Novel missense mutations in gidB gene associated with streptomycin resistance in Mycobacterium tuberculosis: insights from molecular dynamics.

Streptomycin was the first antibiotic used for the treatment of tuberculosis by inhibiting translational proof reading. Point mutation in gidB gene encoding S-adenosyl methionine (SAM)-dependent 7-methylguanosine (m7G) methyltransferase required for methylation of 16S rRNA confers streptomycin resistance. As there was no structural substantiation experimentally, gidB protein model was built by threading algorithm. In this work, molecular dynamics (MD) simulations coupled with binding free energy calculations were performed to outline the mechanism underlying high-level streptomycin resistance associated with three novel missense mutants including S70R, T146M, and R187M. Results from dynamics analyses suggested that the structure distortion in the binding pocket of gidB mutants modulate SAM binding affinity. At the structural level, these conformational changes bring substantial decrease in the number of residues involved in hydrogen bonding and dramatically reduce thermodynamic stability of mutant gidB-SAM complexes. The outcome of comparative analysis of the MD simulation trajectories revealed lower conformational stability associated with higher flexibility in mutants relative to the wild-type, turns to be major factor driving the emergence of drug resistance toward antibiotic. This study will pave way toward design and development of resistant defiant gidB inhibitors as potent anti-TB agents.

Structural basis for drug resistance mechanisms against anaplastic lymphoma kinase.

Drug resistance to anaplastic lymphoma kinase (ALK) inhibitors (crizotinib and ceritinib) is caused by mutation in the region encoding kinase domain of ALK. Compounds with potential ability to inhibit all strains of ALK are a solution to tackle the problem of drug resistance. In this study, we delineated positions of residues possessing the ability to make ALK drug resistant upon mutation by assessing them using five parameters (conservation index, binding-site root-mean-square deviation, protein structure stability, change in ATP, and drug-binding affinity). Four residual positions (Leu 1122, Thr 1151, Phe 1245, and Gly 1269) were ascertained. This study will be beneficial for designing drugs with better proficiency against ALK and the issues of drug resistance. This study can be taken as a pipeline for investigating drug-resistant mutations in other diseases as well.

Structural investigations on mechanism of lapatinib resistance caused by HER-2 mutants.

HER-2 belongs to the human epidermal growth factor receptor (HER) family. Via different signal transduction pathways, HER-2 regulates normal cell proliferation, survival, and differentiation. Recently, it was reported that MCF10A, BT474, and MDA-MB-231 cells bearing the HER2 K753E mutation were resistant to lapatinib. Present study revealed that HER-2 mutant K753E showed some contrasting behaviour as compared to wild, L768S and V773L HER-2 in complex with lapatinib while similar to previously known lapatinib resistant L755S HER-2 mutant. Lapatinib showed stable but reverse orientation in binding site of K753E and the highest binding energy among studied HER2-lapatinib complexes but slightly lesser than L755S mutant. Results indicate that K753E has similar profile as L755S mutant for lapatinib. The interacting residues were also found different from other three studied forms as revealed by free energy decomposition and ligplot analysis.

Alanine mutation of the catalytic sites of Pantothenate Synthetase causes distinct conformational changes in the ATP binding region.

The enzyme Pantothenate synthetase (PS) represents a potential drug target in Mycobacterium tuberculosis. Its X-ray crystallographic structure has demonstrated the significance and importance of conserved active site residues including His44, His47, Asn69, Gln72, Lys160 and Gln164 in substrate binding and formation of pantoyl adenylate intermediate. In the current study, molecular mechanism of decreased affinity of the enzyme for ATP caused by alanine mutations was investigated using molecular dynamics (MD) simulations and free energy calculations. A total of seven systems including wild-type + ATP, H44A + ATP, H47A + ATP, N69A + ATP, Q72A + ATP, K160A + ATP and Q164A + ATP were subjected to 50 ns MD simulations. Docking score, MM-GBSA and interaction profile analysis showed weak interactions between ATP (substrate) and PS (enzyme) in H47A and H160A mutants as compared to wild-type, leading to reduced protein catalytic activity. However, principal component analysis (PCA) and free energy landscape (FEL) analysis revealed that ATP was strongly bound to the catalytic core of the wild-type, limiting its movement to form a stable complex as compared to mutants. The study will give insight about ATP binding to the PS at the atomic level and will facilitate in designing of non-reactive analogue of pantoyl adenylate which will act as a specific inhibitor for PS.

