Inverse Virtual Screening allows the discovery of the biological activity of natural compounds

Repositioning of Small Molecules through the Inverse Virtual Screening in silico Tool: Case of Benzothiazole-Based Inhibitors of Soluble Epoxide Hydrolase (sEH)

Computational techniques accelerate drug discovery by identifying bioactive compounds for specific targets, optimizing molecules with moderate activity, or facilitating the repositioning of inactive items onto new targets. Among them, the Inverse Virtual Screening (IVS) approach is aimed at the evaluation of one or a small set of molecules against a panel of targets for addressing target identification. In this work, a focused library of benzothiazole-based compounds was re-investigated by IVS. Four items, originally synthesized and tested on bromodomain-containing protein 9 (BRD9) but yielding poor binding, were critically re-analyzed, disclosing only a partial fit with 3D structure-based pharmacophore models, which, in the meanwhile, were developed for this target. Afterwards, these compounds were re-evaluated through IVS on a panel of proteins involved in inflammation and cancer, identifying soluble epoxide hydrolase (sEH) as a putative interacting target. Three items were subsequently confirmed as able to interfere with sEH activity, leading to inhibition percentages spanning from 70 % up to 30 % when tested at 10 μM. Finally, one benzothiazole-based compound emerged as the most promising inhibitor featuring an IC50 in the low micromolar range (IC50=6.62±0.13 μM). Our data confirm IVS as a predictive tool for accelerating the target identification and repositioning processes.

Target identification by structure-based computational approaches: Recent advances and perspectives

The use of computational techniques in the early stages of drug discovery has recently experienced a boost, especially in the target identification step. Finding the biological partner(s) for new or existing synthetic and/or natural compounds by "wet" approaches may be challenging; therefore, preliminary in silico screening is even more recommended. After a brief overview of some of the most known target identification techniques, recent advances in structure-based computational approaches for target identification are reported in this digest, focusing on Inverse Virtual Screening and its recent applications. Moreover, future perspectives concerning the use of such methodologies, coupled or not with other approaches, are analyzed.

Phytochemical Analysis of the Methanolic Extract and Essential Oil from Leaves of Industrial Hemp Futura 75 Cultivar: Isolation of a New Cannabinoid Derivative and Biological Profile Using Computational Approaches

Cannabis sativa L. is a plant belonging to the Cannabaceae family, cultivated for its psychoactive cannabinoid (Δ⁹-THC) concentration or for its fiber and nutrient content in industrial use. Industrial hemp shows a low Δ⁹-THC level and is a valuable source of phytochemicals, mainly represented by cannabinoids, flavones, terpenes, and alkaloids, with health-promoting effects. In the present study, we investigated the phytochemical composition of leaves of the industrial hemp cultivar Futura 75, a monoecious cultivar commercially used for food preparations or cosmetic purposes. Leaves are generally discarded, and represent waste products. We analyzed the methanol extract of Futura 75 leaves by HPLC and NMR spectroscopy and the essential oil by GC-MS. In addition, in order to compare the chemical constituents, we prepared the water infusion. One new cannabinoid derivative (1) and seven known components, namely, cannabidiol (2), cannabidiolic acid (3), β-cannabispirol (4), β-cannabispirol (5), canniprene (6), cannabiripsol (7), and cannflavin B (8) were identified. The content of CBD was highest in all preparations. In addition, we present the outcomes of a computational study focused on elucidating the role of 2α-hydroxy-Δ^3,7-cannabitriol (1), CBD (2), and CBDA (3) in inflammation and thrombogenesis.

Synthesis and inverse virtual screening of new bi-cyclic structures towards cancer-relevant cellular targets

We report here synthetic approaches to access new classes of small molecules based on three heterocyclic scaffolds, i.e. 3,7-dihydropyrimido[4,5-d]pyridazine-4,8-dione, 1,8-naphthyridin-4(1H)-one and 4H-pyrido[1,2-a]pyrimidin-4-one. The bi-cyclic structure 3,7-dihydropyrimido[4,5-d]pyridazine-4,8-dione is a new heterocycle, described here for the first time. In silico methodologies of inverse virtual screening have been used to preliminary analyse the molecules, in order to explore their potential as hits for chemical biology investigations. Our computational study has been conducted with 43 synthetically accessible small molecules towards 31 cellular proteins involved in cancer pathogenesis. Binding energies were quantified using molecular docking calculations, allowing to define the relative affinities of the ligands for the cellular targets. Through this methodology, 16 proteins displayed effective interactions with distinct small molecules within the matrix. In addition, 23 ligands have demonstrated high affinity for at least one cellular protein, using as reference the co-crystallised ligand in the X-ray structure. The evaluation of ADME and drug score for selected hits also highlights that these new molecular series can serve as sources of lead candidates for further structure optimisation and biological studies.

