Predicting stable functional peptides from the intergenic space of E. coli

Expression of synthetic proteins from intergenic regions of E. coli and their functional association was recently demonstrated (Dhar et al. in J Biol Eng 3:2, 2009. doi:10.1186/1754-1611-3-2). This gave birth to the question: if one can make 'user-defined' genes from non-coding genome-how big is the artificially translatable genome? (Dinger et al. in PLoS Comput Biol 4, 2008; Frith et al. in RNA Biol 3(1):40-48, 2006a; Frith et al. in PLoS Genet 2(4):e52, 2006b). To answer this question, we performed a bioinformatics study of all reported E. coli intergenic sequences, in search of novel peptides and proteins, unexpressed by nature. Overall, 2500 E. coli intergenic sequences were computationally translated into 'protein sequence equivalents' and matched against all known proteins. Sequences that did not show any resemblance were used for building a comprehensive profile in terms of their structure, function, localization, interactions, stability so on. A total of 362 protein sequences showed evidence of stable tertiary conformations encoded by the intergenic sequences of E. coli genome. Experimental studies are underway to confirm some of the key predictions. This study points to a vast untapped repository of functional molecules lying undiscovered in the non-expressed genome of various organisms.

Abrogation of AuroraA-TPX2 by novel natural inhibitors: molecular dynamics-based mechanistic analysis

INTRODUCTION

Cancer is characterized by uncontrolled cell growth and genetic instabilities. The human Aurora-A kinase protein plays a crucial role in spindle assembly during mitosis and is activated by another candidate oncogene, targeting protein for Xklp2 (TPX2). It has been proposed that dissociation of Aurora A-TPX2 complex leads to disruption of mitotic spindle apparatus, thereby preventing cell division and further tumor growth.

MATERIALS AND METHODS

A large natural compound library was docked against the active site of Aurora A-TPX2 complex. The protein-ligand complexes were subjected to molecular dynamics simulation to ascertain their binding stability. The drug properties of the compounds were analyzed to observe their drug-like properties.

RESULTS

The virtual screening of natural compound library yielded two high scoring compounds, the first compound CTOM [ZINC ID: 38143674] (Glide score: -9.49) was stable for 17 ns while the second TTOM (Glide score: -9.07) was stable for 15 ns. While CTOM interacted with His280, Thr288 of Aurora A and Tyr34, Lys38 of TPX2, TTOM interacted with Arg285 and Arg286 in addition to the residues involved with CTOM.

CONCLUSIONS

We report two natural compounds as potential drugs leads for the disruption of this complex. These ligands show a preferable docking score and have many drugs like properties within in the range of 95% of known drugs. The study provides evidence that CTOM and TTOM can efficiently inhibit the TPX2-mediated activation of Aurora A. Thus, it paves way for an elaborate investigation and establishes the importance of computational approaches as time- and cost-effective techniques.

Identification of chebulinic acid as potent natural inhibitor of M. tuberculosis DNA gyrase and molecular insights into its binding mode of action

Drug resistant tuberculosis has threatened all the advances that have been made in TB control at the global stage in the last few decades. DNA gyrase enzymes are an excellent target for antibacterial drug discovery as they are involved in essential functions like DNA replication. Here we report, a successful application of high throughput virtual screening (HTVS) to identify an inhibitor of Mycobacterium DNA gyrase targeting the wild type and the most prevalent three double mutants of quinolone resistant DNA gyrase namely A90V+D94G, A74S+D94G and A90V+S91P. HTVS of 179.299 compounds gave five compounds with significant binding affinity. Extra presicion (XP) docking and MD simulations gave a clear view of their interaction pattern. Among them, chebulinic acid (CA), a phytocompound obtained from Terminalia chebula was the most potent inhibitor with significantly high XP docking score, -14.63, -16.46, -15.94 and -15.11 against wild type and three variants respectively. Simulation studies for a period of 16 ns indicated stable DNA gyrA-CA complex formation. This stable binding would result in inhibition of the enzyme by two mechanisms. Firstly, binding of CA causes displacement of catalytic Tyr129 away from its target DNA-phosphate molecule from 1.6 Å to 3.8-7.3 Å and secondly, by causing steric hindrance to the binding of DNA strand at DNA binding site of enzyme. The combined effect would result in loss of cleavage and religation activity of enzyme leading to bactericidal effect on tuberculosis. This phytocompound displays desirable quality for carrying forward as a lead compound for anti-tuberculosis drug development. The results presented here are solely based on computations and need to be validated experimentally in order to assert the proposed mechanism of action.

