Orphan Targets Research Articles

Computer-aided pattern scoring – A multitarget dataset-driven workflow to predict ligands of orphan targets

The identification of lead molecules and the exploration of novel pharmacological drug targets are major challenges of medical life sciences today. Genome‐wide association studies, multi-omics, and systems pharmacology steadily reveal new protein networks, extending the known and relevant disease-modifying proteome. Unfortunately, the vast majority of the disease-modifying proteome consists of ‘orphan targets’ of which intrinsic ligands/substrates, (patho)physiological roles, and/or modulators are unknown. Undruggability is a major challenge in drug development today, and medicinal chemistry efforts cannot keep up with hit identification and hit-to-lead optimization studies. New ‘thinking-outside-the-box’ approaches are necessary to identify structurally novel and functionally distinctive ligands for orphan targets. Here we present a unique dataset that includes critical information on the orphan target ABCA1, from which a novel cheminformatic workflow – computer-aided pattern scoring (C@PS) – for the identification of novel ligands was developed. Providing a hit rate of 95.5% and molecules with high potency and molecular-structural diversity, this dataset represents a suitable template for general deorphanization studies.

Inferring molecular inhibition potency with AlphaFold predicted structures

Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.

Medicinal polypharmacology:Exploration and exploitation of the polypharmacolome in modern drug development.

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

De Novo Design of Nurr1 Agonists via Fragment-Augmented Generative Deep Learning in Low-Data Regime.

Generative neural networks trained on SMILES can design innovative bioactive molecules de novo. These so-called chemical language models (CLMs) have typically been trained on tens of template molecules for fine-tuning. However, it is challenging to apply CLM to orphan targets with few known ligands. We have fine-tuned a CLM with a single potent Nurr1 agonist as template in a fragment-augmented fashion and obtained novel Nurr1 agonists using sampling frequency for design prioritization. Nanomolar potency and binding affinity of the top-ranking design and its structural novelty compared to available Nurr1 ligands highlight its value as an early chemical tool and as a lead for Nurr1 agonist development, as well as the applicability of CLM in very low-data scenarios.

Computational Prediction of Compound-Protein Interactions for Orphan Targets Using CGBVS.

A variety of Artificial Intelligence (AI)-based (Machine Learning) techniques have been developed with regard to in silico prediction of Compound–Protein interactions (CPI)—one of which is a technique we refer to as chemical genomics-based virtual screening (CGBVS). Prediction calculations done via pairwise kernel-based support vector machine (SVM) is the main feature of CGBVS which gives high prediction accuracy, with simple implementation and easy handling. We studied whether the CGBVS technique can identify ligands for targets without ligand information (orphan targets) using data from G protein-coupled receptor (GPCR) families. As the validation method, we tested whether the ligand prediction was correct for a virtual orphan GPCR in which all ligand information for one selected target was omitted from the training data. We have specifically expressed the results of this study as applicability index and developed a method to determine whether CGBVS can be used to predict GPCR ligands. Validation results showed that the prediction accuracy of each GPCR differed greatly, but models using Multiple Sequence Alignment (MSA) as the protein descriptor performed well in terms of overall prediction accuracy. We also discovered that the effect of the type compound descriptors on the prediction accuracy was less significant than that of the type of protein descriptors used. Furthermore, we found that the accuracy of the ligand prediction depends on the amount of ligand information with regard to GPCRs related to the target. Additionally, the prediction accuracy tends to be high if a large amount of ligand information for related proteins is used in the training.

A promising orphan target to treat HD

A promising orphan target to treat HD

Novel pharmacological maps of protein lysine methyltransferases: key for target deorphanization

Epigenetic therapies are being investigated for the treatment of cancer, cognitive disorders, metabolic alterations and autoinmune diseases. Among the different epigenetic target families, protein lysine methyltransferases (PKMTs), are especially interesting because it is believed that their inhibition may be highly specific at the functional level. Despite its relevance, there are currently known inhibitors against only 10 out of the 50 SET-domain containing members of the PKMT family. Accordingly, the identification of chemical probes for the validation of the therapeutic impact of epigenetic modulation is key. Moreover, little is known about the mechanisms that dictate their substrate specificity and ligand selectivity. Consequently, it is desirable to explore novel methods to characterize the pharmacological similarity of PKMTs, going beyond classical phylogenetic relationships. Such characterization would enable the prediction of ligand off-target effects caused by lack of ligand selectivity and the repurposing of known compounds against alternative targets. This is particularly relevant in the case of orphan targets with unreported inhibitors. Here, we first perform a systematic study of binding modes of cofactor and substrate bound ligands with all available SET domain-containing PKMTs. Protein ligand interaction fingerprints were applied to identify conserved hot spots and contact-specific residues across subfamilies at each binding site; a relevant analysis for guiding the design of novel, selective compounds. Then, a recently described methodology (GPCR-CoINPocket) that incorporates ligand contact information into classical alignment-based comparisons was applied to the entire family of 50 SET-containing proteins to devise pharmacological similarities between them. The main advantage of this approach is that it is not restricted to proteins for which crystallographic data with bound ligands is available. The resulting family organization from the separate analysis of both sites (cofactor and substrate) was retrospectively and prospectively validated. Of note, three hits (inhibition > 50% at 10 µM) were identified for the orphan NSD1.

