Drug-like density: a method of quantifying the "bindability" of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank

Druggability Analysis of Protein Targets for Drug Discovery to Combat Listeria monocytogenes

Extensive research has been conducted to identify key proteins governing stress responses, virulence, and antimicrobial resistance, as well as to elucidate their interactions within Listeria monocytogenes. While these proteins hold promise as potential targets for novel strategies to control L. monocytogenes, given their critical roles in regulating the pathogen's metabolism, additional analysis is needed to further assess their druggability-the chance of being effectively bound by small-molecule inhibitors. In this work, 535 binding pockets of 46 protein targets for known drugs (mainly antimicrobials) were first analyzed to extract 13 structural features (e.g., hydrophobicity) in a ligand-protein docking platform called Molsoft ICM Pro. The extracted features were used as inputs to develop a logistic regression model to assess the druggability of protein binding pockets, with a value of one if ligands can bind to the protein pocket. The developed druggability model was then used to evaluate 23 key proteins from L. monocytogenes that have been identified in the literature. The following proteins are predicted to be high-potential druggable targets: GroEL, FliH/FliI complex, FliG, FlhB, FlgL, FlgK, InlA, MogR, and PrfA. These findings serve as an initial point for future research to identify specific compounds that can inhibit druggable target proteins and to design experimental work to confirm their effectiveness as drug targets.

Correlation of protein binding pocket properties with hits' chemistries used in generation of ultra-large virtual libraries

Although the size of virtual libraries of synthesizable compounds is growing rapidly, we are still enumerating only tiny fractions of the drug-like chemical universe. Our capability to mine these newly generated libraries also lags their growth. That is why fragment-based approaches that utilize on-demand virtual combinatorial libraries are gaining popularity in drug discovery. These à la carte libraries utilize synthetic blocks found to be effective binders in parts of target protein pockets and a variety of reliable chemistries to connect them. There is, however, no data on the potential impact of the chemistries used for making on-demand libraries on the hit rates during virtual screening. There are also no rules to guide in the selection of these synthetic methods for production of custom libraries. We have used the SAVI (Synthetically Accessible Virtual Inventory) library, constructed using 53 reliable reaction types (transforms), to evaluate the impact of these chemistries on docking hit rates for 40 well-characterized protein pockets. The data shows that the virtual hit rates differ significantly for different chemistries with cross coupling reactions such as Sonogashira, Suzuki-Miyaura, Hiyama and Liebeskind-Srogl coupling producing the highest hit rates. Virtual hit rates appear to depend not only on the property of the formed chemical bond but also on the diversity of available building blocks and the scope of the reaction. The data identifies reactions that deserve wider use through increasing the number of corresponding building blocks and suggests the reactions that are more effective for pockets with certain physical and hydrogen bond-forming properties.

Green synthesis, structure-activity relationships, in silico molecular docking, and antifungal activities of novel prenylated chalcones

A series of 16 novel prenylated chalcones (5A-5P) was synthesized by microwave-assisted green synthesis using 5-prenyloxy-2-hydroxyacetophenone and different benzaldehydes. Comparisons were also performed between the microwave and conventional methods in terms of the reaction times and yields of all compounds, where the reaction times in the microwave and conventional methods were 1-4 min and 12-48 h, respectively. The synthesized compounds were characterized using different spectroscopic techniques, including IR, 1H-NMR, 13C-NMR, and LC-HRMS. The antifungal activities of all compounds were evaluated against Sclerotium rolfsii and Fusarium oxysporum under in vitro conditions and were additionally supported by structure-activity relationship (SAR) and molecular docking studies. Out of the 16 compounds screened, 2'-hydroxy-4-benzyloxy-5'-O-prenylchalcone (5P) showed the highest activity against both S. rolfsii and F. oxysporum, with ED50 of 25.02 and 31.87 mg/L, respectively. The molecular docking studies of the prenylated chalcones within the active sites of the EF1α and RPB2 gene sequences and FoCut5a sequence as the respective receptors for S. rolfsii and F. oxysporum revealed the importance of the compounds, where the binding energies of the docked molecules ranged from -38.3538 to -26.6837 kcal/mol for S. rolfsii and -43.400 to -23.839 kcal/mol for F. oxysporum. Additional docking parameters showed that these compounds formed stable complexes with the protein molecules.

In silico characterization of the novel SDR42E1 as a potential vitamin D modulator

The short-chain dehydrogenase/reductase (SDR) superfamily encompasses enzymes that play essential roles in the metabolism of steroid hormones and lipids. Despite an enigmatic function, recent genetic studies have linked the novel SDR 42 extended-1 (SDR42E1) gene to 25-hydroxyvitamin D levels. This study investigated the potential SDR42E1 functions and interactions with vitamin D using bioinformatics and molecular docking studies. Phylogenetic analysis unveiled that the nucleotide sequences of human SDR42E1 exhibit high evolutionary conservation across nematodes and fruit flies. Molecular docking analysis identified strong binding affinities between SDR42E1 and its orthologs with vitamin D3 and essential precursors, 8-dehydrocholesterol, followed by 7-dehydrocholesterol and 25-hydroxyvitamin D. The hydrophobic interactions observed between the protein residues and vitamin D compounds supported the predicted transmembrane localization of SDR42E1. Our investigation provides valuable insights into the potential role of SDR42E1 in skin vitamin D biosynthesis throughout species. This provides the foundation for future research and development of targeted therapies for vitamin D deficiency and related health conditions.

