Hydrogen bond analysis of the EGFR-ErbB3 heterodimer related to non-small cell lung cancer and drug resistance

Regulatory effects of statins on Akt signaling for prevention of cancers

Statins, which are primarily used as lipid-lowering drugs, have been found to exhibit anti-tumor effects through modulating and interfering with various signaling pathways. In observational studies, statin use has been associated with a significant reduction in the progression of various cancers, including colon, lung, prostate, pancreas, and esophagus cancer, as well as melanoma and B and T cell lymphoma. The mevalonate pathway, which is affected by statins, plays a crucial role in activating Rho, Ras, and Rab proteins, thereby impacting the proliferation and apoptosis of tumor cells. Statins block this pathway, leading to the inhibition of isoprenoid units, which are critical for the activation of these key proteins, thereby affecting cancer cell behavior. Additionally, statins affect MAPK and Cdk2, which in turn reduce the expression of p21 and p27 cyclin-dependent kinase inhibitors. Akt signaling plays a crucial role in key cancer cell features like proliferation, invasion, and apoptosis by activating multiple effectors in downstream pathways such as FOXO, PTEN, NF-κB, GSK3β, and mTOR. The PI3K/Akt signaling is necessary for many events in the metastatic pathway and has been implicated in the resistance to cytostatic drugs. The Akt/PTEN axis is currently attracting great interest for its role in carcinogenesis. Statins have been shown to activate the purinergic receptor P2X7 and affect Akt signaling, which may have important anti-cancer effects. Hence, targeting Akt shows promise as an effective approach to cancer prevention and therapy. This review aims to provide a comprehensive discussion on the specific impact of statins through Akt signaling in different types of cancer.

Computational Methods for the Analysis and Prediction of EGFR-Mutated Lung Cancer Drug Resistance: Recent Advances in Drug Design, Challenges and Future Prospects

Lung cancer is a major cause of cancer deaths worldwide, and has a very low survival rate. Non-small cell lung cancer (NSCLC) is the largest subset of lung cancers, which accounts for about 85% of all cases. It has been well established that a mutation in the epidermal growth factor receptor (EGFR) can lead to lung cancer. EGFR Tyrosine Kinase Inhibitors (TKIs) are developed to target the kinase domain of EGFR. These TKIs produce promising results at the initial stage of therapy, but the efficacy becomes limited due to the development of drug resistance. In this paper, we provide a comprehensive overview of computational methods, for understanding drug resistance mechanisms. The important EGFR mutants and the different generations of EGFR-TKIs, with the survival and response rates are discussed. Next, we evaluate the role of important EGFR parameters in drug resistance mechanism, including structural dynamics, hydrogen bonds, stability, dimerization, binding free energies, and signaling pathways. Personalized drug resistance prediction models, drug response curve, drug synergy, and other data-driven methods are also discussed. Recent advancements in deep learning; such as AlphaFold2, deep generative models, big data analytics, and the applications of statistics and permutation are also highlighted. We explore limitations in the current methodologies, and discuss strategies to overcome them. We believe this review will serve as a reference for researchers; to apply computational techniques for precision medicine, analyzing structures of protein-drug complexes, drug discovery, and understanding the drug response and resistance mechanisms in lung cancer patients.

An overview of epithelial growth factor receptor (EGFR) inhibitors in cancer therapy

Epithelial growth factor receptor (EGFR), a transmembrane receptor on the cell surface, carries extracellular messages into the cell and alters the activity of the nucleus through tyrosine signalling. EGFR-targeted treatments have influenced the new era of precision oncology throughout the last few decades. Despite significant progress, long-term remission from solid tumours is still a distant goal for many oncologists. There are several methods by which tumour cells alter the activity of this protein in solid tumours. EGFR-related oncogenic pathways, resistance mechanisms, and novel avenues to suppress tumour development and metastatic spread were discovered in clinical specimens using preclinical models (cell cultures, xenografts, mouse models), which were then validated in those specimens. EGFR has been implicated in the onset and advancement of a variety of cancers, according to research. An overview of EGFR's structural anatomy and physiology, its role in cancers, and clinical studies that target EGFR in various tumours are included in this review.

