Peptide deformylase is a potential target for anti-Helicobacter pylori drugs: reverse docking, enzymatic assay, and X-ray crystallography validation

Network pharmacology and in vitro experimental verification unveil glycyrrhizin from glycyrrhiza glabra alleviates acute pancreatitis via modulation of MAPK and STAT3 signaling pathways

Acute pancreatitis (AP) is a severe gastrointestinal inflammatory disease with increasing mortality and morbidity. Glycyrrhiza glabra, commonly known as Liquorice, is a widely used plant containing bioactive compounds like Glycyrrhizin, which possesses diverse medicinal properties such as anti-inflammatory, antioxidant, antiviral, and anticancer activities. The objective of this study is to investigate the active components, relevant targets, and underlying mechanisms of the traditional Chinese medicine Glycyrrhiza glabra in the treatment of AP. Utilizing various computational biology methods, we explored the potential targets and molecular mechanisms through Glycyrrhizin supplementation. Computational results indicated that Glycyrrhizin shows promising pharmacological potential, particularly with mitogen-activated protein kinase 3 (MAPK3) protein (degree: 70), forming stable complexes with Glycyrrhizin through ionic and hydrogen bonding interactions, with a binding free energy (ΔGbind) of -33.01 ± 0.08 kcal/mol. Through in vitro experiments, we validated that Glycyrrhizin improves primary pancreatic acinar cell injury by inhibiting the MAPK/STAT3/AKT signaling pathway. Overall, MAPK3 emerges as a reliable target for Glycyrrhizin's therapeutic effects in AP treatment. This study provides novel insights into the active components and potential targets and molecular mechanisms of natural products.

Identification, Genome Sequencing, and Characterizations of Helicobacter pylori Sourced from Pakistan

The stomach's colonization by Helicobacter pylori (H. pylori) results in gastritis, ulcers, and stomach cancer. Frequently, pain is treated with medication, but resistant H. pylori infections are not. Therefore, it is important to find pharmacological targets and improved treatments for resistant H. pylori strains. The aim of the current study was sampling, identification, drug susceptibility testing following genome sequencing and comparative genome-wide analysis of selected H. pylori strains from Pakistan with three representative strains for virulence and drug-resistant characteristics. Based on culture, biochemistry, and molecular biology, 84 strains of H. pylori were identified, which made up 47% of the enrolled cases. Among all H. pylori strains, the highest resistance was reported for metronidazole with 82 H. pylori strains (98%), followed by clarithromycin with 62 resistant strains (74%). Among metronidazole-resistant strains, 38 strains (46%) were also resistant to clarithromycin, contributing 61% of clarithromycin resistant cases. Two strains, HPA1 and HPA2, isolated from 'gastritis' and 'gastric ulcer' patients, respectively, were further processed for WGS. The draft genome sequences of H. pylori strains HPA1 and HPA2 encode 1.66 Mbp and 1.67 Mbp genome size, 24 and 4 contiguous DNA sequences, and 1650 and 1625 coding sequences, respectively. Both the genomes showed greater than 90% similarity with the reference strain H. pylori ATCC 43504/PMSS1. The antibiotic-resistant genes were identified among all the strains with overall similarity above 95%, with minor differences in the sequence similarity. Using the virulent gene data obtained from the Virulence Factor Database, 75 to 85 virulent genes were identified in the five genome assemblies with various key genes such as cytolethal distending toxin (cdt), type IV secretion system, cag PAI, plasticity region, cell-motility- and flagellar-associated genes, neutrophil-activating protein (HP-NAP), T4SS effector cytotoxin-associated gene A (cagA), and urease-associated genes ureA and ureB, etc. Sequence similarity between the virulence factors found in this study and reference genes was at least 90%. In summary, the results of our study showed the relationship between clinical results and specific H. pylori strains' (HPA1 and HPA2) genetics such as antibiotic resistance and specific virulence factors. These findings provide valued understanding of the epidemiology of H. pylori-associated diseases. Moreover, identification and genomics analysis have provided insights into the epidemiology, genetic diversity, pathogenicity, and potential drug resistance genes of H. pylori strains, offering a foundation for developing more targeted and effective medical interventions, including anti-virulent medications.

In silico Methods for Identification of Potential Therapeutic Targets

At the initial stage of drug discovery, identifying novel targets with maximal efficacy and minimal side effects can improve the success rate and portfolio value of drug discovery projects while simultaneously reducing cycle time and cost. However, harnessing the full potential of big data to narrow the range of plausible targets through existing computational methods remains a key issue in this field. This paper reviews two categories of in silico methods—comparative genomics and network-based methods—for finding potential therapeutic targets among cellular functions based on understanding their related biological processes. In addition to describing the principles, databases, software, and applications, we discuss some recent studies and prospects of the methods. While comparative genomics is mostly applied to infectious diseases, network-based methods can be applied to infectious and non-infectious diseases. Nonetheless, the methods often complement each other in their advantages and disadvantages. The information reported here guides toward improving the application of big data-driven computational methods for therapeutic target discovery.

