Machine Learning-based Host–Pathogen Protein–Protein Interaction Prediction

Safari-Alighiarloo N, Taghizadeh M, Rezaei-Tavirani M, et al. Protein-protein interaction networks (PPI) and complex diseases. Gastroenterol Hepatol Bed Bench. 2014;7(1):17–31.

PubMed  PubMed Central  Google Scholar 

Goodacre N, Devkota P, Bae E, et al. Protein-protein interactions of human viruses. Semin Cell Dev Biol. 2020;99:31–9. https://doi.org/10.1016/j.semcdb.2018.07.018.

Article  CAS  PubMed  Google Scholar 

Edward PR. Chapter 7 - Viral pathogenesis. 2023:279–306. https://doi.org/10.1016/B978-0-12-822784-8.00007-6.

Geddes-McAlister J. Pathogenesis of Fungal and Bacterial Microbes. Pathogens. 2020;9(8):602. https://doi.org/10.3390/pathogens9080602.

Article  PubMed  PubMed Central  Google Scholar 

Noack J, Mukherjee S. “Make way”: Pathogen exploitation of membrane traffic. Curr Opin Cell Biol. 2020;65:78–85. https://doi.org/10.1016/j.ceb.2020.02.011.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Schleker S, Sun J, Raghavan B, et al. The current Salmonella-host interactome. Proteomics Clin Appl. 2012;6(1–2):117–33. https://doi.org/10.1002/prca.201100083.

Article  CAS  PubMed  Google Scholar 

Jangra RK, Llabres M, Guardado-Calvo P, et al. Editorial: Influence of Protein-Protein Interactions (PPIs) on the Outcome of Viral Infections. Front Microbiol. 2022;13: 943379. https://doi.org/10.3389/fmicb.2022.943379.

Article  PubMed  PubMed Central  Google Scholar 

Fan T, Gao Y, Al-Shammari A, et al. Yeast two-hybrid screening of MAP kinase cascade identifies cytosolic glutamine synthetase 1b as a tMEK2 interactive protein in wheat. Can J Plant Path. 2009;31(4):407–14. https://doi.org/10.1080/07060660909507615.

Article  CAS  Google Scholar 

Saravanakumar K, Wang S, Dou K, et al. Yeast two-hybrid and label-free proteomics based screening of maize root receptor to cellulase of Trichoderma harzianum. Physiol Mol Plant Pathol. 2018;104:86–94. https://doi.org/10.1016/j.pmpp.2018.10.002.

Article  CAS  Google Scholar 

Lum KK, Cristea IM. Proteomic approaches to uncovering virus-host protein interactions during the progression of viral infection. Expert Rev Proteomics. 2016;13(3):325–40. https://doi.org/10.1586/14789450.2016.1147353.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Haas P, Muralidharan M, Krogan NJ, et al. Proteomic Approaches to Study SARS-CoV-2 Biology and COVID-19 Pathology. J Proteome Res. 2021;20(2):1133–52. https://doi.org/10.1021/acs.jproteome.0c00764.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Alfiky A, L’Haridon F, Abou-Mansour E, et al. Disease Inhibiting Effect of Strain Bacillus subtilis EG21 and Its Metabolites Against Potato Pathogens Phytophthora infestans and Rhizoctonia solani. Phytopathology. 2022;112(10):2099–109. https://doi.org/10.1094/PHYTO-12-21-0530-R.

Article  CAS  PubMed  Google Scholar 

Bian W, Jiang H, Feng S, et al. Protocol for establishing a protein-protein interaction network using tandem affinity purification followed by mass spectrometry in mammalian cells. STAR protocols. 2022;3(3): 101569. https://doi.org/10.1016/j.xpro.2022.101569.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Huaming C, Fuyi L, Lei W, et al. Systematic evaluation of machine learning methods for identifying human–pathogen protein–protein interactions. Oxford University Press. 2020;22(3). https://doi.org/10.1093/bib/bbaa068.

Min S, Byunghan L, Sungroh Y. Deep learning in bioinformatics. Brief Bioinform. 2017;18(5):851–69. https://doi.org/10.1093/bib/bbw068.

Article  PubMed  Google Scholar 

Tang B, Pan Z, Yin K, et al. Recent Advances of Deep Learning in Bioinformatics and Computational Biology. Front Genet. 2019;10:214. https://doi.org/10.3389/fgene.2019.00214.

