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The thermodynamics of protein–nucleic acid interactions (PNIs) is crucial for elucidating the mechanisms of molecular recognition and pathological consequences. The Protein–Nucleic Acid Thermodynamics Database (PNATDB) is a database containing experimentally determined thermodynamic parameters along with sequence, structural, and function data, which is available free online. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Gibberellins (GAs) are phytohormones essential for many developmental processes in plants. In this work, fundamental mechanism of hormone perception by receptor GID1 has been studied by performing computational simulations, revealing a new GA-binding channel of GID1 and a novel hormone perception mechanism involving only one conformational state of GID1. The novel hormone perception mechanism demonstrated here is remarkably different from the previously proposed/speculated mechanism [Murase et al., Nature 2008, 456, 459] involving two conformational states ('OPEN' and 'CLOSED') of GID1. According to the new perception mechanism, GA acts as a 'conformational stabilizer,' rather than the previously speculated 'allosteric inducer,' to induce the recognition of protein DELLA by GID1. The novel mechanistic insights obtained in this study provide a new starting point for further studies on the detailed molecular mechanisms of GID1 interacting with DELLA and various hormones and for mechanism-based rational design of novel, potent growth regulators that target crops and ornamental plants. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Protoporphyrinogen IX oxidase (PPO; EC 1.3.3.4) is an essential enzyme catalyzing the last common step in the pathway leading to heme and chlorophyll biosynthesis. Great interest in PPO inhibitors arises from both its significance to agriculture and medicine. However, the discovery of PPO inhibitors with ultrahigh potency and selectivity is hampered due to lack of structural and mechanistic understanding about the substrate recognition, which remains a longstanding question central in porphyrin biology. To understand the mechanism, a novel binding model of protogen (protoporphyrinogen IX, the substrate) was developed through extensive computational simulations. Subsequently, amino acid residues that are critical for protogen binding identified by computational simulations were substituted by mutagenesis. Kinetic analyses of these mutants indicated that these residues were critical for protogen binding. In addition, the calculated free energies of protogen binding with these mutants correlated well with the experimental data, indicating the reasonability of the binding model. On the basis of this novel model, the fundamental mechanism of substrate recognition was investigated by performing potential of mean force (PMF) calculations, which provided an atomic level description of conformational changes and pathway intermediates. The free energy profile revealed a feedback inhibition mechanism of proto (protoporphyrin IX, the product), which was also in agreement with experimental evidence. The novel mechanistic insights obtained from this study present a new starting point for future rational design of more efficient PPO inhibitors based on the product-bound PPO structure. [ABSTRACT FROM AUTHOR]

Drug resistance has become one of the biggest challenges in drug discovery and/or development and has attracted great research interests worldwide. During the past decade, computational strategies have been developed to predict target mutation-induced drug resistance. Meanwhile, various molecular design strategies, including targeting protein backbone, targeting highly conserved residues and dual/multiple targeting, have been used to design novel inhibitors for combating the drug resistance. In this article we review recent advances in development of computational methods for target mutation-induced drug resistance prediction and strategies for rational design of novel inhibitors that could be effective against the possible drug-resistant mutants of the target. [Copyright &y& Elsevier]

Abstract: Bioactive conformation of drugs is one of the key points for understanding the ligand–receptor interactions. In the present study, by combining density functional theory-based (DFT-based) conformation analysis with quantitative structure–activity relationship analysis (QSAR), we developed successfully a new approach (DFT/QSAR) to carry out bioactive conformation analyses for a series of 25 cyclic imide derivatives as protoporphyrinogen oxidase (PPO) inhibitors. Further potential energy surface scan, molecular docking and molecular dynamic simulation calculations validated that the DFT/QSAR-derived conformation is indeed very similar to the ‘real’ bioactive conformation. We believe the DFT/QSAR approach provides a simple alternative for the bioactive conformation of small molecules, especially in the case that the three-dimensional structure of protein is unknown. [Copyright &y& Elsevier]

Globally, 91% of plant production encounters diverse environmental stresses that adversely affect their growth, leading to severe yield losses of 50–60%. In this case, monitoring the connection between the environment and plant health can balance population demands with environmental protection and resource distribution. Fluorescent chemosensors have shown great progress in monitoring the health and environment of plants due to their high sensitivity and biocompatibility. However, to date, no comprehensive analysis and systematic summary of fluorescent chemosensors used in monitoring the correlation between plant health and their environment have been reported. Thus, herein, we summarize the current fluorescent chemosensors ranging from their design strategies to applications in monitoring plant-environment interaction processes. First, we highlight the types of fluorescent chemosensors with design strategies to resolve the bottlenecks encountered in monitoring the health and living environment of plants. In addition, the applications of fluorescent small-molecule, nano and supramolecular chemosensors in the visualization of the health and living environment of plants are discussed. Finally, the major challenges and perspectives in this field are presented. This work will provide guidance for the design of efficient fluorescent chemosensors to monitor plant health, and then promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Summary: 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. [ABSTRACT FROM AUTHOR]

