Skip to main content
NCBI home page
ACS Omega logoLink to ACS Omega
. 2020 Jun 24;5(26):16307–16314. doi: 10.1021/acsomega.0c02183

Molecular Dynamics Analysis of Binding Sites of Epidermal Growth Factor Receptor Kinase Inhibitors

Dong-Dong Li †,‡, Ting-Ting Wu †,‡, Pan Yu †,‡, Zhen-Zhong Wang §, Wei Xiao §,*, Yan Jiang ‡,*, Lin-Guo Zhao †,‡,*
PMCID: PMC7346266  PMID: 32656454

Abstract

graphic file with name ao0c02183_0006.jpg

The development of an epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) is an ongoing and challenging research field. However, the dynamic motion of the binding site of EGFR has not been accurately depicted, hindering the improvement of EGFR TKI. For this reason, about 33 protein complexes (32 EGFR proteins plus 1 ErbB4 protein) were carefully curated and subsequently studied for dynamic movements of their binding sites by molecular dynamics simulations in this study. The analysis of root mean square deviation (RMSD) revealed that T790M mutation can make an impact on dynamic motion of binding sites; the RMSD value of the EGFR binding site was unrelated to inhibitory activity. The analysis of the radius of gyration (Rg) revealed that T790M can slightly shrink the value of Rg, thereby influencing the shape of the EGFR binding site. More interestingly, the Rg value can exhibit weak correlation with inhibitory activity of most inhibitors. The relationship between Rg and biological activity deserve our serious interest since the best scoring function, Xscore, cannot distinguish highly active EGFR inhibitors. The root mean square fluctuation (RMSF) analysis of key residues derived from binding sites indicated that the most flexible residue was ASP800 with a large RMSF value against the steady residue ALA743 with a small RMSF value, and two other residues (MET793 and LEU844) were supposed to be involved with molecular recognition. In short, the obtained results would be more effective for guiding the development of a novel EGFR kinase inhibitor.

1. Introduction

The epidermal growth factor receptor (EGFR) kinase is a kind of cell surface protein receptor that mediates multiple upstream signal transduction pathways, thus regulating the process of cell growth and differentiation.1 Its prevalent activating mutations such as L858R and an exon 19 deletion are closely involved with the occurrence and development of non-small cell lung cancer (NSCLC).2 In the past 20 years, the EGFR has been validated as a highly effective protein target for the treatment of various types of cancers.

The employment of EGFR tyrosine kinase inhibitors (TKIs) as molecule-targeted drugs leads to statistical improvement in progression-free survival (PFS) via a large number of clinical trials, especially for NSCLC. At present, a total of three generations of EGFR inhibitors have been approved to apply in clinical trials by the Food and Drug Administration (FDA). Gefitinib and erlotinib, two representatives of first-generation inhibitors, can produce dramatic clinical responses in some advanced NSCLCs, but the treated patients would unavoidably generate acquired resistance to these drugs after a 1 year treatment,3 which is mainly due to the secondary mutation (T790M) at the binding pocket. The first approved irreversible kinase inhibitor afatinib, as the second-generation EGFR TKI, still fails to deal with the T790M mutation of exon 20 of the EGFR.4 Subsequently, the third-generation EGFR TKI osimertinib (AZD9291) is a unique FDA-approved drug for the treatment of advanced NSCLC with positive T790M mutation.5 However, a new drug-resistant problem would emerge rapidly, generally after a period of 9–13 months. Correspondingly, C797S mutation located within the EGFR kinase domain had been identified to be a primary mechanism of resistance to the third-generation EGFR TKIs, involving the failure of covalent bond formation at the 797 position.6 In aiming to overcome EGFR (T790M/C797S) resistance, an EGFR allosteric inhibitor EAI045 has been rationally discovered as the fourth-generation EGFR TKI in the last 2 years.7 Therefore, the development of novel EGFR kinase inhibitors will be an ongoing and direct challenge if a newly acquired resistance to the old EGFR TKIs was revealed.