Insight into the inhibitor discrimination by FLT3 F691L.

Fms-like tyrosine kinase 3 (FLT3) belongs to the receptor tyrosine kinase family and expressed in hematopoietic progenitor cells. FLT3 gene mutations are reported in ~30% of acute myeloid leukemia cases. FLT3 kinase domain mutation F691L is one of the common causes of acquired resistance to the FLT3 inhibitors including quizartinib. MZH29 and crenolanib were previously reported to inhibit FLT3 F691L. However, crenolanib was reported for the moderate inhibition. We found that Glu661and Asp829 were the most significant residues to target the FLT3 F691L which contribute most significantly to the binding energy with MZH29 and crenolanib. These interactions were found absent with quizartinib. Further free energy landscape analysis revealed that FLT3 F691L bound to MZH29 and crenolanib was more stable as compared to quizartinib.

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.

Conformational Ensembles of α-Synuclein Derived Peptide with Different Osmolytes from Temperature Replica Exchange Sampling.

Intrinsically disordered proteins (IDP) are a class of proteins that do not have a stable three-dimensional structure and can adopt a range of conformations playing various vital functional role. Alpha-synuclein is one such IDP which can aggregate into toxic protofibrils and has been associated largely with Parkinson's disease (PD) along with other neurodegenerative diseases. Osmolytes are small organic compounds that can alter the environment around the proteins by acting as denaturants or protectants for the proteins. In the present study, we have conducted a series of replica exchange molecular dynamics simulations to explore the role of osmolytes, urea which is a denaturant and TMAO (trimethylamine N-oxide), a protecting osmolyte, in aggregation and conformations of the synuclein peptide. We observed that both the osmolytes have significantly distinct impacts on the peptide and led to transitions of the conformations of the peptide from one state to other. Our findings highlighted that urea attenuated peptide aggregation and resulted in the formation of extended peptide structures whereas TMAO led to compact and folded forms of the peptide.

Structural Basis for Drug Resistance Mechanisms Against EGFR.

Mutations in the kinase domain encoding region of EGFR gene causes drug resistance to EGFR kinase inhibitors such as erlotinib and gefitinib. This problem can be addressed by a new lead compound effective against all mutants of EGFR. To predict positions of residues possessing the potential to render EGFR drug resistant upon mutation, residual positions known to interact with Erlotinib and Gefitinib were assessed using five parameters (conservation index, binding site RMSD, protein structure stability and change in ATP and drug binding affinity). Structural screening protocol was followed to identify novel lead compound. Four positions, Lys 745, Cys 797, Asp 800 and Thr 854, were most likely observed to acquire drug resistance by altering drug binding affinity without destabilizing the protein and ATP binding ability. A compound DHO was observed to possess better binding affinity for all EGFR models in comparison to Erlotinib and Gefitinib, using docking protocol. This information would pave the way for designing drugs effective against wild-type (WT) EGFR as well as against variant EGFRs models. Thus, authors report a lead compound as a long-term potential with the ability to inhibit predicted models of mutant, wild and known SNPs EGFR.

Predicting neurological Adverse Drug Reactions based on biological, chemical and phenotypic properties of drugs using machine learning models.

Adverse drug reactions (ADRs) have become one of the primary reasons for the failure of drugs and a leading cause of deaths. Owing to the severe effects of ADRs, there is an urgent need for the generation of effective models which can accurately predict ADRs during early stages of drug development based on integration of various features of drugs. In the current study, we have focused on neurological ADRs and have used various properties of drugs that include biological properties (targets, transporters and enzymes), chemical properties (substructure fingerprints), phenotypic properties (side effects (SE) and therapeutic indications) and a combinations of the two and three levels of features. We employed relief-based feature selection technique to identify relevant properties and used machine learning approach to generated learned model systems which would predict neurological ADRs prior to preclinical testing. Additionally, in order to explain the efficiency and applicability of the models, we tested them to predict the ADRs for already existing anti-Alzheimer drugs and uncharacterized drugs, respectively in side effect resource (SIDER) database. The generated models were highly accurate and our results showed that the models based on chemical (accuracy 93.20%), phenotypic (accuracy 92.41%) and combination of three properties (accuracy 94.18%) were highly accurate while the models based on biological properties (accuracy 82.11%) were highly informative.

