Prediction of viral oncoproteins through the combination of generative adversarial networks and machine learning techniques

Castel, P., Rauen, K. A. & McCormick, F. The duality of human oncoproteins: drivers of cancer and congenital disorders. Nat. Rev. Cancer. 20, 383–397 (2020).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Shortt, J. & Johnstone, R. W. Oncogenes in cell survival and cell death. Cold Spring Harb Perspect. Biol. 4, a009829–a009829 (2012).

Article 
PubMed 
PubMed Central 

Google Scholar 

Akram, N. et al. Oncogenic role of tumor viruses in humans. Viral Immunol. 30, 20–27 (2017).

Article 
CAS 
PubMed 

Google Scholar 

Mui, U., Haley, C. & Tyring, S. Viral oncology: Molecular biology and pathogenesis. J. Clin. Med. 6, 111 (2017).

Article 
PubMed 
PubMed Central 

Google Scholar 

Guven-Maiorov, E., Tsai, C. J. & Nussinov, R. Oncoviruses can drive cancer by rewiring signaling pathways through interface mimicry. Front. Oncol. 9, (2019).

Roetman, J. J., Apostolova, M. K. I. & Philip, M. Viral and cellular oncogenes promote immune evasion. Oncogene. 41, 921–929 (2022).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Cao, J. & Li, D. Searching for human oncoviruses: Histories, challenges, and opportunities. J. Cell. Biochem. 119, 4897–4906 (2018).

Article 
CAS 
PubMed 

Google Scholar 

Kliger, Y. et al. Mode of action of an antiviral peptide from HIV-1. J. Biol. Chem. 276, 1391–1397 (2001).

Article 
CAS 
PubMed 

Google Scholar 

Miura, M., Naito, T. & Saito, M. Current perspectives in human t-cell leukemia virus type 1 infection and its associated diseases. Front. Med. (Lausanne) 9, (2022).

Han, S. et al. Epstein–Barr virus epithelial cancers—a comprehensive understanding to drive novel therapies. Front. Immunol. 12, (2021).

Vranic, S., Cyprian, F. S. & Akhtar, S. & Al Moustafa, A.-E. The role of Epstein–Barr virus in cervical cancer: A brief update. Front. Oncol. 8, (2018).

Rivière, L., Ducroux, A. & Buendia, M. A. The oncogenic role of hepatitis B virus. in 59–74 doi: (2014). https://doi.org/10.1007/978-3-642-38965-8_4

Pollicino, T. et al. Hepatitis B virus maintains its pro-oncogenic properties in the case of occult HBV infection. Gastroenterology. 126, 102–110 (2004).

Article 
CAS 
PubMed 

Google Scholar 

Zhang, X. et al. Risk factors and prevention of viral hepatitis-related hepatocellular carcinoma. Front. Oncol. 11, (2021).

Kaynarcalidan, O. & Oğuzoğlu, T. Ç. The oncogenic pathways of papillomaviruses. Vet. Comp. Oncol. 19, 7–16 (2021).

Article 
CAS 
PubMed 

Google Scholar 

Boulet, G., Horvath, C., Broeck, D., Vanden, Sahebali, S. & Bogers, J. Human papillomavirus: E6 and E7 oncogenes. Int. J. Biochem. Cell. Biol. 39, 2006–2011 (2007).

Article 
CAS 
PubMed 

Google Scholar 

Moody, C. A. & Laimins, L. A. Human papillomavirus oncoproteins: Pathways to transformation. Nat. Rev. Cancer. 10, 550–560 (2010).

Article 
CAS 
PubMed 

Google Scholar 

Schiffman, M. et al. The carcinogenicity of human papillomavirus types reflects viral evolution. Virology. 337, 76–84 (2005).

Article 
CAS 
PubMed 

Google Scholar 

Jary, A. et al. Kaposi’s Sarcoma-Associated Herpesvirus, the Etiological agent of all epidemiological forms of kaposi’s sarcoma. Cancers (Basel). 13, 6208 (2021).

Article 
CAS 
PubMed 

Google Scholar 

Cesarman, E. et al. Kaposi sarcoma. Nat. Rev. Dis. Primers. 5, 9 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar 

Krump, N. A. & You, J. From merkel cell polyomavirus infection to merkel cell carcinoma oncogenesis. Front. Microbiol. 12, (2021).

