idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Dual-channel hypergraph convolutional network for predicting herb-disease associations

Herbs applicability in disease treatment has been verified through experiences over thousands of years. The understanding of herb-disease associations (HDAs) is yet far from complete due to the complicated mechanism inherent in multi-target and multi-component (MTMC) botanical therapeutics. Most of the existing prediction models fail to incorporate the MTMC mechanism. To overcome this problem, we propose a novel dual-channel hypergraph convolutional network, namely HGHDA, for HDA prediction. Technically, HGHDA first adopts an autoencoder to project components and target protein onto a low-dimensional latent space so as to obtain their embeddings by preserving similarity characteristics in their original feature spaces. To model the high-order relations between herbs and their components, we design a channel in HGHDA to encode a hypergraph that describes the high-order patterns of herb-component relations via hypergraph convolution. The other channel in HGHDA is also established in the same way to model the high-order relations between diseases and target proteins. The embeddings of drugs and diseases are then aggregated through our dual-channel network to obtain the prediction results with a scoring function. To evaluate the performance of HGHDA, a series of extensive experiments have been conducted on two benchmark datasets, and the results demonstrate the superiority of HGHDA over the state-of-the-art algorithms proposed for HDA prediction. Besides, our case study on Chuan Xiong and Astragalus membranaceus is a strong indicator to verify the effectiveness of HGHDA, as seven and eight out of the top 10 diseases predicted by HGHDA for Chuan-Xiong and Astragalus-membranaceus, respectively, have been reported in literature.

Metabolomic profile, anti-trypanosomal potential and molecular docking studies of Thunbergia grandifolia

Trypanosomiasis is a protozoan disease transmitted via Trypanosoma brucei. This study aimed to examine the metabolic profile and anti-trypanosomal effect of methanol extract of Thunbergia grandifolia leaves. The liquid chromatography-high resolution electrospray ionisation mass spectrometry (LC-HRESIMS) revealed the identification of fifteen compounds of iridoid, flavonoid, lignan, phenolic acid, and alkaloid classes. The extract displayed a promising inhibitory activity against T. brucei TC 221 with MIC value of 1.90 μg/mL within 72 h. A subsequent in silico analysis of the dereplicated compounds (i.e. inverse docking, molecular dynamic simulation, and absolute binding free energy) suggested both rhodesain and farnesyl diphosphate synthase as probable targets for two compounds among those dereplicated ones in the plant extract (i.e. diphyllin and avacennone B). The absorption, distribution, metabolism, excretion, and toxicity (ADMET) profiling of diphyllin and avacennone were calculated accordingly, where both compounds showed acceptable drug-like properties. This study highlighted the antiparasitic potential of T. grandifolia leaves.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

ReverseDock: a web server for blind docking of a single ligand to multiple protein targets using AutoDock Vina

Several platforms exist to perform molecular docking to computationally predict binders to a specific protein target from a library of ligands. The reverse, that is, docking a single ligand to various protein targets, can currently be done by very few web servers, which limits the search to a small set of pre-selected human proteins. However, the possibility to in silico predict which targets a compound identified in a high-throughput drug screen bind would help optimize and reduce the costs of the experimental workflow needed to reveal the molecular mechanism of action of a ligand. Here, we present ReverseDock, a blind docking web server based on AutoDock Vina specifically designed to allow users with no computational expertise to dock a ligand to 100 protein structures of their choice. ReverseDock increases the number and type of proteins a ligand can be docked to, making the task of in silico docking of a ligand to entire families of proteins straightforward. We envision ReverseDock will support researchers by providing the possibility to apply inverse docking computations using web browser. ReverseDock is available at: https://reversedock.biologie.uni-freiburg.de/.

Comprehensive survey of target prediction web servers for Traditional Chinese Medicine

Traditional Chinese medicine (TCM) is characterized by multi-components, multiple targets, and complex mechanisms of action and therefore has significant advantages in treating diseases. However, the clinical application of TCM prescriptions is limited due to the difficulty in elucidating the effective substances and the lack of current scientific evidence on the mechanisms of action. In recent years, the development of network pharmacology based on drug systems research has provided a new approach for understanding the complex systems represented by TCM. The determination of drug targets is the core of TCM network pharmacology research. Over the past years, many web tools for drug targets with various features have been developed to facilitate target prediction, significantly promoting drug discovery. Therefore, this review introduces the widely used web tools for compound-target interaction prediction databases and web resources in TCM pharmacology research, and it compares and analyzes each web tool based on their basic properties, including the underlying theory, algorithms, datasets, and search results. Finally, we present the remaining challenges for the promising future of compound-target interaction prediction in TCM pharmacology research. This work may guide researchers in choosing web tools for target prediction and may also help develop more TCM tools based on these existing resources.

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.