Review of Endometriosis Diagnosis through Advances in Biomedical Engineering

Endometriosis is characterized as the ectopic presence of endometrium in various locations within the abdominal cavity, such as the fallopian tube, the pouch of Douglas, the ovaries, outside the ovaries, and more. The inner lining of the uterus, endometrium, is a dynamic tissue that undergoes morphological and functional changes cyclically. The proliferation of endometrial cells during menstruation is influenced by increasing circulating estrogen levels. Adult progenitor stem cells are likely responsible for this remarkable regenerative capacity and hence, enhanced capacity to generate endometriosis. This chronic estrogen-dependent disease is characterized by the ectopic endometrial implant. The disorder occurs in 6%-10% of reproductive-aged women and in 35%-50% of women with pelvic pain and infertility. Currently, the preferred diagnostic methods are laparoscopic inspection by transvaginal ultrasound, MRI, and sensors. Diagnoses with transvaginal ultrasound have 92.7% sensitivity and 97% specificity in detecting endometriotic lesions. On average, MRI diagnoses of DIE have 74% sensitivity and 95% specificity. Lastly, chemical sensors have 91.7% sensitivity and 90.0% specificity in detecting endometriosis. The standard of care includes personalizing the treatment plan based on the individual's set of symptoms and their severity. Advances in biomedical engineering have aided professionals in personalizing the course of treatments as well as to increasing the quality of life of these patients through various therapies for managing pain. Because no one theory provides a full explanation for the manifestation of the disease, hormonal therapies, targeted therapeutics, and surgical options have emerged as elements of disease management. Clinicians are in the process of developing advanced pharmaceutical drugs with specific orphan target receptors of the ectopic tissue. Possible complications that accompany the condition include dyspareunia, hyperalgesia, infertility, and many others.

Diverse Redoxome Reactivity Profiles of Carbon Nucleophiles

Targeted covalent inhibitors have emerged as a powerful approach in the drug discovery pipeline. Key to this process is the identification of signaling pathways (or receptors) specific to (or overexpressed in) disease cells. In this context, fragment-based ligand discovery (FBLD) has significantly expanded our view of the ligandable proteome and affords tool compounds for biological inquiry. To date, such covalent ligand discovery has almost exclusively employed cysteine-reactive small-molecule fragments. However, functional cysteine residues in proteins are often redox-sensitive and can undergo oxidation in cells. Such reactions are particularly relevant in diseases, like cancer, which are linked to excessive production of reactive oxygen species. Once oxidized, the sulfur atom of cysteine is much less reactive toward electrophilic groups used in the traditional FBLD paradigm. To address this limitation, we recently developed a novel library of diverse carbon-based nucleophile fragments that react selectively with cysteine sulfenic acid formed in proteins via oxidation or hydrolysis reactions. Here, we report analysis of sulfenic acid-reactive C-nucleophile fragments screened against a colon cancer cell proteome. Covalent ligands were identified for >1280 S-sulfenylated cysteines present in "druggable" proteins and orphan targets, revealing disparate reactivity profiles and target preferences. Among the unique ligand-protein interactions identified was that of a pyrrolidinedione nucleophile that reacted preferentially with protein tyrosine phosphatases. Fragment-based covalent ligand discovery with C-nucleophiles affords an expansive snapshot of the ligandable "redoxome" with significant implications for covalent inhibitor pharmacology and also affords new chemical tools to investigate redox-regulation of protein function.