Unrealized targets in the discovery of antibiotics for Gram-negative bacterial infections

Advances in areas that include genomics, systems biology, protein structure determination and artificial intelligence provide new opportunities for target-based antibacterial drug discovery. The selection of a 'good' new target for direct-acting antibacterial compounds is the first decision, for which multiple criteria must be explored, integrated and re-evaluated as drug discovery programmes progress. Criteria include essentiality of the target for bacterial survival, its conservation across different strains of the same species, bacterial species and growth conditions (which determines the spectrum of activity of a potential antibiotic) and the level of homology with human genes (which influences the potential for selective inhibition). Additionally, a bacterial target should have the potential to bind to drug-like molecules, and its subcellular location will govern the need for inhibitors to penetrate one or two bacterial membranes, which is a key challenge in targeting Gram-negative bacteria. The risk of the emergence of target-based drug resistance for drugs with single targets also requires consideration. This Review describes promising but as-yet-unrealized targets for antibacterial drugs against Gram-negative bacteria and examples of cognate inhibitors, and highlights lessons learned from past drug discovery programmes.

Computer-aided design of muscarinic acetylcholine receptor M3 inhibitors: Promising compounds among trifluoromethyl containing hexahydropyrimidinones/thiones

The new high selective mAChRs M3 inhibitors with IC50 in nanomolecular ranges, which can be the prototypes for effective COPD and asthma treatment drugs, were discovered with computational approaches among trifluoromethyl containing hexahydropyrimidinones/thiones. Compounds [6-(4-ethoxy-3-methoxy-phenyl)-4-hydroxy-2-thioxo-4-(trifluoromethyl)hexahydropyrimidin-5-yl]-phenyl-methanone (THPT-1) and 5-benzoyl-6-(3,4-dimethoxyphenyl)-4-hydroxy-4-(trifluoromethyl)hexahydropyrimidin-2-one (THPO-4) have been proved to be a highly effective (with IC50 values of 1.62 ⋅ 10-7  M and 3.09 ⋅ 10-9  M, respectively) at the same concentrations significantly competitive inhibit the signal conduction through mAChR3 in comparison with ipratropium bromide, without significant effect on mAChR2, nicotinic cholinergic and adrenergic receptors.

Halo Library, a Tool for Rapid Identification of Ligand Binding Sites on Proteins Using Crystallographic Fragment Screening

X-ray crystallographic fragment screening (XCFS) uses fragment-sized molecules (∼60 to 300 Da) to access binding sites on proteins that may be inaccessible to larger drug-like molecules (>300 Da). Previous studies have shown that fragments containing halogen atoms bind more often to proteins than non-halogenated fragments. Here, we designed the Halo Library containing 46 halogenated fragments (including the "universal fragment" 4-bromopyrazole), a majority of which have been reported to bind to or inhibit one or more targets. The library was screened against the crystals of HIV-1 reverse transcriptase with the drug rilpivirine, yielding an overall hit rate of 26%. Two new binding sites were discovered, and several hot spots were identified. This small library may thus provide a convenient tool for rapidly assessing the feasibility of a target for XCFS, mapping hot spots and cryptic sites, as well as finding fragment binders that can be useful for developing drug leads.

Virtual screening of ultra-large chemical libraries identifies cell-permeable small-molecule inhibitors of a "non-druggable" target, STAT3 N-terminal domain

STAT3 N-terminal domain is a promising molecular target for cancer treatment and modulation of immune responses. However, STAT3 is localized in the cytoplasm, mitochondria, and nuclei, and thus, is inaccessible to therapeutic antibodies. Its N-terminal domain lacks deep pockets on the surface and represents a typical "non-druggable" protein. In order to successfully identify potent and selective inhibitors of the domain, we have used virtual screening of billion structure-sized virtual libraries of make-on-demand screening samples. The results suggest that the expansion of accessible chemical space by cutting-edge ultra-large virtual compound databases can lead to successful development of small molecule drugs for hard-to-target intracellular proteins.

SiteRadar: Utilizing Graph Machine Learning for Precise Mapping of Protein-Ligand-Binding Sites

Identifying ligand-binding sites on the protein surface is a crucial step in the structure-based drug design. Although multiple techniques have been proposed, including those using machine learning algorithms, the existing solutions do not provide significant advantages over nonmachine learning approaches and there is still a big room for improvement. The low ability to identify protein-ligand-binding sites makes available approaches inapplicable to automated drug design. Here, we present SiteRadar, a new algorithm for mapping cavities that are likely to bind a small-molecule ligand. SiteRadar shows higher accuracy in binding site identification compared with FPocket and PUResNet. SiteRadar demonstrates an ability to detect up to 74% of true ligand-binding sites according to the top N + 2 metric and usually covers approximately 80% of ligand atoms. Therefore, SiteRadar can be regarded as a promising solution for implementation into algorithms for automated drug design.

Artificial Intelligence-Based Computational Screening and Functional Assays Identify Candidate Small Molecule Antagonists of PTPmu-Dependent Adhesion

PTPmu (PTPµ) is a member of the receptor protein tyrosine phosphatase IIb family that participates in cell-cell adhesion and signaling. PTPmu is proteolytically downregulated in glioblastoma (glioma), and the resulting extracellular and intracellular fragments are believed to stimulate cancer cell growth and/or migration. Therefore, drugs targeting these fragments may have therapeutic potential. Here, we used the AtomNet^® platform, the first deep learning neural network for drug design and discovery, to screen a molecular library of several million compounds and identified 76 candidates predicted to interact with a groove between the MAM and Ig extracellular domains required for PTPmu-mediated cell adhesion. These candidates were screened in two cell-based assays: PTPmu-dependent aggregation of Sf9 cells and a tumor growth assay where glioma cells grow in three-dimensional spheres. Four compounds inhibited PTPmu-mediated aggregation of Sf9 cells, six compounds inhibited glioma sphere formation/growth, while two priority compounds were effective in both assays. The stronger of these two compounds inhibited PTPmu aggregation in Sf9 cells and inhibited glioma sphere formation down to 25 micromolar. Additionally, this compound was able to inhibit the aggregation of beads coated with an extracellular fragment of PTPmu, directly demonstrating an interaction. This compound presents an interesting starting point for the development of PTPmu-targeting agents for treating cancer including glioblastoma.

