Sclerotinia sclerotiorum infects over 425 host plants and has a near-worldwide distribution, which makes estimating the true economic cost complicated, if not near impossible. The genus also contains at least two other significant plant pathogens, S. minor and S. trifoliorum, which contribute to agricultural losses. Beyond agriculture, Sclerotinia and related genera in the family Sclerotiniaceae present an intriguing case study for host generalism—the ability of a pathogen to infect multiple plant species. This trait, shared with the well-known generalist Botrytis cinerea, offers an opportunity for molecular and genomic investigations to uncover insights into pathogenicity. Some key pathogenicity genes within Sclerotiniaceae have already been explored using comparative genetics, but this only scratches the surface of what could be learned from these pathogens.

The taxonomic history of Sclerotinia is complex and spans nearly two centuries. The oldest name for S. sclerotiorum is Peziza sclerotiorum, described in 1837 by Libert, who noted that its apothecia resembled ascomycetous cup fungi classified in Peziza. Thirty-three years later, Fuckel renamed the species in honor of Libert as Sclerotinia libertiana when he introduced the genus Sclerotinia. De Bary later considered S. libertiana illegitimate and provided the new name Sclerotinia sclerotiorum as a result. Whetzel’s 1945 work represented a convergence point for the histories of S. sclerotiorum, the genus Sclerotinia, and the family Sclerotiniaceae. He proposed major changes to the group when introducing the family, designating Sclerotinia as its type and proposing 12 species within the genus, with S. sclerotiorum as the generic type.

However, this classification was later challenged. Korf and Dumont introduced the genus Whetzelinia in 1972 and renamed S. sclerotiorum as Whetzelinia sclerotiorum, arguing that S. candolleana—which Honey had considered the type of Sclerotinia—should have remained in the genus. Buchwald and Neergaard subsequently proposed conserving Sclerotinia with S. sclerotiorum as the type species, a move approved by the Special Committee on Fungi and Lichens in 1978. This resulted in the classification still in use today, with S. sclerotiorum as the type species of Sclerotinia, which in turn is the type genus of Sclerotiniaceae.

The higher-order taxonomy of Sclerotiniaceae has also been marked by historical instability. Whetzel originally classified the family in the class Discomycetes, which contained all apothecia-producing fungi. Over time, it became clear that Discomycetes belonged to several unrelated lineages, leading to these fungi being reassigned to various taxonomic groups. The order Helotiales, which includes Sclerotiniaceae, was eventually moved to the class Leotiomycetes. Since then, the higher-level taxonomy of Sclerotiniaceae has remained consistent, with its position in Ascomycota, Pezizomycotina, Leotiomycetes, and Helotiales unchanged. Some publications still incorrectly list Sclerotinia species as Discomycetes, likely because the most cited review of S. sclerotiorum lists it in this class.

A significant change came when Holst-Jensen and colleagues split Sclerotiniaceae into two distinct families in 1997. Historically, all stromatic, stipitate discomycetes were grouped into Sclerotiniaceae, but several studies showed that the sclerotial and substratal stromatal taxa likely represented separate lineages. A phylogenetic evaluation supported this two-lineage hypothesis, triggering the formation of Rutstroemiaceae and the subsequent division of genera between the two families. When the family was first described, it included 15 genera. Two additional genera were subsequently described by Kohn in 1979. Following the introduction of Rutstroemiaceae, Sclerotiniaceae retained nine genera and gained five new ones, while several genera were reclassified into Rutstroemiaceae.

Recent phylogenetic studies have offered the most up-to-date insights into the classification of Sclerotiniaceae within Leotiomycetes. Ekanayaka and colleagues produced a maximum-likelihood tree from 482 strains representing 187 Leotiomycetes species based on sequence data from five regions, including the internal transcribed spacer rDNA region (ITS), the 28S nuclear ribosomal large subunit, the 18S nuclear ribosomal small subunit, and partial gene regions of rpb2 and tef1-α. They observed a Sclerotiniales clade that included six families. Johnston and colleagues used three approaches: a phylogenomic tree based on 3,156 single-copy genes from 51 species, a 15-gene phylogeny of 259 selected species, and an ITS phylogeny that included taxa not represented in the other analyses. They identified a sclerotinioid clade comprising four strongly supported subclades. The two studies identified 36 genera as belonging to Sclerotiniaceae, with 26 placed there by both publications. However, six genera showed conflicting placements, and the broad structure of the sclerotinioid clade remains broadly similar but not identical between the studies.

