Computational Approaches to Drug Profiling and Drug-Protein Interactions

Despite substantial increases in R&D spending within the pharmaceutical industry, denovo drug design has become a time-consuming endeavour. High attrition rates led to a long period of stagnation in drug approvals. Due to the extreme costs associated with introducing a drug to the market, locating and understanding the reasons for clinical failure is key to future productivity. As part of this PhD, three main contributions were made in this respect. First, the web platform, LigNFam enables users to interactively explore similarity relationships between ‘drug like’ molecules and the proteins they bind. Secondly, two deep-learning-based binding site comparison tools were developed, competing with the state-of-the-art over benchmark datasets. The models have the ability to predict offtarget interactions and potential candidates for target-based drug repurposing. Finally, the open-source ScaffoldGraph software was presented for the analysis of hierarchical scaffold relationships and has already been used in multiple projects, including integration into a virtual screening pipeline to increase the tractability of ultra-large screening experiments. Together, and with existing tools, the contributions made will aid in the understanding of drug-protein relationships, particularly in the fields of off-target prediction and drug repurposing, helping to design better drugs faster

IN SILICO METHODS FOR DRUG DESIGN AND DISCOVERY

Computer-aided drug design (CADD) methodologies are playing an ever-increasing role in drug discovery that are critical in the cost-effective identification of promising drug candidates. These computational methods are relevant in limiting the use of animal models in pharmacological research, for aiding the rational design of novel and safe drug candidates, and for repositioning marketed drugs, supporting medicinal chemists and pharmacologists during the drug discovery trajectory.Within this field of research, we launched a Research Topic in Frontiers in Chemistry in March 2019 entitled “In silico Methods for Drug Design and Discovery,” which involved two sections of the journal: Medicinal and Pharmaceutical Chemistry and Theoretical and Computational Chemistry. For the reasons mentioned, this Research Topic attracted the attention of scientists and received a large number of submitted manuscripts. Among them 27 Original Research articles, five Review articles, and two Perspective articles have been published within the Research Topic. The Original Research articles cover most of the topics in CADD, reporting advanced in silico methods in drug discovery, while the Review articles offer a point of view of some computer-driven techniques applied to drug research. Finally, the Perspective articles provide a vision of specific computational approaches with an outlook in the modern era of CADD

Structural Cheminformatics for Kinase-Centric Drug Design

Drug development is a long, expensive, and iterative process with a high failure rate, while patients wait impatiently for treatment. Kinases are one of the main drug targets studied for the last decades to combat cancer, the second leading cause of death worldwide. These efforts resulted in a plethora of structural, chemical, and pharmacological kinase data, which are collected in the KLIFS database. In this thesis, we apply ideas from structural cheminformatics to the rich KLIFS dataset, aiming to provide computational tools that speed up the complex drug discovery process. We focus on methods for target prediction and fragment-based drug design that study characteristics of kinase binding sites (also called pockets). First, we introduce the concept of computational target prediction, which is vital in the early stages of drug discovery. This approach identifies biological entities such as proteins that may (i) modulate a disease of interest (targets or on-targets) or (ii) cause unwanted side effects due to their similarity to on-targets (off-targets). We focus on the research field of binding site comparison, which lacked a freely available and efficient tool to determine similarities between the highly conserved kinase pockets. We fill this gap with the novel method KiSSim, which encodes and compares spatial and physicochemical pocket properties for all kinases (kinome) that are structurally resolved. We study kinase similarities in the form of kinome-wide phylogenetic trees and detect expected and unexpected off-targets. To allow multiple perspectives on kinase similarity, we propose an automated and production-ready pipeline; user-defined kinases can be inspected complementarily based on their pocket sequence and structure (KiSSim), pocket-ligand interactions, and ligand profiles. Second, we introduce the concept of fragment-based drug design, which is useful to identify and optimize active and promising molecules (hits and leads). This approach identifies low-molecular-weight molecules (fragments) that bind weakly to a target and are then grown into larger high-affinity drug-like molecules. With the novel method KinFragLib, we provide a fragment dataset for kinases (fragment library) by viewing kinase inhibitors as combinations of fragments. Kinases have a highly conserved pocket with well-defined regions (subpockets); based on the subpockets that they occupy, we fragment kinase inhibitors in experimentally resolved protein-ligand complexes. The resulting dataset is used to generate novel kinase-focused molecules that are recombinations of the previously fragmented kinase inhibitors while considering their subpockets. The KinFragLib and KiSSim methods are published as freely available Python tools. Third, we advocate for open and reproducible research that applies FAIR principles ---data and software shall be findable, accessible, interoperable, and reusable--- and software best practices. In this context, we present the TeachOpenCADD platform that contains pipelines for computer-aided drug design. We use open source software and data to demonstrate ligand-based applications from cheminformatics and structure-based applications from structural bioinformatics. To emphasize the importance of FAIR data, we dedicate several topics to accessing life science databases such as ChEMBL, PubChem, PDB, and KLIFS. These pipelines are not only useful to novices in the field to gain domain-specific skills but can also serve as a starting point to study research questions. Furthermore, we show an example of how to build a stand-alone tool that formalizes reoccurring project-overarching tasks: OpenCADD-KLIFS offers a clean and user-friendly Python API to interact with the KLIFS database and fetch different kinase data types. This tool has been used in this thesis and beyond to support kinase-focused projects. We believe that the FAIR-based methods, tools, and pipelines presented in this thesis (i) are valuable additions to the toolbox for kinase research, (ii) provide relevant material for scientists who seek to learn, teach, or answer questions in the realm of computer-aided drug design, and (iii) contribute to making drug discovery more efficient, reproducible, and reusable

