Ligandability Assessment of IL-1ß by Integrated Hit Identification Approaches.

Human interleukin-1ß (IL-1ß) is a pro-inflammatory cytokine that plays a critical role in the regulation of the immune response and the development of various inflammatory diseases. In this publication, we disclose our efforts toward the discovery of IL-1ß binders that interfere with IL-1ß signaling. To this end, several technologies were used in parallel, including fragment-based screening (FBS), DNA-encoded library (DEL) technology, peptide discovery platform (PDP), and virtual screening. The utilization of distinct technologies resulted in the identification of new chemical entities exploiting three different sites on IL-1ß, all of them also inhibiting the interaction with the IL-1R1 receptor. Moreover, we identified lysine 103 of IL-1ß as a target residue suitable for the development of covalent, low-molecular-weight IL-1ß antagonists.

Simplified quality assessment for small-molecule ligands in the Protein Data Bank.

More than 70% of the experimentally determined macromolecular structures in the Protein Data Bank (PDB) contain small-molecule ligands. Quality indicators of ∼643,000 ligands present in ∼106,000 PDB X-ray crystal structures have been analyzed. Ligand quality varies greatly with regard to goodness of fit between ligand structure and experimental data, deviations in bond lengths and angles from known chemical structures, and inappropriate interatomic clashes between the ligand and its surroundings. Based on principal component analysis, correlated quality indicators of ligand structure have been aggregated into two largely orthogonal composite indicators measuring goodness of fit to experimental data and deviation from ideal chemical structure. Ranking of the composite quality indicators across the PDB archive enabled construction of uniformly distributed composite ranking score. This score is implemented at RCSB.org to compare chemically identical ligands in distinct PDB structures with easy-to-interpret two-dimensional ligand quality plots, allowing PDB users to quickly assess ligand structure quality and select the best exemplars.

High-power screening (HPS) empowered by DNA-encoded libraries.

The world is totally dependent on medications. As science progresses, new, better, and cheaper drugs are needed more than ever. The pharmaceutical industry has been predominantly dependent on high-throughput screening (HTS) for the past three decades. Considering that the discovery rate has been relatively constant, can one hope for a much-needed sudden trend uptick? DNA-encoded libraries (DELs) and similar technologies, that have several orders of magnitude more screening power than HTS, and that we propose to group together under the umbrella term of high-power screening (HPS), are very well positioned to do exactly that. HPS also offers novel screening options such as parallel screening, ex vivo and in vivo screening, as well as a new path to druggable alternatives such as proteolysis targeting chimeras (PROTACs). Altogether, HPS unlocks novel powerful drug discovery avenues.

A Conserved Allosteric Site on Drug-Metabolizing CYPs: A Systematic Computational Assessment.

Cytochrome P450 enzymes (CYPs) are the largest group of enzymes involved in human drug metabolism. Ligand tunnels connect their active site buried at the core of the membrane-anchored protein to the surrounding solvent environment. Recently, evidence of a superficial allosteric site, here denoted as hotspot 1 (H1), involved in the regulation of ligand access in a soluble prokaryotic CYP emerged. Here, we applied multi-scale computational modeling techniques to study the conservation and functionality of this allosteric site in the nine most relevant mammalian CYPs responsible for approximately 70% of drug metabolism. In total, we systematically analyzed over 44 µs of trajectories from conventional MD, cosolvent MD, and metadynamics simulations. Our bioinformatic analysis and simulations with organic probe molecules revealed the site to be well conserved in the CYP2 family with the exception of CYP2E1. In the presence of a ligand bound to the H1 site, we could observe an enlargement of a ligand tunnel in several members of the CYP2 family. Further, we could detect the facilitation of ligand translocation by H1 interactions with statistical significance in CYP2C8 and CYP2D6, even though all other enzymes except for CYP2C19, CYP2E1, and CYP3A4 presented a similar trend. As the detailed comprehension of ligand access and egress phenomena remains one of the most relevant challenges in the field, this work contributes to its elucidation and ultimately helps in estimating the selectivity of metabolic transformations using computational techniques.