Blackberry-Loaded AgNPs Attenuate Hepatic Ischemia/Reperfusion Injury via PI3K/Akt/mTOR Pathway

Liver ischemia-reperfusion injury (IRI) is a pathophysiological insult that often occurs during liver surgery. Blackberry leaves are known for their anti-inflammatory and antioxidant activities. Aims: To achieve site-specific delivery of blackberry leaves extract (BBE) loaded AgNPs to the hepatocyte in IRI and to verify possible molecular mechanisms. Methods: IRI was induced in male Wister rats. Liver injury, hepatic histology, oxidative stress markers, hepatic expression of apoptosis-related proteins were evaluated. Non-targeted metabolomics for chemical characterization of blackberry leaves extract was performed. Key findings: Pre-treatment with BBE protected against the deterioration caused by I/R, depicted by a significant improvement of liver functions and structure, as well as reduction of oxidative stress with a concomitant increase in antioxidants. Additionally, BBE promoted phosphorylation of antiapoptotic proteins; PI3K, Akt and mTOR, while apoptotic proteins; Bax, Casp-9 and cleaved Casp-3 expressions were decreased. LC-HRMS-based metabolomics identified a range of metabolites, mainly flavonoids and anthocyanins. Upon comprehensive virtual screening and molecular dynamics simulation, the major annotated anthocyanins, cyanidin and pelargonidin glucosides, were suggested to act as PLA2 inhibitors. Significance: BBE can ameliorate hepatic IRI augmented by BBE-AgNPs nano-formulation via suppressing, oxidative stress and apoptosis as well as stimulation of PI3K/Akt/mTOR signaling pathway.

Polyphenolic Profile of Herniaria hemistemon Aerial Parts Extract and Assessment of Its Anti-Cryptosporidiosis in a Murine Model: In Silico Supported In Vivo Study

Herniaria hemistemon J.Gay is widely used in folk medicine to treat hernia. The present study aimed to annotate the phytoconstituents of H. hemistemon aerial-part extract and investigate its in vivo anticryptosporidial activity. The chemical characterization was achieved via the LC-ESI-MS/MS technique resulting in the annotation of 37 phytocompounds comprising flavonoids and phenolic acids. Regarding the anticryptosporidial activity, fifty dexamethasone-immunosuppressed mice were separated into five groups: GI, un-infected (normal control); GII, infected but not treated (model); GIII, infected and received NTZ, the reference drug; GIV, infected and received H. hemistemon extract (100 mg/kg); and GV, infected and received H. hemistemon extract (200 mg/kg). When GIII, GIV, and GV were compared to GII, parasitological analyses displayed highly significant differences in the mean numbers of Cryptosporidium parvum oocysts in the stool between the different groups. GV demonstrated the highest efficacy of 79%. Histopathological analyses displayed improvement in the small intestine and liver pathology in the treated groups (GIII, IV, and V) related to the model (GII), with GV showing the highest efficacy. Moreover, the docking-based study tentatively highlighted the potential of benzoic acid derivatives as lactate dehydrogenase inhibitors. The docked compounds showed the same binding interactions as oxamic acid, where they established H-bond interactions with ARG-109, ASN-140, ASP-168, ARG-171, and HIS-195. To sum up, H. hemistemon is a promising natural therapeutic agent for cryptosporidiosis.

The Wound-Healing Potential of Olea europaea L. Cv. Arbequina Leaves Extract: An Integrated In Vitro, In Silico, and In Vivo Investigation

Olea europaea L. Cv. Arbequina (OEA) (Oleaceae) is an olive variety species that has received little attention. Besides our previous work for the chemical profiling of OEA leaves using LC−HRESIMS, an additional 23 compounds are identified. An excision wound model is used to measure wound healing action. Wounds are provided with OEA (2% w/v) or MEBO® cream (marketed treatment). The wound closure rate related to vehicle-treated wounds is significantly increased by OEA. Comparing to vehicle wound tissues, significant levels of TGF-β in OEA and MEBO® (p < 0.05) are displayed by gene expression patterns, with the most significant levels in OEA-treated wounds. Proinflammatory TNF-α and IL-1β levels are substantially reduced in OEA-treated wounds. The capability of several lignan-related compounds to interact with MMP-1 is revealed by extensive in silico investigation of the major OEA compounds (i.e., inverse docking, molecular dynamics simulation, and ΔG calculation), and their role in the wound-healing process is also characterized. The potential of OEA as a potent MMP-1 inhibitor is shown in subsequent in vitro testing (IC50 = 88.0 ± 0.1 nM). In conclusion, OEA is introduced as an interesting therapeutic candidate that can effectively manage wound healing because of its anti-inflammatory and antioxidant properties.

Bioactive Phytochemicals of Citrus reticulata Seeds—An Example of Waste Product Rich in Healthy Skin Promoting Agents

Phytochemical investigation of Egyptian mandarin orange (Citrus reticulata Blanco, F. Rutaceae) seeds afforded thirteen known compounds, 1–13. The structures of isolated compounds were assigned using 1D and 2D NMR and HRESIMS analyses. To characterize the pharmacological activity of these compounds, several integrated virtual screening-based and molecular dynamics simulation-based experiments were applied. As a result, compounds 2, 3 and 5 were putatively identified as hyaluronidase, xanthine oxidase and tyrosinase inhibitors. The subsequent in vitro testing was done to validate the in silico-based experiments to highlight the potential of these flavonoids as promising hyaluronidase, xanthine oxidase and tyrosinase inhibitors with IC50 values ranging from 6.39 ± 0.36 to 73.7 ± 2.33 µM. The present study shed light on the potential of Egyptian mandarin orange’s waste product (i.e., its seeds) as a skin health-promoting natural agent. Additionally, it revealed the applicability of integrated inverse docking-based virtual screening and MDS-based experiments in efficiently predicting the biological potential of natural products.

