Novel CropdocNet Model for Automated Potato Late Blight Disease Detection from Unmanned Aerial Vehicle-Based Hyperspectral Imagery

The accurate and automated diagnosis of potato late blight disease, one of the most destructive potato diseases, is critical for precision agricultural control and management. Recent advances in remote sensing and deep learning offer the opportunity to address this challenge. This study proposes a novel end-to-end deep learning model (CropdocNet) for accurate and automated late blight disease diagnosis from UAV-based hyperspectral imagery. The proposed method considers the potential disease-specific reflectance radiation variance caused by the canopy’s structural diversity and introduces multiple capsule layers to model the part-to-whole relationship between spectral–spatial features and the target classes to represent the rotation invariance of the target classes in the feature space. We evaluate the proposed method with real UAV-based HSI data under controlled and natural field conditions. The effectiveness of the hierarchical features is quantitatively assessed and compared with the existing representative machine learning/deep learning methods on both testing and independent datasets. The experimental results show that the proposed model significantly improves accuracy when considering the hierarchical structure of spectral–spatial features, with average accuracies of 98.09% for the testing dataset and 95.75% for the independent dataset, respectively.

A GPCR-based yeast biosensor for biomedical, biotechnological, and point-of-use cannabinoid determination

The decriminalization of cannabis and the growing interest in cannabinoids as therapeutics require efficient methods to discover novel compounds and monitor cannabinoid levels in human samples and products. However, current methods are limited by the structural diversity of the active compounds. Here, we construct a G-protein coupled receptor-based yeast whole-cell biosensor, optimize it to achieve high sensitivity and dynamic range, and prove its effectiveness in three real-life applications. First, we screen a library of compounds to discover two novel agonists and four antagonists and demonstrate that our biosensor can democratize GPCR drug discovery by enabling low-cost high-throughput analysis using open-source automation. Subsequently, we bioprospect 54 plants to discover a novel phytocannabinoid, dugesialactone. Finally, we develop a robust portable device, analyze body-fluid samples, and confidently detect illicit synthetic drugs like “Spice”/“K2”. Taking advantage of the extensive sensing repertoire of GPCRs, this technology can be extended to detect numerous other compounds.

Bioactive Extracts: Strategies to generate Diversified Natural Product Like Libraries

Natural products have stimulated chemists owing to their abundant structural diversity and complexity. Indeed, natural products have performed an essential role, particularly in the cure of cancerous and infectious diseases, thereby posing medicinal researchers with a scope of unexplored chemotypes for the innovation of new drugs. Fusion of chemical derivatization and combinatorial synthesis forms the basis of the concept of chemo diversification of plants. Diverse libraries of natural product analogs are constructed through existing biological and chemical approaches using unique schemes to expand natural product frameworks. This review aims to present several approaches employed to offer innovative opportunities to synthesize NP-inspired compound libraries. Reactive molecular fragments present in most natural products are chemically converted to chemically engineered extracts (CEEs) or semisynthetic compounds constituting distinct libraries. Bio-guided isolation for natural products required vital tools like reverse phase chromatography and bioautographic assays. Different established strategies from DTS, BIOS, CtD, FOS, FBDD to Late-stage diversification facilitate the expansion of molecules with physicochemical properties. In particular, fragment-like natural products with novel skeletons may be used as preliminary points for chemical biology and medicinal chemistry programs with great capacity. In this review, we sum up how NPs have proven fruitful for the novel methodologies responsible for the diversification of complex natural products; thereby, it is worthy of going over the upcoming integration of natural products with combinatorial chemistry.

Aminoacyl chain translocation catalysed by a type II thioesterase domain in an unusual non-ribosomal peptide synthetase

Non-Ribosomal Peptide Synthetases (NRPSs) assemble a diverse range of natural products with important applications in both medicine and agriculture. They consist of several multienzyme subunits that must interact with each other in a highly controlled manner to facilitate efficient chain transfer, thus ensuring biosynthetic fidelity. Several mechanisms for chain transfer are known for NRPSs, promoting structural diversity. Herein, we report the first biochemically characterized example of a type II thioesterase (TEII) domain capable of catalysing aminoacyl chain transfer between thiolation (T) domains on two separate NRPS subunits responsible for installation of a dehydrobutyrine moiety. Biochemical dissection of this process reveals the central role of the TEII-catalysed chain translocation event and expands the enzymatic scope of TEII domains beyond canonical (amino)acyl chain hydrolysis. The apparent co-evolution of the TEII domain with the NRPS subunits highlights a unique feature of this enzymatic cassette, which will undoubtedly find utility in biosynthetic engineering efforts.

Influence of the Structural Features of Carrageenans from Red Algae of the Far Eastern Seas on Their Antiviral Properties

The structural diversity and unique physicochemical properties of sulphated polysaccharides of red algae carrageenans (CRGs), to a great extent, determine the wide range of their antiviral properties. This work aimed to compare the antiviral activities of different structural types of CRGs: against herpes simplex virus type 1 (HSV-1) and enterovirus (ECHO-1). We found that CRGs significantly increased the resistance of Vero cells to virus infection (preventive effect), directly affected virus particles (virucidal effect), inhibited the attachment and penetration of virus to cells, and were more effective against HSV-1. CRG1 showed the highest virucidal effect on HSV-1 particles with a selective index (SI) of 100. CRG2 exhibited the highest antiviral activity by inhibiting HSV-1 and ECHO-1 plaque formation, with a SI of 110 and 59, respectively, when it was added before virus infection. CRG2 also significantly reduced the attachment of HSV-1 and ECHO-1 to cells compared to other CRGs. It was shown by molecular docking that tetrasaccharides—CRGs are able to bind with the HSV-1 surface glycoprotein, gD, to prevent virus–cell interactions. The revealed differences in the effect of CRGs on different stages of the lifecycle of the viruses are apparently related to the structural features of the investigated compounds.

Bio-inspired Chemical Space Exploration of Terpenoids

Many computational methods are used to expand the open-ended border of chemical spaces. Natural products and their derivatives are an important source for drug discovery, and some algorithms are devoted to rapidly generating pseudo-natural products, while their accessibility and chemical interpretation were often ignored or underestimated, thus hampering experimental synthesis in practice. Herein, a bio-inspired strategy (named TeroGen) is proposed, in which the cyclization and decoration stage of terpenoid biosynthesis were mimicked by meta-dynamics simulations and deep learning models respectively, to explore their chemical space. In the protocol of TeroGen, the synthetic accessibility is validated by reaction energetics (reaction barrier and reaction heat) based on the GFN2-xTB methods. Chemical interpretation is an intrinsic feature as the reaction pathway is bioinspired and triggered by the RMSD-PP method in conjunction with an encoder-decoder architecture. This is quite distinct from conventional library/fragment-based or rule-based strategies, by using TeroGen, new reaction routes are feasibly explored to increase the structural diversity. For example, only a rather limited number of sesterterpenoids in our training set is included in this work, but our TeroGen would predict more than 30000 sesterterpenoids and map out the reaction network with super efficiency, ten times as many as the known sesterterpenoids (less than 2500). In sum, TeroGen not only greatly expands the chemical space of terpenoids but also provides various plausible biosynthetic pathways, which are crucial clues for heterologous biosynthesis, bio-mimic and chemical synthesis of complicated terpenoids.