Rediscovering the sweet spot in drug discovery

Ligandability at the Membrane Interface of GPx4 Revealed through a Reverse Micelle Fragment Screening Platform

While they account for a large portion of drug targets, membrane proteins present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of water-soluble proteins that bind to membranes, are also difficult targets, particularly those that function only when bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, protein-membrane interfaces are underexplored spaces in inhibitor design, and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability at the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase that is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of noncovalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 under bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from >200 to ∼15 μM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane-associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing noncovalent inhibitors of GPx4.

Ligandability at the membrane interface of GPx4 revealed through a reverse micelle fragment screening platform

While they account for a large portion of drug targets, membrane proteins (MPs) present a unique challenge for drug discovery. Peripheral membrane proteins (PMPs), a class of proteins that bind reversibly to membranes, are also difficult targets, particularly those that function only while bound to membranes. The protein-membrane interface in PMPs is often where functional interactions and catalysis occur, making it a logical target for inhibition. However, interfaces are underexplored spaces in inhibitor design and there is a need for enhanced methods for small-molecule ligand discovery. In an effort to better initiate drug discovery efforts for PMPs, this study presents a screening methodology using membrane-mimicking reverse micelles (mmRM) and NMR-based fragment screening to assess ligandability in the protein-membrane interface. The proof-of-principle target, glutathione peroxidase 4 (GPx4), is a lipid hydroperoxidase which is essential for the oxidative protection of membranes and thereby the prevention of ferroptosis. GPx4 inhibition is promising for therapy-resistant cancer therapy, but current inhibitors are generally covalent ligands with limited clinical utility. Presented here is the discovery of non-covalent small-molecule ligands for membrane-bound GPx4 revealed through the mmRM fragment screening methodology. The fragments were tested against GPx4 in bulk aqueous conditions and displayed little to no binding to the protein without embedment into the membrane. The 9 hits had varying affinities and partitioning coefficients and revealed properties of fragments that bind within the protein-membrane interface. Additionally, a secondary screen confirmed the potential to progress the fragments by enhancing the affinity from > 200 μM to ~15 μM with the addition of certain hydrophobic groups. This study presents an advancement of screening capabilities for membrane associated proteins, reveals ligandability within the GPx4 protein-membrane interface, and may serve as a starting point for developing non-covalent inhibitors of GPx4.

Identification of Kv4.2 protein complex and modifications by tandem affinity purification-mass spectrometry in primary neurons

Proteins usually form complexes to fulfill variable physiological functions. In neurons, communication relies on synapses where receptors, channels, and anchoring proteins form complexes to precisely control signal transduction, synaptic integration, and action potential firing. Although there are many published protocols to isolate protein complexes in cell lines, isolation in neurons has not been well established. Here we introduce a method that combines lentiviral protein expression with tandem affinity purification followed by mass-spectrometry (TAP-MS) to identify protein complexes in neurons. This protocol can also be used to identify post-translational modifications (PTMs) of synaptic proteins. We used the A-type voltage-gated K⁺ channel subunit Kv4.2 as the target protein. Kv4.2 is highly expressed in the hippocampus where it contributes to learning and memory through its regulation of neuronal excitability and synaptic plasticity. We tagged Kv4.2 with the calmodulin-binding-peptide (CBP) and streptavidin-binding-peptide (SBP) at its C-terminus and expressed it in neurons via lentivirus. Kv4.2 was purified by two-step TAP and samples were analyzed by MS. MS identified two prominently known Kv4.2 interacting proteins [dipeptidyl peptidase like (DPPs) and Kv channel-interacting proteins (KChIPs)] in addition to novel synaptic proteins including glutamate receptors, a calcium channel, and anchoring proteins. Co-immunoprecipitation and colocalization experiments validated the association of Kv4.2 with glutamate receptors. In addition to protein complex identification, we used TAP-MS to identify Kv4.2 phosphorylation sites. Several known and unknown phosphorylation sites were identified. These findings provide a novel path to identify protein-protein interactions and PTMs in neurons and shed light on mechanisms of neuronal signaling potentially involved in the pathology of neurological diseases.

Screening of Atherosclerotic Druggable Targets from the Proteome Network of Differentially Expressed Genes

Differently expressed genes of atherosclerotic sample analysis are helpful to sort the prominent genes that influence the plaque formation and progression. Scientific evidence-based protein-protein interaction network (PPIN) studies were used to find hub proteins in complex disease conditions. Druggable capacity is one of the important parameters to confirm as a successful drug target. Construction of protein interaction network and principal node analysis (PNA) on atherosclerotic data sets lead to screen the hub proteins. Furthermore, druggable property of protein pocket confirms the targetability of susceptible target candidates and for target selection. Differentially expressed genes are identified through GEO2R analyzer on data sets of various atherosclerotic samples. STRING database and Cytoscape are employed to construct PPIN. Targets were identified by PNA such as centrality measures and clustering algorithm. Gene Ontology enrichment also used as one of the screening parameters to filter the candidates related to atherosclerotic terms. Topological evaluation of target protein was successfully done by ITASSER and GROMACS, respectively. Grid-based principle of DoGSiteScorer is utilized for druggability analysis. Six proteins such as integrin alpha L (ITGAL), metallothionein 1F (MT1F), metallothionein 1X (MT1X), P-selectin glycoprotein ligand-1 (SELPLG), solute carrier family 30 A, zinc transporter protein (SLC30A1), and TNFSF13B are screened as potential biomarkers through network-based analysis. Among the six, ITGAL, SELPLG, SLC30A1, and TNSF13B are identified as better prioritized atherosclerotic targets through druggability efficiency.