Biological Profile of Two Gentiana lutea L. Metabolites Using Computational Approaches and In Vitro Tests

Natural products have been the main source of bioactive molecules for centuries. We tested the biological profile of two metabolites extracted from Gentiana lutea L. by means of computational techniques and in vitro assays. The two molecules (loganic acid and gentiopicroside) were tested in silico using an innovative technique, named Inverse Virtual Screening (IVS), to highlight putative partners among a panel of proteins involved in inflammation and cancer events. A positive binding with cyclooxygenase-2 (COX-2), alpha-1-antichymotrypsin, and alpha-1-acid glycoprotein emerged from the computational experiments and the outcomes from the promising interaction with COX-2 were confirmed by Western blot, highlighting the reliability of IVS in the field of the natural products.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Inverse Virtual Screening for the rapid re-evaluation of the presumed biological safe profile of natural products. The case of steviol from Stevia rebaudiana glycosides on farnesoid X receptor (FXR)

Nonnutritive sweeteners (NNSs) are widely employed as dietary substitutes for classical sugars thanks to their safety profile and low toxicity. In this study, a re-evaluation of the biological effects of steviol (1), the main metabolite from Stevia rebaudiana glycosides, was performed using the Inverse Virtual Screening (IVS) target fishing computational approach. Starting from well-known pharmacological properties of Stevia rebaudiana glycosides, this computational tool was employed for predicting the putative interacting targets of 1 and, afterwards, of its five synthetic ester derivatives 2-6, accounting a large panel of proteins involved in cancer and inflammation events. Applying this methodology, the farnesoid X receptor (FXR) was identified as the putative target partner of 1-6. The predicted ligand-protein interactions were corroborated by transactivation assays, specifically disclosing the agonistic activity of 1 and the antagonistic activities of 2-6 on FXR. The reported results highlight the feasibility of IVS as a fast and potent tool for predicting the interacting targets of query compounds, addressing the re-evaluation of their bioactivity. In light of the obtained results, the presumably safe profile of known compounds, such as the case of steviol (1), is critically discussed.

Inverse Molecular Docking Study of NS3-Helicase and NS5-RNA Polymerase of Zika Virus as Possible Therapeutic Targets of Ligands Derived from Marcetia taxifolia and Its Implications to Dengue Virus

Dengue and Zika are two mosquito-borne diseases of great impact on public health around the world in tropical and subtropical countries. DENV and ZIKV belong to the Flaviviridae family and the Flavivirus genus. Currently, there are no effective therapeutic agents to treat or prevent these pathologies. The main objective of this work was to evaluate potential inhibitors from active compounds obtained from Marcetia taxifolia by performing inverse molecular docking on ZIKV-NS3-helicase and ZIKV-NS5-RNA polymerase as targets. This computational strategy is based on renormalizing the binding scores of the compounds to these two proteins, allowing a direct comparison of the results across the proteins. The crystallographic structures of the ZIKV-NS3-helicase and ZIKV-NS5-RNA-polymerase proteins share a great similarity with DENV homologous proteins. The P-loop active site of the crystallographic structure of ZIKV-NS3-helicase presents a high percentage of homology with the four dengue serotypes. It was found that most ligands of the active compounds (5,3'-dihydroxy-3,6,7,8,4'-pentamethoxyflavone (5DP); 5-hydroxy-3,6,7,8,3',4'-hexamethoxyflavone (5HH); myricetin-3-O-rhamnoside (M3OR)) from Marcetia taxifolia had a better affinity for ZIKV-NS3-helicase than for ZIKV-NS5-RNA polymerase, as indicated by the negative multiple active site correction (MASC) score, except for M3RG that showed a higher affinity for ZIKV-NS5-RNA polymerase. On the other hand, the AutoDock Vina scores showed that M3OR had the highest score value (-9.60 kcal/mol) and the highest normalized score (1.13) against ZIKV-NS3-helicase. These results in silico demonstrated that the nonstructural proteins NS3-helicase and NS5-RNA polymerase, which share similar molecular structures between the selected viruses, could become therapeutic targets for some bioactive compounds derived from Marcetia taxifolia.

