  The evolution of drug-resistant microbial species is one of the major
challenges to global health. The development of new antimicrobial treatments
such as antimicrobial peptides needs to be accelerated to combat this threat.
However, the discovery of novel antimicrobial peptides is hampered by
low-throughput biochemical assays. Computational techniques can be used for
rapid screening of promising antimicrobial peptide candidates prior to testing
in the wet lab. The vast majority of existing antimicrobial peptide predictors
are non-targeted in nature, i.e., they can predict whether a given peptide
sequence is antimicrobial, but they are unable to predict whether the sequence
can target a particular microbial species. In this work, we have developed a
targeted antimicrobial peptide activity predictor that can predict whether a
peptide is effective against a given microbial species or not. This has been
made possible through zero-shot and few-shot machine learning. The proposed
predictor called AMP0 takes in the peptide amino acid sequence and any
N/C-termini modifications together with the genomic sequence of a target
microbial species to generate targeted predictions. It is important to note
that the proposed method can generate predictions for species that are not part
of its training set. The accuracy of predictions for novel test species can be
further improved by providing a few example peptides for that species. Our
computational cross-validation results show that the pro-posed scheme is
particularly effective for targeted antimicrobial prediction in comparison to
existing approaches and can be used for screening potential antimicrobial
peptides in a targeted manner especially for cases in which the number of
training examples is small. The webserver of the method is available at
http://ampzero.pythonanywhere.com.
