Study of Molecular Mechanism of the Interaction Between MEK1/2 and Trametinib with Docking and Molecular Dynamic Simulation

Effects of F53 hotspot mutations on the molecular dynamics of MEK1 protein and the binding of its FDA-approved inhibitors

MEK1 (MAP2K1) could emerge as an oncogenic protein in the presence of certain mutations, and could be inhibited by FDA-approved drugs (trametinib, cobimetinib, binimetinib and selumetinib). However, how the mutations on MEK1's hotspot residue F53 affect the bindings of these therapeutic molecules remained largely unexplored. We performed molecular dynamics (MD) simulations with wild-type and mutated (F53L/V/C/I/Y) MEK1 structures in the presence and absence of these drugs and observed changes on the motion of MEK1. A longer duration in the lowest energy state conformation was observed during the simulations in the presence of F53 mutations. This was complemented by cross-correlated motions of amino acids of MEK1. More importantly, the binding affinities of inhibitors were affected. There was a marked reduction in the calculated binding affinity of trametinib in the presence of F53C mutation. On the other hand, the binding affinities of cobimetinib and selumetinib could overcome F53 mutations on MEK1. Binimetinib interestingly exhibited an increased binding affinity when F53C/I mutations were present. Taken together, our results provide a comprehensive perspective on the structural and drug-binding effects of observed mutations on the hotspot residue F53 within MEK1; warranting further research in stratifying F53 hotspot mutations for effective drug binding.

Ringing medicinal chemistry: The importance of 3-membered rings in drug discovery

Scaffold-based drug design has become increasingly prominent in the pharmaceutical field due to the systematic and effective approach through which it facilitates the development of novel drugs. The identification of key scaffolds provides medicinal chemists with a fundamental framework for subsequent research. With mounting evidence suggesting that increased aromaticity could impede the chances of developmental success for oral drug candidates, there is an imperative need for a more thorough exploration of alternative ring systems to mitigate attrition risks. The unique characteristics exhibited by three-membered rings have led to their application in medicinal chemistry. This review explores the use of cyclopropane-, aziridine-, thiirane-, and epoxide-containing compounds in drug discovery, focusing on their roles in approved medicines and drug candidates. Specifically, the importance of the three-membered ring systems in rending biological activity for each drug molecule was highlighted. The undeniable therapeutic value and intriguing features presented by these compounds suggest significant pharmacological potential, providing justification for their incorporation into the design of novel drug candidates.

Exploring the inhibitory potential of xanthohumol on MEK1/2: a molecular docking and dynamics simulation investigation

Background and purpose

Xanthohumol (Xn), a small molecule found in Humulus lupulus, has shown promise as an anti-cancer compound. This in silico study was performed to understand the mechanism of action of Xn as a natural compound on MEK1/2 by simulation.

Experimental approach

After ligand and protein preparation, the best binding energy was determined using Autodock 4.2. Additionally, molecular dynamics simulations of the MEK1/2-Xn and BRaf-MEK1/2-Xn complexes were conducted using GROMACS 2022.1 software and compared to the complexes of MEK1/2-trametinib (Tra) and BRaf-MEK1/2-Tra.

Findings/Results

The docking results revealed that the best binding energies for MEK1-Xn (-10.70 Kcal/mol), MEK2-Xn (-9.41 Kcal/mol), BRaf-MEK1-Xn (-10.91 Kcal/mol), and BRaf-MEK2-Xn (-8.54 Kcal/mol) were very close to those of the Tra complexes with their targets, MEK1 and MEK2. Furthermore, Xn was found to interact with serine 222 at the active site of these two kinases. The results of the molecular dynamics simulations also indicated that Xn induced changes in the secondary structure of the studied proteins. The root mean square of proteins and the mean radius of gyration showed significant fluctuations.

Conclusion and implications

The findings of the study suggested that Xn, as a novel bioactive compound, potentially inhibits the MEK1/2 function in cancer cells.