Computational Structure-Based De Novo Design of Hypothetical Inhibitors against the Anti- Inflammatory Target COX-2

Cyclooxygenase-2 (COX-2) produces prostaglandins in inflamed tissues and hence has been considered as an important target for the development of anti-inflammatory drugs since long. Administration of traditional non-steroidal anti-inflammatory drugs (NSAIDs) and other COX-2 selective inhibitors (COXIBS) for the treat of inflammation has been found to be associated with side effects, which mainly includes gastro-intestinal (GI) toxicity. The present study involves developing a virtual library of novel molecules with high druglikeliness using structure-based de novo drug designing and 2D fingerprinting approach. A library of 2657 drug like molecules was generated. 2D fingerprinting based screening of the designed library gave a unique set of compounds. Molecular docking approach was then used to identify two compounds highly specific for COX-2 isoform. Molecular dynamics simulations of protein-ligand complexes revealed that the candidate ligands were dynamically stable within the cyclooxygenase binding site of COX-2. The ligands were further analyzed for their druglikeliness, ADMET properties and synthetic accessibility using knowledge based set of rules. The results revealed that the molecules are predicted to selectively bind to COX-2 enzyme thereby potentially overcoming the limitations posed by the drugs in clinical use.

Cheminformatics Based Machine Learning Models for AMA1-RON2 Abrogators for Inhibiting Plasmodium falciparum Erythrocyte Invasion

Malaria remains a dreadful disease by putting every year about 3.4 billion people at risk and resulting into mortality of 627 thousand people worldwide. Existing therapies based upon Quinines and Artemisinin-based combination therapies have started showing resistance, pressing the need for search of anti-malarials with different mechanisms of action. In this respect erythrocyte invasion by Plasmodium is immensely crucial, as being obligate intracellular parasite it must invade host cells. This process is mediated by interaction between conserved Apical Membrane Antigen (AMA1) and Rhoptry Neck (RON2) protein, which is compulsory for successful invasion of erythrocyte by Plasmodium and manifestation of the disease Malaria. Here, using the physicochemical properties of the compounds available from a confirmatory high throughput screening, which were tested for their disruption capability of this crucial molecular interaction, we trained supervised classifiers and validated their robustness by various statistical parameters. Best model was used for screening new compounds from Traditional Chinese Medicine Database. Some of the best hits already find their use as anti-malarials and the model predicts that an essential part of their effectiveness is likely due to inhibition of AMA1-RON2 interaction. Pharmacophoric features have also been identified to ease further designing of possible leads in an effective way.

Checking the STEP-Associated Trafficking and Internalization of Glutamate Receptors for Reduced Cognitive Deficits: A Machine Learning Approach-Based Cheminformatics Study and Its Application for Drug Repurposing

BACKGROUND

Alzheimer's disease, a lethal neurodegenerative disorder that leads to progressive memory loss, is the most common form of dementia. Owing to the complexity of the disease, its root cause still remains unclear. The existing anti-Alzheimer's drugs are unable to cure the disease while the current therapeutic options have provided only limited help in restoring moderate memory and remain ineffective at restricting the disease's progression. The striatal-enriched protein tyrosine phosphatase (STEP) has been shown to be involved in the internalization of the receptor, N-methyl D-aspartate (NMDR) and thus is associated with the disease. The present study was performed using machine learning algorithms, docking protocol and molecular dynamics (MD) simulations to develop STEP inhibitors, which could be novel anti-Alzheimer's molecules.

METHODS

The present study deals with the generation of computational predictive models based on chemical descriptors of compounds using machine learning approaches followed by substructure fragment analysis. To perform this analysis, the 2D molecular descriptors were generated and machine learning algorithms (Naïve Bayes, Random Forest and Sequential Minimization Optimization) were utilized. The binding mechanisms and the molecular interactions between the predicted active compounds and the target protein were modelled using docking methods. Further, the stability of the protein-ligand complex was evaluated using MD simulation studies. The substructure fragment analysis was performed using Substructure fingerprint (SubFp), which was further explored using a predefined dictionary.

RESULTS

The present study demonstrates that the computational methodology used can be employed to examine the biological activities of small molecules and prioritize them for experimental screening. Large unscreened chemical libraries can be screened to identify potential novel hits and accelerate the drug discovery process. Additionally, the chemical libraries can be searched for significant substructure patterns as reported in the present study, thus possibly contributing to the activity of these molecules.