A Guide to In Silico Drug Design

The drug discovery process is a rocky path that is full of challenges, with the result that very few candidates progress from hit compound to a commercially available product, often due to factors, such as poor binding affinity, off-target effects, or physicochemical properties, such as solubility or stability. This process is further complicated by high research and development costs and time requirements. It is thus important to optimise every step of the process in order to maximise the chances of success. As a result of the recent advancements in computer power and technology, computer-aided drug design (CADD) has become an integral part of modern drug discovery to guide and accelerate the process. In this review, we present an overview of the important CADD methods and applications, such as in silico structure prediction, refinement, modelling and target validation, that are commonly used in this area.

ProBiS-Fold Approach for Annotation of Human Structures from the AlphaFold Database with No Corresponding Structure in the PDB to Discover New Druggable Binding Sites

ProBiS (Protein Binding Sites), a local structure-based comparison algorithm, is used in the new ProBiS-Fold web server to annotate human structures from the AlphaFold database without a corresponding structure in the Protein Data Bank (PDB) to discover new druggable binding sites. The ProBiS algorithm is used to compare each query protein structure predicted by the AlphaFold approach with the protein structures from the PDB to identify similarities between known binding sites found in the PDB and yet unknown binding sites in the AlphaFold database. Ligands bound in these identified similar PDB sites are then transferred to each query protein from the AlphaFold database, and binding sites are identified as ligand clusters on an AlphaFold protein. Small molecule binding sites are assigned druggability scores based on the similarity of their ligands to known drugs, allowing them to be ranked according to their perceived and actual potential for drug development. ProBiS-Fold provides interactive and downloadable binding sites for the entire human structural proteome, including more than 3000 new druggable binding sites that have no corresponding structure in the PDB, taking into account AlphaFold's model quality, to enable protein structure-function relationship studies and pharmaceutical drug discovery research. The web server is freely accessible to academic users at http://probis-fold.insilab.org.

Data-driven analysis and druggability assessment methods to accelerate the identification of novel cancer targets

Over the past few decades, drug discovery has greatly improved the outcomes for patients, but several challenges continue to hinder the rapid development of novel drugs. Addressing unmet clinical needs requires the pursuit of drug targets that have a higher likelihood to lead to the development of successful drugs. Here we describe a bioinformatic approach for identifying novel cancer drug targets by performing statistical analysis to ascertain quantitative changes in expression levels between protein-coding genes, as well as co-expression networks to classify these genes into groups. Subsequently, we provide an overview of druggability assessment methodologies to prioritize and select the best targets to pursue.

ProxyBind: a Compendium of Binding Sites for Proximity-Induced Pharmacology

Proximity-induced pharmacology (ProxPharm) is a novel paradigm in drug discovery where a small molecule brings two proteins in close proximity to elicit a signal, generally from one protein onto another. The potential of ProxPharm compounds as a new therapeutic modality is firmly established by proteolysis targeting chimeras (PROTACs) that bring an E3 ubiquitin ligase in proximity to a target protein to induce ubiquitination and subsequent degradation of the target. The concept can be expanded to induce other post-translational modifications via the recruitment of different types of protein-modifying enzymes. To survey the human proteome for opportunities in proximity pharmacology, we systematically mapped non-catalytic drug binding pockets on the structure of protein-modifying enzymes available from the Protein Databank. In addition to binding sites exploited by previously reported ProxPharm compounds, we identified putative ligandable non-catalytic pockets in 236 kinases, 45 phosphatases, 37 deubiquitinases, 14 methyltransferases, 11 acetyltransferases, 13 glycosyltransferases, 4 deacetylases, 7 demethylases and 2 glycosidases, including cavities occupied by chemical matter that may serve as starting points for future ProxPharm compounds. This systematic survey confirms that proximity pharmacology is a versatile modality with largely unexplored and promising potential and reveals novel opportunities to pharmacologically rewire molecular circuitries. All data is available from the ProxyBind database at https://polymorph.sgc.utoronto.ca/proxybind/index.php.

Estimating the Similarity between Protein Pockets

With the exponential increase in publicly available protein structures, the comparison of protein binding sites naturally emerged as a scientific topic to explain observations or generate hypotheses for ligand design, notably to predict ligand selectivity for on- and off-targets, explain polypharmacology, and design target-focused libraries. The current review summarizes the state-of-the-art computational methods applied to pocket detection and comparison as well as structural druggability estimates. The major strengths and weaknesses of current pocket descriptors, alignment methods, and similarity search algorithms are presented. Lastly, an exhaustive survey of both retrospective and prospective applications in diverse medicinal chemistry scenarios illustrates the capability of the existing methods and the hurdle that still needs to be overcome for more accurate predictions.

In silico evidence of beauvericin antiviral activity against SARS-CoV-2

Background

Scientists are still battling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus responsible for the coronavirus 2019 (COVID-19) pandemic so human lives can be saved worldwide. Secondary fungal metabolites are of intense interest due to their broad range of pharmaceutical properties. Beauvericin (BEA) is a secondary metabolite produced by the fungus Beauveria bassiana. Although promising anti-viral activity has previously been reported for BEA, studies investigating its therapeutic potential are limited.

Methods

The objective of this study was to assess the potential usage of BEA as an anti-viral molecule via protein–protein docking approaches using MolSoft.

Results

In-silico results revealed relatively favorable binding energies for BEA to different viral proteins implicated in the vital life stages of this virus. Of particular interest is the capability of BEA to dock to both the main coronavirus protease (Pockets A and B) and spike proteins. These results were validated by molecular dynamic simulation (Gromacs). Several parameters, such as root-mean-square deviation/fluctuation, the radius of gyration, H-bonding, and free binding energy were analyzed. Computational analyses revealed that interaction of BEA with the main protease pockets in addition to the spike glycoprotein remained stable.

