Molecular docking and MD simulations reveal protease inhibitors block the catalytic residues in Prp8 intein of Aspergillus fumigatus: a potential target for antimycotics

Abstract

Resistance to azoles and amphotericin B especially in Aspergillus fumigatus is a growing concern towards the treatment of invasive fungal infection. At this critical juncture, intein splicing would be a productive, and innovative target to establish therapies against resistant strains. Intein splicing is the central event for the activation of host protein, essential for the growth and survival of various microorganisms including A. fumigatus. The splicing process is a four-step protease-like nucleophilic cascade. Thus, we hypothesise that protease inhibitors would successfully halt intein splicing and potentially restrict the growth of the aforementioned pathogen. Using Rosetta Fold and molecular dynamics simulations, we modelled Prp8 intein structure; resembling classic intein fold with horse shoe shaped splicing domain. To fully comprehend the active site of Afu Prp8 intein, C1, T62, H65, H818, N819 from intein sequences and S820, the first C-extein residue are selected. Molecular docking shows that two FDA-approved drugs, i.e. Lufotrelvir and Remdesivir triphosphate efficiently interact with Prp8 intein from the assortment of 212 protease inhibitors. MD simulation portrayed that Prp8 undergoes conformational change upon ligand binding, and inferred the molecular recognition and stability of the docked complexes. Per-residue decomposition analysis confirms the importance of F: block R802, V803, and Q807 binding pocket in intein splicing domain towards recognition of inhibitors, along with active site residues through strong hydrogen bonds and hydrophobic contacts. However, in vitro and in vivo assays are required to confirm the inhibitory action on Prp8 intein splicing; which may pave the way for the development of new antifungals for A. fumigatus.

Communicated by Ramaswamy H. Sarma

Keywords:

• Aspergillus fumigatus
• Prp8 intein
• machine learning
• molecular dynamic simulation
• Lufotrelvir
• Remdesivir triphosphate

Disclosure statement

No potential conflict of interest was reported by the author(s).

Author contributions

Sunita Panda: conceptualization, methodology, software investigation, data curation, visualization, writing original draft. Madhusmita Rout: methodology, software investigation, data curation, visualization, writing original draft. Sarbani Mishra: methodology, software investigation, data curation, visualization. Jyotirmayee Turuk: conceptualization, review and editing, and supervision Budheswar Dehury: conceptualization, methodology, critical review and editing, and supervision. Sanghamitra Pati: writing—review and editing, supervision, and funding acquisition.

Additional information

Funding

The above work is supported by ICMR-Centenary Postdoctoral Fellowship. The authors are thankful to ICMR for providing ICMR-Centenary Postdoctoral Fellowship to SP (No. 3/1/3/PDF(25)/2021-HRD). The authors acknowledge Department of Health Research, Ministry of Health and Family Welfare, Govt. of India for providing financial support under the DHR-young Scientist grant to BD (No. R.12014/41/2021-HR/E-Office:8116317).