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"Ibrahim, Mahmoud"
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Anti-Cancer Peptides: Status and Future Prospects
The dramatic rise in cancer incidence, alongside treatment deficiencies, has elevated cancer to the second-leading cause of death globally. The increasing morbidity and mortality of this disease can be traced back to a number of causes, including treatment-related side effects, drug resistance, inadequate curative treatment and tumor relapse. Recently, anti-cancer bioactive peptides (ACPs) have emerged as a potential therapeutic choice within the pharmaceutical arsenal due to their high penetration, specificity and fewer side effects. In this contribution, we present a general overview of the literature concerning the conformational structures, modes of action and membrane interaction mechanisms of ACPs, as well as provide recent examples of their successful employment as targeting ligands in cancer treatment. The use of ACPs as a diagnostic tool is summarized, and their advantages in these applications are highlighted. This review expounds on the main approaches for peptide synthesis along with their reconstruction and modification needed to enhance their therapeutic effect. Computational approaches that could predict therapeutic efficacy and suggest ACP candidates for experimental studies are discussed. Future research prospects in this rapidly expanding area are also offered.
Journal Article
In Silico Evaluation of Prospective Anti-COVID-19 Drug Candidates as Potential SARS-CoV-2 Main Protease Inhibitors
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a recently emanating human infectious coronavirus that causes COVID-19 disease. On 11th March 2020, it has been announced as a pandemic by the World Health Organization (WHO). Recently, several repositioned drugs have been subjected to clinical investigations as anti-COVID-19 drugs. Here, in silico drug discovery tools were utilized to evaluate the binding affinities and features of eighteen anti-COVID-19 drug candidates against SARS-CoV-2 main protease (Mpro). Molecular docking calculations using Autodock Vina showed considerable binding affinities of the investigated drugs with docking scores ranging from − 5.3 to − 8.3 kcal/mol, with higher binding affinities for HIV drugs compared to the other antiviral drugs. Molecular dynamics (MD) simulations were performed for the predicted drug-Mpro complexes for 50 ns, followed by binding energy calculations utilizing molecular mechanics-generalized Born surface area (MM-GBSA) approach. MM-GBSA calculations demonstrated promising binding affinities of TMC-310911 and ritonavir towards SARS-CoV-2 Mpro, with binding energy values of − 52.8 and − 49.4 kcal/mol, respectively. Surpass potentialities of TMC-310911 and ritonavir are returned to their capabilities of forming multiple hydrogen bonds with the proximal amino acids inside Mpro's binding site. Structural and energetic analyses involving root-mean-square deviation, binding energy per-frame, center-of-mass distance, and hydrogen bond length demonstrated the stability of TMC-310911 and ritonavir inside the Mpro's active site over the 50 ns MD simulation. This study sheds light on HIV protease drugs as prospective SARS-CoV-2 Mpro inhibitors.Graphic Abstract
Journal Article
Repurposing of drug candidates against Epstein–Barr virus: Virtual screening, docking computations, molecular dynamics, and quantum mechanical study
Epstein–Barr virus (EBV) was the first tumor virus identified in humans, and it is mostly linked to lymphomas and cancers of epithelial cells. Nevertheless, there is no FDA-licensed drug feasible for this ubiquitous EBV viral contagion. EBNA1 (Epstein-Barr nuclear antigen 1) plays several roles in the replication and transcriptional of latent gene expression of the EBV, making it an attractive druggable target for the treatment of EBV-related malignancies. The present study targets EBV viral reactivation and upkeep by inhibiting EBNA1 utilizing a drug-repurposing strategy. To hunt novel EBNA1 inhibitors, a SuperDRUG2 database (> 4,600 pharmaceutical ingredients) was virtually screened utilizing docking computations. In accordance with the estimated docking scores, the most promising drug candidates then underwent MDS (molecular dynamics simulations). Besides, the MM-GBSA approach was applied to estimate the binding affinities between the identified drug candidates and EBNA1. On the basis of MM-GBSA//200 ns MDS, bezitramide (SD000308), glyburide (SD001170), glisentide (SD001159), and glimepiride (SD001156) unveiled greater binding affinities towards EBNA1 compared to KWG, a reference inhibitor, with Δ G binding values of −44.3, −44.0, −41.7, −40.2, and −32.4 kcal/mol, respectively. Per-residue decomposition analysis demonstrated that LYS477, ASN519, and LYS586 significantly interacted with the identified drug candidates within the EBNA1 binding pocket. Post-dynamic analyses also demonstrated high constancy of the identified drug candidates in complex with EBNA1 throughout 200 ns MDS. Ultimately, electrostatic potential and frontier molecular orbitals analyses were performed to estimate the chemical reactivity of the identified EBNA1 inhibitors. Considering the current outcomes, this study would be an adequate linchpin for forthcoming research associated with the inhibition of EBNA1; however, experimental assays are required to inspect the efficiency of these candidates.
