In silico prediction and mass spectrometric characterization of botanical antimicrobial peptides

Antimicrobial peptides (AMPs) are promising compounds for the treatment of antibiotic-resistant bacteria and are found across all organisms, including plants. Unlike most antibiotics, AMPs tend to act on more generalized and multiple targets, making development of resistance more difficult. Conventional approaches toward AMP identification include bioactivity-guided fractionation and genome mining. Complementary methods leveraging bioactivity-guided fractionation, cysteine motif-guided in silico AMP prediction, and mass spectrometric approaches can be combined to expand botanical AMP discovery. Herein, we present an integrated workflow which serves to streamline implementation toward a robust botanical AMP discovery pipeline.

Comparison of Current Methods for Signal Peptide Prediction in Phytoplasmas

Although phytoplasma studies are still hampered by the lack of axenic cultivation methods, the availability of genome sequences allowed dramatic advances in the characterization of the virulence mechanisms deployed by phytoplasmas, and highlighted the detection of signal peptides as a crucial step to identify effectors secreted by phytoplasmas. However, various signal peptide prediction methods have been used to mine phytoplasma genomes, and no general evaluation of these methods is available so far for phytoplasma sequences. In this work, we compared the prediction performance of SignalP versions 3.0, 4.0, 4.1, 5.0 and Phobius on several sequence datasets originating from all deposited phytoplasma sequences. SignalP 4.1 with specific parameters showed the most exhaustive and consistent prediction ability. However, the configuration of SignalP 4.1 for increased sensitivity induced a much higher rate of false positives on transmembrane domains located at N-terminus. Moreover, sensitive signal peptide predictions could similarly be achieved by the transmembrane domain prediction ability of TMHMM and Phobius, due to the relatedness between signal peptides and transmembrane regions. Beyond the results presented herein, the datasets assembled in this study form a valuable benchmark to compare and evaluate signal peptide predictors in a field where experimental evidence of secretion is scarce. Additionally, this study illustrates the utility of comparative genomics to strengthen confidence in bioinformatic predictions.

Signal peptide optimization tool for the secretion of recombinant protein from Saccharomyces cerevisiae

The secretion efficiency of foreign proteins in recombinant microbes is strongly dependent on the combination of the signal peptides (SPs) used and the target proteins; therefore, identifying the optimal SP sequence for each target protein is a crucial step in maximizing the efficiency of protein secretion in both prokaryotes and eukaryotes. In this study, we developed a novel method, named the SP optimization tool (SPOT), for the generation and rapid screening of a library of SP-target gene fusion constructs to identify the optimal SP for maximizing target protein secretion. In contrast to libraries generated in previous studies, SPOT fusion constructs are generated without adding the intervening sequences associated with restriction enzyme digestion sites. Therefore, no extra amino acids are inserted at the N-terminus of the target protein that might affect its function or conformational stability. As a model system, β-galactosidase (LacA) from Aspergillus oryzae was used as a target protein for secretion from Saccharomyces cerevisiae. In total, 60 SPs were selected from S. cerevisiae secretory proteins and utilized to generate the SP library. While many of the SP-LacA fusions were not secreted, several of the SPs, AGA2, CRH1, PLB1, and MF(alpha)1, were found to enhance LacA secretion compared to the WT sequence. Our results indicate that SPOT is a valuable method for optimizing the bioproduction of any target protein, and could be adapted to many host strains.