Reverse docking: a powerful tool for drug repositioning and drug rescue

Molecular Docking: Shifting Paradigms in Drug Discovery

Molecular docking is an established in silico structure-based method widely used in drug discovery. Docking enables the identification of novel compounds of therapeutic interest, predicting ligand-target interactions at a molecular level, or delineating structure-activity relationships (SAR), without knowing a priori the chemical structure of other target modulators. Although it was originally developed to help understanding the mechanisms of molecular recognition between small and large molecules, uses and applications of docking in drug discovery have heavily changed over the last years. In this review, we describe how molecular docking was firstly applied to assist in drug discovery tasks. Then, we illustrate newer and emergent uses and applications of docking, including prediction of adverse effects, polypharmacology, drug repurposing, and target fishing and profiling, discussing also future applications and further potential of this technique when combined with emergent techniques, such as artificial intelligence.

Multi-targeting protein-protein interaction inhibitors: Evolution of macrocyclic ligands with embedded carbohydrates (MECs) to improve selectivity

Compounds targeting multiple proteins can have synergistic effects and are therefore of interest in medicinal chemistry. At the same time, inhibiting protein-protein interactions (PPI) is increasingly desired in the treatment of disorders or diseases. The development of non-peptidomimetic inhibitors is still a challenge. Herein we investigate macrocyclic scaffolds with one or two embedded carbohydrates (MECs) that present amino acid side chains, or related isosteres, as pharmacophoric groups. Firstly, retroscreening of the previously reported eannaphane-40 (E40, 40), a MEC presenting two pharmacophoric groups, against a set of 55 receptor-subtypes led to a finding of sub-micromolar inhibitory activity for E40 against three serotonergic isoforms (5HT_1A/_2A/2B) as well as the Na⁺ channel and the NK-2 receptor. We synthesised MECs with an additional pharmacophoric group compared to E40, with a view to identifying compounds where the selectivity profile was altered among the protein hits from the retroscreening. MECs were produced based on scaffolds with two monosaccharide residues, leading to the incorporation of a third pharmacophoric group. Later, homology models were prepared for four proteins (5HT_1A, 5HT_2A, NK₂ and site-2 of the sodium channel) whose 3D structure is unknown. Inverse docking of the synthesised compounds led to the selection of a new MEC (MEC-B) for protein binding assays. MEC-B was found to have its selectivity profile modulated, in line with docking prediction, compared to E40. MEC-B is dual inhibitor of both 5-HT_1A and the sodium channel with improved selectivity for these proteins compared to 5-HT_2A/2B/2C, 5-HT transporter and NK₂ receptor. Thus, a new multitargeting compound, with an improved selectivity profile was identified, based on a MEC peptidomimetic scaffold.

Bioassay Directed Isolation, Biological Evaluation and in Silico Studies of New Isolates from Pteris cretica L

Members of genus Pteris have their established role in the traditional herbal medicine system. In the pursuit to identify its biologically active constituents, the specie Pteris cretica L. (P. cretica) was selected for the bioassay-guided isolation. Two new maleates (F9 and CB18) were identified from the chloroform extract and the structures of the isolates were elucidated through their spectroscopic data. The putative targets, that potentially interact with both of these isolates, were identified through reverse docking by using in silico tools PharmMapper and ReverseScreen3D. On the basis of reverse docking results, both isolates were screened for their antioxidant, acetylcholinesterase (AChE) inhibition, α-glucosidase (GluE) inhibition and antibacterial activities. Both isolates depicted moderate potential for the selected activities. Furthermore, docking studies of both isolates were also studied to investigate the binding mode with respective targets followed by molecular dynamics simulations and binding free energies. Thereby, the current study embodies the poly-pharmacological potential of P. cretica.