GQSAR modeling and combinatorial library generation of 4-phenylquinazoline-2-carboxamide derivatives as antiproliferative agents in human Glioblastoma tumors.

BACKGROUND: TSPO translocator protein, encoded in humans by the Tspo gene plays a crucial role in mitochondria mediated apoptosis and necrotic cell death through its association with Mitochondrial Permeability Transition pore (MPTP). It has been shown that this function can be exploited as a potential treatment for human Glioblastoma Multiforme. In this study, a novel robust fragment based QSAR model has been developed for a series of 4-phenylquinazoline-2-carboxamides experimentally known to be ligands for TSPO, thus triggering apoptotic mechanism cascade. RESULTS: Model developed showed satisfactory statistical parameters for the experimentally reported dataset (r2=0.8259, q2=0.6788, pred_r2=0.8237 and F-test=37.9). Low standard error values (r2_se=0.253, q2_se=0.34, pred_r2_se=0.14) confirmed the accuracy of the generated model. The model obtained had 4 descriptors, namely, R1-Volume, R2-SsCH3E-index, R3-SsCH3count and R5-EpsilonR. Two of them had positive contribution while the other two had negative correlation. CONCLUSION: The high binding affinity and the presence of essential structural features in these compounds make them an ideal choice for the consideration as potent anti-GBM drugs. Activity predicted by GQSAR model reinforces their potential as worthy candidates for drugs against GBM. The detailed analysis carried out in this study provides a substantial basis for the prospective design and development of novel 4-phenylquinazoline-2-carboxamide compounds as TSPO ligands capable of inducing apoptosis in cancer cells.

Natural polyphenolic inhibitors against the antiapoptotic BCL-2.

The apoptotic mechanism is regulated by the BCL-2 family of proteins, such as BCL-2 or Bcl-xL, which block apoptosis while Bad, Bak, Bax, Bid, Bim or Hrk induce apoptosis. The overexpression of BCL-2 was found to be related to the progression of cancer and also providing resistance towards chemotherapeutic treatments. In the present study, we found that all polyphenols (apigenin, fisetin, galangin and luteolin) bind to the hydrophobic groove of BCL-2 and the interaction is stable throughout MD simulation run. Luteolin was found to bind with highest negative binding energy and thus, claimed highest potency towards BCL-2 inhibition followed by fisetin. The hydrophobic interactions were found to be critical for stable complex formation as revealed by the vdW energy and ligplot analysis. Finally, on the basis of data obtained during the study, it can be concluded that these polyphenols have the potential to be used as lead molecules for BCL-2 inhibition.

Double Mutants in DNA Gyrase Lead to Ofloxacin Resistance in Mycobacterium tuberculosis.

Fluoroquinolones are among the most important classes of highly effective antibacterial drugs, exhibiting wide range of activity to cure infectious diseases. Ofloxacin is second generation fluoroquinolone approved by FDA for the treatment of tuberculosis by selectively inhibiting DNA gyrase. However, the emergence of drug resistance owing to mutations in DNA gyrase poses intimidating challenge for the effective therapy of this drug. The double mutants GyrAA90V GyrBD500N and GyrAA90V GyrBT539N are reported to be implicated in conferring higher levels of OFX resistance. The present study was designed to unravel the molecular principles behind development of resistance by the bug against fluoroquinolones. Our results highlighted that polar interactions play critical role in the development of drug resistance and highlight the significant correlation between the free energy calculations predicted by MM-PBSA and stability of the ligand-bound complexes. Modifications at the OFX binding pocket due to amino acid substitution leads to fewer hydrogen bonds in mutants DNA gyrase-OFX complex, which determined the low susceptibility of the ligand in inhibiting the mutant protein. This study provides a structural rationale to the mutation-based resistance to ofloxacin and will pave way for development potent fluoroquinolone-based resistant-defiant drugs. J. Cell. Biochem. 118: 2950-2957, 2017. © 2017 Wiley Periodicals, Inc.

Machine Learning and Molecular Dynamics Based Insights into Mode of Actions of Insulin Degrading Enzyme Modulators.

