COVID-19: Genetics, genomics & molecular structure datasets

MD simulations were performed using Desmond on supercomputer TSUBAME 3.0. SARS-CoV-2 RdRp (also named nsp12) with/without template RNA and Remdesivir triphosphate complex models were placed in the orthorhombic box with a buffer distance of 10 Å in order to create a hydration model. TIP3P water model was used for creation of the hydration model. We performed MD simulations under the NPT ensemble for 1 μs on three complex structures using OPLS3e force field.

The dataset provides five types of k-mers genome representation characterized as k-mers count 1D, k-mers probability 1D, k-mers count 2D, k-mers probability 2D, and k-mers image. The dataset is composed of 1557 virus instances of SARS-CoV-2. Besides, the dataset also provides a data stream of 11540 viruses from the Virus-Host DB dataset and the other three Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

Autotransmutable sequence data. Nucleotide and protein translation data on primers and motifs designed from autotransmutable sequence.

The dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset is composed of 1557 virus instances of SARS-CoV-2. In addition, the dataset also provides a CGR representation of 11540 viruses from the Virus-Host DB dataset and the other three Riboviria viruses from NCBI.

The dataset provides numeric datastream of SARS-CoV-2 virus nucleotide sequences. The dataset is composed of 1557 virus instances of SARS-CoV-2. Besides, the dataset also provides a data stream of 11540 viruses from the Virus-Host DB dataset and the other three Riboviria viruses from NCBI.

This is the supplementary data of the research. The supplementary data 1 is the multiple sequence alignment file. While the other one (no 2) is mainly about the structural annotation.

This dataset was extracted from the Elsevier Pathway Studio, a tool that helps scientists analyze experimental data to answer biologically meaningful questions. The dataset itself consists of biological relationships between diseases (MERS and SARS), proteins and molecules. The relationships are of various types including Regulation, Target, Molecular Transport, etc. You can find a mapping of the relationship name to a description on this support page: https://service.elsevier.com/app/answers/detail/a_id/3014/supporthub/pathway/ The source of these relationships are life sciences and biomedical articles, from various publishers. We make use of taxonomies, curated and maintained by subject matter experts, to extract the right terms from text and map them to the correct identifiers. Subject matter experts have also helped us create the rules and information extraction patterns to optimize the extraction of relationships from text. At last, the pubmed identifiers from which the relationships were extracted are also part of the dataset. The .cypher and .json files can be imported into the graph database neo4j. The .csv files can be used to import into other systems.

MD simulations were performed using Desmond on supercomputer TSUBAME 3.0. The inhibitor-SARS-CoV-2 Mpro complex models were placed in the orthorhombic box with a buffer distance of 10 Å in order to create a hydration model. TIP3P water model was used for creation of the hydration model. We performed MD simulations under the NPT ensemble for 1 μs on three complex structures using OPLS3e force field.

This in silico study evaluated the binding affinities of some natural products (resveratrol, xylopic acid, ellagic acid, kaempferol, and quercetin) to human angiotensin converting enzyme 2 and coronavirus (SARS-coV-2) main protease compared to chloroquine, an inhibitor known to prevent the cellular entry and replication of coronavirus. The respective binding energies of the selected natural compounds, and chloroquine were towards the proteins were computed using Pyrex Virtual Screening tool. The pharmacodynamic and pharmacokinetic attributes of the selected compounds were predicted using admetSAR. The molecular docking analysis showed the natural compoundshad the better scores towards the selected protein compared to chloroquinewith polar amino acid residues present at the binding sites.The predicted ADMET properties revealedthe natural products lower acute oral toxicity compared to chloroquine. The study provided evidence suggesting that the relatively less toxic natural compounds could be repositioned as anti-viral agents to prevent the entry and replication of SARS-CoV-2.