In Silico Investigation of the Anti-Tumor Mechanisms of Epigallocatechin-3-Gallate

The EGCG, an important component of polyphenol in green tea, is well known due to its numerous health benefits. We employed the reverse docking method for the identification of the putative targets of EGCG in the anti-tumor target protein database and these targets were further uploaded to public databases in order to understand the underlying pharmacological mechanisms and search for novel EGCG-associated targets. Similarly, the pharmacological linkage between tumor-related proteins and EGCG was manually constructed in order to provide greater insight into the molecular mechanisms through a systematic integration with applicable bioinformatics. The results indicated that the anti-tumor mechanisms of EGCG may involve 12 signaling transduction pathways and 33 vital target proteins. Moreover, we also discovered four novel putative target proteins of EGCG, including IKBKB, KRAS, WEE1 and NTRK1, which are significantly related to tumorigenesis. In conclusion, this work may provide a useful perspective that will improve our understanding of the pharmacological mechanism of EGCG and identify novel potential therapeutic targets.

Integration of target discovery, drug discovery and drug delivery: A review on computational strategies

Most of the computational tools involved in drug discovery developed during the 1980s were largely based on computational chemistry, quantitative structure-activity relationship (QSAR) and cheminformatics. Subsequently, the advent of genomics in the 2000s gave rise to a huge number of databases and computational tools developed to analyze large quantities of data, through bioinformatics, to obtain valuable information about the genomic regulation of different organisms. Target identification and validation is a long process during which evidence for and against a target is accumulated in the pursuit of developing new drugs. Finally, the drug delivery system appears as a novel approach to improve drug targeting and releasing into the cells, leading to new opportunities to improve drug efficiency and avoid potential secondary effects. In each area: target discovery, drug discovery and drug delivery, different computational strategies are being developed to accelerate the process of selection and discovery of new tools to be applied to different scientific fields. Research on these three topics is growing rapidly, but still requires a global view of this landscape to detect the most challenging bottleneck and how computational tools could be integrated in each topic. This review describes the current state of the art in computational strategies for target discovery, drug discovery and drug delivery and how these fields could be integrated. Finally, we will discuss about the current needs in these fields and how the continuous development of databases and computational tools will impact on the improvement of those areas. This article is categorized under: Therapeutic Approaches and Drug Discovery > Emerging Technologies Therapeutic Approaches and Drug Discovery > Nanomedicine for Infectious Disease Nanotechnology Approaches to Biology > Nanoscale Systems in Biology.

Synthesis and biological evaluation of 7-substituted cycloberberine derivatives as potent antibacterial agents against MRSA

A series of new 7-substituted cycloberberine (CBBR) derivatives were synthesized and evaluated for their antibacterial activities against Gram-positive pathogens, taking CBBR as the lead. The SAR revealed that the introduction of a substituent at the C7 position resulted in a potency against both the reference Gram-positive bacteria and MDR clinical isolates, much higher than that of CBBR. Compound 1f with a 7-phenyl group exhibited higher activities against MRSA and VRE than that of vancomycin, with MIC values of 1-8 μg/mL. Its rapid bactericidal action against MRSA was further confirmed in time-kill study. The preliminary mechanism study indicated that 1f might target bacterial DNA Topo IV ParE subunit, indicating a mode of action distinct from the currently used antibacterial drugs such as quinolones. These results supplemented and enriched the SAR of its kind, and provided powerful information for developing these compounds into a novel class of antibacterial candidates against MRSA.

N-Alkyl-1,5-dideoxy-1,5-imino-l-fucitols as fucosidase inhibitors: Synthesis, molecular modelling and activity against cancer cell lines

1,5-Dideoxy-1,5-imino-l-fucitol (1-deoxyfuconojirimycin, DFJ) is an iminosugar that inhibits fucosidases. Herein, N-alkyl DFJs have been synthesised and tested against the α-fucosidases of T. maritima (bacterial origin) and B. taurus (bovine origin). The N-alkyl derivatives were inactive against the bacterial fucosidase, while inhibiting the bovine enzyme. Docking of inhibitors to homology models, generated for the bovine and human fucosidases, was carried out. N-Decyl-DFJ was toxic to cancer cell lines and was more potent than the other N-alkyl DFJs studied.