Graphical abstract

Atomic level and structural understanding of natural ligands inhibiting Helicobacter pylori peptide deformylase through ligand and receptor based screening, SIFT, molecular dynamics and DFT - a structural computational approach

Helicobacter pylori is a Gram-negative microaerophilic gastric pathogen, responsible for the cause of peptic ulcer around half of the global population. Although several antibiotics and combination therapies have been employed for H. pylori-related gastric ulcer and cancer regiments, identifying potent inhibitors for specific targets of this bacterium will help assessing better treatment periodicity and methods to eradicate H. pylori. Herein, 1,000,000 natural compounds were virtually screened against Helicobacter pylori Peptide deformylase (HpPDF). Pharmacophore hypotheses were created using ligand and receptor-based pharmacophore modeling of GLIDE. Stringent HTVS and IFD docking protocol of GLIDE predicted leads with stable intermolecular bonds and scores. Molecular dynamics simulation of HpPDF was carried out for 100 ns using GROMACS. Hits ZINC00225109 and ZINC44896875 came up with a glide score of -9.967 kcal/mol and -12.114 kcal/mol whereas; reference compound actinonin produced a glide score of -9.730 kcal/mol. Binding energy values of these hits revealed the involvement of significant Van der Waals and Coulomb forces and the deduction of lipophilic forces that portray the deep hydrophobic residues in the S1pocket of H. pylori. The DFT analysis established the electron density-based features of the molecules and observed that the results correlate with intermolecular docking interactions. Analysis of the MD trajectories revealed the crucial residues involved in HpPDF - ligand binding and the conformational changes in the receptor. We have identified and deciphered the crucial features necessary for the potent ligand binding at catalytic site of HpPDF. The resulting ZINC natural compound hits from the study could be further employed for potent drug development.Communicated by Ramaswamy H. Sarma.

From Diospyros kaki L. (Persimmon) Phytochemical Profile and Health Impact to New Product Perspectives and Waste Valorization

Persimmon (Diospyros kaki L.) fruit's phytochemical profile includes carotenoids, proanthocyanidins, and gallic acid among other phenolic compounds and vitamins. A huge antioxidant potential is present given this richness in antioxidant compounds. These bioactive compounds impact on health benefits. The intersection of nutrition and sustainability, the key idea behind the EAT-Lancet Commission, which could improve human health and decrease the global impact of food-related health conditions such as cancer, heart disease, diabetes, and obesity, bring the discussion regarding persimmon beyond the health effects from its consumption, but also on the valorization of a very perishable food that spoils quickly. A broad option of edible products with better storage stability or solutions that apply persimmon and its byproducts in the reinvention of old products or even creating new products, or with new and better packaging for the preservation of food products with postharvest technologies to preserve and extend the shelf-life of persimmon food products. Facing a global food crisis and the climate emergency, new and better day-to-day solutions are needed right now. Therefore, the use of persimmon waste has also been discussed as a good solution to produce biofuel, eco-friendly alternative reductants for fabric dyes, green plant growth regulator, biodegradable and edible films for vegetable packaging, antimicrobial activity against foodborne methicillin-resistant Staphylococcus aureus found in retail pork, anti-Helicobacter pylori agents from pedicel extracts, and persimmon pectin-based emulsifiers to prevent lipid peroxidation, among other solutions presented in the revised literature. It has become clear that the uses for persimmon go far beyond the kitchen table and the health impact consumption demonstrated over the years. The desired sustainable transition is already in progress, however, mechanistic studies and clinical trials are essential and scaling-up is fundamental to the future.

In Silico Approach for Phytocompound-Based Drug Designing to Fight Efflux Pump-Mediated Multidrug-Resistant Mycobacterium tuberculosis

Tuberculosis (TB), caused by the bacteria Mycobacterium tuberculosis, is one of the principal causes of death in the world despite the existence of a significant number of antibiotics aimed against it. This is mainly due to the drug resistance mechanisms present in the bacterium, which leads to multidrug-resistant tuberculosis (MDR-TB). Additionally, the development of new antibiotics has become limited over the years. Although there are various drug resistance mechanisms present, efflux pumps are of utmost importance because they extrude out several dissimilar antitubercular drugs out of the cell. There are many efflux pump proteins present in Mycobacterium tuberculosis. Therefore, blocking these efflux pumps by inhibitors can raise the efficacy of the existing antibiotics and may also pave the path for the discovery and synthesis of new drugs. Plant compounds can act as a resource for the development of efflux pump inhibitors (EPIs), which may eventually replace or augment the current therapeutic options. This is mainly because plants have been traditionally used for ages for food or treatment and are considered safe with little or no side effects. Various computational tools are available which are used for the virtual screening of a large number of phytocompounds within a short span of time. This review aims to highlight the mechanism and appearance of drug resistance in Mycobacterium tuberculosis with emphasis on efflux pumps along with the significance of phytochemicals as inhibitors of these pumps and their screening strategy by computational approaches.