Systematic in silico Evaluation of Leishmania spp. Proteomes for Drug Discovery

Leishmaniasis is a group of neglected infectious diseases, with approximately 1. 3 million new cases each year, for which the available therapies have serious limitations. Therefore, it is extremely important to apply efficient and low-cost methods capable of selecting the best therapeutic targets to speed up the development of new therapies against those diseases. Thus, we propose the use of integrated computational methods capable of evaluating the druggability of the predicted proteomes of Leishmania braziliensis and Leishmania infantum, species responsible for the different clinical manifestations of leishmaniasis in Brazil. The protein members of those proteomes were assessed based on their structural, chemical, and functional contexts applying methods that integrate data on molecular function, biological processes, subcellular localization, drug binding sites, druggability, and gene expression. These data were compared to those extracted from already known drug targets (BindingDB targets), which made it possible to evaluate Leishmania proteomes for their biological relevance and treatability. Through this methodology, we identified more than 100 proteins of each Leishmania species with druggability characteristics, and potential interaction with available drugs. Among those, 31 and 37 proteins of L. braziliensis and L. infantum, respectively, have never been tested as drug targets, and they have shown evidence of gene expression in the evolutionary stage of pharmacological interest. Also, some of those Leishmania targets showed an alignment similarity of <50% when compared to the human proteome, making these proteins pharmacologically attractive, as they present a reduced risk of side effects. The methodology used in this study also allowed the evaluation of opportunities for the repurposing of compounds as anti-leishmaniasis drugs, inferring potential interaction between Leishmania proteins and ~1,000 compounds, of which only 15 have already been tested as a treatment for leishmaniasis. Besides, a list of potential Leishmania targets to be tested using drugs described at BindingDB, such as the potential interaction of the DEAD box RNA helicase, TRYR, and PEPCK proteins with the Staurosporine compound, was made available to the public.

Molecular Context of ADP-ribosylation in Schistosomes for Drug Discovery and Vaccine Development

Schistosome infection is regarded as one of the most important and neglected tropical diseases associated with poor sanitation. Like other living organisms, schistosomes employ multiple biological processes, of which some are regulated by a post-translational modification called Adenosine Diphosphate-ribosylation (ADP-ribosylation), catalyzed by ADP-ribosyltransferases. ADP-ribosylation is the addition of ADP-ribose moieties from Nicotinamide Adenine Dinucleotide (NAD+) to various targets, which include proteins and nucleotides. It is crucial in biological processes such as DNA repair, apoptosis, carbohydrate metabolism and catabolism. In the absence of a vaccine against schistosomiasis, this becomes a promising pathway in the identification of drug targets against various forms of this infection. The tegument of the worm is an encouraging immunogenic target for anti-schistosomal vaccine development. Vaccinology, molecular modeling and target-based drug discovery strategies have been used for years in drug discovery and for vaccine development. In this paper, we outline ADP-ribosylation and other different approaches to drug discovery and vaccine development against schistosomiasis.

Novel Anti-Alzheimer's Therapeutic Molecules Targeting Amyloid Precursor Protein Processing

Alzheimer's disease (AD) is the most common cause of dementia among older people, and the prevalence of this disease is estimated to rise quickly in the upcoming years. Unfortunately, almost all of the drug candidates tested for AD until now have failed to exhibit any efficacy. Henceforth, there is an increased necessity to avert and/or slow down the advancement of AD. It is known that one of the major pathological characteristics of AD is the presence of senile plaques (SPs) in the brain. These SPs are composed of aggregated amyloid beta (Aβ), derived from the amyloid precursor protein (APP). Pharmaceutical companies have conducted a number of studies in order to identify safe and effective anti-Aβ drugs to combat AD. It is known that α-, β-, and γ-secretases are the three proteases that are involved in APP processing. Furthermore, there is a growing interest in these proteases, as they have a contribution to the modulation and production of Aβ. It has been observed that small compounds can be used to target these important proteases. Indeed, these compounds must satisfy the common strict requirements of a drug candidate targeted for brain penetration and selectivity toward different proteases. In this article, we have focused on the auspicious molecules which are under development for targeting APP-processing enzymes. We have also presented several anti-AD molecules targeting Aβ accumulation and phosphorylation signaling in APP processing. This review highlights the structure-activity relationship and other physicochemical features of several pharmacological candidates in order to successfully develop new anti-AD drugs.

Selective Degradation of Target Proteins by Chimeric Small-Molecular Drugs, PROTACs and SNIPERs

New therapeutic modalities are needed to address the problem of pathological but undruggable proteins. One possible approach is the induction of protein degradation by chimeric drugs composed of a ubiquitin ligase (E3) ligand coupled to a ligand for the target protein. This article reviews chimeric drugs that decrease the level of specific proteins such as proteolysis targeting chimeric molecules (PROTACs) and specific and nongenetic inhibitor of apoptosis protein (IAP)-dependent protein erasers (SNIPERs), which target proteins for proteasome-mediated degradation. We cover strategies for increasing the degradation activity induced by small molecules, and their scope for application to undruggable proteins.