BACKGROUND: Alzheimer's disease (AD) is one of the most common lethal neurodegenerative disorders having impact on the lives of millions of people worldwide. The disease lacks effective treatment options and the unavailability of the drugs to cure the disease necessitates the development of effectual anti-Alzheimer drugs. Several mechanisms have been reported underlying the association of the two disorders, diabetes and dementia, one among which is the insulin-degrading enzyme (IDE) which is known to degrade insulin as well beta-amyloid peptides. METHODS: The present study is aimed to generate accurate classification models using machine learning techniques, which could identify IDE modulators from a bioassay dataset consisting of IDE inhibitors as well as non-inhibitors. The identified compounds were subjected to docking and Molecular dynamics (MD) studies for an in-depth analysis of the binding modes along with the complex stability. This study proposes that the identified potential active compounds, STK026154 (PubChem ID: CID2927418) with Glide score of -7.70 kcal/mol and BAS05901102 (PubChem ID: CID3152845) with Glide score of -7.06 kcal/mol, could serve as promising leads for the development of novel drugs against AD. CONCLUSION: The present study shows that such in silico approaches can be effectively used to discover and select active compounds from unseen data for accelerated drug development process. The machine learning models generated in the present study were used to screen Traditional Chinese Medicine (TCM) database to identify the phytocompounds already been reported to have therapeutic effects against AD.

Dissecting the role of mutations in chymase inhibition: Free energy and decomposition analysis.

Chymase enzyme abundantly found in secretory granules of mast cells and catalyzes the hydrolysis of peptide bonds to generate angiotensin II via hydrolysis of angiotensin I and also activates transforming growth factor-b and MMP-9. MMP-9 and TGF-b have significant role in tissue inflammation and fibrosis. In present study, we investigated that Lys192Met mutation leads to a higher loss in binding energy of inhibitors than mutation Arg143Gln in chymase. The energy decomposition revealed that the contributing residues are almost same in all the forms with some change in energy value. All the results pointing that arginine and lysine residues of chymase play the most significant role in inhibitor binding revealed by energy decomposition. The Lys40, Arg90, Lys192 and Arg217 are found to be most prominent residues in two different inhibitor systems but the role of other lysine and arginine also important as they also have significant contribution in the total binding energy.

Molecular dynamics-based identification of novel natural mortalin-p53 abrogators as anticancer agents.

INTRODUCTION: Cancer is one of the leading causes of mortality worldwide that requires attention in terms of extensive study and research. Eradication of mortalin-p53 interaction that leads to the inhibition of transcriptional activation or blocking of p53 from functioning as a suppressor and induction of nuclear translocation of p53 can prove to be one of the useful approaches for cancer management. RESULTS: In this study, we used structure-based approach to target the p53-binding domain of mortalin in order to prevent mortalin-p53 complex formation. We screened compounds from ZINC database against the modeled mortalin protein using Glide virtual screening. The top two compounds, DTOM (ZINC 28639308) and TTOM (ZINC 38143676) with Glide score of -12.27 and -12.16, respectively, were identified with the potential to abrogate mortalin-p53 interaction. Finally, molecular dynamics simulations were used to analyze the dynamic stability of the ligand-bound complex and it was observed that residues Tyr196, Asn198, Val264 and Thr267 were involved in intermolecular interactions in both the simulated ligand-bound complexes, and thus, these residues may have a paramount role in stabilizing the binding of the ligands with the protein. CONCLUSION: These detailed insights can further facilitate the development of potent inhibitors against mortalin-p53 complex.

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.

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease is assumed to be due to the large number of genes involved. Although genetic association studies have revealed a number of potential AD susceptibility genes, there still exists a need for identification of unidentified AD-associated genes and therapeutic targets to have better understanding of the disease-causing mechanisms of Alzheimer's towards development of effective AD therapeutics. RESULTS: In the present study, we have used machine learning approach to identify candidate AD associated genes by integrating topological properties of the genes from the protein-protein interaction networks, sequence features and functional annotations. We also used molecular docking approach and screened already known anti-Alzheimer drugs against the novel predicted probable targets of AD and observed that an investigational drug, AL-108, had high affinity for majority of the possible therapeutic targets. Furthermore, we performed molecular dynamics simulations and MM/GBSA calculations on the docked complexes to validate our preliminary findings. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive study of its kind for identification of putative Alzheimer-associated genes using machine learning approaches and we propose that such computational studies can improve our understanding on the core etiology of AD which could lead to the development of effective anti-Alzheimer drugs.

Molecular Characterization and Enhancement of Anticancer Activity of Caffeic Acid Phenethyl Ester by γ Cyclodextrin.

