Geodesic
Deep learning-assisted design of \emph{de novo} protein binders targeting hepatitis C virus E2 protein
Matematičeskaâ biologiâ i bioinformatika, Tome 19 (2024), pp. 402-417.

Voir la notice de l'article provenant de la source Math-Net.Ru

Hepatitis C virus is a grievous disease with an increased mortality rate worldwide. Chemical-based medications possess deleterious side effects and are considered inefficient in combating viral infections. Advanced therapeutic strategies are being examined with increased specificity against viral proteins such as designing highly regular proteins facilitating the development of highly effective inhibitors. Here, we present for the first time, the use of deep learning-based large language protein model ProtGPT2 with a unique strategy to design novel therapeutic binders that have the potential to mimic host receptors and inhibit the viral protein, especially the HCV envelope glycoprotein E2 for clinically relevant genotype 1a and 1b. We generated five de novo proteins for each host receptor that mimic the human receptors, based on the interacting residues which were identified by the tools of the PDBSum database in the docked host-E2 complexes generated with the ClusPro web server. The Root Mean Square Deviation score revealed that each de novo designed binder exhibited high similarity with the human receptors indicating a successful generation. Furthermore, multiple interactions were observed between these de novo designed proteins and E2 protein, emphasizing the potential of these de novo-designed proteins as significant inhibitors. A comparative analysis of molecular docking between human interacting partners and de novo designed proteins revealed that de novo proteins, such as CD81-D1 and CLDN-D4, are the most effective inhibitors having the lowest binding energy when interacting with the most conserved regions of the E2 protein. These generated proteins may inhibit the interaction of E2 with CD81 and CLDN host receptors. 
@article{MBB_2024_19_a15,
     author = {Noor N. Al-Hayani and Mohammed R. Mohaisen and Sara A. A. Rashid},
     title = {Deep learning-assisted design of \emph{de novo} protein binders targeting hepatitis {C} virus {E2} protein},
     journal = {Matemati\v{c}eska\^a biologi\^a i bioinformatika},
     pages = {402--417},
     publisher = {mathdoc},
     volume = {19},
     year = {2024},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/MBB_2024_19_a15/}
}
TY  - JOUR
AU  - Noor N. Al-Hayani
AU  - Mohammed R. Mohaisen
AU  - Sara A. A. Rashid
TI  - Deep learning-assisted design of \emph{de novo} protein binders targeting hepatitis C virus E2 protein
JO  - Matematičeskaâ biologiâ i bioinformatika
PY  - 2024
SP  - 402
EP  - 417
VL  - 19
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/MBB_2024_19_a15/
LA  - en
ID  - MBB_2024_19_a15
ER  - 
%0 Journal Article
%A Noor N. Al-Hayani
%A Mohammed R. Mohaisen
%A Sara A. A. Rashid
%T Deep learning-assisted design of \emph{de novo} protein binders targeting hepatitis C virus E2 protein
%J Matematičeskaâ biologiâ i bioinformatika
%D 2024
%P 402-417
%V 19
%I mathdoc
%U http://geodesic.mathdoc.fr/item/MBB_2024_19_a15/
%G en
%F MBB_2024_19_a15
Noor N. Al-Hayani; Mohammed R. Mohaisen; Sara A. A. Rashid. Deep learning-assisted design of \emph{de novo} protein binders targeting hepatitis C virus E2 protein. Matematičeskaâ biologiâ i bioinformatika, Tome 19 (2024), pp. 402-417. http://geodesic.mathdoc.fr/item/MBB_2024_19_a15/

[1] P. Mehta, L. M. Grant, A. K.R. Reddivari, “Viral Hepatitis”, StatPearls, StatPearls Publishing, Treasure Island (FL), 2024 (accessed 22.11.2024) <ext-link ext-link-type='uri' href='https://www.ncbi.nlm.nih.gov/books/NBK554549/'>https://www.ncbi.nlm.nih.gov/books/NBK554549/</ext-link>

