Docking and molecular dynamics simulation for therapeutic repurposing in small cell lung cancer (SCLC) patients infected with COVID-19

Background: Mutations in a protein called the Epidermal Growth Factor Receptor (EGFR) can cause Non-Small Cell Lung Cancer (NSCLC), which is the most common form of lung cancer. Many NSCLC cases arise from the L858R mutation, where Leucine (L) is replaced by arginine (R) at the 858th position in the EGFR, and that is also recognized as an exon 21 substitution. Moreover, half of the EKFR-mutated lung cancer patients develop acquired resistance to the first-generation EGFR-TKIs due to another mutation T790M. Objective: In this research work, a novel method is used to investigate the possible reason for the EGFR mutation to takes place in the specific 858th and 790th position, and also, we evaluated the hydrogen bonds to measure the overall stability of different structures. Methods: We performed the molecular dynamics simulation and used Amber tool to achieve our primary objectives and later we use CPPTRAJ to analyze other changes in the hydrogen bonds for different mutational structures of EGFR. Results: First, we investigated the hydrogen bonds in different positions in the EGFR kinase domain and estimated why the first stage mutation (L858R) and resistance mutation (L858R/T790M) take place in the 858th and 790th position respectively. We found the hydrogen bond counts in the 858th and 790th position is lesser than the neighborhood positions and that yields to achieve a least stability in that position. Conclusion: Our method represents an important contribution to molecular dynamics analysis for NSCLC studies. The results obtained from this study provide a useful insight into the NSCLC drug resistance.

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In silico search of triple mutant T790M/C797S allosteric inhibitors to conquer acquired resistance problem in non-small cell lung cancer (NSCLC): a combined approach of structure-based virtual screening and molecular dynamics simulation

Journal of Biomolecular Structure and Dynamics ◽

10.1080/07391102.2020.1734092 ◽

2020 ◽

pp. 1-15

Author(s):

Harun M. Patel ◽

Iqrar Ahmad ◽

Rahul Pawara ◽

Matin Shaikh ◽

Sanjay Surana

Keyword(s):

Lung Cancer ◽

Molecular Dynamics ◽

Molecular Dynamics Simulation ◽

Acquired Resistance ◽

Small Cell ◽

Dynamics Simulation ◽

Combined Approach ◽

Small Cell Lung ◽

Triple Mutant ◽

Allosteric Inhibitors

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Molecular Dynamics of A Biglycan-Rosmarinic Acid Complex with Focal Adhesion Kinase for Possible Arrest of Metastasis in Non-Small Cell Lung Cancer (NSCLC): An In- Silico Study

Journal of Drug Delivery and Therapeutics ◽

10.22270/jddt.v9i4-a.3382 ◽

2019 ◽

Vol 9 (4-A) ◽

pp. 159-166

Author(s):

Hyacinth Highland ◽

Monica Thakur ◽

Pujan Pandya ◽

Archana Mankad ◽

Linz-Buoy George

Keyword(s):

Lung Cancer ◽

Molecular Dynamics ◽

Molecular Docking ◽

Small Cell Lung Cancer ◽

Focal Adhesion Kinase ◽

Focal Adhesion ◽

Cell Lung Cancer ◽

Rosmarinic Acid ◽

Small Cell ◽

Small Cell Lung

Background: Non-small cell lung cancer (NSCLC) is the major cause of mortality all over the world. Significant increase of biglycan is seen in the lung cancer cells when compared with the normal cells. It promotes tumor invasion and metastasis by activating Focal Adhesion Kinase (FAK) signaling pathway. The increased FAK activity may contribute to the metastatic potential of malignant tumors. This study was carried out to establish binding interactions of some selected phytocomponents against biglycan for the possible arrest of metastasis. Methods: Protein-ligand interaction studies were performed using 30 natural compounds from different culinary herbs having potential therapeutic role against the target protein biglycan (BGN). Molegro Virtual Docker (v 5.0) was used as docking tool to evaluate the effectiveness of selected phytocomponents based upon the interaction with the protein’s active site residues with minimal binding energy. Protein-protein docking was performed to observe the interaction of BGN and FAK using Hex (v 8.0.0). Molecular dynamics (10 ns) of BGN-RA-FAK and FAK-RA-BGN was performed in Yasara structure (v 17.8.15) which showed stability of the structure in terms of RMSD values. Results: Molecular docking analysis revealed the selectivity of Rosmarinic acid (RA) towards BGN and FAK. Molecular dynamics trajectory of BGN-RA-FAK and FAK-RA-BGN complexes showed the stability of structure in terms of Time vs Energy and Time vs RMSD values and revealed that binding of RA to BGN will block the interaction of FAK. Conclusions: Hence, investigating the binding interactions of BGN-RA-FAK complex may turn out to be helpful in arresting metastasis in NSCLC. Keywords: Non-small cell lung cancer, Biglycan, Focal adhesion kinase, Phytocomponents, Molecular Docking, Molecular Dynamics

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Primary Erlotinib Resistance in a Patient with Non-Small Cell Lung Cancer Carrying Simultaneous Compound EGFR L718A, Q849H, and L858R Mutations

BioMolecular Concepts ◽

10.1515/bmc-2021-0018 ◽

2021 ◽

Vol 12 (1) ◽

pp. 164-174

Author(s):

Hanifeh Mirtavoos-mahyari ◽

Elham Rismani ◽

Alireza Sarkar Lotfabadi ◽

Azizollah Abbasi Dezfouli ◽

Kambiz Sheikhy ◽

...