Turning to the genus itself, species delineation in Sclerotinia has been the subject of much debate. Before 1979, 279 Sclerotinia species had been described. Their taxonomy was revised by Kohn, who accepted only three species: S. sclerotiorum, S. minor, and S. trifoliorum. Wherever possible, Kohn examined specimens and their published protologues, which led to the synonymization of 21 names with the three accepted species. Two hundred and ten names were removed from Sclerotinia and transferred to other genera. While evaluating these 279 names, she also introduced two new genera, Dumontinia and Elliottinia, to accommodate Sclerotinia tuberosa and Sclerotinia kerneri respectively. No living type material was available for the three species she accepted, leading to the lectotypification of S. sclerotiorum and the neotypification of S. minor and S. trifoliorum.

Kohn’s monograph was also valuable for its extensive review of the morphological characters informative in delimiting Sclerotinia species. Whetzel’s initial description of the genus was limited to species that produce definite, tuberoid sclerotia, have single-celled, hyaline ascospores, and lack a functional conidial state. Kohn further restricted the genus by including only those species in which the cells of the outer excipulum of the apothecium are globose and oriented perpendicularly to the apothecial surface. She distinguished between the three accepted species based on characteristics of their ascospores, apothecia, and sclerotia in agar culture. S. trifoliorum is the only species that exhibits ascospore dimorphism, with a segregation of large and small ascospores typically observed in a 4:4 ratio. Ascospores of S. sclerotiorum contain two nuclei, whereas those of S. minor and S. trifoliorum contain four. S. sclerotiorum and S. trifoliorum typically produce small numbers of large sclerotia on the growing tips of hyphae at the colony periphery, whereas S. minor forms many smaller sclerotia that develop laterally on hyphae dispersed throughout the colony.

Since Kohn’s monograph, 13 new Sclerotinia species have been described, including S. antarctica, S. asari, S. atrostipitata, S. cirsii-spinosissimi, S. ginseng, S. glacialis, S. himalayensis, S. nivalis, S. pseudoplatani, S. subarctica, S. tetraspora, S. trillii, and S. verrucispora. Most of these were described based solely on morphology, and many lack DNA sequence data or living strains deposited in culture collections. The review evaluates each of these species in detail. S. cirsii-spinosissimi has been transferred to Botrytis based on morphological characters and ITS sequence analysis, which showed 98.3 to 98.7 percent BLAST similarity with many Botrytis cinerea sequences. S. pseudoplatani is unlikely to belong in Sclerotinia, as there is no evidence that it meets the morphological criteria of the genus. S. subarctica has been widely used as a provisional name but was never formally described, meaning a valid species description is needed to ensure nomenclatural stability. Excluding S. cirsii-spinosissimi and S. pseudoplatani reduces the total number of possible Sclerotinia species to 14.

The review highlights two major limitations currently hindering the application of phylogenetic approaches in Sclerotinia: the lack of a reliable DNA barcode with sufficient resolution to distinguish among species, and the absence of ex-type cultures with associated molecular data. Although the ITS region is the formal DNA barcode for fungi, its limited variability across Sclerotiniaceae may compromise its effectiveness for species-level resolution in Sclerotinia. Compounding this issue is the scarcity of viable type strains. Although Kohn re-typified the three core species, these are non-living fungarium specimens, restricting their use in molecular analyses. The practical challenges associated with recovering DNA from aged, non-living material or destructively sampling type vouchers present significant technical and ethical obstacles. The stabilization of names through epitypification therefore represents a critical step, as it ensures that species are anchored to well-characterised, publicly accessible living cultures.