[2] V. Khullar, R. J. Firpi, “Hepatitis C cirrhosis: New perspectives for diagnosis and treatment”, World J. Hepatol, 7:14 (2015), 1843–1855 <ext-link ext-link-type='doi' href='https://doi.org/10.4254/wjh.v7.i14.1843'>10.4254/wjh.v7.i14.1843</ext-link>

[3] P. A. Cortesi, C. Fornari, S. Conti, I. C. Antonazzo, P. Ferrara, A. Ahmed, C. L. Andrei, T. Andrei, A. A. Artamonov, M. Banach et al, “Hepatitis B and C in Europe: an update from the Global Burden of Disease Study 2019”, Lancet Public Health, 8:9 (2023), e701–e716 <ext-link ext-link-type='doi' href='https://doi.org/10.1016/S2468-2667(23)00149-4'>10.1016/S2468-2667(23)00149-4</ext-link>

[4] R. J. Center, I. Boo, L. Phu, J. McGregor, P. Poumbourios, H. E. Drummer, “Enhancing the antigenicity and immunogenicity of monomeric forms of hepatitis C virus E2 for use as a preventive vaccine”, J. Biol. Chem, 295:21 (2020), 7179–7192 <ext-link ext-link-type='doi' href='https://doi.org/10.1074/jbc.RA120.013015'>10.1074/jbc.RA120.013015</ext-link>

[5] E. Laugel, C. Hartard, H. Jeulin, S. Berger, V. Venard, J. P. Bronowicki, E. Schvoerer, “Full length genome sequencing of RNA viruses-How the approach can enlighten us on hepatitis C and hepatitis E viruses”, Rev. Med. Virol., 31:4 (2021), e2197 <ext-link ext-link-type='doi' href='https://doi.org/10.1002/rmv.2197'>10.1002/rmv.2197</ext-link>

[6] H. D. Daniel, J. David, S. Raghuraman, M. Gnanamony, G. M. Chandy, G. Sridharan, P. Abraham, “Comparison of Three Different Hepatitis C Virus Genotyping Methods: 5'NCR PCR-RFLP, Core Type-Specific PCR, and NS5b Sequencing in a Tertiary Care Hospital in South India”, J. Clin. Lab. Anal, 31:3 (2017) <ext-link ext-link-type='doi' href='https://doi.org/10.1002/jcla.22045'>10.1002/jcla.22045</ext-link>

[7] D. Pascut, M. Hoang, N. N.Q. Nguyen, M. Y. Pratama, C. Tiribelli, “HCV Proteins Modulate the Host Cell miRNA Expression Contributing to Hepatitis C Pathogenesis and Hepatocellular Carcinoma Development”, Cancers (Basel), 13:10 (2021), 2485 <ext-link ext-link-type='doi' href='https://doi.org/10.3390/cancers13102485'>10.3390/cancers13102485</ext-link>

[8] J. H. Hoofnagle, “Hepatitis C: the clinical spectrum of disease”, Hepatology, 26 (1997), 15S–20S <ext-link ext-link-type='doi' href='https://doi.org/10.1002/hep.510260703'>10.1002/hep.510260703</ext-link>

[9] A. Kumar, T. C. Rohe, E. J. Elrod, A. G. Khan, A. D. Dearborn, R. Kissinger, A. Grakoui, J. Marcotrigiano, “Regions of hepatitis C virus E2 required for membrane association”, Nat. Commun, 14:1 (2023) <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41467-023-36183-y'>10.1038/s41467-023-36183-y</ext-link>

[10] J. Yang, J. L. Qi, X. X. Wang, X. H. Li, R. Jin, B. Y. Liu, H. X. Liu, H. Y. Rao, “The burden of hepatitis C virus in the world. China, India, and the United States from 1990 to 2019”, Front. Public Health, 11 (2023) <ext-link ext-link-type='doi' href='https://doi.org/10.3389/fpubh.2023.1041201'>10.3389/fpubh.2023.1041201</ext-link>

