Browsing by Author "Albadrani, Ghadeer M."

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Antimalarial Drug Discovery Against Malaria Parasites Through Haplopine Modification
(Europe Pub Med Central, 2023-08-24) Akash, Shopnil; Abdelkrim, Guendouzi; Bayil, Imren; Hosen, Md Eram; Mukerjee, Nobendu; Shater, Abdullah F.; Saleh, Fayez M.; Albadrani, Ghadeer M.; Al-Ghadi, Muath Q.; Abdel-Daim, Mohamed M.; Tok, Tuğba Taşkin
The widespread emergence of antimalarial drug resistance has created a major threat to public health. Malaria is a life-threatening infectious disease caused by Plasmodium spp., which includes Apicoplast DNA polymerase and Plasmodium falciparum cysteine protease falcipain-2. These components play a critical role in their life cycle and metabolic pathway, and are involved in the breakdown of erythrocyte hemoglobin in the host, making them promising targets for anti-malarial drug design. Our current study has been designed to explore the potential inhibitors from haplopine derivatives against these two targets using an in silico approach. A total of nine haplopine derivatives were used to perform molecular docking, and the results revealed that Ligands 03 and 05 showed strong binding affinity compared to the control compound atovaquone. Furthermore, these ligand-protein complexes underwent molecular dynamics simulations, and the results demonstrated that the complexes maintained strong stability in terms of RMSD (root mean square deviation), RMSF (root mean square fluctuation), and Rg (radius of gyration) over a 100 ns simulation period. Additionally, PCA (principal component analysis) analysis and the dynamic cross-correlation matrix showed positive outcomes for the protein-ligand complexes. Moreover, the compounds exhibited no violations of the Lipinski rule, and ADMET (absorption, distribution, metabolism, excretion, and toxicity) predictions yielded positive results without indicating any toxicity. Finally, density functional theory (DFT) and molecular electrostatic potential calculations were conducted, revealing that the mentioned derivatives exhibited better stability and outstanding performance. Overall, this computational approach suggests that these haplopine derivatives could serve as a potential source for developing new, effective antimalarial drugs to combat malaria. However, further in vitro or in vivo studies might be conducted to determine their actual effectiveness.
Design of Multi-Epitope Vaccine for Staphylococcus Saprophyticus
(Daffodil International University, 2022-07-27) Yousaf, Maha; Ullah, Asad; Sarosh, Nida; Abbasi, Sumra Wajid; Ismail, Saba; Bibi, Shabana; Hasan, Mohammad Mehedi; Albadrani, Ghadeer M.; Nouh, Nehal Ahmed Talaat; Abdulhakim, Jawaher A.; Abdel-Daim, Mohamed M.; Emran, Talha Bin
Staphylococcus saprophytic us is a Gram-positive coccus responsible for the occurrence of cystitis in sexually active, young females. While effective antibiotics against this organism exist, resistant strains are on the rise. Therefore, prevention via vaccines appears to be a viable solution to address this problem. In comparison to traditional techniques of vaccine design, computationally aided vaccine development demonstrates marked specificity, efficiency, stability, and safety. In the present study, a novel, multi-epitope vaccine construct was developed against S. saprophyticus by targeting fully sequenced proteomes of its five different strains, which were examined using a pangenome and subtractive proteomic strategy to characterize prospective vaccination targets. The three immunogenic vaccine targets which were utilized to map the probable immune epitopes were verified by annotating the entire proteome. The predicted epitopes were further screened on the basis of antigenicity, allergenicity, water solubility, toxicity, virulence, and binding affinity towards the DRB*0101 allele, resulting in 11 potential epitopes, i.e., DLKKQKEKL, NKDLKKQKE, QDKLKDKSD, NVMDNKDLE, TSGTPDSQA, NANSDGSSS, GSDSSSSNN, DSSSSNNDS, DSSSSDRNN, SSSDRNNGD, and SSDDKSKDS. All these epitopes have the efficacy to cover 99.74% of populations globally. Finally, shortlisted epitopes were joined together with linkers and three different adjuvants to find the most stable and immunogenic vaccine construct. The top-ranked vaccine construct was further scrutinized on the basis of its physicochemical characterization and immunological profile. The non-allergenic and antigenic features of modeled vaccine constructs were initially validated and then subjected to docking with immune receptor major histocompatibility complex I and II (MHC-I and II), resulting in strong contact. In silico cloning validations yielded a codon adaptation index (CAI) value of 1 and an ideal percentage of GC contents (46.717%), indicating a putative expression of the vaccine in E. coli. Furthermore, immune simulation demonstrated that, after injecting the proposed MEVC, powerful antibodies were produced, resulting in the sharpest peaks of IgM + IgG formation (>11,500) within 5 to 15 days. Experimental testing against S. saprophytic us can evaluate the safety and efficacy of these prophylactic vaccination designs. Keywords: pan-genome; immuno-informatics; multi-epitope peptide; docking; MD simulation
Development and Performance Analysis of Machine Learning Methods for Predicting Depression Among Menopausal Women
(Frontier Scientific Publishing, 2023-07-11) Shopnil Akash; Baeza, Javiera; Mahmood, Sajjat; Mukerjee, Nobendu; Subramaniyan, Vetriselvan; Islam, Md. Rezaul; Gupta, Gaurav; Rajakumari, Vinibha; Chinni, Suresh V.; Ramachawolran, Gobinath; Saleh, Fayez M.; Albadrani, Ghadeer M.; Sayed, Amany A.; Abdel-Daim, Mohamed M.