The Chemical Profiling, Docking Study, and Antimicrobial and Antibiofilm Activities of the Endophytic fungi Aspergillus sp. AP5

Growing data suggest that Aspergillus niger, an endophytic fungus, is a rich source of natural compounds with a wide range of biological properties. This study aimed to examine the antimicrobial and antibiofilm capabilities of the Phragmites australis-derived endophyte against a set of pathogenic bacteria and fungi. The endophytic fungus Aspergillus sp. AP5 was isolated from the leaves of P. australis. The chemical profile of the fungal crude extract was identified by spectroscopic analysis using LC-HRESIMS. The fungal-derived extract was evaluated for its antimicrobial activity towards a set of pathogenic bacterial and fungal strains including Staphylococcus aureus, Pseudomonas aeruginosa, Proteus vulgaris, Klebsiella sp., Candida albicans, and Aspergillus niger. Moreover, antibiofilm activity toward four resistant biofilm-forming bacteria was also evaluated. Additionally, a neural-networking pharmacophore-based visual screening predicted the most probable bioactive compounds in the obtained extract. The AP5-EtOAc extract was found to have potent antibacterial activities against S. aureus, E. coli, and Klebsiella sp., while it exhibited low antibacterial activity toward P. Vulgaris and P. aeruginosa and displayed anticandidal activity. The AP5-EtOAc extract had significant antibiofilm activity in S. aureus, followed by P. aeruginosa. The active metabolites’ antifungal and/or antibacterial activities may be due to targeting the fungal CYP 51 and/or the bacterial Gyr-B.

Computational Methods for Drug Repurposing

The wealth of knowledge and multi-omics data available in drug research has allowed the rise of several computational methods in the drug discovery field, resulting in a novel and exciting strategy called drug repurposing. Drug repurposing consists in finding new applications for existing drugs. Numerous computational methods perform a high-level integration of different knowledge sources to facilitate the discovery of unknown mechanisms. In this chapter, we present a survey of data resources and computational tools available for drug repositioning.

Anti-otomycotic potential of nanoparticles of Moringa oleifera leaf extract: an integrated in vitro, in silico and phase 0 clinical study

The work flow of the study.

Antiproliferative potential of Physalis peruviana-derived magnolin against pancreatic cancer: a comprehensive in vitro and in silico study

Physalis peruviana L. is a common edible fruit in Egypt and other regional countries.

Phytochemical wedelolactone reverses obesity by prompting adipose browning through SIRT1/AMPK/ PPARα pathway via targeting nicotinamide N-methyltransferase

BACKGROUND

Obesity is the cause of multiple metabolic disorders, and its incidence has been rapidly increasing worldwide. It develops when energy intake exceeds energy expenditure (EE). Wedelolactone (WDL) is a naturally isolated compound from Eclipta prostrata L. and possesses many pharmacological activities. However, little is known about the effect of WDL on obesity and EE.

PURPOSE

The present study aimed to investigate the effect of WDL on obesity and EE in diet-induced obese (DIO) mice and its underlying mechanism.

METHODS

Obese mice were induced by high fat diet. The effects of WDL on obese mice were assessed by examining body weight, fat mass, EE, glucose tolerance, and hepatic and kidney injury. 3T3-L1 cells were differentiated into mature adipocytes and incubated with WDL in vitro. Immunohistochemistry, western blotting, and real-time PCR were used to assess adipose browning. The inhibitory efficiency of WDL on nicotinamide N-methyltransferase (NNMT) was evaluated using a fluorescence assay.

RESULTS

WDL reduced fat mass, suppressed body weight gain, and improved obesity-related metabolic disorders in DIO mice. WDL treatment promoted adipose browning and enhanced EE in both DIO mice and 3T3-L1 cells. These effects were eliminated in AMPK antagonized or PPARα knockdown cells and in PPARα^-/- mice. Furthermore, we identified the target of WDL to be NNMT, an appealing target for regulating energy metabolism. WDL inhibited NNMT with an extremely low IC50 of 0.03 µM. Inhibition of NNMT and activation of SIRT1/AMPK/PPARα explains how WDL reverses obesity by prompting adipose browning.

CONCLUSION

Our findings demonstrate the novel effects of WDL in promoting adipose browning, enhancing EE and attenuating obesity and uncover the underlying mechanism, which includes inhibition of NNMT and subsequently activation of SIRT1/AMPK/PPARα in response to WDL. WDL could be further developed as a therapeutic agent for treating obesity and related metabolic diseases.