Molecular principles behind Boceprevir resistance due to mutations in hepatitis C NS3/4A protease

The hepatitis C virus (HCV) infection is a primary cause of chronic hepatitis which eventually progresses to cirrhosis and in some instances might advance to hepatocellular carcinoma. According to the WHO report, HCV infects 130-150 million people globally and every year 350,000 to 500,000 people die from hepatitis C virus infection. Great achievement has been made in viral treatment evolution, after the development of HCV NS3/4A protease inhibitor (Boceprevir). However, efficacy of Boceprevir is compromised by the emergence of drug resistant variants. The molecular principle behind drug resistance of the protease mutants such as (V36M, T54S and R155K) is still poorly understood. Therefore in this study, we employed a series of computational strategies to analyze the binding of antiviral drug, Boceprevir to HCV NS3/4A protease mutants. Our results clearly demonstrate that the point mutations (V36M, T54S and R155K) in protease are associated with lowering of its binding affinity with Boceprevir. Exhaustive analysis of the simulated Boceprevir-bound wild and mutant complexes revealed variations in hydrophobic interactions, hydrogen bond occupancy and salt bridge interactions. Also, substrate envelope analysis scrutinized that the studied mutations reside outside the substrate envelope which may affect the Boceprevir affinity towards HCV protease but not the protease enzymatic activity. Furthermore, structural analyses of the binding site volume and flexibility show impairment in flexibility and stability of the binding site residues in mutant structures. In order to combat Boceprevir resistance, renovation of binding interactions between the drug and protease may be valuable. The structural insight from this study reveals the mechanism of the Boceprevir resistance and the results can be valuable for the design of new PIs with improved efficiency.

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

BACKGROUND

The epidermal growth factor receptor (EGFR) is a member of the ErbB family that is involved in a number of processes responsible for cancer development and progression such as angiogenesis, apoptosis, cell proliferation and metastatic spread. Malfunction in activation of protein tyrosine kinases has been shown to result in uncontrolled cell growth. The EGFR TK domain has been identified as suitable target in cancer therapy and tyrosine kinase inhibitors such as erlotinib have been used for treatment of cancer. Mutations in the region of the EGFR gene encoding the tyrosine kinase (TK) domain causes altered responses to EGFR TK inhibitors (TKI). In this paper we perform molecular dynamics simulations and PCA analysis on wild-type and mutant (T854A) structures to gain insight into the structural changes observed in the target protein upon mutation. We also report two novel inhibitors identified by combined approach of QSAR model development.

RESULTS

The wild-type and mutant structure was observed to be stable for 26 ns and 24 ns respectively. In PCA analysis, the mutant structure proved to be more flexible than wild-type. We developed a 3D-QSAR model using 38 thiazolyl-pyrazoline compounds which was later used for prediction of inhibitory activity of natural compounds of ZINC library. The 3D-QSAR model was proved to be robust by the statistical parameters such as r2 (0.9751), q2(0.9491) and pred_r2(0.9525).

CONCLUSION

Analysis of molecular dynamics simulations results indicate stability loss and increased flexibility in the mutant structure. This flexibility results in structural changes which render the mutant protein drug resistant against erlotinib. We report two novel compounds having high predicted inhibitory activity to EGFR TK domain with both wild-type and mutant structure.

Cheminformatics models based on machine learning approaches for design of USP1/UAF1 abrogators as anticancer agents

Cancer cells have upregulated DNA repair mechanisms, enabling them survive DNA damage induced during repeated rapid cell divisions and targeted chemotherapeutic treatments. Cancer cell proliferation and survival targeting via inhibition of DNA repair pathways is currently a very promiscuous anti-tumor approach. The deubiquitinating enzyme, USP1 is known to promote DNA repair via complexing with UAF1. The USP1/UAF1 complex is responsible for regulating DNA break repair pathways such as trans-lesion synthesis pathway, Fanconi anemia pathway and homologous recombination. Thus, USP1/UAF1 inhibition poses as an efficient anti-cancer strategy. The recently made available high throughput screen data for anti USP1/UAF1 activity prompted us to compute bioactivity predictive models that could help in screening for potential USP1/UAF1 inhibitors having anti-cancer properties. The current study utilizes publicly available high throughput screen data set of chemical compounds evaluated for their potential USP1/UAF1 inhibitory effect. A machine learning approach was devised for generation of computational models that could predict for potential anti USP1/UAF1 biological activity of novel anticancer compounds. Additional efficacy of active compounds was screened by applying SMARTS filter to eliminate molecules with non-drug like features. The structural fragment analysis was further performed to explore structural properties of the molecules. We demonstrated that modern machine learning approaches could be efficiently employed in building predictive computational models and their predictive performance is statistically accurate. The structure fragment analysis revealed the structures that could play an important role in identification of USP1/UAF1 inhibitors.

Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin

BACKGROUND

A number of microtubule disassembly blocking agents and inhibitors of tubulin polymerization have been elements of great interest in anti-cancer therapy, some of them even entering into the clinical trials. One such class of tubulin assembly inhibitors is of arylthioindole derivatives which results in effective microtubule disorganization responsible for cell apoptosis by interacting with the colchicine binding site of the β-unit of tubulin close to the interface with the α unit. We modelled the human tubulin β unit (chain D) protein and performed docking studies to elucidate the detailed binding mode of actions associated with their inhibition. The activity enhancing structural aspects were evaluated using a fragment-based Group QSAR (G-QSAR) model and was validated statistically to determine its robustness. A combinatorial library was generated keeping the arylthioindole moiety as the template and their activities were predicted.

RESULTS

The G-QSAR model obtained was statistically significant with r2 value of 0.85, cross validated correlation coefficient q2 value of 0.71 and pred_r2 (r2 value for test set) value of 0.89. A high F test value of 65.76 suggests robustness of the model. Screening of the combinatorial library on the basis of predicted activity values yielded two compounds HPI (predicted pIC50 = 6.042) and MSI (predicted pIC50 = 6.001) whose interactions with the D chain of modelled human tubulin protein were evaluated in detail. A toxicity evaluation resulted in MSI being less toxic in comparison to HPI.

CONCLUSIONS

The study provides an insight into the crucial structural requirements and the necessary chemical substitutions required for the arylthioindole moiety to exhibit enhanced inhibitory activity against human tubulin. The two reported compounds HPI and MSI showed promising anti cancer activities and thus can be considered as potent leads against cancer. The toxicity evaluation of these compounds suggests that MSI is a promising therapeutic candidate. This study provided another stepping stone in the direction of evaluating tubulin inhibition and microtubule disassembly degeneration as viable targets for development of novel therapeutics against cancer.

Computational identification of novel natural inhibitors of glucagon receptor for checking type II diabetes mellitus

BACKGROUND

Interaction of the small peptide hormone glucagon with glucagon receptor (GCGR) stimulates the release of glucose from the hepatic cells during fasting; hence GCGR performs a significant function in glucose homeostasis. Inhibiting the interaction between glucagon and its receptor has been reported to control hepatic glucose overproduction and thus GCGR has evolved as an attractive therapeutic target for the treatment of type II diabetes mellitus.

RESULTS

In the present study, a large library of natural compounds was screened against 7 transmembrane domain of GCGR to identify novel therapeutic molecules that can inhibit the binding of glucagon with GCGR. Molecular dynamics simulations were performed to study the dynamic behaviour of the docked complexes and the molecular interactions between the screened compounds and the ligand binding residues of GCGR were analysed in detail. The top scoring compounds were also compared with already documented GCGR inhibitors- MK-0893 and LY2409021 for their binding affinity and other ADME properties. Finally, we have reported two natural drug like compounds PIB and CAA which showed good binding affinity for GCGR and are potent inhibitor of its functional activity.

CONCLUSION

This study contributes evidence for application of these compounds as prospective small ligand molecules against type II diabetes. Novel natural drug like inhibitors against the 7 transmembrane domain of GCGR have been identified which showed high binding affinity and potent inhibition of GCGR.

Embelin inhibits TNF-α converting enzyme and cancer cell metastasis: molecular dynamics and experimental evidence

Background

Embelin, a quinone derivative, is found in the fruits of Embelia ribes Burm (Myrsinaceae). It has been shown to have a variety of therapeutic potentials including anthelmintic, anti-tumor, anti-diabetic, anti-bacterial and anti-inflammation. Inflammation is an immunological response to external harmful stimuli and is regulated by an endogenous pyrogen and pleiotropic pro-inflammatory cytokine, tumor necrosis factor alpha (TNF-α). TNF-α production has been implicated in a variety of other human pathologies including neurodegeneration and cancer. Several studies have shown that the anti-inflammatory activity of embelin is mediated by reduction in TNF-α. The latter is synthesized as a membrane anchored protein (pro-TNF-α); the soluble component of pro-TNF-α is then released into the extracellular space by the action of a protease called TNF-α converting enzyme (TACE). TACE, hence, has been proposed as a therapeutic target for inflammation and cancer.

