The stakes are considerable. Developing a single new synthetic pesticide from laboratory synthesis to production scale is estimated to cost between 250 and 280 million US dollars and take roughly ten years of laboratory and field testing. Computational biology offers a way to trim those costs by predicting molecular target structures and screening candidate compounds before expensive experimental campaigns begin. Structure-based molecular design has already been applied successfully in agrochemical research, for example in the discovery of novel inhibitors of the mitogen-activated protein kinase FgGpmk1, which is vital to the development and virulence of Fusarium graminearum, the causative agent of Fusarium head blight.

The genus Phytophthora contains roughly 200 species and is probably the most economically important group in plant pathology. Although a range of fungicides, including protective, translaminar, curative, systemic and antisporulant formulations, are used against these pathogens, Phytophthora populations are capable of developing resistance, forcing researchers to search continuously for new active molecules or to modernise existing ones. The study’s authors selected P. infestans as a typical representative of the genus and focused on two protein families with well-established links to fungicide biology.

Cytochrome P450 enzymes, heme-containing monooxygenases, play a crucial role in the biology and ecology of fungi, and proteins of the CYP450 system in Phytophthora capsici are known to be involved in the development of resistance to fungicides. Glutathione-S-transferase proteins in P. infestans may likewise participate in fungicide resistance and are actively expressed during infection of potato plants. Because no information on the specific target proteins of the fungicides under study could be found in the scientific literature, the researchers chose these two enzymes as their model targets, creating an opportunity to apply molecular docking to direct targets of the pathogen for the first time.

Fluopicolide, a synthetic benzamide fungicide characterised by its ability to delocalise spectrin-like proteins, and propamocarb, a synthetic carbamide fungicide that inhibits lipid synthesis, were selected for the investigation. Both are typical fungicides against Phytophthora and are used on experimental field farms, targeting oomycetes including Phytophthora and some Pythium species, and controlling root rot on crops such as grapes, hops, potatoes and various vegetables. Prior academic work on propamocarb is sparse, and the researchers found no published study of fluopicolide using molecular interaction modelling methods, making this the first such analysis.

Methodologically, the study combined machine learning prediction with homology-based cross-validation to identify ligand-binding sites on the target proteins. The researchers used the P2Rank algorithm, accessed through the PrankWeb server, to predict molecular cavities where ligands might reside, and then validated these predictions by performing BLAST alignment of reference proteins with annotated homologous structures from the UniProtKB database. This dual approach was designed to overcome a common problem in computational structural biology: when working with proteins obtained by homology modelling or neural network prediction tools such as AlphaFold, the pool of biomolecules often has a low level of experimental evidence, making it difficult to localise molecular cavities with confidence. By superimposing key amino acid residues found in better-annotated homologues onto the predicted cavities, the team could select the most suitable binding sites before refining ligand position through docking.

For the docking itself, the researchers employed rigid intermolecular blind docking using AutoDock version 4.2 with MGLtools version 1.5.6 as a graphical interface. They removed residual water from the protein structures, added protons to amino acid residues at potential binding sites, and defined cubic grid boxes with 40 by 40 by 40 angstrom faces to indicate the docking region. Five docking attempts were performed for each region, and a standard 50-repetition protocol was used for molecular interaction modelling. The resulting complexes were visualised in PyMol version 2.5.4 and Discovery Studio Visualizer to generate two-dimensional and three-dimensional images of the ligand-receptor interactions.

The results reveal a clear hierarchy of binding stability. The lowest minimum binding energy, at minus 9.53 kilocalories per mole, belonged to the CYP450-fluopicolide complex, while the highest, at minus 4.82 kilocalories per mole, belonged to the GST-propamocarb complex. Across all conformation ranks, fluopicolide consistently formed more stable complexes with both target proteins than propamocarb did. The difference in minimum binding energies between the two fungicides was 29.4 percent for CYP450 and 24.3 percent for GST. A total of 21 protein-fungicide complexes were modelled, with between four and six conformation ranks per complex, and the relatively small number of potential positions suggests a high probability of close interaction between ligand and protein site.

