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Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2
Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in humans globally. Considered for a long while a public health issue only in developing countries, the HEV infection is now a global public health concern. Most human infections are caused by the HEV genotypes 1, 2, 3 and 4 (HEV-1 to...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316534/ https://www.ncbi.nlm.nih.gov/pubmed/35891516 http://dx.doi.org/10.3390/v14071537 |
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author | Goulet, Adeline Cambillau, Christian Roussel, Alain Imbert, Isabelle |
author_facet | Goulet, Adeline Cambillau, Christian Roussel, Alain Imbert, Isabelle |
author_sort | Goulet, Adeline |
collection | PubMed |
description | Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in humans globally. Considered for a long while a public health issue only in developing countries, the HEV infection is now a global public health concern. Most human infections are caused by the HEV genotypes 1, 2, 3 and 4 (HEV-1 to HEV-4). Although HEV-3 and HEV-4 can evolve to chronicity in immunocompromised patients, HEV-1 and HEV-2 lead to self-limited infections. HEV has a positive-sense single-stranded RNA genome of ~7.2 kb that is translated into a large pORF1 replicative polyprotein, essential for the viral RNA genome replication and transcription. Unfortunately, the composition and structure of these replicases are still unknown. The recent release of the powerful machine-learning protein structure prediction software AlphaFold2 (AF2) allows us to accurately predict the structure of proteins and their complexes. Here, we used AF2 with the replicase encoded by the polyprotein pORF1 of the human-infecting HEV-3. The boundaries and structures reveal five domains or nonstructural proteins (nsPs): the methyltransferase, Zn-binding domain, macro, helicase, and RNA-dependent RNA polymerase, reliably predicted. Their substrate-binding sites are similar to those observed experimentally for other related viral proteins. Precisely knowing enzyme boundaries and structures is highly valuable to recombinantly produce stable and active proteins and perform structural, functional and inhibition studies. |
format | Online Article Text |
id | pubmed-9316534 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-93165342022-07-27 Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 Goulet, Adeline Cambillau, Christian Roussel, Alain Imbert, Isabelle Viruses Article Hepatitis E virus (HEV) is a major cause of acute viral hepatitis in humans globally. Considered for a long while a public health issue only in developing countries, the HEV infection is now a global public health concern. Most human infections are caused by the HEV genotypes 1, 2, 3 and 4 (HEV-1 to HEV-4). Although HEV-3 and HEV-4 can evolve to chronicity in immunocompromised patients, HEV-1 and HEV-2 lead to self-limited infections. HEV has a positive-sense single-stranded RNA genome of ~7.2 kb that is translated into a large pORF1 replicative polyprotein, essential for the viral RNA genome replication and transcription. Unfortunately, the composition and structure of these replicases are still unknown. The recent release of the powerful machine-learning protein structure prediction software AlphaFold2 (AF2) allows us to accurately predict the structure of proteins and their complexes. Here, we used AF2 with the replicase encoded by the polyprotein pORF1 of the human-infecting HEV-3. The boundaries and structures reveal five domains or nonstructural proteins (nsPs): the methyltransferase, Zn-binding domain, macro, helicase, and RNA-dependent RNA polymerase, reliably predicted. Their substrate-binding sites are similar to those observed experimentally for other related viral proteins. Precisely knowing enzyme boundaries and structures is highly valuable to recombinantly produce stable and active proteins and perform structural, functional and inhibition studies. MDPI 2022-07-14 /pmc/articles/PMC9316534/ /pubmed/35891516 http://dx.doi.org/10.3390/v14071537 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Goulet, Adeline Cambillau, Christian Roussel, Alain Imbert, Isabelle Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 |
title | Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 |
title_full | Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 |
title_fullStr | Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 |
title_full_unstemmed | Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 |
title_short | Structure Prediction and Analysis of Hepatitis E Virus Non-Structural Proteins from the Replication and Transcription Machinery by AlphaFold2 |
title_sort | structure prediction and analysis of hepatitis e virus non-structural proteins from the replication and transcription machinery by alphafold2 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9316534/ https://www.ncbi.nlm.nih.gov/pubmed/35891516 http://dx.doi.org/10.3390/v14071537 |
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