The Lassa virus (LASV), an RNA virus prevalent in West and Central Africa, causes severe hemorrhagic fever with a high fatality rate. However, no FDA-approved treatments or vaccines exist. Two crucial proteins, LASV glycoprotein and nucleoprotein, play vital roles in pathogenesis and are potential therapeutic targets. As effective treatments for many emerging infections remain elusive, cutting-edge drug development approaches are essential, such as identifying molecular targets, screening lead molecules, and repurposing existing drugs. Bioinformatics and computational biology expedite drug discovery pipelines, using data science to identify targets, predict structures, and model interactions. These techniques also facilitate screening leads with optimal drug-like properties, reducing time, cost, and complexities associated with traditional drug development. Researchers have employed advanced computational drug design methods such as molecular docking, pharmacokinetics, drug-likeness, and molecular dynamics simulation to investigate evodiamine derivatives as potential LASV inhibitors. The results revealed remarkable binding affinities, with many outperforming standard compounds. Additionally, molecular active simulation data suggest stability when bound to target receptors. These promising findings indicate that evodiamine derivatives may offer superior pharmacokinetics and drug-likeness properties, serving as a valuable resource for professionals developing synthetic drugs to combat the Lassa virus.
Recognizing Novel Drugs against Keap1 in Alzheimer's disease Using Machine Learning Grounded Computational Studies
(Scopus, 22-12-31) Mukerjee, Nobendu; Al-Khafaji, Khattab; Maitra, Swastika; Wadi, Jaafar Suhail; Sachdeva, Punya; Ghosh, Arabinda; Buchade, Rahul Subhash; Chaudhari, Somdatta Yashwant; Jadhav, Shailaja B.; Das, Padmashree; Hasan, Mohammad Mehedi; Rahman, Md. Habibur; Albadrani, Ghadeer M.; Albadrani, M.; Altyar, Ahmed E.; Kamel, Mohamed; Algahtani, Mohammad; Shinan, Khlood; Theyab, Abdulrahman; Daim, Mohamed M. Abdel-; Ashraf, Md.; Rahman, Md. Mominur; Sharma, Rohit
Alzheimer’s disease (AD) is the most common neurodegenerative disorder in the world, affecting an estimated 50 million individuals. The nerve cells become impaired and die due to the formation of amyloid-beta (Aβ) plaques and neurofibrillary tangles (NFTs). Dementia is one of the most common symptoms seen in people with AD. Genes, lifestyle, mitochondrial dysfunction, oxidative stress, obesity, infections, and head injuries are some of the factors that can contribute to the development and progression of AD. There are just a few FDA-approved treatments without side effects in the market, and their efficacy is restricted due to their narrow target in the etiology of AD. Therefore, our aim is to identify a safe and potent treatment for Alzheimer’s disease. We chose the ursolic acid (UA) and its similar compounds as a compounds’ library. And the ChEMBL database was adopted to obtain the active and inactive chemicals against Keap1. The best Quantitative structure-activity relationship (QSAR) model was created by evaluating standard machine learning techniques, and the best model has the lowest RMSE and greatest R2 (Random Forest Regressor). We chose pIC50 of 6.5 as threshold, where the top five potent medicines (DB06841, DB04310, DB11784, DB12730, and DB12677) with the highest predicted pIC50 (7.091184, 6.900866, 6.800155, 6.768965, and 6.756439) based on QSAR analysis. Furthermore, the top five medicines utilize as ligand molecules were docked in Keap1’s binding region. The structural stability of the nominated medications was then evaluated using molecular dynamics simulations, RMSD, RMSF, Rg, and hydrogen bonding. All models are stable at 20 ns during simulation, with no major fluctuations observed. Finally, the top five medications are shown as prospective inhibitors of Keap1 and are the most promising to battle AD.