ReverseScreen3D: a structure-based ligand matching method to identify protein targets

Significant Broad-Spectrum Antiviral Activity of Bi121 against Different Variants of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has so far infected 762 million people with over 6.9 million deaths worldwide. Broad-spectrum viral inhibitors that block the initial stages of infection by reducing virus binding and proliferation, thereby reducing disease severities, are still an unmet global medical need. We studied Bi121, which is a standardized polyphenolic-rich compound isolated from Pelargonium sidoides, against recombinant vesicular stomatitis virus (rVSV)-pseudotyped SARS-CoV-2S (mutations in the spike protein) of six different variants of SARS-CoV-2. Bi121 was effective at neutralizing all six rVSV-ΔG-SARS-CoV-2S variants. The antiviral activity of Bi121 was also assessed against SARS-CoV-2 variants (USA WA1/2020, Hongkong/VM20001061/2020, B.1.167.2 (Delta), and Omicron) in Vero cells and HEK-ACE2 cell lines using RT-qPCR and plaque assays. Bi121 showed significant antiviral activity against all the four SARS-CoV-2 variants tested, suggesting a broad-spectrum activity. Bi121 fractions generated using HPLC showed antiviral activity in three fractions out of eight against SARS-CoV-2. The dominant compound identified in all three fractions using LC/MS/MS analysis was Neoilludin B. In silico structural modeling studies with Neoilludin B showed that it has a novel RNA-intercalating activity toward RNA viruses. In silico findings and the antiviral activity of this compound against several SARS-CoV-2 variants support further evaluation as a potential treatment of COVID-19.

In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences

Identification and validation of bioactive small-molecule targets is a significant challenge in drug discovery. In recent years, various in-silico approaches have been proposed to expedite time- and resource-consuming experiments for target detection. Herein, we developed several chemogenomic models for target prediction based on multi-scale information of chemical structures and protein sequences. By combining the information of a compound with multiple protein targets together and putting these compound-target pairs into a well-established model, the scores to indicate whether there are interactions between compounds and targets can be derived, and thus a target prediction task can be completed by sorting the outputted scores. To improve the prediction performance, we constructed several chemogenomic models using multi-scale information of chemical structures and protein sequences, and the ensemble model with the best performance was used as our final model. The model was validated by various strategies and external datasets and the promising target prediction capability of the model, i.e., the fraction of known targets identified in the top-k (1 to 10) list of the potential target candidates suggested by the model, was confirmed. Compared with multiple state-of-art target prediction methods, our model showed equivalent or better predictive ability in terms of the top-k predictions. It is expected that our method can be utilized as a powerful computational tool to narrow down the potential targets for experimental testing.

HIDTI: integration of heterogeneous information to predict drug-target interactions

Identification of drug-target interactions (DTIs) plays a crucial role in drug development. Traditional laboratory-based DTI discovery is generally costly and time-consuming. Therefore, computational approaches have been developed to predict interactions between drug candidates and disease-causing proteins. We designed a novel method, termed heterogeneous information integration for DTI prediction (HIDTI), based on the concept of predicting vectors for all of unknown/unavailable heterogeneous drug- and protein-related information. We applied a residual network in HIDTI to extract features of such heterogeneous information for predicting DTIs, and tested the model using drug-based ten-fold cross-validation to examine the prediction performance for unseen drugs. As a result, HIDTI outperformed existing models using heterogeneous information, and was demonstrating that our method predicted heterogeneous information on unseen data better than other models. In conclusion, our study suggests that HIDTI has the potential to advance the field of drug development by accurately predicting the targets of new drugs.

Artificial Intelligence Algorithms for Discovering New Active Compounds Targeting TRPA1 Pain Receptors

Transient receptor potential ankyrin 1 (TRPA1) is a ligand-gated calcium channel activated by cold temperatures and by a plethora of electrophilic environmental irritants (allicin, acrolein, mustard-oil) and endogenously oxidized lipids (15-deoxy-∆12, 14-prostaglandin J2 and 5, 6-eposyeicosatrienoic acid). These oxidized lipids work as agonists, making TRPA1 a key player in inflammatory and neuropathic pain. TRPA1 antagonists acting as non-central pain blockers are a promising choice for future treatment of pain-related conditions having advantages over current therapeutic choices A large variety of in silico methods have been used in drug design to speed up the development of new active compounds such as molecular docking, quantitative structure-activity relationship models (QSAR), and machine learning classification algorithms. Artificial intelligence methods can significantly improve the drug discovery process and it is an attractive field that can bring together computer scientists and experts in drug development. In our paper, we aimed to develop three machine learning algorithms frequently used in drug discovery research: feedforward neural networks (FFNN), random forests (RF), and support vector machines (SVM), for discovering novel TRPA1 antagonists. All three machine learning methods used the same class of independent variables (multilevel neighborhoods of atoms descriptors) as prediction of activity spectra for substances (PASS) software. The model with the highest accuracy and most optimal performance metrics was the random forest algorithm, showing 99% accuracy and 0.9936 ROC AUC. Thus, our study emphasized that simpler and robust machine learning algorithms such as random forests perform better in correctly classifying TRPA1 antagonists since the dimension of the dependent variables dataset is relatively modest.

