Corpus overview


MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

NSP4 (6)

NSP12 (4)

ComplexRdRp (4)

NSP6 (4)

NSP2 (3)


SARS-CoV-2 Proteins
    displaying 1 - 6 records in total 6
    records per page

    Detection of the new SARS-CoV-2 variant B.1.526 with the Spike E484K mutation in South America

    Authors: Juan Fernández Cadena; Mindy Muñoz; Gabriel Morey León; Rubén Armas-González; Darlyn Amaya Márquez; Katheryn Sacheri Viteri; Paúl Cárdenas & USFQ-COVID Consortium; Fernando Valiente-Echeverría; Ricardo Soto Rifo; Derly Andrade Molina

    doi:10.21203/ Date: 2021-02-17 Source: ResearchSquare

    Here, we report two sequences of the new SARS-CoV-2 variant recently detected and designed as B.1.526. This variant carries the immune escape-associated mutation E484K and additional mutations in the S, N, NSP2 PROTEIN NSP2 HGNC, NSP3 HGNC NSP3 PROTEIN, NSP4 PROTEIN NSP4 HGNC, NSP6 PROTEIN, NSP8 PROTEIN, NSP12 PROTEIN and NSP13 PROTEIN genes. Viral sequences were obtained from an individual traveling from the US to Equator with a negative RT-PCR and from one of his closest contacts that became infected. These cases should be considered an alert for the potential circulation of a new variant of concern with the E484K mutation in South America

    SARS-CoV-2 antibody signatures for predicting the outcome of COVID-19 MESHD

    Authors: Qing Lei; Caizheng Yu; Yang Li; Hongyan Hou; Zhaowei Xu; Meian He; Ziyong Sun; Feng Wang; Sheng-ce Tao; Xionglin Fan

    doi:10.1101/2020.11.10.20228890 Date: 2020-11-13 Source: medRxiv

    The COIVD-19 global pandemic is far from ending. There is an urgent need to identify applicable biomarkers for predicting the outcome of COVID-19 MESHD. Growing evidences have revealed that SARS-CoV-2 specific antibodies remain elevated with disease progression and severity in COIVD-19 patients. We assumed that antibodies may serve as biomarkers for predicting disease outcome. By taking advantage of a newly developed SARS-CoV-2 proteome microarray, we surveyed IgM/ IgG responses against 20 SARS-CoV-2 proteins in 1,034 hospitalized COVID-19 MESHD patients on admission, who were followed till 66 days. The microarray results were correlated with clinical information, laboratory test results and patient outcomes. Cox proportional hazards model was used to explore the association between SARS-CoV-2 specific antibodies and COVID-19 MESHD mortality. We found that high level of IgM against ORF7b PROTEIN at the time of hospitalization is an independent predictor of patient survival (p trend = 0.002), while levels of IgG responses to 6 non-structural proteins PROTEIN and 1 accessory protein, i. e PROTEIN., NSP4 HGNC NSP4 PROTEIN, NSP7 PROTEIN, NSP9 PROTEIN, NSP10 PROTEIN, RdRp PROTEIN ( NSP12 PROTEIN), NSP14 PROTEIN, and ORF3b PROTEIN, possess significant predictive power for patient death MESHD, even after further adjustments for demographics, comorbidities, and common laboratory markers for disease severity (all with p trend < 0.05). Spline regression analysis indicated that the correlation between ORF7b PROTEIN IgM, NSP9 PROTEIN IgG, and NSP10 PROTEIN IgG and risk of COVID-19 MESHD mortality is linear (p = 0.0013, 0.0073 and 0.0003, respectively). Their AUCs for predictions, determined by computational cross-validations (validation1), were 0.74 (cut-off = 7.59), 0.66 (cut-off = 9.13), and 0.68 (cut-off = 6.29), respectively. Further validations were conducted in the second and third serial samples of these cases (validation2A, n = 633, validation2B, n = 382), with high accuracy of prediction for outcome. These findings have important implications for improving clinical management, and especially for developing medical interventions and vaccines.