Pharmacoinformatics approaches in the discovery of drug-like antimicrobials of plant origin

Medicinal plants have served as an important source for addressing the ailments of humans and animals alike. The emergence of advanced technologies in the field of drug discovery and development has helped in isolating various bioactive phytochemicals and developing them as drugs. Owing to their significant pharmacological benefits and minimum adverse effects, they not only serve as good candidates for therapeutics themselves but also help in the identification and development of related drug like molecules against various metabolic and infectious diseases. The ever-increasing diversity, severity and incidence of infectious diseases has resulted in an exaggerated mortality and morbidity levels. Geno-proteomic mutations in microbes, irrational prescribing of antibiotics, antimicrobial resistance and human population explosion, all call for continuous efforts to discover and develop alternated therapeutic options against the microbes. This review article describes the pharmacoinformatics tools and methods which are currently used in the discovery of bioactive phytochemicals, thus making the process more efficient and effective. The pharmacological aspects of the drug discovery and development process have also been reviewed with reference to the in silico activities. Communicated by Ramaswamy H. Sarma.

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.

Identification of Potential Drug Targets in Helicobacter pylori Using In Silico Subtractive Proteomics Approaches and Their Possible Inhibition through Drug Repurposing

The class 1 carcinogen, Helicobacter pylori, is one of the World Health Organization's high priority pathogens for antimicrobial development. We used three subtractive proteomics approaches using protein pools retrieved from: chokepoint reactions in the BIOCYC database, the Kyoto Encyclopedia of Genes and Genomes, and the database of essential genes (DEG), to find putative drug targets and their inhibition by drug repurposing. The subtractive channels included non-homology to human proteome, essentiality analysis, sub-cellular localization prediction, conservation, lack of similarity to gut flora, druggability, and broad-spectrum activity. The minimum inhibitory concentration (MIC) of three selected ligands was determined to confirm anti-helicobacter activity. Seventeen protein targets were retrieved. They are involved in motility, cell wall biosynthesis, processing of environmental and genetic information, and synthesis and metabolism of secondary metabolites, amino acids, vitamins, and cofactors. The DEG protein pool approach was superior, as it retrieved all drug targets identified by the other two approaches. Binding ligands (n = 42) were mostly small non-antibiotic compounds. Citric, dipicolinic, and pyrophosphoric acid inhibited H. pylori at an MIC of 1.5-2.5 mg/mL. In conclusion, we identified potential drug targets in H. pylori, and repurposed their binding ligands as possible anti-helicobacter agents, saving time and effort required for the development of new antimicrobial compounds.

Silica bonded N-(propylcarbamoyl)sulfamic acid (SBPCSA) as a highly efficient and recyclable solid catalyst for the synthesis of Benzylidene Acrylate derivatives: Docking and reverse docking integrated approach of network pharmacology

A green approach has been developed for the synthesis of a series of benzylidene acrylate 3(a-p) from differently substituted aromatic/heterocyclic aldehydes and ethyl cyanoacetate in excellent yields (90-98%), and employing silica bonded N-(Propylcarbamoyl)sulfamic acid as a recyclable catalyst under solvent-free condition. The molecular structure of compounds 3b, 3d and 3i were well supported by single-crystal X-ray crystallographic analysis. The present protocol bears wide substrate tolerance and is believed to be more practical, efficient, eco-friendly, and compatible as compared to existing methods. In-silico approaches were implemented to find the biochemical and physiological effects, toxicity, and biological profiles of the synthesized compounds to determine the expected biological nature and confirm a drug-like compound. A molecular docking study of the expected biologically active compound was performed to know the hypothetically binding mode with the receptor. Also, reverse docking is applied to recognize receptors from unknown protein targets for drug-like compounds to explain poly-pharmacology and binding postures with different receptors.

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

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

The potential role of in silico approaches to identify novel bioactive molecules from natural resources

In recent years, integration of in silico approaches to natural product (NP) research reawakened the declined interest in NP-based drug discovery efforts. In particular, advancements in cheminformatics enabled comparison of NP databases with contemporary small-molecule libraries in terms of molecular properties and chemical space localizations. Virtual screening and target fishing approaches were successful in recognizing the untold macromolecular targets for NPs to exploit the unmet therapeutic needs. Developments in molecular docking and scoring methods along with molecular dynamics enabled to predict the target-ligand interactions more accurately taking into consideration the remarkable structural complexity of NPs. Hence, innovative in silico strategies have contributed valuably to the NP research in drug discovery processes as reviewed herein. [Formula: see text].

A Systematic In-silico Analysis of Helicobacter pylori Pathogenic Islands for Identification of Novel Drug Target Candidates

Background:

Helicobacter pylori is associated with inflammation of different areas, such as the duodenum and stomach, causing gastritis and gastric ulcers leading to lymphoma and cancer. Pathogenic islands are a type of clustered mobile elements ranging from 10-200 Kb contributing to the virulence of the respective pathogen coding for one or more virulence factors. Virulence factors are molecules expressed and secreted by pathogen and are responsible for causing disease in the host. Bacterial genes/virulence factors of the pathogenic islands represent a promising source for identifying novel drug targets.

Objective:

The study aimed at identifying novel drug targets from pathogenic islands in H. pylori.

Material & Methods:

The genome of 23 H. pylori strains were screened for pathogenic islands and bacterial genes/virulence factors to identify drug targets. Protein-protein interactions of drug targets were predicted for identifying interacting partners. Further, host-pathogen interactions of interacting partners were predicted to identify important molecules which are closely associated with gastric cancer.