Rigorous risk assessment of chemicals in food and feed is essential to address the growing worldwide concerns about food safety. High-quality toxicological data on food-relevant chemicals are fundamental for risk modeling and assessment in the food safety area. The organization and analysis of substantial toxicity information can positively support decision-making by providing insight into toxicity trends. However, it remains challenging to systematically obtain fragmented toxicity data, and related toxicological resources are required to meet the current demands. In this study, we collected 221,439 experimental toxicity records for 5,657 food-relevant chemicals identified from extensive databases and literature, along with their information on chemical identification, physicochemical properties, environmental fates, and biological targets. Based on the aggregated data, a freely available web-based databank, Food-Relevant Available Chemicals Toxicology Databank (FRAC-TD) is presented, which supports multiple browsing ways and search criterions. Applying FRAC-TD for data-driven analysis, we revealed the underlying toxicity profiles of food-relevant chemicals in humans, mammals, and other species in the food chain. Expectantly, FRAC-TD could positively facilitate toxicological studies, toxicity prediction, and risk assessments in the food industry. [ABSTRACT FROM AUTHOR]

Fks1 is a unique enzyme that makes β-1,3-glucan, a key cell wall component. Blocking Fks1 kills fungi by damaging their cell wall. Fks1 enhances fungal virulence and host infection by contributing to pathogenesis and immune response, while β-1,3-glucan is a biomarker for fungal infections. Fks1 is a promising target for new antifungal drugs. Studies on echinocandins and ibrexafungerp have identified their binding sites and action mechanisms on Fks1. Improvements in existing drugs can increase their antifungal activity. Fks1 research is essential to develop better antifungal drugs. More studies on Fks1 are needed to understand its structure, function, and interaction with antifungal agents crucial for drug optimization and design. 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 β-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. [ABSTRACT FROM AUTHOR]

• Transfer learning enhances PK prediction models by leveraging relevant knowledge transfer. • Transfer learning categorizes into three types based on domain and tasks. • Diverse toolkits offer user-friendly approaches to deploy transfer learning effectively. • Several case studies exhibit the process and outcomes of transfer learning in PK prediction. 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. [ABSTRACT FROM AUTHOR]

Herein, we report an unprecedented skeletal rearrangement reaction of tetrahydro‐β‐carbolines enabled by copper‐catalyzed single‐electron oxidative oxygenation, in which H2O 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. [ABSTRACT FROM AUTHOR]

Herein, we report an unprecedented skeletal rearrangement reaction of tetrahydro‐β‐carbolines enabled by copper‐catalyzed single‐electron oxidative oxygenation, in which H2O 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. [ABSTRACT FROM AUTHOR]

• Automated platforms facilitate organic synthesis study. • Software and configuration are essential for automated platforms of synthesis. • Robotic systems represented a milestone on the road to fully autonomous chemical synthesis. • Automated synthesis could relieve chemists of manual tasks. • Artificial intelligence and deep learning would be useful for the development of synthesis automation. Organic synthesis is a vital process that is a mainstay of drug discovery. However, traditional manual-based approaches to organic synthesis might not be economical, especially in a research environment where budgets are increasingly restricted and the effective use of manpower and materials is crucial. Hence, there is a strong interest in automating the synthesis process, resulting in a growth in synthesis automation, especially of systems and configuration. Here, we systematically summarize recently developed automated systems for organic synthesis. This review will be useful for computational scientists aiming to develop novel tools and also for non-specialists and students to understand the frontier of automated synthesis. [ABSTRACT FROM AUTHOR]

• Online resources facilitate in silico drug-likeness study. • Databases gathering high quality and up to date data are essential for drug-likeness evaluation. • Web servers for drug-likeness prediction offers a useful guideline for further optimization. • Nature products are valuable for selecting drug-like molecules with novel scaffolds. • Deep learning would be useful for building of drug-likeness model. Undesirable pharmacokinetic (PK) properties or unacceptable toxicity are the main causes of the failure of drug candidates at the clinical trial stage. Since the concept of drug-likeness was first proposed, it has become an important consideration in the selection of compounds with desirable bioavailability during the early phases of drug discovery. Over the past decade, online resources have effectively facilitated drug-likeness studies in an economical and time-efficient manner. Here, we provide a comprehensive summary and comparison of current accessible online resources, in terms of their key features, application fields, and performance for in silico drug-likeness studies. We hope that the assembled toolbox will provide useful guidance to facilitate future in silico drug-likeness research. [ABSTRACT FROM AUTHOR]

Protein–protein interactions (PPIs) have vital roles in almost all cellular processes, although they are regarded as 'undruggable' therapeutic targets because of their large, flat, featureless interfaces. Fragment-based drug discovery (FBDD) shows advantages for the design of modulators to inhibit or stabilize PPIs, with two drugs approved and more than ten compounds in clinical trials. Fragments tend to bind at 'hot spots' of PPI interfaces, and the locations of different 'hot spots' provide directions for fragment evolution to discover more potent PPI modulators. Fragment hits with new mode of actions, including covalent fragments and fragments binding at allosteric sites, have become important sources of PPI modulators. Emerging technologies, such as cryo-electron microscopy, covalent tethering, and artificial intelligence, are accelerating the application of FBDD targeting PPIs. 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. [ABSTRACT FROM AUTHOR]