KinomeRun: An interactive utility for kinome target screening and interaction fingerprint analysis towards holistic visualization on kinome tree

Kinases are key targets for many of the pathological conditions. Inverse screening of ligands serves as an essential mode to identify potential kinase targets in modern drug discovery research. Hence, we intend to develop KinomeRun, a robust pipeline for inverse screening and kinome tree visualization through the seamless integration of kinome structures, docking and kinome-drug interaction fingerprint analysis. In this pipeline, the hurdle of residue numbering in kinome is also resolved by creating a common index file with the conserved kinase pocket residues for comparative interaction analysis. KinomeRun can be used to screen the ligands of interest docked against multiple kinase structures in parallel around the kinase binding site and also to filter out the targets with unique interaction patterns. This automation is essential for prioritization of kinase targets that show specificity for a given drug and will also serve as a crucial tool kit for holistic approaches in kinase drug discovery. KinomeRun is developed using python and bash programming language and is distributed freely under the GNU GPL licence-3.0 and can be downloaded at https://github.com/inpacdb/KinomeRun. The tutorial videos for installation, target screening and customized filtration are available at https://www.youtube.com/playlist?list=PLuIaEFtMVgQ7v__WigQH9ilGVxrfI1LKs and also be downloaded for offline viewing from the github link.

Synthesis, Characterization, and Biological Evaluation of Novel 7-Oxo-7H-thiazolo[3,2-b]-1,2,4-triazine-2-carboxylic Acid Derivatives

A series of novel 7-oxo-7H-thiazolo[3,2-b]-1,2,4-triazine-2-carboxylic acid derivatives was synthesized in good yields by a multi-step procedure that included the generation of the S-alkylated derivatives from 6-substituted arylmethyl-3-mercapto-1,2,4-triazin-5-ones with ethyl 2-chloroacetoacetate, intramolecular cyclization with microwave irradiation, hydrolysis and amidation. All of the target compounds were fully characterized through ¹H-NMR, ¹³C-NMR and HRMS spectra. The intramolecular cyclization occurred regioselectively at the N2-position of 1,2,4-triazine ring, which was confirmed by compound 3e using single-crystal X-ray diffraction analysis. The antibacterial and antitubercular activities of the target compounds were evaluated. Compared with Ciprofloxacin and Rifampicin, compounds 5d, 5f and 5g containing the terminal amide fragment exhibited broad spectrum antibacterial activity, and carboxylic acid derivatives or its corresponding ethyl esters had less effect on antibacterial properties. The most potent compound 5f also displayed excellent in vitro antitubercular activity against Mycobacterium smegmatis (minimum inhibitory concentration (MIC) = 50 μg/mL) and better growth inhibition activity of leucyl-tRNA synthetase (78.24 ± 4.05% at 15 μg/mL).

Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as 'virtual screening' (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

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.

Steroids-specific target library for steroids target prediction

Steroids exist universally and play critical roles in various biological processes. Identifying potential targets of steroids is of great significance in studying their physiological and biochemical activities, the side effects and for drug repurposing. Herein, aiming at more precise steroids targets prediction, a steroids-specific target library integrating 3325 PDB or homology modeling structures categorized into 196 proteins was built by considering chemical similarity from DrugBank and biological processes from KEGG. The main properties of this library include: (1) It was manually prepared and checked to eliminate mistakes. (2) The library enriched the possible steroids targets and could decrease the false positives of structure-based target screening for steroids. (3) The ranking by protein name instead of PDB ID could make the screening more efficiency and precise. (4) Protein flexibility was taken into account partially by the different active conformations through the structural redundancy of each category of protein, which leads to more accurate prediction. The case studies of glycocholic acid and 24-epibrassinolide proved its powerful predictive accuracy. In summary, our strategy to build the steroids-specific protein library for steroids target prediction is a promising approach and it provides a novel idea for the target prediction of small molecules.