Target identification for repurposed drugs active against SARS-CoV-2 via high-throughput inverse docking

Screening already approved drugs for activity against a novel pathogen can be an important part of global rapid-response strategies in pandemics. Such high-throughput repurposing screens have already identified several existing drugs with potential to combat SARS-CoV-2. However, moving these hits forward for possible development into drugs specifically against this pathogen requires unambiguous identification of their corresponding targets, something the high-throughput screens are not typically designed to reveal. We present here a new computational inverse-docking protocol that uses all-atom protein structures and a combination of docking methods to rank-order targets for each of several existing drugs for which a plurality of recent high-throughput screens detected anti-SARS-CoV-2 activity. We demonstrate validation of this method with known drug-target pairs, including both non-antiviral and antiviral compounds. We subjected 152 distinct drugs potentially suitable for repurposing to the inverse docking procedure. The most common preferential targets were the human enzymes TMPRSS2 and PIKfyve, followed by the viral enzymes Helicase and PLpro. All compounds that selected TMPRSS2 are known serine protease inhibitors, and those that selected PIKfyve are known tyrosine kinase inhibitors. Detailed structural analysis of the docking poses revealed important insights into why these selections arose, and could potentially lead to more rational design of new drugs against these targets.

An In Vitro and In Silico Study of the Enhanced Antiproliferative and Pro-Oxidant Potential of Olea europaea L. cv. Arbosana Leaf Extract via Elastic Nanovesicles (Spanlastics)

The olive tree is a venerable Mediterranean plant and often used in traditional medicine. The main aim of the present study was to evaluate the effect of Olea europaea L. cv. Arbosana leaf extract (OLE) and its encapsulation within a spanlastic dosage form on the improvement of its pro-oxidant and antiproliferative activity against HepG-2, MCF-7, and Caco-2 human cancer cell lines. The LC-HRESIMS-assisted metabolomic profile of OLE putatively annotated 20 major metabolites and showed considerable in vitro antiproliferative activity against HepG-2, MCF-7, and Caco-2 cell lines with IC₅₀ values of 9.2 ± 0.8, 7.1 ± 0.9, and 6.5 ± 0.7 µg/mL, respectively. The encapsulation of OLE within a (spanlastic) nanocarrier system, using a spraying method and Span 40 and Tween 80 (4:1 molar ratio), was successfully carried out (size 41 ± 2.4 nm, zeta potential 13.6 ± 2.5, and EE 61.43 ± 2.03%). OLE showed enhanced thermal stability, and an improved in vitro antiproliferative effect against HepG-2, MCF-7, and Caco-2 (IC₅₀ 3.6 ± 0.2, 2.3 ± 0.1, and 1.8 ± 0.1 µg/mL, respectively) in comparison to the unprocessed extract. Both preparations were found to exhibit pro-oxidant potential inside the cancer cells, through the potential inhibitory activity of OLE against glutathione reductase and superoxide dismutase (IC₅₀ 1.18 ± 0.12 and 2.33 ± 0.19 µg/mL, respectively). These inhibitory activities were proposed via a comprehensive in silico study to be linked to the presence of certain compounds in OLE. Consequently, we assume that formulating such a herbal extract within a suitable nanocarrier would be a promising improvement of its therapeutic potential.

Challenging Reverse Screening: A Benchmark Study for Comprehensive Evaluation

As an efficient way of computational target prediction, reverse docking can find not only potential targets but also binding modes for a query ligand. Though the number of available docking tools keeps expanding, there is still not a comprehensive evaluation study which can uncover the advantages and limitations of these strategies in the research field of computational target-fishing. In this study, we propose a brand-new evaluation dataset tailor-made for reverse docking, which is composed of a true positive set (the core set) and two negative sets (the similar decoy set and the dissimilar decoy set). The proposed evaluation dataset can assess the prediction performance of docking tools as various values affected by varying degrees of inter-target ranking bias. The performance of four classical docking programs (AutoDock, AutoDock Vina, Glide and GOLD) was evaluated utilizing our dataset, and a biased prediction performance was observed regarding binding site properties. The results demonstrated that Glide (SP) and Glide(XP) had the best capacity to find true targets whether there was inter-target ranking bias or not.

Hyphaene thebaica (doum)-derived extract alleviates hyperglycemia in diabetic rats: a comprehensive in silico, in vitro and in vivo study

In the present study, we investigated the hypoglycemic effect of different extracts (i.e. organic and aqueous) derived from the fruits of Hyphaene thebaica (doum) on male streptozotocin-induced diabetic rats. Blood glucose levels as well as the relative gene expression of insulin, TNF-α, and TGF-β were determined in the pancreatic tissue of the experimental animals. Treatment of STZ-induced diabetic rats with aqueous extracts of the plant fruit over 7 weeks significantly reduced the elevated blood glucose and increased the relative expression of insulin, while the relative expression of inflammatory mediators (i.e. TNF-α and TGF-β) was significantly reduced. Histopathological investigation also revealed that the aqueous extract treatment effectively reversed the β-cell necrosis induced by STZ and restored its normal morphology. Furthermore, liquid chromatography high resolution mass spectrometry (LC-HRMS) and in silico chemical investigation of the aqueous extract elucidated its major bioactive phytochemicals (i.e. flavonoids) and putatively determined the pancreatic K_ATP channel as a target for these bioactive components. In vitro insulin secretion assay revealed that myricetin, luteolin, and apigenin were able to induce insulin secretion by human pancreatic cells (insulin production = 20.9 ± 1.3, 13.74 ± 1.8, and 11.33 ± 1.1 ng mL^-1, respectively). Using molecular docking and dynamics simulations, we were able to shed the light on the insulin secretagogue's mode of action through these identified bioactive compounds and to determine the main structural elements required for its bioactivity. This comprehensive investigation of this native fruit will encourage future clinical studies to recommend edible and widely available fruits like doum to be a part of DM treatment plans.

