 4515-4549.
[PMID: 26309399]
[120]
Zhang, B.; Li, J.; Quan, L.; Chen, Y.; Lü, Q. Sequence-based pre-diction of protein-protein interaction sites by simplified long short-term memory network. Neurocomputing, 2019, 86, 100.
[http://dx.doi.org/10.1016/j.neucom.2019.05.013]
[121]
Tabei, Y. Scalable prediction of compound-protein interaction on compressed molecular fingerprints, Mol. Inform., 2020, 39(1-2), e1900130.
[http://dx.doi.org/10.1002/minf.201900130] [PMID: 31908150]
[122]
Ruas, F.A.D.; Guerra-Sá, R. In silico prediction of protein-protein interaction network induced by Manganese II in Meyerozyma guil-liermondii, Front. Microbiol., 2020, 11, 236.
[http://dx.doi.org/10.3389/fmicb.2020.00236] [PMID: 32140149]
[123]
Basith Mail, S.; Manavalan, B.; Shin, T.H.; Lee, D.; Lee, G. Evolu-tion of machine learning algorithms in the prediction and design of anticancer peptides, Curr. Protein Pept. Sci., 2020.
[http://dx.doi.org/10.2174/1389203721666200117171403] [PMID: 31957610]
[124]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. Meta-4mCpred: a sequence-based meta-predictor for accurate DNA 4mC site prediction using effective feature representation., Mol. Ther. Nucleic Acids, 2019, 16, 733-744.
[http://dx.doi.org/10.1016/j.omtn.2019.04.019] [PMID: 31146255]
[125]
Alkan, F.; Erten, C. SiPAN: simultaneous prediction and alignment of protein-protein interaction networks. Bioinformatics, 2015, 31(14), 2356-2363.
[http://dx.doi.org/10.1093/bioinformatics/btv160] [PMID: 25788620]
[126]
Aloy, P.; Russell, R.B. InterPreTS: protein interaction prediction through tertiary structure Bioinformatics, 2003, 19(1), 161-162.
[http://dx.doi.org/10.1093/bioinformatics/19.1.161] [PMID: 12499311]
[127]
Li, Z.; Nie, R.; You, Z.; Cao, C.; Li, J. Using discriminative vector machine model with 2DPCA to predict interactions among pro-teins BMC Bioinformatics, 2019, 20(S25)(Suppl. 25), 694.
[http://dx.doi.org/10.1186/s12859-019-3268-5] [PMID: 31874626]
[128]
Göbel, U.; Sander, C.; Schneider, R.; Valencia, A. Correlated mu-tations and residue contacts in proteins. Proteins, 1994, 18(4), 309-317.
[http://dx.doi.org/10.1002/prot.340180402] [PMID: 8208723]
[129]
Chen, W.; Feng, P.; Song, X.; Lv, H.; Lin, H. iRNA-m7G: identi-fying N7-methylguanosine sites by fusing multiple features. Mol. Ther. Nucleic Acids, 2019, 18, 269-274.
[http://dx.doi.org/10.1016/j.omtn.2019.08.022] [PMID: 31581051]
[130]
Shatabda, S.; Saha, S.; Sharma, A.; Dehzangi, A. iPHLoc-ES: identification of bacteriophage protein locations using evolutionary and structural features. J. Theor. Biol., 2017, 435, 229-237.
[http://dx.doi.org/10.1016/j.jtbi.2017.09.022] [PMID: 28943403]
[131]
Charoenkwan, P.; Shoombuatong, W.; Lee, H.C.; Chaijaruwanich, J.; Huang, H.L.; Ho, S.Y. SCMCRYS: predicting protein crystal-lization using an ensemble scoring card method with estimating propensity scores of P-collocated amino acid pairs. PLoS One, 2013, 8(9), e72368.
[http://dx.doi.org/10.1371/journal.pone.0072368] [PMID: 24019868]
[132]
Chowdhury, S.Y.; Shatabda, S.; Dehzangi, A. iDNAProt-ES: iden-tification of DNA-binding proteins using evolutionary and structu-ral features. Sci. Rep., 2017, 7(1), 14938.
[http://dx.doi.org/10.1038/s41598-017-14945-1] [PMID: 29097781]
[133]
Hasan, M.M.; Khatun, M.S. Prediction of protein Post-Translational Modifi cation sites: an overview. bann. proteom. bioinform.,, 2018, 2, 049-057.
[134]
Chen, X.; Huang, L.; Xie, D.; Zhao, Q. EGBMMDA: extreme gradient boosting machine for MiRNA-disease association predic-tion, Cell Death Dis., 2018, 9(1), 3.
[http://dx.doi.org/10.1038/s41419-017-0003-x] [PMID: 29305594]
[135]
Li, F.; Chen, J.; Leier, A.; Marquez-Lago, T.; Liu, Q.; Wang, Y.; Revote, J.; Smith, A.I.; Akutsu, T.; Webb, G.I. DeepCleave: a deep learning predictor for caspase and matrix metalloprotease substra-tes and cleavage sites. Bioinformatics, 2019.
[http://dx.doi.org/10.1093/bioinformatics/btz721] [PMID: 31566664]
[136]
Manavalan, B.; Basith, S.; Shin, T.H.; Wei, L.; Lee, G. AtbPpred: A robust sequence-based prediction of anti-tubercular peptides u-sing extremely randomized trees, Comput. Struct. Biotechnol. J., 2019, 17, 972-981.
[http://dx.doi.org/10.1016/j.csbj.2019.06.024] [PMID: 31372196]
[137]
Basith, S.; Manavalan, B.; Hwan Shin, T.; Lee, G. Machine intelli-gence in peptide therapeutics: A next-generation tool for rapid disease screening, Med. Res. Rev., 2020.
[http://dx.doi.org/10.1002/med.21658] [PMID: 31922268]
[138]
Manavalan, B.; Shin, T.H.; Kim, M.O.; Lee, G. AIPpred: sequence-based prediction of anti-inflammatory peptides using random fo-rest, Front. Pharmacol., 2018, 9, 276.
[http://dx.doi.org/10.3389/fphar.2018.00276] [PMID: 29636690]