Advancing our understanding of the mechanisms associated with microbial-mediated signal transduction in plants can enhance our ability to precisely manipulate plant physiology to improve tolerance against biotic and abiotic stresses. Long-distance signal transduction in plants can be initiated by the metabolism of specific microbial taxa in association with plants in the rhizosphere and/or phyllosphere. The transduction of specific long-distance signals in plants can be mediated by mobile peptides, RNA, metabolites, and phytohormones, all of which can be translocated via the vascular system. Embracing the complexity of plant physiological responses and regulation in a holobiont context will advance our knowledge of plant–microbe interactions and foster the development of new chemicals to manipulate plant physiological responses. Microorganisms colonizing the plant rhizosphere and phyllosphere play crucial roles in plant growth and health. Recent studies provide new insights into long-distance communication from plant roots to shoots in association with their commensal microbiome. In brief, these recent advances suggest that specific plant-associated microbial taxa can contribute to systemic plant responses associated with the enhancement of plant health and performance in face of a variety of biotic and abiotic stresses. However, most of the mechanisms associated with microbiome-mediated signal transduction in plants remain poorly understood. In this review, we provide an overview of long-distance signaling mechanisms within plants mediated by the commensal plant-associated microbiomes. We advocate the view of plants and microbes as a holobiont and explore key molecules and mechanisms associated with plant–microbe interactions and changes in plant physiology activated by signal transduction. [ABSTRACT FROM AUTHOR]

Hit to lead (H2L) optimization is a key step for drug and agrochemical discovery. A critical challenge for H2L optimization is the low efficiency due to the lack of predictive method with high accuracy. We described a new computational method called Computational Substitution Optimization (CSO) that has allowed us to rapidly identify compounds with cytochrome bc1 complex inhibitory activity in the nanomolar and subnanomolar range. The comprehensively optimized candidate has proved to be a slow binding inhibitor of bc1 complex, ~73-fold more potent (Ki = 4.1 nM) than the best commercial fungicide azoxystrobin (AZ; Ki = 297.6 nM) and shows excellent in vivo fungicidal activity against downy mildew and powdery mildew disease. The excellent correlation between experimental and calculated binding free-energy shifts together with further crystallographic analysis confirmed the prediction accuracy of CSO method. To the best of our knowledge, CSO is a new computational approach to substitution-scanning mutagenesis of ligand and could be used as a general strategy of H2L optimisation in drug and agrochemical design. [ABSTRACT FROM AUTHOR]

In the conceptual frame of "One Health", the safety crisis caused by various toxicological issues due to chemical exposure is of great concern. The urgent demand for environmentally safe chemicals is increasing the awareness of the importance of designing non-toxic chemical molecules. The expanding toxicological data can provide a foundation for studying interactions between the living organisms and environment. However, a large amount of toxicological data is fragmented and scattered in the literature and online resources. Thus, to promote the more efficient use of toxicological information, further collection and collation of the current toxicity data is urgent. In the present review, we discuss the application of existing toxicological data to support environmental risk assessment and guide green chemical design. The importance of chemical toxicity data for human health and ecological risk assessment, trends of available chemical toxicity data, details of the common structural features associated with different toxicity endpoints, and applications of toxicological big data in toxicity prediction combined with artificial intelligence are described and discussed. Additionally, based on the systematic collection and analysis of the current toxicity data, a reorganized toxicological databank is provided to cover a wider range of organic chemicals and tested species. It is expected that this work can contribute to the study of organic structural toxicity, chemical toxicity prediction, environmental risk assessment, and safer chemical design. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

• Protein–protein interactions (PPIs) exhibit multi-level organization that can aid drug-target discovery. • Multi-level bioinformatics tools focusing on PPIs promote drug-target recognition. • Case studies show the use of bioinformatics tools in actual drug-target research. • The potential challenges and prospects of bioinformatics tools are discussed. 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. [ABSTRACT FROM AUTHOR]

Protein kinases play crucial roles in many cellular signaling processes, making them become important targets for drug discovery. But drug resistance mediated by mutation puts a barrier to the therapeutic effect of kinase inhibitors. Fragment-based drug discovery has been successfully applied to overcome such resistance. However, the complicate kinase-inhibitor fragment interaction and fragment-to-lead process seriously limit the efficiency of kinase inhibitor discovery against resistance caused by mutation. Here, we constructed a comprehensive web platform KinaFrag for the fragment-based kinase inhibitor discovery to overcome resistance. The kinase-inhibitor fragment space was investigated from 7783 crystal kinase-inhibitor fragment complexes, and the structural requirements of kinase subpockets were analyzed. The core fragment-based virtual screening workflow towards specific subpockets was developed to generate new kinase inhibitors. A series of tropomyosin receptor kinase (TRK) inhibitors were designed, and the most potent compound YT9 exhibits up to 70-fold activity improvement than marketed drugs larotrectinib and selitrectinib against G595R, G667C and F589L mutations of TRKA. YT9 shows promising antiproliferative against tumor cells in vitro and effectively inhibits tumor growth in vivo for wild type TRK and TRK mutants. Our results illustrate the great potential of KinaFrag in the kinase inhibitor discovery to combat resistance mediated by mutation. KinaFrag is freely available at http://chemyang.ccnu.edu.cn/ccb/database/KinaFrag/. [ABSTRACT FROM AUTHOR]