Methods

We used molecular docking and experimental approaches to investigate the docking potential and molecular effects of embelin to TACE and human cancer cell characteristics, respectively.

Results

We demonstrate that embelin is a potential inhibitor of TACE. Furthermore, in vitro studies revealed that it inhibits malignant properties of cancer cells through inactivation of metastatic signaling molecules including MMPs, VEGF and hnRNP-K in breast cancer cells.

Conclusion

Based on the molecular dynamics and experimental data, embelin is proposed as a natural anti-inflammatory and anticancer drug.

Molecular modeling and molecular dynamics simulations based structural analysis of the SG2NA protein variants

Background

SG2NA is a member of the striatin sub-family of WD-40 repeat proteins. Striatin family members have been associated with diverse physiological functions. SG2NA has also been shown to have roles in cell cycle progression, signal transduction etc. They have been known to interact with a number of proteins including Caveolin and Calmodulin and also propagate the formation of a multimeric protein unit called striatin-interacting phosphatase and kinase. As a pre-requisite for such interaction ability, these proteins are known to be unstable and primarily disordered in their arrangement. Earlier we had identified that it has multiple isoforms (namely 35, 78, 87 kDa based on its molecular weight) which are generated by alternative splicing. However, detailed structural information of SG2NA is still eluding the researchers.

Results

This study was aimed towards three-dimensional molecular modeling and characterization of SG2NA protein and its isoforms. One structure out of five was selected for each variant having the least value for C score. Out of these, m35 kDa with a C score value of −3.21was the most poorly determined structure in comparison to m78 kDa and m87 kDa variants with C scores of −1.16 and −1.97 respectively. Further evaluation resulted in about 61.6% residues of m35 kDa, 76.6% residues of m78 kDa and 72.1% residues of m87 kDa falling in the favorable regions of Ramchandran Plot. Molecular dynamics simulations were also carried out to obtain biologically relevant structural models and compared with previous atomic coordinates. N-terminal region of all variants was found to be highly disordered.

Conclusion

This study provides first-hand detailed information to understand the structural conformation of SG2NA protein variants (m35 kDa, m78 kDa and m87 kDa). The WD-40 repeat domain was found to constitute antiparallel strands of β-sheets arranged circularly. This study elucidates the crucial structural features of SG2NA proteins which are involved in various protein-protein interactions and also reveals the extent of disorder present in the SG2NA structure crucial for excessive interaction and multimeric protein complexes. The study also potentiates the role of computational approaches for preliminary examination of unknown proteins in the absence of experimental information.

Novel fragment-based QSAR modeling and combinatorial design of pyrazole-derived CRK3 inhibitors as potent antileishmanials

The CRK3 cyclin-dependent kinase of Leishmania plays an important role in regulating the cell-cycle progression at the G2/M phase checkpoint transition, proliferation, and viability inside the host macrophage. In this study, a novel fragment-based QSAR model has been developed using 22 pyrazole-derived compounds exhibiting inhibitory activity against Leishmanial CRK3. Unlike other QSAR methods, this fragment-based method gives flexibility to study the relationship between molecular fragments of interest and their contribution for the variation in the biological response by evaluating cross-term fragment descriptors. Based on the fragment-based QSAR model, a combinatorial library was generated, and top two compounds were reported after predicting their activity. The QSAR model showed satisfactory statistical parameters for the data set (r(2) = 0.8752, q(2) = 0.6690, F-ratio = 30.37, and pred_r(2) = 0.8632) with four descriptors describing the nature of substituent groups and the environment of the substitution site. Evaluation of the model implied that electron-rich substitution at R1 position improves the inhibitory activity, while decline in inhibitory activity was observed in presence of nitrogen at R2 position. The analysis carried out in this study provides a substantial basis for consideration of the designed pyrazole-based leads as potent antileishmanial drugs.

