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

Development of Potential Multi-Target Inhibitors for Human Cholinesterases and Beta-Secretase 1: A Computational Approach

Alzheimer's disease causes chronic neurodegeneration and is the leading cause of dementia in the world. The causes of this disease are not fully understood but seem to involve two essential cerebral pathways: cholinergic and amyloid. The simultaneous inhibition of AChE, BuChE, and BACE-1, essential enzymes involved in those pathways, is a promising therapeutic approach to treat the symptoms and, hopefully, also halt the disease progression. This study sought to identify triple enzymatic inhibitors based on stereo-electronic requirements deduced from molecular modeling of AChE, BuChE, and BACE-1 active sites. A pharmacophore model was built, displaying four hydrophobic centers, three hydrogen bond acceptors, and one positively charged nitrogen, and used to prioritize molecules found in virtual libraries. Compounds showing adequate overlapping rates with the pharmacophore were subjected to molecular docking against the three enzymes and those with an adequate docking score (n = 12) were evaluated for physicochemical and toxicological parameters and commercial availability. The structure exhibiting the greatest inhibitory potential against all three enzymes was subjected to molecular dynamics simulations (100 ns) to assess the stability of the inhibitor-enzyme systems. The results of this in silico approach indicate ZINC1733 can be a potential multi-target inhibitor of AChE, BuChE, and BACE-1, and future enzymatic assays are planned to validate those results.

Identification of Potential Multitarget Compounds against Alzheimer's Disease through Pharmacophore-Based Virtual Screening

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive loss of cognitive functions, and it is the most prevalent type of dementia worldwide, accounting for 60 to 70% of cases. The pathogenesis of AD seems to involve three main factors: deficiency in cholinergic transmission, formation of extracellular deposits of β-amyloid peptide, and accumulation of deposits of a phosphorylated form of the TAU protein. The currently available drugs are prescribed for symptomatic treatment and present adverse effects such as hepatotoxicity, hypertension, and weight loss. There is urgency in finding new drugs capable of preventing the progress of the disease, controlling the symptoms, and increasing the survival of patients with AD. This study aims to present new multipurpose compounds capable of simultaneously inhibiting acetylcholinesterase (AChE), butyrylcholinesterase (BChE)-responsible for recycling acetylcholine in the synaptic cleft-and beta-secretase 1 (BACE-1)-responsible for the generation of amyloid-β plaques. AChE, BChE, and BACE-1 are currently considered the best targets for the treatment of patients with AD. Virtual hierarchical screening based on a pharmacophoric model for BACE-1 inhibitors and a dual pharmacophoric model for AChE and BChE inhibitors were used to filter 214,446 molecules by QFITBACE > 0 and QFITDUAL > 56.34. The molecules selected in this first round were subjected to molecular docking studies with the three targets and further evaluated for their physicochemical and toxicological properties. Three structures: ZINC45068352, ZINC03873986, and ZINC71787288 were selected as good fits for the pharmacophore models, with ZINC03873986 being ultimately prioritized for validation through activity testing and synthesis of derivatives for SAR studies.

The searching for agents for Alzheimer's disease treatment via the system of self-consistent models

Robust quantitative structure-activity relationships (QSARs) for hBACE-1 inhibitors (pIC50) for a large database (n = 1706) are established. New statistical criteria of the predictive potential of models are suggested and tested. These criteria are the index of ideality of correlation (IIC) and the correlation intensity index (CII). The system of self-consistent models is a new approach to validate the predictive potential of QSAR-models. The statistical quality of models obtained using the CORAL software (http://www.insilico.eu/coral) for the validation sets is characterized by the average determination coefficient R²_v= 0.923, and RMSE =0.345. Three new promising molecular structures which can become inhibitors hBACE-1 are suggested.

Changing Paradigm from one Target one Ligand Towards Multi-target Directed Ligand Design for Key Drug Targets of Alzheimer Disease: An Important Role of In Silico Methods in Multi-target Directed Ligands Design

Alzheimer disease (AD) is now considered as a multifactorial neurodegenerative disorder and rapidly increasing to an alarming situation and causing higher death rate. One target one ligand hypothesis does not provide complete solution of AD due to multifactorial nature of the disease and one target one drug fails to provide better treatment against AD. Moreo-ver, currently available treatments are limited and most of the upcoming treatments under clinical trials are based on modulat-ing single target. So, the current AD drug discovery research is shifting towards a new approach for a better solution that simultaneously modulates more than one targets in the neurodegenerative cascade. This can be achieved by network pharma-cology, multi-modal therapies, multifaceted, and/or the more recently proposed term “multi-targeted designed drugs”. Drug discovery project is a tedious, costly and long-term project. Moreover, multi-target AD drug discovery added extra challeng-es such as the good binding affinity of ligands for multiple targets, optimal ADME/T properties, no/less off-target side effect and crossing of the blood-brain barrier. These hurdles may be addressed by insilico methods for an efficient solution in less time and cost as computational methods successfully applied to single target drug discovery project. Here, we are summariz-ing some of the most prominent and computationally explored single targets against AD and further, we discussed a success-ful example of dual or multiple inhibitors for same targets. Moreover, we focused on ligand and structure-based computa-tional approach to design MTDL against AD. However, it is not an easy task to balance dual activity in a single molecule but computational approach such as virtual screening docking, QSAR, simulation and free energy is useful in future MTDLs drug discovery alone or in combination with a fragment-based method. However, rational and logical implementations of computational drug designing methods are capable of assisting AD drug discovery and play an important role in optimizing multi-target drug discovery.