Beyond binding energy, the modelled complexes exhibited structural stability through the formation of multiple bond types, including hydrogen bonds, hydrophobic interactions, alkyl bonds and van der Waals forces. Between three and eight different types of bonds were observed across the various complexes, and between eight and seventeen amino acid residues were bound to the ligands during complex formation. For the CYP450-fluopicolide complex, these included alanine, arginine, histidine, methionine, leucine, asparagine, valine, serine, cysteine, tryptophan and proline, with a quantitative predominance of aliphatic amino acids across all complexes. The CYP450-fluopicolide complex showed the greatest diversity of bonding types, while the CYP450-propamocarb complex showed the least. Notably, docking with CYP450 occurred on the C-terminal side at the heme-binding cofactor site, while docking with GST occurred on the opposite side at the N-terminal domain, at the glutathione-binding G-site, suggesting that blocking these functional sites would likely impair protein function.

The study’s conclusions point towards fluopicolide as the more advantageous option for effectively controlling P. infestans, given its greater propensity to form stable ligand-receptor complexes with all presented target proteins. The researchers emphasise that their algorithm and results are characterised by a high degree of impartiality, making them readily applicable for adoption by fellow researchers in bioinformatics. As fungicide resistance continues to erode the efficacy of existing crop protection agents, computational approaches of this kind may prove increasingly valuable in identifying the molecular mechanisms underlying fungicide action and in guiding the rational design of new compounds before costly laboratory and field trials begin.

Subject of Research: In silico molecular docking of fungicides with Phytophthora infestans target proteins

Article Title: Elucidating fungicide complex formation mechanisms with Phytophthora infestans target proteins: In silico insights

Article References: Timkin, P. D., Gafforov, Y., Wang, M., Qiao, K., Ajiev, A. B., Pamirsky, I. E., & Golokhvast, K. S. (2025). Elucidating fungicide complex formation mechanisms with Phytophthora infestans target proteins: In silico insights. BioRisk, 23, 79-81. https://doi.org/10.3897/biorisk.23.139718

Image Credits: AI Generated

DOI: 10.3897/biorisk.23.139718

Keywords: Elucidating, fungicide, complex, formation, mechanisms, Phytophthora, infestans, target, proteins, silico, insights, scientific research

Researchers at Virginia Tech believe they have found a way to shift that timing dramatically in farmers’ favor. A team at the university has developed a low-cost electrochemical sensor capable of detecting Sclerotinia blight in peanut plants as early as five days after infection. By contrast, the physical symptoms that farmers have traditionally relied upon — the telltale signs of fungal invasion visible on stems and foliage — sometimes do not appear for as long as two months. That gap between infection and visible disease is precisely where the pathogen gains its advantage, spreading silently through a field while a grower remains unaware that anything is wrong.

The research, led by Frank Erukainure, a Ph.D. candidate in the Department of Biological Systems Engineering and an Institute for Critical Technology and Applied Science Doctoral Scholar, was described in a paper published in ACS Agricultural Science & Technology. Erukainure’s work on the project earned him the 2026 Boyd-Scott Graduate Research Award, one of the American Society of Agricultural and Biological Engineers’ top honors for graduate scholarship. The recognition underscores how significant the problem is: Sclerotinia blight is not a marginal nuisance but a persistent threat to one of Virginia’s most valuable row crops.

The stakes are easy to quantify. In 2024, farmers in the southeastern region of Virginia harvested roughly 30,000 acres of peanuts, bringing in a cumulative 37.4 million dollars. Historically, growers have relied on time-consuming physical inspections to detect the fungus, walking their fields and examining plants for signs of disease. The trouble with that approach is that by the time the fungus produces its characteristic structures — irregular, rice grain-sized formations that Erukainure describes as the disease’s most visible calling card — the outbreak is often already beyond easy control. Once those structures appear, the disease spreads quickly across the farm, and the pathogen settles into the soil, where it can survive for years, making eradication extraordinarily difficult.

