Structural investigations of T854A mutation in EGFR and identification of novel inhibitors using structure activity relationships

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.

Structure-based design and synthesis of a novel long-chain 4''-alkyl ether derivative of EGCG as potent EGFR inhibitor: in vitro and in silico studies

Herein, we report the discovery of a novel long-chain ether derivative of (-)-epigallocatechin-3-gallate (EGCG), a major green tea polyphenol as a potent EGFR inhibitor. A series of 4''-alkyl EGCG derivatives have been synthesized via regio-selectively alkylating the 4'' hydroxyl group in the D-ring of EGCG and tested for their antiproliferative activities against high (A431), moderate (HeLa), and low (MCF-7) EGFR-expressing cancer cell lines. The most potent compound, 4''-C14 EGCG showed the lowest IC50 values across all the tested cell lines. 4''-C14 EGCG was also found to be significantly more stable than EGCG under physiological conditions (PBS at pH 7.4). Further western blot analysis and imaging data revealed that 4''-C14 EGCG induced cell death in A431 cells with shrunken nuclei, nuclear fragmentation, membrane blebbing, and increased population of apoptotic cells where BAX upregulation and BCLXL downregulation were observed. In addition, autophosphorylation of EGFR and its downstream signalling proteins Akt and ERK were markedly inhibited by 4''-C14 EGCG. MD simulation and the MM/PBSA analysis disclosed the binding mode of 4''-C14 EGCG in the ATP-binding site of EGFR kinase domain. Taken together, our findings demonstrate that 4''-C14 EGCG can act as a promising potent EGFR inhibitor with enhanced stability.

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

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 (R _g) revealed that T790M can slightly shrink the value of R _g, thereby influencing the shape of the EGFR binding site. More interestingly, the R _g value can exhibit weak correlation with inhibitory activity of most inhibitors. The relationship between R _g 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.

Computational approaches for identifying potential inhibitors on targeting protein interactions in drug discovery

In the era of big data, the interplay of artificial and human intelligence is the demanding job to address the concerns involving exchange of decisions between both sides. Drug discovery is one of the key sources of the big data, which involves synergy among various computational methods to achieve a clinical success. Rightful acquisition, mining and analysis of the data related to ligand and targets are crucial to accomplish reliable outcomes in the entire process. Novel designing and screening tactics are necessary to substantiate a potent and efficient lead compounds. Such methods are emphasized and portrayed in the current review targeting protein-ligand and protein-protein interactions involved in various diseases with potential applications.

Presence and structure-activity relationship of intrinsically disordered regions across mucins

Many members of the mucin family are evolutionarily conserved and are often aberrantly expressed and glycosylated in various benign and malignant pathologies leading to tumor invasion, metastasis, and immune evasion. The large size and extensive glycosylation present challenges to study the mucin structure using traditional methods, including crystallography. We offer the hypothesis that the functional versatility of mucins may be attributed to the presence of intrinsically disordered regions (IDRs) that provide dynamism and flexibility and that the IDRs offer potential therapeutic targets. Herein, we examined the links between the mucin structure and function based on IDRs, posttranslational modifications (PTMs), and potential impact on their interactome. Using sequence-based bioinformatics tools, we observed that mucins are predicted to be moderately (20%-40%) to highly (>40%) disordered and many conserved mucin domains could be disordered. Phosphorylation sites overlap with IDRs throughout the mucin sequences. Additionally, the majority of predicted O- and N- glycosylation sites in the tandem repeat regions occur within IDRs and these IDRs contain a large number of functional motifs, that is, molecular recognition features (MoRFs), which directly influence protein-protein interactions (PPIs). This investigation provides a novel perspective and offers an insight into the complexity and dynamic nature of mucins.

Influence of Amino Acid Mutations and Small Molecules on Targeted Inhibition of Proteins Involved in Cancer

Background:

Protein-protein interactions (PPIs) are of crucial importance in regulating the biological processes of cells both in normal and diseased conditions. Significant progress has been made in targeting PPIs using small molecules and achieved promising results. However, PPI drug discovery should be further accelerated with better understanding of chemical space along with various functional aspects.

Objective:

In this review, we focus on the advancements in computational research for targeted inhibition of protein-protein interactions involved in cancer.