An assessment of the mutational load caused by various reactions used in DNA encoded libraries.

DNA encoded libraries have become an essential hit-finding tool in early drug discovery. Recent advances in synthetic methods for DNA encoded libraries have expanded the available chemical space, but precisely how each type of chemistry affects the DNA is unstudied. Available assays to quantify the damage are limited to write efficiency, where the ability to ligate DNA onto a working encoded library strand is measured, or qPCR is performed to measure the amplifiability of the DNA. These measures read signal quantity and overall integrity, but do not report on specific damages in the encoded information. Herein, we use next generation sequencing (NGS) to measure the quality of the read signal in order to quantify the truthfulness of the retrieved information. We identify CuAAC to be the worst offender in terms of DNA damage amongst commonly used reactions in DELs, causing an increase of G â†’ T transversions. Furthermore, we show that the analysis provides useful information even in fully elaborated DELs; indeed we see that vestiges of the synthetic history, both chemical and biochemical, are written into the mutational spectra of NGS datasets.

Small molecules as tools for functional assessment of deubiquitinating enzyme function.

Deubiquitinating enzymes (DUBs) are a largely understudied and untapped resource in the toolkit of protein degradation functionalities. They comprise a large repertoire of enzymes that remove ubiquitin from substrates in a variety of cellular and pathophysiological contexts, and have enormous potential for research and clinical use. It is only within the last 5 years that potent, selective, and well-characterized small-molecule inhibitors of DUBs have been described. These compounds are now being used to study the biological roles of DUBs. Here, we describe downstream applications of small-molecule inhibitors for studying DUBs and provide a framework for future studies. We highlight recent examples of using these inhibitors to confirm and explore the role of these enzymes in both normal and pathological contexts. These studies represent the first steps in the burgeoning field of pharmacological and chemoproteomic studies of DUBs, which will be critical for the continued advancement of DUB field.

Pharmacology of Free Fatty Acid Receptors and Their Allosteric Modulators.

The physiological function of free fatty acids (FFAs) has long been regarded as indirect in terms of their activities as educts and products in metabolic pathways. The observation that FFAs can also act as signaling molecules at FFA receptors (FFARs), a family of G protein-coupled receptors (GPCRs), has changed the understanding of the interplay of metabolites and host responses. Free fatty acids of different chain lengths and saturation statuses activate FFARs as endogenous agonists via binding at the orthosteric receptor site. After FFAR deorphanization, researchers from the pharmaceutical industry as well as academia have identified several ligands targeting allosteric sites of FFARs with the aim of developing drugs to treat various diseases such as metabolic, (auto)inflammatory, infectious, endocrinological, cardiovascular, and renal disorders. GPCRs are the largest group of transmembrane proteins and constitute the most successful drug targets in medical history. To leverage the rich biology of this target class, the drug industry seeks alternative approaches to address GPCR signaling. Allosteric GPCR ligands are recognized as attractive modalities because of their auspicious pharmacological profiles compared to orthosteric ligands. While the majority of marketed GPCR drugs interact exclusively with the orthosteric binding site, allosteric mechanisms in GPCR biology stay medically underexploited, with only several allosteric ligands currently approved. This review summarizes the current knowledge on the biology of FFAR1 (GPR40), FFAR2 (GPR43), FFAR3 (GPR41), FFAR4 (GPR120), and GPR84, including structural aspects of FFAR1, and discusses the molecular pharmacology of FFAR allosteric ligands as well as the opportunities and challenges in research from the perspective of drug discovery.

Specificity of Molecular Fragments Binding to S100B versus S100A1 as Identified by NMR and Site Identification by Ligand Competitive Saturation (SILCS).

