MolDy: molecular dynamics simulation made easy.

MOTIVATION: Molecular dynamics (MD) is a computational experiment that is crucial for understanding the structure of biological macro and micro molecules, their folding, and the inter-molecular interactions. Accurate knowledge of these structural features is the cornerstone in drug development and elucidating macromolecules functions. The open-source GROMACS biomolecular MD simulation program is recognized as a reliable and frequently used simulation program for its precision. However, the user requires expertise, and scripting skills to carrying out MD simulations. RESULTS: We have developed an end-to-end interactive MD simulation application, MolDy for Gromacs. This front-end application provides a customizable user interface integrated with the Python and Perl-based logical backend connecting the Linux shell and Gromacs software. The tool performs analysis and provides the user with simulation trajectories and graphical representations of relevant biophysical parameters. The advantages of MolDy are (i) user-friendly, does not requiring the researcher to have prior knowledge of Linux; (ii) easy installation by a single command; (iii) freely available for academic research; (iv) can run with minimum configuration of operating systems; (v) has valid default prefilled parameters for beginners, and at the same time provides scope for modifications for expert users. AVAILABILITY AND IMPLEMENTATION: MolDy is available freely as compressed source code files with user manual for installation and operation on GitHub: https://github.com/AIBResearchMolDy/Moldyv01.git and on https://aibresearch.com/innovations.

Nanomedicine for glioblastoma: Progress and future prospects.

Glioblastoma is the most aggressive form of brain tumor, accounting for the highest mortality and morbidity rates. Current treatment for patients with glioblastoma includes maximal safe tumor resection followed by radiation therapy with concomitant temozolomide (TMZ) chemotherapy. The addition of TMZ to the conformal radiation therapy has improved the median survival time only from 12 months to 16 months in patients with glioblastoma. Despite these aggressive treatment strategies, patients' prognosis remains poor. This therapeutic failure is primarily attributed to the blood-brain barrier (BBB) that restricts the transport of TMZ from reaching the tumor site. In recent years, nanomedicine has gained considerable attention among researchers and shown promising developments in clinical applications, including the diagnosis, prognosis, and treatment of glioblastoma tumors. This review sheds light on the morphological and physiological complexity of the BBB. It also explains the development of nanomedicine strategies to enhance the permeability of drug molecules across the BBB.

In-Silico Analysis of Phytocompounds of Olea europaea as Potential Anti-Cancer Agents to Target PKM2 Protein.

Globally, cancer is the second leading cause of mortality and morbidity. The growth and development of cancer are extremely complex. It is caused by a variety of pathways and involves various types of enzymes. Pyruvate kinase M2 (PKM2) is an isoform of pyruvate kinase, that catalyses the last steps of glycolysis to produce energy. PKM2 is relatively more expressed in tumour cells where it tends to exist in a dimer form. Various medicinal plants are available that contain a variety of micronutrients to combat against different cancers. The phytocompounds of the olive tree (Olea europaea) leaves play an important role in inhibiting the proliferation of several cancers. In this study, the phytocompounds of olive leaf extract (OLE) were studied using various in silico tools, such as pkCSM software to predict ADMET properties and PASS Online software to predict anticancer activity. However, the molecular docking study provided the binding energies and inhibition constant and confirmed the interaction between PKM2 and the ligands. The dynamic behaviour, conformational changes, and stability between PKM2 and the top three hit compounds (Verbascoside (Ver), Rutin (Rut), and Luteolin_7_O_glucoside (Lut)) are studied by MD simulations.

Enzyme targeting strategies for prevention and treatment of cancer: Implications for cancer therapy.

