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Signal peptide (SP) is a short peptide located in the N-terminus of proteins.It is essential to target and transfer transmembrane and secreted proteins tocorrect positions. Compared with traditional experimental methods to identifysignal peptides, computational methods are faster and more efficient, which aremore practical for analyzing thousands or even millions of protein sequences,especially for metagenomic data. Here we present Unbiased Organism-agnosticSignal Peptide Network (USPNet), a signal peptide classification and cleavagesite prediction deep learning method that takes advantage of protein languagemodels. We propose to apply label distribution-aware margin loss to handle dataimbalance problems and use evolutionary information of protein to enrichrepresentation and overcome species information dependence.

Unbiased organism-agnostic and highly sensitive signal peptide predictor with deep protein language model