Methods:

Here, we mainly focused on two aspects: (i) understanding the key roles of amino acid mutations in epidermal growth factor receptor (EGFR) as well as mutation-specific inhibitors and (ii) design of small molecule inhibitors for Bcl-2 to disrupt PPIs.

Results:

The paradigm of PPI inhibition to date reflect the certainty that inclination towards novel and versatile strategies enormously dictate the success of PPI inhibition. As the chemical space highly differs from the normal drug like compounds the lead optimization process has to be given the utmost priority to ensure the clinical success. Here, we provided a broader perspective on effect of mutations in oncogene EGFR connected to Bcl-2 PPIs and focused on the potential challenges.

Conclusion:

Understanding and bridging mutations and altered PPIs will provide insights into the alarming signals leading to massive malfunctioning of a biological system in various diseases. Finding rational elucidations from a pharmaceutical stand point will presumably broaden the horizons in future.

Pyrazinamide drug resistance in RpsA mutant (∆438A) of Mycobacterium tuberculosis: Dynamics of essential motions and free-energy landscape analysis

Pyrazinamide is an essential first-line antitubercular drug which plays pivotal role in tuberculosis treatment. It is a prodrug that requires amide hydrolysis by mycobacterial pyrazinamidase enzyme for conversion into pyrazinoic acid (POA). POA is known to target ribosomal protein S1 (RpsA), aspartate decarboxylase (PanD), and some other mycobacterial proteins. Spontaneous chromosomal mutations in RpsA have been reported for phenotypic resistance against pyrazinamide. We have constructed and validated 3D models of the native and Δ438A mutant form of RpsA protein. RpsA protein variants were then docked to POA and long range molecular dynamics simulations were carried out. Per residue binding free-energy calculations, free-energy landscape analysis, and essential dynamics analysis were performed to outline the mechanism underlying the high-level PZA resistance conferred by the most frequently occurring deletion mutant of RpsA. Our study revealed the conformational modulation of POA binding site due to the disruptive collective modes of motions and increased conformational flexibility in the mutant than the native form. Residue wise MMPBSA decomposition and protein-drug interaction pattern revealed the difference of energetically favorable binding site in the wild-type (WT) protein in comparison with the mutant. Analysis of size and shape of minimal energy landscape area delineated higher stability of the WT complex than the mutant form. Our study provides mechanistic insights into pyrazinamide resistance in Δ438A RpsA mutant, and the results arising out of this study will pave way for design of novel and effective inhibitors targeting the resistant strains of Mycobacterium tuberculosis.

Role of cytokines in combinatorial immunotherapeutics of non-small cell lung cancer through systems perspective

Lung cancer is the leading cause of deaths related to cancer and accounts for more than a million deaths per year. Various new strategies have been developed and adapted for treatment; still the survival for 5 years is just 16% in patients with non‐small cell lung cancer (NSCLC). Most of these strategies to combat NSCLC whether it is a drug molecule or immunotherapy/vaccine candidate require a big cost and time. Integration of computational modeling with systems biology has opened new avenues for understanding complex cancer biology. Resolving the complex interactions of various pathways and their crosstalk leading to oncogenic changes could identify new therapeutic targets with lesser cost and time. Herein, this review provides an overview of various aspects of NSCLC along with available strategies for its cure concluding with our insight into how systems approach could serve as a therapeutic intervention dissecting the immunologic parameters and cross talk between various pathways involved.

Molecular dynamics simulations revealed structural differences among WRKY domain-DNA interaction in barley (Hordeum vulgare)

Background

The WRKY transcription factors are a class of DNA-binding proteins involved in diverse plant processes play critical roles in response to abiotic and biotic stresses. Genome-wide divergence analysis of WRKY gene family in Hordeum vulgare provided a framework for molecular evolution and functional roles. So far, the crystal structure of WRKY from barley has not been resolved; moreover, knowledge of the three-dimensional structure of WRKY domain is pre-requisites for exploring the protein-DNA recognition mechanisms. Homology modelling based approach was used to generate structures for WRKY DNA binding domain (DBD) and its variants using AtWRKY1 as a template. Finally, the stability and conformational changes of the generated model in unbound and bound form was examined through atomistic molecular dynamics (MD) simulations for 100 ns time period.