LigBuilder V3: A Multi-Target de novo Drug Design Approach

With the rapid development of systems-based pharmacology and poly-pharmacology, method development for rational design of multi-target drugs has becoming urgent. In this paper, we present the first de novo multi-target drug design program LigBuilder V3, which can be used to design ligands to target multiple receptors, multiple binding sites of one receptor, or various conformations of one receptor. LigBuilder V3 is generally applicable in de novo multi-target drug design and optimization, especially for the design of concise ligands for protein targets with large difference in binding sites. To demonstrate the utility of LigBuilder V3, we have used it to design dual-functional inhibitors targeting HIV protease and HIV reverse transcriptase with three different strategy, including multi-target de novo design, multi-target growing, and multi-target linking. The designed compounds were computational validated by MM/GBSA binding free energy estimation as highly potential multi-target inhibitors for both HIV protease and HIV reverse transcriptase. The LigBuilder V3 program can be downloaded at “http://www.pkumdl.cn/ligbuilder3/”.

Multi-Targeting Anticancer Agents: Rational Approaches, Synthetic Routes and Structure Activity Relationship

We live in a world with complex diseases such as cancer which cannot be cured with one-compound one-target based therapeutic paradigm. This could be due to the involvement of multiple pathogenic mechanisms. One-compound-various-targets stratagem has become a prevailing research topic in anti-cancer drug discovery. The simultaneous interruption of two or more targets has improved the therapeutic efficacy as compared to the specific targeted based therapy. In this review, six types of dual targeting agents along with some interesting strategies used for their design and synthesis are discussed. Their pharmacology with various types of the molecular interactions within their specific targets has also been described. This assemblage will reveal the recent trends and insights in front of the scientific community working in dual inhibitors and help them in designing the next generation of multi-targeted anti-cancer agents.

Towards improved hepatocyte cultures: Progress and limitations

Hepatotoxicity is among the most frequent reasons for drug withdrawal from the market. Therefore, there is an urgent need for reliable predictive in vitro tests, which unfailingly identify hepatotoxic drug candidates, reduce drug development time, expenses and the number of test animals. Currently, human hepatocytes represent the gold standard. However, the use of hepatocytes is challenging since the cells are not constantly available and lose their metabolic activity in culture. To solve these problems many different approaches have been developed in the past decades. The aim of this review is to present these approaches and to discuss the possibilities and limitations as well as future opportunities and directions.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Computational Strategy Revealing the Structural Determinant of Ligand Selectivity towards Highly Similar Protein Targets

BACKGROUND

Poor selectivity of drug candidates may lead to toxicity and side effects accounting for as high as 60% failure rate, thus, the selectivity is consistently significant and challenging for drug discovery.

OBJECTIVE

To find highly specific small molecules towards very similar protein targets, multiple strategies are always employed, including (1) To make use of the diverse shape of binding pocket to avoid steric bump; (2) To increase binding affinities for favorite residues; (3) To achieve selectivity through allosteric regulation of target; (4) To stabalize the inactive conformation of protein target and (5) To occupy dual binding pockets of single target.

CONCLUSION

In this review, we summarize computational strategies along with examples of their successful applications in designing selective ligands, with the aim to provide insights into everdiversifying drug development practice and inspire medicinal chemists to utilize computational strategies to avoid potential side effects due to low selectivity of ligands.

Elucidating the druggability of the human proteome with eFindSite

Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and resources. Inability to properly measure druggability has accounted for a significant portion of failures in drug discovery. This problem is only further exacerbated by the large sample space of proteins involved in human diseases. With these barriers, the druggability space within the human proteome remains unexplored and has made it difficult to develop drugs for numerous diseases. Hence, we present a new feature developed in eFindSite that employs supervised machine learning to predict the druggability of a given protein. Benchmarking calculations against the Non-Redundant data set of Druggable and Less Druggable binding sites demonstrate that an AUC for druggability prediction with eFindSite is as high as 0.88. With eFindSite, we elucidated the human druggability space to be 10,191 proteins. Considering the disease space from the Open Targets Platform and excluding already known targets from the predicted data set reveal 2731 potentially novel therapeutic targets. eFindSite is freely available as a stand-alone software at https://github.com/michal-brylinski/efindsite .

Informatics and Computational Methods in Natural Product Drug Discovery: A Review and Perspectives

The discovery of new pharmaceutical drugs is one of the preeminent tasks-scientifically, economically, and socially-in biomedical research. Advances in informatics and computational biology have increased productivity at many stages of the drug discovery pipeline. Nevertheless, drug discovery has slowed, largely due to the reliance on small molecules as the primary source of novel hypotheses. Natural products (such as plant metabolites, animal toxins, and immunological components) comprise a vast and diverse source of bioactive compounds, some of which are supported by thousands of years of traditional medicine, and are largely disjoint from the set of small molecules used commonly for discovery. However, natural products possess unique characteristics that distinguish them from traditional small molecule drug candidates, requiring new methods and approaches for assessing their therapeutic potential. In this review, we investigate a number of state-of-the-art techniques in bioinformatics, cheminformatics, and knowledge engineering for data-driven drug discovery from natural products. We focus on methods that aim to bridge the gap between traditional small-molecule drug candidates and different classes of natural products. We also explore the current informatics knowledge gaps and other barriers that need to be overcome to fully leverage these compounds for drug discovery. Finally, we conclude with a "road map" of research priorities that seeks to realize this goal.

