Analysis of the relationship between lung cancer drug response level and atom connectivity dynamics based on trimmed Delaunay triangulation

Gefitinib derivatives and drug-resistance: A perspective from molecular dynamics simulations

Epidermal-growth factor receptor (EGFR) is a transmembrane tyrosine kinase (TK) with a significant role in cell survival. EGFR is upregulated in various cancer cells and known as a druggable target. Gefitinib is a first-line TK inhibitor used against metastatic non-small cell lung cancer (NSCLC). Despite initial clinical response, a conserved therapeutic effect could not be achieved due to the occurrence of resistance mechanisms. Point mutations in EGFR genes are one of the major causes of rendered tumor sensitivity. To aid in the development of more efficient TKIs, chemical structures of prevailing drugs and their target binding patterns are very important. The aim of the present study was to propose synthetically-accessible gefitinib congeners with enhanced binding fitness to clinically frequent EGFR mutants. Docking simulations of intended molecules identified 1-(4-(3-chloro-4-fluorophenylamino)-7-methoxyquinazolin-6-yl)-3-(oxazolidin-2-ylmethyl) thiourea (23) as a top-binder structure inside G719S, T790 M, L858R and T790 M/L858R-EGFR active sites. Superior docked complexes were subjected to the entire 400 ns molecular dynamics (MD) simulations. Analysis of data revealed the stability of mutant enzymes upon binding to molecule 23. All mutant complexes with the exception of a T790 M/L858R-EGFR, were majorly stabilized through cooperative hydrophobic contacts. Pairwise analysis of H-bonds proved Met793 as the conserved residue with stable H-bond participations as hydrogen bond donor (Frequency 63-96%). Amino acid decomposition analysis confirmed the probable role of Met793 in complex stabilization. Estimated binding free energies indicated the proper accommodation of molecule 23 inside target active sites. Pairwise energy decompositions of stable binding modes revealed the energetic contribution of key residues. Although wet lab experiments are required to unravel the mechanistic details of mEGFR inhibition, MD results provide structural basis for those events that are difficult to address experimentally. The outputs of the current study may assist to design small molecules with high potency to mEGFRs.

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.

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.

Prediction of sensitivity to gefitinib/erlotinib for EGFR mutations in NSCLC based on structural interaction fingerprints and multilinear principal component analysis

Background

Non-small cell lung cancer (NSCLC) with activating EGFR mutations, especially exon 19 deletions and the L858R point mutation, is particularly responsive to gefitinib and erlotinib. However, the sensitivity varies for less common and rare EGFR mutations. There are various explanations for the low sensitivity of EGFR exon 20 insertions and the exon 20 T790 M point mutation to gefitinib/erlotinib. However, few studies discuss, from a structural perspective, why less common mutations, like G719X and L861Q, have moderate sensitivity to gefitinib/erlotinib.

Results

To decode the drug sensitivity/selectivity of EGFR mutants, it is important to analyze the interaction between EGFR mutants and EGFR inhibitors. In this paper, the 30 most common EGFR mutants were selected and the technique of protein-ligand interaction fingerprint (IFP) was applied to analyze and compare the binding modes of EGFR mutant-gefitinib/erlotinib complexes. Molecular dynamics simulations were employed to obtain the dynamic trajectory and a matrix of IFPs for each EGFR mutant-inhibitor complex. Multilinear Principal Component Analysis (MPCA) was applied for dimensionality reduction and feature selection. The selected features were further analyzed for use as a drug sensitivity predictor. The results showed that the accuracy of prediction of drug sensitivity was very high for both gefitinib and erlotinib. Targeted Projection Pursuit (TPP) was used to show that the data points can be easily separated based on their sensitivities to gefetinib/erlotinib.

Conclusions

We can conclude that the IFP features of EGFR mutant-TKI complexes and the MPCA-based tensor object feature extraction are useful to predict the drug sensitivity of EGFR mutants. The findings provide new insights for studying and predicting drug resistance/sensitivity of EGFR mutations in NSCLC and can be beneficial to the design of future targeted therapies and innovative drug discovery.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2093-6) contains supplementary material, which is available to authorized users.

Deciphering mechanisms of acquired T790M mutation after EGFR inhibitors for NSCLC by computational simulations

Metastatic non-small-cell lung cancer (NSCLC) with activating EGFR mutations responds very well to first and second generation tyrosine-kinase inhibitors (TKI) including gefitinib, erlotinib and afatinib. Unfortunately, drug resistance will eventually develop and about half of the cases are secondary to the emergence of acquired T790M somatic mutation. In this work, we prospectively recruited 68 patients with metastatic EGFR-mutated NSCLC who have developed progressive disease after first-line TKI with or without subsequent TKI and/or other systemic therapy. Liquid biopsy after progression to their last line of systemic therapy were taken for detection of acquired T790M mutation. By performing attribute ranking we found that several attributes, including the initial EGFR mutational type, had a high correlation with the presence of acquired T790M mutation. We also conducted computational studies and discovered that the EGFR mutation delE746_A750 had a lower stability around the residue T790 than delS752_I759 and L858R, which was consistent with our clinical observation that patients with delE746_A750 were more likely to acquire T790M mutation than those with delS752_I759 or L858R. Our results provided new insight to future direction of research on investigating the mechanisms of acquired T790M mutation, which is essential to the development of novel mutation-specific TKIs.