Anticancer Potential of Green Synthesized Silver Nanoparticles of the Soft Coral Cladiella pachyclados Supported by Network Pharmacology and In Silico Analyses

Cladiella-derived natural products have shown promising anticancer properties against many human cancer cell lines. In the present investigation, we found that an ethyl acetate extract of Cladiella pachyclados (CE) collected from the Red Sea could inhibit the human breast cancer (BC) cells (MCF and MDA-MB-231) in vitro (IC50 24.32 ± 1.1 and 9.55 ± 0.19 µg/mL, respectively). The subsequent incorporation of the Cladiella extract into the green synthesis of silver nanoparticles (AgNPs) resulted in significantly more activity against both cancer cell lines (IC50 5.62 ± 0.89 and 1.72 ± 0.36, respectively); the efficacy was comparable to that of doxorubicin with much-enhanced selectivity. To explore the mode of action of this extract, various in silico and network-pharmacology-based analyses were performed in the light of the LC-HRESIMS-identified compounds in the CE extract. Firstly, using two independent machine-learning-based prediction software platforms, most of the identified compounds in CE were predicted to inhibit both MCF7 and MDA-MB-231. Moreover, they were predicted to have low toxicity towards normal cell lines. Secondly, approximately 242 BC-related molecular targets were collected from various databases and used to construct a protein-protein interaction (PPI) network, which revealed the most important molecular targets and signaling pathways in the pathogenesis of BC. All the identified compounds in the extract were then subjected to inverse docking against all proteins hosted in the Protein Data bank (PDB) to discover the BC-related proteins that these compounds can target. Approximately, 10.74% of the collected BC-related proteins were potential targets for 70% of the compounds identified in CE. Further validation of the docking results using molecular dynamic simulations (MDS) and binding free energy calculations revealed that only 2.47% of the collected BC-related proteins could be targeted by 30% of the CE-derived compounds. According to docking and MDS experiments, protein-pathway and compound-protein interaction networks were constructed to determine the signaling pathways that the CE compounds could influence. This paper highlights the potential of marine natural products as effective anticancer agents and reports the discovery of novel anti-breast cancer AgNPs.

Deep learning in target prediction and drug repositioning: Recent advances and challenges

Drug repositioning is an attractive strategy for discovering new therapeutic uses for approved or investigational drugs, with potentially shorter development timelines and lower development costs. Various computational methods have been used in drug repositioning, promoting the efficiency and success rates of this approach. Recently, deep learning (DL) has attracted wide attention for its potential in target prediction and drug repositioning. Here, we provide an overview of the basic principles of commonly used DL architectures and their applications in target prediction and drug repositioning, and discuss possible ways of dealing with current challenges to help achieve its expected potential for drug repositioning.

Anti-Cancer and Anti-Inflammatory Potential of the Green Synthesized Silver Nanoparticles of the Red Sea Sponge Phyllospongia lamellosa Supported by Metabolomics Analysis and Docking Study

BACKGROUND

The Red Sea sponges have been endorsed as a plentiful source of bioactive compounds with promising anti-cancer and anti-inflammatory activities; therefore, exploring their potential as a source of anti-cancer metabolites has stimulated a growing research interest.

PURPOSE

To investigate the anti-cancer and anti-inflammatory potential of the Red Sea sponges, in their bulk and silver nanostructure. Metabolomics analysis of the selected sponge followed by molecular docking studies, will be conducted to explore and predict the secondary metabolites that might provide its capability of inhibiting cancer.

MATERIALS AND METHODS

We prepared a chloroform extract (CE) and ethyl acetate extract (EE) of the Red Sea sponge Phyllospongia lamellosa synthesized silver nanoparticles. The prepared silver nanoparticles were characterized through UV-vis spectrophotometric, transmission electron microscopy (TEM), and Fourier-transform infrared spectroscopy (FTIR) analyses. Testing for their anti-cancer activities was performed against MCF-7, MDB-231, and MCF-10A cells. Anti-inflammatory activity against COX-1 and 2 was assessed. Furthermore, liquid chromatography-mass spectrometry (LC-MS)-based metabolomics analysis and molecular docking were also applied.

