Identification Of Antibacterial Peptides Research Articles

PGAT-ABPp: harnessing protein language models and graph attention networks for antibacterial peptide identification with remarkable accuracy.

The emergence of drug-resistant pathogens represents a formidable challenge to global health. Using computational methods to identify the antibacterial peptides (ABPs), an alternative antimicrobial agent, has demonstrated advantages in further drug design studies. Most of the current approaches, however, rely on handcrafted features and underutilize structural information, which may affect prediction performance. To present an ultra-accurate model for ABP identification, we propose a novel deep learning approach, PGAT-ABPp. PGAT-ABPp leverages structures predicted by AlphaFold2 and a pretrained protein language model, ProtT5-XL-U50 (ProtT5), to construct graphs. Then the graph attention network (GAT) is adopted to learn global discriminative features from the graphs. PGAT-ABPp outperforms the other fourteen state-of-the-art models in terms of accuracy, F1-score and Matthews Correlation Coefficient on the independent test dataset. The results show that ProtT5 has significant advantages in the identification of ABPs and the introduction of spatial information further improves the prediction performance of the model. The interpretability analysis of key residues in known active ABPs further underscores the superiority of PGAT-ABPp. The datasets and source codes for the PGAT-ABPp model are available at https://github.com/moonseter/PGAT-ABPp/.

An effective datasets describing antimicrobial peptide produced from Pediococcus acidilactici - purification and mode of action determined by molecular docking.

Most of the probiotics Bacterial cells, express native antibacterial genes, resulting in the production of, antimicrobial peptides, which have various applications in biotechnology and drug development. But the identification of antibacterial peptide, structural characterization of antimicrobial peptide and prediction on mode of action. Regardless of the significance of protein manufacturing, three individual factors are required for the production method: gene expression, stabilization and specific peptide purification. Our protocol describes a straightforward technique of detecting and characterizing particular extracellular peptides and enhancing the antimicrobial peptide expression we optimized using low molecular weight peptides. This protocol can be used to improve peptide detection and expression. The following are the benefits of this method, (DOI – https://doi.org/10.1016/j.ijbiomac.2019.10.196 [1]).The data briefly describe a simple method in detection identification, characterization of antimicrobial extracellular peptide, predicating the mode of action of peptide in targeting pathogens (In-silico method), brief method on profiling of antimicrobial peptide and its mode of action [1]. Further the protocol can be used to enhance the specific peptide expressions, detection of peptides. The advantages of this technique are presented below:•Characterization protocol of specific antimicrobial peptide•The folded antimicrobial peptide expression were less expressed or non-expressed peptides.•Besides being low cost, less time-consuming, easy to handle, universal and fast to execute, the suggested technique can be used for multiple proteins expressed in probiotics (Lactobacillus species) expression system.

Isolation of Antibacterial Hydrolysates from Hen Egg White Lysozyme and Identification of Antibacterial Peptides.

Native and heated hen egg white lysozyme (HEWL) hydrolysates were isolated by hydrolysis with pepsin at pH 2.0 in situ in a cation exchange membrane to isolate and identify antibacterial peptides of the HEWL hydrolysates. Native and heated HEWL was partially hydrolyzed with pepsin at pH 2.0. The fractions were eluted with 5 M ammonia to identify 23 antibacterial peptides using a tandem mass spectrometry. Then, these fractions were eluted with a solution of NaCl 1 M, and seven positively charged peptides f(23-28) YSLGNW, f(122-129) AWIRGCRL, f(123-129) WIRGCRL, f(124-129) IRGCRL, f(82-96) ALLSSDITASVNCAK, f(103-129) VAWRNRCKGTDVQAWIRGCRL, and f(97-123) KIVSDGNGMNAWVAWRNRCKGT were identified using tandem mass spectrometry. Native HEWL hydrolysate presented an enzymatic activity of 23.0%, heated HEWL hydrolysate at pH 6.0 presented a residual enzymatic activity of 22.0%, and heated HEWL hydrolysate at pH 7.0 presented an enzymatic activity of 21.33%. Native and heated HEWL hydrolysate presented antibacterial activity against Escherichia coli and Staphylococcus carnosus. Native HEWL hydrolysate presented a higher enzymatic activity than heated HEWL hydrolysates.

Identification of antibacterial peptides from Maillard reaction products of half-fin anchovy hydrolysates/glucose via LC-ESI-QTOF-MS analysis

We aimed to identify antibacterial peptides from Maillard reaction products of half-fin anchovy hydrolysates/glucose. Seven peptides from the active fraction of P3-2 derived from DEAE-Sepharose™ CL-6B chromatography and PL aquagel-OH 30 on HPLC system were identified by liquid chromatography-electrospray ionization/multi-stage mass spectrometry, and five peptides were selected for further synthesis on the basis of peptide sequences. The anionic peptide FFTQATDLLSR adopted an α-helical structure, as predicted by Network Protein Sequence @ analysis and helical wheel projection, and further determined using circular dichroism assay. The cationic peptide WLPVVR exhibited stronger inhibition effects against Escherichia coli than other peptides at similar concentration. Furthermore, differential sizes of hydrodynamic diameters of anionic 1,2-dimyristoyl-sn-glycero-3-phosphocholine liposome vesicles treated with peptides were observed, suggesting the clustering and decomposition of peptide on liposome vesicles. To the best of our knowledge, this is the first study that reports antibacterial peptides derived from the MRPs of half-fin anchovy hydrolysates.

Identification of antibacterial peptides from endophytic microbiome.

Endophytes, microorganisms living inside plant tissues, are promising producers of lead compounds for the pharmaceutical industry. However, the majority of endophytes are unculturable and therefore inaccessible for functional studies. To evaluate genetic resources of endophytes, we analyzed the biodiversity of fungal microbiome of black crowberry (Empetrum nigrum L.) by next-generation sequencing and found that it consists mainly of unknown taxa. We then separated the host and the endophyte genomes and constructed a fosmid expression library from the endophytic DNA. This library was screened for antibacterial activity against Staphylococcus aureus. A unique antibacterial clone was selected for further analysis, and a gene En-AP1 was identified with no similarity to known sequences. The expressed, folded protein En-AP1 was not active against S. aureus, while tryptic digests exhibited antimicrobial activity. Seven out of twelve synthesized peptides, predicted antibacterial in silico, exhibited in vitro activity towards both S. aureus and Escherichia coli. We propose that the En-AP1 protein is degraded in the library host E. coli and antimicrobial fragments are released from the cell, explaining the in vitro antibacterial activity of the clone. This is the first report of a novel gene expressed in vitro derived from an endophytic microbiome, demonstrating the potential of finding novel genes and compounds from unculturable endophytes.