Multi-level bioinformatics resources support drug target discovery of protein-protein interactions.

Drug discovery often begins with a new target. Protein-protein interactions (PPIs) are crucial to multitudinous cellular processes and offer a promising avenue for drug-target discovery. PPIs are characterized by multi-level complexity: at the protein level, interaction networks can be used to identify potential targets, whereas at the residue level, the details of the interactions of individual PPIs can be used to examine a target's druggability. Much great progress has been made in target discovery through multi-level PPI-related computational approaches, but these resources have not been fully discussed. Here, we systematically survey bioinformatics tools for identifying and assessing potential drug targets, examining their characteristics, limitations and applications. This work will aid the integration of the broader protein-to-network context with the analysis of detailed binding mechanisms to support the discovery of drug targets.

Targeting Fks1 proteins for novel antifungal drug discovery.

Fungal infections are a major threat to human health. The limited availability of antifungal drugs, the emergence of drug resistance, and a growing susceptible population highlight the critical need for novel antifungal agents. The enzymes involved in fungal cell wall synthesis offer potential targets for antifungal drug development. Recent studies have enhanced our focus on the enzyme Fks1, which synthesizes ß-1,3-glucan, a critical component of the cell wall. These studies provide a deeper understanding of Fks1's function in cell wall biosynthesis, pathogenicity, structural biology, evolutionary conservation across fungi, and interaction with current antifungal drugs. Here, we discuss the role of Fks1 in the survival and adaptation of fungi, guided by insights from evolutionary and structural analyses. Furthermore, we delve into the dynamics of Fks1 modulation with novel antifungal strategies and assess its potential as an antifungal drug target.

Transfer learning empowers accurate pharmacokinetics prediction of small samples.

Accurate assessment of pharmacokinetic (PK) properties is crucial for selecting optimal candidates and avoiding downstream failures. Transfer learning is an innovative machine learning approach enabling high-throughput prediction with limited data. Recently, transfer learning methods showed promise in predicting ADME/PK parameters. Given the prolific growth of research on transfer learning for PK prediction, a comprehensive review of its advantages and challenges is imperative. This study explores the fundamentals, classifications, toolkits and applications of various transfer learning techniques for PK prediction, demonstrating their utility through three practical case studies. This work will serve as a reference for drug design researchers.

Design, Synthesis, and Herbicidal Activity of Substituted 3-(Pyridin-2-yl)Phenylamino Derivatives.

To discover protoporphyrinogen oxidase (PPO) inhibitors with robust herbicidal activity and crop safety, three types of substituted 3-(pyridin-2-yl)phenylamino derivatives bearing amide, urea, or thiourea as side chain were designed via structure splicing strategy. Postemergence herbicidal activity assessment of 33 newly prepared compounds revealed that many of our compounds such as 6a, 7b, and 8d exhibited superior herbicidal activities against broadleaf and monocotyledon weeds to commercial acifluorfen. In particular, compound 8d exhibited excellent herbicidal activities and high crop safety at a dosage range of 37.5-150 g ai/ha. PPO inhibitory studies supported our compounds as typical PPO inhibitors. Molecular docking studies revealed that compound 8d provided effective interactions with Nicotiana tabacum PPO (NtPPO) via diverse interaction models, such as π-π stacking and hydrogen bonds. Molecular dynamics (MD) simulation studies and degradation studies were also conducted to gain insight into the inhibitory mechanism. Our study indicates that compound 8d may be a candidate molecule for the development of novel herbicides.

Wearable sensor supports in-situ and continuous monitoring of plant health in precision agriculture era.

