In silico target prediction for elucidating the mode of action of herbicides including prospective validation

A first-in-class dimethyl 2-acetamido terephthalate inhibitor targeting Conyza canadensis SHMT1 with a novel herbicidal mode-of-action

INTRODUCTION

Herbicide application is a highly efficiency method of weed control that boots agricultural output and assures food security. The development of novel herbicides focuses on improved bioactivity and new modes of action. The amino acid biosynthesis was validated as a promising novel mode of action for herbicidal compounds. However, the amino acid biosynthesis enzyme remains largely unexplored for herbicidal targets.

OBJECTIVES

Serine hydroxymethyl transferase (SHMT) is an essentialenzyme in the photorespiratory cycle. The study aims to explore Conyza canadensis SHMT1 (CcSHMT1) as a promising target for herbicide discovery.

METHODS

Structure determination of CcSHMT1 was resolved by X-ray crystallography. Virtual screening docking experiments were performed with Glide version 5.5. Novel derivatives of dimethyl 2-acetamido terephthalate were further designed, synthesized, and bioassay. The druggability of the inhibitor was evidenced by ultrastructural changes in mitochondria, in vivo and vitro enzyme activity assays, and genetics analysis.

RESULTS

CcSHMT1 has a typical PLP-dependent enzyme 3D structure. The dimethyl 2-acetamido terephthalate-containing compounds had herbicidal activity. Dimethyl 2-(2-(4-(2-(4-bromo-2-chlorophenoxy) acetyl)piperazin-1-yl)acetamido) terephthalate (Compound 9ay, EC50 = 193.8 g a.i./ ha) exhibited the highest herbicidal activity on tested weed among the synthesized compounds. Compound 9ay had no obvious adverse effect on the growth of maize and honeybees. Compound 9ay was verified to target CcSHMT1 as an herbicide candidate.

CONCLUSION

A first-in-class CcSHMT1 inhibitor that could be developed as a potent herbicide with a new mode of action and provide an avenue for discovering novel inhibitors of pyridoxal-5-phosphate-dependent enzymes.

Recent Computer-Aided Studies on Herbicides: A Short Review

Current industrial herbicides have a negative impact on the environment and have widespread resistance, so computational studies on their properties, elimination, and overcoming resistance can be helpful. On the other hand, developing new herbicides, especially bioherbicides, is slow and costly. Therefore, computational studies that guide the design and search for new herbicides that exist in various plant sources, can alleviate the pain associated with the many obstacles. This review summarizes for the first time the most recent studies on both aspects of herbicides over 10 years.

Molecular Design of Novel Herbicide and Insecticide Seed Compounds with Machine Learning

Pesticides are widely used to improve crop productivity by eliminating weeds and pests. Conventional pesticide development involves synthesizing compounds, testing their activities, and studying their effects on the ecosystem. However, as pesticide discovery has an extremely low success rate, many compounds must be synthesized and tested. To overcome the high human, financial, and time costs of this process, machine learning is attracting increasing attention. In this study, we used machine learning for the molecular design of novel seed compounds for herbicides and insecticides. Classification models were constructed by using compounds that had been tested as herbicides and insecticides, and an inverse analysis of the constructed models was conducted. In the molecular design of herbicides, we proposed 186 new samples as herbicides using ensemble learning and a method for expressing explanatory variables that consider the relationships among eight weed species. For the molecular design of insecticides, we used undersampling and ensemble learning for the analysis of unbalanced data. Based on approximately 340,000 compounds, 12 potential insecticides were proposed, of which 2 exhibited actual activity when tested. These results demonstrate the potential of the developed machine-learning method for rapidly identifying novel herbicides and insecticides.

Hyperspectral Analysis for Discriminating Herbicide Site of Action: A Novel Approach for Accelerating Herbicide Research

In agricultural weed management, herbicides are indispensable, yet innovation in their modes of action (MOA)-the general mechanisms affecting plant processes-has slowed. A finer classification within MOA is the site of action (SOA), the specific biochemical pathway in plants targeted by herbicides. The primary objectives of this study were to evaluate the efficacy of hyperspectral imaging in the early detection of herbicide stress and to assess its potential in accelerating the herbicide development process by identifying unique herbicide sites of action (SOA). Employing a novel SOA classification method, eight herbicides with unique SOAs were examined via an automated, high-throughput imaging system equipped with a conveyor-based plant transportation at Purdue University. This is one of the earliest trials to test hyperspectral imaging on a large number of herbicides, and the study aimed to explore the earliest herbicide stress detection/classification date and accelerate the speed of herbicide development. The final models, trained on a dataset with nine treatments with 320 samples in two rounds, achieved an overall accuracy of 81.5% 1 day after treatment. With the high-precision models and rapid screening of numerous compounds in only 7 days, the study results suggest that hyperspectral technology combined with machine learning can contribute to the discovery of new herbicide MOA and help address the challenges associated with herbicide resistance. Although no public research to date has used hyperspectral technology to classify herbicide SOA, the successful evaluation of herbicide damage to crops provides hope to accelerate the progress of herbicide development.

