Recent Developments in Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, as well as a low number of new drugs that are approved each year. To solve these problems, new and innovate technologies are needed that make the drug discovery process of small-molecules more time and cost-efficient, and which allow to target previously undruggable target classes such as protein-protein interactions. Structure-based virtual screenings have become a leading contender in this context. In this review, we give an introduction to the foundations of structure-based virtual screenings, and survey their progress in the past few years. We outline key principles, recent success stories, new methods, available software, and promising future research directions. Virtual screenings have an enormous potential for the development of new small-molecule drugs, and are already starting to transform early-stage drug discovery.Comment: 22 pages, 2 figure

Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists

Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the -opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported -fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale

A systematic approach to identify host targets and rapidly deliver broad-spectrum antivirals.

peer reviewe

A multi-pronged approach targeting SARS-CoV-2 proteins using ultra-large virtual screening.

The unparalleled global effort to combat the continuing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic over the last year has resulted in promising prophylactic measures. However, a need still exists for cheap, effective therapeutics, and targeting multiple points in the viral life cycle could help tackle the current, as well as future, coronaviruses. Here, we leverage our recently developed, ultra-large-scale in silico screening platform, VirtualFlow, to search for inhibitors that target SARS-CoV-2. In this unprecedented structure-based virtual campaign, we screened roughly 1 billion molecules against each of 40 different target sites on 17 different potential viral and host targets. In addition to targeting the active sites of viral enzymes, we also targeted critical auxiliary sites such as functionally important protein-protein interactions

A community effort in SARS-CoV-2 drug discovery.

peer reviewedThe COVID-19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small-molecule drugs that are widely available, including in low- and middle-income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against Covid-19 challenge", to identify small-molecule inhibitors against SARS-CoV-2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding-, cleavage-, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS-CoV-2 treatments.R-AGR-3826 - COVID19-14715687-CovScreen (01/06/2020 - 31/01/2021) - GLAAB Enric