Protein post-translational modifications (PTM) play vital roles in cellular regulation, modulating functions by driving changes in protein structure and dynamics. Exploring comprehensively the influence of PTM on conformational dynamics can facilitate the understanding of the related biological function and molecular mechanism. Currently, a series of excellent computation tools have been designed to analyze the time-dependent structural properties of proteins. However, the protocol aimed to explore conformational dynamics of post-translational modified protein is still a blank. To fill this gap, we present PTMdyna to visually predict the conformational dynamics differences between unmodified and modified proteins, thus indicating the influence of specific PTM. PTMdyna exhibits an AUC of 0.884 tested on 220 protein–protein complex structures. The case of heterochromatin protein 1α complexed with lysine 9-methylated histone H3, which is critical for genomic stability and cell differentiation, was used to demonstrate its applicability. PTMdyna provides a reliable platform to predict the influence of PTM on protein dynamics, making it easier to interpret PTM functionality at the structure level. The web server is freely available at http://ccbportal.com/PTMdyna. [ABSTRACT FROM AUTHOR]

The grand challenge to meet the increasing demands for food by a rapidly growing global population requires protecting crops from pests. Natural active substances play a significant role in the sustainable pests and pathogenic microbes management. In recent years, natural products- (NPs), antimicrobial peptides- (AMPs), medicinal plant- and plant essential oils (EOs)-related online resources have greatly facilitated the development of pests and pathogenic microbes control agents in an efficient and economical manner. However, a comprehensive comparison, analysis and summary of these existing web resources are still lacking. Here, we surveyed these databases of NPs, AMPs, medicinal plants and plant EOs with insecticidal, antibacterial, antiviral and antifungal activity, and we compared their functionality, data volume, data sources and applicability. We comprehensively discussed the limitation of these web resources. This study provides a toolbox for bench scientists working in the pesticide, botany, biomedical and pharmaceutical engineering fields. The aim of the review is to hope that these web resources will facilitate the discovery and development of potential active ingredients of pests and pathogenic microbes control agents. [ABSTRACT FROM AUTHOR]

Abstract: Acetylcholinesterase (AChE) is a key enzyme of the cholinergic nervous system. More than one gene encodes the synaptic AChE target. As the most potent known AChE inhibitor, the syn1-TZ2PA6 isomer was recently shown to have higher affinity as a reversible organic inhibitor of acetylcholinesterase1 (AChE1) than the anti1-TZ2PA6 isomer. Opposite selectivity has been shown for acetylcholinesterase2 (AChE2). In an attempt to understand the selectivity of the syn1-TZ2PA6 and anti1-TZ2PA6 isomers for AChE1 and AChE2, six molecular dynamics (MD) simulations were carried out with mouse AChE (mAChE, type of AChE1), Torpedo californica AChE (TcAChE, type of AChE1), and Drosophila melanogaster AChE (DmAChE, type of AChE2) bound with syn1-TZ2PA6 and anti1-TZ2PA6 isomers. Within the structure of the inhibitor, the 3,8-diamino-6-phenylphenanthridinium subunit and 9-amino-1,2,3,4-tetrahydroacridine subunit, via π–π interactions, made more favorable contributions to syn1-TZ2PA6 or anti1-TZ2PA6 isomer binding in the mAChE/TcAChE enzyme than the 1,2,3-triazole subunit. Compared to AChE1, the triazole subunit had increased binding energy with AChE2 due to a greater negative charge in the active site. The binding free energy calculated using the MM/PBSA method suggests that selectivity between AChE1 and AChE2 is mainly attributed to decreased binding affinity for the inhibitor. [Copyright &y& Elsevier]

• Numbers of research data related to COVID-19 have been produced. • The genome and proteome resources about SARA-CoV-2 are summarized. • Research data for the development of antiviral drug and immunotherapy are organized. • The potential challenges of coronaviruses pandemic are discussed. The infectious disease Coronavirus 2019 (COVID-19) continues to cause a global pandemic and, thus, the need for effective therapeutics remains urgent. Global research targeting COVID-19 treatments has produced numerous therapy-related data and established data repositories. However, these data are disseminated throughout the literature and web resources, which could lead to a reduction in the levels of their use. In this review, we introduce resource repositories for the development of COVID-19 therapeutics, from the genome and proteome to antiviral drugs, vaccines, and monoclonal antibodies. We briefly describe the data and usage, and how they advance research for therapies. Finally, we discuss the opportunities and challenges to preventing the pandemic from developing further. [ABSTRACT FROM AUTHOR]

Food loss due to pathogens is a major concern in agriculture, requiring the need for advanced disease detection and prevention measures to minimize pathogen damage to plants. Novel bioinformatic tools have opened doors for the low-cost rapid identification of pathogens and prevention of disease. The number of these tools is growing fast and a comprehensive and comparative summary of these resources is currently lacking. Here, we review all current bioinformatic tools used to identify the mechanisms of pathogen pathogenicity, plant resistance protein identification, and the detection and treatment of plant disease. We compare functionality, data volume, data sources, performance, and applicability of all tools to provide a comprehensive toolbox for researchers in plant disease management. Appropriate bioinformatic tools supply clues on mechanisms of pathogen pathogenicity and plant immunity as well as strategies on the diagnosis and treatment of plant disease. Analysis tools for plant pathogen genomes, effector proteins, and plant–pathogen interactions can be used to explore pathogen pathogenicity. Homology- and feature-based prediction tools accelerate the identification of new plant resistance proteins. Image-based tools improve the precision of disease detection and disease severity estimation. Agrochemical, biological control, and biopesticide databases facilitate finding active ingredients for plant disease treatment. [ABSTRACT FROM AUTHOR]

