E-SNPs&GO: Embedding of protein sequence and function improves the annotation of pathogenic variants.

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 protein 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 residue variation, can predict whether the variation is related to diseases or not. The proposed method, for the first time, 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 65,888 human protein single residue variants derived from public resources. When tested on a blind set comprising 6,541 variants, our method outperforms recent approaches released in literature for the same task, reaching a MCC score of 0.71. We propose E-SNPs&GO as a suitable, efficient and accurate large-scale annotator of protein variant datasets. Contactpierluigi.martelli@unibo.it