Treatment of Toxoplasmosis and Neosporosis in Farm Ruminants: State of Knowledge and Future Trends

Toxoplasmosis and neosporosis are closely related protozoan diseases that lead to important economic impacts in farm ruminants. Toxoplasma gondii infection mainly causes reproductive failure in small ruminants and is a widespread zoonosis, whereas Neospora caninum infection is one of the most important causes of abortion in cattle worldwide. Vaccination has been considered the most economic measure for controlling these diseases. However, despite vaccine development efforts, only a live-attenuated T. gondii vaccine has been licensed for veterinary use, and no promising vaccines against ne-osporosis have been developed; therefore, vaccine development remains a key goal. Additionally, drug therapy could be a valuable strategy for disease control in farm ruminants, as several drugs that limit T. gondii and N. caninum proliferation and dissemination have been evaluated. This approach may also be relevant to performing an initial drug screening for potential human therapy for zoonotic parasites. Treat-ments can be applied against infections in adult ruminants to minimize the outcomes of a primo-infection or the reactivation of a chronic infection during gestation or in newborn ruminants to avoid infection chronification. In this review, the current status of drug development against toxoplasmosis and neosporo-sis in farm ruminants is presented, and in an effort to promote additional treatment options, prospective drugs that have shown efficacy in vitro and in laboratory animal models of toxoplasmosis and neosporosis are examined

Liposome delivery systems for the treatment of Alzheimer's disease

Alzheimer's disease (AD) will affect around 115 million people worldwide by the year 2050. It is associated with the accumulation of misfolded and aggregated proteins (β-amyloid and tau) in the senile plaques and neurofibrillary tangles found in the brain. Currently available drugs for AD only temporarily alleviate symptoms and do not slow the inevitable progression of this disease. New drugs are required that act on key pathologies in order to arrest or reverse cognitive decline. However, there has been a spectacular failure rate in clinical trials of conventional small molecule drugs or biological agents. Targeted nanoliposomes represent a viable and promising drug delivery system for AD that have not yet reached clinical trials. They are biocompatible, highly flexible, and have the potential to carry many different types of therapeutic molecules across the blood-brain barrier (BBB) and into brain cells. They can be tailored to extend blood circulation time and can be directed against individual or multiple pathological targets. Modifications so far have included the use of brain-penetrating peptides, together with Aβ-targeting ligands, such as phosphatidic acid, curcumin, and a retro-inverted peptide that inhibits Aβ aggregation. Combining several modifications together into multifunctional liposomes is currently a research area of great interest. This review focuses on recent liposomal approaches to AD therapy, including mechanisms involved in facilitating their passage across the BBB, and the evaluation of new therapeutic agents for blocking Aβ and/or tau aggregation.

Synthesis & molecular modeling studies of bronchodilatory active indole–pyridine conjugates

Aim: Synthesis of novel bronchodilatory active indole–pyridine conjugates. Results/methodology: Indole–pyridine conjugates (6a–n, 8a–i and 10a–c) were synthesized in a facile pathway through reaction of 2-[(1-alkyl-1H-indol-3-yl)methylene]malononitriles 4a,b with the corresponding ketone-containing compounds (5a–f, 7a–c and 9a,b) in the presence of sodium alkoxide. Single (6l, 8 g) and powder (6k, 8d) x-ray studies supported the structures. Results: Histamine precontracted isolated tracheal rings of guinea pig exhibited the potent bronchodilation properties of 6c (about double-fold potency relative to the standard reference, theophylline). Some of the synthesized conjugates (8d, 6c, 6f and 6e) revealed promising reduction of IL-8 production during lipopolysaccharide-induced airway inflammatory bioassay. Computational studies (3D pharmacophore, 2D-QSAR ‘quantitative structure–activity relationship’) showed high approximations to the bronchodilation properties and explained the parameters controlling biological observations.

Defining Pharmacological Targets by Analysis of Virus-Host Protein Interactions

Viruses are obligate parasites that depend on cellular factors for replication. Pharmacological inhibition of essential viral proteins, mostly enzymes, is an effective therapeutic alternative in the absence of effective vaccines. However, this strategy commonly encounters drug resistance mechanisms that allow these pathogens to evade control. Due to the dependency on host factors for viral replication, pharmacological disruption of the host-pathogen protein-protein interactions (PPIs) is an important therapeutic alternative to block viral replication. In this review we discuss salient aspects of PPIs implicated in viral replication and advances in the development of small molecules that inhibit viral replication through antagonism of these interactions.

Multiple target-based pharmacophore design from active site structures

Health care systems have benefitted from rational drug discovery processes like vHTS, virtual high throughput screening pharmacophores and quantitative structure-activity relationships, and many challenges have been explored using such techniques: decisions on specificity and selectivity are made after screening millions of molecules for multiple targets. Recent challenges in drug research emphasize the design of drugs that bind with more than one target of interest (multi-target) and do not bind with undesirable targets. This work attempts to use a three-dimensional interaction profile of the active site of a class of proteins, identify selective positions for the binding of functional groups, called features, and develop ensembles of multi-targeted pharmacophores that retain specificity and selectivity. The goal of this study is to develop multi-target pharmacophores by computational methods using protein structures alone to guide the discovery of novel inhibitors of plasmepsins, displaying selectivity over their human homologs, cathepsin D and pepsin. The development of such novel tools is attempted using a combination of different approaches such as the molecular interaction field, clique graph and inductive logic programming to identify and compare specific and selective complementary features. The identification of selective combinations of features has resulted in the design of multi-featured specific and selective pharmacophores that are validated using antimalarial compounds in ChEMBL that are known for their anti-plasmepsin II activity. This novel method is computationally less intensive and is applicable to any known class of target structures for finding specific and selective binders simultaneously.

Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain

While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.

Computational Multitarget Drug Design

Designing drugs that can simultaneously interact with multiple targets is a promising approach for treating complicated diseases. Compared to using combinations of single target drugs, multitarget drugs have advantages of higher efficacy, improved safety profile, and simpler administration. Many in silico methods have been developed to approach different aspects of this polypharmacology-guided drug design, particularly for drug repurposing and multitarget drug design. In this review, we summarize recent progress in computational multitarget drug design and discuss their advantages and limitations. Perspectives for future drug development will also be discussed.

Quebrachitol: Global Status and Basic Research

Recently, there has been a renewed interest in the natural-products-inspired drugs. Quebrachitol (QCT) is one of naturally occurring optically active cyclitols that has now received considerable attention. Until the last decade, it came to be a starting point for the lead discovery. In this review, we had a discussion on the basic research of QCT, including its source, structure, properties, and the recent advances on its application. The biological activities and QCT-inspired leads that are potentially effective for treating human diseases were also discussed.

Pharmacophore-based screening and drug repurposing exemplified on glycogen synthase kinase-3 inhibitors

The current study was conducted to elaborate a novel pharmacophore model to accurately map selective glycogen synthase kinase-3 (GSK-3) inhibitors, and perform virtual screening and drug repurposing. Pharmacophore modeling was developed using PHASE on a data set of 203 maleimides. Two benchmarking validation data sets with focus on selectivity were assembled using ChEMBL and PubChem GSK-3 confirmatory assays. A drug repurposing experiment linking pharmacophore matching with drug information originating from multiple data sources was performed. A five-point pharmacophore model was built consisting of a hydrogen bond acceptor (A), hydrogen bond donor (D), hydrophobic (H), and two rings (RR). An atom-based 3D quantitative structure-activity relationship (QSAR) model showed good correlative and satisfactory predictive abilities (training set [Formula: see text]; test set: [Formula: see text]; whole data set: stability [Formula: see text]). Virtual screening experiments revealed that selective GSK-3 inhibitors are ranked preferentially by Hypo-1, but fail to retrieve nonselective compounds. The pharmacophore and 3D QSAR models can provide assistance to design novel, potential GSK-3 inhibitors with high potency and selectivity pattern, with potential application for the treatment of GSK-3-driven diseases. A class of purine nucleoside antileukemic drugs was identified as potential inhibitor of GSK-3, suggesting the reassessment of the target range of these drugs.

Global vision of druggability issues: applications and perspectives

During the preliminary stage of a drug discovery project, the lack of druggability information and poor target selection are the main causes of frequent failures. Elaborating on accurate computational druggability prediction methods is a requirement for prioritizing target selection, designing new drugs and avoiding side effects. In this review, we describe a survey of recently reported druggability prediction methods mainly based on networks, statistical pocket druggability predictions and virtual screening. An application for a frequent mutation of p53 tumor suppressor is presented, illustrating the complementarity of druggability prediction approaches, the remaining challenges and potential new drug development perspectives.

Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets

In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

Towards pharmacological intervention in inositol pyrophosphate signalling

To help define the molecular basis of cellular signalling cascades, and their biological functions, there is considerable value in utilizing a high-quality chemical 'probe' that has a well-defined interaction with a specific cellular protein. Such reagents include inhibitors of protein kinases and small molecule kinases, as well as mimics or antagonists of intracellular signals. The purpose of this review is to consider recent progress and promising future directions for the development of novel molecules that can interrogate and manipulate the cellular actions of inositol pyrophosphates (PP-IPs)--a specialized, 'energetic' group of cell-signalling molecules in which multiple phosphate and diphosphate groups are crammed around a cyclohexane polyol scaffold.

Recent progress in repositioning Alzheimer's disease drugs based on a multitarget strategy

Alzheimer's disease (AD) is a serious progressive neurological disorder, characterized by impaired cognition and profound irreversible memory loss. The multifactorial nature of AD and the absence of a cure so far have stimulated medicinal chemists worldwide to follow multitarget drug-design strategies based on repositioning approved drugs. This review describes a summary of recently published works focused on tailoring new derivatives of US FDA-approved acetylcholinesterase inhibitors, in addition to huperzine (a drug approved in China), either by hybridization with other pharmacophore elements (to hit more AD targets), or by combination of two FDA-approved drugs. Besides the capacity for improving the cholinergic activity, these polyfunctional derivatives are also able to tackle other important neuroprotective properties, such as anti-β-amyloid aggregation, scavenging of radical oxygen species, modulation of redox-active metals or inhibition of monoamine oxidase, thereby resulting in potentially novel and more effective therapeutics for the treatment of AD.