Recent Advances in In Silico Target Fishing

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data

Traditional techniques to identify macromolecular targets for drugs utilize solely the information on a query drug and a putative target. Nonetheless, the mechanisms of action of many drugs depend not only on their binding affinity toward a single protein, but also on the signal transduction through cascades of molecular interactions leading to certain phenotypes. Although using protein-protein interaction networks and drug-perturbed gene expression profiles can facilitate system-level investigations of drug-target interactions, utilizing such large and heterogeneous data poses notable challenges. To improve the state-of-the-art in drug target identification, we developed GraphDTI, a robust machine learning framework integrating the molecular-level information on drugs, proteins, and binding sites with the system-level information on gene expression and protein-protein interactions. In order to properly evaluate the performance of GraphDTI, we compiled a high-quality benchmarking dataset and devised a new cluster-based cross-validation protocol. Encouragingly, GraphDTI not only yields an AUC of 0.996 against the validation dataset, but it also generalizes well to unseen data with an AUC of 0.939, significantly outperforming other predictors. Finally, selected examples of identified drugtarget interactions are validated against the biomedical literature. Numerous applications of GraphDTI include the investigation of drug polypharmacological effects, side effects through offtarget binding, and repositioning opportunities.

MDock: A Suite for Molecular Inverse Docking and Target Prediction

Molecular docking is commonly used for identification of drug candidates targeting a specified protein of known structure. With the increasing emphasis on drug repurposing over recent decades, molecular inverse docking has been widely applied to prediction of the potential protein targets of a specified molecule. In practice, inverse docking has many advantages, including early supervision of drugs' side effects and toxicity. MDock developed from our laboratory is a protein-ligand docking software based on a knowledge-based scoring function and has numerous applications to lead identification. In addition to its computational efficiency on ensemble docking for multiple protein conformations, MDock is well suited for inverse docking. In this chapter, we focus on introducing the protocol of inverse docking with MDock. For academic users, the MDock package is freely available at http://zoulab.dalton.missouri.edu/mdock.htm .

The latest automated docking technologies for novel drug discovery

INTRODUCTION

Molecular docking has been consolidated as one of the most important methods in the molecular modeling field. It has been recognized as a prominent tool in the study of protein-ligand complexes, to describe intermolecular interactions, to accurately predict poses of multiple ligands, to discover novel promising bioactive compounds. Molecular docking methods have evolved in terms of their accuracy and reliability; but there are pending issues to solve for improving the connection between the docking results and the experimental evidence.

AREAS COVERED

In this article, the author reviews very recent innovative molecular docking applications with special emphasis on reverse docking, treatment of protein flexibility, the use of experimental data to guide the selection of docking poses, the application of Quantum mechanics(QM) in docking, and covalent docking.

EXPERT OPINION

There are several issues being worked on in recent years that will lead to important breakthroughs in molecular docking methods in the near future These developments are related to more efficient exploration of large datasets and receptor conformations, advances in electronic description, and the use of structural information for guiding the selection of results.

Glycogen Phosphorylase: A Drug Target of Amino Alcohols in Echinococcus granulosus, Predicted by a Computer-Aided Method

Echinococcosis is an important parasitic disease that threats human health and animal husbandry worldwide. However, the low cure rate of clinical drugs for this disease is a challenge. Hence, novel compounds and specific drug targets are urgently needed. In this study, we identified drug targets of amino alcohols with effects on Echinococcus species. The drug targets were predicted with the idTarget web server. Corresponding three-dimensional structures of the drug targets were built after sequence BLAST analysis and homology modeling. After further screening by molecular docking, the activities of the candidate targets were validated in vitro. We ultimately identified glycogen phosphorylase as a potential drug target for amino alcohols. There are two genes coding glycogen phosphorylase in Echinococcus granulosus (EgGp1 and EgGp2). EgGp1 was abundant in E. granulosus PSCs, while EgGp2 was abundant in the cysts. These proteins were located at suckers and somas of E. granulosus PSCs and near the rostellum of cysts developed from PSCs. The effective compounds docked into a pocket consisting of E124, K543 and K654 and affected (either inhibited or enhanced) the activity of E. granulosus glycogen phosphorylase. In this study, we designed a method to predict drug targets for echinococcosis treatment based on inverse docking. The candidate targets found by this method can contribute not only to understanding of the modes of action of amino alcohols but also to modeling-aided drug design based on targets.

Computational/in silico methods in drug target and lead prediction

Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.

An up-to-date overview of computational polypharmacology in modern drug discovery

INTRODUCTION

In recent years, computational polypharmacology has gained significant attention to study the promiscuous nature of drugs. Despite tremendous challenges, community-wide efforts have led to a variety of novel approaches for predicting drug polypharmacology. In particular, some rapid advances using machine learning and artificial intelligence have been reported with great success.

AREAS COVERED

In this article, the authors provide a comprehensive update on the current state-of-the-art polypharmacology approaches and their applications, focusing on those reports published after our 2017 review article. The authors particularly discuss some novel, groundbreaking concepts, and methods that have been developed recently and applied to drug polypharmacology studies.

EXPERT OPINION

Polypharmacology is evolving and novel concepts are being introduced to counter the current challenges in the field. However, major hurdles remain including incompleteness of high-quality experimental data, lack of in vitro and in vivo assays to characterize multi-targeting agents, shortage of robust computational methods, and challenges to identify the best target combinations and design effective multi-targeting agents. Fortunately, numerous national/international efforts including multi-omics and artificial intelligence initiatives as well as most recent collaborations on addressing the COVID-19 pandemic have shown significant promise to propel the field of polypharmacology forward.