Extensive growth of cancer in humans is a major cause of death. Numerous studies are being conducted to improve the early diagnosis, prevention, and treatment of cancer. Recent technological advancements in medical science and research indicate molecular target therapy holds much promise in cancer treatment. In the past, therapeutic and diagnostic targeting of non-glycolytic and glycolytic enzymes in cancer have been successful, and discoveries of biomarker enzymes in cancer hold promise for therapeutic treatments. In this review, we discuss the roles of several cancer-associated enzymes that could potentially act as therapeutic targets, and place special focus on non-glycolytic and glycolytic enzymes. This review indicates that the targeting of metabolic signaling offers a promising means of developing novel anti-cancer therapies.

Deciphering the emerging role of phytocompounds: Implications in the management of drug-resistant tuberculosis and ATDs-induced hepatic damage.

Tuberculosis is a disease of poverty, discrimination, and socioeconomic burden. Epidemiological studies suggest that the mortality and incidence of tuberculosis are unacceptably higher worldwide. Genomic mutations in embCAB, embR, katG, inhA, ahpC, rpoB, pncA, rrs, rpsL, gyrA, gyrB, and ethR contribute to drug resistance reducing the susceptibility of Mycobacterium tuberculosis to many antibiotics. Additionally, treating tuberculosis with antibiotics also poses a serious risk of hepatotoxicity in the patient's body. Emerging data on drug-induced liver injury showed that anti-tuberculosis drugs remarkably altered levels of hepatotoxicity biomarkers. The review is an attempt to explore the anti-mycobacterial potential of selected, commonly available, and well-known phytocompounds and extracts of medicinal plants against strains of Mycobacterium tuberculosis. Many studies have demonstrated that phytocompounds such as flavonoids, alkaloids, terpenoids, and phenolic compounds have antibacterial action against Mycobacterium species, inhibiting the bacteria's growth and replication, and sometimes, causing cell death. Phytocompounds act by disrupting bacterial cell walls and membranes, reducing enzyme activity, and interfering with essential metabolic processes. The combination of these processes reduces the overall survivability of the bacteria. Moreover, several phytochemicals have synergistic effects with antibiotics routinely used to treat TB, improving their efficacy and decreasing the risk of resistance development. Interestingly, phytocompounds have been presented to reduce isoniazid- and ethambutol-induced hepatotoxicity by reversing serum levels of AST, ALP, ALT, bilirubin, MDA, urea, creatinine, and albumin to their normal range, leading to attenuation of inflammation and hepatic necrosis. As a result, phytochemicals represent a promising field of research for the development of new TB medicines.

Development of machine learning models for the screening of potential HSP90 inhibitors.

Heat shock protein 90 (Hsp90) is a molecular chaperone playing a significant role in the folding of client proteins. This cellular protein is linked to the progression of several cancer types, including breast cancer, lung cancer, and gastrointestinal stromal tumors. Several oncogenic kinases are Hsp90 clients and their activity depends on this molecular chaperone. This makes HSP90 a prominent therapeutic target for cancer treatment. Studies have confirmed the inhibition of HSP90 as a striking therapeutic treatment for cancer management. In this study, we have utilized machine learning and different in silico approaches to screen the KCB database to identify the potential HSP90 inhibitors. Further evaluation of these inhibitors on various cancer cell lines showed favorable inhibitory activity. These inhibitors could serve as a basis for future development of effective HSP90 inhibitors.

Combining structure-based pharmacophore modeling and machine learning for the identification of novel BTK inhibitors.

Bruton's tyrosine kinase (BTK) is a critical enzyme which is involved in multiple signaling pathways that regulate cellular survival, activation, and proliferation, making it a major cancer therapeutic target. We applied the novel integrated structure-based pharmacophore modeling, machine learning, and other in silico studies to screen the Korean chemical database (KCB) to identify the potential BTK inhibitors (BTKi). Further evaluation of these inhibitors on three different human cancer cell lines showed significant cell growth inhibitory activity. Among the 13 compounds shortlisted, four demonstrated consistent cell inhibition activity among breast, gastric, and lung cancer cells (IC50 below 3 µM). The selected compounds also showed significant kinase inhibition activity (IC50 below 5 µM). The current study suggests the potential of these inhibitors for targeting BTK malignant tumors.