Plant health is intricately linked to crop quality, food security and agricultural productivity. Obtaining accurate plant health information is of paramount importance in the realm of precision agriculture. Wearable sensors offer an exceptional avenue for investigating plant health status and fundamental plant science, as they enable real-time and continuous in-situ monitoring of physiological biomarkers. However, a comprehensive overview that integrates and critically assesses wearable plant sensors across various facets, including their fundamental elements, classification, design, sensing mechanism, fabrication, characterization and application, remains elusive. In this study, we provide a meticulous description and systematic synthesis of recent research progress in wearable sensor properties, technology and their application in monitoring plant health information. This work endeavours to serve as a guiding resource for the utilization of wearable plant sensors, empowering the advancement of plant health within the precision agriculture paradigm.

Human-virus protein-protein interactions maps assist in revealing the pathogenesis of viral infection.

Many significant viral infections have been recorded in human history, which have caused enormous negative impacts worldwide. Human-virus protein-protein interactions (PPIs) mediate viral infection and immune processes in the host. The identification, quantification, localization, and construction of human-virus PPIs maps are critical prerequisites for understanding the biophysical basis of the viral invasion process and characterising the framework for all protein functions. With the technological revolution and the introduction of artificial intelligence, the human-virus PPIs maps have been expanded rapidly in the past decade and shed light on solving complicated biomedical problems. However, there is still a lack of prospective insight into the field. In this work, we comprehensively review and compare the effectiveness, potential, and limitations of diverse approaches for constructing large-scale PPIs maps in human-virus, including experimental methods based on biophysics and biochemistry, databases of human-virus PPIs, computational methods based on artificial intelligence, and tools for visualising PPIs maps. The work aims to provide a toolbox for researchers, hoping to better assist in deciphering the relationship between humans and viruses.

PlantPAD: a platform for large-scale image phenomics analysis of disease in plant science.

Plant disease, a huge burden, can cause yield loss of up to 100% and thus reduce food security. Actually, smart diagnosing diseases with plant phenomics is crucial for recovering the most yield loss, which usually requires sufficient image information. Hence, phenomics is being pursued as an independent discipline to enable the development of high-throughput phenotyping for plant disease. However, we often face challenges in sharing large-scale image data due to incompatibilities in formats and descriptions provided by different communities, limiting multidisciplinary research exploration. To this end, we build a Plant Phenomics Analysis of Disease (PlantPAD) platform with large-scale information on disease. Our platform contains 421 314 images, 63 crops and 310 diseases. Compared to other databases, PlantPAD has extensive, well-annotated image data and in-depth disease information, and offers pre-trained deep-learning models for accurate plant disease diagnosis. PlantPAD supports various valuable applications across multiple disciplines, including intelligent disease diagnosis, disease education and efficient disease detection and control. Through three applications of PlantPAD, we show the easy-to-use and convenient functions. PlantPAD is mainly oriented towards biologists, computer scientists, plant pathologists, farm managers and pesticide scientists, which may easily explore multidisciplinary research to fight against plant diseases. PlantPAD is freely available at http://plantpad.samlab.cn.

Facile access to benzofuran derivatives through radical reactions with heteroatom-centered super-electron-donors.

The development of suitable electron donors is critical to single-electron-transfer (SET) processes. The use of heteroatom-centered anions as super-electron-donors (SEDs) for direct SET reactions has rarely been studied. Here we show that heteroatom anions can be applied as SEDs to initiate radical reactions for facile synthesis of 3-substituted benzofurans. Phosphines, thiols and anilines bearing different substitution patterns work well in this inter-molecular radical coupling reaction and the 3-functionalized benzofuran products bearing heteroatomic functionalities are given in moderate to excellent yields. The reaction mechanism is elucidated via control experiments and computational methods. The afforded products show promising applications in both organic synthesis and pesticide development.

Copper-Catalyzed Oxygenative Skeletal Rearrangement of Tetrahydro-ß-carbolines Using H2 O and O2 as Oxygen Sources.