Understanding Cryptic Pocket Formation in Protein Targets by Enhanced Sampling Simulations

Cryptic pockets, that is, sites on protein targets that only become apparent when drugs bind, provide a promising alternative to classical binding sites for drug development. Here, we investigate the nature and dynamical properties of cryptic sites in four pharmacologically relevant targets, while comparing the efficacy of various simulation-based approaches in discovering them. We find that the studied cryptic sites do not correspond to local minima in the computed conformational free energy landscape of the unliganded proteins. They thus promptly close in all of the molecular dynamics simulations performed, irrespective of the force-field used. Temperature-based enhanced sampling approaches, such as Parallel Tempering, do not improve the situation, as the entropic term does not help in the opening of the sites. The use of fragment probes helps, as in long simulations occasionally it leads to the opening and binding to the cryptic sites. Our observed mechanism of cryptic site formation is suggestive of an interplay between two classical mechanisms: induced-fit and conformational selection. Employing this insight, we developed a novel Hamiltonian Replica Exchange-based method "SWISH" (Sampling Water Interfaces through Scaled Hamiltonians), which combined with probes resulted in a promising general approach for cryptic site discovery. We also addressed the issue of "false-positives" and propose a simple approach to distinguish them from druggable cryptic pockets. Our simulations, whose cumulative sampling time was more than 200 μs, help in clarifying the molecular mechanism of pocket formation, providing a solid basis for the choice of an efficient computational method.

Increasing the Endoplasmic Reticulum Pool of the F508del Allele of the Cystic Fibrosis Transmembrane Conductance Regulator Leads to Greater Folding Correction by Small Molecule Therapeutics

Small molecules that correct the folding defects and enhance surface localization of the F508del mutation in the Cystic Fibrosis Transmembrane conductance Regulator (CFTR) comprise an important therapeutic strategy for cystic fibrosis lung disease. However, compounds that rescue the F508del mutant protein to wild type (WT) levels have not been identified. In this report, we consider obstacles to obtaining robust and therapeutically relevant levels of F508del CFTR. For example, markedly diminished steady state amounts of F508del CFTR compared to WT CFTR are present in recombinant bronchial epithelial cell lines, even when much higher levels of mutant transcript are present. In human primary airway cells, the paucity of Band B F508del is even more pronounced, although F508del and WT mRNA concentrations are comparable. Therefore, to augment levels of “repairable” F508del CFTR and identify small molecules that then correct this pool, we developed compound library screening protocols based on automated protein detection. First, cell-based imaging measurements were used to semi-quantitatively estimate distribution of F508del CFTR by high content analysis of two-dimensional images. We evaluated ~2,000 known bioactive compounds from the NIH Roadmap Molecular Libraries Small Molecule Repository in a pilot screen and identified agents that increase the F508del protein pool. Second, we analyzed ~10,000 compounds representing diverse chemical scaffolds for effects on total CFTR expression using a multi-plate fluorescence protocol and describe compounds that promote F508del maturation. Together, our findings demonstrate proof of principle that agents identified in this fashion can augment the level of endoplasmic reticulum (ER) resident “Band B” F508del CFTR suitable for pharmacologic correction. As further evidence in support of this strategy, PYR-41—a compound that inhibits the E1 ubiquitin activating enzyme—was shown to synergistically enhance F508del rescue by C18, a small molecule corrector. Our combined results indicate that increasing the levels of ER-localized CFTR available for repair provides a novel route to correct F508del CFTR.

Identification of protein-ligand binding sites by the level-set variational implicit-solvent approach

Protein–ligand binding is a key biological process at the molecular level. The identification and characterization of small-molecule binding sites on therapeutically relevant proteins have tremendous implications for target evaluation and rational drug design. In this work, we used the recently developed level-set variational implicit-solvent model (VISM) with the Coulomb field approximation (CFA) to locate and characterize potential protein–small-molecule binding sites. We applied our method to a data set of 515 protein–ligand complexes and found that 96.9% of the cocrystallized ligands bind to the VISM-CFA-identified pockets and that 71.8% of the identified pockets are occupied by cocrystallized ligands. For 228 tight-binding protein–ligand complexes (i.e, complexes with experimental pK_d values larger than 6), 99.1% of the cocrystallized ligands are in the VISM-CFA-identified pockets. In addition, it was found that the ligand binding orientations are consistent with the hydrophilic and hydrophobic descriptions provided by VISM. Quantitative characterization of binding pockets with topological and physicochemical parameters was used to assess the “ligandability” of the pockets. The results illustrate the key interactions between ligands and receptors and can be very informative for rational drug design.

Systems Pharmacology in Small Molecular Drug Discovery

Drug discovery is a risky, costly and time-consuming process depending on multidisciplinary methods to create safe and effective medicines. Although considerable progress has been made by high-throughput screening methods in drug design, the cost of developing contemporary approved drugs did not match that in the past decade. The major reason is the late-stage clinical failures in Phases II and III because of the complicated interactions between drug-specific, human body and environmental aspects affecting the safety and efficacy of a drug. There is a growing hope that systems-level consideration may provide a new perspective to overcome such current difficulties of drug discovery and development. The systems pharmacology method emerged as a holistic approach and has attracted more and more attention recently. The applications of systems pharmacology not only provide the pharmacodynamic evaluation and target identification of drug molecules, but also give a systems-level of understanding the interaction mechanism between drugs and complex disease. Therefore, the present review is an attempt to introduce how holistic systems pharmacology that integrated in silico ADME/T (i.e., absorption, distribution, metabolism, excretion and toxicity), target fishing and network pharmacology facilitates the discovery of small molecular drugs at the system level.

