Mind the Gap - Deciphering GPCR Pharmacology Using 3D Pharmacophores and Artificial Intelligence

G protein-coupled receptors (GPCRs) are amongst the most pharmaceutically relevant and well-studied protein targets, yet unanswered questions in the field leave significant gaps in our understanding of their nuanced structure and function. Three-dimensional pharmacophore models are powerful computational tools in in silico drug discovery, presenting myriad opportunities for the integration of GPCR structural biology and cheminformatics. This review highlights success stories in the application of 3D pharmacophore modeling to de novo drug design, the discovery of biased and allosteric ligands, scaffold hopping, QSAR analysis, hit-to-lead optimization, GPCR de-orphanization, mechanistic understanding of GPCR pharmacology and the elucidation of ligand–receptor interactions. Furthermore, advances in the incorporation of dynamics and machine learning are highlighted. The review will analyze challenges in the field of GPCR drug discovery, detailing how 3D pharmacophore modeling can be used to address them. Finally, we will present opportunities afforded by 3D pharmacophore modeling in the advancement of our understanding and targeting of GPCRs

Next generation 3D pharmacophore modeling

3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond

Identification of G-quadruplex DNA/RNA binders: Structure-based virtual screening and biophysical characterization

Background Recent findings demonstrated that, in mammalian cells, telomere DNA (Tel) is transcribed into telomeric repeat-containing RNA (TERRA), which is involved in fundamental biological processes, thus representing a promising anticancer target. For this reason, the discovery of dual (as well as selective) Tel/TERRA G-quadruplex (G4) binders could represent an innovative strategy to enhance telomerase inhibition. Methods Initially, docking simulations of known Tel and TERRA active ligands were performed on the 3D coordinates of bimolecular G4 Tel DNA (Tel2) and TERRA (TERRA2). Structure-based pharmacophore models were generated on the best complexes and employed for the virtual screening of ~ 257,000 natural compounds. The 20 best candidates were submitted to biophysical assays, which included circular dichroism and mass spectrometry at different K+ concentrations. Results Three hits were here identified and characterized by biophysical assays. Compound 7 acts as dual Tel2/TERRA2 G4-ligand at physiological KCl concentration, while hits 15 and 17 show preferential thermal stabilization for Tel2 DNA. The different molecular recognition against the two targets was also discussed. Conclusions Our successful results pave the way to further lead optimization to achieve both increased selectivity and stabilizing effect against TERRA and Tel DNA G4s. General significance The current study combines for the first time molecular modelling and biophysical assays applied to bimolecular DNA and RNA G4s, leading to the identification of innovative ligand chemical scaffolds with a promising anticancer profile. This article is part of a Special Issue entitled "G-quadruplex" Guest Editor: Dr. Concetta Giancola and Dr. Daniela Montesarchio

Structure Prediction and Validation of the ERK8 Kinase Domain

Extracellular signal-regulated kinase 8 (ERK8) has been already implicated in cell transformation and in the protection of genomic integrity and, therefore, proposed as a novel potential therapeutic target for cancer. In the absence of a crystal structure, we developed a three-dimensional model for its kinase domain. To validate our model we applied a structure- based virtual screening protocol consisting of pharmacophore screening and molecular docking. Experimental characterization of the hit compounds confirmed that a high percentage of the identified scaffolds was able to inhibit ERK8. We also confirmed an ATP competitive mechanism of action for the two best-performing molecules. Ultimately, we identified an ERK8 drug-resistant \u27\u27gatekeeper\u27\u27 mutant that corroborated the predicted molecular binding mode, confirming the reliability of the generated structure. We expect that our model will be a valuable tool for the development of specific ERK8 kinase inhibitors

The benefits of in silico modeling to identify possible small-molecule drugs and their off-target interactions

Accepted for publication in a future issue of Future Medicinal Chemistry.The research into the use of small molecules as drugs continues to be a key driver in the development of molecular databases, computer-aided drug design software and collaborative platforms. The evolution of computational approaches is driven by the essential criteria that a drug molecule has to fulfill, from the affinity to targets to minimal side effects while having adequate absorption, distribution, metabolism, and excretion (ADME) properties. A combination of ligand- and structure-based drug development approaches is already used to obtain consensus predictions of small molecule activities and their off-target interactions. Further integration of these methods into easy-to-use workflows informed by systems biology could realize the full potential of available data in the drug discovery and reduce the attrition of drug candidates.Peer reviewe

Mechanistic Elucidation of Protease–Substrate and Protein–Protein Interactions for Targeting Viral Infections

Viral infections represent an old threat to global health, with multiple epidemics and pandemics in the history of mankind. Despite several advances in the development of antiviral substances and vaccines, many viral species are still not targeted. Additionally, new viral species emerge, posing a menace without precedent to humans and animals and causing fatalities, disabilities, environmental harm, and economic losses. In this thesis, we present rational modeling approaches for targeting specific protease-substrate and protein-protein interactions pivotal for the viral replication cycle. Over the course of this work, antiviral research is supported beginning with the development of small molecular antiviral substances, going through the modeling of a potential immunogenic epitope for vaccine development, towards the establishment of descriptors for susceptibility of animals to a viral infection. Notably, all the research was done under scarce data availability, highlighting the predictive power of computational methods and complementarity between in-silico and in-vitro or in-vivo methods