Genomic resources represent a largely untapped opportunity for resolving these taxonomic issues. As of August 2025, more than 160 Sclerotiniaceae genomes covering nine genera are publicly available through NCBI, although Botrytis is overrepresented, with 92 genome sequences. Although some genomes have been used to investigate the biology and pathogenicity of S. sclerotiorum, they remain underutilised for taxonomic purposes. Genome-scale data could be readily incorporated into multilocus phylogenies and phylogenomic analyses, accelerating efforts to clarify species boundaries and uncover cryptic diversity. The situation closely parallels the former state of Ceratocystis, a genus that also encompassed diverse but morphologically similar taxa until multigene phylogenies prompted its reclassification into multiple genera. As with Ceratocystis, the development of a robust phylogenetic framework for Sclerotinia would clarify generic and species boundaries across the group.

Despite its economic importance and long history of study, the taxonomy of Sclerotinia remains largely unresolved. However, incorporating molecular data offers a clear path forward. Establishing a robust taxonomic framework supported by validated reference strains and informative DNA barcode markers will allow clarification of the currently tenuous identities of some newly described species, such as S. trillii and S. glacialis, and will refine species limits within the core taxa. A clearer understanding of the taxonomic relationships among Sclerotinia species will also facilitate research into areas such as pathogenicity and population genetics, which may ultimately contribute to improved management of the devastating diseases caused by these pathogens. The ongoing ambiguity surrounding Sclerotinia borealis exemplifies how unresolved classifications continue to undermine taxonomic clarity, and the review charts a course toward a clearer understanding of taxonomic relationships among Sclerotinia species that will benefit researchers across multiple disciplines.

Subject of Research: Advancing the taxonomy of Sclerotinia (Helotiales, Sclerotiniaceae): a review and recommendations for an important plant-pathogenic genus

Article Title: Advancing the taxonomy of Sclerotinia ( Helotiales , Sclerotiniaceae ): a review and recommendations for an important plant-pathogenic genus

Article References: Thomas, C., Wilken, P. M., Coetzee, M. P. A., & Visagie, C. M. (2026). Advancing the taxonomy of Sclerotinia (Helotiales, Sclerotiniaceae): a review and recommendations for an important plant-pathogenic genus. IMA Fungus, 17, Article e175737. https://doi.org/10.3897/imafungus.17.175737

Image Credits: AI Generated

DOI: 10.3897/imafungus.17.175737

Keywords: Advancing, taxonomy, Sclerotinia, Helotiales, Sclerotiniaceae, review, recommendations, important, plant-pathogenic, genus, scientific research

The significance of the work begins with the target itself. Chitin, composed of repeating units of N-acetyl-D-glucosamine linked by beta-1,4-glycosidic bonds, is the second most abundant biopolymer in nature, found in insect cuticles, fungal cell walls, and even some plants. Chitinases, the enzymes that cleave these bonds, belong to glycoside hydrolase families GH18 and GH19, and those in the GH18 family fold into a characteristic alpha/beta barrel domain that carries out catalysis. Fungal chitinases are of particular interest because they can be deployed against agricultural pests and pathogens, dissolving the protective armor of organisms such as Colletotrichum gloeosporioides, Rhizoctonia solani, Fusarium graminearum, and Fusarium oxysporum. Yet despite their promise, the structural and functional properties of fungal chitinases remain poorly charted, largely because experimental techniques such as X-ray crystallography are costly and technically demanding.

Paraphaeosphaeria minitans, formerly known as Coniothyrium minitans, is a mycoparasite that attacks Sclerotinia species, a group of fungal pathogens that infect more than 400 plant species, including soybeans, sunflowers, canola, and beans. The economic stakes are enormous: outbreaks of Sclerotinia stem rot caused losses of roughly 227 million dollars in 1997, 344 million dollars in 2004, and 560 million dollars in 2009. P. minitans invades the pathogen’s resilient sclerotia, and its chitinase is central to this parasitic process, helping the mycoparasite penetrate and dismantle the fungal cell walls of its host. Understanding how this enzyme works at the atomic level is therefore not just an academic exercise but a step toward more effective and sustainable crop protection.