Results

In this study, we investigated the comparative binding pattern of WRKY domain and its variants with W-box cis-regulatory element using molecular docking and dynamics (MD) simulations assays. The atomic insight into WRKY domain exhibited significant variation in the intermolecular hydrogen bonding pattern, leading to the structural anomalies in the variant type and differences in the DNA-binding specificities. Based on the MD analysis, residual contribution and interaction contour, wild-type WRKY (HvWRKY46) were found to interact with DNA through highly conserved heptapeptide in the pre- and post-MD simulated complexes, whereas heptapeptide interaction with DNA was missing in variants (I and II) in post-MD complexes. Consequently, through principal component analysis, wild-type WRKY was also found to be more stable by obscuring a reduced conformational space than the variant I (HvWRKY34). Lastly, high binding free energy for wild-type and variant II allowed us to conclude that wild-type WRKY-DNA complex was more stable relative to variants I.

Conclusions

The results of our study revealed complete dynamic and structural information about WRKY domain-DNA interactions. However, no structure base information reported to date for WRKY variants and their mechanism of interaction with DNA. Our findings highlighted the importance of selecting a sequence to generate newer transgenic plants that would be increasingly tolerance to stress conditions.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4506-3) contains supplementary material, which is available to authorized users.

Investigating mutation-specific biological activities of small molecules using quantitative structure-activity relationship for epidermal growth factor receptor in cancer

Epidermal Growth Factor Receptor (EGFR) is a potential drug target in cancer therapy. Missense mutations play major roles in influencing the protein function, leading to abnormal cell proliferation and tumorigenesis. A number of EGFR inhibitor molecules targeting ATP binding domain were developed for the past two decades. Unfortunately, they become inactive due to resistance caused by new mutations in patients, and previous studies have also reported noticeable differences in inhibitor binding to distinct known driver mutants as well. Hence, there is a high demand for identification of EGFR mutation-specific inhibitors. In our present study, we derived a set of anti-cancer compounds with biological activities against eight typical EGFR known driver mutations and developed quantitative structure-activity relationship (QSAR) models for each separately. The compounds are grouped based on their functional scaffolds, which enhanced the correlation between compound features and respective biological activities. The models for different mutants performed well with a correlation coefficient, (r) in the range of 0.72-0.91 on jack-knife test. Further, we analyzed the selected features in different models and observed that hydrogen bond and aromaticity-related features play important roles in predicting the biological activity of a compound. This analysis is complimented with docking studies, which showed the binding patterns and interactions of ligands with EGFR mutants that could influence their activities.

Structural insights into impact of Y134F mutation and discovery of novel fungicidal compounds against CYP51 in Puccinia triticina

Sterol 14α-Demethylase Cytochrome P450 (CYP51) protein involved in ergosterol biosynthesis pathways turn out to be a crucial target for the fungicidal compound. However, the recognition mechanism and dynamic behavior of CYP51 in wheat leaf rust pathogen, Puccinia triticina, is still obscure. Previously, a mutation at position 134 (Y134F) was reported in five European isolates of P. triticina, conversely, structural basis of this mutation remains unclear. To address this problem, three-dimensional structure of CYP51 protein from P. triticina was successfully built using homology modeling approach. To assess the protein structure stability, wild and mutant-type CYP51 proteins bound with azole fungicide was subjected to 50 ns molecular dynamics (MD) simulations run. Observably, the comparative protein-ligand interaction analysis and binding free energy results revealed that impact of the mutation on the thermodynamics and conformational stability of the CYP51 protein was negligible. In addition, we carried out structure-based virtual screening and identified potent novel fungicidal compounds from four different databases and libraries. Consequently, through MD simulation and thermodynamic integration, four novel compounds such as CoCoCo54211 (CoCoCo database), ZINC04089470 (ZINC database), Allyl pyrocatechol 3,4 diacetate (Natural compound library), and 9-octadecenoic acid (Traditional Chinese Medicine database) has been predicted as potent fungicidal compound against CYP51 with XPGlide docking score of -11.41, -13.64, -7.40, and -6.55 kcal/mol, respectively. These compounds were found to form hydrogen bonds with heme group of CYP51, subsequently disturbing the stability and survival of fungus and can be used to control leaf rust in wheat.