Results:

Screening the genome of 23 H. pylori strains revealed 642 bacterial genes/virulence factors in 31 pathogenic islands. Further analysis identified 101 genes which were non-homologous to human and essential for the survival of the pathogen, among them 31 are potential drug targets. Protein-protein interactions for 31 drug targets predicted 609 interacting partners. Predicted interacting partners were further subjected to host-pathogen interactions leading to identification of important molecules like TNF receptor associated factor 6, (TRAF6) and MAPKKK7 which are closely associated with gastric cancer.

Conclusion:

These provocative studies enabled us to identify important molecules in H. pylori and their counter interacting molecules in the host leading to gastric cancer and also a pool of novel drug targets for therapeutic intervention of gastric cancer.

Chinese Herbal Medicine Meets Biological Networks of Complex Diseases: A Computational Perspective

With the rapid development of cheminformatics, computational biology, and systems biology, great progress has been made recently in the computational research of Chinese herbal medicine with in-depth understanding towards pharmacognosy. This paper summarized these studies in the aspects of computational methods, traditional Chinese medicine (TCM) compound databases, and TCM network pharmacology. Furthermore, we chose arachidonic acid metabolic network as a case study to demonstrate the regulatory function of herbal medicine in the treatment of inflammation at network level. Finally, a computational workflow for the network-based TCM study, derived from our previous successful applications, was proposed.

Using reverse docking to identify potential targets for ginsenosides

Background

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

Methods

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

Results

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

Conclusion

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

Design, Synthesis, and Biological Evaluation of Novel Tetrahydroprotoberberine Derivatives (THPBs) as Selective α1A-Adrenoceptor Antagonists

A novel series of tetrahydroprotoberberine derivatives (THPBs) were designed, synthesized, and evaluated as selective α_1A-adrenergic receptors (AR) antagonists for the treatment of benign prostatic hyperplasia. On the basis of the pharmacophore model of the marketed drug silodosin, THPBs were modified by introducing an indole segment into their core scaffolds. In calcium assays, 7 out of 32 compounds displayed excellent antagonistic activities against α_1A-ARs, with IC₅₀ less than 250 nM. Among them, compound (S)-27 had the most potent biological activity; its IC₅₀ toward α_1A-AR was 12.8 ± 2.2 nM, which is 781 and 20 times more selective than that toward α_1B- and α_1D-AR, respectively. In the functional assay using isolated rat tissues, compound (S)-27 inhibited norepinephrine-induced urethra smooth muscle contraction potently (IC₅₀ = 0.5 ± 0.3 nM), without inhibiting the aortic contraction (IC₅₀ > 1000 nM), displaying a better tissue selectivity than the marketed drug silodosin. Additional results of preliminary safety studies (acute toxicity and hERG inhibition) and pharmacokinetics studies indicated the potential druggability for compound (S)-27 which is a promising lead for the development of selective α_1A-AR antagonists for the treatment of BPH.

RepurposeVS: A Drug Repurposing-Focused Computational Method for Accurate Drug-Target Signature Predictions

We describe here RepurposeVS for the reliable prediction of drug-target signatures using X-ray protein crystal structures. RepurposeVS is a virtual screening method that incorporates docking, drug-centric and protein-centric 2D/3D fingerprints with a rigorous mathematical normalization procedure to account for the variability in units and provide high-resolution contextual information for drug-target binding. Validity was confirmed by the following: (1) providing the greatest enrichment of known drug binders for multiple protein targets in virtual screening experiments, (2) determining that similarly shaped protein target pockets are predicted to bind drugs of similar 3D shapes when RepurposeVS is applied to 2,335 human protein targets, and (3) determining true biological associations in vitro for mebendazole (MBZ) across many predicted kinase targets for potential cancer repurposing. Since RepurposeVS is a drug repurposing-focused method, benchmarking was conducted on a set of 3,671 FDA approved and experimental drugs rather than the Database of Useful Decoys (DUDE) so as to streamline downstream repurposing experiments. We further apply RepurposeVS to explore the overall potential drug repurposing space for currently approved drugs. RepurposeVS is not computationally intensive and increases performance accuracy, thus serving as an efficient and powerful in silico tool to predict drug-target associations in drug repurposing.

Using reverse docking for target identification and its applications for drug discovery

INTRODUCTION

In contrast to traditional molecular docking, inverse or reverse docking is used for identifying receptors for a given ligand among a large number of receptors. Reverse docking can be used to discover new targets for existing drugs and natural compounds, explain polypharmacology and the molecular mechanism of a substance, find alternative indications of drugs through drug repositioning, and detecting adverse drug reactions and drug toxicity.

AREAS COVERED

In this review, the authors examine how reverse docking methods have evolved over the past fifteen years and how they have been used for target identification and related applications for drug discovery. They discuss various aspects of target databases, reverse docking tools and servers.

EXPERT OPINION

There are several issues related to reverse docking methods such as target structure dataset construction, computational efficiency, how to include receptor flexibility, and most importantly, how to properly normalize the docking scores. In order for reverse docking to become a truly useful tool for the drug discovery, these issues need to be adequately resolved.