Identification of Novel and Natural High Affinity Peptide Inhibitors of KcsA by Phage-Display Reveals an Unexpected Mechanism of Pore Blockade

Peptide neurotoxins are powerful tools for research, diagnosis and treatment of disease. Limiting broader use, most receptors lack an identified toxin that binds with high-affinity and specificity. Here we describe isolation of toxins for one such orphan target, KcsA, a K+ channel that has been fundamental to delineating the structural basis for ion channel function. A phage display strategy is employed whereby ∼1.5 million novel and natural peptides are fabricated on the scaffold present in ShK, a sea anemone type I (SAK1) toxin stabilized by three disulfide bonds. We focus here on two of nine toxins selected by sorting on purified KcsA, one novel (Hui1, 34 residues) and one natural (HmK, 35 residues). Hui1 is potent, blocking single KcsA channels in planar lipid bilayers half-maximally (Ki) at 1 nM. Hui1 is also specific, inhibiting KcsA-Shaker channels in Xenopus oocytes with a Ki of 0.5 nM while Shaker, Kv1.2 and Kv1.3 channels are blocked over 200-fold less well. In contrast, HmK is potent but promiscuous, blocking KcsA-Shaker, Shaker, Kv1.2 and Kv1.3 channels with Ki of 1 to 4 nM. As anticipated, one Hui1 blocks the KcsA pore and two conserved toxin residues, Lys21 and Tyr22, are essential for high affinity binding. Unexpectedly, K+ ions traversing the channel from the inside confer voltage-sensitivity to the Hui1 off-rate via Arg23, indicating that Lys21 is not in the pore. The 3D structure of Hui1 reveals a SAK1 fold, rationalizes KcsA inhibition, and validates the scaffold-based approach for isolation of high-affinity toxins for orphan receptors.

Inhibition of Bacterial Carbonic Anhydrases and Zinc Proteases: From Orphan Targets to Innovative New Antibiotic Drugs

Zinc-containing enzymes, such as carbonic anhydrases (CAs) and metalloproteases (MPs) play critical functions in bacteria, being involved in various steps of their life cycle, which are important for survival, colonization, acquisition of nutrients for growth and proliferation, facilitation of dissemination, invasion and pathogenicity. The development of resistance to many classes of clinically used antibiotics emphasizes the need of new antibacterial drug targets to be explored. There is a wealth of data regarding bacterial CAs and zinc MPs present in many pathogenic species, such as Neisseria spp., Helycobacter pylori Escherichia coli, Mycobacterium tuberculosis, Brucella spp., Streptococcus pneumoniae, Salmonella enterica, Haemophilus influenzae, Listeria spp, Vibrio spp., Pseudomonas aeruginosa, Legionella pneumophila, Streptomyces spp., Clostridium spp., Enterococcus spp., etc. Some of these enzymes have been cloned, purified and characterized by crystallographic techniques. However, for the moment, few potent and specific inhibitors for bacterial MPs have been reported except for Clostridium histolyticum collagenase, botulinum and tetanus neurotoxin and anthrax lethal factor, which will be reviewed in this article. Bacteria encode α-,β-, and/or γ-CA families, but up to now only the first two classes have been investigated in some detail in different species. The α-CAs from Neisseria spp. and H. pylori as well as the β-class enzymes from E. coli, H. pylori, M. tuberculosis, Brucella spp., S. pneumoniae, S. enterica and H. influenzae have been cloned and characterized. The catalytic/inhibition mechanisms of these CAs are well understood as X-ray crystal structures are available for some of them, but no adducts of these enzymes with inhibitors have been characterized so far. In vitro and in vivo studies with various classes of inhibitors, such as anions, sulfonamides and sulfamates have been reported. Only for Neisseria spp., H. pylori, B. suis and S. pneumoniae CAs it has been possible to evidence inhibition of bacterial growth in vivo. Thus, bacterial CAs and MPs represent at this moment very promising targets for obtaining antibacterials devoid of the resistance problems of the clinically used such agents but further studies are needed to validate these and other less investigated enzymes as novel drug targets.

EBI2, GPR18, and GPR17 – Three Structurally Related but Biologically Distinct 7TM Receptors

7TM receptors constitute one of the largest superfamilies of proteins in the human genome. They are involved in a large number of physiological and pathological processes in the human body and thus represent major and important drug targets for the pharmaceutical industry. Although the majority have been deorphanized, many remain orphan, and these orphan receptors constitute a large pool of potential drug targets. This review focuses on one of these orphan targets, the Epstein-Barr Virus-induced receptor 2, EBI2 (or GPR183), together with two structurally related receptors, GPR17 and GPR18. The pharmacology and "druggability" of these three receptors is reviewed through a thorough description of their structural and functional properties and in vivo biology together with a status of currently available ligands for these receptors.