A computational method for the identification of candidate drugs for non-small cell lung cancer

Lung cancer causes a large number of deaths per year. Until now, a cure for this disease has not been found or developed. Finding an effective drug through traditional experimental methods invariably costs millions of dollars and takes several years. It is imperative that computational methods be developed to integrate several types of existing information to identify candidate drugs for further study, which could reduce the cost and time of development. In this study, we tried to advance this effort by proposing a computational method to identify candidate drugs for non-small cell lung cancer (NSCLC), a major type of lung cancer. The method used three steps: (1) preliminary screening, (2) screening compounds by an association test and a permutation test, (3) screening compounds using an EM clustering algorithm. In the first step, based on the chemical-chemical interaction information reported in STITCH, a well-known database that reports interactions between chemicals and proteins, and approved NSCLC drugs, compounds that can interact with at least one approved NSCLC drug were picked. In the second step, the association test selected compounds that can interact with at least one NSCLC-related chemical and at least one NSCLC-related gene, and subsequently, the permutation test was used to discard nonspecific compounds from the remaining compounds. In the final step, core compounds were selected using a powerful clustering algorithm, the EM algorithm. Six putative compounds, protoporphyrin IX, hematoporphyrin, canertinib, lapatinib, pelitinib, and dacomitinib, were identified by this method. Previously published data show that all of the selected compounds have been reported to possess anti-NSCLC activity, indicating high probabilities of these compounds being novel candidate drugs for NSCLC.

Long non-coding RNAs associated with non-small cell lung cancer

Lung cancer, with 80–85% being non-small cell lung cancer (NSCLC), is the leading cause of cancer-related death in both men and women. Long non-coding RNAs (lncRNAs), always defined as non-protein-coding RNA molecules longer than 200 nucleotides, are now thought as a new frontier in the study of human malignant diseases including NSCLC. As researches continue, increasing number of roles that lncRNAs play in NSCLC has been found, and more and more evidences show lncRNAs have a close relationship with patients’ response to radiochemotherapy or molecular therapy. The aim of this review is to disclose the roles that lncRNAs play in NSCLC and how lncRANs influence the treatment of NSCLC.

Dynamics of fluoroquinolones induced resistance in DNA gyrase of Mycobacterium tuberculosis

DNA gyrase is a validated target of fluoroquinolones which are key components of multidrug resistance tuberculosis (TB) treatment. Most frequent occurring mutations associated with high level of resistance to fluoroquinolone in clinical isolates of TB patients are A90V, D94G, and A90V-D94G (double mutant [DM]), present in the larger subunit of DNA Gyrase. In order to explicate the molecular mechanism of drug resistance corresponding to these mutations, molecular dynamics (MD) and mechanics approach was applied. Structure-based molecular docking of complex comprised of DNA bound with Gyrase A (large subunit) and Gyrase C (small subunit) with moxifloxacin (MFX) revealed high binding affinity to wild type with considerably high Glide XP docking score of -7.88 kcal/mol. MFX affinity decreases toward single mutants and was minimum toward the DM with a docking score of -3.82 kcal/mol. Docking studies were also performed against 8-Methyl-moxifloxacin which exhibited higher binding affinity against wild and mutants DNA gyrase when compared to MFX. Molecular Mechanics/Generalized Born Surface Area method predicted the binding free energy of the wild, A90V, D94G, and DM complexes to be -55.81, -25.87, -20.45, and -12.29 kcal/mol, respectively. These complexes were further subjected to 30 ns long MD simulations to examine significant interactions and conformational flexibilities in terms of root mean square deviation, root mean square fluctuation, and strength of hydrogen bond formed. This comparative drug interaction analysis provides systematic insights into the mechanism behind drug resistance and also paves way toward identifying potent lead compounds that could combat drug resistance of DNA gyrase due to mutations.