Study on Wangzaozin-A-Inducing Cancer Apoptosis and Its Theoretical Protein Targets

Wangzaozin A is the most representative cytotoxic C-20-nonoxide compound of Isodon plants of Labiatae. The protein targets of the 2 isomers (X and X') of Wangzaozin A are, respectively, extracted from target fishing dock Web site. Each isomer has 23 targets with good energy scores. The binding modes of each isomer and its targets are, respectively, analyzed by Dock. The energy score of the binding mode between the isomer X and the inositol-1(or 4)-monophosphatase is the smallest. The apoptosis, antiproliferative, and lethal effects of human gastric cancer cells induced by Wangzaozin A are assessed by trypan blue exclusion, hoechst33258 stain in SGC-7901, and flow cytometric measurement. The mechanism of Wangzaozin-A-inducing cancer apoptosis is analyzed. Wangzaozin A inhibits the growth of human gastric cancer SGC-7901 cell lines at the lower concentration (<4.0 µmol/L) and results in the lethal effect at the relative higher concentration (>8.0 µmol/L).

Histidine 352 (His352) and tryptophan 355 (Trp355) are essential for flax UGT74S1 glucosylation activity toward secoisolariciresinol

Flax secoisolariciresinol diglucoside (SDG) lignan is a natural phytoestrogen for which a positive role in metabolic diseases is emerging. Until recently however, much less was known about SDG and its monoglucoside (SMG) biosynthesis. Lately, flax UGT74S1 was identified and characterized as an enzyme sequentially glucosylating secoisolariciresinol (SECO) into SMG and SDG when expressed in yeast. However, the amino acids critical for UGT74S1 glucosyltransferase activity were unknown. A 3D structural modeling and docking, site-directed mutagenesis of five amino acids in the plant secondary product glycosyltransferase (PSPG) motif, and enzyme assays were conducted. UGT74S1 appeared to be structurally similar to the Arabidopsis thaliana UGT72B1 model. The ligand docking predicted Ser357 and Trp355 as binding to the phosphate and hydroxyl groups of UDP-glucose, whereas Cys335, Gln337 and Trp355 were predicted to bind the 7-OH, 2-OCH3 and 17-OCH3 of SECO. Site-directed mutagenesis of Cys335, Gln337, His352, Trp355 and Ser357, and enzyme assays revealed an alteration of these binding sites and a significant reduction of UGT74S1 glucosyltransferase catalytic activity towards SECO and UDP-glucose in all mutants. A complete abolition of UGT74S1 activity was observed when Trp355 was substituted to Ala355 and Gly355 or when changing His352 to Asp352, and an altered metabolite profile was observed in Cys335Ala, Gln337Ala, and Ser357Ala mutants. This study provided for the first time evidence that Trp355 and His352 are critical for UGT74S1's glucosylation activity toward SECO and suggested the possibility for SMG production in vitro.

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.

Comparison of ultra-fast 2D and 3D ligand and target descriptors for side effect prediction and network analysis in polypharmacology

BACKGROUND AND PURPOSE

Some existing computational methods are used to infer protein targets of small molecules and can therefore be used to find new targets for existing drugs, with the goals of re-directing the molecule towards a different therapeutic purpose or explaining off-target effects due to multiple targeting. Inherent limitations, however, arise from the fact that chemical analogy is calculated on the basis of common frameworks or scaffolds and also because target information is neglected. The method we present addresses these issues by taking into account 3D information from both the ligand and the target.

EXPERIMENTAL APPROACH

ElectroShape is an established method for ultra-fast comparison of the shapes and charge distributions of ligands that is validated here for prediction of on-target activities, off-target profiles and adverse effects of drugs and drug-like molecules taken from the DrugBank database.

KEY RESULTS

The method is shown to predict polypharmacology profiles and relate targets from two complementary viewpoints (ligand- and target-based networks).

CONCLUSIONS AND IMPLICATIONS

The open-access web tool presented here (http://ub.cbm.uam.es/chemogenomics/) allows interactive navigation in a unified 'pharmacological space' from the viewpoints of both ligands and targets. It also enables prediction of pharmacological profiles, including likely side effects, for new compounds. We hope this web interface will help many pharmacologists to become aware of this new paradigm (up to now mostly used in the realm of the so-called 'chemical biology') and encourage its use with a view to revealing 'hidden' relationships between new and existing compounds and pharmacologically relevant targets.

New technologies in computer-aided drug design: Toward target identification and new chemical entity discovery

In the postgenomic era, computer-aided drug design (CADD) has considerably extended its range of applications, spanning almost all stages in the drug discovery pipeline, from target identification to lead discovery, from lead optimization to preclinical or clinical trials. Two new technologies of CADD associated with target identification and new chemical entity discovery will be the focus of this review.

Li-he Zhang – School of Pharmaceutical Science, Peking University, Beijing, China

Kaixian Chen – Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion

Background

Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound.

Results

We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities.

Conclusions

With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Identification of Similar Binding Sites to Detect Distant Polypharmacology

The ability of small molecules to interact with multiple proteins is referred to as polypharmacology. This property is often linked to the therapeutic action of drugs but it is known also to be responsible for many of their side effects. Because of its importance, the development of computational methods that can predict drug polypharmacology has become an important line of research that led recently to the identification of many novel targets for known drugs. Nowadays, the majority of these methods are based on measuring the similarity of a query molecule against the hundreds of thousands of molecules for which pharmacological data on thousands of proteins are available in public sources. However, similarity-based methods are inherently biased by the chemical coverage offered by the active molecules present in those public repositories, which limits significantly their capacity to predict interactions with proteins structurally and functionally unrelated to any of the already known targets for drugs. It is in this respect that structure-based methods aiming at identifying similar binding sites may offer an alternative complementary means to ligand-based methods for detecting distant polypharmacology. The different existing approaches to binding site detection, representation, comparison, and fragmentation are reviewed and recent successful applications presented.