    Different mutations in SARS-CoV-2 associate with severe and mild outcome

    Authors: Adam Nagy; Sandor Pongor; Balazs Gyorffy

    doi:10.1101/2020.10.16.20213710 Date: 2020-10-20 Source: medRxiv

    Introduction. Genomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance. Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome. Methods. Mutations in viral sequences from the GISAID virus repository were evaluated by using hCoV-19/Wuhan/WIV04/2019 as the reference. Patient outcomes were classified as mild disease, hospitalization and severe disease ( death MESHD or documented treatment in an intensive-care unit). Chi-square test was applied to examine the association between each mutation and patient outcome. False discovery rate was computed to correct for multiple hypothesis testing and results passing a FDR cutoff of 5% were accepted as significant. Results. Mutations were mapped to amino acid changes for 2,120 non-silent mutations. Mutations correlated to mild outcome were located in the ORF8 PROTEIN, NSP6 PROTEIN, ORF3a PROTEIN, NSP4 PROTEIN NSP4 HGNC, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface ( S) glycoprotein PROTEIN, in the RNA dependent RNA polymerase PROTEIN, in the 3'-to5' exonuclease, in ORF3a PROTEIN, NSP2 HGNC NSP2 PROTEIN and N. Mutations leading to severe outcome with low prevalence were found in the surface ( S) glycoprotein PROTEIN and in NSP7 PROTEIN. Five out of 17 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome. Conclusions. We demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome. Our results help to quickly identify SARS-CoV-2 infections MESHD harboring mutations related to severe outcome.

    Global variation in the SARS-CoV-2 proteome reveals the mutational hotspots in the drug and vaccine candidates

    Authors: L Ponoop Prasad Patro; Chakkarai Sathyaseelan; Patil Pranita Uttamrao; Thenmalarchelvi Rathinavelan

    doi:10.1101/2020.07.31.230987 Date: 2020-07-31 Source: bioRxiv

    To accelerate the drug and vaccine development against the severe acute respiratory syndrome MESHD virus 2 (SARS-CoV-2), a comparative analysis of SARS-CoV-2 proteome has been performed in two phases by considering manually curated 31389 whole genome sequences from 84 countries. Among the 9 mutations that occur at a high significance (T85I-NPS2, L37F- NSP6 PROTEIN, P323L- NSP12 PROTEIN, D614G-spike, Q57H- ORF3a PROTEIN, G251V- ORF3a PROTEIN, L84S- ORF8 PROTEIN, R203K-nucleocapsid and G204R-nucleocapsid), R203K-nucleocapsid and G204R-nucleocapsid are co-occurring (dependent) mutations and P323L- NSP12 PROTEIN and D614G-spike often appear simultaneously. Other notable variations that appear with a moderate to low significance are, M85- NSP1 HGNC deletion, D268- NSP2 HGNC NSP2 PROTEIN deletion, 112 amino acids deletion in ORF8 PROTEIN, a phenylalanine insertion amidst F34-F36 ( NSP6 PROTEIN) and several co-existing (dependent) substitution/deletion (I559V & P585S in NSP2 HGNC NSP2 PROTEIN, P504L & Y541C in NSP13 PROTEIN, G82 & H83 deletions in NSP1 HGNC and K141, S142 & F143 deletions in NSP2 HGNC NSP2 PROTEIN) mutations. P323L- NSP12 PROTEIN, D614G-spike, L37F- NSP6 PROTEIN, L84S- ORF8 PROTEIN and the sequences deficient of the high significant mutations have led to 4 major SARS-CoV-2 clades. The top 5 countries bearing all the high significant and majority of the moderate significant mutations are: USA, England, Wales, Australia and Scotland. Further, the majority of the significant mutations have evolved in the first phase and have already transmitted around the globe indicating the positive selection pressure. Among the 26 SARS-CoV-2 proteins, nucleocapsid PROTEIN protein, ORF3a PROTEIN, ORF8 PROTEIN, RNA dependent RNA polymerase PROTEIN and spike exhibit a higher heterogeneity compared with the rest of the proteins. However, NSP9 PROTEIN, NSP10 PROTEIN, NSP8 PROTEIN, the envelope protein PROTEIN and NSP4 HGNC NSP4 PROTEIN are highly resistant to mutations and can be exploited for drug/vaccine development.