Therapeutic efficacy of RAS inhibitor trametinib using a juvenile myelomonocytic leukemia patient-derived xenograft model

Juvenile myelomonocytic leukemia (JMML) is an aggressive pediatric leukemia with few effective treatments and poor outcomes even after stem cell transplantation, the only current curative treatment. We developed a JMML patient-derived xenograft (PDX) mouse model and demonstrated the in vivo therapeutic efficacy and confirmed the target of trametinib, a RAS-RAF-MEK-ERK pathway inhibitor, in this model. A PDX model was created through transplantation of patient JMML cells into mice, up to the second generation, and successful engraftment was confirmed using flow cytometry. JMML PDX mice were treated with trametinib versus vehicle control, with a median survival of 194 days in the treatment group versus 124 days in the control group (p = 0.02). Trametinib's target as a RAS pathway inhibitor was verified by showing inhibition of ERK phosphorylation using immunoblot assays. In conclusion, trametinib monotherapy significantly prolongs survival in our JMML PDX model by inhibiting the RAS pathway. Our model can be effectively used for assessment of novel targeted treatments, including potential combination therapies, to improve JMML outcomes.

Synthesis, molecular docking analysis, molecular dynamic simulation, ADMET, DFT, and drug likeness studies: Novel Indeno[1,2-b]pyrrol-4(1H)-one as SARS-CoV-2 main protease inhibitors

BACKGROUND

The COVID-19 pandemic began in 2019 as a result of the advent of a novel coronavirus, SARS-CoV-2. At present, there are a limited number of approved antiviral agents for the treatment of COVID-19. Remdesivir, Molnupiravir, and Paxlovid have been approved by the FDA to treat COVID-19 infections. Research has shown that the main protease enzyme (Mpro) of SARS-CoV-2 plays a crucial role in the enzymatic processing of viral polyproteins. This makes Mpro an interesting therapeutic target for combating infections caused by emerging coronaviruses.

METHODS

The pharmacological effects of pyrroles and their derivatives have a wide range of applications. In our study, we focused on synthesizing nine novel derivatives of 2-arylamino-dihydro-indeno[1,2-b] pyrrol-4(1H)-one, with a particular emphasis on their antiviral properties. Using in silico studies involving molecular docking and DFT analyses in the gas phase using the B3LYP/6-31++G(d,p) basis set, we studied these compounds with respect to their interactions with the Mpro of SARS-CoV-2. The results of the docking analysis revealed that the synthesized compounds exhibited favorable inhibitory effects. Notably, compound 5f demonstrated the highest effectiveness against the target protein. Furthermore, the pharmacokinetic and drug-like properties of the synthesized derivatives of 2-arylamino-dihydroindeno[1,2-b] pyrrol-4(1H)-one indicated their potential as promising candidates for further development as inhibitors targeting SARS-CoV-2. However, it is imperative to determine the in vitro efficacy of these compounds through comprehensive biochemical and structural analyses.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

Erianin suppresses constitutive activation of MAPK signaling pathway by inhibition of CRAF and MEK1/2

Constitutive activation of RAS-RAF-MEK-ERK signaling pathway (MAPK pathway) frequently occurs in many cancers harboring RAS or RAF oncogenic mutations. Because of the paradoxical activation induced by a single use of BRAF or MEK inhibitors, dual-target RAF and MEK treatment is thought to be a promising strategy. In this work, we evaluated erianin is a novel inhibitor of CRAF and MEK1/2 kinases, thus suppressing constitutive activation of the MAPK signaling pathway induced by BRAF V600E or RAS mutations. KinaseProfiler enzyme profiling, surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), cellular thermal shift assay, computational docking, and molecular dynamics simulations were utilized to screen and identify erianin binding to CRAF and MEK1/2. Kinase assay, luminescent ADP detection assay, and enzyme kinetics assay were investigated to identify the efficiency of erianin in CRAF and MEK1/2 kinase activity. Notably, erianin suppressed BRAF V600E or RAS mutant melanoma and colorectal cancer cell by inhibiting MEK1/2 and CRAF but not BRAF kinase activity. Moreover, erianin attenuated melanoma and colorectal cancer in vivo. Overall, we provide a promising leading compound for BRAF V600E or RAS mutant melanoma and colorectal cancer through dual targeting of CRAF and MEK1/2.

