Unlocking the secrets of SARS-CoV-2 nsp3 by combining experiments with AlphaFold2 domain prediction

Nonstructural protein 3 (nsp3) is crucial for SARS-CoV-2 infection. It is the largest protein of the virus with roughly 2000 residues, and a major drug target. However, because of its size, disordered regions, and transmembrane domains, the atomic structure of the whole protein has not yet been established. Only 10 out of its 16 domains were individually determined in experiments. Here, we demonstrate how structural bioinformatics, AI-based fold prediction, and traditional experiments complement each other and can shed light on the makeup of this important protein, both in SARS-CoV-2 and in related viruses. Our method can be generalized for other multidomain proteins. Our prediction-based approach reveals a previously undescribed folded domain, which we could confirm experimentally. Our research also suggests a potential function of the domain Y1: this domain may be involved in the assembly of nsp3, nsp4, and nsp6 into the hexameric pore, which was discovered by electron tomography and exports RNA into the cytosol. The Y1 hexamer, however, could not be expressed on its own. We revise domain segmentation and nomenclature of nsp3 domains.

Authors

Institutions

Publication Details

Journal
Life Science Alliance
Published
2026-01-15
DOI
https://doi.org/10.26508/lsa.202503247
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Unlocking the secrets of SARS-CoV-2 nsp3 by combining experiments with AlphaFold2 domain prediction

Andrea Thorn, David C. Briggs, Maximilian Edich, Yunyun Gao
Life Science Alliance
Machine Learning in Bioinformatics
article

Unlocking the secrets of SARS-CoV-2 nsp3 by combining experiments with AlphaFold2 domain prediction

Andrea Thorn, David C. Briggs, Maximilian Edich, Yunyun Gao
article en

Abstract

Nonstructural protein 3 (nsp3) is crucial for SARS-CoV-2 infection. It is the largest protein of the virus with roughly 2000 residues, and a major drug target. However, because of its size, disordered regions, and transmembrane domains, the atomic structure of the whole protein has not yet been established. Only 10 out of its 16 domains were individually determined in experiments. Here, we demonstrate how structural bioinformatics, AI-based fold prediction, and traditional experiments complement each other and can shed light on the makeup of this important protein, both in SARS-CoV-2 and in related viruses. Our method can be generalized for other multidomain proteins. Our prediction-based approach reveals a previously undescribed folded domain, which we could confirm experimentally. Our research also suggests a potential function of the domain Y1: this domain may be involved in the assembly of nsp3, nsp4, and nsp6 into the hexameric pore, which was discovered by electron tomography and exports RNA into the cytosol. The Y1 hexamer, however, could not be expressed on its own. We revise domain segmentation and nomenclature of nsp3 domains.

Life Science AllianceVol. 9(4)
Universität Hamburg (DE), Helmholtz-Zentrum Berlin für Materialien und Energie (DE), The Francis Crick Institute (GB)
Openalex Percentile: Top 47%
Machine Learning in Bioinformatics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.