To aid in the development of new EGFR TKIs, several groups have investigated EGFR drug resistance mechanisms between activating mutations and inhibitors at the atomic level by molecular dynamics (MD) simulation8−10 and developed an in silico prediction model for individualized administration over NSCLC patients harboring rare EGFR mutations.11 Despite the fact that a large number of MD studies about wild/mutant EGFR kinase domains or EGFR TKIs were reported in recent years, the dynamic movement of EGFR kinase binding sites involving key residues have not yet been discussed. To date, there are more than 100 EGFR protein crystal complexes deposited in the Protein Data Bank (PDB),12 almost including all the four generation inhibitors with varied scaffolds, which can be a kind of good resource for the dynamic study of the EGFR binding site. Accordingly, as shown in Figure 1, 33 EGFR complex structures were selected rationally based on analysis of several aspects, including X-ray resolution, protein conformation, activity, scaffold, and reversibility of kinase inhibitors. Each protein complex was performed by molecular dynamics simulation step by step, and then, root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg) of its binding site, and relative MM-GBSA calculation were studied in succession. Finally, the analysis derived from these obtained results would provide meaningful information used for (i) how the key residues at the binding site of the EGFR respond to the inhibitor effectively; (ii) what the correlation is between dynamic movements of binding sites and the inhibitory activities of TKIs. Hence, this study would give rise to new insight into the development of novel EGFR kinase inhibitors.

Figure 1.

Figure 1

Open in a new tab

(A) Flowchart of the molecular dynamics (MD) study for the EGFR protein kinase. (B) DFG-in and DFG-out conformation of the EGFR kinase domain. DFG refers to a conserved Asp-Phe-Gly motif of the EGFR sequence. (B1) The DFG-in motif makes the EGFR protein adopt an active state conformation (PDB ID: 1 M17 bound to erlotinib) where only the side chain of the Asp can extend to the inhibitor binding site. (B2) The DFG-out motif makes the EGFR protein adopt an inactive state conformation (PDB ID: 1XKK bound to lapatinib) where the side chains of the two residues (Asp and Phe) can extend to the inhibitor binding site.

2. Results and Discussion

2.1. Preparation of EGFR Protein–Ligand Complexes and Molecular Dynamics

As a result of the EGFR being an exceedingly successful drug target, there are over 200 human EGFR protein crystals deposited in the Protein Data Bank.12 According to Figure 1A, 100 and 28 EGFR X-ray structures with a resolution of less than 3 Å were selected and downloaded from the PDB database. Subsequently, 60 kinds of EGFR reversible inhibitor complexes (Table S1) were screened by removing dozens of EGFR proteins with no inhibitors. Due to many duplicates of EGFR inhibitors among 60 protein complexes, it was very essential to perform cluster analysis for these 60 inhibitors under the condition of considering the range of inhibitory activity (Table S2) as well as EGFR conformation types. Two common kinds of cluster methods (hierarchical and k-means) based on the MACCS fingerprints of inhibitors were used for the further selection of EGFR complexes. The detailed results are displayed in Table S3. Accordingly, 33 EGFR protein complexes were collected and prepared for the molecular dynamics (MD) study in succession. Moreover, Figure S1 shows that EGFR protein complexes can reach equilibrium within the 1 ns simulation time, taking the 12 ns MD study of 1 M17 for example, whereas for the result above, the simulation time of molecular dynamics that was set to 2.4 ns (Figure 1A) could be acceptable.

Two common conformations of protein tyrosine kinase (DFG-in and DFG-out; Figure 1B) are frequently discussed.19 In this respect, 33 EGFR complexes can be classified into two groups. As shown in Figure 2A and Table S4, there are 22 items adopting active conformation, and the other 11 items are adopting inactive conformation. According to residue mutation of the binding site, each group would be further divided into two categories: wild type and mutant EGFR. Correspondingly, subsequent analysis of RMSD, Rg, and RMSF of the binding site would be discussed separately in accordance with these four groups.

Figure 2.

