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<p>Anthracnose is the most destructive disease afflicting <i>Phaseolus vulgaris</i> L., and in our research, we have gained insights into potential control strategies at the molecular level by virtue of Molecular docking. Stress responsive proteins from <i>Phaseolus vulgaris</i> L. against <i>Colletotrichum lindemuthianum</i> were screened out from literature. Protein sequences were retrieved from NCBI, and 3D structures were obtained either from the Protein Data Bank (PGIP, Arcelin) or generated through homology modelling (phenylalanine ammonia lyase, β-1,3-glucanase, NPR1, peroxidase, lipoxygenase, PvPR1, PvPR2, and a putative calcium-binding protein). The models were validated using standard tools and found to be of good quality. These proteins were then prepared in PyMOL for docking with ligands from plant growth-promoting rhizobacteria. Different metabolites from Plant growth promoting rhizobia were taken as ligands, and these were passed through “The pains remover” and then through “Lipinsky’s rule of 5”. The protein and ligand pdbqt files were finally prepared in Autodock Vina. The best docking model was formed between beta-1,3-glucanase and Enterobactin forming the highest number of hydrogen bonds (14) while beta-1,3-glucanase with Violacein exhibited the strongest binding affinity (− 11.4&#xa0;kcal/mol) respectively.</p>

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Molecular docking and interaction studies of stress responsive protein in Phaseolus vulgaris L. an in-silico perspective on comprehending stress tolerance mechanism

  • 期刊论文

  • 发表日期：

    2026年7月11日

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  • Barkha Parihar,
  • Shreya Proch,
  • Brijmohan Singh Bhau

摘要

Anthracnose is the most destructive disease afflicting Phaseolus vulgaris L., and in our research, we have gained insights into potential control strategies at the molecular level by virtue of Molecular docking. Stress responsive proteins from Phaseolus vulgaris L. against Colletotrichum lindemuthianum were screened out from literature. Protein sequences were retrieved from NCBI, and 3D structures were obtained either from the Protein Data Bank (PGIP, Arcelin) or generated through homology modelling (phenylalanine ammonia lyase, β-1,3-glucanase, NPR1, peroxidase, lipoxygenase, PvPR1, PvPR2, and a putative calcium-binding protein). The models were validated using standard tools and found to be of good quality. These proteins were then prepared in PyMOL for docking with ligands from plant growth-promoting rhizobacteria. Different metabolites from Plant growth promoting rhizobia were taken as ligands, and these were passed through “The pains remover” and then through “Lipinsky’s rule of 5”. The protein and ligand pdbqt files were finally prepared in Autodock Vina. The best docking model was formed between beta-1,3-glucanase and Enterobactin forming the highest number of hydrogen bonds (14) while beta-1,3-glucanase with Violacein exhibited the strongest binding affinity (− 11.4 kcal/mol) respectively.