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.

Repurposing strategies on pyridazinone-based series by pharmacophore- and structure-driven screening

We report here in silico repurposing studies on 52 new pyridazinone-based small-molecules through inverse virtual screening (iVS) methodologies. These analogues were originally designed as formyl peptide receptor (FPR) ligands. As it is sometimes the case in drug discovery programmes, subsequent biological screening demonstrated the inefficacy of the molecules in binding FPRs, failing in the identification of new hits. Through a focussed drug-repurposing approach we have defined a variety of potential targets that are suitable to interact with this library of pyridazinone-based analogues. A two-step approach has been conducted for computational analysis. Specifically, the molecules were initially processed through a pharmacophore-based screening. Secondly, the resulting features of binding were investigated by docking studies and following molecular dynamic simulations, in order to univocally confirm "pyridazinone-based ligand-target protein" interactions. Our findings propose aspartate aminotransferase as the most favourable repurposed target for this small-molecule series, worth of additional medicinal chemistry investigations in the field.

Synthesis and identification of a novel derivative of salidroside as a selective, competitive inhibitor of monoamine oxidase B with enhanced neuroprotective properties

Salidroside [(2R,3S,4S,5R,6R)-2-(hydroxymethyl)-6-(4-hydroxyphenethoxy)tetrahy-dro-2H-pyran-3,4,5-triol] is an antioxidant, anti-inflammatory and neuroprotective agent, but its drug-like properties are unoptimized and its mechanism of actions is uncertain. We synthesized twenty-six novel derivatives of salidroside and examined them in CoCl₂-treated PC12 cells using MTT assay. pOBz, synthesized by esterifying the phenolic hydroxyl group of salidroside with benzoyl chloride, was one of five derivatives that were more cytoprotective than salidroside, with an EC₅₀ of 0.038 μM versus 0.30 μM for salidroside. pOBz was also more lipophilic, with log P of 1.44 versus -0.89 for salidroside. Reverse virtual docking predicted that pOBz would bind strongly with monoamine oxidase (MAO) B by occupying its entrance and substrate cavities, and by interacting with the inter-cavity gating residue Ile199 and Tyr435 of the substrate cavity. Enzymatic studies confirmed that pOBz competitively inhibited the activity of purified human MAO-B (K_i = 0.041 μM versus K_i = 0.92 μM for salidroside), and pOBz was highly selective for MAO-B over MAO-A. In vivo, pOBz inhibited cerebral MAO activity after middle cerebral artery occlusion with reperfusion in rats, and it reduced cerebral infarct volume, improved neurological function and NeuN expression, and inhibited complement C3 expression and apoptosis. Our results suggest that pOBz is a structurally novel type of competitive and selective MAO-B inhibitor, with potent neuroprotective properties after cerebral ischemia-reperfusion injury in rats.

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.

Accelerating the repurposing of FDA-approved drugs against coronavirus disease-19 (COVID-19)

The recent release of the main protein structures belonging to SARS CoV-2, responsible for the coronavirus disease-19 (COVID-19), strongly pushed for identifying valuable drug treatments. With this aim, we show a repurposing study on FDA-approved drugs applying a new computational protocol and introducing a novel parameter called IVS_ratio. Starting with a virtual screening against three SARS CoV-2 targets (main protease, papain-like protease, spike protein), the top-ranked molecules were reassessed combining the Inverse Virtual Screening novel approach and MM-GBSA calculations. Applying this protocol, a list of drugs was identified against the three investigated targets. Also, the top-ranked selected compounds on each target (rutin vs. main protease, velpatasvir vs. papain-like protease, lomitapide vs. spike protein) were further tested with molecular dynamics simulations to confirm the promising binding modes, obtaining encouraging results such as high stability of the complex during the simulation and a good protein–ligand interaction network involving some important residues of each target. Moreover, the recent outcomes highlighting the inhibitory activity of quercetin, a natural compound strictly related to rutin, on the SARS-CoV-2 main protease, strengthened the applicability of the proposed workflow.

