Hybrid structure-based virtual screening protocol for the identification of novel BACE1 inhibitors

Computer-Aided Drug Design of β-Secretase, γ-Secretase and Anti-Tau Inhibitors for the Discovery of Novel Alzheimer's Therapeutics

Aging-associated neurodegenerative diseases, which are characterized by progressive neuronal death and synapses loss in human brain, are rapidly growing affecting millions of people globally. Alzheimer's is the most common neurodegenerative disease and it can be caused by genetic and environmental risk factors. This review describes the amyloid-β and Tau hypotheses leading to amyloid plaques and neurofibrillary tangles, respectively which are the predominant pathways for the development of anti-Alzheimer's small molecule inhibitors. The function and structure of the druggable targets of these two pathways including β-secretase, γ-secretase, and Tau are discussed in this review article. Computer-Aided Drug Design including computational structure-based design and ligand-based design have been employed successfully to develop inhibitors for biomolecular targets involved in Alzheimer's. The application of computational molecular modeling for the discovery of small molecule inhibitors and modulators for β-secretase and γ-secretase is summarized. Examples of computational approaches employed for the development of anti-amyloid aggregation and anti-Tau phosphorylation, proteolysis and aggregation inhibitors are also reported.

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.

Computational protein design and protein-ligand interaction studies for the improvement of organophosphorus degrading potential of Deinococcus radiodurans

The organophosphorus hydrolase enzyme is involved in the catalyzing reaction that involve hydrolysis of organophosphate toxic compounds. An enzyme from Deinococcus radiodurans reported as homologous to phosphotriesterase and show activity against organophosphate. In the past activity of this enzyme is low and efforts made to improve the activity by experimental mutation study. However only very few organophosphates tested against very few catalytic site mutations. In order to improve the catalytic power of the organophosphorus hydrolase enzyme, we carried out systematic functional hotspot based protein engineering strategy. The mutants tested against 46 know organophosphate compounds using molecular docking study. Finally, we carried out an extensive molecular docking study to predict the binding of 46 organophosphate compounds to wild-type protein and mutant organophosphorus hydrolase enzyme. At the end we are able to improve the degrading potential of organophosphorus hydrolase enzyme against organophosphate toxic compounds. This preliminary study and the outcome would be useful guide for the experimental scientist involved in the bioremediation of toxic organophosphate compounds.

Fragment-based virtual screening approach and molecular dynamics simulation studies for identification of BACE1 inhibitor leads

Traditional structure-based virtual screening method to identify drug-like small molecules for BACE1 is so far unsuccessful. Location of BACE1, poor Blood Brain Barrier permeability and P-glycoprotein (Pgp) susceptibility of the inhibitors make it even more difficult. Fragment-based drug design method is suitable for efficient optimization of initial hit molecules for target like BACE1. We have developed a fragment-based virtual screening approach to identify/optimize the fragment molecules as a starting point. This method combines the shape, electrostatic, and pharmacophoric features of known fragment molecules, bound to protein conjugate crystal structure, and aims to identify both chemically and energetically feasible small fragment ligands that bind to BACE1 active site. The two top-ranked fragment hits were subjected for a 53 ns MD simulation. Principle component analysis and free energy landscape analysis reveal that the new ligands show the characteristic features of established BACE1 inhibitors. The potent method employed in this study may serve for the development of potential lead molecules for BACE1-directed Alzheimer's disease therapeutics.

Discovery of novel dual VEGFR2 and Src inhibitors using a multistep virtual screening approach

AIM

Simultaneous inhibition of VEGFR2 and Src may enhance the efficacy of VEGFR2-targeted cancer therapeutics. Hence, development of dual inhibitors on VEGFR2 and Src can be a useful strategy for such treatments.

MATERIALS & METHODS

A multistep virtual screening protocol, comprising ligand-based support vector machines method, drug-likeness rules filter and structure-based molecular docking, was developed and employed to identify dual inhibitors of VEGFR2 and Src from a large commercial chemical library. Kinase inhibitory assays and cell viability assays were then used for experimental validation.

