ArticleBioinformatics (Oxford, England)2022
E-SNPs&GO: embedding of protein sequence and function improves the annotation of human pathogenic variants.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed, 40 citations in OpenAlex.
- Telmisartan Repurposing Targets Novel Biomarkers for Precision Colorectal Cancer Therapy.Pharmaceutics · 2026Article
- Congenital Myasthenic Syndrome in Doberman Pinscher Dogs Is Associated with a Homozygous Missense Variant inBiomolecules · 2026Article
- Bridging local-global transmembrane protein contexts with contrastive pretraining for alignment-free pathogenicity prediction.Briefings in bioinformatics · 2026Article
- In Silico Identification and Structural Characterization of High-Risk Missense SNVs in the HumanGenes · 2026Article
- Deep Learning-Based Protein Half-Life Prediction for Identifying Rate-Limiting Enzymes in Metabolic Pathways to Alleviate Bottleneck Reactions.Journal of microbiology and biotechnology · 2026Article
- Computational analysis of the structural and functional impact of the deleterious nsSNPs in the humanBiochemistry and biophysics reports · 2026Article
- A survey of downstream applications of evolutionary scale modeling protein language models.Quantitative biology (Beijing, China) · 2026Review
- Integrated Computational Analysis Reveals Structurally Destabilizing Missense Variants in theGenes · 2026Article
- Computational analysis of MYC gene variants: structural and functional impact of non-synonymous SNPs.Journal of applied genetics · 2026Article
- Computational analysis and validation of UGT1A1/4 missense variants impacting tecovirimat metabolism in monkeypox patients.Frontiers in systems biology · 2026Article
- Article
- Integrative structural profiling and ligand optimisation across the transthyretin mutational landscape.NPJ systems biology and applications · 2025Article
- Computational Identification of RNF114 nsSNPs with Potential Roles in Psoriasis and Immune Dysregulation.Medical sciences (Basel, Switzerland) · 2025Article
- Advancing the Accuracy of Anti-MRSA Peptide Prediction Through Integrating Multi-Source Protein Language Models.Interdisciplinary sciences, computational life sciences · 2025Article
- Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.NAR genomics and bioinformatics · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Evaluation of enzyme activity predictions for variants of unknown significance in Arylsulfatase A.Human genetics · 2025Article
- An augmented transformer model trained on protein family specific variant data leads to improved prediction of variants of uncertain significance.Human genetics · 2025Article
- CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs).Human genetics · 2025Article
- Computational Identification and Functional Analysis of Potentially Pathogenic nsSNPs in the NLRP3 Gene Linked to Alzheimer's Disease.Cell biochemistry and biophysics · 2025Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
motivationThe advent of massive DNA sequencing technologies is producing a huge number of human single-nucleotide polymorphisms occurring in protein-coding regions and possibly changing their sequences. Discriminating harmful protein variations from neutral ones is one of the crucial challenges in precision medicine. Computational tools based on artificial intelligence provide models for protein sequence encoding, bypassing database searches for evolutionary information. We leverage the new encoding schemes for an efficient annotation of protein variants.
resultsE-SNPs&GO is a novel method that, given an input protein sequence and a single amino acid variation, can predict whether the variation is related to diseases or not. The proposed method adopts an input encoding completely based on protein language models and embedding techniques, specifically devised to encode protein sequences and GO functional annotations. We trained our model on a newly generated dataset of 101 146 human protein single amino acid variants in 13 661 proteins, derived from public resources. When tested on a blind set comprising 10 266 variants, our method well compares to recent approaches released in literature for the same task, reaching a Matthews Correlation Coefficient score of 0.72. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. AVAILABILITY AND IMPLEMENTATION: The method is available as a webserver at https://esnpsandgo.biocomp.unibo.it. Datasets and predictions are available at https://esnpsandgo.biocomp.unibo.it/datasets. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.