Figure 2

Open in a new tab

(A) Classification of the EGFR kinase domain according to the residue mutation and conformation form. (B) Root mean square deviation (RMSD) of the binding site composed of several residues from the EGFR active pocket: (B1–B4) from the EGFR kinase domain in an active conformation (DFG-in motif) and (B5, B6) from the EGFR kinase domain in an inactive conformation (DFG-out motif). In addition, 5 wild type (WT) crystal complexes of EGFR kinase domains in panel (B1), 4 non-T790M mutants in panel (B2), 3 T790M mutants in panel (B3), 10 T790M/L858R mutants in panel (B4), 7 WT EGFR kinase domains in panel (B5), and 4 mutants in panel (B6). (C) Correlation between RMSD values: (C1) RMSD of the binding site backbone and that of inhibitors; (C2) RMSD of the binding site backbone and that of the side chain; Rp, Pearson coefficient; Rs, Spearman coefficient.

2.2. RMSD and Rg Analysis Based on the Molecular Dynamics Trajectory

All the residues of binding sites of the EGFR complexes are obtained from the 2D graph embedded in Figure S2. After the MD experiment, RMSD curves of all the binding sites had been collected on the basis of the MD trajectory. As shown in Figure 2B, most of the RMSD curves should be less than 1 Å during the 2.4 ns simulation time, even if the largest one cannot touch to 2 Å. This implies that all the structures of EGFR active pockets should reach at the equilibrium state rapidly. From an insight into Figure 2B1,B3,B6, several RMSD curves (4JQ8, 5X2F, and 3W2O) seem to fluctuate a bit obviously. This is probably related to the structure of corresponding inhibitors. However, what is interesting is that the T790M mutation might contribute to the fluctuation of 5X2F when comparing to 5X28, mainly due to the fact that these two protein complexes contain the same inhibitor 7XU. In addition, there are other five pairs of protein complexes containing the same inhibitor (Table S4): 1XKK and 3BBT; 2ITP and 2ITT; 2ITZ and 3UG2; 3W2R and 3W2S; 5EM7 and 5EM8, successively. Although both of 1XKK and 3BBT are located in the inactive/WT group (Figure 2B5), the protein of 3BBT is an ErbB4 kinase that can be explained for much difference in inhibitory activity. Similarly, 2ITP and 2ITT are located in the active/mutant group (Figure 2B2), but the former has a G719S point mutation while the latter has a L858R point mutation, implying that the non-T790M mutation might take no effect for curve fluctuation. By comparison of the curves of 2ITZ and 3UG2, the T790M mutation deserves our serious interest owing to the same fact that 3UG2 has the T790M mutation, and its curve fluctuates a bit more than 2ITZ. In two inactive groups (Figure 2B5,B6), the RMSD curve of the EGFR protein 3W2R harboring T790M mutation seems to fluctuate a bit more than 3W2S. Therefore, in addition to inhibitors with different scaffolds impacting on dynamic motion of binding sites, T790M mutation is also worthy of more consideration. In addition, as well as the binding site, the RMSD curves of the whole proteins are displayed in Figure S3, which demonstrated that all the proteins in the 2.4 ns molecular dynamics simulation could reach equilibrium quickly. To study how to move in coordination with the related inhibitor, correlation analysis between RMSD of the binding site backbone and that of the inhibitor was carried out, as shown in Figure 2C. The results showed that there is a weak correlation between RMSD of the binding site backbone and that of the corresponding inhibitor as well as that of the binding site side chain. The detailed data is retained in Table S5. However, RMSD values of the protein backbone were highly relevant with those of the protein side chain (Figure S4). This probably attributed to the structural specificity of binding sites.