To Hit or Not to Hit, That Is the Question - Genome-wide Structure-Based Druggability Predictions for Pseudomonas aeruginosa Proteins

Pseudomonas aeruginosa is a Gram-negative bacterium known to cause opportunistic infections in immune-compromised or immunosuppressed individuals that often prove fatal. New drugs to combat this organism are therefore sought after. To this end, we subjected the gene products of predicted perturbative genes to structure-based druggability predictions using DrugPred. Making this approach suitable for large-scale predictions required the introduction of new methods for calculation of descriptors, development of a workflow to identify suitable pockets in homologous proteins and establishment of criteria to obtain valid druggability predictions based on homologs. We were able to identify 29 perturbative proteins of P. aeruginosa that may contain druggable pockets, including some of them with no or no drug-like inhibitors deposited in ChEMBL. These proteins form promising novel targets for drug discovery against P. aeruginosa.

Novel antibacterial active quinolone-fluoroquinolone conjugates and 2D-QSAR studies

Novel, quinolone-fluoroquinolone conjugates 10a-f, 11a-f, 13a-f and 14a-f with amino acid linkers were synthesized in good yields utilizing benzotriazole chemistry. Antibacterial bioassay showed the synthesized bis-conjugates exhibit anti-bacterial properties comparable with the parent drugs.

Dual Inhibitors Against Topoisomerases and Histone Deacetylases

Topoisomerases and histone deacetylases (HDACs) are considered as important therapeutic targets for a wide range of cancers, due to their association with the initiation, proliferation and survival of cancer cells. Topoisomerases are involved in the cleavage and religation processes of DNA, while HDACs regulate a dynamic epigenetic modification of the lysine amino acid on various proteins. Extensive studies have been undertaken to discover small molecule inhibitor of each protein and thereby, several drugs have been transpired from this effort and successfully approved for clinical use. However, the inherent heterogeneity and multiple genetic abnormalities of cancers challenge the clinical application of these single targeted drugs. In order to overcome the limitations of a single target approach, a novel approach, simultaneously targeting topoisomerases and HDACs with a single molecule has been recently employed and attracted much attention of medicinal chemists in drug discovery. This review highlights the current studies on the discovery of dual inhibitors against topoisomerases and HDACs, provides their pharmacological aspects and advantages, and discusses the challenges and promise of the dual inhibitors.

Could miltefosine be used as a therapy for toxoplasmosis?

Toxoplasmosis is a zoonotic protozoal disease affecting more than a billion people worldwide. The shortfalls of the current treatment options necessitate the development of non-toxic and well-tolerated, efficient alternatives especially against the cyst form. The current study was undertaken to investigate, for the first time, the potential potency of miltefosine against Toxoplasma gondii infection in acute and chronic experimental toxoplasmosis. Results showed that there is no evidence of anti-parasitic activity of miltefosine against T. gondii tachyzoites in acute experimental toxoplasmosis. However, anti-parasitic activity of miltefosine against T. gondii cyst stage in chronic experimental toxoplasmosis could not be excluded as demonstrated by significant reduction in brain cyst burden. Moreover, considerable morphological changes in the cysts were revealed by light and electron microscopy study and also by amelioration of pathological changes in the brain. Future studies should focus on enhancement of anti-toxoplasma activity of miltefosine against chronic toxoplasmosis using formulation based nanotechnology. To the best of our knowledge, this is the first study highlighting efficacy of miltefosine against chronic toxoplasmosis, thus, increasing the list of diseases that can be targeted by this drug.

Insights into the structure and inhibition of Giardia intestinalis arginine deiminase: homology modeling, docking, and molecular dynamics studies

Giardia intestinalis arginine deiminase (GiADI) is an important metabolic enzyme involved in the energy production and defense of this protozoan parasite. The lack of this enzyme in the human host makes GiADI an attractive target for drug design against G. intestinalis. One approach in the design of inhibitors of GiADI could be computer-assisted studies of its crystal structure, such as docking; however, the required crystallographic structure of the enzyme still remains unresolved. Because of its relevance, in this work, we present a three-dimensional structure of GiADI obtained from its amino acid sequence using the homology modeling approximation. Furthermore, we present an approximation of the most stable dimeric structure of GiADI identified through molecular dynamics simulation studies. An in silico analysis of druggability using the structure of GiADI was carried out in order to know if it is a good target for design and optimization of selective inhibitors. Potential GiADI inhibitors were identified by docking of a set of 3196 commercial and 19 in-house benzimidazole derivatives, and molecular dynamics simulation studies were used to evaluate the stability of the ligand-enzyme complexes.

PockDrug-Server: a new web server for predicting pocket druggability on holo and apo proteins

Predicting protein pocket's ability to bind drug-like molecules with high affinity, i.e. druggability, is of major interest in the target identification phase of drug discovery. Therefore, pocket druggability investigations represent a key step of compound clinical progression projects. Currently computational druggability prediction models are attached to one unique pocket estimation method despite pocket estimation uncertainties. In this paper, we propose ‘PockDrug-Server’ to predict pocket druggability, efficient on both (i) estimated pockets guided by the ligand proximity (extracted by proximity to a ligand from a holo protein structure) and (ii) estimated pockets based solely on protein structure information (based on amino atoms that form the surface of potential binding cavities). PockDrug-Server provides consistent druggability results using different pocket estimation methods. It is robust with respect to pocket boundary and estimation uncertainties, thus efficient using apo pockets that are challenging to estimate. It clearly distinguishes druggable from less druggable pockets using different estimation methods and outperformed recent druggability models for apo pockets. It can be carried out from one or a set of apo/holo proteins using different pocket estimation methods proposed by our web server or from any pocket previously estimated by the user. PockDrug-Server is publicly available at: http://pockdrug.rpbs.univ-paris-diderot.fr.