S100B, a biomarker of malignant melanoma, interacts with the p53 protein and diminishes its tumor suppressor function, which makes this S100 family member a promising therapeutic target for treating malignant melanoma. However, it is a challenge to design inhibitors that are specific for S100B in melanoma versus other S100-family members that are important for normal cellular activities. For example, S100A1 is most similar in sequence and structure to S100B, and this S100 protein is important for normal skeletal and cardiac muscle function. Therefore, a combination of NMR and computer aided drug design (CADD) was used to initiate the design of specific S100B inhibitors. Fragment-based screening by NMR, also termed "SAR by NMR," is a well-established method, and was used to examine spectral perturbations in 2D [1H, 15N]-HSQC spectra of Ca2+-bound S100B and Ca2+-bound S100A1, side-by-side, and under identical conditions for comparison. Of the 1000 compounds screened, two were found to be specific for binding Ca2+-bound S100A1 and four were found to be specific for Ca2+-bound S100B, respectively. The NMR spectral perturbations observed in these six data sets were then used to model how each of these small molecule fragments showed specificity for one S100 versus the other using a CADD approach termed Site Identification by Ligand Competitive Saturation (SILCS). In summary, the combination of NMR and computational approaches provided insight into how S100A1 versus S100B bind small molecules specifically, which will enable improved drug design efforts to inhibit elevated S100B in melanoma. Such a fragment-based approach can be used generally to initiate the design of specific inhibitors for other highly homologous drug targets.

An automated and high-throughput-screening compatible pluripotent stem cell-based test platform for developmental and reproductive toxicity assessment of small molecule compounds.

The embryonic stem cell test (EST) represents the only validated and accepted in vitro system for the detection and classification of compounds according to their developmental and reproductive teratogenic potency. The widespread implementation of the EST, however, in particular for routine application in pharmaceutical development, has not been achieved so far. Several drawbacks still limit the high-throughput screening of potential drug candidates in this format: The long assay period, the use of non-homogeneous viability assays, the low throughput analysis of marker protein expression and the compatibility of the assay procedures to automation. We have therefore introduced several advancements into the EST workflow: A reduction of the assay period, an introduction of homogeneous viability assays, and a straightforward analysis of marker proteins by flow cytometry and high content imaging to assess the impact of small molecules on differentiation capacity. Most importantly, essential parts of the assay procedure have been adapted to lab automation in 96-well format, thus enabling the interrogation of several compounds in parallel. In addition, extensive investigations were performed to explore the predictive capacity of this next-generation EST, by testing a set of well-known embryotoxicants that encompasses the full range of chemical-inherent embryotoxic potencies possible. Due to these significant improvements, the augmented workflow provides a basis for a sensitive, more rapid, and reproducible high throughput screening compatible platform to predict in vivo developmental toxicity from in vitro data which paves the road towards application in an industrial setting. Graphical abstract •The embryonic stem cell test to predict teratogenicity was made automation-compatible. •Several key improvements to the assay procedure have been introduced to increase performance. •The workflow was adapted to human iPS cells and isogenic fibroblast donor cells.

Assessment of NR4A Ligands That Directly Bind and Modulate the Orphan Nuclear Receptor Nurr1.

Nurr1/NR4A2 is an orphan nuclear receptor transcription factor implicated as a drug target for neurological disorders including Alzheimer's and Parkinson's diseases. Previous studies identified small-molecule NR4A nuclear receptor modulators, but it remains unclear if these ligands affect transcription via direct binding to Nurr1. We assessed 12 ligands reported to affect NR4A activity for Nurr1-dependent and Nurr1-independent transcriptional effects and the ability to bind the Nurr1 ligand-binding domain (LBD). Protein NMR structural footprinting data show that amodiaquine, chloroquine, and cytosporone B bind the Nurr1 LBD; ligands that do not bind include C-DIM12, celastrol, camptothecin, IP7e, isoalantolactone, ethyl 2-[2,3,4-trimethoxy-6-(1-octanoyl)phenyl]acetate (TMPA), and three high-throughput screening hit derivatives. Importantly, ligands that modulate Nurr1 transcription also show Nurr1-independent effects on transcription in a cell type-specific manner, indicating that care should be taken when interpreting the functional response of these ligands in transcriptional assays. These findings should help focus medicinal chemistry efforts that desire to optimize Nurr1-binding ligands.

Deep-Learning-Assisted Assessment of DNA Damage Based on Foci Images and Its Application in High-Content Screening of Lead Compounds.

