OBJECTIVE: The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools.
MATERIALS AND METHODS: The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated.
RESULTS: The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate.
CONCLUSION: The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates.
MATERIALS AND METHODS: Yeast isolates were collected in Sultan Abdul Halim Hospital, North Malaysia, from October 2020 to October 2021. Chromogenic Candida differential agar media and PCR-RFLP were used to identify yeast species.
RESULTS: A total of 206 yeast isolates were collected from different body sites of patients. The majority of the yeast isolates (n=104) were obtained from the urine. Other isolates were extracted from blood (n=52), vaginal swabs (n=45), ear discharge (n=2), tracheal aspirate (n=2), tissue (n=2), skin (n=1), nail (n=1), sputum (n=1), and cerebrospinal fluid (n=1). In total, 200 yeast samples were identified as single species, and six isolates were a mixture of Candida species.
CONCLUSION: Malaysia lacks accurate epidemiological data on mixed yeast infections. We identified all samples to the species level, including mixed yeast cultures, using the MspI enzyme and PCR-RFLP.
MATERIALS AND METHODS: Yeast isolates were collected from Sultan Abdul Halim Hospital, Kedah, Malaysia, from October 2020 to October 2021. Molecular identification of the isolates was performed by one enzyme-based polymerase chain reaction-restriction fragment length polymorphism method.
RESULTS: Candida albicans was the most prevalent species, accounting for 120 isolates (59%) in total. The most prevalent non-albicans Candida species were C. tropicalis (n=33, 16%), C. krusei (Pichia kudriavzevii) (n=12, 5.8%), C. glabrata (n=12, 5.8%), and C. parapsilosis (n=6, 3%). Other unusual Candida species were C. guilliermondii (2), C. metapsilosis (2), C. orthopsilosis (1), C. lusitaniae (1), C. rugosa (1), C. haemulonii (1), C. bracarensis (1), and C. dubliniensis (1). Moreover, Talaromyces marneffei (1), Kodamaea ohmeri (1), Cryptococcus neoformans (3), and Cryptococcus laurentii (1) were among the other yeasts identified.
CONCLUSION: The Molecular technique used in this study identified 96% of isolates, including mixed species. According to the findings, the most prevalent species are C. albicans, C. tropicalis, C. krusei, and C. glabrata.