Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource

Portelli, Stephanie; Olshansky, Moshe; Rodrigues, Carlos H M; D'Souza, Elston N; Myung, Yoochan; Silk, Michael; Alavi, Azadeh; Pires, Douglas E. V.; Ascher, David B.

doi:10.1038/s41588-020-0693-3

Cited by 58 publications

(59 citation statements)

References 17 publications

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“…This could possibly explain immune evasion as suggested by Gupta et al for asymptomatic reinfection of this mutant in two healthcare workers from India [17]. Interestingly, we observed 100% co-occurrence on N440K mutation along C64F mutation in membrane glycoprotein of SARS-CoV-2 apart from globally dominant D614G (S protein) and P323L (ORF1ab) [4, 9]. The other new mutations in S protein showed co-occurrence with only D614G-P323L type [9].…”

Section: Figuresupporting

confidence: 52%

“…These proteins were selected owing to their curial role in host-viral interactome. Structural effects of SARS-CoV-2 mutations were studied using variant analysis module of COVID-3D suite [9, 10]. Images were created using PyMol [11].…”

Section: Figurementioning

confidence: 99%

“…Interestingly, we observed 100% co-occurrence on N440K mutation along C64F mutation in membrane glycoprotein of SARS-CoV-2 apart from globally dominant D614G (S protein) and P323L (ORF1ab) [4, 9]. The other new mutations in S protein showed co-occurrence with only D614G-P323L type [9]. Taken together, the naturally occurring mutations might confer resistance against one antibody, not other.…”

Section: Figurementioning

confidence: 99%

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Structure-function investigation of a new VUI-202012/01 SARS-CoV-2 variant

Singh

Rahman³

et al. 2021

Preprint

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The SARS-CoV-2 (Severe Acute Respiratory Syndrome-Coronavirus) has accumulated multiple mutations during its global circulation. Recently, a new strain of SARS-CoV-2 (VUI 202012/01) had been identified leading to sudden spike in COVID-19 cases in South-East England. The strain has accumulated 23 mutations which have been linked to its immune evasion and higher transmission capabilities. Here, we have highlighted structural-function impact of crucial mutations occurring in spike (S), ORF8 and nucleocapsid (N) protein of SARS-CoV-2. Some of these mutations might confer higher fitness to SARS-CoV-2.SummarySince initial outbreak of COVID-19 in Wuhan city of central China, its causative agent; SARS-CoV-2 virus has claimed more than 1.7 million lives out of 77 million populations and still counting. As a result of global research efforts involving public-private-partnerships, more than 0.2 million complete genome sequences have been made available through Global Initiative on Sharing All Influenza Data (GISAID). Similar to previously characterized coronaviruses (CoVs), the positive-sense single-stranded RNA SARS-CoV-2 genome codes for ORF1ab non-structural proteins (nsp(s)) followed by ten or more structural/nsps [1, 2]. The structural proteins include crucial spike (S), nucleocapsid (N), membrane (M), and envelope (E) proteins. The S protein mediates initial contacts with human hosts while the E and M proteins function in viral assembly and budding. In recent reports on evolution of SARS-CoV-2, three lineage defining non-synonymous mutations; namely D614G in S protein (Clade G), G251V in ORF3a (Clade V) and L84S in ORF 8 (Clade S) were observed [2–4]. The latest pioneering works by Plante et al and Hou et al have shown that compared to ancestral strain, the ubiquitous D614G variant (clade G) of SARS-CoV-2 exhibits efficient replication in upper respiratory tract epithelial cells and transmission, thereby conferring higher fitness [5, 6]. As per latest WHO reports on COVID-19, a new strain referred as SARS-CoV-2 VUI 202012/01 (Variant Under Investigation, year 2020, month 12, variant 01) had been identified as a part of virological and epidemiological analysis, due to sudden rise in COVID-19 detected cases in South-East England [7]. Preliminary reports from UK suggested higher transmissibility (increase by 40-70%) of this strain, escalating Ro (basic reproduction number) of virus to 1.5-1.7 [7, 8]. This apparent fast spreading variant inculcates 23 mutations; 13 non-synonymous, 6 synonymous and 4 amino acid deletions [7]. In the current scenario, where immunization programs have already commenced in nations highly affected by COVID-19, advent of this new strain variant has raised concerns worldwide on its possible role in disease severity and antibody responses. The mutations also could also have significant impact on diagnostic assays owing to S gene target failures.