Screening Strategies and Methods for Better Off-Target Liability Prediction and Identification of Small-Molecule Pharmaceuticals

Pharmaceutical discovery and development is a long and expensive process that, unfortunately, still results in a low success rate, with drug safety continuing to be a major impedance. Improved safety screening strategies and methods are needed to more effectively fill this critical gap. Recent advances in informatics are now making it possible to manage bigger data sets and integrate multiple sources of screening data in a manner that can potentially improve the selection of higher-quality drug candidates. Integrated screening paradigms have become the norm in Pharma, both in discovery screening and in the identification of off-target toxicity mechanisms during later-stage development. Furthermore, advances in computational methods are making in silico screens more relevant and suggest that they may represent a feasible option for augmenting the current screening paradigm. This paper outlines several fundamental methods of the current drug screening processes across Pharma and emerging techniques/technologies that promise to improve molecule selection. In addition, the authors discuss integrated screening strategies and provide examples of advanced screening paradigms.

Reverse Screening Methods to Search for the Protein Targets of Chemopreventive Compounds

This article is a systematic review of reverse screening methods used to search for the protein targets of chemopreventive compounds or drugs. Typical chemopreventive compounds include components of traditional Chinese medicine, natural compounds and Food and Drug Administration (FDA)-approved drugs. Such compounds are somewhat selective but are predisposed to bind multiple protein targets distributed throughout diverse signaling pathways in human cells. In contrast to conventional virtual screening, which identifies the ligands of a targeted protein from a compound database, reverse screening is used to identify the potential targets or unintended targets of a given compound from a large number of receptors by examining their known ligands or crystal structures. This method, also known as in silico or computational target fishing, is highly valuable for discovering the target receptors of query molecules from terrestrial or marine natural products, exploring the molecular mechanisms of chemopreventive compounds, finding alternative indications of existing drugs by drug repositioning, and detecting adverse drug reactions and drug toxicity. Reverse screening can be divided into three major groups: shape screening, pharmacophore screening and reverse docking. Several large software packages, such as Schrödinger and Discovery Studio; typical software/network services such as ChemMapper, PharmMapper, idTarget, and INVDOCK; and practical databases of known target ligands and receptor crystal structures, such as ChEMBL, BindingDB, and the Protein Data Bank (PDB), are available for use in these computational methods. Different programs, online services and databases have different applications and constraints. Here, we conducted a systematic analysis and multilevel classification of the computational programs, online services and compound libraries available for shape screening, pharmacophore screening and reverse docking to enable non-specialist users to quickly learn and grasp the types of calculations used in protein target fishing. In addition, we review the main features of these methods, programs and databases and provide a variety of examples illustrating the application of one or a combination of reverse screening methods for accurate target prediction.

Inverse docking based screening and identification of protein targets for Cassiarin alkaloids against Plasmodium falciparum

Various reports have shown Cassiarin alkaloids, selective in vitro activities against various strains of Plasmodium falciparum with low cytotoxicity, which indicates their possible candidature as antimalarial drug. However, poor recognition of their protein targets and molecular binding behaviour, certainly limits their exploration as antimalarial drug candidature. To address this, we utilises inverse screening, based on three different docking methodologies in order to find their most putative protein targets. In our study, we screened 1047 protein structures from protein data bank, which belongs to 147 different proteins. Our investigation identified 16 protein targets for Cassiarins. In few cases of identified protein targets, the binding site was poorly studied, which encouraged us to perform comparative sequence and structural studies with their homologous proteins, like as in case of Kelch motif associated protein, Armadillo repeats only protein and Methionine aminopeptidase 1b. In our study, we also found Tryptophanyl-tRNA synthetase and 1-Deoxy-D-Xylose-5-phosphate reductoisomerase proteins are the most common targets for Cassiarins.