Herein, we report an unprecedented skeletal rearrangement reaction of tetrahydro-ß-carbolines enabled by copper-catalyzed single-electron oxidative oxygenation, in which H2 O and O2 act as oxygen sources to generate a unique 2-hydroxyl-3-peroxide indoline intermediate. The synthetic reactivity of 2-hydroxyl-3-peroxide indoline species was demonstrated by a unique multi-step bond cleavage and formation cascade. Using a readily available copper catalyst under open-air conditions, highly important yet synthetically difficult spiro[pyrrolidone-(3,1-benzoxazine)] products were obtained in a single operation. The synthetic utility of this methodology is demonstrated by the efficient synthesis of the natural products donaxanine and chimonamidine, as well as the 3-hydroxyl-pyrroloindoline scaffold, in just one or two steps.

GIFTdb: a useful gene database for plant fruit traits improving.

Fruit traits are critical determinants of plant fitness, resource diversity, productive and quality. Gene regulatory networks in plants play an essential role in determining fruit traits, such as fruit size, yield, firmness, aroma and other important features. Many research studies have focused on elucidating the associated signaling pathways and gene interaction mechanism to better utilize gene resources for regulating fruit traits. However, the availability of specific database of genes related to fruit traits for use by the plant research community remains limited. To address this limitation, we developed the Gene Improvements for Fruit Trait Database (GIFTdb, http://giftdb.agroda.cn). GIFTdb contains 35 365 genes, including 896 derived from the FR database 1.0, 305 derived from 30 882 articles from 2014 to 2021, 236 derived from the Universal Protein Resource (UniProt) database, and 33 928 identified through homology analysis. The database supports several aided analysis tools, including signal transduction pathways, gene ontology terms, protein-protein interactions, DNAWorks, Basic Local Alignment Search Tool (BLAST), and Protein Subcellular Localization Prediction (WoLF PSORT). To provide information about genes currently unsupported in GIFTdb, potential fruit trait-related genes can be searched based on homology with the supported genes. GIFTdb can provide valuable assistance in determining the function of fruit trait-related genes, such as MYB306-like, by conducting a straightforward search. We believe that GIFTdb will be a valuable resource for researchers working on gene function annotation and molecular breeding to improve fruit traits.

Knowledge Distillation Facilitates the Lightweight and Efficient Plant Diseases Detection Model.

Plant disease diagnosis in time can inhibit the spread of the disease and prevent a large-scale drop in production, which benefits food production. Object detection-based plant disease diagnosis methods have attracted widespread attention due to their accuracy in classifying and locating diseases. However, existing methods are still limited to single crop disease diagnosis. More importantly, the existing model has a large number of parameters, which is not conducive to deploying it to agricultural mobile devices. Nonetheless, reducing the number of model parameters tends to cause a decrease in model accuracy. To solve these problems, we propose a plant disease detection method based on knowledge distillation to achieve a lightweight and efficient diagnosis of multiple diseases across multiple crops. In detail, we design 2 strategies to build 4 different lightweight models as student models: the YOLOR-Light-v1, YOLOR-Light-v2, Mobile-YOLOR-v1, and Mobile-YOLOR-v2 models, and adopt the YOLOR model as the teacher model. We develop a multistage knowledge distillation method to improve lightweight model performance, achieving 60.4% mAP@ .5 in the PlantDoc dataset with small model parameters, outperforming existing methods. Overall, the multistage knowledge distillation technique can make the model lighter while maintaining high accuracy. Not only that, the technique can be extended to other tasks, such as image classification and image segmentation, to obtain automated plant disease diagnostic models with a wider range of lightweight applicability in smart agriculture. Our code is available at https://github.com/QDH/MSKD.

Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses.

