Structural and energetic profiling of SARS-CoV-2 receptor binding domain antibody recognition and the impact of circulating variants

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1. Version published to 10.1371/journal.pcbi.1009380

   Sep 7, 2021
2. 

   ScreenIT

   Mar 24, 2021

   SciScore for 10.1101/2021.03.21.436311: (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

   Antibodies
   Sentences	Resources
   To permit consistency among antibody-RBD complex structures, and to facilitate calculations, antibodies were truncated to include variable domains, and full spike glycoproteins were truncated to include only RBD residues (residues 330-530) of the sole or major target of the antibody.	antibody-RBD suggested: None
   Antibodies with > 20 atomic clashes with ACE2 were classified as likely to block ACE2 binding.	ACE2 suggested: None
   Four antibodies that engaged the closed spike and exhibited cross-protomer binding, as confirmed by inspection of antibody-spike complex structures (S2M11, C144, mNb6,	C144 suggested: None

   SciScore for 10.1101/2021.03.21.436311: (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

   Antibodies
   Sentences	Resources
   To permit consistency among antibody-RBD complex structures, and to facilitate calculations, antibodies were truncated to include variable domains, and full spike glycoproteins were truncated to include only RBD residues (residues 330-530) of the sole or major target of the antibody.	antibody-RBD suggested: None
   Antibodies with > 20 atomic clashes with ACE2 were classified as likely to block ACE2 binding.	ACE2 suggested: None
   Four antibodies that engaged the closed spike and exhibited cross-protomer binding, as confirmed by inspection of antibody-spike complex structures (S2M11, C144, mNb6,	C144 suggested: None
   An in-house Perl script was used to analyze SARS-CoV-2 antibody- antigen interfaces and calculate epitope conservation.	antibody- suggested: None
   Software and Algorithms
   Sentences	Resources
   Hierarchical clustering of antibody RMSDs was performed in R version 4.0.3 (www.r-project.org) with the distance matrix of RMSDs as input, and Ward’s minimum variance method (“ward.D2” method in hclust).	hclust suggested: (HCLUST, RRID:SCR_009154)
   Principal component analysis of antibody-RBD contact profile data was performed with the scikit-learn Python module.	Python suggested: (IPython, RRID:SCR_001658)
   Buried surface areas (BSAs) were calculated using the naccess program (v. 2.1.1) (Hubbard and Thornton, 1993), subtracting the solvent accessible surface area of the antibody-RBD complex structure from the total solvent accessible surface area of the separate antibody and RBD structures, dividing by two to avoid double-counting interface area and to make BSA values commensurate with those from other tools including PISA (http://www.ebi.ac.uk/pdbe/prot_int/pistart.html).	PISA suggested: (PISA, RRID:SCR_015749)
   Pearson correlation coefficients (PCC) between measured and predicted ΔΔG values, and receiver operating characteristic area under the curve (AUC) values for prediction of hotspot residues (measured ΔΔG for alanine residue substitution > 1 kcal/mol), were calculated using scipy and scikit-learn (sklearn	scipy suggested: (SciPy, RRID:SCR_008058) scikit-learn suggested: (scikit-learn, RRID:SCR_002577)
   An example command line used for flex ddG calculations in this study is: rosetta_scripts.linuxgccrelease -s input.pdb -parser:protocol flexddg.xml -parser:script_vars chainstomove=1,2 mutate_resfile_relpath=input.resfile number_backrub_trials=35000 max_minimization_iter=5000 abs_score_convergence_thresh=1.0 backrub_trajectory_stride=7000 -restore_talaris_behavior -in:file:fullatom - ignore_unrecognized_res -ignore_zero_occupancy false -ex1 -ex2 For ΔΔG calculations in FoldX (Schymkowitz et al., 2005), complex structures were pre- processed using the FoldX RepairPDB protocol, and ΔΔG values were calculated using the FoldX PSSM protocol.	FoldX suggested: (FoldX, RRID:SCR_008522)
   Sequence conservation: Assessment of sequence conservation of SARS-CoV-2 RBD positions in the SARS-CoV-1 sequence was performed using SARS-CoV-2 (GenBank: QHD43416) and SARS-CoV-1 (GenBank: AAP13441) spike reference sequences aligned with BLAST (Altschul et al., 1990).	BLAST suggested: (BLASTX, RRID:SCR_001653)
   Figures: Figures of structures were generated using PyMOL version 1.8 (Schrodinger, Inc.).	PyMOL suggested: (PyMOL, RRID:SCR_000305)
   Boxplots and dendrograms were generated using the ggplot2 (Wickham, 2016) and factoextra (Kassambara and Mundt, 2020) packages in R, and heatmaps were generated using the ComplexHeatmap package (Gu et al., 2016) in R.	ggplot2 suggested: (ggplot2, RRID:SCR_014601) ComplexHeatmap suggested: (ComplexHeatmap, RRID:SCR_017270)

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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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

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

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3. 

   Version published to 10.1101/2021.03.21.436311 on bioRxiv

   Mar 21, 2021