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.

Ranking Enzyme Structures in the PDB by Bound Ligand Similarity to Biological Substrates

There are numerous applications that use the structures of protein-ligand complexes from the PDB, such as 3D pharmacophore identification, virtual screening, and fragment-based drug design. The structures underlying these applications are potentially much more informative if they contain biologically relevant bound ligands, with high similarity to the cognate ligands. We present a study of ligand-enzyme complexes that compares the similarity of bound and cognate ligands, enabling the best matches to be identified. We calculate the molecular similarity scores using a method called PARITY (proportion of atoms residing in identical topology), which can conveniently be combined to give a similarity score for all cognate reactants or products in the reaction. Thus, we generate a rank-ordered list of related PDB structures, according to the biological similarity of the ligands bound in the structures.

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We present PARITY, matching atoms in identical topology to gauge ligand similarity

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Bound-cognate ligand similarity is a useful metric for ranking PDB structures

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Only 26% of enzyme structures in the PDB have bound-cognate ligand similarity ≥0.7

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We provide rank-ordered lists of PDBs with the most biologically relevant ligands

Structure-Based Kinase Profiling To Understand the Polypharmacological Behavior of Therapeutic Molecules

Several drugs elicit their therapeutic efficacy by modulating multiple cellular targets and possess varied polypharmacological actions. The identification of the molecular targets of a potent bioactive molecule is essential in determining its overall polypharmacological profile. Experimental procedures are expensive and time-consuming. Therefore, computational approaches are actively implemented in rational drug discovery. Here, we demonstrate a computational pipeline, based on reverse virtual screening technique using several consensus scoring strategies, and perform structure-based kinase profiling of 12 FDA-approved drugs. This target prediction showed an overall good performance, with an average AU-ROC greater than 0.85 for most drugs, and identified the true targets even at the top 2% cutoff. In contrast, 10 non-kinase binder drugs exhibited lower binding efficiency and appeared in the bottom of ranking list. Subsequently, we validated this pipeline on a potent therapeutic molecule, mahanine, whose polypharmacological profile related to targeting kinases is unknown. Our target-prediction method identified different kinases. Furthermore, we have experimentally validated that mahanine is able to modulate multiple kinases that are involved in cross-talk with different signaling molecules, which thereby exhibits its polypharmacological action. More importantly, in vitro kinase assay exhibited the inhibitory effect of mahanine on two such predicted kinases' (mTOR and VEGFR2) activity, with IC₅₀ values being ∼12 and ∼22 μM, respectively. Next, we generated a comprehensive drug-protein interaction fingerprint that explained the basis of their target selectivity. We observed that it is controlled by variations in kinase conformations followed by significant differences in crucial hydrogen-bond and van der Waals interactions. Such structure-based kinase profiling could provide useful information in revealing the unknown targets of therapeutic molecules from their polypharmacological behavior and would assist in drug discovery.

Evaluation of 6-chloro-N-[3,4-disubstituted-1,3-thiazol-2(3H)-ylidene]-1,3-benzothiazol-2-amine Using Drug Design Concept for Their Targeted Activity Against Colon Cancer Cell Lines HCT-116, HCT15, and HT29

BACKGROUND Colorectal adenocarcinoma is the second leading cause of cancer-related death in the world. The stage of the disease is related to the survival of the patient, and in early phases surgery is the main modality of treatment. The main aim of modern medicinal chemistry is to synthesize small molecules via drug designing, especially by targeting tumor cells. MATERIAL AND METHODS A new series of 19 compounds containing benzothiazole and thiazole were designed. Molecular docking studies were performed on the designed series of molecules. Compounds showing good binding affinity towards the EGFR receptor were selected for synthetic studies. Characterization of the synthesized compounds was done by FTIR, 1HNMR, Mass and C, H, N, analysis. RESULTS The anticancer evaluation of the synthesized compounds was done at NIC, USA at a single dose against colon cancer cell lines HCT 116, HCT15, and HC 29. The active compounds were further evaluated for the 5-dose testing. Compounds were designed by using docking analysis. To ascertain the interaction of EGFR tyrosine kinase binding, energy calculation was used. CONCLUSIONS The results of the present study indicate that the designed compounds show good activity against colon cancer cell lines, which may be further studied to design new potential molecules.