Flavonoids as Potential anti-MRSA Agents through Modulation of PBP2a: A Computational and Experimental Study

Recently, the interest in plant-derived antimicrobial agents has increased. However, there are no sufficient studies dealing with their modes of action. Herein, we investigate an in-house library of common plant-based phenolic compounds for their potential antibacterial effects against the methicillin-resistant Staphylococcus aureus (MRSA), a widespread life-threatening superbug. Flavonoids, which are considered major constituents in the plant kingdom, were found to be a promising class of compounds against MRSA, particularly the non-glycosylated ones. On the other hand, the glycosylated derivatives, along with the flavonolignan silibinin A, were able to restore the inhibitory activity of ampicillin against MRSA. To explore the mode of action of this class, they were subjected to an extensive inverse virtual screening (IVS), which suggested penicillin-binding protein 2a (PBP2a) as a possible target that mediates both the antibacterial and the antibiotic-synergistic effects of this class of compounds. Further molecular docking and molecular dynamic simulation experiments were conducted to support the primary IVS and the in vitro results and to study their binding modes with PBP2a. Our findings shed a light on plant-derived natural products, notably flavonoids, as a promising and readily available source for future adjuvant antimicrobial therapy against resistant strains.

Development of simultaneous interaction prediction approach (SiPA) for the expansion of interaction network of traditional Chinese medicine

Background

Due to the lack of enough interaction data among compositions, targets and diseases, it is difficult to construct a complete network of Traditional Chinese Medicine (TCM) that comprehensively reflects active compositions and their synergistic network in terms of specific diseases. Therefore, mapping of the full spectrum of interaction between compounds and their targets is of central importance when we use network pharmacology approach to explore the therapeutic potential of the TCM.

Methods

To address this challenge, we developed a large-scale simultaneous interaction prediction approach (SiPA) integrated one interaction network based simple inference model (SIM), focusing on ‘logical relevance’ between compounds, proteins or diseases, and another compound-target correlation space based interaction prediction model (CTCS-IPM) that was built on the basis of the canonical correlation analysis (CCA) to estimate the position of compounds (or targets) in compound-protein correlated space. Then SiPA was applied to discover reliable multiple interactions for interaction network expansion of a TCM, compound Salvia miltiorrhiza. By means of network analysis, potential active compounds and their related network synergy underlying cardiovascular diseases were evaluated between expanded and original interaction networks. Part of new interactions were validated with existing experimental evidence and molecular docking.

Results

As evaluated with known test dataset, the established combination approach was proved to make highly accurate prediction, showing a well prediction performance for the SIM and a high recall rate of 85.2% for the CTCS-IPM. Then 710 pairs of new compound-target interactions, 24 pairs of new compound-cardiovascular disease interactions and 294 pairs of new cardiovascular disease-protein interactions were predicted for compound Salvia miltiorrhiza. Results of network analysis suggested the network expansion could dramatically improve the completeness and effectiveness of the network. Validation results of literature and molecular docking manifested that inferred interactions had good reliability.

Conclusions

We provided a practical and efficient way for large-scale inference of multiple interactions of TCM ingredients, which was not limited by the lack of negative samples, sample size and target 3D structures. SiPA could help researchers more accurately prioritize the effective compounds and more completely explore network synergy of TCM for treating specific diseases, indicating a potential way for effectively identifying candidate compound (or target) in drug discovery.

GalaxySagittarius: Structure- and Similarity-Based Prediction of Protein Targets for Druglike Compounds

Computational techniques for predicting interactions of proteins and druglike molecules have often been used to search for compounds that bind a given protein with high affinity. More recently, such tools have also been applied to the reverse procedure of searching protein targets for a given compound. Among methods for predicting protein-ligand interactions, ligand-based methods relying on similarity to ligands of known interactions are effective only when similar protein-ligand interactions are known. Receptor-based methods predicting protein-ligand interactions by molecular docking are effective only when high-accuracy receptor structures and binding sites are available. Moreover, the computational cost of molecular docking tends to be too high to be applied to the entire protein structure database. In this paper, an effective target prediction method, which combines ligand similarity-based and receptor structure-based approaches, is introduced. In this method, protein-ligand docking is performed after efficient structure- and similarity-based screening. The enriched protein target database by predicted binding ligands and sites allows detection of protein targets with previously unknown ligand interactions. The method, called GalaxySagittarius, is freely available as a web server at http://galaxy.seoklab.org/sagittarius.

Large-scale prediction of binding affinity in protein-small ligand complexes: the PRODIGY-LIG web server

SUMMARY

Recently we published PROtein binDIng enerGY (PRODIGY), a web-server for the prediction of binding affinity in protein-protein complexes. By using a combination of simple structural properties, such as the residue-contacts made at the interface, PRODIGY has demonstrated a top performance compared with other state-of-the-art predictors in the literature. Here we present an extension of it, named PRODIGY-LIG, aimed at the prediction of affinity in protein-small ligand complexes. The predictive method, properly readapted for small ligand by making use of atomic instead of residue contacts, has been successfully applied for the blind prediction of 102 protein-ligand complexes during the D3R Grand Challenge 2. PRODIGY-LIG has the advantage of being simple, generic and applicable to any kind of protein-ligand complex. It provides an automatic, fast and user-friendly tool ensuring broad accessibility.

