Methods
SARS-CoV-2 and SARS-CoV-2-related coronavirus (SC2r-CoV) sequences
Our genomic survey of SARS-CoV-2 positive samples sequenced by the Fiocruz COVID-19 Genomic Surveillance Network between 12th March 2020 and 28th February 2021 identified 11 sequences with mutations of concern in the RBD and indels in the NTD (Table 1). The SARS-CoV-2 genomes were recovered using Illumina sequencing protocols as previously described 33,34. The FASTQ reads obtained were imported into the CLC Genomics Workbench version 20.0.4 (Qiagen A/S, Denmark), trimmed, and mapped against the reference sequence EPI_ISL_402124 available in EpiCoV database in the GISAID (https://www.gisaid.org/). The alignment was refined using the InDels and Structural Variants module. Additionally, the same reads were imported in a different pipeline 35 based on Bowtie2 and bcftools 36 mapping and consensus generation allowing us to further confirm the indels for all samples sequenced in this study. BAM files were used as input to generate sequencing coverage plots onto indels using the Karyoploter R package 37. Sequences were combined with SARS-CoV-2 and SC2r-CoV from bats and pangolins available in the EpiCoV database in GISAID by 1st March 2021 (Appendix Table 1). This study was approved by the FIOCRUZ-IOC (68118417.6.0000.5248 and CAAE 32333120.4.0000.5190) and the Amazonas State University Ethics Committee (CAAE: 25430719.6.0000.5016) and the Brazilian Ministry of the Environment (MMA) A1767C3.
Maximum Likelihood Phylogenetic Analyses
SARS-COV-2 sequences here obtained were aligned with high quality (<1% of N) and complete (>29 kb) lineages B.1.1.28, P.1, P2 and B.1.1.33 sequences that were available in EpiCoV database in the GISAID (https://www.gisaid.org/) at March 1st, 2021 and subjected to maximum-likelihood (ML) phylogenetic analysis using IQ-TREE v2.1.2 38. The S amino acid sequences from selected SARS-CoV-2 and SC2r-CoV lineages available in the EpiCoV database were also aligned using Clustal W 39 adjusted by visual inspection.
Structural Modeling
The resolved crystallographic structure of SARS-CoV-2 NTD protein bound to the neutralizing antibody 2-51 was retrieved from the Protein Databank (PDB) under the accession code 7L2C 28. Missing residues of the chain A, corresponding to the NTD coordinates, were modeled using the user template mode of the Swiss-Model web server (https://swissmodel.expasy.org/) 40 and was used as starting structure for the NTD wildtype. This structure was then used as a template to model the NTD variants using the Swiss-Model. The modeled structures of the NTDs variants were superimposed onto the coordinates of the PDB ID 7L2C to visualize the differences between the NTD-antibody binding interfaces. Image rendering was carried out using Visual Molecular Dynamics (VMD) software 41. The NTD-antibodies complexes were geometry optimized using a maximum of 5,000 steps or until it reached a convergence value of 0.001 REU (Rosetta energy units) using the limited-memory BroydenFletcher-Goldfarb-Shanno algorithm, complying with the Armijo-Goldstein condition, as implemented in the Rosetta suite of software 3.12 42. Geometry optimization was accomplished through the atomistic Rosetta energy function 2015 (REF15), while preserving backbone torsion angles. Protein-protein interface analyses were performed using the Protein Interactions Calculator (PIC) webserver (http://pic.mbu.iisc.ernet.in/)43, the ‘Protein interfaces, surfaces and assemblies’ service (PISA) at the European Bioinformatics Institute (PDBe < PISA < EMBL-EBI) 44 and the InterfaceAnalyzer protocol of the Rosetta package interfaced with the RosettaScripts scripting language 45. For the interface analyzer, the maximum SASA that is allowed for an atom to be defined as buried is 0.01 Å2, with a SASA probe radius of 1.2 Å.
Epitope prediction
Epitopes in the NTD region were predicted by the ElliPro Antibody Epitope Prediction server 46. NTD are shown as predicted linear epitopes when using PDB accession codes 6VXX 47 and 6VSB 48, (structural coordinates corresponding to the entire S protein), along with a minimum score of 0.9, i.e., a highly strict criterion.
Acknowledgments
The authors wish to thank all the health care workers and scientists who have worked hard to deal with this pandemic threat, the GISAID team, and all the EpiCoV database’s submitters, GISAID acknowledgment table containing sequences used in this study are attached to this post (Appendix Table 3 - gisaid_hcov-19_acknowledgement_table_final.pdf (102.7 KB). We also appreciate the support of the Fiocruz COVID-19 Genomic Surveillance Network (http://www.genomahcov.fiocruz.br/) members, the Respiratory Viruses Genomic Surveillance Network of the General Laboratory Coordination (CGLab), Brazilian Ministry of Health (MoH), Brazilian Central Laboratory States (LACENs) and the Amazonas surveillance teams for the partnership in the viral surveillance in Brazil. Financial support was provided by FAPEAM (PCTI-EmergeSaude/AM call 005/2020 and Rede Genômica de Vigilancia em Saúde - REGESAM); Ministério da Ciência, Tecnologia, Inovações e Comunicações/Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq/Ministério da Saúde - MS/FNDCT/SCTIE/Decit (grants 402457/2020-9 and 403276/2020-9); Inova Fiocruz/Fundação Oswaldo Cruz (Grants VPPCB-007-FIO-18-2-30 and VPPCB-005-FIO-20-2-87) and INCT-FCx (465259/2014-6). Computer allocation was partly granted by the Brazilian National Scientific Computing Center (LNCC). FGN, GLW, RDL and GB are supported by the CNPq through their productivity research fellowships (306146/2017-7, 303902/2019-1, 425997/2018-9 and 302317/2017-1 respectively). G.B. is also funded by the Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro – FAPERJ (Grant number E-26/202.896/2018).