Investigating a Library of Flavonoids as Potential Inhibitors of a Cancer Therapeutic Target MEK2 Using in Silico Methods

The second leading cause of death in the world is cancer. Mitogen-activated protein kinase (MAPK) and extracellular signal-regulated protein kinase (ERK) 1 and 2 (MEK1/2) stand out among the different anticancer therapeutic targets. Many MEK1/2 inhibitors are approved and widely used as anticancer drugs. The class of natural compounds known as flavonoids is well-known for their therapeutic potential. In this study, we focus on discovering novel inhibitors of MEK2 from flavonoids using virtual screening, molecular docking analyses, pharmacokinetic prediction, and molecular dynamics (MD) simulations. A library of drug-like flavonoids containing 1289 chemical compounds prepared in-house was screened against the MEK2 allosteric site using molecular docking. The ten highest-scoring compounds based on docking binding affinity (highest score: -11.3 kcal/mol) were selected for further analysis. Lipinski's rule of five was used to test their drug-likeness, followed by ADMET predictions to study their pharmacokinetic properties. The stability of the best-docked flavonoid complex with MEK2 was examined for a 150 ns MD simulation. The proposed flavonoids are suggested as potential inhibitors of MEK2 and drug candidates for cancer therapy.

Computational targeting of allosteric site of MEK1 by quinoline-based molecules

MEK1 is an attractive target due to its role in selective extracellular-signal-regulated kinase phosphorylation, which plays a pivotal role in regulating cell proliferation. Another benefit of targeting the MEK protein is its unique hydrophobic pocket that can accommodate highly selective allosteric inhibitors. To date, various MEK1 inhibitors have reached clinical trials against several cancers, but they were discarded due to their severe toxicity and low efficacy. Thus, the development of allosteric inhibitors for MEK1 is the demand of the hour. In this in-silico study, molecular docking, long-term molecular dynamics (5 µs), and molecular mechanics Poisson-Boltzmann surface area analysis were undertaken to address the potential of quinolines as allosteric inhibitors. We selected four reference MEK1 inhibitors for the comparative analysis. The drug-likeness and toxicity of these molecules were also examined based on their ADMET and Toxicity Prediction by Komputer Assisted Technology profiles. The outcome of the analysis revealed that the quinolines (4m, 4o, 4s, and 4n) exhibited better stability and binding affinity while being nontoxic compared to reference inhibitors. We have reached the conclusion that these quinoline molecules could be checked by experimental studies to validate their use as allosteric inhibitors against MEK1.

Exploring the Natural Compounds in Flavonoids for Their Potential Inhibition of Cancer Therapeutic Target MEK1 Using Computational Methods

The Mitogen-Activated Protein Kinase (MAPK) signaling pathway plays an important role in cancer cell proliferation and survival. MAPKs' protein kinases MEK1/2 serve as important targets in drug designing against cancer. The natural compounds' flavonoids are known for their anticancer activity. This study aims to explore flavonoids for their inhibition ability, targeting MEK1 using virtual screening, molecular docking, ADMET prediction, and molecular dynamics (MD) simulations. Flavonoids (n = 1289) were virtually screened using molecular docking and have revealed possible inhibitors of MEK1. The top five scoring flavonoids based on binding affinity (highest score for MEK1 is -10.8 kcal/mol) have been selected for further protein-ligand interaction analysis. Lipinski's rule (drug-likeness) and absorption, distribution, metabolism, excretion, and toxicity predictions were followed to find a good balance of potency. The selected flavonoids of MEK1 have been refined with 30 (ns) molecular dynamics (MD) simulation. The five selected flavonoids are strongly suggested to be promising potent inhibitors for drug development as anticancer therapeutics of the therapeutic target MEK1.