Computational Screening and Exploration of Disease-Associated Genes in Alzheimer's Disease

Alzheimer's is a neurodegenerative disease affecting large populations worldwide characterized mainly by progressive loss of memory along with various other symptoms. The foremost cause of the disease is still unclear, however various mechanisms have been proposed to cause the disease that include amyloid hypothesis, tau hypothesis, and cholinergic hypothesis in addition to genetic factors. Various genes have been known to be involved which are APOE, PSEN1, PSEN2, and APP among others. In the present study, we have used computational methods to examine the pathogenic effects of non-synonymous single nucleotide polymorphisms (SNPs) associated with ABCA7, CR1, MS4A6A, CD2AP, PSEN1, PSEN2, and APP genes. The SNPs were obtained from dbSNP database followed by identification of deleterious SNPs and prediction of their functional impact. Prediction of disease-associated mutations was performed and the impact of the mutations on the stability of the protein was carried out. To study the structural significance of the computationally prioritized mutations on the proteins, molecular dynamics simulation studies were carried out. On analysis, the SNPs with IDs rs76282929 ABCA7; CR1 rs55962594; MS4A6A rs601172; CD2AP rs61747098; PSEN1 rs63750231, rs63750265, rs63750526, rs63750577, rs63750687, rs63750815, rs63750900, rs63751037, rs63751163, rs63751399; PSEN2 rs63749851; and APP rs63749964, rs63750066, rs63750734, and rs63751039 were predicted to be deleterious and disease-associated having significant structural impact on the proteins. The current study proposes a precise computational methodology for the identification of disease-associated SNPs. J. Cell. Biochem. 118: 1471-1479, 2017. © 2016 Wiley Periodicals, Inc.

Molecular modeling and lead design of substituted zanamivir derivatives as potent anti-influenza drugs

Background

Influenza virus spreads infection by two main surface glycoproteins, namely hemagglutinin (HA) and neuraminidase (NA). NA cleaves the sialic acid receptors eventually releasing newly formed virus particles which then invade new cells. Inhibition of NA could limit the replication of virus to one round which is insufficient to cause the disease.

Results

An experimentally reported series of acylguanidine zanamivir derivatives was used to develop GQSAR model targeting NA in different strains of influenza virus, H1N1 and H3N2. A combinatorial library was developed and their inhibitory activities were predicted using the GQSAR model.

Conclusion

The top leads were analyzed by docking which revealed the binding modes of these inhibitors in the active site of NA (150-loop). The top compound (AMA) was selected for carrying out molecular dynamics simulations for 15 ns which provided insights into the time dependent dynamics of the designed leads. AMA possessed a docking score of −8.26 Kcal/mol with H1N1 strain and −7.00 Kcal/mol with H3N2 strain. Ligand-bound complexes of both H1N1 and H3N2 were observed to be stable for 11 ns and 7 ns respectively. ADME descriptors were also calculated to study the pharmacokinetic properties of AMA which revealed its drug-like properties.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1374-1) contains supplementary material, which is available to authorized users.

Novel group-based QSAR and combinatorial design of CK-1δ inhibitors as neuroprotective agents

Background

Tar DNA binding protein 43 (TDP-43) hyperphosphorylation, caused by Casein kinase 1 (CK-1) protein isoforms, is associated with the onset and progression of Amyotrophic Lateral Sclerosis (ALS). Among the reported isoforms and splice variants of CK-1 protein superfamily, CK-1δ is known to phosphorylate different serine and threonine sites on TDP-43 protein in vitro and thus qualifies as a potential target for ALS treatment.

Results

The developed GQSAR (group based quantitative structure activity relationship) model displayed satisfactory statistical parameters for the dataset of experimentally reported N-Benzothiazolyl-2-Phenyl Acetamide derivatives. A combinatorial library of molecules was also generated and the activities were predicted using the statistically sound GQSAR model. Compounds with higher predicted inhibitory activity were screened against CK-1δ that resulted in to the potential novel leads for CK-1δ inhibition.

Conclusions

In this study, a robust fragment based QSAR model was developed on a congeneric set of experimentally reported molecules and using combinatorial library approach, a series of molecules were generated from which we report two top scoring, CK-1δ inhibitors i.e., CHC (6-benzyl-2-cyclopropyl-4-{[(4-cyclopropyl-6-ethyl-1,3-benzothiazol-2-yl)carbamoyl]methyl}j-3-fluorophenyl hydrogen carbonate) and DHC (6-benzyl-4-{[(4-cyclopropyl-6-ethyl-1,3-benzothiazol-2-yl)carbamoyl]methyl}-2-(decahydronaphthalen-1-yl)-3-hydroxyphenyl hydrogen carbonate) with binding energy of −6.11 and −6.01 kcal/mol, respectively.