TY - JOUR
T1 - Antifungal drug discovery for targeting Candida albicans morphogenesis through structural dynamics study
AU - Rabaan, Ali A.
AU - Alfouzan, Wadha A.
AU - Garout, Mohammed
AU - Halwani, Muhammad A.
AU - Alotaibi, Nouf
AU - Alfaresi, Mubarak
AU - Al Kaabi, Nawal A.
AU - Almansour, Zainab H.
AU - Bueid, Ahmed S.
AU - Yousuf, Amjad A.
AU - Eid, Hamza M.A.
AU - Alissa, Mohammed
N1 - Publisher Copyright:
© 2024 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2024
Y1 - 2024
N2 - In response to the escalating threat of drug-resistant fungi to human health, there is an urgent need for innovative strategies. Our focus is on addressing this challenge by exploring a previously untapped target, yeast casein kinase (Yck2), as a potential space for antifungal development. To identify promising antifungal candidates, we conducted a thorough screening of the diverse-lib drug-like molecule library, comprising 99,288 molecules. Five notable drug-like compounds with diverse-lib IDs 24334243, 24342416, 17516746, 17407455, and 24360740 were selected based on their binding energy scores surpassing 11 Kcal/mol. Our investigation delved into the interaction studies and dynamic stability of these compounds. Remarkably, all selected molecules demonstrated acceptable RMSD values during the 200 ns simulation, indicating their stable nature. Further analysis through Principal Component Analysis (PCA)-based Free Energy Landscape (FEL) revealed minimal energy transitions for most compounds, signifying dynamic stability. Notably, the two compounds exhibited slightly different behaviour in terms of energy transitions. These findings mark a significant breakthrough in the realm of antifungal drugs against C. albicans by targeting the Yck2 protein. However, it is crucial to note that additional experimental validation is imperative to assess the efficacy of these molecules as potential antifungal candidates. This study serves as a promising starting point for further exploration and development in the quest for effective antifungal solutions. Communicated by Ramaswamy H. Sarma.
AB - In response to the escalating threat of drug-resistant fungi to human health, there is an urgent need for innovative strategies. Our focus is on addressing this challenge by exploring a previously untapped target, yeast casein kinase (Yck2), as a potential space for antifungal development. To identify promising antifungal candidates, we conducted a thorough screening of the diverse-lib drug-like molecule library, comprising 99,288 molecules. Five notable drug-like compounds with diverse-lib IDs 24334243, 24342416, 17516746, 17407455, and 24360740 were selected based on their binding energy scores surpassing 11 Kcal/mol. Our investigation delved into the interaction studies and dynamic stability of these compounds. Remarkably, all selected molecules demonstrated acceptable RMSD values during the 200 ns simulation, indicating their stable nature. Further analysis through Principal Component Analysis (PCA)-based Free Energy Landscape (FEL) revealed minimal energy transitions for most compounds, signifying dynamic stability. Notably, the two compounds exhibited slightly different behaviour in terms of energy transitions. These findings mark a significant breakthrough in the realm of antifungal drugs against C. albicans by targeting the Yck2 protein. However, it is crucial to note that additional experimental validation is imperative to assess the efficacy of these molecules as potential antifungal candidates. This study serves as a promising starting point for further exploration and development in the quest for effective antifungal solutions. Communicated by Ramaswamy H. Sarma.
KW - Candida albicans
KW - Yck2
KW - antifungal
KW - drug-resistant
KW - molecular dynamics
UR - http://www.scopus.com/inward/record.url?scp=85190601856&partnerID=8YFLogxK
U2 - 10.1080/07391102.2024.2332507
DO - 10.1080/07391102.2024.2332507
M3 - Article
AN - SCOPUS:85190601856
SN - 0739-1102
JO - Journal of Biomolecular Structure and Dynamics
JF - Journal of Biomolecular Structure and Dynamics
ER -