ABSTRACT
The current coronavirus disease of 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-CoV)-2, has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions can provide fine-grained resolution of the mechanisms behind the virus biology and the human organism response. We present a curated dataset of physical molecular interactions focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family that has been manually extracted by International Molecular Exchange (IMEx) Consortium curators. Currently, the dataset comprises over 4400 binarized interactions extracted from 151 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website (https://www.ebi.ac.uk/intact) and will be continuously updated as research on COVID-19 progresses.
Subject(s)
Betacoronavirus , Coronaviridae , Coronavirus Infections , Host-Pathogen Interactions , Pandemics , Pneumonia, Viral , Protein Interaction Maps , COVID-19 , Humans , Organ Specificity , Proteomics , SARS-CoV-2 , Viral ProteinsABSTRACT
The current Coronavirus Disease 2019 (COVID-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has spurred a wave of research of nearly unprecedented scale. Among the different strategies that are being used to understand the disease and develop effective treatments, the study of physical molecular interactions enables studying fine-grained resolution of the mechanisms behind the virus biology and the human organism response. Here we present a curated dataset of physical molecular interactions, manually extracted by IMEx Consortium curators focused on proteins from SARS-CoV-2, SARS-CoV-1 and other members of the Coronaviridae family. Currently, the dataset comprises over 2,200 binarized interactions extracted from 86 publications. The dataset can be accessed in the standard formats recommended by the Proteomics Standards Initiative (HUPO-PSI) at the IntAct database website ( www.ebi.ac.uk/intact ), and will be continuously updated as research on COVID-19 progresses.
ABSTRACT
In the original HTML version of this Article, the order of authors within the author list was incorrect. The IMEx Consortium contributing authors were incorrectly listed as the last author and should have been listed as the first author. This error has been corrected in the HTML version of the Article; the PDF version was correct at the time of publication.
ABSTRACT
The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule's interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.
Subject(s)
Amino Acid Substitution , Genetic Variation , Molecular Sequence Annotation , Point Mutation , Protein Interaction Maps , Animals , Disease/genetics , HumansABSTRACT
The evidence that two molecules interact in a living cell is often inferred from multiple different experiments. Experimental data is captured in multiple repositories, but there is no simple way to assess the evidence of an interaction occurring in a cellular environment. Merging and scoring of data are commonly required operations after querying for the details of specific molecular interactions, to remove redundancy and assess the strength of accompanying experimental evidence. We have developed both a merging algorithm and a scoring system for molecular interactions based on the proteomics standard initiative-molecular interaction standards. In this manuscript, we introduce these two algorithms and provide community access to the tool suite, describe examples of how these tools are useful to selectively present molecular interaction data and demonstrate a case where the algorithms were successfully used to identify a systematic error in an existing dataset.