DNA damage is one of major culprits in many complex diseases; thus, there is great interest in the discovery of novel lead compounds regulating DNA damage. However, there remain plenty of challenges to evaluate DNA damage through counting the amount of intranuclear foci. Herein, a deep-learning-based open-source pipeline, FociNet, was developed to automatically segment full-field fluorescent images and dissect DNA damage of each cell. We annotated 6000 single-nucleus images to train the classification ability of the proposed computational pipeline. Results showed that FociNet achieved satisfying performance in classifying a single cell into a normal, damaged, or nonsignaling (no fusion-protein expression) state and exhibited excellent compatibility in the assessment of DNA damage based on fluorescent foci images from various imaging platforms. Furthermore, FociNet was employed to analyze a data set of over 5000 foci images from a high-content screening of 315 natural compounds from traditional Chinese medicine. It was successfully applied to identify several novel active compounds including evodiamine, isoliquiritigenin, and herbacetin, which were found to reduce 53BP1 foci for the first time. Among them, isoliquiritigenin from Glycyrrhiza uralensis Fisch. exerts a significant effect on attenuating double strand breaks as indicated by the comet assay. In conclusion, this work provides an artificial intelligence tool to evaluate DNA damage on the basis of microscopy images as well as a potential strategy for high-content screening of active compounds.

Druggability Assessment for Selected Serine Proteases in a Pharmaceutical Industry Setting.

Target druggability assessment is an integral part of the early target characterization and selection process in pharmaceutical industry. Here, we investigate a set of five different serine proteases from the blood coagulation cascade. The aim of this study is twofold. Firstly, leveraging the wealth of available in-house high-throughput screening (HTS) data, we analyze HTS hit rates and discuss their predictive value for the development of small molecule (SMOL) candidates. Purely structure-activity relationship (SAR) based druggability ratings are compared with computational protein-structure based druggability assessments. Secondly, we evaluate the impact of using conformational ensembles from molecular dynamics (MD) simulations instead of single static crystal structures as basis for computational druggability assessments. Based on this study, we recommend incorporating molecular dynamics routinely into the early target characterization process, especially if only a single X-ray structure is available.

Multispecific drugs herald a new era of biopharmaceutical innovation.

The modern biopharmaceutical industry traces its roots to the dawn of the twentieth century, coincident with marketing of aspirin-a signature event in the history of modern drug development. Although the archetypal discovery process did not change markedly in the first seven decades of the industry, the past fifty years have seen two successive waves of transformative innovation in the development of drug molecules: the rise of 'rational drug discovery' methodology in the 1970s, followed by the invention of recombinant protein-based therapeutic agents in the 1980s. An incipient fourth wave is the advent of multispecific drugs. The successful development of prospectively designed multispecific drugs has the potential to reconfigure our ideas of how target-based therapeutic molecules can work, and what it is possible to achieve with them. Here I review the two major classes of multispecific drugs: those that enrich a therapeutic agent at a particular site of action and those that link a therapeutic target to a biological effector. The latter class-being freed from the constraint of having to directly modulate the target upon binding-may enable access to components of the proteome that currently cannot be targeted by drugs.

A Comparative Assessment Study of Known Small-Molecule Keap1-Nrf2 Protein-Protein Interaction Inhibitors: Chemical Synthesis, Binding Properties, and Cellular Activity.

Inhibiting the protein-protein interaction (PPI) between the transcription factor Nrf2 and its repressor protein Keap1 has emerged as a promising strategy to target oxidative stress in diseases, including central nervous system (CNS) disorders. Numerous non-covalent small-molecule Keap1-Nrf2 PPI inhibitors have been reported to date, but many feature suboptimal physicochemical properties for permeating the blood-brain barrier, while others contain problematic structural moieties. Here, we present the first side-by-side assessment of all reported Keap1-Nrf2 PPI inhibitor classes using fluorescence polarization, thermal shift assay, and surface plasmon resonance-and further evaluate the compounds in an NQO1 induction cell assay and in counter tests for nonspecific activities. Surprisingly, half of the compounds were inactive or deviated substantially from reported activities, while we confirm the cross-assay activities for others. Through this study, we have identified the most promising Keap1-Nrf2 inhibitors that can serve as pharmacological probes or starting points for developing CNS-active Keap1 inhibitors.