To build their model, the researchers retrieved the protein sequence of the enzyme, designated PmChi, from the UniProt database and employed homology modeling powered by deep learning through the RaptorX platform, followed by refinement with DeepRefiner. The resulting structure was validated with a battery of computational tools, including Procheck, ProSA, Verify 3D, and ERRAT. The model showed a Z-score of -7.21, indicating that most residues sit in energetically favorable states, and a QMEAN score of 0.65, confirming reliability. Ramachandran analysis placed 79.6 percent of residues in the most favored regions, and the ERRAT quality score reached 90.824, all pointing to a structurally sound model suitable for downstream analysis.

The physicochemical portrait of PmChi proved equally informative. The enzyme has a molecular weight of 47 kilodaltons and a theoretical isoelectric point of 4.96, reflecting an abundance of acidic amino acids that also favor protein solubility. Its instability index of 32.60 falls below the threshold of 40, predicting stability in vivo, while an aliphatic index of 74.94 suggests thermostability. Secondary structure analysis revealed that random coils dominate at 51.24 percent, followed by alpha helices at 28.44 percent and extended strands at 14.22 percent. Two N-glycosylation sites were identified, which likely assist secretion, folding, and stability. Sequence analysis confirmed that PmChi belongs to glycosyl hydrolase family 18, subfamily II, spanning residues 52 to 416, and contains a chitinase insertion domain composed of antiparallel beta strands and an alpha helix nested within the TIM barrel.

At the heart of the enzyme lies a conserved catalytic motif. GH18 chitinases typically carry the sequence DxxDxDxE, and PmChi carries FDGLDIDWE at positions 178 to 186. Within this motif, the glutamic acid acts as the catalytic proton donor, while the aspartic acid stabilizes the protein by accommodating substrate distortion during the reaction. Docking studies with AutoDock 4.2 identified the key residues involved in substrate recognition and catalysis: Trp146, Asp184, Glu186, Tyr187, Pro228, Met252, Tyr254, Asp255, Trp393, Arg310, and Glu395. The calculated Gibbs free energy for binding chitotriose was -6.62 kilocalories per mole, indicating a spontaneous and stable interaction, with a ligand binding efficiency of -0.196 kilocalories per mole and a pKd of 4.94, suggesting moderate affinity with room for optimization through protein engineering.

The molecular dynamics simulations, run for 200 nanoseconds with GROMACS using the CHARMM27 force field, delivered the study’s most striking dynamic insights. The root mean square deviation of the free enzyme stabilized at approximately 1 nanometer, but the PmChi-chitotriose complex settled at around 0.5 nanometer and showed none of the early fluctuations seen in the unbound protein between 0 and 80 nanoseconds. This enhanced stability reflects the anchoring effect of chitotriose binding, which locks the active site into a catalytically competent conformation through hydrogen bonds, hydrophobic contacts, and van der Waals forces. Root mean square fluctuation analysis reinforced the picture: free PmChi displayed greater residue flexibility, while the ligand-bound complex was markedly more rigid, underscoring how substrate binding restrains protein motion and preserves the structural integrity of the active site during catalysis.

Hydrogen bond analysis revealed that the enzyme-carbohydrate interaction was maintained by four to ten hydrogen bonds throughout the simulation, with Tyr254 and Trp185 emerging as the principal residues forming these contacts. Solvent accessibility calculations showed that the enzyme cavity, initially highly exposed to solvent, became progressively less accessible over the 200-nanosecond trajectory, suggesting that chitotriose binding induces a more closed and compact conformation that stabilizes the enzyme-substrate complex. Binding free energy calculations using both MM/GBSA and MM/PBSA methods decomposed the interaction residue by residue. In the MM/GBSA analysis, Glu186 contributed approximately -7.0 kilocalories per mole, the largest single contribution, followed by Trp146 and Asp255 at around -4.0 each, while Arg310 was the only residue contributing unfavorably. The Poisson-Boltzmann-based MM/PBSA approach proved more accurate for analyzing enzyme-carbohydrate interactions, consistent with its theoretical rigor.

