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.

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.

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.

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.

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.

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.

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/.

Protein-nucleic acid thermodynamic databases for specific uses.

In response to Gromiha and Harini, we review the currently available thermodynamic databases for protein-nucleic acid interactions. These databases are designed for particular uses. We give general comments on them to facilitate browsing and exploration.

CIPDB: A biological structure databank for studying cation and π interactions.

As major forces for modulating protein folding and molecular recognition, cation and π interactions are extensively identified in protein structures. They are even more competitive than hydrogen bonds in molecular recognition, thus, are vital in numerous biological processes. In this review, we introduce the methods for the identification and quantification of cation and π interactions, provide insights into the characteristics of cation and π interactions in the natural state, and reveal their biological function together with our developed database (Cation and π Interaction in Protein Data Bank; CIPDB; http://chemyang.ccnu.edu.cn/ccb/database/CIPDB). This review lays the foundation for the in-depth study of cation and π interactions and will guide the use of molecular design for drug discovery.

Mapping cryptic binding sites of drug targets to overcome drug resistance.

The emergence of drug resistance is a primary obstacle for successful chemotherapy. Drugs that target cryptic binding sites (CBSs) represent a novel strategy for overcoming drug resistance. In this short communication, we explain and discuss how the discovery of CBSs and their inhibitors can overcome drug resistance.

Bioinformatics toolbox for exploring target mutation-induced drug resistance.

Drug resistance is increasingly among the main issues affecting human health and threatening agriculture and food security. In particular, developing approaches to overcome target mutation-induced drug resistance has long been an essential part of biological research. During the past decade, many bioinformatics tools have been developed to explore this type of drug resistance, and they have become popular for elucidating drug resistance mechanisms in a low cost, fast and effective way. However, these resources are scattered and underutilized, and their strengths and limitations have not been systematically analyzed and compared. Here, we systematically surveyed 59 freely available bioinformatics tools for exploring target mutation-induced drug resistance. We analyzed and summarized these resources based on their functionality, data volume, data source, operating principle, performance, etc. And we concisely discussed the strengths, limitations and application examples of these tools. Specifically, we tested some predictive tools and offered some thoughts from the clinician's perspective. Hopefully, this work will provide a useful toolbox for researchers working in the biomedical, pesticide, bioinformatics and pharmaceutical engineering fields, and a good platform for non-specialists to quickly understand drug resistance prediction.

Fragment-based drug discovery supports drugging 'undruggable' protein-protein interactions.

Protein-protein interactions (PPIs) have important roles in various cellular processes, but are commonly described as 'undruggable' therapeutic targets due to their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) has achieved great success in modulating PPIs, with more than ten compounds in clinical trials. Here, we highlight the progress of FBDD in modulating PPIs for therapeutic development. Targeting hot spots that have essential roles in both fragment binding and PPIs provides a shortcut for the development of PPI modulators via FBDD. We highlight successful cases of cracking the 'undruggable' problems of PPIs using fragment-based approaches. We also introduce new technologies and future trends. Thus, we hope that this review will provide useful guidance for drug discovery targeting PPIs.

Deciphering Nonbioavailable Substructures Improves the Bioavailability of Antidepressants by Serotonin Transporter.

Inadequate bioavailability is one of the most critical reasons for the failure of oral drug development. However, the way that substructures affect bioavailability remains largely unknown. Serotonin transporter (SERT) inhibitors are first-line drugs for major depression disorder, and improving their bioavailability may be able to decrease side-effects by reducing daily dose. Thus, it is an excellent model to probe the relationship between substructures and bioavailability. Here, we proposed the concept of "nonbioavailable substructures", referring to substructures that are unfavorable to bioavailability. A machine learning model was developed to identify nonbioavailable substructures based on their molecular properties and shows the accuracy of 83.5%. A more potent SERT inhibitor DH4 was discovered with a bioavailability of 83.28% in rats by replacing the nonbioavailable substructure of approved drug vilazodone. DH4 exhibits promising anti-depression efficacy in animal experiments. The concept of nonbioavailable substructures may open up a new venue for the improvement of drug bioavailability.

Protein Kinases as Potential Targets Contribute to the Development of Agrochemicals.

Using agrochemicals against pest insects, fungi, and weeds plays a major part in maintaining and improving crop yields, which helps to solve the issue of food security. Due to the limited targets and resistance of agrochemicals, protein kinases are regarded as attractive potential targets to develop new agrochemicals. Recently, a lot of investigations have shown the extension of agrochemicals by targeting protein kinases, implying an increasing concern for this kind of method. However, few people have summarized and discussed the targetability of protein kinases contributing to the development of agrochemicals. In this work, we introduce the research on protein kinases as potential targets used in crop protection and discuss the prospects of protein kinases in the field of agrochemical development. This study may not only provide guidance for the contribution of protein kinases to the development of agrochemicals but also help nonprofessionals such as students learn and understand the role of protein kinases quickly.