Protein–nucleic acid interactions play essential roles in many biological processes, such as transcription, replication and translation. In protein–nucleic acid interfaces, hotspot residues contribute the majority of binding affinity toward molecular recognition. Hotspot residues are commonly regarded as potential binding sites for compound molecules in drug design projects. The dynamic property is a considerable factor that affects the binding of ligands. Computational approaches have been developed to expedite the prediction of hotspot residues on protein–nucleic acid interfaces. However, existing approaches overlook hotspot dynamics, despite their essential role in protein function. Here, we report a web server named Hotspots In silico Scanning on Nucleic Acid and Protein Interface (HISNAPI) to analyze hotspot residue dynamics by integrating molecular dynamics simulation and one-step free energy perturbation. HISNAPI is capable of not only predicting the hotspot residues in protein–nucleic acid interfaces but also providing insights into their intensity and correlation of dynamic motion. Protein dynamics have been recognized as a vital factor that has an effect on the interaction specificity and affinity of the binding partners. We applied HISNAPI to the case of SARS-CoV-2 RNA-dependent RNA polymerase, a vital target of the antiviral drug for the treatment of coronavirus disease 2019. We identified the hotspot residues and characterized their dynamic behaviors, which might provide insight into the target site for antiviral drug design. The web server is freely available via a user-friendly web interface at http://chemyang.ccnu.edu.cn/ccb/server/HISNAPI/ and http://agroda.gzu.edu.cn:9999/ccb/server/HISNAPI/. [ABSTRACT FROM AUTHOR]

Protein kinases (PKs) are important drug targets, but kinases selectivity poses a challenge to protein kinase inhibitors (PKIs) design. Fragment-based drug discovery (FBDD) has achieved great success in the discovery of highly specific PKIs. It makes full use of kinase–fragment interaction in target kinase subpockets to obtain promising selectivity. However, it's difficult to understand the complicated kinase–fragment interaction space, and systemic discussion of these interactions is still lacking. Herein, we introduce the advantages of the FBDD strategy in PKIs design. Key features of the selectivity of kinase–fragment interactions are summarized and analyzed. Some promising PKIs are introduced as case studies to help understand the fragment-to-lead (F2L) optimization process. Novel strategies and technologies for FBDD in PKIs discovery are also outlooked. Protein kinases inhibitors play vital roles in the treatment of multiple diseases. The problem of selectivity poses a challenge to the development of kinase inhibitors. Fragment-based drug discovery (FBDD) could maximize the kinase–fragment interaction in target kinase subpockets. And the abundant kinase–inhibitor complexes could provide necessary structural information for FBDD to obtain promising selectivity. Understanding kinase–fragment interactions in each kinase subpocket is significant for FBDD. Special interactions targeting subpockets in the back cleft or FP-I/FP-II subpockets of the front cleft are important for selectivity. Development of kinase inhibitors with novel mode of action like allosteric inhibitors via FBDD method is an efficient and potent way to achieve good selectivity. [ABSTRACT FROM AUTHOR]

Effective drug discovery contributes to the treatment of numerous diseases but is limited by high costs and long cycles. The Quantitative Structure–Activity Relationship (QSAR) method was introduced to evaluate the activity of a large number of compounds virtually, reducing the time and labor costs required for chemical synthesis and experimental determination. Hence, this method increases the efficiency of drug discovery. To meet the needs of researchers to utilize this technology, numerous QSAR-related web servers, such as Web-4D-QSAR and DPubChem, have been developed in recent years. However, none of the servers mentioned above can perform a complete QSAR modeling and supply activity prediction functions. We introduce Cloud 3D-QSAR by integrating the functions of molecular structure generation, alignment, molecular interaction field (MIF) computing and results analysis to provide a one-stop solution. We rigidly validated this server, and the activity prediction correlation was R 2 = 0.934 in 834 test molecules. The sensitivity, specificity and accuracy were 86.9%, 94.5% and 91.5%, respectively, with AUC = 0.981, AUCPR = 0.971. The Cloud 3D-QSAR server may facilitate the development of good QSAR models in drug discovery. Our server is free and now available at http://chemyang.ccnu.edu.cn/ccb/server/cloud3dQSAR/ and http://agroda.gzu.edu.cn:9999/ccb/server/cloud3dQSAR/. [ABSTRACT FROM AUTHOR]

In the 21st century, drought has been the main cause of shortages in world grain production and has created problems with food security. Abscisic acid (ABA) is a key plant hormone involved in the response to abiotic stress, especially drought. The pyrabactin resistance (PYR)/PYR1-like (PYL)/regulatory component of abscisic acid receptor (RCAR) family of proteins (simplified as PYLs) is a well-known ABA receptor family, which can be divided into dimeric and monomeric forms. PYLs can recognize ABA and activate downstream plant drought-resistance signals. However, the difference between monomeric and dimeric receptors in the mechanism of the response to ABA is unclear. Here, we reveal that monomeric receptors have a competitive advantage over dimeric receptors for binding to ABA, driven by the energy penalty resulting from dimer dissociation. ABA also plays different roles with the monomer and the dimer: in the monomer, it acts as a 'conformational stabilizer' for stabilizing the closed gate, whereas for the dimer, it serves as an 'allosteric promoter' for promoting gate closure, which leads to dissociation of the two subunits. This work illustrates how receptor oligomerization could modulate hormonal responses and provides a new concept for novel engineered plants based on ABA binding of monomers. [ABSTRACT FROM AUTHOR]

