Year: 2023
Type: article
Abstract: Distinguishing between damaging and neutral missense variants is an ongoing challenge in human genetics, with profound implications for clinical diagnosis, genetic studies and protein engineering. Recently, deep-learning models have achieved state-of-the-art performance in classifying variants as pathogenic or benign. However, these models are currently unable to provide predictions over all missense variants, either because of dependency on close protein homologs or due to software limitations. Here we leveraged ESM1b, a 650M-parameter protein language model, to predict the functional impact of human coding variation at scale. To overcome existing technical limitations, we developed a modified ESM1b workflow and functionalized, for the first time, all proteins in the human genome, resulting in predictions for all ∼450M possible missense variant effects. ESM1b was able to distinguish between pathogenic and benign variants across ∼150K variants annotated in ClinVar and HGMD, outperforming existing state-of-the-art methods. ESM1b also exceeded the state of the art at predicting the experimental results of deep mutational scans. We further annotated ∼2M variants across ∼9K alternatively-spliced genes as damaging in certain protein isoforms while neutral in others, demonstrating the importance of considering all isoforms when functionalizing variant effects. The complete catalog of variant effect predictions is available at: https://huggingface.co/spaces/ntranoslab/esm_variants .
Source: Nature Genetics
Language: English
Topic: Genomics and Rare Diseases
Subfield: Genetics
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $11,690
Funders: Cancer Research Institute, Parker Institute for Cancer Immunotherapy, Chan Zuckerberg Initiative, Cardiff University, National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Arthritis and Musculoskeletal and Skin Diseases
Awards: Fine mapping rheumatic disease variants using functional genomic sequencing, Fine mapping rheumatic disease variants using functional genomic sequencing, BMI Bioinformatics Training Grant, G518370680, Genetic regulation and immunological function of ERAP2 haplotypes, Mapping gene-by-environment interactions using multiplexed single cell RNA-sequencing
OpenAlex ID: W4385737488