RESULTS

A set of compounds belonging to six different molecular scaffolds was identified and sent for biological evaluation. Compound 3c belonging to the 2-amino-3-cyanopyridine scaffold exhibited good antiproliferative effect and dual-target activities against VEGFR2 and Src.

CONCLUSION

This study demonstrated the ability of the multistep virtual screening approach to identify novel multitarget agents.

Drug Design for CNS Diseases: Polypharmacological Profiling of Compounds Using Cheminformatic, 3D-QSAR and Virtual Screening Methodologies

HIGHLIGHTS

Many CNS targets are being explored for multi-target drug design

New databases and cheminformatic methods enable prediction of primary pharmaceutical target and off-targets of compounds

QSAR, virtual screening and docking methods increase the potential of rational drug design

The diverse cerebral mechanisms implicated in Central Nervous System (CNS) diseases together with the heterogeneous and overlapping nature of phenotypes indicated that multitarget strategies may be appropriate for the improved treatment of complex brain diseases. Understanding how the neurotransmitter systems interact is also important in optimizing therapeutic strategies. Pharmacological intervention on one target will often influence another one, such as the well-established serotonin-dopamine interaction or the dopamine-glutamate interaction. It is now accepted that drug action can involve plural targets and that polypharmacological interaction with multiple targets, to address disease in more subtle and effective ways, is a key concept for development of novel drug candidates against complex CNS diseases. A multi-target therapeutic strategy for Alzheimer‘s disease resulted in the development of very effective Multi-Target Designed Ligands (MTDL) that act on both the cholinergic and monoaminergic systems, and also retard the progression of neurodegeneration by inhibiting amyloid aggregation. Many compounds already in databases have been investigated as ligands for multiple targets in drug-discovery programs. A probabilistic method, the Parzen-Rosenblatt Window approach, was used to build a “predictor” model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. Based on all these findings, it is concluded that multipotent ligands targeting AChE/MAO-A/MAO-B and also D₁-R/D₂-R/5-HT_2A-R/H₃-R are promising novel drug candidates with improved efficacy and beneficial neuroleptic and procognitive activities in treatment of Alzheimer's and related neurodegenerative diseases. Structural information for drug targets permits docking and virtual screening and exploration of the molecular determinants of binding, hence facilitating the design of multi-targeted drugs. The crystal structures and models of enzymes of the monoaminergic and cholinergic systems have been used to investigate the structural origins of target selectivity and to identify molecular determinants, in order to design MTDLs.

Target specific proteochemometric model development for BACE1 – protein flexibility and structural water are critical in virtual screening

Structural water and protein plasticity are important factors for BACE1 targeted ligand virtual screening.

Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors

Aminopeptidase N (APN) inhibitors have been reported to be effective in treating of life threatening diseases including cancer. Validated ligand- and structure-based pharmacophore mapping approaches were combined with Bayesian modeling and recursive partitioning to identify structural and physicochemical requirements for highly active APN inhibitors. Based on the assumption that ligand- and structure-based pharmacophore models are complementary, the efficacy of 'multiple pharmacophore screening' for filtering true positive virtual hits was investigated. These multiple pharmacophore screening methods were utilized to search novel virtual hits for APN inhibition. The number of hits was refined and reduced by recursive partitioning, drug-likeliness, pharmacokinetic property prediction, and comparative molecular-docking studies. Four compounds were proposed as the potential virtual hits for APN enzyme inhibition.

Exploring polypharmacology using a ROCS-based target fishing approach

Polypharmacology has emerged as a new theme in drug discovery. In this paper, we studied polypharmacology using a ligand-based target fishing (LBTF) protocol. To implement the protocol, we first generated a chemogenomic database that links individual protein targets with a specified set of drugs or target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/chemistry overlap between the query molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures). We validated this approach using the Directory of Useful Decoys (DUD). DUD contains 2950 active compounds, each with 36 property-matched decoys, against 40 protein targets. We chose a set of known drugs to represent each DUD target, and we carried out ligand-based virtual screens using data sets of DUD actives seeded into DUD decoys for each target. We computed Receiver Operator Characteristic (ROC) curves and associated area under the curve (AUC) values. For the majority of targets studied, the AUC values were significantly better than for the case of a random selection of compounds. In a second test, the method successfully identified off-targets for drugs such as rimantadine, propranolol, and domperidone that were consistent with those identified by recent experiments. The results from our ROCS-based target fishing approach are promising and have potential application in drug repurposing for single and multiple targets, identifying targets for orphan compounds, and adverse effect prediction.

