Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

Abstract

The current wealth of genomic variation data identified at the nucleotide level has provided us with 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 amino acid variation of a molecule's sequence has on its molecular interactions is a key step towards connecting a full mechanistic characterization of nonsynonymous variation to cellular phenotype. Herewe present an open access resource created by IMEx database curators over 14 years, featuring 28,000 annotations fully describing the effect of individual point sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data content is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website at www.ebi.ac.uk/intact/resources/datasets#mutationDs and is being enhanced with every monthly release. MainCells process information and respond to their environments through dynamic networks of molecular interactions, where the nodes are bio-molecules (e.g. proteins, genes, metabolites, miRNAs) and edges represent functional relationships, including physical protein-protein interactions, transcriptional regulation, genetic interactions and gene/protein modifications. Comprehensive and systematic characterization of these networks is essential to gain a full understanding of complex biological . CC-BY 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/346833 doi: bioRxiv preprint first posted online Jun. 14, 2018; processes, of how cells behave in response to specific cues, and of how individual components of the network contribute to the whole phenotype, in physiological, pathological or synthetic conditions.Interactions between molecules may be inherently stable and essentially irreversible, resulting in the formation of stable macromolecular complexes, or weak transient interactions characterized by a dissociation constant (KD) in the micromolar range and a lifetime of seconds. A change to a single amino acid in a protein chain can be enough to disrupt a protein binding site and may then alter the composition of a sub-network of transient binders or the formation of a protein complex. A variant leading to the inactivation of a protein kinase molecule may result in widespread disruption of posttranslation phosphorylation events and the rewiring of related signalling networks.Many diseases are caused by specific mutations, and prognosis or response to treatment is frequently mutation-specific. The study of how mutations affect molecular interact...

Inferring the Effects of Protein Variants on Protein–Protein Interactions with Interpretable Transformer Representations

“…This is achieved by predicting one of the following as the outcome of the mutation effect type on PPI: strengthens the interaction (“increasing”), reduces the interaction (“decreasing”), suspends the interaction (“disrupting”), and does not affect the interaction (“no effect”). These 4 types of mutation effects were defined by the IMEx dataset [ 21 ] (Fig. 1 A, released version: 2020 May 20), which contained a set of experimentally validated variant impacts on molecular interactions ( N = 28,000).…”

“…Given that the IMEx dataset [ 21 ] was manually curated from thousands of experimental studies under various conditions, there might be different mutation effects reported in IMEx for the same PPI carrying the same missense mutation. Thus, we excluded this type of entry with multiple mutation effect annotations from the preprocessing step before constructing the MIPPI model (see Table S6).…”

“…Here, we used a protein mutation dataset from the IMEx Consortium [ 21 ]. This dataset features more than 50,000 annotations describing the effect of amino acid sequence changes on physical protein interactions with tested experimental evidences.…”

“…To address these challenges, we leveraged the experimentally validated data provided by the IMEx consortium [ 21 ] and developed a new method, mutation impact on protein–protein interaction (MIPPI), a predictor based on a deep learning network that only requires sequence information, to make predictions. The IMEx dataset contains a set of validated experimental results on how the interaction strength changes when a mutation affects a protein in a PPI pair.…”