The scoring bias in reverse docking and the score normalization strategy to improve success rate of target fishing

Target fishing often relies on the use of reverse docking to identify potential target proteins of ligands from protein database. The limitation of reverse docking is the accuracy of current scoring funtions used to distinguish true target from non-target proteins. Many contemporary scoring functions are designed for the virtual screening of small molecules without special optimization for reverse docking, which would be easily influenced by the properties of protein pockets, resulting in scoring bias to the proteins with certain properties. This bias would cause lots of false positives in reverse docking, interferring the identification of true targets. In this paper, we have conducted a large-scale reverse docking (5000 molecules to 100 proteins) to study the scoring bias in reverse docking by DOCK, Glide, and AutoDock Vina. And we found that there were actually some frequency hits, namely interference proteins in all three docking procedures. After analyzing the differences of pocket properties between these interference proteins and the others, we speculated that the interference proteins have larger contact area (related to the size and shape of protein pockets) with ligands (for all three docking programs) or higher hydrophobicity (for Glide), which could be the causes of scoring bias. Then we applied the score normalization method to eliminate this scoring bias, which was effective to make docking score more balanced between different proteins in the reverse docking of benchmark dataset. Later, the Astex Diver Set was utilized to validate the effect of score normalization on actual cases of reverse docking, showing that the accuracy of target prediction significantly increased by 21.5% in the reverse docking by Glide after score normalization, though there was no obvious change in the reverse docking by DOCK and AutoDock Vina. Our results demonstrate the effectiveness of score normalization to eliminate the scoring bias and improve the accuracy of target prediction in reverse docking. Moreover, the properties of protein pockets causing scoring bias to certain proteins we found here can provide the theory basis to further optimize the scoring functions of docking programs for future research.

Efficient synthesis of α-fluoromethylhistidine di-hydrochloride and demonstration of its efficacy as a glutathione S-transferase inhibitor

Histidine decarboxylase (HDC) is an enzyme that converts histidine to histamine. Inhibition of HDC has several medical applications, and HDC inhibitors are of considerable interest for the study of histidine metabolism. (S)-α-Fluoromethylhistidine di-hydrochloride (α-FMH) is a potent HDC inhibitor that is commercially available at high cost in small amounts only. Here we report a novel, inexpensive, and efficient method for synthesis of α-FMH using methyl 2-aziridinyl-3-(N-triphenylmethyl-4-imidazolyl) propionate and HF/pyridine, with experimental yield of 57%. To identify novel targets for α-FMH, we developed a three step in silico work-flow for identifying physically plausible protein targets. The work-flow resulted in 21 protein target hits, including several enzymes involved in glutathione metabolism, and notably, two isozymes of the glutathione S-transferase (GST) superfamily, which plays a central role in drug metabolism. In view of this predictive data, the efficacy of α-FMH as a GST inhibitor was investigated in vitro. α-FMH was demonstrated to be an effective inhibitor of GST at micromolar concentration, suggesting that off-target effects of α-FMH may limit physiological drug metabolism and elimination by GST-dependent mechanisms. The present study therefore provides new avenues for obtaining α-FMH and for studying its biochemical effects, with potential implications for drug development.

Building a virtual ligand screening pipeline using free software: a survey

Virtual screening, the search for bioactive compounds via computational methods, provides a wide range of opportunities to speed up drug development and reduce the associated risks and costs. While virtual screening is already a standard practice in pharmaceutical companies, its applications in preclinical academic research still remain under-exploited, in spite of an increasing availability of dedicated free databases and software tools. In this survey, an overview of recent developments in this field is presented, focusing on free software and data repositories for screening as alternatives to their commercial counterparts, and outlining how available resources can be interlinked into a comprehensive virtual screening pipeline using typical academic computing facilities. Finally, to facilitate the set-up of corresponding pipelines, a downloadable software system is provided, using platform virtualization to integrate pre-installed screening tools and scripts for reproducible application across different operating systems.

Connecting proteins with drug-like compounds: Open source drug discovery workflows with BindingDB and KNIME

Today's large, public databases of protein-small molecule interaction data are creating important new opportunities for data mining and integration. At the same time, new graphical user interface-based workflow tools offer facile alternatives to custom scripting for informatics and data analysis. Here, we illustrate how the large protein-ligand database BindingDB may be incorporated into KNIME workflows as a step toward the integration of pharmacological data with broader biomolecular analyses. Thus, we describe a collection of KNIME workflows that access BindingDB data via RESTful webservices and, for more intensive queries, via a local distillation of the full BindingDB dataset. We focus in particular on the KNIME implementation of knowledge-based tools to generate informed hypotheses regarding protein targets of bioactive compounds, based on notions of chemical similarity. A number of variants of this basic approach are tested for seven existing drugs with relatively ill-defined therapeutic targets, leading to replication of some previously confirmed results and discovery of new, high-quality hits. Implications for future development are discussed. Database URL: www.bindingdb.org.

