The WRKY transcription factors are a class of DNA-binding proteins involved in diverse plant processes play critical roles in response to abiotic and biotic stresses. Genome-wide divergence analysis of WRKY gene family in Hordeum vulgare provided a framework for molecular evolution and functional roles. So far, the crystal structure of WRKY from barley has not been resolved; moreover, knowledge of the three-dimensional structure of WRKY domain is pre-requisites for exploring the protein-DNA recognition mechanisms. Homology modelling based approach was used to generate structures for WRKY DNA binding domain (DBD) and its variants using AtWRKY1 as a template. Finally, the stability and conformational changes of the generated model in unbound and bound form was examined through atomistic molecular dynamics (MD) simulations for 100 ns time period. In this study, we investigated the comparative binding pattern of WRKY domain and its variants with W-box cis-regulatory element using molecular docking and dynamics (MD) simulations assays. The atomic insight into WRKY domain exhibited significant variation in the intermolecular hydrogen bonding pattern, leading to the structural anomalies in the variant type and differences in the DNA-binding specificities. Based on the MD analysis, residual contribution and interaction contour, wild-type WRKY (HvWRKY46) were found to interact with DNA through highly conserved heptapeptide in the pre- and post-MD simulated complexes, whereas heptapeptide interaction with DNA was missing in variants (I and II) in post-MD complexes. Consequently, through principal component analysis, wild-type WRKY was also found to be more stable by obscuring a reduced conformational space than the variant I (HvWRKY34). Lastly, high binding free energy for wild-type and variant II allowed us to conclude that wild-type WRKY-DNA complex was more stable relative to variants I. The results of our study revealed complete dynamic and structural information about WRKY domain-DNA interactions. However, no structure base information reported to date for WRKY variants and their mechanism of interaction with DNA. Our findings highlighted the importance of selecting a sequence to generate newer transgenic plants that would be increasingly tolerance to stress conditions.

The MYB transcription factor (TF) family is essential for various plant growth and development processes, including responses to biotic and abiotic stresses. This study investigated the R2R3-MYB protein structure from five plants, including cereal crops. The R2R3–MYB protein structure was docked with the DNA structure, and the best complexes were selected for two runs of molecular dynamics (MD) simulations to investigate the key interacting residues and conformational changes in the R2R3-MYB proteins caused by DNA binding. The MM/PBSA method calculated the binding free energy for each R2R3–MYB protein–DNA complex, showing strong interaction. Hydrophobic and hydrogen bonds significantly stabilized the R2R3–MYB protein–DNA complexes. The principal component analysis showed high restrictions on the movement of protein atoms in the phase space. A similar MD simulation analysis was performed using the crystal structure of the R2R3–MYB protein–DNA complex from Arabidopsis thaliana, and the generated complexes resembled the X-ray crystal structure. This is the first detailed study on the R2R3–MYB protein–DNA complex in cereal crops, providing a cost-effective solution to identify the key interacting residues and analyze the conformational changes in the MYB domain before and after DNA binding. Communicated by Ramaswamy H. Sarma

Wheat, a globally vital staple crop, is under threats from a variety of pathogens, highlighting the urgent need for efficient identification of disease resistance (R) genes. Traditional experimental methods are slow and resource-intensive. To address this, we present WheatRPred, a web-based deep learning framework for accurate classification of R proteins in wheat and its wild relatives. Our integrative approach combines CNN, BiLSTM, and Transformer models which capture spatial, sequential, and contextual features from protein sequences. Trained on a curated dataset of validated R and non-R proteins, the models achieved 95-97% accuracy, outperforming traditional machine learning classifiers. WheatRPred allows users to submit protein sequences and obtain predictions classifying each sequence as resistance or non-resistance, along with confidence scores, thereby accelerating disease resistance research and genomic breeding in wheat. This platform provides a practical resource for researchers and breeders, supporting rapid R gene discovery, marker-assisted selection, and the development of disease-resistant wheat varieties.