Machine learning (ML) has pervaded most areas of protein engineering, including stability and stereoselectivity. Using limonene epoxide hydrolase as the model enzyme and innov'SAR as the ML platform, comprising a digital signal process, we achieved high protein robustness that can resist unfolding with concomitant detrimental aggregation. Fourier transform (FT) allows us to take into account the order of the protein sequence and the nonlinear interactions between positions, and thus to grasp epistatic phenomena. The innov'SAR approach is interpolative, extrapolative and makes outside‐the‐box, predictions not found in other state‐of‐the‐art ML or deep learning approaches. Equally significant is the finding that our approach to ML in the present context, flanked by advanced molecular dynamics simulations, uncovers the connection between epistatic mutational interactions and protein robustness.
The microbial communities of plant ecosystems are in relation to plant growing environment, but the alteration in biodiversity of rhizosphere and phyllosphere microbial communities in closed and controlled environments is unknown. The purpose of this study is to analyze the change regularity of microbial communities with wheat plants dependent-cultivated in a closed artificial ecosystem. The microbial community structures in closed-environment treatment plants were investigated by a culture-dependent approach, polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), and Illumina Miseq high-throughput sequencing. The results indicated that the number of microbes decreased along with time, and the magnitude of bacteria, fungi, and actinomycetes were 10(7)-10(8), 10(5), and 10(3)-10(4) CFU/g (dry weight), respectively. The analysis of PCR-DGGE and Illumina Miseq revealed that the wheat leaf surface and near-root substrate had different microbial communities at different periods of wheat ecosystem development and showed that the relative highest diversity of microbial communities appeared at late and middle periods of the plant ecosystem, respectively. The results also indicated that the wheat leaf and substrate had different microbial community compositions, and the wheat substrate had higher richness of microbial community than the leaf. Flavobacterium, Pseudomonas, Paenibacillus, Enterobacter, Penicillium, Rhodotorula, Acremonium, and Alternaria were dominant in the wheat leaf samples, and Pedobacter, Flavobacterium, Halomonas, Marinobacter, Salinimicrobium, Lysobacter, Pseudomonas, Halobacillus, Xanthomonas, Acremonium, Monographella, and Penicillium were dominant populations in the wheat near-root substrate samples.
Xenocoumacin 1 (Xcn1), which is produced by Xenorhabdus nematophila CB6, exhibits strong inhibition activity against plant pathogens, especially fungi and oomycetes. Therefore, it has attracted interest in developing it into a novel biofungicide applicable for plant protection. However, its low yield with concomitant high cost during the fermentation process limits its widespread application. In this study, we replaced the native promoter of xcnA with the arabinose-inducible araBAD promoter (PBAD), a well-known and widely used promoter for expressing heterologous genes, to evaluate its effects on Xcn1 yield and antimicrobial activity. Compared with wildtype strain, the fermentation yield of Xcn1 was improved from 68.5 mg/L to 249.7 mg/L (3.6-fold) and 234.9 mg/L (3.4-fold) at 0.5% and 1.0% L-arabinose concentration, respectively. We further explored the transcription level of the biosynthesis related genes of Xcn1 and found that their upregulation resulted in the yield improvement of Xcn1. Moreover, the antimicrobial activity of Xcn1 against Bacillus subtilis and Phytophthora capsici was determined by agar diffusion plate and growth inhibition assay, as expected, it was also found to be enhanced. The promoter-replacement strategy utilized here improves the yield of Xcn1 efficiently, which provides a basis for the industrial production of Xcn1.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.