Group-based QSAR and molecular dynamics mechanistic analysis revealing the mode of action of novel piperidinone derived protein-protein inhibitors of p53-MDM2

Tumour suppressor p53 is known to play a central role in prevention of tumour development, DNA repair, senescence and apoptosis which is in normal cells maintained by negative feedback regulator MDM2 (Murine Double Minute 2). In case of dysfunctioning of this regulatory loop, tumour development starts thus resulting in cancerous condition. Inhibition of p53-MDM2 binding would result in activation of the tumour suppressor. In this study, a novel robust fragment-based QSAR model has been developed for piperidinone derived compounds experimentally known to inhibit p53-MDM2 interaction. The QSAR model developed showed satisfactory statistical parameters for the experimentally reported dataset (r(2)=0.9415, q(2)=0.8958, pred_r(2)=0.8894 and F-test=112.7314), thus judging the robustness of the model. Low standard error values (r(2)_se=0.3003, q(2)_se=0.4009 and pred_r(2)_se=0.3315) confirmed the accuracy of the developed model. The regression equation obtained constituted three descriptors (R2-DeltaEpsilonA, R1-RotatableBondCount and R2-SssOCount), two of which had positive contribution while third showed negative correlation. Based on the developed QSAR model, a combinatorial library was generated and activities of the compounds were predicted. These compounds were docked with MDM2 and two top scoring compounds with binding affinities of -10.13 and -9.80kcal/mol were selected. The binding modes of actions of these complexes were analyzed using molecular dynamics simulations. Analysis of the developed fragment-based QSAR model revealed that addition of unsaturated electronegative groups at R2 site and groups with more rotatable bonds at R1 improved the inhibitory activity of these potent lead compounds. The detailed analysis carried out in this study provides a considerable basis for the design and development of novel piperidinone-based lead molecules against cancer and also provides mechanistic insights into their mode of actions.

Structural insights into mode of actions of novel natural Mycobacterium protein tyrosine phosphatase B inhibitors

Background

Tuberculosis has become a major health problem being the second leading cause of death worldwide. Mycobacterium tuberculosis secretes a virulence factor, protein tyrosine phosphatase B (mPTPB) in the cytoplasm of host macrophage which suppresses its natural innate immune response and helps the pathogen survive and proliferate in the phagosome. The present study aims at indentifying potent inhibitors of mPTPB by using computational approaches of ligand based molecular modeling and docking studies.

Results

A 3D QSAR model was developed using a set of benzofuran salicylic acid based mPTPB inhibitors with experimentally known IC₅₀ values. The model was generated using the statistical method of principle component regression analysis in combination with step wise forward variable selection algorithm. It was observed that steric and hydrophobic descriptors positively contribute towards the inhibitory activity of the ligands. The developed model had a robust internal as well as external predictive power as indicated by the q² value of 0.8920 and predicted r² value of 0.8006 respectively. Hence, the generated model was used to screen a large set of naturally occurring chemical compounds and predict their biological activity to identify more potent natural compounds targeting mPTPB. The two top potential hits (with pIC₅₀ value of 1.459 and 1.677 respectively) had a similar interaction pattern as that of the most potent compound (pIC₅₀ = 1.42) of the congeneric series.

Conclusion

The contour plot provided a better understanding of the relationship between structural features of substituted benzofuran salicylic acid derivatives and their activities which would facilitate design of novel mPTPB inhibitors. The QSAR modeling was used to obtain an equation, correlating the important steric and hydrophobic descriptors with the pIC₅₀ value. Thus, we report two natural compounds of inhibitory nature active against mPTPB enzyme of Mycobacterium tuberculosis. These inhibitors have the potential to evolve as lead molecules in the development of drugs for the treatment of tuberculosis.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-S1-S3) contains supplementary material, which is available to authorized users.

Mechanistic insights into mode of action of potent natural antagonists of BACE-1 for checking Alzheimer's plaque pathology

Alzheimer's is a neurodegenerative disorder resulting in memory loss and decline in cognitive abilities. Accumulation of extracellular beta amyloidal plaques is one of the major pathology associated with this disease. β-Secretase or BACE-1 performs the initial and rate limiting step of amyloidic pathway in which 37-43 amino acid long peptides are generated which aggregate to form plaques. Inhibition of this enzyme offers a viable prospect to check the growth of these plaques. Numerous efforts have been made in recent years for the generation of BACE-1 inhibitors but many of them failed during the preclinical or clinical trials due to drug related or drug induced toxicity. In the present work, we have used computational methods to screen a large dataset of natural compounds to search for small molecules having BACE-1 inhibitory activity with low toxicity to normal cells. Molecular dynamics simulations were performed to analyze molecular interactions between the screened compounds and the active residues of the enzyme. Herein, we report two natural compounds of inhibitory nature active against β-secretase enzyme of amyloidic pathway and are potent lead molecules against Alzheimer's disease.