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<p><i>Aspergillus fumigatus (A. fumigatus)</i>, an opportunistic fungal pathogen, poses a significant threat to immunocompromised individuals due to increasing resistance to existing antifungal therapies. Targeting fungus-specific enzymes, such as A. fumigatus chitinase B1 (AfChiB1), which plays a critical role in cell wall remodeling, represents a promising alternative therapeutic strategy. In this study, an integrated computational workflow was employed to screen a food-derived natural compound library for potential AfChiB1 inhibitors. Three candidate inhibitors—Plantacyanin, Amentoflavone, and Conferone—along with caffeine as a reference compound, were identified through library screening followed by re-docking of DFT-optimized structures. Molecular dynamics (MD) simulations exceeding 500 ns demonstrated that Conferone exhibited the highest binding stability, supported by consistently low RMSD and RMSF values. Although Amentoflavone showed favorable QM/MM energy values, it displayed pronounced ligand drift, while Plantacyanin exhibited moderate stability. Principal component analysis and free energy landscape profiling corroborated the MD results, revealing that Conferone occupied deep energy minima with limited conformational flexibility. QM/MM calculations further confirmed the electronically favorable nature of Amentoflavone and the robust dynamic behavior of Conferone. Superimposition of initial and refined MD structures indicated stable binding conformations for all ligands. Overall, Conferone emerged as the most promising computationally predicted AfChiB1 inhibitor, although experimental validation will be required to confirm its antifungal activity. These findings highlight the antifungal potential of food-derived bioactive compounds and the value of integrated computational approaches, although experimental validation is required to substantiate these predictions. </p> Graphical Abstract <p></p>

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Investigation of chitinase B1 inhibitors targeting the cell wall biosynthetic machinery in Aspergillus fumigatus through integrated molecular dynamics and QM/MM

  • 期刊论文

  • 发表日期：

    2026年4月30日

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  • Perwez Alam,
  • Ali Akhtar,
  • Zafrul Hasan,
  • Mohammad Serajuddin,
  • Omer M. Almarfadi,
  • Sarfaraz Ahmed

摘要

Aspergillus fumigatus (A. fumigatus), an opportunistic fungal pathogen, poses a significant threat to immunocompromised individuals due to increasing resistance to existing antifungal therapies. Targeting fungus-specific enzymes, such as A. fumigatus chitinase B1 (AfChiB1), which plays a critical role in cell wall remodeling, represents a promising alternative therapeutic strategy. In this study, an integrated computational workflow was employed to screen a food-derived natural compound library for potential AfChiB1 inhibitors. Three candidate inhibitors—Plantacyanin, Amentoflavone, and Conferone—along with caffeine as a reference compound, were identified through library screening followed by re-docking of DFT-optimized structures. Molecular dynamics (MD) simulations exceeding 500 ns demonstrated that Conferone exhibited the highest binding stability, supported by consistently low RMSD and RMSF values. Although Amentoflavone showed favorable QM/MM energy values, it displayed pronounced ligand drift, while Plantacyanin exhibited moderate stability. Principal component analysis and free energy landscape profiling corroborated the MD results, revealing that Conferone occupied deep energy minima with limited conformational flexibility. QM/MM calculations further confirmed the electronically favorable nature of Amentoflavone and the robust dynamic behavior of Conferone. Superimposition of initial and refined MD structures indicated stable binding conformations for all ligands. Overall, Conferone emerged as the most promising computationally predicted AfChiB1 inhibitor, although experimental validation will be required to confirm its antifungal activity. These findings highlight the antifungal potential of food-derived bioactive compounds and the value of integrated computational approaches, although experimental validation is required to substantiate these predictions.

Graphical Abstract