Identification of anti-cancer targets of eco-friendly waste Punica granatum peel by dual reverse virtual screening and binding analysis

BACKGROUND

Punica granatum (family: Lythraceae) is mainly found in Iran, which is considered to be its primary centre of origin. Studies on pomegranate peel have revealed antioxidant, anti-inflammatory, anti- angiogenesis activities, with prevention of premature aging and reducing inflammation. In addition to this it is also useful in treating various diseases like diabetes, maintaining blood pressure and treatment of neoplasms such as prostate and breast cancer.

OBJECTIVES

In this study we identified anti-cancer targets of active compounds like corilagin (tannins), quercetin (flavonoids) and pseudopelletierine (alkaloids) present in pomegranate peel by employing dual reverse screening and binding analysis.

MATERIALS AND METHODS

The potent targets of the pomegranate peel were annotated by the PharmMapper and ReverseScreen 3D, then compared with targets identified from different Bioassay databases (NPACT and HIT's). Docking was then further employed using AutoDock pyrx and validated through discovery studio for studying molecular interactions.

RESULTS

A number of potent anti-cancerous targets were attained from the PharmMapper server according to their fit score and from ReverseScreen 3D server according to decreasing 3D scores.

CONCLUSION

The identified targets now need to be further validated through in vitro and in vivo studies.

Using quantitative systems pharmacology for novel drug discovery

INTRODUCTION

Over the past three decades, the predominant paradigm in drug discovery was designing selective ligands for a specific target to avoid unwanted side effects. However, in the last 5 years, the aim has shifted to take into account the biological network in which they interact. Quantitative and Systems Pharmacology (QSP) is a new paradigm that aims to understand how drugs modulate cellular networks in space and time, in order to predict drug targets and their role in human pathophysiology.

AREAS COVERED

This review discusses existing computational and experimental QSP approaches such as polypharmacology techniques combined with systems biology information and considers the use of new tools and ideas in a wider 'systems-level' context in order to design new drugs with improved efficacy and fewer unwanted off-target effects.

EXPERT OPINION

The use of network biology produces valuable information such as new indications for approved drugs, drug-drug interactions, proteins-drug side effects and pathways-gene associations. However, we are still far from the aim of QSP, both because of the huge effort needed to model precisely biological network models and the limited accuracy that we are able to reach with those. Hence, moving from 'one molecule for one target to give one therapeutic effect' to the 'big systems-based picture' seems obvious moving forward although whether our current tools are sufficient for such a step is still under debate.

Prediction of protein targets of kinetin using in silico and in vitro methods: a case study on spinach seed germination mechanism

Kinetin, a cytokinin which promotes seed germination by inhibiting the action of abscisic acid, is an important molecule known to trigger various molecular mechanisms by interacting with an array of proteins shown from experimental observations in various model organisms. We report here the prediction of most probable protein targets of kinetin from spinach proteome using in silico approaches. Inverse docking and ligand-based similarity search was performed using kinetin as molecule. The former method prioritized six spinach proteins, whereas the latter method provided a list of protein targets retrieved from several model organisms. The most probable protein targets were selected by comparing the rank list of docking and ligand similarity methods. Both of these methods prioritized chitinase as the most probable protein target (ΔG pred = 5.064 kcal/mol) supported by the experimental structure of yeast chitinase 1 complex with kinetin (PDB: 2UY5) and Gliocladium roseum chitinase complex with 3,7-dihydro-1,3,7-trimethyl-1H-purine-2,6-dione (caffeine; 3G6M) which bears a 3D similarity of 0.43 with kinetin. An in vitro study to evaluate the effect of kinetin on spinach seed germination indicated that a very low concentration of kinetin (0.5 mg/l) did not show a significant effect compared to control in inducing seed germination process. Further, higher levels of kinetin (>0.5 mg/l) constituted an antagonist effect on spinach seed germination. It is anticipated that kinetin may have a molecular interaction with prioritized protein targets synthesized during the seed germination process and reduces growth. Thus, it appears that kinetin may not be a suitable hormone for enhancing spinach seed germination in vitro.