[11] S. Roger, A. Ducancelle, H. Le Guillou-Guillemette, C. Gaudy, F. Lunel, “HCV virology and diagnosis”, Clin. Res. Hepatol. Gastroenterol, 45:3 (2021) <ext-link ext-link-type='doi' href='https://doi.org/10.1016/j.clinre.2021.101626'>10.1016/j.clinre.2021.101626</ext-link>

[12] M. Umer, M. Iqbal, “Hepatitis C virus prevalence and genotype distribution in Pakistan: Comprehensive review of recent data”, World J. Gastroenterol, 22:4 (2016), 1684–1700 <ext-link ext-link-type='doi' href='https://doi.org/10.3748/wjg.v22.i4.1684'>10.3748/wjg.v22.i4.1684</ext-link><ext-link ext-link-type='mr-item-id' href='http://mathscinet.ams.org/mathscinet-getitem?mr=4333466'>4333466</ext-link>

[13] X. Pan, T. Kortemme, “Recent advances in de novo protein design: Principles, methods, and applications”, J. Biol. Chem, 296 (2021), 100558 <ext-link ext-link-type='doi' href='https://doi.org/10.1016/j.jbc.2021.100558'>10.1016/j.jbc.2021.100558</ext-link>

[14] J. C. Jakobsen, E. E. Nielsen, J. Feinberg, K. K. Katakam, K. Fobian, G. Hauser, G. Poropat, S. Djurisic, K. H. Weiss, M. Bjelakovic et al, “Direct-acting antivirals for chronic hepatitis C”, Cochrane Database Syst. Rev, 2017, no. 9, CD012143 <ext-link ext-link-type='doi' href='https://doi.org/10.1002/14651858.CD012143.pub3'>10.1002/14651858.CD012143.pub3</ext-link>

[15] J. A. Gonzales Zamora, “Adverse Effects of Direct Acting Antivirals in HIV/HCV Coinfected Patients: A 4-Year Experience in Miami, Florida”, Diseases, 6:2 (2018) <ext-link ext-link-type='doi' href='https://doi.org/10.3390/diseases6020051'>10.3390/diseases6020051</ext-link>

[16] B. G. Pierce, Z. Y. Keck, R. Wang, P. Lau, K. Garagusi, K. Elkholy, E. A. Toth, R. A. Urbanowicz, J. D. Guest, P. Agnihotri et al, “Structure-Based Design of Hepatitis C Virus E2 Glycoprotein Improves Serum Binding and Cross-Neutralization”, J. Virol, 94:22 (2020), 51 <ext-link ext-link-type='doi' href='https://doi.org/10.1128/JVI.00704-20'>10.1128/JVI.00704-20</ext-link>

[17] R. A. Ali, E. A. Awadalla, Y. A. Amin, S. S. Fouad, M. A. E. B. Ahmed, M. H. Hassan, E. Abdel Kahaar, R. H. Abdel-Aziz, “The deleterious effects of sofosbuvir and ribavirin (antiviral drugs against hepatitis C virus) on different body systems in male albino rats regarding reproductive, hematological, biochemical, hepatic, and renal profiles and histopathological changes”, Sci. Rep, 14:1 (2024), 5682 <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41598-024-55950-5'>10.1038/s41598-024-55950-5</ext-link>

[18] D. Sepulveda-Crespo, S. Resino, I. Martinez, “Hepatitis C virus vaccine design: focus on the humoral immune response”, J. Biomed. Sci, 27:1 (2020), 78 <ext-link ext-link-type='doi' href='https://doi.org/10.1186/s12929-020-00669-4'>10.1186/s12929-020-00669-4</ext-link>

[19] T. Kucera, M. Togninalli, L. Meng-Papaxanthos, “Conditional generative modeling for de novo protein design with hierarchical functions”, Bioinformatics, 38:13 (2022), 3454–3461 <ext-link ext-link-type='doi' href='https://doi.org/10.1093/bioinformatics/btac353'>10.1093/bioinformatics/btac353</ext-link>

[20] S. Ovchinnikov, P. S. Huang, “Structure-based protein design with deep learning”, Curr. Opin. Chem. Biol, 65 (2021), 136–144 <ext-link ext-link-type='doi' href='https://doi.org/10.1016/j.cbpa.2021.08.004'>10.1016/j.cbpa.2021.08.004</ext-link>