In silico Tools for Target Identification and Drug Molecular Docking in Leishmania

Neglected tropical diseases represent a significant health burden in large parts of the world. Drug discovery is currently a key bottleneck in the pipeline of these diseases. In this chapter, the in silico approaches used for the processes involved in drug discovery, identification and validation of druggable Leishmania targets, and design and optimisation of new anti-leishmanial drugs are discussed. We also provide a general view of the different computational tools that can be employed in pursuit of this aim, along with the most interesting cases found in the literature.

Application of reverse docking for target prediction of marine compounds with anti-tumor activity

A large number of structures of anti-cancer drug targets have been solved and deposited to the protein data bank already. Identification of the targets for marine compounds with anti-tumor activity presents a challenge for marine natural products scientists. In this study, fast and efficient computational reverse docking was applied to predict the probable targeting proteins of the marine compounds with anti-tumor activity. Crystal structures of the proteins involved in tumor genesis, growth and metastasis were collected from PDB to construct the anti-tumor protein database (APD) for reverse docking. Two non-commercial docking programs, AutoDock Vina and LeDock, were used to perform the docking. Our results suggest that reverse docking is efficient for target fishing of compounds with known anti-tumor activities. In addition, the results show that performance of reverse docking using LeDock is superior to that using AutoDock Vina. Overall, reverse docking is a fast and efficient computational method to identify the probable target of the compounds with anti-tumor activities, and it can be complementary to the biological testing methods.

Design and synthesis of a novel inhibitor of T. Viride chitinase through an in silico target fishing protocol

In the last ten years, we identified and developed a new therapeutic class of antifungal agents, the macrocyclic amidinoureas. These compounds are active against several Candida species, including clinical isolates resistant to currently available antifungal drugs. The mode of action of these molecules is still unknown. In this work, we developed an in-silico target fishing procedure to identify a possible target for this class of compounds based on shape similarity, inverse docking procedure and consensus score rank-by-rank. Chitinase enzyme emerged as possible target. To confirm this hypothesis a novel macrocyclic derivative has been produced, specifically designed to increase the inhibition of the chitinase. Biological evaluation highlights a stronger enzymatic inhibition for the new derivative, while its antifungal activity drops probably because of pharmacokinetic issues. Collectively, our data suggest that chitinase represent at least one of the main target of macrocyclic amidinoureas.

Using reverse docking to identify potential targets for ginsenosides

Background

Ginsenosides are the main ingredients of ginseng, which, in traditional Eastern medicine, has been claimed to have therapeutic values for many diseases. In order to verify the effects of ginseng that have been empirically observed, we utilized the reverse docking method to screen for target proteins that are linked to specific diseases.

Methods

We constructed a target protein database including 1,078 proteins associated with various kinds of diseases, based on the Potential Drug Target Database, with an added list of kinase proteins. We screened 26 kinds of ginsenosides of this target protein database using docking.

Results

We found four potential target proteins for ginsenosides, based on docking scores. Implications of these “hit” targets are discussed. From this screening, we also found four targets linked to possible side effects and toxicities, based on docking scores.

Conclusion

Our method and results can be helpful for finding new targets and developing new drugs from natural products.

A computational study on role of 6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol in the regulation of blood glucose level

6-(hydroxymethyl)-3-[3,4,5-trihydroxy-6-[(3,4,5-trihydroxyoxan-2-yl)oxymethyl]oxan-2-yl]oxyoxane-2,4,5-triol (SID 242078875) was isolated from the fruits of Syzygium densiflorum Wall. ex Wight & Arn (Myrtaceae), which has been traditionally used in the treatment of diabetes by the tribes of The Nilgiris, Tamil Nadu, India. In this study, reverse pharmacophore mapping approach and text-based database search identified the dipeptidyl peptidase-IV, protein-tyrosine phosphatase 1B, phosphoenolpyruvate carboxykinase, glycogen synthase kinase-3β and glucokinase as potential targets of SID 242078875 in diabetes management. Further, molecular docking was performed to predict the binding pose of SID 242078875 in the active site region of the target protein. In addition, dynamic behaviour and stability of protein-ligand complexes were observed for a period of 50 ns through molecular dynamics simulation.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

Identification of a Potential Target of Capsaicin by Computational Target Fishing