An in silico insight into novel therapeutic interaction of LTNF peptide-LT10 and design of structure based peptidomimetics for putative anti-diabetic activity

Lethal Toxin Neutralizing Factor (LTNF) obtained from Opossum serum (Didephis virginiana) is known to exhibit toxin-neutralizing activity for envenomation caused by animals, plants and bacteria. Small synthetic peptide- LT10 (10mer) derived from N-terminal fraction of LTNF exhibit similar anti-lethal and anti-allergic property. In our in silico study, we identified Insulin Degrading Enzyme (IDE) as a potential target of LT10 peptide followed by molecular docking and molecular dynamic (MD) simulation studies which revealed relatively stable interaction of LT10 peptide with IDE. Moreover, their detailed interaction analyses dictate IDE-inhibitory interactions of LT10 peptide. This prediction of LT10 peptide as a novel putative IDE-inhibitor suggests its possible role in anti-diabetic treatment since IDE- inhibitors are known to assist treatment of Diabetes mellitus by enhancing insulin signalling. Furthermore, series of structure based peptidomimetics were designed from LT10 peptide and screened for their inhibitory interactions which ultimately led to a small set of peptidomimetic inhibitors of IDE. These peptidomimetic thus might provide a new class of IDE-inhibitors, those derived from LT10 peptide.

Elucidation of molecular targets of bioactive principles of black cumin relevant to its anti-tumour functionality - An Insilico target fishing approach

Black cumin (Nigella sativa) is a spice having medicinal properties with pungent and bitter odour. It is used since thousands of years to treat various ailments, including cancer mainly in South Asia and Middle Eastern regions. Substantial evidence in multiple research studies emphasizes about the therapeutic importance of bioactive principles of N. sativa in cancer bioassays; however, the exact mechanism of their anti-tumour action is still to be fully comprehended. The current study makes an attempt in this direction by exploiting the advancements in the Insilico reverse screening technology. In this study, three different Insilico Reverse Screening approaches have been employed for identifying the putative molecular targets of the bioactive principles in Black cumin (thymoquinone, alpha-hederin, dithymoquinone and thymohydroquinone) relevant to its anti-tumour functionality. The identified set of putative targets is further compared with the existing set of experimentally validated targets, so as to estimate the performance of insilico platforms. Subsequently, molecular docking simulations studies were performed to elucidate the molecular interactions between the bioactive compounds & their respective identified targets. The molecular interactions of one such target identified i.e. VEGF2 along with thymoquinone depicted one H-bond formed at the catalytic site. The molecular targets identified in this study need further confirmatory tests on cancer bioassays, in order to justify the research findings from Insilico platforms. This study has brought to light the effectiveness of usage of Insilico Reverse Screening protocols to characterise the un-identified target-ome of poly pharmacological bioactive agents in spices.

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

Reverse or inverse docking is proving to be a powerful tool for drug repositioning and drug rescue. It involves docking a small-molecule drug/ligand in the potential binding cavities of a set of clinically relevant macromolecular targets. Detailed analyses of the binding characteristics lead to ranking of the targets according to the tightness of binding. This process can potentially identify novel molecular targets for the drug/ligand which may be relevant for its mechanism of action and/or side effect profile. Another potential application of reverse docking is during the lead discovery and optimization stages of the drug-discovery cycle. This review summarizes the state-of-the-art and future prospects of the reverse docking with particular emphasis on computational molecular design.

Facing the challenges of structure-based target prediction by inverse virtual screening

Computational target prediction for bioactive compounds is a promising field in assessing off-target effects. Structure-based methods not only predict off-targets, but, simultaneously, binding modes, which are essential for understanding the mode of action and rationally designing selective compounds. Here, we highlight the current open challenges of computational target prediction methods based on protein structures and show why inverse screening rather than sequential pairwise protein-ligand docking methods are needed. A new inverse screening method based on triangle descriptors is introduced: iRAISE (inverse Rapid Index-based Screening Engine). A Scoring Cascade considering the reference ligand as well as the ligand and active site coverage is applied to overcome interprotein scoring noise of common protein-ligand scoring functions. Furthermore, a statistical evaluation of a score cutoff for each individual protein pocket is used. The ranking and binding mode prediction capabilities are evaluated on different datasets and compared to inverse docking and pharmacophore-based methods. On the Astex Diverse Set, iRAISE ranks more than 35% of the targets to the first position and predicts more than 80% of the binding modes with a root-mean-square deviation (RMSD) accuracy of <2.0 Å. With a median computing time of 5 s per protein, large amounts of protein structures can be screened rapidly. On a test set with 7915 protein structures and 117 query ligands, iRAISE predicts the first true positive in a ranked list among the top eight ranks (median), i.e., among 0.28% of the targets.