New computational protocol applied to a repurposing campaign against SARS-CoV-2.

Computer-based techniques for lead identification and optimization I: Basics

This chapter focuses on computational techniques for identifying and optimizing lead molecules, with a special emphasis on natural compounds. A number of case studies have been specifically discussed, such as the case of the naphthyridine scaffold, discovered through a structure-based virtual screening (SBVS) and proposed as the starting point for further lead optimization process, to enhance its telomeric RNA selectivity. Another example is the case of Liphagal, a tetracyclic meroterpenoid extracted fromAka coralliphaga, known as PI3Kα inhibitor, provide an evidence for the design of new active congeners against PI3Kα using molecular dynamics (MD) simulations. These are only two of the numerous examples of the computational techniques’ powerful in drug design and drug discovery fields. Finally, the design of drugs that can simultaneously interact with multiple targets as a promising approach for treating complicated diseases has been reported. An example of polypharmacological agents are the compounds extracted from mushrooms identified by means of molecular docking experiments. This chapter may be a useful manual of molecular modeling techniques used in the lead-optimization and lead identification processes.

Discovery of High Affinity Receptors for Dityrosine through Inverse Virtual Screening and Docking and Molecular Dynamics

Dityrosine is the product of oxidation that has been linked to a number of serious pathological conditions. Evidence indicates that high amounts of dityrosine exist in oxidized milk powders and some milk related foodstuffs, further reducing the nutritional value of oxidized proteins. Therefore, we hypothesize that some receptors related to special diseases would be targets for dityrosine. However, the mechanisms of the interaction of dityrosine with probable targets are still unknown. In the present work, an inverse virtual screening approach was performed to screen possible novel targets for dityrosine. Molecular docking studies were performed on a panel of targets extracted from the potential drug target database (PDTD) to optimize and validate the screening results. Firstly, two different conformations cis- and trans- were found for dityrosine during minimization. Moreover, Tubulin (αT) (−11.0 kcal/mol) was identified as a target for cis-dityrosine (CDT), targets including αT (−11.2 kcal/mol) and thyroid hormone receptor beta-1 (−10.7 kcal/mol) presented high binding affinities for trans-dityrosine (TDT). Furthermore, in order to provide binding complexes with higher precision, the three docked systems were further refined by performing thermo dynamic simulations. A series of techniques for searching for the most stable binding pose and the calculation of binding free energy are elaborately provided in this work. The major interactions between these targets and dityrosine were hydrophobic, electrostatic and hydrogen bonding. The application of inverse virtual screening method may facilitate the prediction of unknown targets for known ligands, and direct future experimental assays.

iVS analysis to evaluate the impact of scaffold diversity in the binding to cellular targets relevant in cancer

This study reports the application of inverse virtual screening (iVS) methodologies to identify cellular proteins as suitable targets for a library of heterocyclic small-molecules, with potential pharmacological implications. Standard synthetic procedures allow facile generation of these ligands showing a high degree of core scaffold diversity. Specifically, we have computationally investigated the binding efficacy of the new series for target proteins which are involved in cancer pathogenesis. As a result, nine macromolecules demonstrated efficient binding interactions for the molecular dataset, in comparison to the co-crystallised ligand for each target. Moreover, the iVS analysis led us to confirm that 27 analogues have high affinity for one or more examined cellular proteins. The additional evaluation of ADME and drug score for selected hits also highlights their capability as drug candidates, demonstrating valuable leads for further structure optimisation and biological studies.

The Hop Polyphenols Xanthohumol and 8-Prenyl-Naringenin Antagonize the Estrogenic Effects of Fusarium Mycotoxins in Human Endometrial Cancer Cells