The radius of gyration (Rg) can be used for indicating protein stability during simulation. In other words, the Rg curve of this protein would reach a plateau during simulation when a protein had been folded well. Changes in the structure of a protein can be monitored by Rg fluctuation. Figure 3A reveals that most of EGFR binding sites had very stable structures, whether in active or inactive groups. However, the Rg curves of 5X2F, 3UG2 (Figure 3A3), and 3W2O (Figure 3A6) fluctuate a bit more than others. According to the foregoing conjecture, T790M mutation of the EGFR binding site probably led to the variation of Rg during MD simulation. We also discovered that T790M mutation might shrink the value of Rg when comparing 5X2F, 3UG2, 3W2R, and 5EM7 to 5X28, 2ITZ, 3W2S, and 5EM8, respectively. As seen in Table S6, for example, the mean values of Rg of 5X2F and 5X28 are 8.06 and 9.43, respectively; the mean values of Rg of 3W2R and 3W2S are 9.08 and 9.38, respectively. Thus, it seems that T790M is closely related to the change of Rg.

Figure 3.

Figure 3

Open in a new tab

(A) Radius of gyration (Rg) of the EGFR binding site from 33 EGFR protein complexes: (A1) active/WT group, (A2) active/non-T790M group, (A3) active/T790M group, (A4) active/T790M/L858 group, (A5) inactive/WT group, and (A6) inactive/mutant group; WT, wild type. (B) Correlation analysis: (B1) Rg of the binding site backbone and that of the side chain, (B2) Rg of the protein complex and pIC50, (B3) Rg of the binding site backbone and pIC50, and (B4) SD of Rg values of the binding site backbone and pIC50; pIC50, logarithm of half inhibition concentration; SD, standard deviation; Rp, Pearson coefficient; Rs, Spearman coefficient.

Unlike RMSD, the correlation of Rg values between the side chain and backbone is vastly high; the Pearson coefficient of which reaches to 0.83 (Figure 3B1). This is probably due to the fact that Rg describes the shape change of protein, while RMSD describes residue structure stability. As seen from Figure 3B2,B3, the Rg value can exhibit a weak correlation with inhibitory activity of most inhibitors, no matter the Rg value of protein or that of only the binding site. It is very confusing that the change in the shape of the binding site might correlate with the activity. However, the standard deviation (SD; Figure 3B4) showed a kind of negative correlation with the activity. Further, the relationship between Rg and biological activity should need more comprehensive study, which might be beneficial for the development of a new EGFR inhibitor.

In addition, all the conformations of protein complexes derived from each MD study were collected and clustered reasonably. Ten representatives of complex structures for each protein were selected by the Schrodinger Suite 2015.15 Based on these conformations, MM-GBSA binding energy calculation20 had been performed, and the obtained results are displayed in Figure 4A. Second, the results of correlation analysis in Figure 4B,C revealed that neither MM-GBSA or Xscore21 can correlate better with PIC50 (logarithm of half inhibition concentration). It should be noted that the original EGFR complex structures were employed for the docking experiment of Xscore. All the calculated data are retained in Table S7. To be honest, there is basically no correlation between these two kinds of predicted values and inhibitory activities. It is well known that Xscore performs best than other scoring functions, whereas for Rg correlating with PIC50, it would be surmised that molecular dynamics techniques probably promote rapid development of novel scoring function.

Figure 4.

Figure 4

Open in a new tab

(A) Binding energy of these EGFR inhibitors via MM-GBSA calculation. MM-GBSA, molecular mechanics-generalized Born surface area. (B) Correlation analysis between interaction energy and pIC50. (C) Correlation analysis between the docking score of Xscore and pIC50. pIC50, logarithm of half inhibition concentration; Xscore, a kind of scoring function; Rp, Pearson coefficient; Rs, Spearman coefficient.

2.3. RMSF Analysis of Key Residues Derived from EGFR Binding Sites

Root mean square fluctuation (RMSF) of the residue can be used for evaluating structural movement and flexibility. It is widely accepted that the binding site is mainly composed of several key residues in the active pocket, and monitoring the behavior of these residues would contribute to the study of protein–ligand interaction. For this purpose, 33 kinds of EGFR protein–ligand contact graphs are exported into Figure S5 in proper sequence. As displayed in these graphs, the hydrogen bond (H-bond) and hydrophobic effect principally accounted for the interaction between the EGFR protein and inhibitors. One H-bond interaction between Met793 and the inhibitor seemed very significant. Meanwhile, Figure 5A reveals a dynamic process of H-bond of Met793 among 33 EGFR complexes during simulation. The detailed data are deposited in Table S7. Most of EGFR kinase inhibitors can interact with Met793 via a hydrogen bond except 3W20 and 5ZWJ, and maybe it was because that these two proteins adopted an inactive conformation during the MD study and that conformation possessed a larger active pocket.

