In Silico target identification and drug discovery for 5-proFAR isomerase inhibitors in critical fungal pathogens
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Hemantha Mani Kumar Chakravarthi Chanda
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Hari Priya Narra
Abstract
Fungal infections, especially in immunocompromised individuals, are emerging as a significant global health threat, contributing to high morbidity and mortality rates. The growing resistance of pathogens like Cryptococcus neoformans, Aspergillus fumigatus, Candida albicans and Candida auris coupled with limited treatment options, underscores the urgent need for improved diagnostics and antifungal therapies. Utilizing a comprehensive comparative proteomic approach, we identified the critical role of 1-(5-phosphoribosyl)-5-[(5-phosphoribosylamino) methylideneamino] imidazole-4-carboxamide (5-proFAR) isomerase in Amino acid transport metabolism, a vital pathway for fungal growth and virulence. Through advanced in silico techniques, including molecular docking, molecular dynamics simulations (MDS), and Principal Component Analysis (PCA), we screened potential inhibitors and identified Mugineic acid and 9-amino-2-deoxy-2,3-dehydro-n-acetyl-neuraminate as the most promising candidates, displaying favorable binding affinity, stability, and strong interactions with key residues within the enzyme’s active site. These compounds exhibited low RMSD and RMSF values, indicative of high conformational stability, making them ideal candidates for further experimental validation. Our findings suggest that targeting 5-proFAR isomerase offers a broad-spectrum, resistance-evading therapeutic strategy that could significantly mitigate the global burden of fungal infections, particularly in immunocompromised individuals, thus paving the way for the development of next-generation antifungal drugs to combat the growing challenge of multidrug-resistant fungal pathogens.
Acknowledgments
Authors are highly thankful to VFSTR (Deemed to be University) for providing faculty seed grant (F.No. VFSTR/REG/A6/30/2023-24/01 dated 16-05-2023).
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission. Hemantha Mani Kumar Chakravarthi Chanda: Executed in silico analyses including molecular docking and molecular dynamics simulations, and contributed to the writing and editing of the manuscript. Haripriya Narra: Assisted with data interpretation, prepared figures and tables, and contributed to drafting the manuscript. Sudheer Kumar Katari: Contributed to the study’s conceptualization and design, and conducted in silico analysis, including target prediction and protein-protein interaction (PPI) network analysis.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: All other authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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