Article  PubMed  PubMed Central  Google Scholar 

Nikhil M, Tuan T, Banafsheh R, et al. Predicting human–pathogen protein–protein interactions using Natural Language Processing methods. Inform Med Unlocked. 2021;26: 100738. https://doi.org/10.1016/j.imu.2021.100738.

Article  Google Scholar 

Rakesh K, Cristian DL, Naveen D, et al. deepHPI: a comprehensive deep learning platform for accurate prediction and visualization of host–pathogen protein–protein interactions. Oxford University Press. 2022;23(3). https://doi.org/10.1093/bib/bbac125.

Ammari MG, Gresham CR, McCarthy FM, et al. HPIDB 20: a curated database for host-pathogen interactions. Database. 2016;2016:baw103. https://doi.org/10.1093/database/baw103.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Del Toro N, Shrivastava A, Ragueneau E, et al. The IntAct database: efficient access to fine-grained molecular interaction data. Nucleic Acids Res. 2022;50(D1):D648–53. https://doi.org/10.1093/nar/gkab1006.

Article  CAS  PubMed  Google Scholar 

Oughtred R, Rust J, Chang C, et al. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 2021;30(1):187–200. https://doi.org/10.1002/pro.3978.

Article  CAS  PubMed  Google Scholar 

Kotlyar M, Pastrello C, Sheahan N, et al. Integrated interactions database: tissue-specific view of the human and model organism interactomes. Nucleic Acids Res. 2016;44(D1):D536–41. https://doi.org/10.1093/nar/gkv1115.

Article  CAS  PubMed  Google Scholar 

Calderone A, Licata L, Cesareni G. VirusMentha: a new resource for virus-host protein interactions. Nucleic Acids Res. 2015;43:D588-92. https://doi.org/10.1093/nar/gku830.

Article  CAS  PubMed  Google Scholar 

Chatr-aryamontri A, Ceol A, Peluso D, et al. VirusMINT: a viral protein interaction database. Nucleic Acids Res. 2009;37:D669-73. https://doi.org/10.1093/nar/gkn739.

Article  CAS  PubMed  Google Scholar 

Guirimand T, Delmotte S, Navratil V. VirHostNet 2.0: surfing on the web of virus/host molecular interactions data. Nucleic Acids Res. 2015;43:D583-7. https://doi.org/10.1093/nar/gku1121.

Article  CAS  PubMed  Google Scholar 

de Chassey B, Navratil V, Tafforeau L, et al. Hepatitis C virus infection protein network. Mol Syst Biol. 2008;4:230. https://doi.org/10.1038/msb.2008.66.

Article  CAS  PubMed  PubMed Central  Google Scholar 

UniProt C. UniProt: the Universal Protein Knowledgebase in 2023. Nucleic Acids Res. 2023;51(D1):D523–31. https://doi.org/10.1093/nar/gkac1052.

Article  CAS  Google Scholar 

Basenko EY, Pulman JA, Shanmugasundram A, et al. FungiDB: An Integrated Bioinformatic Resource for Fungi and Oomycetes. J Fungi. 2018;4(1):39. https://doi.org/10.3390/jof4010039.

Article  CAS  Google Scholar 

Wattam AR, Davis JJ, Assaf R, et al. Improvements to PATRIC, the all-bacterial Bioinformatics Database and Analysis Resource Center. Nucleic Acids Res. 2017;45(D1):D535–42. https://doi.org/10.1093/nar/gkw1017.

Article  CAS  PubMed  Google Scholar 

Alvarez-Jarreta J, Amos B, Aurrecoechea C, et al. VEuPathDB: the eukaryotic pathogen, vector and host bioinformatics resource center in 2023. Nucleic Acids Res. 2024;52(D1):D808–16. https://doi.org/10.1093/nar/gkad1003.

Article  CAS  PubMed  Google Scholar 

Neumann D, Roy S, Minhas F, et al. On the choice of negative examples for prediction of host-pathogen protein interactions. Front Bioinform. 2022;2:1083292. https://doi.org/10.3389/fbinf.2022.1083292.

Article  PubMed  PubMed Central  Google Scholar 

Blohm P, Frishman G, Smialowski P, et al. Negatome 20: a database of non-interacting proteins derived by literature mining, manual annotation and protein structure analysis. Nucleic Acids Res. 2014;42:D396-400.

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