Figure 5.

Figure 5

Open in a new tab

(A) Mean values of distance from MET793 to the kinase inhibitor, which generally can form a hydrogen bond (H-bond). (B–E) Root mean square fluctuation (RMSF) of key residues from the EGFR binding site, including the active/WT group, active/mutant group, inactive/WT group, and inactive/mutant group. (F) Mean values of RMSF of seven key residues.

Based on analysis of these protein–ligand contact graphs (Figure S5) , seven key residues composed of LEU718, VAL726, ALA743, THR790 (or MET790 mutation), MET793, ASP800, and LEU844 were selected for RMSF calculation. All the raw data are recorded in Table S8, and Figure 5B–E shows the distribution of RMSF values of key residues in the form of line charts. Additionally, Figure 5F calculates the mean of RMSF values of key residues. After careful examination, we can obtain certain meaningful results. First, the change of RMSF values of seven residues for each EGFR protein was basically in lockstep. This reflected that these residues can move together as an integral whole. Second, statistically, the most flexible residue was ASP800 with a large RMSF value, and the steady residue was ALA743 with a small RMSF value. This point might be helpful to guide the design of the novel EGFR kinase inhibitor, whether the type I or type II inhibitor.19 Third, these seven residues can be classified into two groups: hydrophobic residues (718, 726, 743, and 844) and polar residues (790, 793, and 800). As a matter of fact, except residue 800, both residues 793 and 844 exhibited more flexibility than other residues in a series of MD studies, implying that these two residues should be more involved with molecular recognition. Moreover, as to the point mutation indicating a higher degree of flexibility in mutant in comparison to WT,22 it should be cautiously discussed for specific point mutation.

3. Conclusions

In the present study, 33 protein complexes (32 EGFR proteins plus 1 ErbB4 protein) were curated and studied for dynamic movement of the binding site by means of molecular dynamics simulations. The analysis of RMSD revealed that T790M mutation can make an impact on dynamic motion of binding sites. The RMSD values of EGFR binding sites cannot been associated with inhibitory activity (data not shown). The analysis of Rg revealed that T790M can shrink the value of Rg, thereby influencing the shape change of the binding site. More interestingly, the Rg value can exhibit a weak correlation with inhibitory activity of most inhibitors. The relationship between Rg and biological activity deserve our serious interest regarding the fact that the best scoring function Xscore cannot distinguish highly active EGFR inhibitors. The RMSF analysis of key residues derived from binding sites indicated that the most flexible residue was ASP800 with a large RMSF value against the steady residue ALA743 with a small RMSF value, and two other residues (MET793 and LEU844) were supposed to be involved with molecular recognition. Hence, the two questions mentioned in the Introduction section could be replied as follows: (i) some key residues such as residues 793, 800, and 844 can effectively respond to the inhibitor via two fundamental interactions (hydrogen bond and hydrophobic effect), which can be tracked by the MD study; (ii) Rg values of binding sites can weakly correlate with the inhibitory activities of TKIs. These key residues could be ranked as follows: ASP800, MET793, LEU844, and ALA743 if possible. In short, the obtained results would be beneficial for the development of a novel EGFR kinase inhibitor.

4. Materials and Methods

4.1. The Collection of the Protein–Inhibitor Data Set of the EGFR Kinase Domain

There are 100 and 28 items of EGFR protein X-ray crystal structures with high resolution (<3 Å) derived from Homo sapiens deposited in the Protein Data Bank12 by September 2019 (Figure 1). First, 100 and 28 EGFR crystal structures were retrieved and downloaded. Second, 60 EGFR reversible inhibitor complexes were obtained by filtering 68 EGFR structures with nonbiological ligands such as experimental additives, ANP analogs, and peptides. Third, a total of 33 EGFR protein complexes were manually selected based on cluster analysis and binding affinities of the inhibitors of 60 structures.