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Section: Figuresupporting

confidence: 52%

Section: Figurementioning

confidence: 99%

Section: Figurementioning

confidence: 99%

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Structure-function investigation of a new VUI-202012/01 SARS-CoV-2 variant

Singh

Rahman³

et al. 2021

Preprint

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“…These single amino acid changes can readily disrupt the intricate network of intramolecular interactions, affecting how a protein folds, its stability, dynamics, and ultimately protein function. Beyond phenotypic outcomes, 1–22 it also has direct implications for their experimental study, protein engineering, 23,24 drug design, 25–30 and use in industrial processes 31 …”

Section: Introductionmentioning

confidence: 99%

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

2020

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Predicting the effect of missense variations on protein stability and dynamics is important for understanding their role in diseases, and the link between protein structure and function. Approaches to estimate these changes have been proposed, but most only consider single‐point missense variants and a static state of the protein, with those that incorporate dynamics are computationally expensive. Here we present DynaMut2, a web server that combines Normal Mode Analysis (NMA) methods to capture protein motion and our graph‐based signatures to represent the wildtype environment to investigate the effects of single and multiple point mutations on protein stability and dynamics. DynaMut2 was able to accurately predict the effects of missense mutations on protein stability, achieving Pearson's correlation of up to 0.72 (RMSE: 1.02 kcal/mol) on a single point and 0.64 (RMSE: 1.80 kcal/mol) on multiple‐point missense mutations across 10‐fold cross‐validation and independent blind tests. For single‐point mutations, DynaMut2 achieved comparable performance with other methods when predicting variations in Gibbs Free Energy (ΔΔG) and in melting temperature (ΔTm). We anticipate our tool to be a valuable suite for the study of protein flexibility analysis and the study of the role of variants in disease. DynaMut2 is freely available as a web server and API at http://biosig.unimelb.edu.au/dynamut2.

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“…Further to that, its clinical applicability in non-mycobacteria has been achieved in spite of low sequence identity with Mtb, making SUSPECT-RIF the first structure-based diagnostic tool which consistently performs across bacterial species. This highlights the power and broad applicability of this approach for predicting resistance from both clinical 65,66 and drug development perspectives [67][68][69][70] .…”

Section: Discussionmentioning

confidence: 96%

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

Portelli

Myung

Furnham

et al. 2020

Sci Rep

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Rifampicin resistance is a major therapeutic challenge, particularly in tuberculosis, leprosy, P. aeruginosa and S. aureus infections, where it develops via missense mutations in gene rpoB. Previously we have highlighted that these mutations reduce protein affinities within the RNA polymerase complex, subsequently reducing nucleic acid affinity. Here, we have used these insights to develop a computational rifampicin resistance predictor capable of identifying resistant mutations even outside the well-defined rifampicin resistance determining region (RRDR), using clinical M. tuberculosis sequencing information. Our tool successfully identified up to 90.9% of M. tuberculosis rpoB variants correctly, with sensitivity of 92.2%, specificity of 83.6% and MCC of 0.69, outperforming the current gold-standard GeneXpert-MTB/RIF. We show our model can be translated to other clinically relevant organisms: M. leprae, P. aeruginosa and S. aureus, despite weak sequence identity. Our method was implemented as an interactive tool, SUSPECT-RIF (StrUctural Susceptibility PrEdiCTion for RIFampicin), freely available at https://biosig.unimelb.edu.au/suspect_rif/.

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Exploring the structural distribution of genetic variation in SARS-CoV-2 with the COVID-3D online resource

Cited by 58 publications

References 17 publications

Structure-function investigation of a new VUI-202012/01 SARS-CoV-2 variant

Structure-function investigation of a new VUI-202012/01 SARS-CoV-2 variant

DynaMut2: Assessing changes in stability and flexibility upon single and multiple point missense mutations

Prediction of rifampicin resistance beyond the RRDR using structure-based machine learning approaches

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