Ahmed, M. M., Cushman, C. H. & DeCaprio, J. A. Merkel cell polyomavirus: Oncogenesis in a stable genome. Viruses. 14, 58 (2021).

Article 
PubMed 
PubMed Central 

Google Scholar 

Chang, Y., Moore, P. S. & Weiss, R. A. Human oncogenic viruses: Nature and discovery. Philosophical Trans. Royal Soc. B: Biol. Sci. 372, 20160264 (2017).

Article 

Google Scholar 

Tempera, I. & Lieberman, P. M. Oncogenic viruses as entropic drivers of cancer evolution. Front. Virol. 1, (2021).

Krump, N. A. & You, J. Molecular mechanisms of viral oncogenesis in humans. Nat. Rev. Microbiol. 16, 684–698 (2018).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Carlson, C. J., Zipfel, C. M., Garnier, R. & Bansal, S. Global estimates of mammalian viral diversity accounting for host sharing. Nat. Ecol. Evol. 3, 1070–1075 (2019).

Article 
PubMed 

Google Scholar 

Butel, J. S. Viral carcinogenesis: Revelation of molecular mechanisms and etiology of human disease. Carcinogenesis. 21, 405–426 (2000).

Article 
CAS 
PubMed 

Google Scholar 

Thakur, N., Qureshi, A. & Kumar, M. AVPpred: Collection and prediction of highly effective antiviral peptides. Nucleic Acids Res. 40, W199–W204 (2012).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Joseph, S., Karnik, S., Nilawe, P., Jayaraman, V. K. & Idicula-Thomas, S. ClassAMP: A prediction tool for classification of antimicrobial peptides. IEEE/ACM Trans. Comput. Biol. Bioinform. 9, 1535–1538 (2012).

Article 
PubMed 

Google Scholar 

Beltrán Lissabet, J. F., Belén, L. H. & Farias, J. G. AntiVPP 1.0: A portable tool for prediction of antiviral peptides. Comput. Biol. Med. 107, 127–130 (2019).

Article 
PubMed 
PubMed Central 

Google Scholar 

Lawrence, T. J. et al. amPEPpy 1.0: a portable and accurate antimicrobial peptide prediction tool. Bioinformatics. 37, 2058–2060 (2021).

Article 
CAS 
PubMed 

Google Scholar 

Yan, J. et al. Deep-AmPEP30: Improve short antimicrobial peptides prediction with deep learning. Mol. Ther. Nucleic Acids. 20, 882–894 (2020).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Beltrán Lissabet, J. F., Herrera Belén, L. & Farias, J. G. TTAgP 1.0: A computational tool for the specific prediction of tumor T cell antigens. Comput. Biol. Chem. 83, 107103 (2019).

Article 
PubMed 

Google Scholar 

Pang, Y., Yao, L., Jhong, J. H., Wang, Z. & Lee, T. Y. AVPIden: A new scheme for identification and functional prediction of antiviral peptides based on machine learning approaches. Brief. Bioinform 22, (2021).

Herrera-Bravo, J., Herrera Belén, L., Farias, J. G. & Beltrán, J. F. TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties. Comput. Biol. Chem. 91, 107452 (2021).

Article 
CAS 
PubMed 

Google Scholar 

Almagro Armenteros, J. J., Sønderby, C. K., Sønderby, S. K., Nielsen, H. & Winther, O. DeepLoc: prediction of protein subcellular localization using deep learning. Bioinformatics. 33, 4049–4049 (2017).

Article 
PubMed 

Google Scholar 

Gudenas, B. L. & Wang, L. Prediction of LncRNA subcellular localization with deep learning from sequence features. Sci. Rep. 8, 16385 (2018).

Article 
ADS 
PubMed 
PubMed Central 

Google Scholar 

Sharma, N., Naorem, L. D., Jain, S. & Raghava, G. P. S. ToxinPred2: an improved method for predicting toxicity of proteins. Brief. Bioinform 23, (2022).

Khan, A. et al. AFP-SPTS: An accurate prediction of antifreeze proteins using sequential and pseudo-tri-slicing evolutionary features with an extremely randomized tree. J. Chem. Inf. Model. 63, 826–834 (2023).

Article 
CAS 
PubMed 

Google Scholar 

Khan, A. et al. Prediction of antifreeze proteins using machine learning. Sci. Rep. 12, 20672 (2022).

Article 
ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Shen, H. B. & Chou, K. C. Virus-mPLoc: A fusion classifier for viral protein subcellular location prediction by incorporating multiple sites. J. Biomol. Struct. Dyn. 28, 175–186 (2010).

