Abstract
Antimicrobial peptides (AMPs), as an important part of the innate immune system of an organism, is a kind of promising drug candidate for novel antibiotics due to their unique antibacterial mechanism. However, the discovery of novel AMPs is facing a great challenge due to the complexity of systematic experiments and the poor predictability of antimicrobial activity. Here, a novel and comprehensive screening system, the Multiple Descriptor Multiple Strategy (MultiDS), was proposed based on 59 physicochemical and structural parameters, three strategies, and four algorithms for the mining of α-helical AMPs. This approach was applied to mine the encrypted peptide antibiotics from the global human genome, including introns and exons. A library of approximately 70 billion peptides with 15-25 amino acid residues was screened by the MultiDS system and generated a list of peptides with the Multiple Descriptor Index (MD index) scores, which was the core part of the MultiDS system. Sixty peptides with top MD scores were chemically synthesized and experimentally tested their antimicrobial activity against 10 kinds of Gram-positive bacteria, Gram-negative bacteria (including drug-resistant pathogens). A total of fifty-nine out of 60 (98.3%) peptides exhibited antimicrobial activity (MIC ≤ 64 μg/mL), and 24 out of 60 (40%) peptides showed high activity (MIC ≤ 2 μg/mL), validating the MultiDS system was an effective and predictive screening tool with high hit rate and superior antimicrobial activity. For further investigation, AMPs S1, S2, and S3 with the highest MD scores were used to treat the skin infection mouse models in vivo caused by Escherichia coli, drug-resistance Escherichia coli, and Staphylococcus aureus, respectively. All of S1, S2, and S3 showed comparable therapeutic effects on promoting infection healing to or even better than the positive drug levofloxacin. A mechanism study discovered that rapid bactericidal action was caused by cell membrane disruption and content leakage. The MultiDS system not only provides a high-throughput approach that allows for the mining of candidate AMPs from the global genome sequence but also opens up a new route to accelerate the discovery of peptide antibiotics.