Conclusion

Altogether, our results suggest that BEA might be considered as a potential competitive and allosteric agonist inhibitor with therapeutic options for treating COVID-19 pending in vitro and in vivo validation.

Amilorides inhibit SARS-CoV-2 replication in vitro by targeting RNA structures

The SARS-CoV-2 pandemic, and the likelihood of future coronavirus pandemics, emphasized the urgent need for development of novel antivirals. Small-molecule chemical probes offer both to reveal aspects of virus replication and to serve as leads for antiviral therapeutic development. Here, we report on the identification of amiloride-based small molecules that potently inhibit OC43 and SARS-CoV-2 replication through targeting of conserved structured elements within the viral 5′-end. Nuclear magnetic resonance–based structural studies revealed specific amiloride interactions with stem loops containing bulge like structures and were predicted to be strongly bound by the lead amilorides in retrospective docking studies. Amilorides represent the first antiviral small molecules that target RNA structures within the 5′ untranslated regions and proximal region of the CoV genomes. These molecules will serve as chemical probes to further understand CoV RNA biology and can pave the way for the development of specific CoV RNA–targeted antivirals.

ProBiS-Dock Database: A Web Server and Interactive Web Repository of Small Ligand-Protein Binding Sites for Drug Design

We have developed a new system, ProBiS-Dock, which can be used to determine the different types of protein binding sites for small ligands. The binding sites identified this way are then used to construct a new binding site database, the ProBiS-Dock Database, that allows for the ranking of binding sites according to their utility for drug development. The newly constructed database currently has more than 1.4 million binding sites and offers the possibility to investigate potential drug targets originating from different biological species. The interactive ProBiS-Dock Database, a web server and repository that consists of all small-molecule ligand binding sites in all of the protein structures in the Protein Data Bank, is freely available at http://probis-dock-database.insilab.org. The ProBiS-Dock Database will be regularly updated to keep pace with the growth of the Protein Data Bank, and our anticipation is that it will be useful in drug discovery.

DrugPred_RNA-A Tool for Structure-Based Druggability Predictions for RNA Binding Sites

RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predictor DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered predictions for selected RNA binding sites that agreed with manual assignment. In addition, most drug-like ligands contained in an RNA test set were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational and sequence changes in the binding sites and can contribute to direct drug discovery efforts for RNA targets.

From Genome to Drugs: New Approaches in Antimicrobial Discovery

Decades of successful use of antibiotics is currently challenged by the emergence of increasingly resistant bacterial strains. Novel drugs are urgently required but, in a scenario where private investment in the development of new antimicrobials is declining, efforts to combat drug-resistant infections become a worldwide public health problem. Reasons behind unsuccessful new antimicrobial development projects range from inadequate selection of the molecular targets to a lack of innovation. In this context, increasingly available omics data for multiple pathogens has created new drug discovery and development opportunities to fight infectious diseases. Identification of an appropriate molecular target is currently accepted as a critical step of the drug discovery process. Here, we review how diverse layers of multi-omics data in conjunction with structural/functional analysis and systems biology can be used to prioritize the best candidate proteins. Once the target is selected, virtual screening can be used as a robust methodology to explore molecular scaffolds that could act as inhibitors, guiding the development of new drug lead compounds. This review focuses on how the advent of omics and the development and application of bioinformatics strategies conduct a "big-data era" that improves target selection and lead compound identification in a cost-effective and shortened timeline.

Molecular docking studies, in-silico ADMET predictions and synthesis of novel PEGA-nucleosides as antimicrobial agents targeting class B1 metallo-β-lactamases

Class B1 metallo-β-lactamases (MBLs) are metalloenzymes found in drug resistant bacteria. The enzyme requires zinc ions, along with conserved amino acid coordination for nucleophilic attack of the lactam ring to induce hydrolysis and inactivation of β-lactam and some carbapenem antibiotics. To this date there are no clinically relevant class B1 MBL inhibitors, however L-captopril has shown significant results against NDM-1, the most difficult MBL to inhibit. Herein, we report the synthesis and evaluation of novel nucleoside analogues modified with polyethylene glycolamino (PEGA) as potential inhibitors for class B1 MBLs. Molecular dynamics simulations, using internal coordinate mechanics (ICM) algorithm, were performed on subclass B1 enzyme complex models screened with twenty-one possible PEGA-nucleosides. Analogue A, 3'-deoxy-3'-(2-(2-hydroxyethoxy)ethanamino)-β-D-xylofuranosyluracil showed superior binding, with high specificity to the conserved zinc ions in the class B1 MBL active site by utilizing key β-lactam mimic points in the uridine nucleobase. The PEGA moiety showed chelating activity with zinc and disrupted the metal-binding amino acid geometry. In all subclass B1 proteins tested, analogue A had the most effective inhibition when compared to penicillin or L-captopril. Chemical synthesis was performed by condensation of the corresponding keto ribonucleoside with PEGA, followed by enantioselective reduction of the formed imine to produce the amino derivative with desired configuration. Pharmacokinetic and pharmacodynamic screenings revealed that PEGA-pyrimidine nucleosides are not toxic, nor violate Lipinski's rules. These results suggested that analogue A can be proposed as a potential metalloenzyme inhibitor against the widespread antibiotic resistant bacteria and is worth further in vitro and in vivo investigations.