Capsaicin, the component responsible for the pungency of chili peppers, shows beneficial effects in many diseases, although the underlying mechanisms remain unclear. In the present study, the potential targets of capsaicin were predicted using PharmMapper and confirmed via chemical-protein interactome (CPI) and molecular docking. Carbonic anhydrase 2 was identified as the main disease-related target, with the pharmacophore model matching well with the molecular features of capsaicin. The relation was confirmed by CPI and molecular docking and supported by previous research showing that capsaicin is a potent inhibitor of carbonic anhydrase isoenzymes. The present study provides a basis for understanding the mechanisms of action of capsaicin or those of other natural compounds.

Hit Recycling: Discovery of a Potent Carbonic Anhydrase Inhibitor by in Silico Target Fishing

In silico target fishing is an emerging tool in drug discovery, which is mostly used for primary target or off-target prediction and drug repositioning. In this work, we developed an in silico target fishing protocol to identify the primary target of GV2-20, a false-positive hit highlighted in a cell-based screen for 14-3-3 modulators. Although GV2-20 does not bind to 14-3-3 proteins, it showed remarkable antiproliferative effects in CML cells, thus raising interest toward the identification of its primary target. Six potential targets of GV2-20 were prioritized in silico and tested in vitro. Our results show that the molecule is a potent inhibitor of carbonic anhydrase 2 (CA2), thus confirming the predictive capability of our protocol. Most notably, GV2-20 experienced a remarkable selectivity for CA2, CA7, CA9, and CA12, and its scaffold was never explored before as a chemotype for CA inhibition, thus becoming an interesting lead candidate for further development.

In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

Background

Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies.

Methods

In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targets was applied to predict the related biological activities, signal pathways and processes networks of the compound by using MetaCore platform. After that, some most relevant regulating networks were considered, which included the nodes and relevant pathways of dioscin.

Results

71 potential targets of dioscin from humans, 7 from rats and 8 from mice were screened, and the prediction results showed that the most likely targets of dioscin were cyclin A2, calmodulin, hemoglobin subunit beta, DNA topoisomerase I, DNA polymerase lambda, nitric oxide synthase and UDP-N-acetylhexosamine pyrophosphorylase, etc. Many diseases including experimental autoimmune encephalomyelitis of human, temporal lobe epilepsy of rat and ankylosing spondylitis of mouse, may be inhibited by dioscin through regulating immune response alternative complement pathway, G-protein signaling RhoB regulation pathway and immune response antiviral actions of interferons, etc. The most relevant networks (5 from human, 3 from rat and 5 from mouse) indicated that dioscin may be a TOP1 inhibitor, which can treat cancer though the cell cycle– transition and termination of DNA replication pathway. Dioscin can down regulate EGFR and EGF to inhibit cancer, and also has anti-inflammation activity by regulating JNK signaling pathway.

Conclusions

The predictions of the possible targets, biological activities, signal pathways and relevant regulating networks of dioscin provide valuable information to guide further investigation of dioscin on pharmacodynamics and molecular mechanisms, which also suggests a practical and effective method for studies on the mechanism of other chemicals.

Maximizing computational tools for successful drug discovery

Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.

Docking to multiple pockets or ligand fields for screening, activity prediction and scaffold hopping

BACKGROUND

Two recent technological advances dramatically reducing the rate of false-negatives in activity prediction by docking flexible 3D models of compounds include multi-conformational docking (mPockDock) and the docking of candidates to atomic property fields derived by co-crystallized ligands (mApfDock).

RESULTS

The mApfDock and mPockDock provide the AUC of 90.4 and 83.8%, respectively. The mApfDock gave better performance when compounds required large induced-fit pocket changes unseen in crystallography, whereas the mPockDock is superior when the co-crystallized ligands do not represent sufficient chemical and binding location diversity.

CONCLUSION

Both approaches proved to be efficient for scaffold hopping; they are complementary when the coverage of the co-crystallized complexes is poor but become convergent when the complexes are diverse enough.