Computer-Aided Drug Discovery in Plant Pathology

Pharmacophore Modeling and Binding Affinity of Secondary Metabolites from Angelica keiskei to HMG Co-A Reductase

Statins are cholesterol-lowering drugs with a mechanism of inhibiting 3-hydroxy-3-methylglutaryl-CoA reductase, but long-term use can cause side effects. An example of a plant capable of reducing cholesterol levels is Angelica keiskei (ashitaba). Therefore, this study aimed to obtain suitable compounds with inhibitory activity against the HMG-CoA reductase enzyme from ashitaba through in silico tests. The experiment began with screening and pharmacophore modeling, followed by molecular docking on ashitaba's compounds, statins groups, and the native ligand was (3R,5R)-7-[4-(benzyl carbamoyl)-2-(4-fluorophenyl)-5-(1-methylethyl)-1H-imidazole-1-yl]-3,5-dihydroxyheptanoic acid (4HI). Based on the results of the molecular docking simulations, 15 hit compounds had a small binding energy (ΔG). Pitavastatin, as the comparator drug (ΔG = -8.24 kcal/mol; Ki = 2.11 µM), had a lower ΔG and inhibition constant (Ki) than the native ligand 4HI (ΔG = -7.84 kcal/mol; Ki = 7.96µM). From ashitaba's compounds, it was found that 4'-O-geranylnaringenin, luteolin, isobavachalcone, dorsmannin A, and 3'-carboxymethyl-4,2'-dihydroxy-4'-methoxychalcone have low ΔG of below -6 kcal/mol. The lowest ΔG value was found in 3'-carboxymethyl-4,2'-dihydroxy-4'-methoxy chalcone with a ΔG of -6.67 kcal/mol and Ki value of 16.66 µM, which was lower than the ΔG value of the other comparator drugs, atorvastatin (ΔG = -5.49 kcal/mol; Ki = 1148.17 µM) and simvastatin (ΔG = -6.50 kcal/mol; Ki = 22.34 µM). This compound also binds to the important amino acid residues, including ASN755D, ASP690C, GLU559D, LYS735D, LYS691C, and SER684C, through hydrogen bonds. Based on the results, the compound effectively binds to six important amino acids with good binding affinity and only requires a small concentration to reduce half of the enzyme activity.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

Identification of Fungicide Combinations for Overcoming Plasmopara viticola and Botrytis cinerea Fungicide Resistance

Fungal diseases, including downy mildew (caused by Plasmopara viticola) and gray mold (caused by Botrytis cinerea), significantly impact the marketable yield of grapes produced worldwide. Cytochrome b of the mitochondrial respiratory chain of these two fungi is a key target for Quinone outside inhibitor (QoI)-based fungicide development. Since the mode of action (MOA) of QoI fungicides is restricted to a single site, the extensive usage of these fungicides has resulted in fungicide resistance. The use of fungicide combinations with multiple targets is an effective way to counter and slow down the development of fungicide resistance. Due to the high cost of in planta trials, in silico techniques can be used for the rapid screening of potential fungicides. In this study, a combination of in silico simulations that include Schrödinger Glide docking, molecular dynamics, and Molecular Mechanism-Generalized Born Surface Area calculation were used to screen the most potent QoI and non-QoI-based fungicide combinations to wild-type, G143A-mutated, F129L-mutated, and double-mutated versions that had both G143A and F129L mutations of fungal cytochrome b. In silico docking studies indicated that mandestrobin, famoxadone, captan, and thiram have a high affinity toward WT cytochrome b of Botrytis cinerea. Although the QoIs mandestrobin and famoxadone were effective for WT based on in vitro results, they were not broadly effective against G143A-mutated isolates. Famoxadone was only effective against one isolate with G143A-mutated cytochrome b. The non-QoI fungicides thiram and captan were effective against both WT and isolates with G143A-mutated cytochrome b. Follow-up in silico docking and molecular dynamics studies suggested that fungicide combinations consisting of famoxadone, mandestrobin, fenamidone, and thiram should be considered in field testing targeting Plasmopara viticola and Botrytis cinerea fungicide resistance.

Precision drug design against Acidovorax oryzae: leveraging bioinformatics to combat rice brown stripe disease