The Fusarium toxin zearalenone (ZEN) and its reductive metabolite α-zearalenol (α-ZEL) are well-documented endocrine disruptors that are frequently found to contaminate cereal products, including beer. But also hop is known to represent a source for endocrine active compounds, containing amongst others xanthohumol (XAN), which might be converted to the potent phytoestrogen 8-prenylnaringenin (8-PN). In the present study, we investigated the interaction of these xenoestrogens in mixtures which might occur in beer. Estrogenicity was measured as induction of alkaline phosphatase (AlP) expression in estrogen-sensitive Ishikawa cells. In binary combinations, XAN was found to act as a potent antagonist of mycotoxin-induced estrogenicity, significantly suppressing the AlP-inducing impact of both ZEN and α-ZEL at nanomolar concentrations. Also 8-PN antagonized the estrogenic stimulus of the two fungal metabolites, although less pronounced. These effects also manifested in combinations of three or four test compounds, and at the level of cell proliferation, that was assessed via an E-screen-like approach in Ishikawa cells. Of note, co-exposure to the investigated myco- and phyto-estrogens did not result in additive or overadditive/synergistic estrogenic effects in the applied test system. Being aware that the actual study is still limited to the in vitro situation, our results even suggest that prenylated chalkones from hops might protect against Fusarium toxin–induced endocrine disruptive activities at concentrations that can be reached by moderate beer consumption.

Glucopyranosylbianthrones from the Algerian Asphodelus tenuifolius: Structural Insights and Biological Evaluation on Melanoma Cancer Cells

Two new glucopyranosylbianthrones (1 and 2) were isolated from the aerial part of the plant Asphodelus tenuifolius, collected in Southwest Algeria. The 2D structures of 1 and 2 were defined by NMR analysis, HRESIMS data, and comparison with literature data. The comparison of experimental and calculated electronic circular dichroism and NMR data led to characterization of the ( M) and ( P) atropisomeric forms of the glucopyranosylbianthrones, asphodelins (1) and (2), respectively. The in vitro activities of these two metabolites were evaluated in human melanoma A375 cells, and both the compounds inhibited cell proliferation in a concentration-dependent manner, with IC₅₀ values of 20.6 ± 0.8 and 23.2 ± 1.1 μM, respectively. Considering their biological profile, an inverse virtual screening approach was employed to identify and suggest putative anticancer interacting targets.

Discovery of benzimidazole-based Leishmania mexicana cysteine protease CPB2.8ΔCTE inhibitors as potential therapeutics for leishmaniasis

Chemotherapy is currently the only effective approach to treat all forms of leishmaniasis. However, its effectiveness is severely limited due to high toxicity, long treatment length, drug resistance, or inadequate mode of administration. As a consequence, there is a need to identify new molecular scaffolds and targets as potential therapeutics for the treatment of this disease. We report a small series of 1,2-substituted-1H-benzo[d]imidazole derivatives (9a-d) showing affinity in the submicromolar range (K_i  = 0.15-0.69 μM) toward Leishmania mexicanaCPB2.8ΔCTE, one of the more promising targets for antileishmanial drug design. The compounds confirmed activity in vitro against intracellular amastigotes of Leishmania infantum with the best result being obtained with derivative 9d (IC₅₀  = 6.8 μM), although with some degree of cytotoxicity (CC₅₀  = 8.0 μM on PMM and CC₅₀  = 32.0 μM on MCR-5). In silico molecular docking studies and ADME-Tox properties prediction were performed to validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of these derivatives.

Discovery of new erbB4 inhibitors: Repositioning an orphan chemical library by inverse virtual screening

Inverse Virtual Screening (IVS) is a docking based approach aimed to the evaluation of the virtual ability of a single compound to interact with a library of proteins. For the first time, we applied this methodology to a library of synthetic compounds, which proved to be inactive towards the target they were initially designed for. Trifluoromethyl-benzenesulfonamides 3-21 were repositioned by means of IVS identifying new lead compounds (14-16, 19 and 20) for the inhibition of erbB4 in the low micromolar range. Among these, compound 20 exhibited an interesting value of IC50 on MCF7 cell lines, thus validating IVS in lead repurposing.

Docking-based inverse virtual screening: methods, applications, and challenges

Identifying potential protein targets for a small-compound ligand query is crucial to the process of drug development. However, there are tens of thousands of proteins in human alone, and it is almost impossible to scan all the existing proteins for a query ligand using current experimental methods. Recently, a computational technology called docking-based inverse virtual screening (IVS) has attracted much attention. In docking-based IVS, a panel of proteins is screened by a molecular docking program to identify potential targets for a query ligand. Ever since the first paper describing a docking-based IVS program was published about a decade ago, the approach has been gradually improved and utilized for a variety of purposes in the field of drug discovery. In this article, the methods employed in docking-based IVS are reviewed in detail, including target databases, docking engines, and scoring function methodologies. Several web servers developed for non-expert users are also reviewed. Then, a number of applications are presented according to different research purposes, such as target identification, side effects/toxicity, drug repositioning, drug-target network development, and receptor design. The review concludes by discussing the challenges that docking-based IVS needs to overcome to become a robust tool for pharmaceutical engineering.

