A 3D structural SARS-CoV-2–human interactome to explore genetic and drug perturbations

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1. Version published to 10.1038/s41592-021-01318-w

   Nov 29, 2021
2. 

   ScreenIT

   Oct 15, 2020

   SciScore for 10.1101/2020.10.13.308676: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Finally, the calculation of structural features for the viral protein were overruled to use the manually provided homology models instead of pulling structures from the PDB or ModBase.	ModBase suggested: (ModBase, RRID:SCR_004642)
   In order to map these gnomAD DNA-level SNPs to equivalent protein-level UniProt annotations, we used the Ensembl Variant Effect Predictor (VEP)110.	Ensembl Variant Effect Predictor suggested: None Variant suggested: (VARIANT, RRID:SCR_005194)
   Curation of Disease Associated Variants: To explore whether human proteins interacting with SARS-CoV-2 proteins were enriched for …

   SciScore for 10.1101/2020.10.13.308676: (What is this?)

   Please note, not all rigor criteria are appropriate for all manuscripts.

   Table 1: Rigor

   NIH rigor criteria are not applicable to paper type.

   Table 2: Resources

   Software and Algorithms
   Sentences	Resources
   Finally, the calculation of structural features for the viral protein were overruled to use the manually provided homology models instead of pulling structures from the PDB or ModBase.	ModBase suggested: (ModBase, RRID:SCR_004642)
   In order to map these gnomAD DNA-level SNPs to equivalent protein-level UniProt annotations, we used the Ensembl Variant Effect Predictor (VEP)110.	Ensembl Variant Effect Predictor suggested: None Variant suggested: (VARIANT, RRID:SCR_005194)
   Curation of Disease Associated Variants: To explore whether human proteins interacting with SARS-CoV-2 proteins were enriched for disease or trait associated variants, three datasets were curated; the Human Gene Mutation Database (HGMD)65, ClinVar66, and the NHGRI-EBI GWAS Catalog67.	Human Gene Mutation Database suggested: (Human Gene Mutation Database, RRID:SCR_001621)
   Disease annotations from HGMD and ClinVar were obtained directly from their respective downloads pages and mapped to UniProt.	ClinVar suggested: (ClinVar, RRID:SCR_006169)
   For overall enrichment of individual disease terms among all human proteins interacting with SARS-CoV-2, disease terms were linked in an ontology based on the NCBI MedGen term relationships (https://ftp.ncbi.nlm.nih.gov/pub/medgen/MGREL.	MedGen suggested: (MedGen, RRID:SCR_000111)
   Proxy SNPs in high linkage disequilibrium (LD) (Parameters: R2 > 0.8; pop: “ALL”) for individual lead SNPs were obtained through programmatic queries to the LDproxy API112, which used phase 3 haplotype data from the 1000 Genomes Project as reference for calculating pairwise metrics of LD.	1000 Genomes Project suggested: (1000 Genomes Project and AWS, RRID:SCR_008801)

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   Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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   Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:

   However, our work is not without limitation. Firstly, we note that although structural coverage from our homology modelling of SARS-CoV-2 proteins was robust (Supplemental Figure 1), the same could not be universally said of the human proteins. Although guided molecular docking was always done to orient the most likely interface residues on each structure towards each other, protein-protein docking using incomplete protein models introduces some bias and low coverage may exclude some true interface residues. For this reason, the initial ECLAIR interface annotations—which are less subject to structural coverage limitations—may provide orthogonal value. We additionally note that direct quantitative interpretation of predicted ΔΔG values using the Rosetta scoring function is often difficult since different term weights can be used in different setups. Although the same scoring function was used for all predictions described here, the relative magnitude of each term may change based on the size and composition of different proteins between interactions. For these reasons, we only employ a relative qualitative comparison of similar predictions when interpreting our scanning mutagenesis results. Moreover, the structure optimization after each mutation is applied focuses on side-chain repacking, therefore, our results focus only on mutations at or near the interface where the impact of side-chain repacking could be measured. We expect there may be some mutations with significant imp...

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   Results from TrialIdentifier: No clinical trial numbers were referenced.

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   Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

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   Results from JetFighter: We did not find any issues relating to colormaps.

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   Results from rtransparent:

   • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
   • No funding statement was detected.
   • No protocol registration statement was detected.

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3. 

   Version published to 10.1101/2020.10.13.308676v1 on bioRxiv

   Oct 13, 2020