Bacterial brown stripe disease caused by Acidovorax oryzae is a major threat to crop yields, and the current reliance on pesticides for control is unsustainable due to environmental pollution and resistance. To address this, bacterial-based ligands have been explored as a potential treatment solution. In this study, we developed a protein-protein interaction (PPI) network for A. oryzae by utilizing shared differentially expressed genes (DEGs) and the STRING database. Using a maximal clique centrality (MCC) approach through CytoHubba and Network Analyzer, we identified hub genes within the PPI network. We then analyzed the genomic data of the top 10 proteins, and further narrowed them down to 2 proteins by utilizing betweenness, closeness, degree, and eigenvector studies. Finally, we used molecular docking to screen 100 compounds against the final two proteins (guaA and metG), and Enfumafungin was selected as a potential treatment for bacterial resistance caused by A. oryzae based on their binding affinity and interaction energy. Our approach demonstrates the potential of utilizing bioinformatics and molecular docking to identify novel drug candidates for precision treatment of bacterial brown stripe disease caused by A. oryzae, paving the way for more targeted and sustainable control strategies. The efficacy of Enfumafungin in inhibiting the growth of A. oryzae strain RS-1 was investigated through both computational and wet lab methods. The models of the protein were built using the Swiss model, and their accuracy was confirmed via a Ramachandran plot. Additionally, Enfumafungin demonstrated potent inhibitory action against the bacterial strain, with an MIC of 100 µg/mL, reducing OD600 values by up to 91%. The effectiveness of Enfumafungin was further evidenced through agar well diffusion assays, which exhibited the highest zone of inhibition at 1.42 cm when the concentration of Enfumafungin was at 100 µg/mL. Moreover, Enfumafungin was also able to effectively reduce the biofilm of A. oryzae RS-1 in a concentration-dependent manner. The swarming motility of A. oryzae RS-1 was also found to be significantly inhibited by Enfumafungin. Further validation through TEM observation revealed that bacterial cells exposed to Enfumafungin displayed mostly red fluorescence, indicating destruction of the bacterial cell membrane.

In-silico prediction, characterization, molecular docking and dynamic simulation studies for screening potential fungicides against leaf rust of Triticum aestivum

Triticum aestivum is an important crop worldwide, which is a large source of food grain. T.aestivum demands on developed countries will grow every year, this increase in the demand is profoundly serious especially in the light climate change which would lead to a 29% reduction in final productivity. Rust fungus attacks the T.aestivum, specifically newly planted T.aestivum plants, which block the vascular system, stun, and finally damage grain and tillers. In present study we predict the 3D structure then find the binding pocket and conserved domains for MAPkinase-1 of Puccinia triticina. After that, screen the FungiPAD, PubChem, NPAtlas databases by physicochemical properties, docking, clustering, ADME (Absorption, distribution, metabolism, and excretion) and PAINS (pan assay interference compounds) filter analysis. Through this screening process screen the nine compounds, which are benzovindiflupyr, furametpyr, isopyrazam, fenaminstrobin, and flumorph from Fungicide database: zoxamide, vinclozolin, pentachloronitrobenzene, and dithianon from PubChem database, based on the binding energy, clustering, ADME and PAINS analysis. All these nine compounds bind in the same pocket and show the same pattern of interaction. Among these nine compounds, select the two compounds (PubChem:122087 (-6.96 kcal/mol) and FDBD02904 (-8.62 kcal/mol)) based on binding energy for 100 ns MD simulation and free energy calculation. MD simulation shows stability throughout the simulation, and it shows the sable interaction when compounds bind to the MAPKinase 1 protein which may help to protein kinase pathways in plant defense response. This result helps to design alternative fungicide against the wheat rust disease.Communicated by Ramaswamy H. Sarma.

Computational Strategy for Minimizing Mycotoxins in Cereal Crops: Assessment of the Biological Activity of Compounds Resulting from Virtual Screening

Cereal crops are frequently affected by toxigenic Fusarium species, among which the most common and worrying in Europe are Fusarium graminearum and Fusarium culmorum. These species are the causal agents of grain contamination with type B trichothecene (TCTB) mycotoxins. To help reduce the use of synthetic fungicides while guaranteeing low mycotoxin levels, there is an urgent need to develop new, efficient and environmentally-friendly plant protection solutions. Previously, F. graminearum proteins that could serve as putative targets to block the fungal spread and toxin production were identified and a virtual screening undertaken. Here, two selected compounds, M1 and M2, predicted, respectively, as the top compounds acting on the trichodiene synthase, a key enzyme of TCTB biosynthesis, and the 24-sterol-C-methyltransferase, a protein involved in ergosterol biosynthesis, were submitted for biological tests. Corroborating in silico predictions, M1 was shown to significantly inhibit TCTB yield by a panel of strains. Results were less obvious with M2 that induced only a slight reduction in fungal biomass. To go further, seven M1 analogs were assessed, which allowed evidencing of the physicochemical properties crucial for the anti-mycotoxin activity. Altogether, our results provide the first evidence of the promising potential of computational approaches to discover new anti-mycotoxin solutions

Plant-Derived Protectants in Combating Soil-Borne Fungal Infections in Tomato and Chilli

Fungal infections transmitted through the soil continue to pose a threat to a variety of horticultural and agricultural products, including tomato and chilli. The indiscriminate use of synthetic pesticides has resulted in a slew of unintended consequences for the surrounding ecosystem. To achieve sustainable productivity, experts have turned their attention to natural alternatives. Due to their biodegradability, varied mode of action, and minimal toxicity to non-target organisms, plant-derived protectants (PDPs) are being hailed as a superior replacement for plant pesticides. This review outlines PDPs’ critical functions (including formulations) in regulating soil-borne fungal diseases, keeping tomato and chilli pathogens in the spotlight. An in-depth examination of the impact of PDPs on pathogen activity will be a priority. Additionally, this review emphasises the advantages of the in silico approach over conventional approaches for screening plants’ secondary metabolites with target-specific fungicidal activity. Despite the recent advances in our understanding of the fungicidal capabilities of various PDPs, it is taking much longer for that information to be applied to commercially available pesticides. The restrictions to solving this issue can be lifted by breakthroughs in formulation technology, governmental support, and a willingness to pursue green alternatives among farmers and industries.

