Browsing by Author "M. Bui, Francis"

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Exploring the therapeutic targets of stevioside in management of type 2 diabetes by network pharmacology and in-silico approach
(2024-08-14) Dutta, Amit; Hossain, Md. Arju; Somadder, Pratul Dipta; Moli, Mahmuda Akter; Ahmed, Kawsar; Rahman, Md Masuder; M. Bui, Francis
The main objective of the current study is to investigate the pathways and therapeutic targets linked to stevioside in the management of T2D using computational approaches. We collected RNA-seq datasets from NCBI, then employed GREIN to retrieve differentially expressed genes (DEGs). Computer-assisted techniques DAVID, STRING and NetworkAnalyst were used to explore common significant pathways and therapeutic targets associated with T2D and stevioside. Molecular docking and dynamics simulations were conducted to validate the interaction between stevioside and therapeutic targets. Gene ontology and KEGG analysis revealed that prostaglandin synthesis, IL-17 signaling, inflammatory response, and interleukin signaling were potential pathways targeted by stevioside in T2D. Protein-protein interactions (PPI) analysis identified six common hub proteins (PPARG, PTGS2, CXCL8, CCL2, PTPRC, and EDN1). Molecular docking results showed best binding of stevioside to PPARG (−8 kcal/mol) and PTGS2 (−10.1 kcal/mol). Finally, 100 ns molecular dynamics demonstrated that the binding stability between stevioside and target protein (PPARG and PTGS2) falls within the acceptable range. This study reveals that stevioside exhibits significant potential in controlling T2D by targeting key pathways and stably binding to PPARG and PTGS2. Further research is necessary to confirm and expand upon these significant computational results.
Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease
(2024-12-15) Alam, Mohammad Khursheed; Hosen, Md. Faruk; Ganji, Kiran Kumar; Ahmed, Kawsar; M. Bui, Francis
Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease. Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach. These three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity. Predictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.
MLAFP-XN: Leveraging neural network model for development of antifungal peptide identification tool
(2024-09-30) Sultana, Md. Fahim; Shaona, Md. Shazzad Hossain; Karima, Tasmin; Ali, Md. Mamun; Hasana, Md. Zahid; Ahmed, Kawsar; M. Bui, Francis; Dhasarathang, Vigneswaran; Ali Moni, Mohammad
Infectious fungi have been an increasing global concern in the present era. A promising approach to tackle this pressing concern involves utilizing Antifungal peptides (AFP) to develop an antifungal drug that can selectively eliminate fungal pathogens from a host with minimal toxicity to the host. Accordingly, identifying precise therapeutic antifungal peptides is crucial for developing effective drugs and treatments. This study proposed MLAFP-XN, a neural network-based strategy for accurately detecting active AFP in sequencing data to achieve this objective. In this work, eight feature extraction techniques and the XGB feature selection strategy are utilized together to present an enhanced methodology. A total of 24 classification models were evaluated, and the most effective four have been selected. Each of these models demonstrated superior accuracy on independent test sets, with respective scores of 97.93 %, 99.47 %, and 99.48 %. Our model outperforms current state of the art methods. In addition, we created a companion website to demonstrate our AFP recognition process and use SHAP to identify the most influential properties.