In-silico based designing of inhibitors against thevirulence and filamentation of Candida albicans, a common human pathogen

The human pathogen <i>Mycobacterium tuberculosis</i> ( MTB) is indeed one of the renowned important longtime infectious diseases that cause tuberculosis (TB). Interestingly, MTB infection has become one of the world's leading causes of human death. In trehalose synthase, the protein NCGM 946K2 146 found in MTB has an important role. For carbohydrate transport and metabolism, trehalose synthase is required. The protein is not clarified yet, however. In this research, an <i>in silico</i> approach was therefore formulated for functional and structural documentation of the uncharacterized protein NCGM946K2 146. Three different servers, including the Modeller, the Phyre2, and the Swiss Model, were used to evaluate the predicted tertiary structure. The top materials are selected using structural evaluations conducted with the analysis of Ramachandran Plot, Swiss-Model Interactive Workplace, Prosa-web, Verify 3D, and Z scores. This analysis aimed to uncover the value of the NCGM946K2 146 protein of MTB. This research will, therefore, improve our pathogenesis awareness and give us a chance to target the protein compound.

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Comparative analysis of potency of Azole derivatives to target ASL and GPI proteins responsible for pathogenesis of Candida albicans using in silico approach

Archives of Agriculture and Environmental Science ◽

10.26832/24566632.2019.0402020 ◽

2019 ◽

Vol 4 (2) ◽

pp. 256-260

Author(s):

P. Raperia ◽

N. Chaudhry ◽

R. Mittal

Keyword(s):

Candida Albicans ◽

Comparative Analysis ◽

In Silico ◽

Gpi Proteins

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Linalool-induced oxidative stress processes in the human pathogen Candida albicans

Acta Biologica Hungarica ◽

10.1556/018.68.2017.2.9 ◽

2017 ◽

Vol 68 (2) ◽

pp. 220-231 ◽

Cited By ~ 3

Author(s):

Gábor Máté ◽

Dominika Kovács ◽

Zoltán Gazdag ◽

Miklós Pesti ◽

Árpád Szántó

Keyword(s):

Oxidative Stress ◽

Candida Albicans ◽

Human Pathogen ◽

Human Pathogen Candida Albicans

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Metabolic modeling predicts specific gut bacteria as key determinants for Candida albicans colonization levels

The ISME Journal ◽

10.1038/s41396-020-00848-z ◽

2020 ◽

Author(s):

Mohammad H. Mirhakkak ◽

Sascha Schäuble ◽

Tilman E. Klassert ◽

Sascha Brunke ◽

Philipp Brandt ◽

...

Keyword(s):

Candida Albicans ◽

In Silico ◽

Human Subjects ◽

Bacterial Species ◽

Fungal Growth ◽

Metagenomic Sequencing ◽

Growth Data ◽

Predictive Capacity ◽

Fungal Abundance ◽

A Genome

AbstractCandida albicans is a leading cause of life-threatening hospital-acquired infections and can lead to Candidemia with sepsis-like symptoms and high mortality rates. We reconstructed a genome-scale C. albicans metabolic model to investigate bacterial-fungal metabolic interactions in the gut as determinants of fungal abundance. We optimized the predictive capacity of our model using wild type and mutant C. albicans growth data and used it for in silico metabolic interaction predictions. Our analysis of more than 900 paired fungal–bacterial metabolic models predicted key gut bacterial species modulating C. albicans colonization levels. Among the studied microbes, Alistipes putredinis was predicted to negatively affect C. albicans levels. We confirmed these findings by metagenomic sequencing of stool samples from 24 human subjects and by fungal growth experiments in bacterial spent media. Furthermore, our pairwise simulations guided us to specific metabolites with promoting or inhibitory effect to the fungus when exposed in defined media under carbon and nitrogen limitation. Our study demonstrates that in silico metabolic prediction can lead to the identification of gut microbiome features that can significantly affect potentially harmful levels of C. albicans.

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