Article 
CAS 
PubMed 

Google Scholar 

Cheng, X., Xiao, X. & Chou, K. C. pLoc-mVirus: Predict subcellular localization of multi-location virus proteins via incorporating the optimal GO information into general PseAAC. Gene. 628, 315–321 (2017).

Article 
CAS 
PubMed 

Google Scholar 

Yang, X., Yang, S., Li, Q., Wuchty, S. & Zhang, Z. Prediction of human-virus protein-protein interactions through a sequence embedding-based machine learning method. Comput. Struct. Biotechnol. J. 18, 153–161 (2020).

Article 
CAS 
PubMed 

Google Scholar 

Tsukiyama, S., Hasan, M. M., Fujii, S. & Kurata, H. LSTM-PHV: Prediction of human-virus protein–protein interactions by LSTM with word2vec. Brief. Bioinform 22, (2021).

Doytchinova, I. A. & Flower, D. R. VaxiJen: A server for prediction of protective antigens, tumour antigens and subunit vaccines. BMC Bioinform. 8, 4 (2007).

Article 

Google Scholar 

Beltrán, J. F. et al. VirusHound-I: prediction of viral proteins involved in the evasion of host adaptive immune response using the random forest algorithm and generative adversarial network for data augmentation. Brief. Bioinform 25, (2023).

Bateman, A. et al. UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 49, D480–D489 (2021).

Article 
ADS 

Google Scholar 

Chou, K. C. Prediction of protein cellular attributes using pseudo-amino acid composition. Proteins Struct. Funct. Genet. 43, 246–255 (2001).

Article 
CAS 
PubMed 

Google Scholar 

Chou, K. C. Pseudo amino acid composition and its applications in bioinformatics, proteomics and system biology. Curr. Proteom. 6, 262–274 (2009).

Article 
CAS 

Google Scholar 

Xu, J. et al. Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides. Brief. Bioinform 22, (2021).

Beltrán, J. F. et al. MultiToxPred 1.0: A novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach. BMC Bioinform. 25, 148 (2024).

Article 

Google Scholar 

Lissabet, J. F. B., Belén, L. H., Farias, J. G. & PPLK + C A Bioinformatics tool for predicting peptide ligands of potassium channels based on primary structure information. Interdiscip Sci. 12, 258–263 (2020).

Article 
PubMed 

Google Scholar 

Lefin, N., Herrera-Belén, L., Farias, J. G. & Beltrán, J. F. Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides. Mol. Divers. (2023).

Article 
PubMed 

Google Scholar 

Herrera-Bravo, J. et al. VirVACPRED: A web server for prediction of protective viral antigens. Int. J. Pept. Res. Ther. 28, 35 (2022).

Article 
CAS 
PubMed 

Google Scholar 

Bergonzini, V., Salata, C., Calistri, A., Parolin, C. & Palù, G. View and review on viral oncology research. Infect. Agent Cancer. 5, 11 (2010).

Article 
PubMed 
PubMed Central 

Google Scholar 

Venuti, A. et al. Papillomavirus E5: the smallest oncoprotein with many functions. Mol. Cancer. 10, 140 (2011).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Münger, K. et al. Biological activities and molecular targets of the human papillomavirus E7 oncoprotein. Oncogene. 20, 7888–7898 (2001).

Article 
PubMed 

Google Scholar 

Kha, Q. H. et al. An interpretable deep learning model for classifying adaptor protein complexes from sequence information. Methods. 207, 90–96 (2022).

Article 
CAS 
PubMed 

Google Scholar 

Zhao, Z., Gui, J., Yao, A., Le, N. Q. K. & Chua, M. C. H. Improved prediction model of protein and peptide toxicity by integrating channel attention into a convolutional neural network and gated recurrent units. ACS Omega. 7, 40569–40577 (2022).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Mei, S. et al. A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction. Brief. Bioinform. 21, 1119–1135 (2020).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Wang, T. et al. scMultiGAN: cell-specific imputation for single-cell transcriptomes with multiple deep generative adversarial networks. Brief. Bioinform 24, (2023).

Wang, T. et al. Exploring causal effects of sarcopenia on risk and progression of Parkinson disease by Mendelian randomization. NPJ Parkinsons Dis. 10, 164 (2024).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Wang, T. et al. Accurately deciphering spatial domains for spatially resolved transcriptomics with stCluster. Brief. Bioinform 25, (2024).