Modern Approaches for the Development of New Herbicides Based on Natural Compounds

Weeds are a permanent component of anthropogenic ecosystems. They require strict control to avoid the accumulation of their long-lasting seeds in the soil. With high crop infestation, many elements of crop production technologies (fertilization, productive varieties, growth stimulators, etc.) turn out to be practically meaningless due to high yield losses. Intensive use of chemical herbicides (CHs) has led to undesirable consequences: contamination of soil and wastewater, accumulation of their residues in the crop, and the emergence of CH-resistant populations of weeds. In this regard, the development of environmentally friendly CHs with new mechanisms of action is relevant. The natural phytotoxins of plant or microbial origin may be explored directly in herbicidal formulations (biorational CHs) or indirectly as scaffolds for nature-derived CHs. This review considers (1) the main current trends in the development of CHs that may be important for the enhancement of biorational herbicides; (2) the advances in the development and practical application of natural compounds for weed control; (3) the use of phytotoxins as prototypes of synthetic herbicides. Some modern approaches, such as computational methods of virtual screening and design of herbicidal molecules, development of modern formulations, and determination of molecular targets, are stressed as crucial to make the exploration of natural compounds more effective.

Hierarchical cluster analysis of herbicide modes of action reveals distinct classes of multiple resistance in weeds

BACKGROUND

The number of weed species resistant to multiple herbicide modes of action (MoAs) has increased over the last 30 years and may in the future render existing herbicide MoAs obsolete for many cropping systems. Yet few predictive tools exist to manage this risk. Using a worldwide dataset of weed species resistant to multiple herbicide MoAs, hierarchical clustering was used to classify MoAs into similar groups in relation to the suite of resistant weed species they have in common. Network analyses then were used to explore the relative importance of species prevalence and similarity in cluster patterns.

RESULTS

Hierarchical clustering identified three similarly sized clusters of herbicide MoAs that were linked by the co-occurrence of resistant weeds: Herbicide Resistance Action Committee (HRAC) groups 2, 4, 5 and 9; HRAC groups 12, 14 and 15; and HRAC groups 1, 3 and 22. Cluster membership was consistent with similarities in the physiological or biochemical target of the herbicide MoAs. Network analyses revealed that the number of weed species resistant to two different MoAs was related to the number of weeds known to be resistant to each individual herbicide MoA.

CONCLUSIONS

Hierarchical cluster analysis provided new insights into the risk of weeds becoming resistant to more than one herbicide MoA. By clustering herbicide MoAs into three distinct groups, the potential exists for farmers to manage resistance by rotating herbicides between rather than within clusters, as far as crop, weed and environmental conditions allow.

NMR as a "Gold Standard" Method in Drug Design and Discovery

Studying disease models at the molecular level is vital for drug development in order to improve treatment and prevent a wide range of human pathologies. Microbial infections are still a major challenge because pathogens rapidly and continually evolve developing drug resistance. Cancer cells also change genetically, and current therapeutic techniques may be (or may become) ineffective in many cases. The pathology of many neurological diseases remains an enigma, and the exact etiology and underlying mechanisms are still largely unknown. Viral infections spread and develop much more quickly than does the corresponding research needed to prevent and combat these infections; the present and most relevant outbreak of SARS-CoV-2, which originated in Wuhan, China, illustrates the critical and immediate need to improve drug design and development techniques. Modern day drug discovery is a time-consuming, expensive process. Each new drug takes in excess of 10 years to develop and costs on average more than a billion US dollars. This demonstrates the need of a complete redesign or novel strategies. Nuclear Magnetic Resonance (NMR) has played a critical role in drug discovery ever since its introduction several decades ago. In just three decades, NMR has become a "gold standard" platform technology in medical and pharmacology studies. In this review, we present the major applications of NMR spectroscopy in medical drug discovery and development. The basic concepts, theories, and applications of the most commonly used NMR techniques are presented. We also summarize the advantages and limitations of the primary NMR methods in drug development.

Herbicidal activity of isobenzofuranones and in silico identification of their enzyme target

BACKGROUND

Given the weed resistance to various herbicides with different mechanisms of action, the search for new compounds that are more effective and exhibit low levels of impact to other species in nature has been imperative in the field of the agriculture. For this purpose, 16 phthalides, and furan-2(5H)-one were synthetized and evaluated for their effectiveness as herbicides in seeds of Sorghum bicolor (sorghum), Cucumis sativus (cucumber), and Allium cepa (onion). Furthermore, a preliminary in silico study was carried out to identify the enzyme target of the most active compounds.

RESULTS

In the assays with S. bicolor, the mixture rac-(3aR,4R,5S,6S,7S,7aS)-5,6-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one + rac-(3aR,4R,5R,6R,7S,7aS)-5,6-dibromohexahydro-4,7-methanoisobenzofuran-1(3H)-one (15a + 15b) showed comparable inhibitory activity to (S)-metolachlor, which was used as control herbicide at concentrations ranging from 50 μm to 1000 μm. The developments of the seeds evaluated were altered by all 17 compounds, either stimulating or inhibiting. The best results were presented by compounds 15a, and 15b, either in their pure form or as a mixture.

CONCLUSION

The results presented by 15a, and 15b were superior to the activity of the commercial herbicide (S)-metolachlor in the assays with C. sativus, and A. cepa. The in silico study provides strong evidence that the most active compounds bind to strigolactones esterases D14 through the same binding site of (5R)-5-hydroxy-3-methylfuran-2(5H)-one (H3M), which is one of the strigolactones (SLs) cleavage products. © 2019 Society of Chemical Industry.