Expression, crystallization and preliminary X-ray crystallographic analysis of peptide deformylase from Campylobacter jejuni

Campylobacter jejuni is one of the major foodborne pathogens causing human infection. Peptide deformylase, a metallohydrolase, catalyzes the deformylation of N-formylated methionine in newly synthesized polypeptides in prokaryotes and some eukaryotic organelles. The deformylation process is an essential step in protein synthesis and has attracted much attention as a potential target for the development of novel antibacterial agents. Here, the cloned codon-optimized def gene from C. jejuni was synthesized and the protein was expressed, purified and crystallized. C. jejuni peptide deformylase crystals obtained at pH 7.0 and pH 6.5 diffracted to 2.9 Å resolution and belonged to the trigonal space group R3, with unit-cell parameters a=b=105.7, c=58.0 Å. One monomer existed in the asymmetric unit, with a corresponding VM of 3.1 Å3 Da(-1) and a solvent content of 60.4%.

Computational methods for drug design and discovery: focus on China

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The leading computational techniques for drug design and discovery are reviewed.

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Successful applications of computational techniques are provided.

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A novel drug–target binding kinetics calculation approach is introduced.

In the past decades, China's computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of China's status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.

Proteome-scale docking: myth and reality

Docking is the computational method of choice to quickly predict how a low molecular-weight ligand binds to its macromolecular target. Despite persistent problems in predicting binding free energies, docking has undergone significant advances in numerous topics (throughput, target flexibility). The ever increasing availability of high-resolution X-ray structures and the development of more reliable comparative models for proteins of pharmacological interest paved the way to apply protein–ligand docking to multiple targets to predict main and off-targets for bioactive compounds and even to repurpose existing drugs. Applying docking to multiple targets brings an additional level of complexity in scoring numerous and heterogeneous docking poses. Despite undeniable successes, proteomewide docking should, however, be considered with caution with regard to recall and precision of the predictions.

TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database

Target identification of the known bioactive compounds and novel synthetic analogs is a very important research field in medicinal chemistry, biochemistry, and pharmacology. It is also a challenging and costly step towards chemical biology and phenotypic screening. In silico identification of potential biological targets for chemical compounds offers an alternative avenue for the exploration of ligand-target interactions and biochemical mechanisms, as well as for investigation of drug repurposing. Computational target fishing mines biologically annotated chemical databases and then maps compound structures into chemogenomical space in order to predict the biological targets. We summarize the recent advances and applications in computational target fishing, such as chemical similarity searching, data mining/machine learning, panel docking, and the bioactivity spectral analysis for target identification. We then described in detail a new web-based target prediction tool, TargetHunter (http://www.cbligand.org/TargetHunter). This web portal implements a novel in silico target prediction algorithm, the Targets Associated with its MOst SImilar Counterparts, by exploring the largest chemogenomical databases, ChEMBL. Prediction accuracy reached 91.1% from the top 3 guesses on a subset of high-potency compounds from the ChEMBL database, which outperformed a published algorithm, multiple-category models. TargetHunter also features an embedded geography tool, BioassayGeoMap, developed to allow the user easily to search for potential collaborators that can experimentally validate the predicted biological target(s) or off target(s). TargetHunter therefore provides a promising alternative to bridge the knowledge gap between biology and chemistry, and significantly boost the productivity of chemogenomics researchers for in silico drug design and discovery.

Large-scale reverse docking profiles and their applications

BACKGROUND

Reverse docking approaches have been explored in previous studies on drug discovery to overcome some problems in traditional virtual screening. However, current reverse docking approaches are problematic in that the target spaces of those studies were rather small, and their applications were limited to identifying new drug targets. In this study, we expanded the scope of target space to a set of all protein structures currently available and developed several new applications of reverse docking method.

RESULTS

We generated 2D Matrix of docking scores among all the possible protein structures in yeast and human and 35 famous drugs. By clustering the docking profile data and then comparing them with fingerprint-based clustering of drugs, we first showed that our data contained accurate information on their chemical properties. Next, we showed that our method could be used to predict the druggability of target proteins. We also showed that a combination of sequence similarity and docking profile similarity could predict the enzyme EC numbers more accurately than sequence similarity alone. In two case studies, 5-fluorouracil and cycloheximide, we showed that our method can successfully find identifying target proteins.

CONCLUSIONS

By using a large number of protein structures, we improved the sensitivity of reverse docking and showed that using as many protein structure as possible was important in finding real binding targets.