Tailoring Toll-like Receptor 8 Ligands for Balancing Immune Response and Inflammation

Toll-like receptors (TLRs) play a central role in innate immunity by recognising invading pathogens and host-derived danger signals and initiating the inflammatory response. Aberrant TLR response is involved in the pathogenesis of cancers, infections, autoimmune disorders and allergic diseases. Therefore, TLRs represent attractive targets for novel therapeutic agents. The PhD project's main research aim is to discover novel small molecule modulators of Toll-like receptor 8 (TLR8) and understand their mechanisms of action using computational approaches. TLR8 crystal structure is solved, and several modulators are known from previous drug screens. Therefore, TLR8 is a promising target for rational computer-aided development of novel drug candidates. In the initial phase of the project, the main goal was to study relevant structural features in available crystal structures of TLR8. The focus was on the dimerisation interface because of its role in the binding of ligands and subsequent activation of the receptor. Additionally, we studied the conservation of the relevant structural features across the closely related TLRs. The second part shifts the focus to the binding of the small molecules to TLR8. We investigated interactions between the known ligands and TLR8 and used it to develop the most plausible 3D pharmacophore model. Subsequently, we employed the developed 3D pharmacophore model in virtual screening to identify novel modulators of TLR8. We identified a pyrimidine-based compound that inhibits TLR8-mediated signalling in the micromolar concentration range. The potent anti-inflammatory and dose-dependent response has been confirmed in a series of derivatives of this initial virtual hit, which allowed for a detailed elucidation of structure-activity relationships (SAR) and more precise description of the binding mode. Conclusively, we have developed a novel and promising pyrimidine-based TLR8 inhibitors in silico and confirmed their biological activity, selectivity and low cytotoxicity in vitro. Results from the study on TLR8 represent a solid basis for the future design of small molecule TLR modulators as novel therapeutic agents for modulating immune response and inflammation.Toll-like Rezeptoren (TLRs) spielen eine zentrale Rolle in angeborenen Immunsystem, indem sie eindringende Pathogene sowie endogene Gefahrensignale erkennen und Entzündungsreaktionen einleiten. TLRs sind an der Pathogenese von Krebserkrankungen, Infektionen, Autoimmunerkrankungen und allergischen Erkrankungen beteiligt. Aus diesem Grund stellen TLRs attraktive Ziele für neue, niedermolekulare Wirkstoffe dar. Das Hauptziel dieses Promotionsprojekts ist die Entdeckung neuer niedermolekularer Modulatoren des Toll-like-Rezeptors 8 (TLR8) und das Verständnis ihrer Wirkmechanismen mit Hilfe computergestützter Ansätze. Die Kristallstruktur von TLR8 ist verfügbar und mehrere Modulatoren sind aus früheren Wirkstoffscreens bekannt. Daher ist TLR8 ein vielversprechendes Ziel für die rationale computergestützte Entwicklung neuer Wirkstoffkandidaten. Am Beginn des Projekts bestand das Hauptziel darin, relevante strukturelle Merkmale in den verfügbaren Kristallstrukturen von TLR8 zu untersuchen. Der Fokus lag dabei auf dem Dimerisierungsbereich, da dieser eine wichtige Rolle bei der Bindung von Liganden und der anschließenden Aktivierung des Rezeptors spielt. Zusätzlich untersuchten wir die Konservierung der relevanten Strukturmerkmale über die eng verwandten TLRs hinweg. Der zweite Teil verlagert den Fokus auf die Bindung kleiner Moleküle an TLR8. Wir untersuchten die Interaktionen zwischen den bekannten Liganden und TLR8 und entwickelten daraus systemtisch ein 3D-Pharmakophormodell. Anschließend setzten wir das entwickelte 3D-Pharmakophormodell im virtuellen Screening ein, um neuartige Modulatoren des TLR8 zu identifizieren. Wir identifizierten ein Pyrimidin-Analogon, das die TLR8- vermittelte Signalweiterleitung im mikromolaren Konzentrationsbereich hemmt. Die potente entzündungshemmende und dosisabhängige Wirkung wurde in einer kleinen Serie von Analoga bestätigt. Schließlich optimierten wir die identifizierten Pyrimidinverbindungen weiter, was eine detailliertere Struktur-Aktivitäts-Analyse und eine genauere Aufklärung des Bindungsmodus ermöglichte. Zusammenfassend haben wir neuartige und vielversprechende TLR8-Inhibitoren auf Pyrimidinbasis in silico entwickelt und ihre in vitro biologische Aktivität, Selektivität und geringe Zytotoxizität bestätigt. Die Ergebnisse der Studie zu TLR8 helfen uns, die Prozesse zu verstehen, die für ein erfolgreiches Wirkstoffdesign auch bei anderen TLR notwendig sind und stellen eine gute Ausgangsbasis dar, um in Zukunft optimierte, niedermolekulare TLR- Modulatoren zu entwickeln und damit Entzündung und die Immunreaktion effizient zu modulieren