[21] N. Ferruz, M. Heinzinger, M. Akdel, A. Goncearenco, L. Naef, C. Dallago, “From sequence to function through structure: Deep learning for protein design”, Comput. Struct. Biotechnol. J., 21 (2023), 238–250 <ext-link ext-link-type='doi' href='https://doi.org/10.1016/j.csbj.2022.11.014'>10.1016/j.csbj.2022.11.014</ext-link>

[22] P. Koehl, M. Levitt, “De novo protein design. I. In search of stability and specificity”, J. Mol. Biol, 293:5 (1999), 1161–1181 <ext-link ext-link-type='doi' href='https://doi.org/10.1006/jmbi.1999.3211'>10.1006/jmbi.1999.3211</ext-link>

[23] The UniProt Consortium, “UniProt: the universal protein knowledgebase in 2021”, Nucleic Acids Res., 49:D1 (2021), D480-D489 <ext-link ext-link-type='doi' href='https://doi.org/10.1093/nar/gkaa1100'>10.1093/nar/gkaa1100</ext-link>

[24] J. Segura, Y. Rose, C. Bi, J. Duarte, S. K. Burley, S. Bittrich, “RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins”, Front. Bioinform, 3 (2023), 1311287 <ext-link ext-link-type='doi' href='https://doi.org/10.3389/fbinf.2023.1311287'>10.3389/fbinf.2023.1311287</ext-link>

[25] C. C. Huang, E. C. Meng, J. H. Morris, E. F. Pettersen, T. E. Ferrin, “Enhancing UCSF Chimera through web services”, Nucleic Acids Res., 42 (2014), W478-W484 <ext-link ext-link-type='doi' href='https://doi.org/10.1093/nar/gku377'>10.1093/nar/gku377</ext-link>

[26] T. Paysan-Lafosse, M. Blum, S. Chuguransky, T. Grego, B. L. Pinto, G. A. Salazar, M. L. Bileschi, P. Bork, A. Bridge, L. Colwell et al, “InterPro in 2022”, Nucleic Acids Res., 51 (2023), D418-D427 <ext-link ext-link-type='doi' href='https://doi.org/10.1093/nar/gkac993'>10.1093/nar/gkac993</ext-link>

[27] M. A. Lill, M. L. Danielson, “Computer-aided drug design platform using PyMOL”, J. Comput. Aided Mol. Des, 25:1 (2010), 13–19 <ext-link ext-link-type='doi' href='https://doi.org/10.1007/s10822-010-9395-8'>10.1007/s10822-010-9395-8</ext-link>

[28] M. Varadi, S. Anyango, M. Deshpande, S. Nair, C. Natassia, G. Yordanova, D. Yuan, O. Stroe, G. Wood, A. Laydon et al, “AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models”, Nucleic Acids Res., 50:D1 (2022), D439-D444 <ext-link ext-link-type='doi' href='https://doi.org/10.1093/nar/gkab1061'>10.1093/nar/gkab1061</ext-link>

[29] F. N. Haron, A. Azazi, K. H. Chua, Y. A.L. Lim, P. C. Lee, C. H. Chew, “In silico structural modeling and quality assessment of Plasmodium knowlesi apical membrane antigen 1 using comparative protein models”, Trop. Biomed, 39:3 (2022), 394–401 <ext-link ext-link-type='doi' href='https://doi.org/10.47665/tb.39.3.009'>10.47665/tb.39.3.009</ext-link>

[30] A. Messaoudi, H. Belguith, J. Ben Hamida, “Homology modeling and virtual screening approaches to identify potent inhibitors of VEB-1-lactamase”, Theor. Biol. Med. Model, 10 (2013), 22 <ext-link ext-link-type='doi' href='https://doi.org/10.1186/1742-4682-10-22'>10.1186/1742-4682-10-22</ext-link>