The latest automated docking technologies for novel drug discovery

INTRODUCTION

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

AREAS COVERED

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

EXPERT OPINION

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

Amilorides inhibit SARS-CoV-2 replication in vitro by targeting RNA structures

The SARS-CoV-2 pandemic, and the likelihood of future coronavirus pandemics, has rendered our understanding of coronavirus biology more essential than ever. Small molecule chemical probes offer to both reveal novel aspects of virus replication and to serve as leads for antiviral therapeutic development. The RNA-biased amiloride scaffold was recently tuned to target a viral RNA structure critical for translation in enterovirus 71, ultimately uncovering a novel mechanism to modulate positive-sense RNA viral translation and replication. Analysis of CoV RNA genomes reveal many conserved RNA structures in the 5'-UTR and proximal region critical for viral translation and replication, including several containing bulge-like secondary structures suitable for small molecule targeting. Following phylogenetic conservation analysis of this region, we screened an amiloride-based small molecule library against a less virulent human coronavirus, OC43, to identify lead ligands. Amilorides inhibited OC43 replication as seen in viral plaque assays. Select amilorides also potently inhibited replication competent SARS-CoV-2 as evident in the decreased levels of cell free virions in cell culture supernatants of treated cells. Reporter screens confirmed the importance of RNA structures in the 5'-end of the viral genome for small molecule activity. Finally, NMR chemical shift perturbation studies of the first six stem loops of the 5'-end revealed specific amiloride interactions with stem loops 4, 5a, and 6, all of which contain bulge like structures and were predicted to be strongly bound by the lead amilorides in retrospective docking studies. Taken together, the use of multiple orthogonal approaches allowed us to identify the first small molecules aimed at targeting RNA structures within the 5'-UTR and proximal region of the CoV genome. These molecules will serve as chemical probes to further understand CoV RNA biology and can pave the way for the development of specific CoV RNA-targeted antivirals.

Structural Fluctuations of Aromatic Residues in an Apo-Form Reveal Cryptic Binding Sites: Implications for Fragment-Based Drug Design

Cryptic sites are binding pockets that are transiently formed in an apo form or that are induced by ligand binding. The investigation of cryptic sites is crucial for drug discovery, since these sites are ubiquitous in disease-related human proteins, and targeting them expands the number of drug targets greatly. However, although many computational studies have attempted to identify cryptic sites, the detection remains challenging. Here, we aimed to characterize and detect cryptic sites in terms of structural fluctuations in an apo form, investigating proteins each of which possesses a cryptic site. From their X-ray structures, we saw that aromatic residues tended to be found in cryptic sites. To examine structural fluctuations of the apo forms, we performed molecular dynamics (MD) simulations, producing probability distributions of the solvent-accessible surface area per aromatic residue. To detect aromatic residues in cryptic sites, we have proposed a "cryptic-site index" based on the distribution, demonstrating the performance via several measures, such as recall and specificity. Besides, we found that high-ranking aromatic residues were likely to probe concaves in a cryptic site. This implies that such fluctuations provide a profile of scaffolds of compounds with the potential to bind to a particular cryptic site.

Analysis of Benzenoid Substitution Patterns in Small Molecule Active Pharmaceutical Ingredients

Previous analyses have revealed that benzenoid rings are prevalent scaffolds in active pharmaceutical ingredients (APIs). Here, we analyze the substitution patterns of benzenoid rings in small molecule APIs approved by the FDA through 2019 and show that only a few substitution patterns (1-, 1,2-, 1,4-, and 1,2,4-) prevail, and the distribution has remained relatively constant over time. We postulate the connection between available synthetic methods and the occurrence of a few benzenoid substitution patterns by providing an overview of synthetic methods that elaborate existing substitution patterns and those that create new substitution patterns, including those of the former that are favored by medicinal chemists. Finally, we calculated medicinal chemistry properties of benzenoid containing APIs that are often used by practitioners as design elements, including "druglikeness", shape, complexity, and similarity/diversity and discuss these properties in the context of synthesis.

Druggability and drug-likeness concepts in drug design: are biomodelling and predictive tools having their say?

The drug discovery process typically involves target identification and design of suitable drug molecules against these targets. Despite decades of experimental investigations in the drug discovery domain, about 96% overall failure rate has been recorded in drug development due to the "undruggability" of various identified disease targets, in addition to other challenges. Likewise, the high attrition rate of drug candidates in the drug discovery process has also become an enormous challenge for the pharmaceutical industry. To alleviate this negative outlook, new trends in drug discovery have emerged. By drifting away from experimental research methods, computational tools and big data are becoming valuable in the prediction of biological target druggability and the drug-likeness of potential therapeutic agents. These tools have proven to be useful in saving time and reducing research costs. As with any emerging technique, however, controversial opinions have been presented regarding the validation of predictive computational tools. To address the challenges associated with these varying opinions, this review attempts to highlight the principles of druggability and drug-likeness and their recent advancements in the drug discovery field. Herein, we present the different computational tools and their reliability of predictive analysis in the drug discovery domain. We believe that this report would serve as a comprehensive guide towards computational-oriented drug discovery research. Graphical abstract Highlights of methods for assessing the druggability of biological targets.

AlphaSpace 2.0: Representing Concave Biomolecular Surfaces Using β-Clusters

Modern rational modulator design and structure-function characterization often concentrate on concave regions of biomolecular surfaces, ranging from well-defined small-molecule binding sites to large protein-protein interaction interfaces. Here, we introduce a β-cluster as a pseudomolecular representation of fragment-centric pockets detected by AlphaSpace [J. Chem. Inf. Model. 2015, 55, 1585], a recently developed computational analysis tool for topographical mapping of biomolecular concavities. By mimicking the shape as well as atomic details of potential molecular binders, this new β-cluster representation allows direct pocket-to-ligand shape comparison and can be used to guide ligand optimization. Furthermore, we defined the β-score, the optimal Vina score of the β-cluster, as an indicator of pocket ligandability and developed an ensemble β-cluster approach, which allows one-to-one pocket mapping and comparison among aligned protein structures. We demonstrated the utility of β-cluster representation by applying the approach to a wide variety of problems including binding site detection and comparison, characterization of protein-protein interactions, and fragment-based ligand optimization. These new β-cluster functionalities have been implemented in AlphaSpace 2.0, which is freely available on the web at http://www.nyu.edu/projects/yzhang/AlphaSpace2.