Exploring Polypharmacology in Drug Design

Nowadays it is widely accepted that one compound can be able to hit several targets at once. This "magic shotgun" approach for drug development properly describes the mechanism of biomolecular recognition. The need to take into account the polypharmacology in structure-based drug design has led to the development of several computational tools. Here we present a computational protocol to identify promising compounds against several biological targets, a protocol known as inverse docking.

Molecular modeling studies of pseudouridine isoxazolidinyl nucleoside analogues as potential inhibitors of the pseudouridine 5'-monophosphate glycosidase

In this paper, we investigated the hypothesis that pseudouridine isoxazolidinyl nucleoside analogues could act as potential inhibitors of the pseudouridine 5'-monophosphate glycosidase. This purpose was pursued using molecular modeling and in silico ADME-Tox profiling. From these studies emerged that the isoxazolidinyl derivative 1 5'-monophosphate can be effectively accommodated within the active site of the enzyme with a ligand efficiency higher than that of the natural substrate. In this context, the poor nucleofugality of the N-protonated isoxazolidine prevents or slows down, the first mechanistic step proposed for the degradation of the pseudouridine 5'-monophosphate glycosidase, leading to the enzyme inhibition. Finally, the results of the physicochemical and ADME-Tox informative analysis pointed out that compound 1 is weakly bounded to plasma protein, only moderately permeate the blood-brain barrier, and is non-carcinogen in rat and mouse. To the best of our knowledge, this is the first paper that introduces the possibility of inhibition of pseudouridine 5'-monophosphate glycosidase by a molecule that competing with the natural substrate hinders the glycosidic C-C bond cleavage.

Inhibition of Wnt/β-Catenin pathway and Histone acetyltransferase activity by Rimonabant: a therapeutic target for colon cancer

In a high percentage (≥85%) of both sporadic and familial adenomatous polyposis forms of colorectal cancer (CRC), the inactivation of the APC tumor suppressor gene initiates tumor formation and modulates the Wnt/β-Catenin transduction pathways involved in the control of cell proliferation, adhesion and metastasis. Increasing evidence showed that the endocannabinoids control tumor growth and progression, both in vitro and in vivo. We evaluated the effect of Rimonabant, a Cannabinoid Receptor 1 (CB1) inverse agonist, on the Wnt/β-Catenin pathway in HCT116 and SW48 cell lines carrying the genetic profile of metastatic CRC poorly responsive to chemotherapies. In these models, Rimonabant inhibited the Wnt/β-Catenin canonical pathway and increased β-Catenin phosphorylation; in HCT116 cells, but not in SW48, the compound also triggered the Wnt/β-Catenin non canonical pathway activation through induction of Wnt5A and activation of CaMKII. The Rimonabant-induced downregulation of Wnt/β-Catenin target genes was partially ascribable to a direct inhibition of p300/KAT3B histone acetyltransferase, a coactivator of β-Catenin dependent gene regulation. Finally, in HCT116 xenografts, Rimonabant significantly reduced tumor growth and destabilized the nuclear localization of β-Catenin. Obtained data heavily supported the rationale for the use of cannabinoids in combined therapies for metastatic CRC harbouring activating mutations of β-Catenin.

Ligand Selectivity between the ADP-Ribosylating Toxins: An Inverse-Docking Study for Multitarget Drug Discovery

Bacterial adenosine 5'-diphosphate-ribosylating toxins are encoded by several human pathogens, such as Pseudomonas aeruginosa (exotoxin A (ETA)), Corynebacterium diphtheriae (diphtheria toxin (DT)), and Vibrio cholerae (cholix toxin (CT)). The toxins modify eukaryotic elongation factor 2, an essential human enzyme in protein synthesis, thereby causing cell death. Targeting external virulence factors, such as the above toxins, is a promising alternative for developing new antibiotics, while at the same time avoiding drug resistance. This study aims to establish a reliable computational methodology to find a "silver bullet" able to target all three toxins. Herein, we have undertaken a detailed analysis of the active sites of ETA, DT, and CT, followed by the determination of the most appropriate selection of the size of the docking sphere. Thereafter, we tested two different approaches for normalizing the docking scores and used these to verify the best target (toxin) for each ligand. The results indicate that the methodology is suitable for identifying selective as well as multitoxin inhibitors, further validating the robustness of inverse docking for target-fishing experiments.

