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.

Computationally predicting binding affinity in protein-ligand complexes: free energy-based simulations and machine learning-based scoring functions.

Accurately predicting protein-ligand binding affinities can substantially facilitate the drug discovery process, but it remains as a difficult problem. To tackle the challenge, many computational methods have been proposed. Among these methods, free energy-based simulations and machine learning-based scoring functions can potentially provide accurate predictions. In this paper, we review these two classes of methods, following a number of thermodynamic cycles for the free energy-based simulations and a feature-representation taxonomy for the machine learning-based scoring functions. More recent deep learning-based predictions, where a hierarchy of feature representations are generally extracted, are also reviewed. Strengths and weaknesses of the two classes of methods, coupled with future directions for improvements, are comparatively discussed.

Predicting the impacts of mutations on protein-ligand binding affinity based on molecular dynamics simulations and machine learning methods.

PURPOSE: Mutation-induced variation of protein-ligand binding affinity is the key to many genetic diseases and the emergence of drug resistance, and therefore predicting such mutation impacts is of great importance. In this work, we aim to predict the mutation impacts on protein-ligand binding affinity using efficient structure-based, computational methods. METHODS: Relying on consolidated databases of experimentally determined data we characterize the affinity change upon mutation based on a number of local geometrical features and monitor such feature differences upon mutation during molecular dynamics (MD) simulations. The differences are quantified according to average difference, trajectory-wise distance or time-vary differences. Machine-learning methods are employed to predict the mutation impacts using the resulting conventional or time-series features. Predictions based on estimation of energy and based on investigation of molecular descriptors were conducted as benchmarks. RESULTS: Our method (machine-learning techniques using time-series features) outperformed the benchmark methods, especially in terms of the balanced F1 score. Particularly, deep-learning models led to the best prediction performance with distinct improvements in balanced F1 score and a sustained accuracy. CONCLUSION: Our work highlights the effectiveness of the characterization of affinity change upon mutations. Furthermore, deep-learning techniques are well designed for handling the extracted time-series features. This study can lead to a deeper understanding of mutation-induced diseases and resistance, and further guide the development of innovative drug design.