Structure-based Druggability Assessment of Anti-virulence Targets from Pseudomonas aeruginosa

Antimicrobial Resistance (AMR) represents a serious threat to health and the global economy. However, interest in antibacterial drug development has decreased substantially in recent decades. Meanwhile, anti-virulence drug development has emerged as an attractive alternative to fight AMR. Although several macromolecular targets have been explored for this goal, their druggability is a vital piece of information that has been overlooked. This review explores this subject by showing how structure- based freely available in silico tools, such as PockDrug and FTMap, might be useful for designing novel inhibitors of the pyocyanin biosynthesis pathway and improving the potency/selectivity of compounds that target the Pseudomonas aeruginosa quorum sensing mechanism. The information provided by hotspot analysis, along with binding site features, reveals novel druggable targets (PhzA and PhzS) that remain largely unexplored. However, it also highlights that in silico druggability prediction tools have several limitations that might be overcome in the near future. Meanwhile, anti-virulence drug targets should be assessed by complementary methods, such as the combined use of FTMap/PockDrug, once the consensus druggability classification reduces the risk of wasting resources on undruggable proteins.

Druggable exosites of the human kino-pocketome

Small molecules binding at any of the multiple regulatory sites on the molecular surface of a protein kinase may stabilize or disrupt the corresponding interaction, leading to consequent modulation of the kinase cellular activity. As such, each of these sites represents a potential drug target. Even targeting sites outside the immediate ATP site, the so-called exosites, may cause desirable biological effects through an allosteric mechanism. Targeting exosites can alleviate adverse effects and toxicity that is common when ATP-site compounds bind promiscuously to many other types of kinases. In this study we have identified, catalogued, and annotated all potentially druggable exosites on the protein kinase domains within the existing structural human kinome. We then priority-ranked these exosites by those most amenable to drug design. In order to identify pockets that are either consistent across the kinome, or unique and specific to a particular structure, we have also implemented a normalized representation of all pockets, and displayed these graphically. Finally, we have built a database and designed a web-based interface for users interested in accessing the 3-dimensional representations of these pockets. We envision this information will assist drug discovery efforts searching for untargeted binding pockets in the human kinome.

Sequence-Derived Markers of Drug Targets and Potentially Druggable Human Proteins

Recent research shows that majority of the druggable human proteome is yet to be annotated and explored. Accurate identification of these unexplored druggable proteins would facilitate development, screening, repurposing, and repositioning of drugs, as well as prediction of new drug–protein interactions. We contrast the current drug targets against the datasets of non-druggable and possibly druggable proteins to formulate markers that could be used to identify druggable proteins. We focus on the markers that can be extracted from protein sequences or names/identifiers to ensure that they can be applied across the entire human proteome. These markers quantify key features covered in the past works (topological features of PPIs, cellular functions, and subcellular locations) and several novel factors (intrinsic disorder, residue-level conservation, alternative splicing isoforms, domains, and sequence-derived solvent accessibility). We find that the possibly druggable proteins have significantly higher abundance of alternative splicing isoforms, relatively large number of domains, higher degree of centrality in the protein-protein interaction networks, and lower numbers of conserved and surface residues, when compared with the non-druggable proteins. We show that the current drug targets and possibly druggable proteins share involvement in the catalytic and signaling functions. However, unlike the drug targets, the possibly druggable proteins participate in the metabolic and biosynthesis processes, are enriched in the intrinsic disorder, interact with proteins and nucleic acids, and are localized across the cell. To sum up, we formulate several markers that can help with finding novel druggable human proteins and provide interesting insights into the cellular functions and subcellular locations of the current drug targets and potentially druggable proteins.

Cytotoxic unsaturated electrophilic compounds commonly target the ubiquitin proteasome system

A large number of natural products have been advocated as anticancer agents. Many of these compounds contain functional groups characterized by chemical reactivity. It is not clear whether distinct mechanisms of action can be attributed to such compounds. We used a chemical library screening approach to demonstrate that a substantial fraction (~20%) of cytotoxic synthetic compounds containing Michael acceptor groups inhibit proteasome substrate processing and induce a cellular response characteristic of proteasome inhibition. Biochemical and structural analyses showed binding to and inhibition of proteasome-associated cysteine deubiquitinases, in particular ubiquitin specific peptidase 14 (USP14). The results suggested that compounds bind to a crevice close to the USP14 active site with modest affinity, followed by covalent binding. A subset of compounds was identified where cell death induction was closely associated with proteasome inhibition and that showed significant antineoplastic activity in a zebrafish embryo model. These findings suggest that proteasome inhibition is a relatively common mode of action by cytotoxic compounds containing Michael acceptor groups and help to explain previous reports on the antineoplastic effects of natural products containing such functional groups.

Glycine-induced formation and druggability score prediction of protein surface pockets

Nowadays, it is well established that most of the human diseases which are not related to pathogen infections have their origin from DNA disorders. Thus, DNA mutations, waiting for the availability of CRISPR-like remedies, will propagate into proteomics, offering the possibility to select natural or synthetic molecules to fight against the effects of malfunctioning proteins. Drug discovery, indeed, is a flourishing field of biotechnological research to improve human health, even though the development of a new drug is increasingly more expensive in spite of the massive use of informatics in Medicinal Chemistry. CRISPR technology adds new alternatives to cure diseases by removing DNA defects responsible of genome-related pathologies. In principle, the same technology, however, could also be exploited to induce protein mutations whose effects are controlled by the presence of suitable ligands. In this paper, a new idea is proposed for the realization of mutated proteins, on the surface of which more spacious transient pockets are formed and, therefore, are more suitable for hosting drugs. In particular, new allosteric sites are obtained by replacing amino-acids with bulky side chains with glycine, Gly, the smallest natural amino-acid. We also present a machine learning approach to evaluate the druggability score of new (or enlarged) pockets. Preliminary experimental results are very promising, showing that 10% of the sites created by the Gly-pipe software are druggable.