[31] I. T. Desta, S. Kotelnikov, G. Jones, U. Ghani, M. Abyzov, Y. Kholodov, D. M. Standley, D. Beglov, S. Vajda, D. Kozakov, “The ClusPro AbEMap web server for the prediction of antibody epitopes”, Nat. Protoc, 18:6 (2023), 1814–1840 <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41596-023 00826-7'>10.1038/s41596-023 00826-7</ext-link>

[32] A. Alekseenko, M. Ignatov, G. Jones, M. Sabitova, D. Kozakov, “Protein-Protein and Protein Peptide Docking with ClusPro Server”, Methods Mol. Biol, 2165 (2020), 157–174 <ext-link ext-link-type='doi' href='https://doi.org/10.1007/978-1-0716-0708-4_9'>10.1007/978-1-0716-0708-4_9</ext-link>

[33] R. A. Laskowski, J. M. Thornton, “PDBsum extras: SARS-CoV-2 and AlphaFold models”, Protein Sci, 31:1 (2022), 283–289 <ext-link ext-link-type='doi' href='https://doi.org/10.1002/pro.4238'>10.1002/pro.4238</ext-link>

[34] R. A. Laskowski, J. Jablonska, L. Pravda, R. S. Varekova, J. M. Thornton, “PDBsum: Structural summaries of PDB entries”, Protein Sci, 27:1 (2018), 129–134 <ext-link ext-link-type='doi' href='https://doi.org/10.1002/pro.3289'>10.1002/pro.3289</ext-link>

[35] N. Ferruz, S. Schmidt, B. Hocker, “ProtGPT2 is a deep unsupervised language model for protein design”, Nat. Commun, 13:1 (2022) <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41467 022-32007-7'>10.1038/s41467 022-32007-7</ext-link>

[36] P. Bradley, K. M. Misura, D. Baker, “Toward high-resolution de novo structure prediction for small proteins”, Science, 309 (2005), 1868–1871 <ext-link ext-link-type='doi' href='https://doi.org/10.1126/science.1113801'>10.1126/science.1113801</ext-link>

[37] J. C. Booth, U. Kumar, D. Webster, J. Monjardino, H. C. Thomas, “Comparison of the rate of sequence variation in the hypervariable region of E2/NS1 region of hepatitis C virus in normal and hypogammaglobulinemic patients”, Hepatology, 27:1 (1998), 223–227 <ext-link ext-link-type='doi' href='https://doi.org/10.1002/hep.510270134'>10.1002/hep.510270134</ext-link>

[38] U. A. Ashfaq, T. Javed, S. Rehman, Z. Nawaz, S. Riazuddin, “An overview of HCV molecular biology, replication and immune responses”, Virol. J., 8 (2011), 161 <ext-link ext-link-type='doi' href='https://doi.org/10.1186/1743-422X-8-161'>10.1186/1743-422X-8-161</ext-link>

[39] M. Martinello, S. S. Solomon, N. A. Terrault, G. J. Dore, “Hepatitis C”, Lancet, 402:10407 (2023), 1085–1096 <ext-link ext-link-type='doi' href='https://doi.org/10.1016/S0140-6736(23)01320-X'>10.1016/S0140-6736(23)01320-X</ext-link>

[40] P. Piselli, D. Serraino, M. Fusco, E. Girardi, A. Pirozzi, F. Toffolutti, C. Cimaglia, M. Taborelli; Collaborating Study Group, “Hepatitis C virus infection and risk of liver-related and non liver-related deaths: a population-based cohort study in Naples, southern Italy”, BMC Infect. Dis, 21:1 (2021) <ext-link ext-link-type='doi' href='https://doi.org/10.1186/s12879-021-06336-9'>10.1186/s12879-021-06336-9</ext-link>

[41] J. L. Watson, D. Juergens, N. R. Bennett, B. L. Trippe, J. Yim, H. E. Eisenach, W. Ahern, A. J. Borst, R. J. Ragotte, L. F. Milles et al, “De novo design of protein structure and function with RFdiffusion”, Nature, 620:7976 (2023), 1089–1100 <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41586-023 06415-8'>10.1038/s41586-023 06415-8</ext-link>