Keyword(s):

Lung Cancer ◽

Tyrosine Kinase ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Kinase Domain ◽

Small Cell ◽

Dynamics Simulation ◽

Egfr Mutations ◽

Small Cell Lung ◽

Egfr Tyrosine Kinase

Abstract Nowadays, mutations in the epidermal growth factor receptor (EGFR) kinase domain are studied in targeted therapy of non-small cell lung cancer (NSCLC) with EGFR tyrosine kinase inhibitors including gefitinib and erlotinib. The present study reports a rare case of a patient harboring three simultaneous EGFR mutations (L718A, Q849H, and L858R). The development of erlotinib resistance was detected in the subsequent treatment. Using a computational approach, the current study investigated the conformational changes of wild-type and mutant EGFR's kinase domains in the interaction with erlotinib. Their binding modes with erlotinib were elucidated during molecular dynamics simulation, where higher fluctuations were detected in the mutated forms of the EGFR tyrosine kinase domain. Prediction of stability and functional effect of mutations revealed that amino acidic substitutions have decreased the protein stability as well as the binding affinity to erlotinib. These results may be useful for a recommendation of EGFR mutational analysis for patients with NSCLC carcinoma.

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Discovery of a Potent Candidate for RET-Specific Non-Small-Cell Lung Cancer—A Combined In Silico and In Vitro Strategy

Pharmaceutics ◽

10.3390/pharmaceutics13111775 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1775

Author(s):

Priyanka Ramesh ◽

Woong-Hee Shin ◽

Shanthi Veerappapillai

Keyword(s):

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

In Silico ◽

Kinase Inhibitors ◽

Small Cell ◽

Dynamics Simulation ◽

Small Cell Lung ◽

Glide Docking

Rearranged during transfection (RET) is a tyrosine kinase oncogenic receptor, activated in several cancers including non-small-cell lung cancer (NSCLC). Multiple kinase inhibitors vandetanib and cabozantinib are commonly used in the treatment of RET-positive NSCLC. However, specificity, toxicity, and reduced efficacy limit the usage of multiple kinase inhibitors in targeting RET protein. Thus, in the present investigation, we aimed to figure out novel and potent candidates for the inhibition of RET protein using combined in silico and in vitro strategies. In the present study, screening of 11,808 compounds from the DrugBank repository was accomplished by different hypotheses such as pharmacophore, e-pharmacophore, and receptor cavity-based models in the initial stage. The results from the different hypotheses were then integrated to eliminate the false positive prediction. The inhibitory activities of the screened compounds were tested by the glide docking algorithm. Moreover, RF score, Tanimoto coefficient, prime-MM/GBSA, and density functional theory calculations were utilized to re-score the binding free energy of the docked complexes with high precision. This procedure resulted in three lead molecules, namely DB07194, DB03496, and DB11982, against the RET protein. The screened lead molecules together with reference compounds were then subjected to a long molecular dynamics simulation with a 200 ns time duration to validate the inhibitory activity. Further analysis of compounds using MM-PBSA and mutation studies resulted in the identification of potent compound DB07194. In essence, a cell viability assay with RET-specific lung cancer cell line LC-2/ad was also carried out to confirm the in vitro biological activity of the resultant compound, DB07194. Indeed, the results from our study conclude that DB07194 can be effectively translated for this new therapeutic purpose, in contrast to the properties for which it was originally designed and synthesized.

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A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer

Biomolecules ◽

10.3390/biom11030477 ◽

2021 ◽

Vol 11 (3) ◽

pp. 477

Author(s):

Hong-Yi Zhi ◽

Lu Zhao ◽

Cheng-Chun Lee ◽

Calvin Yu-Chian Chen

Keyword(s):

Machine Learning ◽

Lung Cancer ◽

Small Cell Lung Cancer ◽

Cell Lung Cancer ◽

Small Cell ◽

Dynamics Simulation ◽

Support Vector ◽

Dihydroorotate Dehydrogenase ◽

Small Cell Lung ◽

Novel Concept

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a therapeutic target for SCLC. Network pharmacology analysis and virtual screening were utilized to find out related proteins and investigate candidates with high docking capacity to multiple targets. Graph neural networks (GNNs) and machine learning were used to build reliable predicted models. We proposed a novel concept of multi-GNNs, and then built three multi-GNN models called GIAN, GIAT, and SGCA, which achieved satisfactory results in our dataset containing 532 molecules with all R^2 values greater than 0.92 on the training set and higher than 0.8 on the test set. Compared with machine learning algorithms, random forest (RF), and support vector regression (SVR), multi-GNNs had a better modeling effect and higher precision. Furthermore, the long-time 300 ns molecular dynamics simulation verified the stability of the protein–ligand complexes. The result showed that ZINC8577218, ZINC95618747, and ZINC4261765 might be the potentially potent inhibitors for DHODH. Multi-GNNs show great performance in practice, making them a promising field for future research. We therefore suggest that this novel concept of multi-GNNs is a promising protocol for drug discovery.

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