Integrating sampling techniques and inverse virtual screening: toward the discovery of artificial peptide-based receptors for ligands

A novel heuristic using an iterative select-and-purge strategy is proposed. It combines statistical techniques for sampling and classification by rigid molecular docking through an inverse virtual screening scheme. This approach aims to the de novo discovery of short peptides that may act as docking receptors for small target molecules when there are no data available about known association complexes between them. The algorithm performs an unbiased stochastic exploration of the sample space, acting as a binary classifier when analyzing the entire peptides population. It uses a novel and effective criterion for weighting the likelihood of a given peptide to form an association complex with a particular ligand molecule based on amino acid sequences. The exploratory analysis relies on chemical information of peptides composition, sequence patterns, and association free energies (docking scores) in order to converge to those peptides forming the association complexes with higher affinities. Statistical estimations support these results providing an association probability by improving predictions accuracy even in cases where only a fraction of all possible combinations are sampled. False positives/false negatives ratio was also improved with this method. A simple rigid-body docking approach together with the proper information about amino acid sequences was used. The methodology was applied in a retrospective docking study to all 8000 possible tripeptide combinations using the 20 natural amino acids, screened against a training set of 77 different ligands with diverse functional groups. Afterward, all tripeptides were screened against a test set of 82 ligands, also containing different functional groups. Results show that our integrated methodology is capable of finding a representative group of the top-scoring tripeptides. The associated probability of identifying the best receptor or a group of the top-ranked receptors is more than double and about 10 times higher, respectively, when compared to classical random sampling methods.

GeauxDock: A novel approach for mixed-resolution ligand docking using a descriptor-based force field

Molecular docking is an important component of computer-aided drug discovery. In this communication, we describe GeauxDock, a new docking approach that builds on the ideas of ligand homology modeling. GeauxDock features a descriptor-based scoring function integrating evolutionary constraints with physics-based energy terms, a mixed-resolution molecular representation of protein-ligand complexes, and an efficient Monte Carlo sampling protocol. To drive docking simulations toward experimental conformations, the scoring function was carefully optimized to produce a correlation between the total pseudoenergy and the native-likeness of binding poses. Indeed, benchmarking calculations demonstrate that GeauxDock has a strong capacity to identify near-native conformations across docking trajectories with the area under receiver operating characteristics of 0.85. By excluding closely related templates, we show that GeauxDock maintains its accuracy at lower levels of homology through the increased contribution from physics-based energy terms compensating for weak evolutionary constraints. GeauxDock is available at http://www.institute.loni.org/lasigma/package/dock/.

Panel docking of small-molecule libraries - Prospects to improve efficiency of lead compound discovery

Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

Calculating an optimal box size for ligand docking and virtual screening against experimental and predicted binding pockets

Background

Computational approaches have emerged as an instrumental methodology in modern research. For example, virtual screening by molecular docking is routinely used in computer-aided drug discovery. One of the critical parameters for ligand docking is the size of a search space used to identify low-energy binding poses of drug candidates. Currently available docking packages often come with a default protocol for calculating the box size, however, many of these procedures have not been systematically evaluated.

Methods

In this study, we investigate how the docking accuracy of AutoDock Vina is affected by the selection of a search space. We propose a new procedure for calculating the optimal docking box size that maximizes the accuracy of binding pose prediction against a non-redundant and representative dataset of 3,659 protein-ligand complexes selected from the Protein Data Bank. Subsequently, we use the Directory of Useful Decoys, Enhanced to demonstrate that the optimized docking box size also yields an improved ranking in virtual screening. Binding pockets in both datasets are derived from the experimental complex structures and, additionally, predicted by eFindSite.