Purification and identification of metabolites produced by Bacillus cereus and B. subtilis active against Meloidogyne exigua, and their in silico interaction with a putative phosphoribosyltransferase from M. incognita

To contribute to the development of products to control Meloidogyne exigua, the bacteria Bacillus cereus and B. subtilis were cultivated in liquid medium to produce metabolites active against this plant-parasitic nematode. Fractionation of the crude dichloromethane extracts obtained from the cultures afforded uracil, 9H-purine and dihydrouracil. All compounds were active against M. exigua, the latter being the most efficient. This substance presented a LC50 of 204 µg/mL against the nematode, while a LC50 of 260 µg/mL was observed for the commercial nematicide carbofuran. A search for protein-ligand complexes in which the ligands were structurally similar to dihydrouracil resulted in the selection of phosphoribosyltransferases, the sequences of which were used in an in silico search in the genome of M. incognita for a similar sequence of amino acids. The resulting sequence was modelled and dihydrouracil and 9H-purine were inserted in the active site of this putative phosphoribosyltransferase resulting in protein-ligand complexes that underwent molecular dynamics simulations. Calculation of the binding free-energies of these complexes revealed that the dissociation constant of dihydrouracil and 9H-purine to this protein is around 8.3 x 10-7 and 1.6 x 10-6 M, respectively. Consequently, these substances and the putative phosphoribosyltransferase are promising for the development of new products to control M. exigua.

Implementation of pseudoreceptor-based pharmacophore queries in the prediction of probable protein targets: explorations in the protein structural profile of Zea mays

Molecular docking plays an important role in the protein target identification by prioritizing probable druggable proteins using docking energies. Due to the limitations of docking scoring schemes, there arises a need for structure-based approaches to acquire confidence in theoretical binding affinities. In this direction, we present here a receptor (protein)-based approach to predict probable protein targets using a small molecule of interest. We adopted a reverse approach wherein the ligand pharmacophore features were used to decipher interaction complementary amino acids of protein cavities (a pseudoreceptor) and expressed as queries to match the cavities or binding sites of the protein dataset. These pseudoreceptor-based pharmacophore queries were used to estimate total probabilities of each protein cavity thereby representing the ligand binding efficiency of the protein. We applied this approach to predict 3 experimental protein targets among 28 Zea mays structural data using 3 co-crystallized ligands as inputs and compared its effectiveness using conventional docking results. We suggest that the combination of total probabilities and docking energies increases the confidence in prioritizing probable protein targets using docking methods. These prediction hypotheses were further supported by DrugScoreX (DSX) pair potential calculations and molecular dynamic simulations.

Predictive in silico off-target profiling in drug discovery

Drug action can be rationalized as interaction of a molecule with proteins in a regulatory network of targets from a specific biological system. Both drug and side effects are often governed by interaction of the drug molecule with many, often unrelated, targets. Accordingly, arrays of protein–ligand interaction data from numerous in vitro profiling assays today provide growing evidence of polypharmacological drug interactions, even for marketed drugs. In vitro off-target profiling has therefore become an important tool in early drug discovery to learn about potential off-target liabilities, which are sometimes beneficial, but more often safety relevant. The rapidly developing field of in silico profiling approaches is complementing in vitro profiling. These approaches capitalize from large amounts of biochemical data from multiple sources to be exploited for optimizing undesirable side effects in pharmaceutical research. Therefore, current in silico profiling models are nowadays perceived as valuable tools in drug discovery, and promise a platform to support optimally informed decisions.

Identification of proapoptopic, anti-inflammatory, anti- proliferative, anti-invasive and anti-angiogenic targets of essential oils in cardamom by dual reverse virtual screening and binding pose analysis

BACKGROUND

Cardamom (Elettaria cardamom), also known as "Queen of Spices", has been traditionally used as a culinary ingredient due to its pleasant aroma and taste. In addition to this role, studies on cardamom have demonstrated cancer chemopreventive potential in in vitro and in vivo systems. Nevertheless, the precise poly-pharmacological nature of naturally occurring chemo-preventive compounds in cardamom has still not been fully demystified.

METHODS

In this study, an effort has been made to identify the proapoptopic, anti-inflammatory, anti-proliferative, anti-invasive and anti-angiogenic targets of Cardamom's bioactive principles (eucalyptol, alpha-pinene, beta-pinene, d-limonene and geraniol) by employing a dual reverse virtual screening protocol. Experimentally proven target information of the bioactive principles was annotated from bioassay databases and compared with the virtually screened set of targets to evaluate the reliability of the computational identification. To study the molecular interaction pattern of the anti-tumor action, molecular docking simulation was performed with Auto Dock Pyrx. Interaction studies of binding pose of eucalyptol with Caspase 3 were conducted to obtain an insight into the interacting amino acids and their inter-molecular bondings.