Self-subunit swapping occurs in another gene type of cobalt nitrile hydratase

Self-subunit swapping is one of the post-translational maturation of the cobalt-containing nitrile hydratase (Co-NHase) family of enzymes. All of these NHases possess a gene organization of <β-subunit> <α-subunit> <activator protein>, which allows the activator protein to easily form a mediatory complex with the α-subunit of the NHase after translation. Here, we discovered that the incorporation of cobalt into another type of Co-NHase, with a gene organization of <α-subunit> <β-subunit> <activator protein>, was also dependent on self-subunit swapping. We successfully isolated a recombinant NHase activator protein (P14K) of Pseudomonas putida NRRL-18668 by adding a Strep-tag N-terminal to the P14K gene. P14K was found to form a complex [α(StrepP14K)₂] with the α-subunit of the NHase. The incorporation of cobalt into the NHase of P. putida was confirmed to be dependent on the α-subunit substitution between the cobalt-containing α(StrepP14K)₂ and the cobalt-free NHase. Cobalt was inserted into cobalt-free α(StrepP14K)₂ but not into cobalt-free NHase, suggesting that P14K functions not only as a self-subunit swapping chaperone but also as a metallochaperone. In addition, NHase from P. putida was also expressed by a mutant gene that was designed with a <β-subunit> <α-subunit> <P14K> order. Our findings expand the general features of self-subunit swapping maturation.

Discovery of structurally diverse and bioactive compounds from plant resources in China

This review describes the major discoveries of structurally diverse and/or biologically significant compounds from plant resources in China, mainly from the traditional Chinese medicines (TCMs) since the establishment of our research group in 1999. In the past decade, a large array of biologically significant and novel structures has been identified from plant resources (or TCM) in our laboratory. The structural modification of several biologically important compounds led to more than 400 derivatives, some of which exhibited significantly improved activities and provided opportunities to elucidate the structure-activity relationship of the related compound class. These findings are important for drug discovery and help us understand the biological basis for the traditional applications of these plants in TCM.

Computational drug discovery

Computational drug discovery is an effective strategy for accelerating and economizing drug discovery and development process. Because of the dramatic increase in the availability of biological macromolecule and small molecule information, the applicability of computational drug discovery has been extended and broadly applied to nearly every stage in the drug discovery and development workflow, including target identification and validation, lead discovery and optimization and preclinical tests. Over the past decades, computational drug discovery methods such as molecular docking, pharmacophore modeling and mapping, de novo design, molecular similarity calculation and sequence-based virtual screening have been greatly improved. In this review, we present an overview of these important computational methods, platforms and successful applications in this field.

Structures of Staphylococcus aureus peptide deformylase in complex with two classes of new inhibitors

Peptide deformylase (PDF) catalyzes the removal of the formyl group from the N-terminal methionine residue in newly synthesized polypeptides, which is an essential process in bacteria. Four new inhibitors of PDF that belong to two different classes, hydroxamate/pseudopeptide compounds [PMT387 (7a) and PMT497] and reverse-hydroxamate/nonpeptide compounds [PMT1039 (15e) and PMT1067], have been developed. These compounds inhibited the growth of several pathogens involved in respiratory-tract infections, such as Streptococcus pneumoniae, Moraxella catarrhalis and Haemophilus influenzae, and leading nosocomial pathogens such as Staphylococcus aureus and Klebsiella pneumoniae with a minimum inhibitory concentration (MIC) in the range 0.1-0.8 mg ml(-1). Interestingly, the reverse-hydroxamate/nonpeptide compounds showed a 250-fold higher antimicrobial activity towards S. aureus, although the four compounds showed similar K(i) values against S. aureus PDF enzymes, with K(i) values in the 11-85 nM range. To provide a structural basis for the discovery of additional PDF inhibitors, the crystal structures of S. aureus PDF in complex with the four inhibitors were determined at resolutions of 1.90-2.30 Å. The inhibitor-bound structures displayed distinct deviations depending on the inhibitor class. The distance between the Zn(2+) ion and the carbonyl O atom of the hydroxamate inhibitors (or the hydroxyl O atom of the reverse-hydroxamate inhibitors) appears to be correlated to S. aureus inhibition activity. The structural information reported in this study should aid in the discovery of new PDF inhibitors that can be used as novel antibacterial drugs.

In-silico studies in Chinese herbal medicines' research: evaluation of in-silico methodologies and phytochemical data sources, and a review of research to date

The available databases that catalogue information on traditional Chinese medicines are reviewed in terms of their content and utility for in-silico research on Chinese herbal medicines, as too are the various protein database resources, and the software available for use in such studies. The software available for bioinformatics and 'omics studies of Chinese herbal medicines are summarised, and a critical evaluation given of the various in-silico methods applied in screening Chinese herbal medicines, including classification trees, neural networks, support vector machines, docking and inverse docking algorithms. Recommendations are made regarding any future in-silico studies of Chinese herbal medicines.

SwissDock, a protein-small molecule docking web service based on EADock DSS

Most life science processes involve, at the atomic scale, recognition between two molecules. The prediction of such interactions at the molecular level, by so-called docking software, is a non-trivial task. Docking programs have a wide range of applications ranging from protein engineering to drug design. This article presents SwissDock, a web server dedicated to the docking of small molecules on target proteins. It is based on the EADock DSS engine, combined with setup scripts for curating common problems and for preparing both the target protein and the ligand input files. An efficient Ajax/HTML interface was designed and implemented so that scientists can easily submit dockings and retrieve the predicted complexes. For automated docking tasks, a programmatic SOAP interface has been set up and template programs can be downloaded in Perl, Python and PHP. The web site also provides an access to a database of manually curated complexes, based on the Ligand Protein Database. A wiki and a forum are available to the community to promote interactions between users. The SwissDock web site is available online at http://www.swissdock.ch. We believe it constitutes a step toward generalizing the use of docking tools beyond the traditional molecular modeling community.