AVAILABILITY AND IMPLEMENTATION

PRODIGY-LIG is freely available without registration requirements at http://milou.science.uu.nl/services/PRODIGY-LIG.

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.

PatchSearch: a web server for off-target protein identification

The large number of proteins found in the human body implies that a drug may interact with many proteins, called off-target proteins, besides its intended target. The PatchSearch web server provides an automated workflow that allows users to identify structurally conserved binding sites at the protein surfaces in a set of user-supplied protein structures. Thus, this web server may help to detect potential off-target protein. It takes as input a protein complexed with a ligand and identifies within user-defined or predefined collections of protein structures, those having a binding site compatible with this ligand in terms of geometry and physicochemical properties. It is based on a non-sequential local alignment of the patch over the entire protein surface. Then the PatchSearch web server proposes a ligand binding mode for the potential off-target, as well as an estimated affinity calculated by the Vinardo scoring function. This novel tool is able to efficiently detects potential interactions of ligands with distant off-target proteins. Furthermore, by facilitating the discovery of unexpected off-targets, PatchSearch could contribute to the repurposing of existing drugs. The server is freely available at http://bioserv.rpbs.univ-paris-diderot.fr/services/PatchSearch.

Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace

The interplay between life sciences and advancing technology drives a continuous cycle of chemical data growth; these data are most often stored in open or partially open databases. In parallel, many different types of algorithms are being developed to manipulate these chemical objects and associated bioactivity data. Virtual screening methods are among the most popular computational approaches in pharmaceutical research. Today, user-friendly web-based tools are available to help scientists perform virtual screening experiments. This article provides an overview of internet resources enabling and supporting chemical biology and early drug discovery with a main emphasis on web servers dedicated to virtual ligand screening and small-molecule docking. This survey first introduces some key concepts and then presents recent and easily accessible virtual screening and related target-fishing tools as well as briefly discusses case studies enabled by some of these web services. Notwithstanding further improvements, already available web-based tools not only contribute to the design of bioactive molecules and assist drug repositioning but also help to generate new ideas and explore different hypotheses in a timely fashion while contributing to teaching in the field of drug development.

ACID: a free tool for drug repurposing using consensus inverse docking strategy

Drug repurposing offers a promising alternative to dramatically shorten the process of traditional de novo development of a drug. These efforts leverage the fact that a single molecule can act on multiple targets and could be beneficial to indications where the additional targets are relevant. Hence, extensive research efforts have been directed toward developing drug based computational approaches. However, many drug based approaches are known to incur low successful rates, due to incomplete modeling of drug-target interactions. There are also many technical limitations to transform theoretical computational models into practical use. Drug based approaches may, thus, still face challenges for drug repurposing task. Upon this challenge, we developed a consensus inverse docking (CID) workflow, which has a ~ 10% enhancement in success rate compared with current best method. Besides, an easily accessible web server named auto in silico consensus inverse docking (ACID) was designed based on this workflow (http://chemyang.ccnu.edu.cn/ccb/server/ACID).

Current computational methods for predicting protein interactions of natural products

Natural products (NPs) are an indispensable source of drugs and they have a better coverage of the pharmacological space than synthetic compounds, owing to their high structural diversity. The prediction of their interaction profiles with druggable protein targets remains a major challenge in modern drug discovery. Experimental (off-)target predictions of NPs are cost- and time-consuming, whereas computational methods, on the other hand, are much faster and cheaper. As a result, computational predictions are preferentially used in the first instance for NP profiling, prior to experimental validations. This review covers recent advances in computational approaches which have been developed to aid the annotation of unknown drug-target interactions (DTIs), by focusing on three broad classes, namely: ligand-based, target-based, and target-ligand-based (hybrid) approaches. Computational DTI prediction methods have the potential to significantly advance the discovery and development of novel selective drugs exhibiting minimal side effects. We highlight some inherent caveats of these methods which must be overcome to enable them to realize their full potential, and a future outlook is given.

A platform for target prediction of phenotypic screening hit molecules

Many drug discovery programmes, particularly for infectious diseases, are conducted phenotypically. Identifying the targets of phenotypic screening hits experimentally can be complex, time-consuming, and expensive. However, it would be valuable to know what the molecular target(s) is, as knowledge of the binding pose of the hit molecule in the binding site can facilitate the compound optimisation. Furthermore, knowing the target would allow de-prioritisation of less attractive chemical series or molecular targets. To generate target-hypotheses for phenotypic active compounds, an in silico platform was developed that utilises both ligand and protein-structure information to generate a ranked set of predicted molecular targets. As a result of the web-based workflow the user obtains a set of 3D structures of the predicted targets with the active molecule bound. The platform was exemplified using Mycobacterium tuberculosis, the causative organism of tuberculosis. In a test that we performed, the platform was able to predict the targets of 60% of compounds investigated, where there was some similarity to a ligand in the protein database.

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An algorithm to predict the molecular target(s) of phenotypic hits against TB.

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Uses information based on the ligand and protein structure.

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Allow visualisation of proposed binding pose.

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Web interface developed.

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.

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.