Two kinds of cluster analyses (hierarchical and k-means13) applying into EGFR inhibitors of these 60 EGFR protein complexes mentioned above based on a MACCS fingerprint descriptor were performed.14 The MACCS fingerprints and cluster analysis of inhibitors were computed by the Canvas module of the Schrodinger Suite 2015.15 In addition, the binding affinities of EGFR inhibitors were carefully curated from the original references of EGFR protein structures.

4.2. Molecular Dynamics (MD) Simulations

Each EGFR protein was prepared using the Protein Preparation Wizard of the Schrodinger Suite 201515 and subsequently imported into molecular dynamics software as the starting structure. For each EGFR crystal complex with several chains, one chain containing an inhibitor had been retained. Each MD simulation was performed using Desmond 4.2 with the standard RESPA integration and a 2 fs time step. The TIP4P water model was employed for the generation of a solvent system and would work well under the OPLS force field. Each simulation system including the prepared protein, one substrate, and several Cl– added to achieve charge neutrality was immersed in a cubic box (10 Å). First, all the prepared systems were minimized until a gradient threshold (25 kcal/mol/Å) was reached by the steepest-descent (SD) method and then coupled to a Berenson thermostat with a 300 K reference temperature and a Berendsen barostat with a 1.01325 bar reference pressure. The calculation of long-range electrostatics was based on the Particle mesh Ewald method. The cutoff for coulomb interaction was set at 9.0 Å. After equilibration, all the systems began to run in the NPT ensemble for 2.4 ns.

4.3. Simulation Data Analyses

The root mean square deviation (RMSD), root mean square fluctuation (RMSF), and other measures for the distance between residues and inhibitors were calculated based on the analysis of the MD trajectories by the VMD (version 1.9.1)16 or in-house Perl scripts. Most of the diagrams of curves in this paper were plotted by Xmgrace.17 The correlation analysis among the values of RMSD, RMSF, and PIC50 was conducted by Origin 9.0.18

Acknowledgments

This work was supported by the Natural Science Foundation of Jiangsu Higher Education Institutions (grant number: 18KJB350004), the Project for Young Teachers of Nanjing Forestry University (grant number: CX2017005), a project funded by the National First-Class Disciplines (PNFD), and the Doctorate Fellowship Foundation of Nanjing Forestry University (163030748).

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.0c02183.

  • Protein–ligand crystal complexes of the epidermal growth factor receptor family tyrosine kinase domain from the Protein Data Bank, binding affinities of EGFR inhibitors in the crystal complexes, results of hierarchical and k-means cluster analysis, EGFR protein complexes selected for the molecular dynamics study, RMSD values of EGFR protein complexes selected for the molecular dynamics study, Rg values of EGFR protein complexes selected for the molecular dynamics study, calculated data derived from the molecular dynamics study of 33 EGFR protein complexes, RMSF values of key residues of EGFR binding sites during the molecular dynamics study, 12 ns molecular dynamics of the EGFR protein–ligand complex, residues at the binding pockets of EGFR kinase domains and corresponding EGFR inhibitors, RMSD of the backbone of 33 EGFR structures, correlation between RMSD values of the whole protein backbone and that of the protein side chain, and 33 kinds of EGFR protein–ligand contact graphs derived from the molecular dynamic simulations (PDF)

Author Contributions

# D.-D.L. and T.-T.W. equally contributed to this work.

The authors declare no competing financial interest.

