ABSTRACT
Antimicrobial peptides (AMPs) have gained the attention of the research community for being an alternative to conventional antimicrobials to fight antibiotic resistance and for displaying other pharmacologically relevant activities, such as cell penetration, autophagy induction, immunomodulation, among others. The identification of AMPs had been accomplished by combining computational and experimental approaches and have been mostly restricted to self-contained peptides despite accumulated evidence indicating AMPs may be found embedded within proteins, the functions of which are not necessarily associated with antimicrobials. To address this limitation, we propose a machine-learning (ML)-based pipeline to identify AMPs that are embedded in proteomes. Our method performs an in-silico digestion of every protein in the proteome to generate unique k-mers of different lengths, computes a set of molecular descriptors for each k-mer, and performs an antimicrobial activity prediction. To show the efficiency of the method we used the shrimp proteome, and the pipeline analyzed all k-mers between 10 and 60 amino acids in length to predict all AMPs in less than 20 min. As an application example we predicted AMPs in different rodents (common cuy, common rat, and naked mole rat) with different reported longevities and found a relation between species longevity and the number of predicted AMPs. The analysis shows as the longevity of the species is higher, the number of predicted AMPs is also higher. The pipeline is available as a web service.
ABSTRACT
Although most antibiotics act on cells that are actively dividing and non-dividing cells such as in microbe sporulation or cancer stem cells represent a new paradigm for the control of disease. In addition to their relevance to health, such antibiotics may promote our understanding of the relationship between the cell cycle and cell death. No antibiotic specifically acting on microbial cells arrested in their cell cycle has been identified until the present time. In this study we used an antimicrobial peptide derived from α-pheromone, IP-1, targeted against MATa Saccharomyces cerevisiae cells in order to assess its dependence on cell cycle arrest to kill cells. Analysis by flow cytometry and fluorescence microscopy of various null mutations of genes involved in biological processes activated by the pheromone pathway (the mitogen-activated protein kinase pathway, cell cycle arrest, cell proliferation, autophagy, calcium influx) showed that IP-1 requires arrest in G0/G1 in order to kill yeast cells. Isolating cells in different cell cycle phases by elutriation provided further evidence that entry into cell cycle arrest, and not into G1 phase, is necessary if our peptide is to kill yeast cells. We also describe a variant of IP-1 that does not activate the pheromone pathway and consequently does not kill yeast cells that express the pheromone's receptor; the use of this variant peptide in combination with different cell cycle inhibitors that induce cell cycle arrest independently of the pheromone pathway confirmed that it is cell cycle arrest that is required for the cell death induced by this peptide in yeast. We show that the cell death induced by IP-1 differs from that induced by α-pheromone and depends on FIG1 in a way independent of the cell cycle arrest induced by the pheromone. Thus, IP-1 is the first molecule described that specifically kills microbial cells during cell cycle arrest, a subject of interest beyond the process of mating in yeast cells. The experimental system described in this study should be useful in the study of the mechanisms at play in the communication between cell cycle arrest and cell death on other organisms, hence promoting the development of new antibiotics.