Journal Article
Benzothiazinone analogs as Anti-Mycobacterium tuberculosis DprE1 irreversible inhibitors: Covalent docking, validation, and molecular dynamics simulations
Mycobacterium tuberculosis is a lethal human pathogen, with the key flavoenzyme for catalyzing bacterial cell-wall biosynthesis, decaprenylphosphoryl-D-ribose oxidase (DprE1), considered an Achilles heal for tuberculosis (TB) progression. Inhibition of DprE1 blocks cell wall biosynthesis and is a highly promising antitubercular target. Macozinone (PBTZ169, a benzothiazinone (BTZ) derivative) is an irreversible DprE1 inhibitor that has attracted considerable attention because it exhibits an additive activity when combined with other anti-TB drugs. Herein, 754 BTZ analogs were assembled in a virtual library and evaluated against the DprE1 target using a covalent docking approach. After validation of the employed covalent docking approach, BTZ analogs were screened. Analogs with a docking score less than –9.0 kcal/mol were advanced for molecular dynamics (MD) simulations, followed by binding energy evaluations utilizing the MM-GBSA approach. Three BTZ analogs–namely, PubChem-155-924-621, PubChem-127-032-794, and PubChem-155-923-972– exhibited higher binding affinities against DprE1 compared to PBTZ169 with Δ G binding values of –77.2, –74.3, and –65.4 kcal/mol, versus –49.8 kcal/mol, respectively. Structural and energetical analyses were performed for the identified analogs against DprE1 throughout the 100 ns MD simulations, and the results demonstrated the great stability of the identified BTZ analogs. Physicochemical and ADMET characteristics indicated the oral bioavailability of the identified BTZ analogs. The obtained in-silico results provide promising anti-TB inhibitors that are worth being subjected to in-vitro and in-vivo investigations.
Journal Article
Exploration of African natural products as VP35 inhibitors to combat Marburg virus infection: Molecular docking, molecular dynamics, and quantum mechanical computations
Marburg virus (MBV) is a highly lethal filovirus responsible for hemorrhagic fever with case fatality rates of up to 88%. MBV was first recognized in 1967 during simultaneous outbreaks in Marburg and Frankfurt, Germany, and Belgrade, then part of Yugoslavia (now Serbia), following exposure to infected African green monkeys imported from Uganda. Currently, no approved treatment exists for MBV infection. The viral protein (VP35) plays a critical role in viral replication, transcription, and nucleocapsid assembly, making it a promising antiviral target. Consequently, obstructing the function of VP35 offers a potential strategy for combating MBV. Herein, the African Natural Products (ANP) database, which encompasses over 6,500 compounds, was subjected to virtual screening against VP35 employing docking computations. For inhibitors exhibiting a docking score <−8.0 kcal/mol against VP35, molecular dynamics simulations (MDS) were conducted, along with binding energy assessment utilizing the MM/GBSA approach. Upon the MM/GBSA//250 ns MDS, ANPDB6426, ANPDB5109, and ANPDB6357 demonstrated promising binding affinities toward the VP35, with Δ G binding values of −37.9, −34.6, and −34.2 kcal/mol, respectively. The post-MD analyses demonstrated that all three ANPs remained remarkably stable within the VP35 binding pocket over the full 250 ns MDS. Furthermore, the identified ANPs unveiled favorable oral bioavailability, pharmacokinetic, and safety profiles. Density functional theory calculations further supported the chemical reactivity of the identified ANPs. Compared to galidesivir and favipiravir, reference inhibitors, the estimated MM/GBSA binding energies of the identified ANPs with VP35 were about two times lower than galidesivir and favipiravir. These results highlighted the efficacy of computational methods in recognizing putative VP35 inhibitors, providing promising avenues for additional experimental research and prospective curative advancement toward MBV.