Correlated Motions and Dynamics in Different Domains of Epidermal Growth Factor Receptor With L858R and T790M Mutations

Non-small cell lung cancer with an activating epidermal growth factor receptor (EGFR) mutation responds well to targeted drugs. In most cases, drug resistance appears after about a year. Several studies have been conducted on the kinase domain of EGFR to understand the drug resistance mechanism. Since EGFR is a multi-domain protein, mutation in the kinase domain may affect the other domains as well. In this study, we examine the complete structure of the multi-domain EGFR protein and its mutants. We performed molecular dynamics simulations for wildtype EGFR, EGFR with L858R mutation, and EGFR with L858R and T790M mutations. We applied normal mode analysis and complex network analysis to extract the correlated motions in the domains of EGFR. The normal modes are used to construct the dynamic cross-correlation map (DCCM). Simulation results show different patterns of correlated motions in each domain of EGFR mutants compared to the wildtype. In Domains 1 and 3 of the extracellular region, a small number of weak positively correlated motions are extracted. Domains 2 and 4 show large numbers of both positive and negative motions. However, the negatively correlated motions are stronger in mutant structures compared to the wildtype. In Domain 7, some residues showed a positive correlation around the main diagonal. We also identified different communities, nodes and crucial residues in the domains of the structures, which can be important for the function of EGFR. Moreover, hydrogen bond analysis is performed for the stability analysis. The mutant structures have fewer hydrogen bonds compared to the wildtype. Overall, these findings are useful for understanding the dynamics and communications in EGFR domains.

Molecular Insights into Binding Mode and Interactions of Structure-Based Virtually Screened Inhibitors for Pseudomonas aeruginosa Multiple Virulence Factor Regulator (MvfR)

Multi-drug resistance (MDR) bacterial pathogens pose a threat to global health and warrant the discovery of new therapeutic molecules, particularly those that can neutralize their virulence and stop the evolution of new resistant mechanisms. The superbug nosocomial pathogen, Pseudomonas aeruginosa, uses a multiple virulence factor regulator (MvfR) to regulate the expression of multiple virulence proteins during acute and persistent infections. The present study targeted MvfR with the intention of designing novel anti-virulent compounds, which will function in two ways: first, they will block the virulence and pathogenesis P. aeruginosa by disrupting the quorum-sensing network of the bacteria, and second, they will stop the evolution of new resistant mechanisms. A structure-based virtual screening (SBVS) method was used to screen druglike compounds from the Asinex antibacterial library (~5968 molecules) and the comprehensive marine natural products database (CMNPD) (~32 thousand compounds), against the ligand-binding domain (LBD) of MvfR, to identify molecules that show high binding potential for the relevant pocket. In this way, two compounds were identified: Top-1 (4-((carbamoyloxy)methyl)-10,10-dihydroxy-2,6-diiminiodecahydropyrrolo[1,2-c]purin-9-yl sulfate) and Top-2 (10,10-dihydroxy-2,6-diiminio-4-(((sulfonatocarbamoyl)oxy)methyl)decahydropyrrolo[1,2-c]purin-9-yl sulfate), in contrast to the co-crystallized M64 control. Both of the screened leads were found to show deep pocket binding and interactions with several key residues through a network of hydrophobic and hydrophilic interactions. The docking results were validated by a long run of 200 ns of molecular dynamics simulation and MM-PB/GBSA binding free energies. All of these analyses confirmed the presence of strong complex formation and rigorous intermolecular interactions. An additional analysis of normal mode entropy and a WaterSwap assay were also performed to complement the aforementioned studies. Lastly, the compounds were found to show an acceptable range of pharmacokinetic properties, making both compounds potential candidates for further experimental studies to decipher their real biological potency.