Supplementary Material

ao0c02183_si_001.pdf (5.5MB, pdf)

References

  1. Jorissen R. N.; Walker F.; Pouliot N.; Garrett T. P.; Ward C. W.; Burgess A. W. Epidermal growth factor receptor: mechanisms of activation and signalling. Exp. Cell Res. 2003, 284, 31–53. 10.1016/S0014-4827(02)00098-8. [DOI] [PubMed] [Google Scholar]
  2. Maemondo M.; Inoue A.; Kobayashi K.; Sugawara S.; Oizumi S.; Isobe H.; Gemma A.; Harada M.; Yoshizawa H.; Kinoshita I.; Fujita Y.; Okinaga S.; Hirano H.; Yoshimori K.; Harada T.; Ogura T.; Ando M.; Miyazawa H.; Tanaka T.; Saijo Y.; Hagiwara K.; Morita S.; Nukiwa T. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N. Engl. J. Med. 2010, 362, 2380–2388. 10.1056/NEJMoa0909530. [DOI] [PubMed] [Google Scholar]
  3. Engelman J. A.; Janne P. A. Mechanisms of acquired resistance to epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer. Clin. Cancer. Res. 2008, 14, 2895–2899. 10.1158/1078-0432.CCR-07-2248. [DOI] [PubMed] [Google Scholar]
  4. Liao B.-C.; Lin C.-C.; Yang J. C.-H. Second and third-generation epidermal growth factor receptor tyrosine kinase inhibitors in advanced nonsmall cell lung cancer. Curr. Opin. Oncol. 2015, 27, 94–101. 10.1097/CCO.0000000000000164. [DOI] [PubMed] [Google Scholar]
  5. Cross D. A. E.; Ashton S. E.; Ghiorghiu S.; Eberlein C.; Nebhan C. A.; Spitzler P. J.; Orme J. P.; Finlay M. R. V.; Ward R. A.; Mellor M. J.; Hughes G.; Rahi A.; Jacobs V. N.; Brewer M. R.; Ichihara E.; Sun J.; Jin H.; Ballard P.; Al-Kadhimi K.; Rowlinson R.; Klinowska T.; Richmond G. H. P.; Cantarini M.; Kim D. W.; Ranson M. R.; Pao W. AZD9291, an irreversible EGFR TKI, overcomes T790M-mediated resistance to EGFR inhibitors in lung cancer. Cancer Discovery 2014, 4, 1046–1061. 10.1158/2159-8290.CD-14-0337. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Thress K. S.; Paweletz C. P.; Felip E.; Cho B. C.; Stetson D.; Dougherty B.; Lai Z.; Markovets A.; Vivancos A.; Kuang Y.; Ercan D.; Matthews S. E.; Cantarini M.; Barrett J. C.; Jänne P. A.; Oxnard G. R. Acquired EGFR C797S mutation mediates resistance to AZD9291 in non-small cell lung cancer harboring EGFR T790M. Nat. Med. 2015, 21, 560–562. 10.1038/nm.3854. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Jia Y.; Yun C. H.; Park E.; Ercan D.; Manuia M.; Juarez J.; Xu C.; Rhee K.; Chen T.; Zhang H.; Palakurthi S.; Jang J.; Lelais G.; DiDonato M.; Bursulaya B.; Michellys P. Y.; Epple R.; Marsilje T. H.; McNeill M.; Lu W.; Harris J.; Bender S.; Wong K. K.; Jänne P. A.; Eck M. J. Overcoming EGFR(T790M) and EGFR(C797S) resistance with mutant-selective allosteric inhibitors. Nature 2016, 534, 129–132. 10.1038/nature17960. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Park J.; McDonald J. J.; Petter R. C.; Houk K. N. Molecular Dynamics Analysis of Binding of Kinase Inhibitors to WT EGFR and the T790M Mutant. J. Chem. Theory Comput. 2016, 12, 2066–2078. 10.1021/acs.jctc.5b01221. [DOI] [PubMed] [Google Scholar]
  9. Bello M. Binding mechanism of kinase inhibitors to EGFR and T790M, L858R and L858R/T790M mutants through structural and energetic analysis. Int. J. Biol. Macromol. 2018, 118, 1948–1962. 10.1016/j.ijbiomac.2018.07.042. [DOI] [PubMed] [Google Scholar]