PockDrug: A Model for Predicting Pocket Druggability That Overcomes Pocket Estimation Uncertainties

Predicting protein druggability is a key interest in the target identification phase of drug discovery. Here, we assess the pocket estimation methods' influence on druggability predictions by comparing statistical models constructed from pockets estimated using different pocket estimation methods: a proximity of either 4 or 5.5 Å to a cocrystallized ligand or DoGSite and fpocket estimation methods. We developed PockDrug, a robust pocket druggability model that copes with uncertainties in pocket boundaries. It is based on a linear discriminant analysis from a pool of 52 descriptors combined with a selection of the most stable and efficient models using different pocket estimation methods. PockDrug retains the best combinations of three pocket properties which impact druggability: geometry, hydrophobicity, and aromaticity. It results in an average accuracy of 87.9% ± 4.7% using a test set and exhibits higher accuracy (∼5-10%) than previous studies that used an identical apo set. In conclusion, this study confirms the influence of pocket estimation on pocket druggability prediction and proposes PockDrug as a new model that overcomes pocket estimation variability.

Simple chalcones and bis-chalcones ethers as possible pleiotropic agents

The synthesis, the antioxidative properties and the lipoxygenase (LOX) and acetylcholinesterase (AChE) inhibition of a number of 4-hydroxy-chalcones diversely substituted as well as of a series of bis-chalcones ether derivatives are reported. The chalcones derivatives were readily produced using a Claisen-Schmidt condensation in a ultra sound bath in good yields. The structures of the synthesized compounds were confirmed by spectral and elemental analysis. Their lipophilicity is experimentally determined by reversed-phase thin-layer chromatography method. Most of them are potent in vitro inhibitors of lipid peroxidation and of LOX. Compounds b2 and b3 were found to be the most potent LOX and AChE inhibitors among the tested derivatives with a significant anti-lipid peroxidation profile. The results led us to propose these enone derivatives as new multifunctional compounds against Alzheimer's disease. The results are discussed in terms of structural and physicochemical characteristics of the compounds. Moreover, the pharmacokinetic profile of these compounds was investigated using computational methods.

The use of affinity sorbents in targeted proteomics

The complexity of most proteomes is too high to obtain a truly comprehensive view of all expressed proteins by any of the current technologies. Prefractionation techniques are thus necessary to dig deeper into the low-abundance region of the proteome. Progress in the application of affinity sorbents to focus on particular protein classes of low abundance, while at the same time adding understanding about their function through the way they interact with the affinity ligands, will be described.:

In pursuit of the ideal antifungal agent for Candida infections: high-throughput screening of small molecules

Candida infections have created a great burden on the public healthcare sector. The situation is worsened by recent epidemiological changes. Furthermore, the current arsenal of antifungal agents is limited and associated with undesirable drawbacks. Therefore, new antifungal agents that surpass the existing ones are urgently needed. High-throughput screening of small molecule libraries enables rapid hit identification and, possibly, increases hit rate. Moreover, the identified hits could be associated with unrecognized or multiple drug targets, which would provide novel insights into the biological processes of the pathogen. Hence, it is proposed that high-throughput screening of small molecules is particularly important in the pursuit of the ideal antifungal agents for Candida infections.

Chemical, target, and bioactive properties of allosteric modulation

Allosteric modulators are ligands for proteins that exert their effects via a different binding site than the natural (orthosteric) ligand site and hence form a conceptually distinct class of ligands for a target of interest. Here, the physicochemical and structural features of a large set of allosteric and non-allosteric ligands from the ChEMBL database of bioactive molecules are analyzed. In general allosteric modulators are relatively smaller, more lipophilic and more rigid compounds, though large differences exist between different targets and target classes. Furthermore, there are differences in the distribution of targets that bind these allosteric modulators. Allosteric modulators are over-represented in membrane receptors, ligand-gated ion channels and nuclear receptor targets, but are underrepresented in enzymes (primarily proteases and kinases). Moreover, allosteric modulators tend to bind to their targets with a slightly lower potency (5.96 log units versus 6.66 log units, p<0.01). However, this lower absolute affinity is compensated by their lower molecular weight and more lipophilic nature, leading to similar binding efficiency and surface efficiency indices. Subsequently a series of classifier models are trained, initially target class independent models followed by finer-grained target (architecture/functional class) based models using the target hierarchy of the ChEMBL database. Applications of these insights include the selection of likely allosteric modulators from existing compound collections, the design of novel chemical libraries biased towards allosteric regulators and the selection of targets potentially likely to yield allosteric modulators on screening. All data sets used in the paper are available for download.

The physicochemistry and topography of ligand binding sites is generally conserved amongst related proteins, however, comparisons of the pharmacology of related targets (and even the same target) are often confounded by the existence of multiple, distinct, binding sites within the same protein. Importantly, these multiple binding sites can have ‘druggability’ or selectivity properties, and can therefore offer attractive novel approaches to develop new therapeutic agents. In this paper, sets of known ligands binding to the same target are classified as being either allosteric (binding at a site that is non-competitive for a natural ligand/substrate) or non-allosteric (binding at the same site as a natural substrate), it is demonstrated that there are differences in the profiles of ligands discovered empirically against these sites. Finally predictive models are developed with several useful applications in drug discovery.