A Local Outlier Factor-Based Detection of Copy Number Variations From NGS Data

Copy number variation (CNV) is a major type of genomic structural variations that play an important role in human disorders. Next generation sequencing (NGS) has fueled the advancement in algorithm design to detect CNVs at base-pair resolution. However, accurate detection of CNVs of low amplitudes remains a challenging task. This paper proposes a new computational method, CNV-LOF, to identify CNVs of full-range amplitudes from NGS data. CNV-LOF is distinctly different from traditional methods, which mainly consider aberrations from a global perspective and rely on some assumed distribution of NGS read depths. In contrast, CNV-LOF takes a local view on the read depths and assigns an outlier factor to each genome segment. With the outlier factor profile, CNV-LOF uses a boxplot procedure to declare CNVs without the reliance of any distribution assumptions. Simulation experiments indicate that CNV-LOF outperforms five existing methods with respect to F1-measure, sensitivity, and precision. CNV-LOF is further validated on real sequencing samples, yielding highly consistent results with peer methods. CNV-LOF is able to detect CNVs of low and moderate amplitudes where the other existing methods fail, and it is expected to become a routine approach for the discovery of novel CNVs on whole sequencing genome.

Antagonizing S1P3 Receptor with Cell-Penetrating Pepducins in Skeletal Muscle Fibrosis

S1P is the final product of sphingolipid metabolism, which interacts with five widely expressed GPCRs (S1P_1-5). Increasing numbers of studies have indicated the importance of S1P₃ in various pathophysiological processes. Recently, we have identified a pepducin (compound KRX-725-II) acting as an S1P₃ receptor antagonist. Here, aiming to optimize the activity and selectivity profile of the described compound, we have synthesized a series of derivatives in which Tyr, in position 4, has been substituted with several natural aromatic and unnatural aromatic and non-aromatic amino acids. All the compounds were evaluated for their ability to inhibit vascular relaxation induced by KRX-725 (as S1P₃ selective pepducin agonist) and KRX-722 (an S1P₁-selective pepducin agonist). Those selective towards S1P₃ (compounds V and VII) were also evaluated for their ability to inhibit skeletal muscle fibrosis. Finally, molecular dynamics simulations were performed to derive information on the preferred conformations of selective and unselective antagonists.

Genotype-determined EGFR-RTK heterodimerization and its effects on drug resistance in lung Cancer treatment revealed by molecular dynamics simulations

BACKGROUND

Epidermal growth factor receptor (EGFR) and its signaling pathways play a vital role in pathogenesis of lung cancer. By disturbing EGFR signaling, mutations of EGFR may lead to progression of cancer or the emergence of resistance to EGFR-targeted drugs.

RESULTS

We investigated the correlation between EGFR mutations and EGFR-receptor tyrosine kinase (RTK) crosstalk in the signaling network, in order to uncover the drug resistance mechanism induced by EGFR mutations. For several EGFR wild type (WT) or mutated proteins, we measured the EGFR-RTK interactions using several computational methods based on molecular dynamics (MD) simulations, including geometrical characterization of the interfaces and conventional estimation of free energy of binding. Geometrical properties, namely the matching rate of atomic solid angles in the interfaces and center-of-mass distances between interacting atoms, were extracted relying on Alpha Shape modeling. For a couple of RTK partners (c-Met, ErbB2 and IGF-1R), results have shown a looser EGFR-RTK crosstalk for the drug-sensitive EGFR mutant while a tighter crosstalk for the drug-resistant mutant. It guarantees the genotype-determined EGFR-RTK crosstalk, and further proposes a potential drug resistance mechanism by amplified EGFR-RTK crosstalk induced by EGFR mutations.

CONCLUSIONS

This study will lead to a deeper understanding of EGFR mutation-induced drug resistance mechanisms and promote the design of innovative drugs.