Beyond the active site, the simulations illuminated the broader interaction network that keeps PmChi stable. Hydrophobic bonds increased from 702 before the simulation to 827 after it, revealing a non-polar protein surface primed for intermolecular interactions even as the active site itself is rich in catalytic aspartate and glutamate residues. The enzyme also maintained 16 pi-pi stacking interactions before the simulation and 14 after, interactions known to support protein stability, carbohydrate recognition, catalysis, and self-assembly. Salt bridge analysis identified six such interactions, dominated by Lys/Asp pairs at 59 percent, with Arg/Asp pairs accounting for 22 percent, all shorter than 4.0 angstroms. These non-covalent interactions collectively explain how the enzyme withstands the conformational stresses of substrate binding and hydrolysis in an aqueous environment.

The study’s authors note that extending simulations beyond 500 nanoseconds will help delineate the long-term structural stability and interaction dynamics of the PmChi-chitotriose complex. Even now, however, the work delivers a platform for future research and applications. By pinpointing the residues that govern substrate binding and catalysis, and by demonstrating that ligand binding stabilizes rather than destabilizes the enzyme, the study provides a rational starting point for protein engineering aimed at boosting catalytic efficiency or tailoring substrate specificity. In a world seeking alternatives to synthetic pesticides, understanding the molecular mechanics of a fungus that parasitizes crop pathogens is a powerful step toward greener agriculture, and this computational portrait of PmChi brings that goal measurably closer.

The broader context of biocontrol helps explain why this enzyme matters. Microbial inoculants such as P. minitans suppress plant disease through several complementary mechanisms, including the secretion of cell-wall-degrading enzymes, the release of siderophores that sequester iron from the rhizosphere, competition for space and nutrients, and the induction of systemic resistance within the plant itself. Chitinase production sits squarely within this enzymatic arsenal, physically dissolving the chitin-rich walls of target fungi and enabling the mycoparasite to breach its host.

The products of chitin hydrolysis also carry commercial weight. Chitooligomers liberated by chitinases are finding uses across agriculture, food processing, and biomedicine, which means an enzyme optimized for efficient chitin cleavage could serve dual purposes: strengthening biological control in the field and supplying valuable oligosaccharides for downstream applications. This dual utility aligns the work with green chemistry goals, replacing synthetic pesticides and harsh chemical depolymerization routes with enzymatic alternatives.

The computational strategy itself deserves note. Homology modeling guided by deep learning, combined with docking and extended molecular dynamics, offers a route to structural insight when experimental crystallography is impractical, and the validation metrics reported here suggest such models can be reliable enough to nominate specific residues for mutagenesis. The finding that MM/PBSA outperformed MM/GBSA for this enzyme-carbohydrate pair adds a practical benchmark for future simulation studies of glycoside hydrolases.

Finally, the moderate binding affinity observed with chitotriose hints at natural substrate tuning. Because chitin in fungal walls is polymeric, the enzyme likely engages longer chains in vivo, and the residues identified here provide concrete targets for engineering variants with enhanced processivity, thermostability, or antifungal potency tailored to specific crop pathogens.

Subject of Research: Molecular dynamics analysis of the chitinase enzyme from the mycoparasitic biocontrol fungus Paraphaeosphaeria minitans and its interaction with chitotriose

Article Title: Unveiling the interaction between fungal biocontrol agent Paraphaeosphaeria minitans chitinase and chitotriose through molecular dynamics study

Article References: Jana, U. K., & Kango, N. (2026). Unveiling the interaction between fungal biocontrol agent Paraphaeosphaeria minitans chitinase and chitotriose through molecular dynamics study. Discover Green Chemistry, 1(1), Article 30. https://doi.org/10.1007/s44509-026-00029-9

Image Credits: AI Generated

DOI: 10.1007/s44509-026-00029-9

Keywords: chitinase, Paraphaeosphaeria minitans, molecular dynamics, chitotriose, biocontrol, GH18, homology modeling, molecular docking, MM/PBSA, Sclerotinia, chitin, protein engineering