Results

A systematic analysis of ligand binding poses generated by AutoDock Vina shows that the highest accuracy is achieved when the dimensions of the search space are 2.9 times larger than the radius of gyration of a docking compound. Subsequent virtual screening benchmarks demonstrate that this optimized docking box size also improves compound ranking. For instance, using predicted ligand binding sites, the average enrichment factor calculated for the top 1 % (10 %) of the screening library is 8.20 (3.28) for the optimized protocol, compared to 7.67 (3.19) for the default procedure. Depending on the evaluation metric, the optimal docking box size gives better ranking in virtual screening for about two-thirds of target proteins.

Conclusions

This fully automated procedure can be used to optimize docking protocols in order to improve the ranking accuracy in production virtual screening simulations. Importantly, the optimized search space systematically yields better results than the default method not only for experimental pockets, but also for those predicted from protein structures. A script for calculating the optimal docking box size is freely available at www.brylinski.org/content/docking-box-size.

Graphical Abstract

Natural products as leads in schistosome drug discovery

Schistosomiasis is a neglected parasitic tropical disease that claims around 200,000 human lives every year. Praziquantel (PZQ), the only drug recommended by the World Health Organization for the treatment and control of human schistosomiasis, is now facing the threat of drug resistance, indicating the urgent need for new effective compounds to treat this disease. Therefore, globally, there is renewed interest in natural products (NPs) as a starting point for drug discovery and development for schistosomiasis. Recent advances in genomics, proteomics, bioinformatics, and cheminformatics have brought about unprecedented opportunities for the rapid and more cost-effective discovery of new bioactive compounds against neglected tropical diseases. This review highlights the main contributions that NP drug discovery and development have made in the treatment of schistosomiasis and it discusses how integration with virtual screening (VS) strategies may contribute to accelerating the development of new schistosomidal leads, especially through the identification of unexplored, biologically active chemical scaffolds and structural optimization of NPs with previously established activity.

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.

Virtual screening: an in silico tool for interlacing the chemical universe with the proteome

In silico screening both in the forward (traditional virtual screening) and reverse sense (inverse virtual screening (IVS)) are helpful techniques for interlacing the chemical universe of small molecules with the proteome. The former, which is using a protein structure and a large chemical database, is well-known by the scientific community. We have chosen here to provide an overview on the latter, focusing on validation and target prioritization strategies. By comparing it to complementary or alternative wet-lab approaches, we put IVS in the broader context of chemical genomics, target discovery and drug design. By giving examples from the literature and an own example on how to validate the approach, we provide guidance on the issues related to IVS.

Antioxidant activity and spectroscopic data of isoxanthohomol oxime and related compounds

Oximes of isoxanthohumol (IXN), naringenin (N) and flavanone (FL) were synthesized with yields of 88-95% and their antioxidant activity was evaluated using the 1,1-diphenyl-2-picrylhydrazyl radical (DPPH) method. Although naringenin oxime (NOX) and flavanone oxime (FLOX) did not have any significant antioxidant effect (EC50=2.21 mM and 78.7 mM, respectively), isoxanthohumol oxime (IXNOX) showed a strong antioxidant activity (EC50=0.0411 mM), comparable to the activity of ascorbic acid (EC50=0.0181 mM). The structure of new compound IXNOX was established using (1)H NMR, (13)C NMR, IR and UV-VIS spectroscopy, by comparison to IXN, NOX and FLOX.

Isolation and characterization of bioactive compounds from plant resources: the role of analysis in the ethnopharmacological approach

The phytochemical research based on ethnopharmacology is considered an effective approach in the discovery of novel chemicals entities with potential as drug leads. Plants/plant extracts/decoctions, used by folklore traditions for treating several diseases, represent a source of chemical entities but no information are available on their nature. Starting from this viewpoint, the aim of this review is to address natural-products chemists to the choice of the best methodologies, which include the combination of extraction/sample preparation tools and analytical techniques, for isolating and characterizing bioactive secondary metabolites from plants, as potential lead compounds in the drug discovery process. The work is distributed according to the different steps involved in the ethnopharmacological approach (extraction, sample preparation, biological screening, etc.), discussing the analytical techniques employed for the isolation and identification of compound/s responsible for the biological activity claimed in the traditional use (separation, spectroscopic, hyphenated techniques, etc.). Particular emphasis will be on herbal medicines applications and developments achieved from 2010 up to date.