A clear systematic delineation of the interactions between phosphorylation sites on substrates and their effector kinases plays a fundamental role in revealing cellular activities, understanding signaling modulation mechanisms and proposing novel hypotheses. The emergence of bioinformatics tools contributes to studying phosphorylation network. Some of them feature the visualization of network, enabling more effective trace of the underlying biological problems in a clear and succinct way. In this review, we aimed to provide a toolbox for exploring phosphorylation network. We first systematically surveyed 19 tools that are available for exploring phosphorylation networks, and subsequently comparatively analyzed and summarized these tools to guide tool selection in terms of functionality, data sources, performance, network visualization and implementation, and finally briefly discussed the application cases of these tools. In different scenarios, the conclusion on the suitability of a tool for a specific user may vary. Nevertheless, easily accessible bioinformatics tools are proved to facilitate biological findings. Hopefully, this work might also assist non-specialists, students, as well as computational scientists who aim at developing novel tools in the field of phosphorylation modification. [ABSTRACT FROM AUTHOR]

Protein dynamics is central to all biological processes, including signal transduction, cellular regulation and biological catalysis. Among them, in-depth exploration of ligand-driven protein dynamics contributes to an optimal understanding of protein function, which is particularly relevant to drug discovery. Hence, a wide range of computational tools have been designed to investigate the important dynamic information in proteins. However, performing and analyzing protein dynamics is still challenging due to the complicated operation steps, giving rise to great difficulty, especially for nonexperts. Moreover, there is a lack of web protocol to provide online facility to investigate and visualize ligand-driven protein dynamics. To this end, in this study, we integrated several bioinformatic tools to develop a protocol, named Ligand and Receptor Molecular Dynamics (LARMD, http://chemyang.ccnu.edu.cn/ccb/server/LARMD/ and http://agroda.gzu.edu.cn:9999/ccb/server/LARMD/), for profiling ligand-driven protein dynamics. To be specific, estrogen receptor (ER) was used as a case to reveal ERβ-selective mechanism, which plays a vital role in the treatment of inflammatory diseases and many types of cancers in clinical practice. Two different residues (Ile373/Met421 and Met336/Leu384) in the pocket of ERβ/ERα were the significant determinants for selectivity, especially Met336 of ERβ. The helix H8, helix H11 and H7-H8 loop influenced the migration of selective agonist (WAY-244). These computational results were consistent with the experimental results. Therefore, LARMD provides a user-friendly online protocol to study the dynamic property of protein and to design new ligand or site-directed mutagenesis. [ABSTRACT FROM AUTHOR]

The phytohormone abscisic acid (ABA) is the best‐known stress signaling molecule in plants. ABA protects sessile land plants from biotic and abiotic stresses. The conserved pyrabactin resistance/pyrabactin resistance‐like/regulatory component of ABA receptors (PYR/PYL/RCAR) perceives ABA and triggers a cascade of signaling events. A thorough knowledge of the sequential steps of ABA signaling will be necessary for the development of chemicals that control plant stress responses. The core components of the ABA signaling pathway have been identified with adequate characterization. The information available concerning ABA biosynthesis, transport, perception, and metabolism has enabled detailed functional studies on how the protective ability of ABA in plants might be modified to increase plant resistance to stress. Some of the significant contributions to chemical manipulation include ABA biosynthesis inhibitors, and ABA receptor agonists and antagonists. Chemical manipulation of key control points in ABA signaling is important for abiotic and biotic stress management in agriculture. However, a comprehensive review of the current knowledge of chemical manipulation of ABA signaling is lacking. Here, a thorough analysis of recent reports on small‐molecule modulation of ABA signaling is provided. The challenges and prospects in the chemical manipulation of ABA signaling for the development of ABA‐based agrochemicals are also discussed. [ABSTRACT FROM AUTHOR]

The impact of pesticides on insect pollinators has caused worldwide concern. Both global bee decline and stopping the use of pesticides may have serious consequences for food security. Automated and accurate prediction of chemical poisoning of honey bees is a challenging task owing to a lack of understanding of chemical toxicity and introspection. Deep learning (DL) shows potential utility for general and highly variable tasks across fields. Here, we developed a new DL model of deep graph attention convolutional neural networks (GACNN) with the combination of undirected graph (UG) and attention convolutional neural networks (ACNN) to accurately classify chemical poisoning of honey bees. We used a training dataset of 720 pesticides and an external validation dataset of 90 pesticides, which is one order of magnitude larger than the previous datasets. We tested its performance in two ways: poisonous versus non-poisonous and GACNN versus other frequently-used machine learning models. The first case represents the accuracy in identifying bee poisonous chemicals. The second represents performance advantages. The GACNN achieved ~6% higher performance for predicting toxic samples and more stable with ~7% Matthews Correlation Coefficient (MCC) higher compared to all tested models, demonstrating GACNN is capable of accurately classifying chemicals and has considerable potential in practical applications. In addition, we also summarized and evaluated the mechanisms underlying the response of honey bees to chemical exposure based on the mapping of molecular similarity. Moreover, our cloud platform (http://beetox.cn) of this model provides low-cost universal access to information, which could vitally enhance environmental risk assessment. [ABSTRACT FROM AUTHOR]

