With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-consuming and costly process. Deep learning has been applied to the de novo design of AMPs and address AMP classification with high efficiency. In this study, several natural language processing models were combined to design and identify AMPs, i.e. sequence generative adversarial nets, bidirectional encoder representations from transformers and multilayer perceptron. Then, six candidate AMPs were screened by AlphaFold2 structure prediction and molecular dynamic simulations. These peptides show low homology with known AMPs and belong to a novel class of AMPs. After initial bioactivity testing, one of the peptides, A-222, showed inhibition against gram-positive and gram-negative bacteria. The structural analysis of this novel peptide A-222 obtained by nuclear magnetic resonance confirmed the presence of an alpha-helix, which was consistent with the results predicted by AlphaFold2. We then performed a structure–activity relationship study to design a new series of peptide analogs and found that the activities of these analogs could be increased by 4–8-fold against Stenotrophomonas maltophilia WH 006 and Pseudomonas aeruginosa PAO1. Overall, deep learning shows great potential in accelerating the discovery of novel AMPs and holds promise as an important tool for developing novel AMPs.
Background and aims In the Gurbantunggut Desert, Haloxylon ammodendron and Syntrichia caninervis are often found at the base of the dunes. In these areas, bare patches usually form under the H. ammodendron canopy, but not under other shrub canopies. Methods We compared the soil chemical properties under H. ammodendron canopy inside the bare patches (UC) and of soil under moss crust outside of H. ammodendron canopy bare patches (UM), and used UHPLC-MS/MS to analyze soil metabolites and metagenomic sequencing to characterize the structure of soil microflora. Results A total of 951 metabolites were identified in the soil samples, and 518 differential metabolites were observed. The content of amides, such as oleamide, in UC soil was significantly higher than that in UM soil, suggesting that the amides may be the main allelochemicals inhibiting S. caninervis. The differences in soil chemical properties and metabolites impacted soil microorganisms, but the structure and function of microbial communities did not differ significantly. Conclusions The amides secreted by H. ammodendron roots create a concentration gradient under its canopy, with high concentrations inhibiting S. caninervis, causing changes in soil chemical factors inside and outside the bare patch. These changes affect the abundance of microbial species and relevant metabolic pathways. The differences in microbial communities and functions are caused by a combination of soil chemical properties and metabolites, rather than a direct effect of high levels of soil metabolites such as amides.
Conopeptides are peptides in the venom of marine cone snails that are used for capturing prey or as a defense against predators. A new cysteine-poor conopeptide, Czon1107, has exhibited non-competitive inhibition with an undefined allosteric mechanism in the human (h) α3β4 nicotinic acetylcholine receptors (nAChRs). In this study, the binding mode of Czon1107 to hα3β4 nAChR was investigated using molecular dynamics simulations coupled with mutagenesis studies of the peptide and electrophysiology studies on heterologous hα3β4 nAChRs. Overall, this study clarifies the structure–activity relationship of Czon1107 and hα3β4 nAChR and provides an important experimental and theoretical basis for the development of new peptide drugs.
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