Wang, T. et al. DFinder: A novel end-to-end graph embedding-based method to identify drug–food interactions. Bioinformatics 39, (2023).

Rukh, G., Akbar, S., Rehman, G., Alarfaj, F. K. & Zou, Q. StackedEnC-AOP: Prediction of antioxidant proteins using transform evolutionary and sequential features based multi-scale vector with stacked ensemble learning. BMC Bioinform. 25, 256 (2024).

Article 
CAS 

Google Scholar 

Ullah, M., Akbar, S., Raza, A. & Zou, Q. DeepAVP-TPPred: identification of antiviral peptides using transformed image-based localized descriptors and binary tree growth algorithm. Bioinformatics 40, (2024).

Akbar, S., Raza, A. & Zou, Q. Deepstacked-AVPs: predicting antiviral peptides using tri-segment evolutionary profile and word embedding based multi-perspective features with deep stacking model. BMC Bioinform. 25, 102 (2024).

Article 
CAS 

Google Scholar 

Raza, A. et al. AIPs-SnTCN: Predicting anti-inflammatory peptides using fasttext and transformer encoder-based hybrid word embedding with self-normalized temporal convolutional networks. J. Chem. Inf. Model. 63, 6537–6554 (2023).

Article 
CAS 
PubMed 

Google Scholar 

Akbar, S., Zou, Q., Raza, A. & Alarfaj, F. K. iAFPs-Mv-BiTCN: Predicting antifungal peptides using self-attention transformer embedding and transform evolutionary based multi-view features with bidirectional temporal convolutional networks. Artif. Intell. Med. 151, 102860 (2024).

Article 
PubMed 

Google Scholar 

Lee, M. Recent advances in generative adversarial networks for gene expression data: A comprehensive review. Mathematics. 11, 3055 (2023).

Article 

Google Scholar 

Lin, T. T. et al. AI4AVP: An antiviral peptides predictor in deep learning approach with generative adversarial network data augmentation. Bioinf. Adv. 2, (2022).

Kumar, A. & Singh, D. Generative adversarial network-based augmentation with noval 2-step authentication for anti-coronavirus peptide prediction. IEEE/ACM Trans. Comput. Biol. Bioinform. 1–13 (2024).

Achuthan, S. et al. Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks. J. Biosci. 47, 43 (2022).

Article 
PubMed 

Google Scholar 

Schaduangrat, N., Nantasenamat, C., Prachayasittikul, V., Shoombuatong, W. & ACPred A computational tool for the prediction and analysis of anticancer peptides. Molecules. 24, 1973 (2019).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Shoombuatong, W., Schaduangrat, N., Pratiwi, R., Nantasenamat, C. & THPep A machine learning-based approach for predicting tumor homing peptides. Comput. Biol. Chem. 80, 441–451 (2019).

Article 
CAS 
PubMed 

Google Scholar 

Laengsri, V. et al. TargetAntiAngio: A sequence-based tool for the prediction and analysis of anti-angiogenic peptides. Int. J. Mol. Sci. 20, 2950 (2019).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Wani, M. A., Garg, P. & Roy, K. K. Machine learning-enabled predictive modeling to precisely identify the antimicrobial peptides. Med. Biol. Eng. Comput. 59, 2397–2408 (2021).

Article 
PubMed 

Google Scholar 

Bournez, C. et al. CalcAMP: A new machine learning model for the accurate prediction of antimicrobial activity of peptides. Antibiotics. 12, 725 (2023).

Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 

Poorinmohammad, N. & Mohabatkar, H. A. Comparison of different machine learning algorithms for the prediction of Anti-HIV-1 peptides based on their sequence-related properties. Int. J. Pept. Res. Ther. 21, 57–62 (2015).

Article 
CAS 

Google Scholar 

Khan, Y. D. et al. iProtease-PseAAC(2L): A two-layer predictor for identifying proteases and their types using Chou’s 5-step-rule and general PseAAC. Anal. Biochem. 588, 113477 (2020).

Article 
CAS 
PubMed 

Google Scholar 

Nanni, L., Brahnam, S. & Lumini, A. High performance set of PseAAC and sequence based descriptors for protein classification. J. Theor. Biol. 266, 1–10 (2010).

Article 
ADS 
CAS 
PubMed 

Google Scholar 

Repecka, D. et al. Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324–333 (2021).

Article 

Google Scholar