Journal Article
Fake It Till You Make It: Guidelines for Effective Synthetic Data Generation
Synthetic data provides a privacy protecting mechanism for the broad usage and sharing of healthcare data for secondary purposes. It is considered a safe approach for the sharing of sensitive data as it generates an artificial dataset that contains no identifiable information. Synthetic data is increasing in popularity with multiple synthetic data generators developed in the past decade, yet its utility is still a subject of research. This paper is concerned with evaluating the effect of various synthetic data generation and usage settings on the utility of the generated synthetic data and its derived models. Specifically, we investigate (i) the effect of data pre-processing on the utility of the synthetic data generated, (ii) whether tuning should be applied to the synthetic datasets when generating supervised machine learning models, and (iii) whether sharing preliminary machine learning results can improve the synthetic data models. Lastly, (iv) we investigate whether one utility measure (Propensity score) can predict the accuracy of the machine learning models generated from the synthetic data when employed in real life. We use two popular measures of synthetic data utility, propensity score and classification accuracy, to compare the different settings. We adopt a recent mechanism for the calculation of propensity, which looks carefully into the choice of model for the propensity score calculation. Accordingly, this paper takes a new direction with investigating the effect of various data generation and usage settings on the quality of the generated data and its ensuing models. The goal is to inform on the best strategies to follow when generating and using synthetic data.
Journal Article
Interactions of Apigenin and Safranal with the 5HT1A and 5HT2A Receptors and Behavioral Effects in Depression and Anxiety: A Molecular Docking, Lipid-Mediated Molecular Dynamics, and In Vivo Analysis
Background: The current study utilizes in silico molecular docking/molecular dynamics to evaluate the binding affinity of apigenin and safranal with 5HT1AR/5HT2AR, followed by assessment of in vivo effects of these compounds on depressive and anxious behavior. Methods: The docking between apigenin and safranal and the 5HT1A and 5HT2A receptors was performed utilizing AutoDock Vina software, while MD and protein-lipid molecular dynamics simulations were executed by AMBER16 software. For in vivo analysis, healthy control (HC), disease control (DC), fluoxetine-, and apigenin-safranal-treated rats were tested for changes in depression and anxiety using the forced swim test (FST) and the elevated plus-maze test (EPMT), respectively. Results: The binding affinity estimations identified the superior interacting capacity of apigenin over safranal for 5HT1A/5HT2A receptors over 200 ns MD simulations. Both compounds exhibit oral bioavailability and absorbance. In the rodent model, there was a significant increase in the overall mobility time in the FST, while in the EPMT, there was a decrease in latency and an increase in the number of entries for the treated and HC rats compared with the DC rats, suggesting a reduction in depressive/anxiety symptoms after treatment. Conclusions: Our analyses suggest apigenin and safranal as prospective medication options to treat depression and anxiety.
Journal Article
SuperNatural inhibitors to reverse multidrug resistance emerged by ABCB1 transporter: Database mining, lipid-mediated molecular dynamics, and pharmacokinetics study
An effective approach to reverse multidrug resistance (MDR) is P-glycoprotein (P-gp, ABCB1) transport inhibition. To identify such molecular regulators, the SuperNatural II database, which comprises > 326,000 compounds, was virtually screened for ABCB1 transporter inhibitors. The Lipinski rule was utilized to initially screen the SuperNatural II database, identifying 128,126 compounds. Those natural compounds were docked against the ABCB1 transporter, and those with docking scores less than zosuquidar (ZQU) inhibitor were subjected to molecular dynamics (MD) simulations. Based on MM-GBA binding energy (Δ G binding ) estimations, UMHSN00009999 and UMHSN00097206 demonstrated Δ G binding values of –68.3 and –64.1 kcal/mol, respectively, compared to ZQU with a Δ G binding value of –49.8 kcal/mol. For an investigation of stability, structural and energetic analyses for UMHSN00009999- and UMHSN00097206-ABCB1 complexes were performed and proved the high steadiness of these complexes throughout 100 ns MD simulations. Pharmacokinetic properties of the identified compounds were also predicted. To mimic the physiological conditions, MD simulations in POPC membrane surroundings were applied to the UMHSN00009999- and UMHSN00097206-ABCB1 complexes. These results demonstrated that UMHSN00009999 and UMHSN00097206 are promising ABCB1 inhibitors for reversing MDR in cancer and warrant additional in-vitro / in-vivo studies.
Journal Article