N4-hydroxycytidine, the active compound of Molnupiravir, promotes SARS-CoV-2 mutagenesis and escape from a neutralizing nanobody

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Summary

N4-hydroxycytidine (NHC), the active compound of the drug Molnupiravir, is incorporated into SARS-CoV-2 RNA, causing false base pairing. The desired result is an “error catastrophe,” but this bears the risk of mutated virus progeny. To address this experimentally, we propagated the initial SARS-CoV-2 strain in the presence of NHC. Deep sequencing revealed numerous NHC-induced mutations and host-cell-adapted virus variants. The presence of the neutralizing nanobody Re5D06 selected for immune escape mutations, in particular p.E484K and p.F490S, which are key mutations of the Beta/Gamma and Omicron-XBB strains, respectively. With NHC treatment, nanobody resistance occurred two passages earlier than without. Thus, within the limitations of this purely in vitro study, we conclude that the combined action of Molnupiravir and a spike-neutralizing antagonist leads to the rapid emergence of escape mutants. We propose caution use and supervision when using Molnupiravir, especially when patients are still at risk of spreading virus.

Graphical abstract

Highlights

•

Molnupiravir might give rise to new SARS-CoV-2 variants when used without precautions
•

In vitro, the active compound of Molnupiravir gives rise to nanobody-resistant variants
•

We provide an integrated view on the selection of nanobody-escape mutants

Abstract

Pharmaceutical science; Immunology; Virology; Structural biology

Related collections

Most cited references 84

Record: found
Abstract: found
Article: found

Is Open Access

The Sequence Alignment/Map format and SAMtools

Heng Li, Bob Handsaker, Alec Wysoker … (2009)

Summary: The Sequence Alignment/Map (SAM) format is a generic alignment format for storing read alignments against reference sequences, supporting short and long reads (up to 128 Mbp) produced by different sequencing platforms. It is flexible in style, compact in size, efficient in random access and is the format in which alignments from the 1000 Genomes Project are released. SAMtools implements various utilities for post-processing alignments in the SAM format, such as indexing, variant caller and alignment viewer, and thus provides universal tools for processing read alignments. Availability: http://samtools.sourceforge.net Contact: rd@sanger.ac.uk

0 comments Cited 14090 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Fast and accurate short read alignment with Burrows–Wheeler transform

Heng Li, Richard Durbin (2009)

Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk

0 comments Cited 10562 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Highly accurate protein structure prediction with AlphaFold

John Jumper, Richard Evans, Alexander Pritzel … (2021)

Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

0 comments Cited 8917 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Matthias Dobbelstein

Journal

Journal ID (nlm-ta): iScience

Journal ID (iso-abbrev): iScience

Title: iScience

Publisher: Elsevier

ISSN (Electronic): 2589-0042

Publication date PMC-release: 30 August 2023

Publication date Collection: 20 October 2023

Publication date (Electronic): 30 August 2023

Volume: 26

Issue: 10

Electronic Location Identifier: 107786

Affiliations

[1 ]Department of Human Genetics, University Medical Center Göttingen, 37073 Göttingen, Germany

[2 ]Department of Mathematics and Technology, University of Applied Sciences Koblenz, 53424 Remagen, Germany

[3 ]Department of Informatics, Technical University of Munich, 81675 Munich, Germany

[4 ]Department of Molecular Oncology, Göttingen Center of Molecular Biosciences (GZMB), University Medical Center Göttingen, 37077 Göttingen, Germany

[5 ]Max Planck Institute for Biology, 72076 Tübingen, Germany

[6 ]NGS Integrative Genomics Core Unit, Department of Human Genetics, University Medical Center Göttingen, 37077 Göttingen, Germany

[7 ]Department of Medical Microbiology and Virology, University Medical Center Göttingen, 37075 Göttingen, Germany

[8 ]Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany

[9 ]Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, 37075 Göttingen, Germany

Author notes

[∗ ]Corresponding author mdobbel@ 123456uni-goettingen.de

[10]

These authors contributed equally

[11]

Senior author

[12]

Lead contact

Article

Publisher Item ID: S2589-0042(23)01863-1 Publisher ID: 107786

DOI: 10.1016/j.isci.2023.107786

PMC ID: 10507161

PubMed ID: 37731621

SO-VID: dc740f28-7ffe-4141-9a11-910d363e1900

License:

This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

History

Date received : 20 January 2023

Date revision received : 27 July 2023

Date accepted : 28 August 2023

Comments

Comment on this article

scite_

Cited by 2

See all cited by

Most referenced authors 2,247

See all reference authors

- Version 1

N4-hydroxycytidine, the active compound of Molnupiravir, promotes SARS-CoV-2 mutagenesis and escape from a neutralizing nanobody

Read this article at

Summary

Graphical abstract

Highlights

Abstract

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 84

The Sequence Alignment/Map format and SAMtools

Fast and accurate short read alignment with Burrows–Wheeler transform

Highly accurate protein structure prediction with AlphaFold

Author and article information

Contributors

Journal

Affiliations

Author notes

Article

History

Categories

Comments

Comment on this article

Similar content 953

Cited by 2

Most referenced authors 2,247