Integrated virtual screening and molecular dynamics simulation revealed promising drug candidates of p53-MDM2 interaction

Computational-based drug design of novel small molecules targeting p53-MDMX interaction

The regulation of the p53 tumor suppressor pathway is critically dependent on the activity of Murine Double Minute 2 (MDM2) and Murine Double Minute X (MDMX) proteins. In certain types of cancer cells, excessive amount of MDMX can poly-ubiquitinate p53, which can result in its degradation, leading to a subsequent reduction in the levels of this protein. Therefore, the design of small-molecule inhibitors targeting the MDMX-p53 interaction has emerged as a promising strategy for cancer therapy. In this study, we employed computational techniques including pharmacophore modeling and molecular docking to identify three potential small molecule inhibitors (CID_25094615, CID_137634453, and CID_25094344) of the MDMX-p53 interaction from a PubChem database. Molecular dynamics of 100000 ps were conducted to assess the stability of the MDMX-inhibitor complexes. Our results showed that all three compounds exhibit stable binding with MDMX, with significantly lower root mean square deviation (RMSD) and fluctuation (RMSF) values than the control ligand, indicating superior stability. Additionally, the three compounds exhibit stronger intermolecular hydrogen bond (HBOND) interactions compared to the control, suggesting stronger stability. Overall, our findings highlight the potential of these compounds as lead candidates for the development of novel anticancer agents that target the MDMX-p53 interaction.Communicated by Ramaswamy H. Sarma.

Unveiling therapeutic prospects: targeting MDM-2 in non-muscle invasive bladder cancer

Non-muscle invasive bladder cancer (NMIBC) is considered one of the most costly malignancies, requiring significant surgical and therapeutic measures. However, progression and non-responsiveness to immunotherapy are common outcomes of treatment. In this study, we conducted comparative transcriptomic analysis of non-responders from two distinct populations (Asian and American) and identified six common genes associated with disease prognosis. Among these genes, MDM-2 is a major oncogenic protein linked to seven different types of cancers, as it is involved in the degradation of the tumor suppressor protein p53. To address this, we explored novel therapeutic drugs to block the binding of p53 and MDM-2 using a targeted molecular docking approach. High-throughput screening of 2500 drugs from the FDA-approved drug database led to the identification of three drug compounds: Mol-126, Mol-679, and Mol-768. Subsequently, we evaluated the structural stability and binding of these drugs for the targeted inhibition of the MDM-2 active site (hydrophobic cleft) using molecular dynamics simulations. Analysis of five trajectories, including RMSD, RMSF, hydrogen bond, radius of gyration, coulomb short-range electrostatic spectra, and free binding energy, confirmed that Mol-126 exhibited the highest structural stability compared to the reference drug (Alrizomadlin). Notably, Mol-126 is a derivative of 3-phenoxypropionic acid, which shows promise for the development of alternative therapeutic treatments for non-responsive bladder cancer patients.Communicated by Ramaswamy H. Sarma.

Identification of promising small-molecule inhibitors targeting STK17B for cancer therapeutics: molecular docking and molecular dynamics investigations

Cancer is a complex disease characterized by the uncontrolled growth of abnormal cells, leading to the formation of tumours. STK17B, a member of the DAPK family, has been implicated in various cancers and is considered a potential therapeutic target. However, no drug in the market has been approved for the treatment of STK17 B-associated cancer disease. This research aimed to identify direct inhibitors of STK17B using computational techniques. Ligand-based virtual screening and molecular docking were performed, resulting in the selection of three lead compounds (CID_135698391, CID_135453100, CID_136599608) with superior binding affinities compared to the reference compound dovitinib. While molecular docking simulation revealed specific interactions between the lead compounds and key amino acid residues at the binding pocket of STK17B, molecular dynamics simulations demonstrated that CID_135453100 and CID_136599608 exhibit stable conformations and comparable flexibility to dovitinib. However, CID_135698391 did not perform well using this metric as it displayed poor stability. Overall, small-molecule compounds CID_135453100 and CID_136599608 showed promising binding interactions and stability, suggesting their potential as direct inhibitors of STK17B. These findings could contribute to the exploration of novel therapeutic options targeting STK17B in cancer treatment.Communicated by Ramaswamy H. Sarma.

Pharmacophoric analogs of sotorasib-entrapped KRAS G12C in its inactive GDP-bound conformation: covalent docking and molecular dynamics investigations

For decades, KRAS G12C was considered an undruggable target. However, in recent times, a covalent inhibitor known as sotorasib was discovered and approved for the treatment of patients with KRAS G12C-driven cancers. Ever since the discovery of this drug, several preclinical efforts have focused on identifying novel therapeutic candidates that could act as covalent binders of KRAS G12C. Despite these intensive efforts, only a few KRAS G12C inhibitors have entered clinical trials. Hence, this highlights the need to develop effective drug candidates that could be used in the treatment of KRAS G12C-driven cancers. Herein, we embarked on a virtual screening campaign that involves the identification of pharmacophores of sotorasib that could act as covalent arsenals against the KRAS G12C target. To our knowledge, this is the first computational study that involves the compilation of sotorasib pharmacophores from an online chemical database against KRAS G12C. After this library of chemical entities was compiled, we conducted a covalent docking-based virtual screening that revealed three promising drug candidates (CID_146235944, CID_160070181, and CID_140956845) binding covalently to the crucial nucleophilic side chain of Cys12 and interact with the residues that form the cryptic allosteric pocket of KRAS G12C in its inactive GDP-bound conformation. Subsequently, ADMET profiling portrayed the covalent inhibitors as lead-like candidates, while 100 ns molecular dynamics was used to substantiate their stability. Although our overall computational study has shown the promising potential of the lead-like candidates in impeding oncogenic RAS signaling, more experimental efforts are needed to validate and establish their preclinical relevance. Implication of KRAS G12C in cancer and computational approach towards impeding the KRAS G12C RAS signaling.

Docking covalent targets for drug discovery: stimulating the computer-aided drug design community of possible pitfalls and erroneous practices

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the 'best' program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

Identification of EGFR inhibitors as potential agents for cancer therapy: pharmacophore-based modeling, molecular docking, and molecular dynamics investigations

CONTEXT

As a member of a large family of proteins that together regulate various aspects of cell growth and development, the epidermal growth factor receptor (EGFR) is a validated target for the development of new drugs. Herein, we compiled a library of 62 compounds from the PubChem database with similar pharmacophores as osimertinib, which to our knowledge represents the only drug capable of overcoming EGFR-T790M-mutated NSCLC until date. Subsequently, we launched a docking-based virtual screening campaign against the EGFR kinase with the compiled chemical entities. The virtual screen identified 3 hit candidates (CID_126667097, CID_137660592, and CID_137659061) with lower binding energy/higher affinity (- 8.7 kcal/mol, - 8.6 kcal/mol, and - 8.5 kcal/mol, respectively) than the standard osimertinib (- 8.4 kcal/mol). Molecular dynamics metrics such as RMSD, RMSF, ROG, and intermolecular H-bond were used to substantiate the stability of the promising drug candidates at the binding pocket of EGFR after 100,000 ps production run. Overall, our molecular modeling study portrayed CID_126667097, CID_137660592, and CID_137659061 as lead-like drug candidates that may be further developed for the treatment of EGFR-associated cancer disease.

METHODS

Molecular docking was conducted with Autodock Vina. A total of 62 compounds were compiled for the docking screen, which were then downloaded in SMILE format and converted to Protein Data Bank (PDB) format using the Openbabel online server. Finally, Gromacs 2022.3 was used to perform MD simulation to substantiate the stability of the hit candidates.