Plant phenotyping is important for plants to cope with environmental changes and ensure plant health. Imaging techniques are perceived as the most critical and reliable tools for studying plant phenotypes. Thermal imaging has opened up new opportunities for nondestructive imaging of plant phenotyping. However, a comprehensive summary of thermal imaging in plant phenotyping is still lacking. Here we discuss the progress and future prospects of thermal imaging for assessing plant growth and stress responses. First, we classify thermal imaging into ground-based and aerial platforms based on their adaptability to different experimental environments (including laboratory, greenhouse, and field). It is convenient to collect phenotypic information of different dimensions. Second, in order to enhance the efficiency of thermal image processing, automatic algorithms based on deep learning are employed instead of traditional manual methods, greatly reducing the time cost of experiments. Considering its ease of implementation, handling and instant response, thermal imaging has been widely used in research on environmental stress, crop yield, and seed vigor. We have found that thermal imaging can detect thermal energy dissipation caused by living organisms (e.g., pests, viruses, bacteria, fungi, and oomycetes), enabling early disease diagnosis. It also recognizes changes leaf surface temperatures resulting from reduced transpiration rates caused by nutrient deficiency, drought, salinity, or freezing. Furthermore, thermal imaging predicts crop yield under different water states and forecasts the viability of dormant seeds after water absorption by monitoring temperature changes in the seeds. This work will assist biologists and agronomists in studying plant phenotypes and serve a guide for breeders to develop high-yielding, stress-tolerant, and superior crops.

Grasping cryptic binding sites to neutralize drug resistance in the field of anticancer.

Drug resistance is a significant obstacle to successful cancer treatment. The utilization and development of cryptic binding sites (CBSs) in proteins involved in cancer-related drug-resistance (CRDR) could help to overcome that drug resistance. However, there is no comprehensive review of the successful use of CBSs in addressing CRDR. Here, we have systematically summarized and analyzed the opportunities and challenges of using CBSs in addressing CRDR and revealed the key role that CBSs have in targeting CRDR. First, we have identified the CRDR targets and the corresponding CBSs. Second, we discuss the mechanisms by which CBSs can overcome CRDR. Finally, we have provided examples of successful CBS applications in addressing CRDR. We hope that this approach will provide guidance to biologists and chemists in effectively utilizing CBSs for the development of new drugs to alleviate CRDR.

In silico resources help combat cancer drug resistance mediated by target mutations.

Drug resistance causes catastrophic cancer treatment failures. Mutations in target proteins with altered drug binding indicate a main mechanism of cancer drug resistance (CDR). Global research has generated considerable CDR-related data and well-established knowledge bases and predictive tools. Unfortunately, these resources are fragmented and underutilized. Here, we examine computational resources for exploring CDR caused by target mutations, analyzing these tools based on their functional characteristics, data capacity, data sources, methodologies and performance. We also discuss their disadvantages and provide examples of how potential inhibitors of CDR have been discovered using these resources. This toolkit is designed to help specialists explore resistance occurrence effectively and to explain resistance prediction to non-specialists easily.

Carboxylic Acid Derivatives in Herbicide Development.

Carboxylic acids and their derivatives are extensively found in natural and non-natural compounds with proven bioactivities. They have made great contributions to the development of herbicides and herbicidal lead structures in the past 70 years. Carboxylic acid-related herbicidal molecules have targeted a diversity of biosynthetic pathways, proteins, enzymes, energetic metabolism systems, and other reaction sites through different mechanisms. It is significant and helpful for us to know the herbicidal targets and mechanisms of the carboxylic acid-related herbicides as well as the basic rules for the design and development of herbicidal lead structures. We therefore summarize here the overall development of carboxyl group-containing herbicides and herbicidal molecules in the past 20 years based on their structural properties and herbicidal mechanisms.

Oxidative post-translational modification of catalase confers salt stress acclimatization by regulating H2O2 homeostasis in Malus hupehensis.

Reactive oxygen species (ROS) play an essential role as both signaling molecule and damage agent during salt stress. As a signaling molecule, proper accumulation of H2O2 is crucial to trigger stress response and enhance stress tolerance. However, the dynamic regulation mechanism of H2O2 remains unclear. Here, we show that MhCAT2 (catalase 2 in Malus hupehensis) undergoes oxidative modification in an O2•--dependent manner and that oxidation at His225 residue reduces the MhCAT2 activity. Furthermore, the substitution of His225 with Tyr weakens the activity of MhCAT2. The oxidation modification provides a post-translational brake mechanism for the excessive scavenging of H2O2 caused by salt stress-induced catalase (CAT) over-expression. Overall, this finding provides mechanistic insights on stress tolerance augmentation by an O2•--mediated switch that regulates H2O2 homeostasis in Malus hupehensis.