Evidence for ligandable sites in structured RNA throughout the Protein Data Bank

RNA has attracted considerable attention as a target for small molecules. However, methods to identify, study, and characterize suitable RNA targets have lagged behind strategies for protein targets. One approach that has received considerable attention for protein targets has been to utilize computational analysis to investigate ligandable "pockets" on proteins that are amenable to small molecule binding. These studies have shown that selected physical properties of pockets are important parameters that govern the ability of a structure to bind to small molecules. This work describes a similar analysis to study pockets on all RNAs in the Protein Data Bank (PDB). Using parameters such as buriedness, hydrophobicity, volume, and other properties, the set of all RNAs is analyzed and compared to all proteins. Considerable overlap is observed between the properties of pockets on RNAs and proteins. Thus, many RNAs are capable of populating conformations with pockets that are likely suitable for small molecule binding. Further, principal moment of inertia (PMI) calculations reveal that liganded RNAs exist in diverse structural space, much of which overlaps with protein structural space. Taken together, these results suggest that complex folded RNAs adopt unique structures with pockets that may represent viable opportunities for small molecule targeting.

An overview of recent molecular dynamics applications as medicinal chemistry tools for the undruggable site challenge

Molecular dynamics (MD) has become increasingly popular due to the development of hardware and software solutions and the improvement in algorithms, which allowed researchers to scale up calculations in order to speed them up. MD simulations are usually used to address protein folding issues or protein-ligand complex stability through energy profile analysis over time. In recent years, the development of new tools able to deeply explore a potential energy surface (PES) has allowed researchers to focus on the dynamic nature of the binding recognition process and binding-induced protein conformational changes. Moreover, modern approaches have been demonstrated to be effective and reliable in calculating some kinetic and thermodynamic parameters behind the host-guest recognition process. Starting from all of these considerations, several efforts have been made in order to integrate MD within the virtual screening process in drug discovery. Knowledge retrieved from MD can, in fact, be exploited as a starting point to build pharmacophores or docking constraints in the early stage of the screening campaign as well as to define key features, in order to unravel hidden binding modes and help the optimisation of the molecular structure of a lead compound. Based on these outcomes, researchers are nowadays using MD as an invaluable tool to discover and target previously considered undruggable binding sites, including protein-protein interactions and allosteric sites on a protein surface. As a matter of fact, the use of MD has been recognised as vital to the discovery of selective protein-protein interaction modulators. The use of a dynamic overview on how the host-guest recognition occurs and of the relative conformational modifications induced allows researchers to optimise small molecules and small peptides capable of tightly interacting within the cleft between two proteins. In this review, we aim to present the most recent applications of MD as an integrated tool to be used in the rational design of small molecules or small peptides able to modulate undruggable targets, such as allosteric sites and protein-protein interactions.

Quantitative metrics for drug-target ligandability

Ligandability is a prerequisite for druggability and is a much easier concept to understand, model and predict because it does not depend on the complex pharmacodynamic and pharmacokinetic mechanisms in the human body. In this review, we consider a metric for quantifying ligandability from experimental data. We discuss ligandability in terms of the balance between effort and reward. The metric is evaluated for a standard set of well-studied drug targets - some traditionally considered to be ligandable and some regarded as difficult. We suggest that this metric should be used to systematically improve computational predictions of ligandability, which can then be applied to novel drug targets to predict their tractability.

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.

Bioinformatics in translational drug discovery

Bioinformatics approaches are becoming ever more essential in translational drug discovery both in academia and within the pharmaceutical industry. Computational exploitation of the increasing volumes of data generated during all phases of drug discovery is enabling key challenges of the process to be addressed. Here, we highlight some of the areas in which bioinformatics resources and methods are being developed to support the drug discovery pipeline. These include the creation of large data warehouses, bioinformatics algorithms to analyse 'big data' that identify novel drug targets and/or biomarkers, programs to assess the tractability of targets, and prediction of repositioning opportunities that use licensed drugs to treat additional indications.

The impact of in silico screening in the discovery of novel and safer drug candidates

Drug discovery is a multidisciplinary and multivariate optimization endeavor. As such, in silico screening tools have gained considerable importance to archive, analyze and exploit the vast and ever-increasing amount of experimental data generated throughout the process. The current review will focus on the computer-aided prediction of the numerous properties that need to be controlled during the discovery of a preliminary hit and its promotion to a viable clinical candidate. It does not pretend to the almost impossible task of an exhaustive report but will highlight a few key points that need to be collectively addressed both by chemists and biologists to fuel the drug discovery pipeline with innovative and safe drug candidates.

ENRI: A tool for selecting structure-based virtual screening target conformations

Finding pharmaceutically relevant target conformations from an arbitrary set of protein conformations remains a challenge in structure-based virtual screening (SBVS). The growth in the number of available conformations, either experimentally determined or computationally derived, obscures the situation further. While the inflated conformation space potentially contains viable druggable targets, the increase of conformational complexity, as a consequence, poses a selection problem. To address this challenge, we took advantage of machine learning methods, namely an over-sampling and a binary classification procedure, and present a novel method to select druggable receptor conformations. Specifically, we trained a binary classifier on a set of nuclear receptor conformations, wherein each conformation was labeled with an enrichment measure for a corresponding SBVS. The classifier enabled us to formulate suggestions and identify enriching SBVS targets for six of seven nuclear receptors. Further, the classifier can be extended to other proteins of interest simply by feeding new training data sets to the classifier. Our work, thus, provides a methodology to identify pharmaceutically interesting receptor conformations for nuclear receptors and other drug targets.

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.