SUMMARY: Intron‐containing genes have the ability to generate multiple transcript isoforms by splicing, thereby greatly expanding the eukaryotic transcriptome and proteome. In eukaryotic cells, precursor mRNA (pre‐mRNA) splicing is performed by a mega‐macromolecular complex defined as a spliceosome. Among its splicing components, U1 small nuclear ribonucleoprotein (U1 snRNP) is the smallest subcomplex involved in early spliceosome assembly and 5′‐splice site recognition. Its central component, named U1‐70K, has been extensively characterized in animals and yeast. Very few investigations on U1‐70K genes have been conducted in plants, however. To this end, we performed a comprehensive study to systematically identify 115 U1‐70K genes from 67 plant species, ranging from algae to angiosperms. Phylogenetic analysis suggested that the expansion of the plant U1‐70K gene family was likely to have been driven by whole‐genome duplications. Subsequent comparisons of gene structures, protein domains, promoter regions and conserved splicing patterns indicated that plant U1‐70Ks are likely to preserve their conserved molecular function across plant lineages and play an important functional role in response to environmental stresses. Furthermore, genetic analysis using T‐DNA insertion mutants suggested that Arabidopsis U1‐70K may be involved in response to osmotic stress. Our results provide a general overview of this gene family in Viridiplantae and will act as a reference source for future mechanistic studies on this U1 snRNP‐specific splicing factor. Significance Statement: This study describes a comprehensive analysis to systematically name and identify 115 U1‐70K genes from 67 plant species, ranging from algae to angiosperms. [ABSTRACT FROM AUTHOR]

The plant hormone abscisic acid (ABA) plays a crucial role during the plant life cycle as well as in adaptive responses to environmental stresses. The core regulatory components of ABA signaling in plants are the pyrabactin resistance1/PYR1-like/regulatory component of ABA receptor family (PYLs), which comprise the largest plant hormone receptor family known. They act as negative regulators of members of the protein phosphatase type 2C family. Due to the biological importance of PYLs, many researchers have focused on their genetic redundancy and consequent functional divergence. However, little is understood of their evolution and its impact on the generation of regulatory diversity. In this study, we identify positive selection and functional divergence in PYLs through phylogenetic reconstruction, gene structure and expression pattern analysis, positive selection analysis, functional divergence analysis, and structure comparison. We found the correlation of desensitization of PYLs under specific modifications in the molecular recognition domain with functional diversification. Hence, an interesting antagonistic co-evolutionary mechanism is proposed for the functional diversification of ABA receptor family proteins. We believe a compensatory evolutionary pathway may have occurred. [ABSTRACT FROM AUTHOR]

Drug resistance is one of the most intractable issues for successful treatment in current clinical practice. Although many mutations contributing to drug resistance have been identified, the relationship between the mutations and the related pharmacological profile of drug candidates has yet to be fully elucidated, which is valuable both for the molecular dissection of drug resistance mechanisms and for suggestion of promising treatment strategies to counter resistant. Hence, effective prediction approach for estimating the sensitivity of mutations to agents is a new opportunity that counters drug resistance and creates a high interest in pharmaceutical research. However, this task is always hampered by limited known resistance training samples and accurately estimation of binding affinity. Upon this challenge, we successfully developed Auto In Silico Macromolecular Mutation Scanning (AIMMS), a web server for computer-aided de novo drug resistance prediction for any ligand–protein systems. AIMMS can qualitatively estimate the free energy consequences of any mutations through a fast mutagenesis scanning calculation based on a single molecular dynamics trajectory, which is differentiated with other web services by a statistical learning system. AIMMS suite is available at http://chemyang.ccnu.edu.cn/ccb/server/AIMMS/. [ABSTRACT FROM AUTHOR]

• Cryptic binding sites against two carcinogenic mechanisms: target mutation and signaling pathways. • Cryptic binding sites were found in 16 cancer resistance targets, ten of which engineered inhibitors to address resistance. • The effect of dual medicinal drugs based on the cryptic binding site and active site is significant. • Discovery of cryptic binding site in RET, CK2α, TEAD and successful cases of drug design based on it. 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. [ABSTRACT FROM AUTHOR]

• Gather online knowledge bases of drug resistance mutations in human cancer. • Discuss tools that can predict mutational effects on protein–ligand interactions. • Give examples of potential inhibitor discovery to combat cancer drug resistance. • Challenges and opportunities from a clinician's perspective in using these tools. 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. [ABSTRACT FROM AUTHOR]

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 H 2 O 2 is crucial to trigger stress response and enhance stress tolerance. However, the dynamic regulation mechanism of H 2 O 2 remains unclear. Here, we show that MhCAT2 (catalase 2 in Malus hupehensis) undergoes oxidative modification in an O 2 •--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 H 2 O 2 caused by salt stress-induced catalase (CAT) over-expression. Overall, this finding provides mechanistic insights on stress tolerance augmentation by an O 2 •--mediated switch that regulates H 2 O 2 homeostasis in Malus hupehensis. • We identified the mechanism of decreased CAT2 activity in Malus hupehensis under salt stress. • MhCAT2 undergoes oxidative modification in a O 2 ·--dependent manner, causing its reduced activity. • The substitution of His225 with Tyr weakens the activity of MhCAT2. • The oxidation modification of catalase provided a post-translational brake mechanism for the excessive scavenging of H 2 O 2. [ABSTRACT FROM AUTHOR]