Identification of the functional food ingredients with antithrombotic properties via virtual screen and experimental studies

Thrombin is a key therapeutic target protein of thrombosis. To date, massive studies have focused on the exploration of antithrombotic compounds. Here we capitalize on molecular docking, molecular simulations and spectroscopic experiments for virtually screening natural products that can inhibit thrombin and elucidating their interaction mechanism. Six compounds are screened from a natural product database by a cross-analysis based on two semi-flexible molecular docking methods. We show that four compounds can effectively inhibit thrombin and Calceolarioside B is the most competitive one based on enzyme inhibition experiments. Moreover, the binding free energies of these compounds with thrombin exhibit a consistent rank trend with their enzyme inhibition assay results. In addition, the Van der Waals is the main force to drive the interaction between the ligands and the receptor, which can be deduced from the fluorescence spectral results. This work provides a new insight into the development of antithrombotic natural compounds.

Bioinformatics Approaches for Anti-cancer Drug Discovery

Drug discovery is important in cancer therapy and precision medicines. Traditional approaches of drug discovery are mainly based on in vivo animal experiments and in vitro drug screening, but these methods are usually expensive and laborious. In the last decade, omics data explosion provides an opportunity for computational prediction of anti-cancer drugs, improving the efficiency of drug discovery. High-throughput transcriptome data were widely used in biomarkers' identification and drug prediction by integrating with drug-response data. Moreover, biological network theory and methodology were also successfully applied to the anti-cancer drug discovery, such as studies based on protein-protein interaction network, drug-target network and disease-gene network. In this review, we summarized and discussed the bioinformatics approaches for predicting anti-cancer drugs and drug combinations based on the multi-omic data, including transcriptomics, toxicogenomics, functional genomics and biological network. We believe that the general overview of available databases and current computational methods will be helpful for the development of novel cancer therapy strategies.

Docking-based virtual screening of TβR1 inhibitors: evaluation of pose prediction and scoring functions

To improve the reliability of virtual screening for transforming growth factor-beta type 1 receptor (TβR1) inhibitors, 2 docking methods and 11 scoring functions in Discovery Studio software were evaluated and validated in this study. LibDock and CDOCKER protocols were performed on a test set of 24 TβR1 protein-ligand complexes. Based on the root-mean-square deviation (RMSD) values (in Å) between the docking poses and co-crystal conformations, the CDOCKER protocol can be efficiently applied to obtain more accurate dockings in medium-size virtual screening experiments of TβR1, with a successful docking rate of 95%. A dataset including 281 known active and 8677 inactive ligands was used to determine the best scoring function. The receiver operating characteristic (ROC) curves were used to compare the performance of scoring functions in attributing best scores to active than inactive ligands. The results show that Ludi 1, PMF, Ludi 2, Ludi 3, PMF04, PLP1, PLP2, LigScore2, Jain and LigScore1 are better scoring functions than the random distribution model, with AUC of 0.864, 0.856, 0.842, 0.812, 0.776, 0.774, 0.769, 0.762, 0.697 and 0.660, respectively. Based on the pairwise comparison of ROC curves, Ludi 1 and PMF were chosen as the best scoring functions for virtual screening of TβR1 inhibitors. Further enrichment factors (EF) analysis also supports PMF and Ludi 1 as the top two scoring functions.

