Identifying the targets of an antimicrobial peptide is a fundamental step in studying the innate immuneresponse and combating antibiotic resistance, and more broadly, precision medicine and public health. Therehave been extensive studies on the statistical and computational approaches to identify (i) whether a peptide is anantimicrobial peptide (AMP) or a non-AMP and (ii) which targets are these sequences effective to (Gram-positive,Gram-negative, etc.). Despite the existing deep learning methods on this problem, most of them are unable tohandle the small AMP classes (anti-insect, anti-parasite, etc.). And more importantly, some AMPs can havemultiple targets, which the previous methods fail to consider. In this study, we build a diverse and comprehensivemulti-label protein sequence database by collecting and cleaning amino acids from various AMP databases.To generate efficient representations and features for the small classes dataset, we take advantage of a proteinlanguage model trained on 250 million protein sequences. Based on that, we develop an end-to-end hierarchicalmulti-label deep forest framework, HMD-AMP, to annotate AMP comprehensively. After identifying an AMP, itfurther predicts what targets the AMP can effectively kill from eleven available classes. Extensive experimentssuggest that our framework outperforms state-of-the-art models in both the binary classification task and themulti-label classification task, especially on the minor classes. Compared with the previous deep learning methods,our method improves the performance on macro-AUROC by 11%. The model is robust against reduced featuresand small perturbations and produces promising results. We believe HMD-AMP contribute to both the future wet-lab investigations of the innate structural properties of different antimicrobial peptides and build promising empirical underpinnings for precise medicine with antibiotics.
HMD-AMP: Protein Language-Powered Hierarchical Multi-label Deep Forest for Annotating Antimicrobial Peptides