Slow‐binding inhibitors with long residence time on the target often display superior efficacy in vivo. Rationally designing inhibitors with low off‐target rates is restricted by a limited understanding of the structural basis of slow‐binding inhibition kinetics in enzyme–drug interactions. 4‐Hydroxyphenylpyruvate dioxygenase (HPPD) is an important target for drug and herbicide development. Although the time‐dependent behavior of HPPD inhibitors has been studied for decades, its structural basis and mechanism remain unclear. Herein, we report a detailed experimental and computational study that explores structures for illustrating the slow‐binding inhibition kinetics of HPPD. We observed the conformational change of Phe428 at the C‐terminal α‐helix in the inhibitor‐bound structures and further identified that the inhibition kinetics of drugs are related to steric hindrance of Phe428. These detailed structural and mechanistic insights illustrate that steric hindrance is highly associated with the time‐dependent behavior of HPPD inhibitors. These findings may enable rational design of new potent HPPD‐targeted drugs or herbicides with longer target residence time and improved properties. Database: Structure data are available in the PDB under the accession numbers 5CTO (released), 5DHW (released), and 5YWG (released). 4‐Hydroxyphenylpyruvate dioxygenase (HPPD) is a target for drug and herbicide development. Although the slow‐binding behavior of HPPD inhibitors has been studied for decades, its mechanism remains unclear. We combined the enzyme kinetics, crystallography and computational simulations study to explore the mechanism, and identified that the steric hindrance of Phe428 of Arabidopsis thaliana HPPD is highly associated with the slow‐binding behavior of HPPD inhibitors. These findings enable the rational design of novel HPPD‐targeted inhibitors with improved properties. [ABSTRACT FROM AUTHOR]

• CIPDB is a database containing 417,203 classified cation and π pairs. • CIPDB is a bioinformatic tool for facilitating the analysis of cation and π interactions in protein. • Cationic ligands form much more diverse interaction modes than arene ligands. • Cation and π interactions directly affect the function of targets involved in a biological system. 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. [ABSTRACT FROM AUTHOR]

In eukaryotes, mechanisms such as alternative splicing ( AS) and alternative translation initiation ( ATI) contribute to organismal protein diversity. Specifically, splicing factors play crucial roles in responses to environment and development cues; however, the underlying mechanisms are not well investigated in plants. Here, we report the parallel employment of short-read RNA sequencing, single molecule long-read sequencing and proteomic identification to unravel AS isoforms and previously unannotated proteins in response to abscisic acid ( ABA) treatment. Combining the data from the two sequencing methods, approximately 83.4% of intron-containing genes were alternatively spliced. Two AS types, which are referred to as alternative first exon ( AFE) and alternative last exon ( ALE), were more abundant than intron retention ( IR); however, by contrast to AS events detected under normal conditions, differentially expressed AS isoforms were more likely to be translated. ABA extensively affects the AS pattern, indicated by the increasing number of non-conventional splicing sites. This work also identified thousands of unannotated peptides and proteins by ATI based on mass spectrometry and a virtual peptide library deduced from both strands of coding regions within the Arabidopsis genome. The results enhance our understanding of AS and alternative translation mechanisms under normal conditions, and in response to ABA treatment. [ABSTRACT FROM AUTHOR]

Mutation-induced resistance to targeted drug treatment poses a serious threat to successful chemotherapy. Multiple mutations underlying drug resistance remain a largely unsolved scientific issue. Tropomyosin receptor kinases (TRKs) are promising therapeutic targets for several malignant human cancers, but they have become less effective due to multiple resistance mutations. Thus, TRKs are representative cases to explore the problem of multiple resistance mutations. Here, we proposed a conformational adjustment strategy of drug design to overcome multiple resistance mutations in cancer treatments. A representative inhibitor, TIY-7, exhibited remarkable inhibitory activity against five TRK mutants, showing an IC 50 value of 1.1 nM against the most severe mutant TRKA-G595R. Moreover, it displayed superior tumor growth inhibitory activity compared with the clinically used drug selitrectinib. These results validated our strategy to design a new inhibitor structure to overcome multiple resistance mutations. [Display omitted] • TRK is a promising drug target for NTRK gene fusion cancers. • Multiple resistance mutations are a great challenge for cancer therapy. • A novel inhibitor TIY-7 was designed and tested, showing high inhibitory activity. • In vivo study revealed the greater anticancer potency of TIY-7 than selitrectinib. [ABSTRACT FROM AUTHOR]

4-Hydroxyphenylpyruvate dioxygenase (HPPD), an essential enzyme in tyrosine catabolism, is an important target for treating type I tyrosinemia. Inhibition of HPPD can effectively alleviate the symptoms of type I tyrosinemia. However, only one commercial HPPD inhibitor, 2-(2-nitro-4-trifluoromethylbenzoyl) cyclohexane-1,3-dione (NTBC), has been available for clinical use so far. In the present study, a series of novel pyrazole-benzimidazolone hybrids were designed, synthesized and evaluated as potent human HPPD inhibitors. Most of the new compounds displayed significant inhibitory activity against the recombinant human HPPD. Moreover, compound 9l was identified as the most potent candidate with IC 50 value of 0.021 μM against recombinant human HPPD, about 3-fold more potent than NTBC. Thus the pyrazole-benzimidazolone hybrid has great potential to be further developed for the treatment of type I tyrosinemia. [ABSTRACT FROM AUTHOR]