Prediction of conformational states in a coronavirus channel using Alphafold-2 and DeepMSA2: Strengths and limitations

The envelope (E) protein is present in all coronavirus genera. This protein can form pentameric oligomers with ion channel activity which have been proposed as a possible therapeutic target. However, high resolution structures of E channels are limited to those of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the recent COVID-19 pandemic. In the present work, we used Alphafold-2 (AF2), in ColabFold without templates, to predict the transmembrane domain (TMD) structure of six E-channels representative of genera alpha-, beta- and gamma-coronaviruses in the <i>Coronaviridae</i> family. High-confidence models were produced in all cases when combining multiple sequence alignments (MSAs) obtained from DeepMSA2. Overall, AF2 predicted at least two possible orientations of the α-helices in E-TMD channels: one where a conserved polar residue (Asn-15 in the SARS sequence) is oriented towards the center of the channel, 'polar-in', and one where this residue is in an interhelical orientation 'polar-inter'. For the SARS models, the comparison with the two experimental models 'closed' (PDB: 7K3G) and 'open' (PDB: 8SUZ) is described, and suggests a ∼60˚ α-helix rotation mechanism involving either the full TMD or only its N-terminal half, to allow the passage of ions. While the results obtained are not identical to the two high resolution models available, they suggest various conformational states with striking similarities to those models. We believe these results can be further optimized by means of MSA subsampling, and guide future high resolution structural studies in these and other viral channels.

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Publication Details

Journal
Computational and Structural Biotechnology Journal
Published
2024-10-16
DOI
https://doi.org/10.1016/j.csbj.2024.10.021
Citations
6
Primary Topic
Machine Learning in Bioinformatics
Type
article
Field-Weighted Citation Impact
1.40

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article

Prediction of conformational states in a coronavirus channel using Alphafold-2 and DeepMSA2: Strengths and limitations

Jaume Torres, Konstantin Pervushin, Wahyu Surya
6 citations
Computational and Structural Biotechnology Journal
Machine Learning in Bioinformatics
1.40
article

Prediction of conformational states in a coronavirus channel using Alphafold-2 and DeepMSA2: Strengths and limitations

Jaume Torres, Konstantin Pervushin, Wahyu Surya
article en
6 citations

Abstract

The envelope (E) protein is present in all coronavirus genera. This protein can form pentameric oligomers with ion channel activity which have been proposed as a possible therapeutic target. However, high resolution structures of E channels are limited to those of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the recent COVID-19 pandemic. In the present work, we used Alphafold-2 (AF2), in ColabFold without templates, to predict the transmembrane domain (TMD) structure of six E-channels representative of genera alpha-, beta- and gamma-coronaviruses in the <i>Coronaviridae</i> family. High-confidence models were produced in all cases when combining multiple sequence alignments (MSAs) obtained from DeepMSA2. Overall, AF2 predicted at least two possible orientations of the α-helices in E-TMD channels: one where a conserved polar residue (Asn-15 in the SARS sequence) is oriented towards the center of the channel, 'polar-in', and one where this residue is in an interhelical orientation 'polar-inter'. For the SARS models, the comparison with the two experimental models 'closed' (PDB: 7K3G) and 'open' (PDB: 8SUZ) is described, and suggests a ∼60˚ α-helix rotation mechanism involving either the full TMD or only its N-terminal half, to allow the passage of ions. While the results obtained are not identical to the two high resolution models available, they suggest various conformational states with striking similarities to those models. We believe these results can be further optimized by means of MSA subsampling, and guide future high resolution structural studies in these and other viral channels.

Computational and Structural Biotechnology JournalVol. 23
Nanyang Technological University (SG)
Ministry of Education, India
Openalex Percentile: Top 18%
Machine Learning in Bioinformatics
1.40
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Prediction of conformational states in a coronavirus channel using Alphafold-2 and DeepMSA2: Strengths and limitations — Jaume Torres, Konstantin Pervushin, et al. · Computational and Structural Biotechnology Journal (2024) | TGRS Research Map | TGRS