In silico Analysis of Allium sativum Bioactive Compounds against Effector Protein from Pseudomonas syringae pv. pisi

Allium sativum commonly known as Garlic is a familiar herb, highly studied for its valuable medicinal properties. The main objective involved in the current research is to analyze inhibitor and antibacterial action of bioactive compounds (ligands) present in the methanolic extract of Allium sativum bulbs against phytopathogen protein (receptor) through molecular docking. The effector protein AvrRps4 (4B6X) from phytopathogen Pseudomonas syringae pv. pisi, a protein responsible for Effector triggered immunity (ETI) activation and to subvert host responses in Pea plant was selected as protein target. The docking interactions between opted ligands and target protein, with ampicillin as control was done using PyRx software tool and analysed using Discovery studio 3.1 Visualiser. The outcomes obtained from in silico analysis suggested that the bioactive compound namely Diethyl 3-methyl-5-[(2,2,2-trifluoroacetyl)amino]thiophene-2,4-dicarboxylate bind effectively showing -5.7 binding energy value in comparison with antibiotic ampicillin which showed binding energy -5.8 value. This research study concluded that the bioactive compounds from methanolic extract of Allium sativum bulbs displayed a potential inhibitory activity against effector AvrRps4 protein exhibiting antibacterial properties and may be considered as possible substantial lead molecules in future prospects.

Effect of shape and anthocyanin capping on antibacterial activity of CuI particles

The recent upsurge of antibiotic-resistant infections has posed to be a serious health concern worldwide. In the present paper, the effect of shape & capping agent on the antibacterial activity (on Skin and Urinary Tract Infection (UTI) causing bacteria) of copper iodide (CuI) particles was probed. CuI synthesized without a capping agent was leaf-like, and that with one was prismatic in shape. XRD of the synthesized CuI confirmed their high crystalline nature and purity. The average crystallite sizes of capped and uncapped CuI were calculated to be 91.10 nm and 89.01 nm respectively from X-Ray powder diffraction data. The average particle size of capped CuI was found to be 492.7 nm and that of uncapped CuI was found to be 2.96 μm using HR-SEM analysis. The crystals obtained were further characterized using EDAX, FTIR spectroscopy and UV-Visible spectroscopy. Antibacterial activity of prismatic CuI capped with the flower extract of Hibiscus rosa-sinensis showed better activity than that of uncapped CuI. AFM analysis was carried out to confirm the proposed mechanism for antibacterial activity through the morphological changes on the bacterial cell wall in the presence of capped CuI. Molecular docking studies were performed to reaffirm the enhanced antibacterial activity of prismatic CuI further. The present study demonstrates the superior antibacterial propensity of prismatic CuI, consequently making it a potent antibacterial agent.

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.

Synergism of Plant Compound With Traditional Antimicrobials Against Streptococcus spp. Isolated From Bovine Mastitis

Mastitis is an inflammation of the mammary gland that causes major losses in the dairy industry. Streptococcus spp. are among the main agents of this disease. Increased resistance to antibiotics is one of the causes of therapeutic failure. Plants, due to their broad chemodiversity, are an interesting source of new molecules with antibacterial activity. Using these compounds along with traditional antibiotics is a possible method for reversing resistance. The objective of this work was to determine the interactions between the activities of guttiferone-A and 7-epiclusianone, two active substances isolated from the fruits of Garcinia brasiliensis, and traditional antibiotics against Streptococcus spp. isolated from bovine mastitis and known to be resistant to them. First, the MIC for the antibiotics and bioactive compounds was determined, followed by their activities, alone and in combination. Then, their cytotoxicity was measured in bovine mammary epithelial cells. Finally, molecular docking simulations were performed to elucidate molecular details of the interactions between β-lactamase and the compounds binding to it (clavulanic acid, ampicillin, 7-epiclusianone, and guttiferone-A). The bacterial isolates were resistant to ampicillin and gentamicin. Both antibiotics showed predominantly synergistic antibacterial activities in combination with guttiferone-A or 7-epiclusianone. These two active substances were not cytotoxic at synergistic concentrations and both showed strong binding to β-lactamase, which may explain the reversal of ampicillin resistance. These substances are promising for the treatment of bovine mastitis.