  10. Zou B.; Lee V. H. F.; Chen L.; Ma L.; Wang D. D.; Yan H. Deciphering mechanisms of acquired T790M mutation after EGFR inhibitors for NSCLC by computational simulations. Sci. Rep. 2017, 7, 6595–6607. 10.1038/s41598-017-06632-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Ikemura S.; Yasuda H.; Matsumoto S.; Kamada M.; Hamamoto J.; Masuzawa K.; Kobayashi K.; Manabe T.; Arai D.; Nakachi I.; Kawada I.; Ishioka K.; Nakamura M.; Namkoong H.; Naoki K.; Ono F.; Araki M.; Kanada R.; Ma B.; Hayashi Y.; Mimaki S.; Yoh K.; Kobayashi S. S.; Kohno T.; Okuno Y.; Goto K.; Tsuchihara K.; Soejima K. Molecular dynamics simulation-guided drug sensitivity prediction for lung cancer with rare EGFR mutations. Proc. Natl. Acad. Sci. U. S. A. 2019, 116, 10025–10030. 10.1073/pnas.1819430116. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Sussman J. L.; Abola E. E.; Lin D.; Jiang J.; Manning N. O.; Prilusky J. The protein data bank. Bridging the gap between the sequence and 3D structure world. Genetica 1999, 106, 149–158. [DOI] [PubMed] [Google Scholar]
  13. Li W.; Zhou Y.; Xia S. A Novel Clustering Algorithm Based on Hierarchical and K-means Clustering. Control Conference. 2007, 605. [Google Scholar]
  14. Duan J.; Dixon S. L.; Lowrie J. F.; Sherman W. Analysis and comparison of 2D fingerprints: insights into database screening performance using eight fingerprint methods. J. Mol. Graphics Modell. 2010, 29, 157–170. 10.1016/j.jmgm.2010.05.008. [DOI] [PubMed] [Google Scholar]
  15. Canvas S.LLC; New York, NY, 2015. [Google Scholar]
  16. Humphrey W.; Dalke A.; Schulten K. VMD: visual molecular dynamics. J. Mol. Graph. 1996, 14, 33–38. 10.1016/0263-7855(96)00018-5. [DOI] [PubMed] [Google Scholar]
  17. Grace http://plasma-gate.weizmann.ac.il/Grace/, 2013.
  18. Hao C.; Sun W.; Feng Y.; Sun R. Discussion on the Application of Origin9.0 Software in the Experiment of Microwave Technology. Phys. Exp. Coll. 2017, 93–96. [Google Scholar]
  19. Vijayan R. S. K.; He P.; Modi V.; Duong-Ly K. C.; Ma H.; Peterson J. R.; Dunbrack R. J. Jr.; Levy R. M. Conformational analysis of the DFG-out kinase motif and biochemical profiling of structurally validated type II inhibitors. J. Med. Chem. 2015, 58, 466–479. 10.1021/jm501603h. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Sirin S.; Kumar R.; Martinez C.; Karmilowicz M. J.; Ghosh P.; Abramov Y. A.; Martin V.; Sherman W. A computational approach to enzyme design: predicting ω-aminotransferase catalytic activity using docking and MM-GBSA scoring. J. Chem. Inf. Model. 2014, 54, 2334–2346. 10.1021/ci5002185. [DOI] [PubMed] [Google Scholar]
  21. Thompson M. A. Molecular docking using arguslab: An efficient shape-based search algorithm and an enhanced xscore scoring function. Abstr. Pap. Am. Chem. Soc. 2004, 228, U360. [Google Scholar]
  22. Goyal S.; Jamal S.; Shanker A.; Grover A. Structural investigations of T854A mutation in EGFR and identification of novel inhibitors using structure activity relationships. BMC Genomics 2015, 16, S8. 10.1186/1471-2164-16-S5-S8. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ao0c02183_si_001.pdf (5.5MB, pdf)

Articles from ACS Omega are provided here courtesy of American Chemical Society

RESOURCES