Molecular dynamics simulation and experimental study of the surface-display of SPA protein via Lpp-OmpA system for screening of IgG

Staphylococcal protein A (SpA) is a major virulence factor of Staphylococcus aureus. S. aureus is able to escape detection by the immune system by the surface display of protein A. The SpA protein is broadly used to purify immunoglobulin G (IgG) antibodies. This study investigates the fusion ability of Lpp'-OmpA (46-159) to anchor and display five replicate domains of protein A with 295 residues length (SpA295) of S. aureus on the surface of Escherichia coli to develop a novel bioadsorbent. First, the binding between Lpp'-OmpA-SPA295 and IgGFc and the three-dimensional structure was investigated using molecular dynamics simulation. Then high IgG recovery from human serum by the surface-displayed system of Lpp'-OmpA-SPA295 performed experimentally. In silico analysis was demonstrated the binding potential of SPA295 to IgG after expression on LPP-OmpA surface. Surface-engineered E. coli displaying SpA protein and IgG-binding assay with SDS-PAGE analysis exhibited high potential of the expressed complex on the E. coli surface for IgG capture from human serum which is applicable to conventional immune precipitation.

In silico identification of potential inhibitors of key SARS-CoV-2 3CL hydrolase (Mpro) via molecular docking, MMGBSA predictive binding energy calculations, and molecular dynamics simulation

The incidence of 2019 novel corona virus (SARS-CoV-2) has created a medical emergency throughout the world. Various efforts have been made to develop the vaccine or effective treatments against the disease. The discovery of crystal structure of SARS-CoV-2 main protease has made the in silico identification of its inhibitors possible. Based on its critical role in viral replication, the viral protease can prove to be a promising “target” for antiviral drug therapy. We have systematically screened an in-house library of 15,754 natural and synthetic compounds, established at International Center for Chemical and Biological Sciences, University of Karachi. The in silico search for potential viral protease inhibitors resulted in nine top ranked ligands (compounds 1–9) against SARS-CoV-2 main protease (PDB ID: 6LU7) based on docking scores, and predictive binding energies. The in silico studies were updated via carrying out the docking, and predictive binding energy estimation, with a recently reported crystal structure of main protease (PDB ID: 6Y2F) at a better resolution i.e., 1.95 Å. Compound 2 (molecular bank code AAA396) was found to have highest negative binding energy of −71.63 kcal/mol for 6LU7. While compound 3 (molecular bank code AAD146) exhibited highest negative binding energy of -81.92 kcal/mol for 6Y2F. The stability of the compounds- in complex with viral protease was analyzed by Molecular Dynamics simulation studies, and was found to be stable over the course of 20 ns simulation time. Compound 2, and 3 were predicted to be the significant inhibitors of SARS-CoV-2 3CL hydrolase (Mpro) among the nine short listed compounds.

Gibbs Free Energy Calculation of Mutation in PncA and RpsA Associated With Pyrazinamide Resistance

A central approach for better understanding the forces involved in maintaining protein structures is to investigate the protein folding and thermodynamic properties. The effect of the folding process is often disturbed in mutated states. To explore the dynamic properties behind mutations, molecular dynamic (MD) simulations have been widely performed, especially in unveiling the mechanism of drug failure behind mutation. When comparing wild type (WT) and mutants (MTs), the structural changes along with solvation free energy (SFE), and Gibbs free energy (GFE) are calculated after the MD simulation, to measure the effect of mutations on protein structure. Pyrazinamide (PZA) is one of the first-line drugs, effective against latent Mycobacterium tuberculosis isolates, affecting the global TB control program 2030. Resistance to this drug emerges due to mutations in pncA and rpsA genes, encoding pyrazinamidase (PZase) and ribosomal protein S1 (RpsA) respectively. The question of how the GFE may be a measure of PZase and RpsA stabilities, has been addressed in the current review. The GFE and SFE of MTs have been compared with WT, which were already found to be PZA-resistant. WT structures attained a more stable state in comparison with MTs. The physiological effect of a mutation in PZase and RpsA may be due to the difference in energies. This difference between WT and MTs, depicted through GFE plots, might be useful in predicting the stability and PZA-resistance behind mutation. This study provides useful information for better management of drug resistance, to control the global TB problem.