Discovery of safe and orally effective 4-aminoquinaldine analogues as apoptotic inducers with activity against experimental visceral leishmaniasis

Novel antileishmanials are urgently required to overcome emergence of drug resistance, cytotoxic effects, and difficulties in oral delivery. Toward this, we investigated a series of novel 4-aminoquinaldine derivatives, a new class of molecules, as potential antileishmanials. 4-Aminoquinaldine derivatives presented inhibitory effects on L. donovani promastigotes and amastigotes (50% inhibitory concentration range, 0.94 to 127 μM). Of these, PP-9 and PP-10 were the most effective in vitro and demonstrated strong efficacies in vivo through the intraperitoneal route. They were also found to be effective against both sodium antimony gluconate-sensitive and -resistant Leishmania donovani strains in BALB/c mice when treated orally, resulting in more than 95% protection. Investigation of their mode of action revealed that killing by PP-10 involved moderate inhibition of dihydrofolate reductase and elicitation of the apoptotic cascade. Our studies implicate that PP-10 augments reactive oxygen species generation, evidenced from decreased glutathione levels and increased lipid peroxidation. Subsequent disruption of Leishmania promastigote mitochondrial membrane potential and activation of cytosolic proteases initiated the apoptotic pathway, resulting in DNA fragmentation and parasite death. Our results demonstrate that PP-9 and PP-10 are promising lead compounds with the potential for treating visceral leishmaniasis (VL) through the oral route.

Identification of novel malarial cysteine protease inhibitors using structure-based virtual screening of a focused cysteine protease inhibitor library

Malaria, in particular that caused by Plasmodium falciparum , is prevalent across the tropics, and its medicinal control is limited by widespread drug resistance. Cysteine proteases of P. falciparum , falcipain-2 (FP-2) and falcipain-3 (FP-3), are major hemoglobinases, validated as potential antimalarial drug targets. Structure-based virtual screening of a focused cysteine protease inhibitor library built with soft rather than hard electrophiles was performed against an X-ray crystal structure of FP-2 using the Glide docking program. An enrichment study was performed to select a suitable scoring function and to retrieve potential candidates against FP-2 from a large chemical database. Biological evaluation of 50 selected compounds identified 21 diverse nonpeptidic inhibitors of FP-2 with a hit rate of 42%. Atomic Fukui indices were used to predict the most electrophilic center and its electrophilicity in the identified hits. Comparison of predicted electrophilicity of electrophiles in identified hits with those in known irreversible inhibitors suggested the soft-nature of electrophiles in the selected target compounds. The present study highlights the importance of focused libraries and enrichment studies in structure-based virtual screening. In addition, few compounds were screened against homologous human cysteine proteases for selectivity analysis. Further evaluation of structure-activity relationships around these nonpeptidic scaffolds could help in the development of selective leads for antimalarial chemotherapy.

Novel non-peptide beta-secretase inhibitors derived from structure-based virtual screening and bioassay

This Letter describes an efficient approach by integrating virtual screening with bioassay technology for finding small organic inhibitors targeting beta-secretase (BACE-1). Fifteen hits with inhibitory potencies ranging from 2.8 to 118 microM (IC(50)) against beta-secretase were successfully identified. Compound 12 with IC(50) of 2.8 microM is the most potent hit against BACE-1. Docking simulation from gold 3.0 suggests putative binding mode of 12 in BACE-1 and potential key pharmacophore groups for further designing of non-peptide compounds as more powerful inhibitors against BACE-1.