Systems-wide analysis of A. fumigatus using kinetic modeling of metabolic pathways to identify putative drug targets

Abstract

Aspergillosis is a major causative factor for morbidity in those with impaired immune systems, often caused by Aspergillus fumigatus. The diagnosis and treatment are difficult due to the diversity of individuals and risk factors and still pose a challenge for medical professionals. To understand the pathogenicity of any organism, it is critical to identify the significant metabolic pathways that are involved. Our work focused on developing kinetic models of critical pathways crucial for the survival of A. fumigatus using COPASI. While focusing on the folate biosynthesis, ergosterol biosynthesis and glycolytic pathway; sensitivity, time-course and steady-state analysis were performed to find the proteins/enzymes that are essential in the pathway and can be considered as potential drug targets. For further analysis of the interaction of drug targets identified, a protein–protein interaction (PPI) network was built, and hub nodes were identified using the Cytohubba package from Cytoscape. Based on the findings, dihydropteroate-synthase, dihydrofolate-reductase, 4-amino-4-deoxychorismate synthase, HMG-CoA-reductase, PG-isomerase and hexokinase could act as potential drug targets. Further, molecular docking and MM-GBSA analysis were performed with ligands chosen from DrugBank, and PubChem, and validated by experimental evidence and existing literature based on results from kinetic modeling and PPI network analysis. Based on docking scores and MM-GBSA results, molecular simulations were carried out for 1AJ2-dapsone, 1DIS-sulfamethazine, 1T02-lovastatin and 70YL-3-bromopyruvic acid complexes, which validated our findings. Our study provides a deeper insight into the mechanisms of A. fumigatus’s metabolism to reveal dapsone, sulfamethazine, lovastatin and 3-bromopyruvic acid as potential drugs for the treatment of Aspergillosis.

Communicated by Ramaswamy H. Sarma

Keywords:

• Aspergillosis
• kinetic modeling
• metabolic pathways
• drug targets
• protein-protein interaction network
• molecular docking &
• MD simulation

Acknowledgments

The authors would like to express our gratitude to Dr. Ashok K. Chauhan, Founder and President of Amity University in Uttar Pradesh, for granting us permission to conduct research. They also thank the Amity Institute of Biotechnology’s Centre for Computational Biology and Bioinformatics for having provided us with the resource base we needed.

Contributions

P.N., S.K., P.G., L.G., P.V. and A.S. carried out Conceptualization, Supervision, Validation, Visualization, and Writing (Review and Editing). A.C., V.G., M.K. and R.S. carried out Data curation, Formal analysis, Investigation, Methodology, Visualization, and Writing (original draft). All authors read and approved the final manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Participant consent and ethics approval

Not applicable.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.