Toxicity Tests for Chemical Pesticide Registration: Requirement Differences among the United States, the European Union, Japan, and China?

Pesticide registration is a scientific, legal, and administrative process that checks if a pesticide is safe and effective for its intended use before it can be used. The toxicity test is a key part of pesticide registration, which includes human health and ecological effect testing. Different countries adopt their own toxicity test criteria for pesticide registration guidelines. However, these differences, which may help accelerate the progress of pesticide registration and reduce the number of animals used, are yet to be explored and compared. Herein, we outlined the details and compared the differences between the toxicity tests in the United States, the European Union, Japan, and China. Some differences lie in the types and waiver policy, while others are in new approach methodologies (NAMs). On the basis of these differences, there is great potential for the optimization of NAMs during the toxicity tests. It is expected that this perspective can contribute to developing and adopting NAMs.

ACFIS 2.0: an improved web-server for fragment-based drug discovery via a dynamic screening strategy.

Drug discovery, which plays a vital role in maintaining human health, is a persistent challenge. Fragment-based drug discovery (FBDD) is one of the strategies for the discovery of novel candidate compounds. Computational tools in FBDD could help to identify potential drug leads in a cost-efficient and time-saving manner. The Auto Core Fragment in silico Screening (ACFIS) server is a well-established and effective online tool for FBDD. However, the accurate prediction of protein-fragment binding mode and affinity is still a major challenge for FBDD due to weak binding affinity. Here, we present an updated version (ACFIS 2.0), that incorporates a dynamic fragment growing strategy to consider protein flexibility. The major improvements of ACFIS 2.0 include (i) increased accuracy of hit compound identification (from 75.4% to 88.5% using the same test set), (ii) improved rationality of the protein-fragment binding mode, (iii) increased structural diversity due to expanded fragment libraries and (iv) inclusion of more comprehensive functionality for predicting molecular properties. Three successful cases of drug lead discovery using ACFIS 2.0 are described, including drugs leads to treat Parkinson's disease, cancer, and major depressive disorder. These cases demonstrate the utility of this web-based server. ACFIS 2.0 is freely available at http://chemyang.ccnu.edu.cn/ccb/server/ACFIS2/.

PDDD-PreTrain: A Series of Commonly Used Pre-Trained Models Support Image-Based Plant Disease Diagnosis.

Plant diseases threaten global food security by reducing crop yield; thus, diagnosing plant diseases is critical to agricultural production. Artificial intelligence technologies gradually replace traditional plant disease diagnosis methods due to their time-consuming, costly, inefficient, and subjective disadvantages. As a mainstream AI method, deep learning has substantially improved plant disease detection and diagnosis for precision agriculture. In the meantime, most of the existing plant disease diagnosis methods usually adopt a pre-trained deep learning model to support diagnosing diseased leaves. However, the commonly used pre-trained models are from the computer vision dataset, not the botany dataset, which barely provides the pre-trained models sufficient domain knowledge about plant disease. Furthermore, this pre-trained way makes the final diagnosis model more difficult to distinguish between different plant diseases and lowers the diagnostic precision. To address this issue, we propose a series of commonly used pre-trained models based on plant disease images to promote the performance of disease diagnosis. In addition, we have experimented with the plant disease pre-trained model on plant disease diagnosis tasks such as plant disease identification, plant disease detection, plant disease segmentation, and other subtasks. The extended experiments prove that the plant disease pre-trained model can achieve higher accuracy than the existing pre-trained model with less training time, thereby supporting the better diagnosis of plant diseases. In addition, our pre-trained models will be open-sourced at https://pd.samlab.cn/ and Zenodo platform https://doi.org/10.5281/zenodo.7856293.