    Identification of novel mutations in SARS-COV-2 isolates from Turkey

    Authors: Shazia Rehman; Tariq Mahmood; Ejaz Aziz; Riffat Batool

    doi:10.21203/ Date: 2020-07-30 Source: ResearchSquare

    Severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), originally emerged from Wuhan, has caused an unprecedented worldwide pandemic in the first half of 2020. Since the first report of SARS-CoV-2 on March 10th, 2020 in Turkey, more than 150,000 people in the country have been infected with this virus. In this study, a total of 80 genomic virulent strains from Turkey which were uploaded in NCBI and GISAID database were analyzed with other genomic sequences from different countries with the aim to characterize notable genomic features of SARS-CoV-2 and to identify some novel mutations. Consistent with other studies, the combination of variants at positions C3037T, C14408T and A23403G were most common mutations (73%), that exist together in isolates from Turkey. Our secondary structure prediction analysis also highlighted 11 unique non-substitutional mutations from viral SARS-COV-2 isolates of Turkey in different regions such as in spike (S) protein PROTEIN and non-structural proteins (Nsp2, Nsp3, NSP4 PROTEIN, and NSP12 PROTEIN/ RdRP PROTEIN). Of these 11 mutations, nine of them have been found to be involved in structural alterations at different sites. 3/9 mutants (A771V, T1238I and G1251V) cause alteration in structure of S protein PROTEIN, while the rest of them induces structural changes in Nsp2 (A206T, R207C, T265I), Nsp3 (A1824V), Nsp4 (M2796I) and NSP12 PROTEIN (A4489V). These mutations identified here might have significant functional implications that needs to be addressed for future studies in the context of vaccine engineering and therapeutic interventions. Moreover, transmission and phylogenetic analysis revealed multiple independent sources of introductions for infection of hCovs in Turkey and close phylogenetic relationship of Turkish strains with Saudi strains.

    The first report of the most important sequential differences between COVID-19 MESHD and MERS viruses by attribute weighting models, the importance of Nucleocapsid (N) protein PROTEIN

    Authors: Mansour Ebrahimi; Boris Novikov; Esmaeil Ebrahimie; Alexey Spilman; Reza Ahsan; Mohammad Reza Tahsili; Mojtaba Najafi; Samaneh Navvabi; Faridoddin Shariaty

    doi:10.21203/ Date: 2020-06-13 Source: ResearchSquare

    COVID-19 MESHD and the Middle East respiratory syndrome MESHD-related coronavirus (MERS) viruses are from coronaviridae family; the former became a pandemic while the latter confined to a limited region. Their pathogenicity and infection rates are also different; the high mortality rate for MERS with low spreading capability. To investigate the possible structural changes at RNA sequences of both virus, 1621 and 125 sequences of COVID-19 MESHD and MERS downloaded and converted to polynomial datasets and seven attribute weighting (feature selection) approaches have been used for the analysis of genomic sequences of COVID-19 MESHD and MERS viruses. The end nucleotide sequences (from 29288 to the end genome positions) selected by the most attribute weighting models to be significantly different between two virus classes followed by smaller piece at 5700 and 1750 and 7600 nucleotide positions. These parts encode Nucleocapsid (N PROTEIN), Papin-like protease ( NSP3 PROTEIN) and NSP4 PROTEIN proteins of COVID-19 MESHD. The finding for the first time reports the structural differences between two important viruses at the sequential level and paves the road to decipher new emerging COVID-19 MESHD virus high pathogenicity.

The ZB MED preprint Viewer preVIEW includes all COVID-19 related preprints from medRxiv and bioRxiv, from ChemRxiv, from ResearchSquare, from arXiv and from and is updated on a daily basis (7am CET/CEST).
The web page can also be accessed via API.



MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins

Export subcorpus as...

This service is developed in the project nfdi4health task force covid-19 which is a part of nfdi4health.

nfdi4health is one of the funded consortia of the National Research Data Infrastructure programme of the DFG.