The technical insight behind the new sensor lies in plant biochemistry. When peanut plants are infected with Sclerotinia minor, they produce abundant quantities of oxalic acid, a chemical compound that accumulates in the plant’s sap. This elevated oxalic acid serves as an early biochemical fingerprint of infection, a molecular distress signal that appears long before any outward symptom. The Virginia Tech sensor is designed to read that signal directly. Peanut plant sap is extracted from the plant and placed on the sensor, which is built from 3D-printed resin substrates fitted with platinum electrodes. The electrochemical platform measures the concentration of oxalic acid in the sample, flagging infections that would otherwise remain hidden for weeks.

The choice of materials is not incidental. By relying on 3D-printed resin and a relatively simple electrode arrangement, the team has kept the sensor inexpensive to produce, a critical consideration for any technology that hopes to reach working farms. Cost, scalability, and the number of sensors a grower would need to monitor large acreages are all questions the researchers are actively evaluating as development continues. These practical factors will shape how the technology is ultimately deployed on plants in real farm environments, and the team is candid that additional field testing is needed before the sensors can move from validated greenhouse trials into routine agricultural use.

The early detection capability itself has already been validated in greenhouse trials, an important milestone that demonstrates the sensor works under controlled conditions. If the fungus is caught early enough, the infected plant can be treated with a fungicide to prevent the disease from spreading to healthy parts of the plant and to neighboring crops. That intervention window — the days between infection and visible symptom onset — is exactly what the sensor opens up. Instead of reacting to an established outbreak, growers could treat a nascent one, protecting both their yields and the soil beneath their fields from long-term contamination.

The peanut blight sensor is not the team’s first foray into plant-mounted diagnostics. In a previous study published in ACS Sensors, Erukainure and his advisor Abhilash Chandel, assistant professor of precision agriculture and data management, demonstrated a wearable microneedle sensor designed to monitor glucose and water stress in crop plants, providing real-time sensor readings directly from living plants. That device attaches to the stem of a plant and streams plant health data to a phone application, giving growers a continuous window into the physiological state of their crops. The team now hopes to develop a similar wearable sensor for detecting Sclerotinia blight in peanut plants, extending the same continuous-monitoring philosophy to fungal disease.

Chandel frames the broader shift in stark terms: technologies like these are transforming plant production. By monitoring plant health in real time, he argues, such sensors enable proactive intervention, improving crop resilience, productivity, and sustainability. The vision is one in which disease management moves from periodic, labor-intensive scouting to continuous, data-driven surveillance — a model familiar from precision medicine, applied instead to the physiology of crops. For a disease like Sclerotinia blight, whose survival structures can lurk in soil for years and sabotage subsequent growing seasons, the value of catching an infection at day five rather than day sixty is difficult to overstate.

The next step is already on the calendar. The researchers plan to connect with farmers in the Tidewater region of Virginia to pilot test the sensors in the coming year, a crucial transition from controlled greenhouse experiments to the messier realities of commercial production. That pilot phase will determine how the technology performs across variable field conditions, how growers integrate it into existing management routines, and whether the promise of early detection translates into measurably healthier harvests. If it does, a modest 3D-printed sensor with platinum electrodes could become an essential tool in protecting a crop that Virginia has grown for two hundred years — and in demonstrating that the future of plant disease management may be measured not in seasons, but in days.

Subject of Research: Electrochemical biosensor detection of Sclerotinia blight fungal infection in peanut crops

Article Title: Protecting the peanut: Sensors help fend off fungus

Article References: Protecting the peanut: Sensors help fend off fungus. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: peanuts, Sclerotinia blight, Sclerotinia minor, electrochemical sensor, oxalic acid, plant disease detection, precision agriculture, Virginia Tech, fungicide, wearable plant sensors, crop yield loss, 3D-printed sensors