Ligand Prediction for Orphan Targets Using Support Vector Machines and Various Target-Ligand Kernels Is Dominated by Nearest Neighbor Effects

Support vector machine (SVM) calculations combining protein and small molecule information have been applied to identify ligands for simulated orphan targets (i.e., targets for which no ligands were available). The combination of protein and ligand information was facilitated through the design of target-ligand kernel functions that account for pairwise ligand and target similarity. The design and biological information content of such kernel functions was expected to play a major role for target-directed ligand prediction. Therefore, a variety of target-ligand kernels were implemented to capture different types of target information including sequence, secondary structure, tertiary structure, biophysical properties, ontologies, or structural taxonomy. These kernels were tested in ligand predictions for simulated orphan targets in two target protein systems characterized by the presence of different intertarget relationships. Surprisingly, although there were target- and set-specific differences in prediction rates for alternative target-ligand kernels, the performance of these kernels was overall similar and also similar to SVM linear combinations. Test calculations designed to better understand possible reasons for these observations revealed that ligand information provided by nearest neighbors of orphan targets significantly influenced SVM performance, much more so than the inclusion of protein information. As long as ligands of closely related neighbors of orphan targets were available for SVM learning, orphan target ligands could be well predicted, regardless of the type and sophistication of the kernel function that was used. These findings suggest simplified strategies for SVM-based ligand prediction for orphan targets.

Orphan targets for reperfusion injury

Cardiomyocyte death secondary to transient ischaemia occurs mainly during the first minutes of reperfusion in the form of contraction band necrosis. Research on the mechanisms leading to sarcolemmal rupture and necrosis during initial reperfusion identified several promising pharmacological targets directed either to correct the alterations in Ca(2+) handling occurring during this period (Na(+)/H(+)-exchanger, reverse mode of Na(+)/Ca(2+)-exchanger, sarcoplasmic reticulum) or to interfere with its consequences [hypercontracture, calpain activation, and mitochondrial permeability transition pore (mPTP) opening]. However, despite the fact that pharmacological tools against some of these targets have consistently demonstrated that it is possible to reduce infarct size in experimental studies by interventions applied at the time of reperfusion, the translation of these approaches to clinical practice has failed due in part to the lack of drugs able to be tested in humans. Recently, the benefits of both post-conditioning and inhibition of mPTP have been supported by proof-of-concept trials demonstrating the clinical applicability of strategies aimed at preventing lethal reperfusion injury. These promising results should stimulate efforts to develop drugs testable in humans against known, unexploited targets involved in reperfusion injury and to identify and validate additional ones.

Ligand Prediction from Protein Sequence and Small Molecule Information Using Support Vector Machines and Fingerprint Descriptors

Support vector machine (SVM) database search strategies are presented that aim at the identification of small molecule ligands for targets for which no ligand information is currently available. In pharmaceutical research and chemical biology, this situation is faced, for example, when studying orphan targets or newly identified members of protein families. To investigate methods for de novo ligand identification in the absence of known three-dimensional target structures or active molecules, we have focused on combining sequence and ligand information for closely and distantly related proteins. To provide a basis for these investigations, a set of 11 protease targets from different families was assembled together with more than 2000 inhibitors directed against individual proteases. We have compared SVM approaches that combine protein sequence and ligand information in different ways and utilize 2D fingerprints as ligand descriptors. These methodologies were applied to search for inhibitors of individual proteases not taken into account during learning. A target sequence-ligand kernel and, in particular, a linear combination of multiple target-directed SVMs consistently identified inhibitors with high accuracy including test cases where homology-based similarity searching using data fusion and conventional SVM ranking nearly or completely failed. The SVM linear combination and target-ligand kernel methods described herein are intuitive and straightforward to adopt for ligand prediction against other targets.

A universal method for fishing target proteins from mixtures of biomolecules using isothermal titration calorimetry

The most challenging tasks in biology include the identification of (1) the orphan receptor for a ligand, (2) the ligand for an orphan receptor protein, and (3) the target protein(s) for a given drug or a lead compound that are critical for the pharmacological or side effects. At present, several approaches are available, including cell- or animal-based assays, affinity labeling, solid-phase binding assays, surface plasmon resonance, and nuclear magnetic resonance. Most of these techniques are not easy to apply when the target protein is unknown and the compound is not amenable to labeling, chemical modification, or immobilization. Here we demonstrate a new universal method for fishing orphan target proteins from a complex mixture of biomolecules using isothermal titration calorimetry (ITC) as a tracking tool. We took snake venom, a crude mixture of several hundred proteins/peptides, as a model to demonstrate our proposed ITC method in tracking the isolation and purification of two distinct target proteins, a major component and a minor component. Identities of fished out target proteins were confirmed by amino acid sequencing and inhibition assays. This method has the potential to make a significant advancement in the area of identifying orphan target proteins and inhibitor screening in drug discovery and characterization.