Entwicklung, Implementierung, und Anwendung in der Wirkstoffentwicklung

Computational science has the potential to solve most of the problems which pharmaceutical research is facing these days. In this field the most pivotal property is arguably the free energy of binding. Yet methods to predict this quantity with sufficient accuracy, reliability and efficiency remain elusive, and are thus not yet able to replace experimental determinations, which remains one of the unattained holy grails of computer-aided drug design (CADD). The situation is similar for methods which are used to identify promising new drug candidates with high binding affinity, which resembles a closely related endeavor in this field. In this thesis the development of a new free energy method (QSTAR) was in the focus. It is able to explicitly take into account the quantum nature of atomic nuclei which so far was not done in binding free energy simulations of biomolecular systems. However, it can be expected to play a substantial role in such systems in particular due to the abundance of hydrogen atoms which posses one of the strongest nuclear delocalizations of all atoms. To take these nuclear quantum effects into account Feynman’s path integral formulation is used and combined in a synergistic way with a novel alchemical transformation scheme. QSTAR makes also available the first readily available single topology approach for electronic structure methods (ESMs). Moreover, an extended alchemical scheme for relative binding free energies was developed to address van der Waals endpoint problems. QSTAR and the alchemical schemes were implemented in HyperQ, a new free energy simulation suite which is highly automated and scalable. Most ESMs methods become soon prohibitively expensive with the size of the system, a restriction which can be circumvented by quantum mechanics/molecular mechanics (QM/MM) methods. In order to be able to apply QSTAR together with ESMs on biomolecular systems an enhanced QM/MM scheme was developed. It is a method for diffusive systems based on restraining potentials, and allows to define QM regions of customizable shape while being computationally fast. It was implemented in a novel client for i-PI, and together with HyperQ allows to carry out free energy simulations of biomolecular systems with potentials of very high accuracy. One of the most promising ways to identify new hit compounds in CADD is provided by structure- based virtual screenings (SBVSs) which make use of free energy methods. In this thesis it is argued that the larger the scale of virtual screenings the higher their success. And a novel workflow system was developed called Virtual Flow, allowing to carry out SBVS-related tasks on computer clusters with virtually perfect scaling behavior and no practically relevant bounds regarding the number of nodes/CPUs. Two versions were implemented, VFLP and VFVS, dedicated to the preparation of large ligand databases and for carrying out the SBVS procedure itself. As a primary application of the new methods and software a dedicated drug design project was started involving three regions on the novel target EBP1, expected to be located on protein-protein interfaces which are extremely challenging to inhibit. Three multistage SBVSs were carried out each involving more than 100 million compounds. Subsequent experimental binding assays indicated a remarkably high true hit rate of above 30 %. Subsequent fluorescence microscopy of one selected compound exhibited favorable biological activities in cancer cells. Other applied projects included computational hit and lead discovery for several other types of anti- cancer drugs, anti-Herpes medications, as well as antibacterials.Die Simulationswissenschaft, auch wissenschaftliches Rechnen genannt, hat das Potential viele der aktuellen Probleme der pharmazeutischen Forschung zu lösen. Eine der zentralen physikalischen Größen in diesem Gebiet ist die Bindungsenergie zwischen Molekülen. Eines der Hauptziele in der computergestützter Wirkstoffentwicklung (CADD) ist es, diese Größe mit solcher Genauigkeit, Verlässlichkeit und Effizienz vorherzusagen, dass dassexperimentelle Bestimmungen nicht mehr notwendig sind. Jedoch sind derartige Methoden derzeit noch nicht verfügbar. Ein Schwerpunkt der vorliegenden Arbeit war die Entwicklung einer neuen Methode (QSTAR) zur Vorhersage von freien Energien. Diese Methode ist fähig die quantenmechanische Natur von Atomkernen explizit zu berücksichtigen, was in bisherigen Simulationen für freie Bindungsenergien in biomolekularen Simulationen vernachlässig wurde. Es kann angenommen werden, dass die quantenmechanische Delokalisation der Atomkerne in solchen Systemen eine erhebliche Rolle spielen kann, vor allem aufgrund der Wasserstoffatome, welche zu den Atomarten mit den stärksten Quantendelokalisationen gehören. Um diese nuklearen Quanteneffekte zu berücksichtigen, wurde der Feynman’sche Pfadintegral Formalismus verwendet und synergetisch mit einem neuen alchemischen Transformationsschema kombiniert. QSTAR stellt auch den ersten direkt anwendbaren Einfach- Topologieansatz für Elektronenstrukturmodelle (ESMs) zur Verfügung. Des Weiteren wurde ein erweitertes alchemisches Schema für relative freie Bindungsenergien entwickelt, um das van der Waals Endpunktproblem zu umgehen. QSTAR und die alchemischen Schemen wurden in HyperQ implementiert, einer neuen skalierbaren Software, welche in der Lage ist, Simulationen der freien Energie automatisch durchzuführen. Die meisten ESMs werden mit zunehmender Größe des Systems schnell prohibitiv teuer. Dies ist eine Einschränkung, welche mit QM/MM Methoden umgangen werden kann. Um QSTAR mit ESMs on auf biomolekulare Systeme anwenden zu können, wurde ein erweitertes QM/MM Schema entwickelt. Dieses Schema ist eine Methode für diffusive Systeme, welche auf Rückhaltepotentialen beruht. Diese erlaubt, QM Regionen von angepasster Form zu definieren, und ist rechnerisch sehr effizient. Die Methode wurde in einem neuen Klienten für i-PI implementiert, und sie ermöglicht es, Simulationen der freien Energie von biomolekularen Systemen mit einer sehr hohen Genauigkeit durchzuführen. Einer der vielversprechendsten Ansätze um neue Hit-Kandidaten im CADD zu identifizieren sind strukturbasierte virtuelle Screenings (SBVS) welche Methoden zur Schätzung von freien Bindungsenergien nutzen. In dieser Arbeit wird argumentiert, dass je umfangreicher das virtuelle Screening ist desto besser die Ergebnisse im Sinne der Bindungsaffinität sowie der Erfolgsrate werden. Ein neues Workflowsystem, Virtual Flow, wurde entwickelt, welches erlaubt Aufgaben im Zusammenhang von SBVSs mit fast perfektem Skalierungsverhalten ohne praktisch relevante Grenzen bezüglich der Anzahl der Knoten/CPUs auf Computerclustern durchzuführen. Zwei Versionen wurden bisher implementiert, VFLP und VFVS, welche spezialisiert sind auf die Aufbereitung von Moleküldatenbanken sowie die virtuelle Screeningprozedur selbst. Eine neues Wirkstoffentwicklungsprojekt wurde begonnen, um die neuen Methoden und Software unter realistischen Bedingungen zu testen und anzuwenden. Das Ziel dieses Projektes ist es, mögliche Inhibitoren für drei Regionen an der Oberfläche des Proteins EBP1 zu identifizieren, welche sich aller Erkenntnis nach auf Protein-Protein-Interfaces befinden und eine große Herausforderung darstellen. Drei zweistufige virtuelle Screenings wurden ausgeführt mit jeweils über 100 Millionen Molekülen. Nachfolgende Bindungsexperimente deuten auf eine relativ hohe Hitrate von über 30 % hin, und Fluoreszenzmikrosopie von mindestens einem Molekül weist auf gewünschte Effekte in Krebszellen hin. Weitere Projekte, in welchen die neuen Methoden und Software für die computergestützte Hit/Lead-Identifizierung angewendet wurden, beinhalteten die Zielmoleküle SHP2 (Krebs), eIF4E (Krebs), MED15 (Krebs), UL50/UL53 (Herpes), KEAP1 (Krebs), und die Peptidoglykane (Antibiotika)

Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists

Abstract Opioids are essential pharmaceuticals due to their analgesic properties, however, lethal side effects, addiction, and opioid tolerance are extremely challenging. The development of novel molecules targeting the $$\mu$$ μ -opioid receptor (MOR) in inflamed, but not in healthy tissue, could significantly reduce these unwanted effects. Finding such novel molecules can be achieved by maximizing the binding affinity to the MOR at acidic pH while minimizing it at neutral pH, thus combining two conflicting objectives. Here, this multi-objective optimal affinity approach is presented, together with a virtual drug discovery pipeline for its practical implementation. When applied to finding pH-specific drug candidates, it combines protonation state-dependent structure and ligand preparation with high-throughput virtual screening. We employ this pipeline to characterize a set of MOR agonists identifying a morphine-like opioid derivative with higher predicted binding affinities to the MOR at low pH compared to neutral pH. Our results also confirm existing experimental evidence that NFEPP, a previously described fentanyl derivative with reduced side effects, and recently reported $$\beta$$ β -fluorofentanyls and -morphines show an increased specificity for the MOR at acidic pH when compared to fentanyl and morphine. We further applied our approach to screen a >50K ligand library identifying novel molecules with pH-specific predicted binding affinities to the MOR. The presented differential docking pipeline can be applied to perform multi-objective affinity optimization to identify safer and more specific drug candidates at large scale

VirtualFlow Ants—Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization

The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery

Cryo-EM structure of an activated GPCR-G protein complex in lipid nanodiscs

G-protein-coupled receptors (GPCRs) are the largest superfamily of transmembrane proteins and the targets of over 30% of currently marketed pharmaceuticals. Although several structures have been solved for GPCR–G protein complexes, few are in a lipid membrane environment. Here, we report cryo-EM structures of complexes of neurotensin, neurotensin receptor 1 and Gαi1β1γ1 in two conformational states, resolved to resolutions of 4.1 and 4.2 Å. The structures, determined in a lipid bilayer without any stabilizing antibodies or nanobodies, reveal an extended network of protein–protein interactions at the GPCR–G protein interface as compared to structures obtained in detergent micelles. The findings show that the lipid membrane modulates the structure and dynamics of complex formation and provide a molecular explanation for the stronger interaction between GPCRs and G proteins in lipid bilayers. We propose an allosteric mechanism for GDP release, providing new insights into the activation of G proteins for downstream signaling

Non-covalent SARS-CoV-2 Mpro inhibitors developed from in silico screen hits.

Mpro, the main protease of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is essential for the viral life cycle. Accordingly, several groups have performed in silico screens to identify Mpro inhibitors that might be used to treat SARS-CoV-2 infections. We selected more than five hundred compounds from the top-ranking hits of two very large in silico screens for on-demand synthesis. We then examined whether these compounds could bind to Mpro and inhibit its protease activity. Two interesting chemotypes were identified, which were further evaluated by characterizing an additional five hundred synthesis on-demand analogues. The compounds of the first chemotype denatured Mpro and were considered not useful for further development. The compounds of the second chemotype bound to and enhanced the melting temperature of Mpro. The most active compound from this chemotype inhibited Mpro in vitro with an IC50 value of 1 μM and suppressed replication of the SARS-CoV-2 virus in tissue culture cells. Its mode of binding to Mpro was determined by X-ray crystallography, revealing that it is a non-covalent inhibitor. We propose that the inhibitors described here could form the basis for medicinal chemistry efforts that could lead to the development of clinically relevant inhibitors