Abstract

The fungal keratitis (FK) infections that cause cornea inflammations are more virulent than other bacterial keratitis infections and remain one of the most ethereal and challenging infections for ophthalmologists to diagnose and treat. Thus, the urgency in understanding the current perspectives of antifungal agents and their interactions with novel therapeutic targets and the identification of novel anti-fungal agents are at the frontline of studies in the pharmaceutical industry. In this study, DNA dependent RNA polymerase was modelled and virtually screened against eight antifungal agents, and it was found that Itraconazole (−22.0427 kJ/mol), Ketoconazole (−20.2194 kJ/mol), and Voriconazole (−12.6388 kJ/mol) exhibited better binding interactions. further, the structural and electronic properties of Itraconazole calculated through density functional theory studies revealed the sites of chemical reactivity that are vital in the compounds for possible interactions with RNA polymerase (RNAP). Hence, this study explores the binding efficacies of various anti-fungal agents through docking studies and their chemical entities, which might pave a significant path for the design of novel anti-fungal agents against hyalohyphomycetes causing keratitis.

Highlights

  • Cornea inflammations caused by fungal infections remain one of the most ethereal and challenging infections for ophthalmologists to diagnose and treat [1].These fungal keratitis (FK) infections are more likely to perforate the cornea and more virulent than other bacterial keratitis infections [2]

  • In the last few decades, many research groups have suggested the usage of structural based drug design (SBDD), especially molecular docking methods, as a prospective method to screen potential novel compounds against many diseases [25]

  • The binding efficiencies of eight anti-fungal compounds against RNA polymerase (RNAP) from keratitis causing hyalohyphomycete, A. flavus, and A. fumigatus, are explored through docking studies, and the chemical stability of the best docked Itraconazole is explored through Density functional theory (DFT) studies

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Summary

Introduction

Cornea inflammations caused by fungal infections (fungal keratitis or keratomycosis) remain one of the most ethereal and challenging infections for ophthalmologists to diagnose and treat [1]. The major groups of antifungal drugs are azoles and polyenes, which are fungistatic with interference in protein synthesis and fungicidal with an effect on cell wall function, which has been accepted for the treatment of keratitis [15,16]; their efficacies are limited for several reasons, including the increasing resistance to antifungal drugs, their limited effect on certain fungal species, and their exclusively topical route of administration, which limits the use of those drugs to treat the superficial fungal infections, as opposed to deep fungal invasions [17]. A few in-vitro studies [19,20,21,22] have reported that a combination of amphotericin B and rifampin, an antibacterial agent that inhibits RNA polymerase, synergistically interacts against a variety of fungi In line with this result, the DNA dependent RNA polymerase (RNAP) that is responsible for transcribing DNA has been considered as a direct or indirect target for the treatment of various fungal infections, including fungal keratitis [23]. This study explores the binding efficacies of various anti-fungal agents through docking studies, and their chemical entities might pave a significant path for the design of novel anti-fungal agents against hyalohyphomycetes causing keratitis

Result and Discussion
Target–Template Alignment for Homology Modeling
Homology
Theoretical
Binding Site Prediction
Anti-Fungal Agents
Docking Studies
Docking Interactions of Anti-Fungal Agents against Aspergillus Flavus
Docking Interactions of Anti-Fungal Agents against Aspergillus Fumigatus
In-Vitro Studies
2.10. Density Functional Theory Analysis
Materials and Methods
Model Validation
Binding Pocket Prediction
Molecular Docking
Docking Interactions
In Vitro Studies for Rate of Germination
Density Functional Theory Study
Findings
Methods
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