Orphan G-Protein-Coupled Receptors

G-protein-coupled receptors (GPCRs) are key regulators of intercellular interactions, participating in almost every physiological response. They exert their effects by being activated by a variety of endogenous ligands. Traditionally, these ligands were identified first, providing tools to characterise the receptors. However, since the late 1980s, homology screening approaches have allowed the GPCRs to be found first, and in turn used as orphan targets to identify their ligands. Over the last decade this method has led to the identification of 12 novel neuropeptide families. Interestingly, four of these deorphanised GPCR systems, melanin-concentrating hormone, ghrelin, orexin and neuropeptide B/neuropeptide W, have been found to play a role in the control of energy balance. This article reviews the role of these GPCR systems in the control of food intake and energy expenditure, and discusses their potential use in therapies directed at eating disorders. As obesity has reached epidemic proportions across the developed world, pharmacotherapy has focused on this condition. However, difficulties in weight control also characterise disorders of binge eating such as bulimia and binge-eating disorder. Consequently, hypophagic treatments may be of potential benefit in normal, overweight or obese individuals displaying aberrant (out of control) eating behaviour.

Developing an Antituberculosis Compounds Database and Data Mining in the Search of a Motif Responsible for the Activity of a Diverse Class of Antituberculosis Agents

A novel data mining procedure to look for new antitubercular agents and targets as well as to find a minimum common bioactive substructure (MCBS), has been reported here. The methodology extracts MCBS, both across the diverse chemical classes and within the particular chemical class, known to be present in the various marketed drugs alongside antimycobacterial compounds with known MICs. For this purpose a small in-house database of compounds has been created, for which MICs against Mycobacterium are known. The compounds have been collected from literature available on the synthetic compounds, having known MICs against Mycobacterium tuberculosis. An elaborate HQSAR (Hologram QSAR) study has been attempted to extract active fragment from a diverse class of compounds, in combination with the clustering technique to select a homogeneous group of compounds having good a profile toward the activity. The 2D pharmacophore (the 2D fragments extracted from HQSAR) has been validated searching the database. It has been found further that this validated 2D pharmacophore could be used for searching the orphan target in Mycobacterium effectively.

104 Novel drug discovery strategies based on aptamer inhibitors which are especially useful for orphan targets

Aptamers have been identified against various targets as a type of chemical or nucleic acid ligand by systematic evolution of ligands by exponential enrichment (SELEX) with high sensitivity and specificity. Aptamers show remarkable advantages over antibodies due to the nucleic acid nature and target-induced structure-switching properties and are widely used to design various fluorescent, electrochemical, or colorimetric biosensors. However, the practical applications of aptamer-based sensing and diagnostics are still lagging behind those of antibody-based tests. Lateral flow immunoassay (LFIA) represents a well established and appropriate technology among rapid assays because of its low cost and user-friendliness. The antibody-based platform is utilized to detect numerous targets, but it is always hampered by the antibody preparation time, antibody stability, and effect of modification on the antibody. Seeking alternatives to antibodies is an area of active research and is of tremendous importance. Aptamers are receiving increasing attention in lateral flow applications because of a number of important potential performance advantages. We speculate that aptamer-based LFIA may be one of the first platforms for commercial use of aptamer-based diagnosis. This review first gives an introduction to aptamer including the selection process SELEX with its focus on aptamer advantages over antibodies, and then depicts LFIA with its focus on aptamer opportunities in LFIA over antibodies. Furthermore, we summarize the recent advances in the development of aptamer-based lateral flow biosensing assays with the aim to provide a general guide for the design of aptamer-based lateral flow biosensing assays.

Mimicry of a G protein mutation by pertussis toxin expression in transgenic Caenorhabditis elegans.

Pathogens produce virulence factors that interact directly with host molecules, but in many cases the host targets are unknown. The genetic and molecular identification of these orphan targets is often not feasible with mammalian experimental models. However, a substantial number of known targets are molecules and pathways that are conserved among eukaryotes, and therefore the use of nonmammalian model hosts to identify orphan targets may prove useful. To demonstrate the feasibility of this approach, we transformed the nematode Caenorhabditis elegans with a gene encoding the catalytic subunit of pertussis toxin (PTX), which in mammals inactivates G(o/i)alpha proteins. Expression of PTX in C. elegans produced phenotypes almost identical to those of a null mutation in the nematode gene encoding G(o/i)alpha. Furthermore, PTX suppressed the phenotype of a constitutively active form of nematode G(o/i)alpha protein. These results indicate that PTX is functional in nematodes and acts specifically on the C. elegans homologue of the mammalian target.