Burkholderia pseudomallei d-alanine-d-alanine ligase; detailed characterisation and assessment of a potential antibiotic drug target.

Burkholderia pseudomallei is a serious, difficult to treat Gram-negative pathogen and an increase in the occurrence of drug-resistant strains has been detected. We have directed efforts to identify and to evaluate potential drug targets relevant to treatment of infection by B. pseudomallei. We have selected and characterised the essential enzyme d-alanine-d-alanine ligase (BpDdl), required for the ATP-assisted biosynthesis of a peptidoglycan precursor. A recombinant supply of protein supported high-resolution crystallographic and biophysical studies with ligands (AMP and AMP+d-Ala-d-Ala), and comparisons with orthologues enzymes suggest a ligand-induced conformational change occurring that might be relevant to the catalytic cycle. The detailed biochemical characterisation of the enzyme, development and optimisation of ligand binding assays supported the search for novel inhibitors by screening of selected compound libraries. In a similar manner to that observed previously in other studies, we note a paucity of hits that are worth follow-up and then in combination with a computational analysis of the active site, we conclude that this ligase represents a difficult target for drug discovery. Nevertheless, our reagents, protocols and data can underpin future efforts exploiting more diverse chemical libraries and structure-based approaches.

Dynamic combinatorial chemistry as a rapid method for discovering sequence-selective RNA-binding compounds.

The ever-growing number of RNA species that are recognized as having a role in human disease is driving a demand for novel molecular probes and therapeutics. Producing sequence-selective RNA-binding molecules remains a substantial challenge, however. One approach that has been successful in producing molecules with high affinity and specificity for disease-relevant RNAs is the use of dynamic combinatorial chemistry, a fragment-based method in which fragments combine reversibly in the presence of the target. We describe methods for the design, synthesis, and screening of dynamic combinatorial libraries targeting RNA.

Activity assessment of small drug molecules in estrogen receptor using multilevel prediction model.

The authors have proposed an efficient multilevel prediction model for better activity assessment to test whether certain chemical compounds can disrupt processes in the human body that may create negative health effects. Here, a computational method (in-silico) is proposed for the quality prediction of drugs in terms of their activity, activity score, potency, and efficacy for estrogen receptors (ERs) by using various physicochemical properties (molecular descriptors). PaDEL-Descriptor is used for features extraction. The ER dataset has 8481 drug molecules where 1084 are active, and 7397 are inactive, and each drug molecule has 1444 features. This dataset is highly imbalanced and has a substantial number of features. Initially, a class imbalance problem is resolved through synthetic minority oversampling technique algorithm, and feature selection is done using FSelector library of R. A machine learning based multilevel prediction model is developed where classification is performed on its first level and regression on its second level. By using all these strategies simultaneously, outperformed accuracy is achieved in comparison to many other computational approaches. The K-fold cross-validation is performed to measure the consistency of the model for all the target classes. Finally, the validity of the proposed method on some AIDS therapy's drug molecules is proved.

In Silico Target Druggability Assessment: From Structural to Systemic Approaches.

This chapter will focus on today's in silico direct and indirect approaches to assess therapeutic target druggability. The direct approach tries to infer from the 3D structure the capacity of the target protein to bind small molecule in order to modulate its biological function. Algorithms to recognize and characterize the quality of the ligand interaction sites whether within buried protein cavities or within large protein-protein interface will be reviewed in the first part of the paper. In the case a ligand-binding site is already identified, indirect aspects of target druggability can be assessed. These indirect approaches focus first on target promiscuity and the potential difficulties in developing specific drugs. It is based on large-scale comparison of protein-binding sites. The second aspect concerns the capacity of the target to induce resistant pathway once it is inhibited or activated by a drug. The emergence of drug-resistant pathways can be assessed through systemic analysis of biological networks implementing metabolism and/or cell regulation signaling.