Visualization of Protein-Drug Interactions for the Analysis of Drug Resistance in Lung Cancer

Non-small cell lung cancer (NSCLC) caused by mutation of the epidermal growth factor receptor (EGFR) is a major cause of death worldwide. Tyrosine kinase inhibitors (TKIs) of EGFR have been developed and show promising results at the initial stage of therapy. However, in most cases, their efficacy becomes limited due to the emergence of secondary mutations causing drug resistance after about a year. In this work, we investigated the mechanism of drug resistance due to these mutations. We performed molecular dynamics (MD) simulations of EGFR-drug interactions to obtain Euclidean distance and binding free energy values to analyse drug resistance and visualize drug-protein interactions. A PCA-based method is proposed to find normal, rigid, flexible, and critical residues. We have established a systematic method for the visualization of protein-drug interactions, which provides an effective framework for the analysis of drug resistance in lung cancer at the atomic level.

Prediction and evaluation of deleterious and disease causing non-synonymous SNPs (nsSNPs) in human NF2 gene responsible for neurofibromatosis type 2 (NF2)

The majority of genetic variations in the human genome that lead to variety of different diseases are caused by non-synonymous single nucleotide polymorphisms (nsSNPs). Neurofibromatosis type 2 (NF2) is a deadly disease caused by nsSNPs in the NF2 gene that encodes for a protein called merlin. This study used various in silico methods, SIFT, Polyphen-2, PhD-SNP and MutPred, to investigate the pathogenic effect of 14 nsSNPs in the merlin FERM domain. The G197C and L234R mutations were found to be two deleterious and disease mutations associated with the mild and severe forms of NF2, respectively. Molecular dynamics (MD) simulations were conducted to understand the stability, structure and dynamics of these mutations. Both mutant structures experienced larger flexibility compared to the wildtype. The L234R mutant suffered from more prominent structural instability, which may help to explain why it is associated with the more severe form of NF2. The intramolecular hydrogen bonding in L234R mutation decreased from the wildtype, while intermolecular hydrogen bonding of L234R mutation with solvent greatly increased. The native contacts were also found to be important. Protein-protein docking revealed that L234R mutation decreased the binding complementarity and binding affinity of LATS2 to merlin, which may have an impact on merlin's ability to regulate the Hippo signaling pathway. The calculated binding affinity of the LATS2 to L234R mutant and wildtype merlin protein is found to be 21.73 and -11 kcal/mol, respectively. The binding affinity of the wildtype merlin agreed very well with the experimental value, -8 kcal/mol.Communicated by Ramaswamy H. Sarma.

BBB-penetrating codelivery liposomes treat brain metastasis of non-small cell lung cancer with EGFRT790M mutation

EGFR TKI therapy has become a first-line regimen for non-small cell lung cancer (NSCLC) patients with EGRF mutations. However, there are two big challenges against effective therapy--the secondary EGFR mutation-associated TKI resistance and brain metastasis (BMs) of lung cancer. The BMs is a major cause of death for advanced NSCLC patients, and the treatment of BMs with TKI resistance remains difficult.

Methods: Tumor-associated macrophages (TAM) is a promising drug target for inhibiting tumor growth, overcoming drug resistance, and anti-metastasis. TAM also plays an essential role in regulating tumor microenvironment. We developed a dual-targeting liposomal system with modification of anti-PD-L1 nanobody and transferrin receptor (TfR)-binding peptide T12 for codelivery of simvastatin/gefitinib to treat BMs of NSCLC.

Results: The dual-targeting liposomes could efficiently penetrate the blood-brain barrier (BBB) and enter the BMs, acting on TAM repolarization and reversal of EGFR^T790M-associated drug resistance. The treatment mechanisms were related to the elevating ROS and the suppression of the EGFR/Akt/Erk signaling pathway.

Conclusion: The dual-targeting liposomal codelivery system offers a promising strategy for treating the advanced EGFR^T790M NSCLC patients with BMs.