High tolerance to mutations in a Chlamydia trachomatis peptide deformylase loop

AIM: To determine if and how a loop region in the peptide deformylase (PDF) of Chlamydia trachomatis regulates enzyme function.

METHODS: Molecular dynamics simulation was used to study a structural model of the chlamydial PDF (cPDF) and predict the temperature factor per residue for the protein backbone atoms. Site-directed mutagenesis was performed to construct cPDF variants. Catalytic properties of the resulting variants were determined by an enzyme assay using formyl-Met-Ala-Ser as a substrate.

RESULTS: In silico analysis predicted a significant increase in atomic motion in the DGELV sequence (residues 68-72) of a loop region in a cPDF mutant, which is resistant to PDF inhibitors due to two amino acid substitutions near the active site, as compared to wild-type cPDF. The D68R and D68R/E70R cPDF variants demonstrated significantly increased catalytic efficiency. The E70R mutant showed only slightly decreased efficiency. Although deletion of residues 68-72 resulted in a nearly threefold loss in substrate binding, this deficiency was compensated for by increased catalytic efficiency.

CONCLUSION: Movement of the DGELV loop region is involved in a rate-limiting conformational change of the enzyme during catalysis. However, there is no stringent sequence requirement for this region for cPDF enzyme activity.

Evolution in an oncogenic bacterial species with extreme genome plasticity: Helicobacter pylori East Asian genomes

BACKGROUND

The genome of Helicobacter pylori, an oncogenic bacterium in the human stomach, rapidly evolves and shows wide geographical divergence. The high incidence of stomach cancer in East Asia might be related to bacterial genotype. We used newly developed comparative methods to follow the evolution of East Asian H. pylori genomes using 20 complete genome sequences from Japanese, Korean, Amerind, European, and West African strains.

RESULTS

A phylogenetic tree of concatenated well-defined core genes supported divergence of the East Asian lineage (hspEAsia; Japanese and Korean) from the European lineage ancestor, and then from the Amerind lineage ancestor. Phylogenetic profiling revealed a large difference in the repertoire of outer membrane proteins (including oipA, hopMN, babABC, sabAB and vacA-2) through gene loss, gain, and mutation. All known functions associated with molybdenum, a rare element essential to nearly all organisms that catalyzes two-electron-transfer oxidation-reduction reactions, appeared to be inactivated. Two pathways linking acetyl~CoA and acetate appeared intact in some Japanese strains. Phylogenetic analysis revealed greater divergence between the East Asian (hspEAsia) and the European (hpEurope) genomes in proteins in host interaction, specifically virulence factors (tipα), outer membrane proteins, and lipopolysaccharide synthesis (human Lewis antigen mimicry) enzymes. Divergence was also seen in proteins in electron transfer and translation fidelity (miaA, tilS), a DNA recombinase/exonuclease that recognizes genome identity (addA), and DNA/RNA hybrid nucleases (rnhAB). Positively selected amino acid changes between hspEAsia and hpEurope were mapped to products of cagA, vacA, homC (outer membrane protein), sotB (sugar transport), and a translation fidelity factor (miaA). Large divergence was seen in genes related to antibiotics: frxA (metronidazole resistance), def (peptide deformylase, drug target), and ftsA (actin-like, drug target).

CONCLUSIONS

These results demonstrate dramatic genome evolution within a species, especially in likely host interaction genes. The East Asian strains appear to differ greatly from the European strains in electron transfer and redox reactions. These findings also suggest a model of adaptive evolution through proteome diversification and selection through modulation of translational fidelity. The results define H. pylori East Asian lineages and provide essential information for understanding their pathogenesis and designing drugs and therapies that target them.

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

Ligand promiscuity, which is now recognized as an extremely common phenomenon, is a major underlying cause of drug toxicity. We have developed a new reverse virtual screening (VS) method called ReverseScreen3D, which can be used to predict the potential protein targets of a query compound of interest. The method uses a 2D fingerprint-based method to select a ligand template from each unique binding site of each protein within a target database. The target database contains only the structurally determined bioactive conformations of known ligands. The 2D comparison is followed by a 3D structural comparison to the selected query ligand using a geometric matching method, in order to prioritize each target binding site in the database. We have evaluated the performance of the ReverseScreen2D and 3D methods using a diverse set of small molecule protein inhibitors known to have multiple targets, and have shown that they are able to provide a highly significant enrichment of true targets in the database. Furthermore, we have shown that the 3D structural comparison improves early enrichment when compared with the 2D method alone, and that the 3D method performs well even in the absence of 2D similarity to the template ligands. By carrying out further experimental screening on the prioritized list of targets, it may be possible to determine the potential targets of a new compound or determine the off-targets of an existing drug. The ReverseScreen3D method has been incorporated into a Web server, which is freely available at http://www.modelling.leeds.ac.uk/ReverseScreen3D .