Can We Rely on Computational Predictions To Correctly Identify Ligand Binding Sites on Novel Protein Drug Targets? Assessment of Binding Site Prediction Methods and a Protocol for Validation of Predicted Binding Sites

In the field of medicinal chemistry there is increasing focus on identifying key proteins whose biochemical functions can firmly be linked to serious diseases. Such proteins become targets for drug or inhibitor molecules that could treat or halt the disease through therapeutic action or by blocking the protein function respectively. The protein must be targeted at the relevant biologically active site for drug or inhibitor binding to be effective. As insufficient experimental data is available to confirm the biologically active binding site for novel protein targets, researchers often rely on computational prediction methods to identify binding sites. Presented herein is a short review on structure-based computational methods that (i) predict putative binding sites and (ii) assess the druggability of predicted binding sites on protein targets. This review briefly covers the principles upon which these methods are based, where they can be accessed and their reliability in identifying the correct binding site on a protein target. Based on this review, we believe that these methods are useful in predicting putative binding sites, but as they do not account for the dynamic nature of protein-ligand binding interactions, they cannot definitively identify the correct site from a ranked list of putative sites. To overcome this shortcoming, we strongly recommend using molecular docking to predict the most likely protein-ligand binding site(s) and mode(s), followed by molecular dynamics simulations and binding thermodynamics calculations to validate the docking results. This protocol provides a valuable platform for experimental and computational efforts to design novel drugs and inhibitors that target disease-related proteins.

Exploring the role of water in molecular recognition: predicting protein ligandability using a combinatorial search of surface hydration sites

The interaction between any two biological molecules must compete with their interaction with water molecules. This makes water the most important molecule in medicine, as it controls the interactions of every therapeutic with its target. A small molecule binding to a protein is able to recognize a unique binding site on a protein by displacing bound water molecules from specific hydration sites. Quantifying the interactions of these water molecules allows us to estimate the potential of the protein to bind a small molecule. This is referred to as ligandability. In the study, we describe a method to predict ligandability by performing a search of all possible combinations of hydration sites on protein surfaces. We predict ligandability as the summed binding free energy for each of the constituent hydration sites, computed using inhomogeneous fluid solvation theory. We compared the predicted ligandability with the maximum observed binding affinity for 20 proteins in the human bromodomain family. Based on this comparison, it was determined that effective inhibitors have been developed for the majority of bromodomains, in the range from 10 to 100 nM. However, we predict that more potent inhibitors can be developed for the bromodomains BPTF and BRD7 with relative ease, but that further efforts to develop inhibitors for ATAD2 will be extremely challenging. We have also made predictions for the 14 bromodomains with no reported small molecule K d values by isothermal titration calorimetry. The calculations predict that PBRM1(1) will be a challenging target, while others such as TAF1L(2), PBRM1(4) and TAF1(2), should be highly ligandable. As an outcome of this work, we assembled a database of experimental maximal K d that can serve as a community resource assisting medicinal chemistry efforts focused on BRDs. Effective prediction of ligandability would be a very useful tool in the drug discovery process.

CryptoSite: Expanding the Druggable Proteome by Characterization and Prediction of Cryptic Binding Sites

Many proteins have small-molecule binding pockets that are not easily detectable in the ligand-free structures. These cryptic sites require a conformational change to become apparent; a cryptic site can therefore be defined as a site that forms a pocket in a holo structure, but not in the apo structure. Because many proteins appear to lack druggable pockets, understanding and accurately identifying cryptic sites could expand the set of drug targets. Previously, cryptic sites were identified experimentally by fragment-based ligand discovery and computationally by long molecular dynamics simulations and fragment docking. Here, we begin by constructing a set of structurally defined apo-holo pairs with cryptic sites. Next, we comprehensively characterize the cryptic sites in terms of their sequence, structure, and dynamics attributes. We find that cryptic sites tend to be as conserved in evolution as traditional binding pockets but are less hydrophobic and more flexible. Relying on this characterization, we use machine learning to predict cryptic sites with relatively high accuracy (for our benchmark, the true positive and false positive rates are 73% and 29%, respectively). We then predict cryptic sites in the entire structurally characterized human proteome (11,201 structures, covering 23% of all residues in the proteome). CryptoSite increases the size of the potentially "druggable" human proteome from ~40% to ~78% of disease-associated proteins. Finally, to demonstrate the utility of our approach in practice, we experimentally validate a cryptic site in protein tyrosine phosphatase 1B using a covalent ligand and NMR spectroscopy. The CryptoSite Web server is available at http://salilab.org/cryptosite.

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.

New Frontiers in Druggability

A powerful early approach to evaluating the druggability of proteins involved determining the hit rate in NMR-based screening of a library of small compounds. Here, we show that a computational analog of this method, based on mapping proteins using small molecules as probes, can reliably reproduce druggability results from NMR-based screening and can provide a more meaningful assessment in cases where the two approaches disagree. We apply the method to a large set of proteins. The results show that, because the method is based on the biophysics of binding rather than on empirical parametrization, meaningful information can be gained about classes of proteins and classes of compounds beyond those resembling validated targets and conventionally druglike ligands. In particular, the method identifies targets that, while not druggable by druglike compounds, may become druggable using compound classes such as macrocycles or other large molecules beyond the rule-of-five limit.

Overcoming Chemical, Biological, and Computational Challenges in the Development of Inhibitors Targeting Protein-Protein Interactions

Protein-protein interactions (PPIs) underlie the majority of biological processes, signaling, and disease. Approaches to modulate PPIs with small molecules have therefore attracted increasing interest over the past decade. However, there are a number of challenges inherent in developing small-molecule PPI inhibitors that have prevented these approaches from reaching their full potential. From target validation to small-molecule screening and lead optimization, identifying therapeutically relevant PPIs that can be successfully modulated by small molecules is not a simple task. Following the recent review by Arkin et al., which summarized the lessons learnt from prior successes, we focus in this article on the specific challenges of developing PPI inhibitors and detail the recent advances in chemistry, biology, and computation that facilitate overcoming them. We conclude by providing a perspective on the field and outlining four innovations that we see as key enabling steps for successful development of small-molecule inhibitors targeting PPIs.