[42] L. Kong, E. Giang, T. Nieusma, R. U. Kadam, K. E. Cogburn, Y. Hua, X. Dai, R. L. Stanfield, D. R. Burton, A. B. Ward, I. A. Wilson, M. Law, “Hepatitis C virus E2 envelope glycoprotein core structure”, Science, 342:6162 (2013), 1090–1094 <ext-link ext-link-type='doi' href='https://doi.org/10.1126/science.1243876'>10.1126/science.1243876</ext-link>

[43] M. O. Dobrica, A. van Eerde, C. Tucureanu, A. Onu, L. Paruch, I. Caras, E. Vlase, H. Steen, S. Haugslien, D. Alonzi et al, “Hepatitis C virus E2 envelope glycoprotein produced in Nicotiana benthamiana triggers humoral response with virus-neutralizing activity in vaccinated mice”, Plant Biotechnol. J., 19:10 (2021), 2027–2039 <ext-link ext-link-type='doi' href='https://doi.org/10.1111/pbi.13631'>10.1111/pbi.13631</ext-link>

[44] W. Zhong, A. S. Uss, E. Ferrari, J. Y. Lau, Z. Hong, “De novo initiation of RNA synthesis by hepatitis C virus nonstructural protein 5B polymerase”, J. Virol, 74:4 (2000), 2017–2022 <ext-link ext-link-type='doi' href='https://doi.org/10.1128/jvi.74.4.2017-2022.2000'>10.1128/jvi.74.4.2017-2022.2000</ext-link>

[45] L. He, N. Tzarum, X. Lin, B. Shapero, C. Sou, C. J. Mann, A. Stano, L. Zhang, K. Nagy, E. Giang et al, “Proof of concept for rational design of hepatitis C virus E2 core nanoparticle vaccines”, Sci. Adv, 6:16 (2020), eaaz6225 <ext-link ext-link-type='doi' href='https://doi.org/10.1126/sciadv.aaz6225'>10.1126/sciadv.aaz6225</ext-link>

[46] Y. Shirasago, H. Fukazawa, S. Nagase, Y. Shimizu, T. Mizukami, T. Wakita, T. Suzuki, H. Tani, M. Kondoh, T. Kuroda et al, “A single mutation in the E2 glycoprotein of hepatitis C virus broadens the claudin specificity for its infection”, Sci. Rep, 12:1 (2022), 20243 <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41598-022-23824-3'>10.1038/s41598-022-23824-3</ext-link>

[47] E. G. Cormier, R. J. Durso, F. Tsamis, L. Boussemart, C. Manix, W. C. Olson, J. P. Gardner, T. Dragic, “L-SIGN (CD209L) and DC-SIGN (CD209) mediate transinfection of liver cells by hepatitis C virus”, Proc. Natl. Acad. Sci. USA, 101:39 (2004), 14067–14072 <ext-link ext-link-type='doi' href='https://doi.org/10.1073/pnas.0405695101'>10.1073/pnas.0405695101</ext-link>

[48] N. N.T. Nguyen, Y. S. Lim, L. P. Nguyen, S. C. Tran, T. T.D. Luong, T. T.T. Nguyen, H. T. Pham, H. N. Mai, J. W. Choi, S. S. Han et al, “Hepatitis C Virus Modulates Solute carrier family 3 member 2 for Viral Propagation”, Sci. Rep, 8:1 (2018), 15486 <ext-link ext-link-type='doi' href='https://doi.org/10.1038/s41598-018-33861-6'>10.1038/s41598-018-33861-6</ext-link>

[49] J. Lyu, H. Imachi, K. Fukunaga, T. Yoshimoto, H. Zhang, K. Murao, “Roles of lipoprotein receptors in the entry of hepatitis C virus”, World J. Hepatol, 7:24 (2015), 2535–2542 <ext-link ext-link-type='doi' href='https://doi.org/10.4254/wjh.v7.i24.2535'>10.4254/wjh.v7.i24.2535</ext-link>