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.

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.

Fragment based G-QSAR and molecular dynamics based mechanistic simulations into hydroxamic-based HDAC inhibitors against spinocerebellar ataxia

Expansion of polyglutamine (CAG) triplets within the coding gene ataxin 2 results in transcriptional repression, forming the molecular basis of the neurodegenerative disorder named spinocerebellar ataxia type-2 (SCA2). HDAC inhibitors (HDACi) have been elements of great interest in polyglutamine disorders such as Huntington's and Ataxia's. In this study, we have selected hydroxamic acid derivatives as HDACi and performed fragment-based G-QSAR, molecular docking studies and molecular dynamics simulations for elucidating the dynamic mode of action of HDACi with His-Asp catalytic dyad of HDAC4. The model was statistically validated to establish its predictive robustness. The model was statistically significant with r(2) value of .6297, cross-validated co-relation coefficient q(2) value of .5905 and pred_r(2) (predicted square co-relation coefficient) value of .85. An F-test value of 56.11 confirms absolute robustness of the model. Two combinatorial libraries comprising of 3180 compounds were created with hydroxamate moiety as the template and their pIC50 activities were predicted based on the G-QSAR model. The combinatorial library created was screened on the basis of predicted activity (pIC50), with two resultant top scoring compounds, HIC and DHC. The interaction of the compounds with His-Asp dyad in terms of H-bond interactions with His802, Asp840, Pro942, and Gly975 residues of HDAC4 was evaluated by docking and 20 ns long molecular dynamics simulations. This study provides valuable leads for structural substitutions required for hydroxamate moiety to exhibit enhanced inhibitory activity against HDAC4. The reported compounds demonstrated good binding and thus can be considered as potent therapeutic leads against ataxia.

Structural studies on molecular mechanisms of Nelfinavir resistance caused by non-active site mutation V77I in HIV-1 protease

Background

The human immunodeficiency virus (HIV-1) is a retrovirus causing acquired immunodeficiency syndrome (AIDS), which has become a serious problem across the world and has no cure reported to date. Human immunodeficiency virus (HIV-1) protease is an attractive target for antiviral treatment and a number of therapeutically useful inhibitors have been designed against it. The emergence of drug resistant mutants of HIV-1 poses a serious problem for conventional therapies that have been used so far. Until now, thirteen protease inhibitors (PIs), major mutation sites and many secondary mutations have been listed in the HIV Drug Resistance Database. In this study, we have studied the effect of the V77I mutation in HIV-PR along with the co-occurring mutations L33F and K20T through multi-nanosecond molecular dynamics simulations. V77I is known to cause Nelfinavir (NFV) resistance in the subtype B population of HIV-1 protease. We have for the first time reported the effect of this clinically relevant mutation on the binding of Nelfinavir and the conformational flexibility of the protease.

Results

Two HIV-PR mutants have been considered in this study - the Double Mutant Protease (DBM) V77I-L33F and Triple Mutant Protease (TPM) V77I-K20T-L33F. The molecular dynamics simulation studies were carried out and the RMSD trajectories of the unliganded wild type and mutated protease were found to be stable. The binding affinity of NFV with wild type HIV-PR was very high with a Glide XP docking score of -9.3 Kcal/mol. NFV showed decreased affinity towards DBM with a docking score of -8.0 Kcal/mol, whereas its affinity increased towards TPM (Glide XP score: -10.3). Prime/MM-GBSA binding free energy of the wild type, DBM and TPM HIV-PR docked structures were calculated as -38.9, -11.1 and -42.6 Kcal/mol respectively. The binding site cavity volumes of wild type, DBM and TPM protease were 1186.1, 1375.5 and 1042.5 ų respectively.

Conclusion

In this study, we have studied the structural roles of the two HIV-PR mutations by conducting molecular dynamics simulation studies of the wild type and mutant HIV-1 PRs. The present study proposes that DBM protease showed greater flexibility and the flap separation was greater with respect to the wild type protease. The cavity size of the MD-stabilized DBM was also found to be increased, which may be responsible for the decreased interaction of Nelfinavir with the cavity residues, thus explaining the decreased binding affinity. On the other hand, the binding affinity of NFV for TPM was found to be enhanced, accounted for by the decrease in cavity size of the mutant which facilitated strong interactions with the flap residues. The flap separation of TPM was less than the wild type protease and the decreased cavity size may be responsible for its lower resistance, and hence, may be the reason for its lower clinical relevance.

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.

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.

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.