Caffeic Acid Phenethyl Ester (CAPE) is a key component in New Zealand propolis, known for a variety of health promoting and therapeutic potentials. We investigated the molecular mechanism of anticancer and anti-metastasis activities of CAPE. cDNA array performed on the control and CAPE-treated breast cancer cells revealed activation of DNA damage signaling involving upregulation of GADD45α and p53 tumor suppressor proteins. Molecular docking analysis revealed that CAPE is capable of disrupting mortalin-p53 complexes. We provide experimental evidence and demonstrate that CAPE induced disruption of mortalin-p53 complexes led to nuclear translocation and activation of p53 resulting in growth arrest in cancer cells. Furthermore, CAPE-treated cells exhibited downregulation of mortalin and several other key regulators of cell migration accountable for its anti-metastasis activity. Of note, we found that whereas CAPE was unstable in the culture medium (as it gets degraded into caffeic acid by secreted esterases), its complex with gamma cyclodextrin (γCD) showed high efficacy in anti-tumor and anti-metastasis assays in vitro and in vivo (when administered through either intraperitoneal or oral route). The data proposes that CAPE-γCD complex is a potent anti-cancer and anti-metastasis reagent.

Molecular Modeling and Virtual Screening Approach to Discover Potential Antileishmanial Inhibitors Against Ornithine Decarboxylase.

BACKGROUND: Visceral leishmaniasis (VL) is a tropical neglected disease, which encounters poorest of poor people living in Asia, Africa and Latin America; causing the mortality of more than 30,000 people worldwide. The armamentarium for the treatment of VL cases is limited and continuously facing decreasing of efficacy for existing drugs. Ornithine decarboxylase (ODC) is one of the interesting drug targets in Leishmania donovani, due to its association with redox metabolism. OBJECTIVE: To search an antileishmanial compound showing the inhibitory effect against ornithine decarboxylase of Leishmania donovani Method: In this study, we have modelled the three dimensional structure of ODC using Phyre2 (Protein Homology/analog Y Recognition Engine V 2.0), followed by validation using VADAR (Volume, Area, Dihedral Angle Reporter), RAMPAGE, ERRAT, Verify3D and ProSA (Protein Structure Analysis). In order to develop potential antileishmanial, we conducted a high throughput virtual screening of ZINC database ligands comprising of 135,966 compounds. Furthermore, QikProp, ADMET predictor and MM-GBSA was performed for ADME (Absorption, Distribution, Metabolism and Elimination), toxicity and binding energy prediction for top ligands, respectively. Finally, molecular dynamics simulation was performed to get potential antileishmanial compounds. RESULT: Screening of zinc database compounds using high throughput virtual screening has given twelve compounds with good inhibition activity against ornithine decarboxylase. Furthermore, the molecular dynamics simulation work reveals that ZINC67909154 could be a potent inhibitor and this compound can be used to combat VL disease Conclusion: This study concludes that ZINC67909154 has the great potential to inhibit L. donovani ODC and would add to the drug discovery process against visceral leishmaniasis.

Cheminformatics Based Machine Learning Approaches for Assessing Glycolytic Pathway Antagonists of Mycobacterium tuberculosis.

BACKGROUND: Tuberculosis is the second leading cause of death from an infectious disease worldwide after HIV, thus reasoning the expeditions in antituberculosis research. The rising number of cases of infection by resistant forms of M. tuberculosis has given impetus to the development of novel drugs that have different targets and mechanisms of action against the bacterium. METHODS: In this study, we have used machine learning algorithms on the available high throughput screening data of inhibitors of fructose bisphosphate aldolase, an enzyme central to the glycolysis pathway in M. tuberculosis, to build predictive classification models to identify actives against Mycobacterium tuberculosis, the causative organism of tuberculosis. We used Naïve Bayes, Random Forest and C4.5 J48 algorithms available from Weka were used for building predictive classification models. Additionally, a set of most relevant attributes was selected using genetic search algorithm which offered improved model performance by avoiding over fitting and generating faster and cost effective models. RESULTS: The model built using machine learning methods in this study provided good accuracy of classification of test compounds which suggests that in silico methods can be successfully used for screening of large datasets to identify potential drug leads. The substructure fragment analysis serves to further potentiate the M. tuberculosis drug development process as it would facilitate identification of structural fragments that are responsible for biological activity against this crucial glycolysis pathway target.