RESULTS

A prioritized list of target proteins associated with multiple forms of cancer and ranked by their Fit Score (Pharm Mapper) and descending 3D score (Reverse Screen 3D) were obtained from the two independent inverse screening platforms. Molecular docking studies exploring the bioactive principle targeted action revealed that H- bonds and electrostatic interactions forms the chief contributing factor in inter-molecular interactions associated with anti-tumor activity. Eucalyptol binds to the Caspase 3 with a specific framework that is well-suited for nucleophilic attacks by polar residues inside the Caspase 3 catalytic site.

CONCLUSION

This study revealed vital information about the poly-pharmacological anti-tumor mode-of-action of essential oils in cardamom. In addition, a probabilistic set of anti-tumor targets for cardamom was generated, which can be further confirmed by in vivo and in vitro experiments.

One hundred thousand mouse clicks down the road: selected online resources supporting drug discovery collected over a decade

Online resources enabling and supporting drug discovery have blossomed during the past ten years. However, drug hunters commonly find themselves overwhelmed by the proliferation of these computer-based resources. Ten years ago, we, the authors of this review, felt that a comprehensive list of in silico resources relating to drug discovery was needed. Especially because the internet provides a wealth of inspiring tools that, if fully exploited, could greatly assist the process. We present here a compilation of online tools and databases collected over the past decade. The tools were essentially found through literature and internet searches and, currently, our list contains over 1500 URLs. We also briefly highlight some recently reported services and comment about ongoing and future efforts in the field.

Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction

Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.

Established and emerging trends in computational drug discovery in the structural genomics era

Bioinformatics and chemoinformatics approaches contribute to hit discovery, hit-to-lead optimization, safety profiling, and target identification and enhance our overall understanding of the health and disease states. A vast repertoire of computational methods has been reported and increasingly combined in order to address more and more challenging targets or complex molecular mechanisms in the context of large-scale integration of structure and bioactivity data produced by private and public drug research. This review explores some key computational methods directly linked to drug discovery and chemical biology with a special emphasis on compound collection preparation, virtual screening, protein docking, and systems pharmacology. A list of generally freely available software packages and online resources is provided, and examples of successful applications are briefly commented upon.

Identifying and characterizing promiscuous targets: implications for virtual screening

INTRODUCTION

Ligand-based shape matching approaches have become established as important and popular virtual screening (VS) techniques. However, despite their relative success, the question of how to best choose the initial query compounds and their conformations remains largely unsolved. This issue gains importance when dealing with promiscuous targets, that is, proteins that bind multiple ligand scaffold families in one or more binding site. Conventional shape matching VS approaches assume that there is only one binding mode for a given protein target. This may be true for some targets, but it is certainly not true in all cases. Several recent studies have shown that some protein targets bind to different ligands in different ways.

AREAS COVERED

The authors discuss the concept of promiscuity in the context of virtual drug screening, and present and analyze several examples of promiscuous targets. The article also reports on the impact of the query conformation on the performance of shape-based VS and the potential to improve VS performance by using consensus shape clustering techniques.

EXPERT OPINION

The notion of polypharmacology is becoming highly relevant in drug discovery. Understanding and exploiting promiscuity present challenges and opportunities for drug discovery endeavors. The examples of promiscuity presented here suggest that promiscuous targets and ligands are much more common than previously assumed, and this should be taken into account in practical VS protocols. Although some progress has been made, there is a need to develop more sophisticated computational techniques and protocols that can identify and characterize promiscuous targets on a genomic scale.

From in silico target prediction to multi-target drug design: current databases, methods and applications

Given the tremendous growth of bioactivity databases, the use of computational tools to predict protein targets of small molecules has been gaining importance in recent years. Applications span a wide range, from the 'designed polypharmacology' of compounds to mode-of-action analysis. In this review, we firstly survey databases that can be used for ligand-based target prediction and which have grown tremendously in size in the past. We furthermore outline methods for target prediction that exist, both based on the knowledge of bioactivities from the ligand side and methods that can be applied in situations when a protein structure is known. Applications of successful in silico target identification attempts are discussed in detail, which were based partly or in whole on computational target predictions in the first instance. This includes the authors' own experience using target prediction tools, in this case considering phenotypic antibacterial screens and the analysis of high-throughput screening data. Finally, we will conclude with the prospective application of databases to not only predict, retrospectively, the protein targets of a small molecule, but also how to design ligands with desired polypharmacology in a prospective manner.