In Silico Strategies for Prospective Drug Repositionings

The discovery of new drugs is one of pharmaceutical research's most exciting and challenging tasks. Unfortunately, the conventional drug discovery procedure is chronophagous and seldom successful; furthermore, new drugs are needed to address our clinical challenges (e.g., new antibiotics, new anticancer drugs, new antivirals).Within this framework, drug repositioning—finding new pharmacodynamic properties for already approved drugs—becomes a worthy drug discovery strategy.Recent drug discovery techniques combine traditional tools with in silico strategies to identify previously unaccounted properties for drugs already in use. Indeed, big data exploration techniques capitalize on the ever-growing knowledge of drugs' structural and physicochemical properties, drug–target and drug–drug interactions, advances in human biochemistry, and the latest molecular and cellular biology discoveries.Following this new and exciting trend, this book is a collection of papers introducing innovative computational methods to identify potential candidates for drug repositioning. Thus, the papers in the Special Issue In Silico Strategies for Prospective Drug Repositionings introduce a wide array of in silico strategies such as complex network analysis, big data, machine learning, molecular docking, molecular dynamics simulation, and QSAR; these strategies target diverse diseases and medical conditions: COVID-19 and post-COVID-19 pulmonary fibrosis, non-small lung cancer, multiple sclerosis, toxoplasmosis, psychiatric disorders, or skin conditions

Improved approaches to ligand growing through fragment docking and fragment-based library design

Die Fragment-basierte Wirkstoffforschung (“fragment-based drug discovery“ – FBDD) hat in den vergangenen zwei Jahrzehnten kontinuierlich an Beliebtheit gewonnen und sich zu einem dominanten Instrument der Erforschung neuer chemischer Moleküle als potentielle bioaktive Modulatoren entwickelt. FBDD ist eng mit Ansätzen zur Fragment-Erweiterung, wie etwa dem Fragment-„growing“, „merging“ oder dem „linking“, verknüpft. Diese Entwicklungsansätze können mit Hilfe von Computerprogrammen oder teilautomatischen Prozessen der „de novo“ Wirkstoffentwicklung beschleunigt werden. Obwohl Computer mühelos Millionen von Vorschlägen generieren können, geschieht dies allerdings oft auf Kosten unsicherer synthetischer Realisierbarkeit der Verbindungen mit einer potentiellen Sackgasse im Optimierungsprozess. Dieses Manuskript beschreibt die Entwicklung zweier computerbasierter Instrumente, PINGUI und SCUBIDOO, mit dem Ziel den FBDD Ausarbeitungs-Zyklus zu fördern. PINGUI ist ein halbautomatischer Arbeitsablauf zur Fragment-Erweiterung basierend auf der Proteinstruktur unter Berücksichtigung der synthetischen Umsetzbarkeit. SCUBIDOO ist eine freizugängliche Datenbank mit aktuell 21 Millionen verfügbaren virtuellen Produkten, entwickelt durch die Kombination kommerziell verfügbarer Bausteine („building blocks“) mit bewährten organischen Reaktionen. Zu jedem erzeugten virtuellen Produkt wird somit eine Synthesevorschrift geliefert. Die entscheidenden Funktionen von PINGUI, wie die Erzeugung abgeleiteter Bibliotheken oder das Anwenden organischer Reaktionen, wurden daraufhin in die SCUBIDOO Webseite integriert. PINGUI als auch SCUBIDOO wurden des Weiteren zur Erforschung Fragment-basierter Liganden („fragment-based ligand discovery“) mit dem β-2 adrenergen Rezeptor (β-2-AR) und der PIM1 Kinase als Zielproteine („targets“) eingesetzt. Im Rahmen einer ersten Studie zum β-2-AR wurden mit PINGUI acht unterschiedliche Erweiterungen für verschiedene Fragment-Treffer („hits“) vorhergesagt (ausgewählt?). Alle acht Verbindungen konnten dabei erfolgreich synthetisiert werden und vier der acht Produkte zeigten im Vergleich zu den Ausgangsfragmenten eine erhöhte Affinität zum target. Eine zweite Studie umfasste die Anwendung von SCUBIDOO zur schnellen Identifikation von Fragmenten und deren möglichen Erweiterungen mit potentieller Bindungsaktivität zur PIM-1 Kinase. Als Ergebnis ergab sich ein Fragment-Treffer mit der dazugehörigen Kristallstruktur. Weitere Folgeprodukte befinden sich derzeit in Synthese. Abschließend wurde SCUBIDOO an eine automatische Roboter- Synthese gekoppelt, wodurch hunderte von Verbindungen effizient parallel synthetisiert werden können. 127 der 240 vorhergesagten Produkte (53%) wurden mit dem Ziel an den β-2-AR zu binden bereits synthetisiert und werden in Kürze weitergehend getestet. Die beiden vorgestellten Computer-Tools könnten zur Verbesserung im Anfangsstadium befindlicher Projekte zur Fragment-basierten Wirkstoffentwicklung, vor allem hinsichtlich der Strategien im Bereich der Fragment Erweiterung, eingesetzt werden. PINGUI zum Beispiel generiert Vorschläge zur Fragment- Erweiterung, die sich mit hoher Wahrscheinlichkeit an die Zielstruktur anlagern, und stellt somit ein nützliches und kreatives Werkzeug zur Untersuchung von Struktur-Wirkungsbeziehungen („structure-activity relationship“ – SAR) dar. SCUBIDOO zeigte sich mit einem bisherigen 53-prozentigen Synthese-Erfolg als zugänglich für die Integration an die effiziente automatisierte Roboter-Synthese. Jede zukünftige Synthese liefert neue Kenntnisse innerhalb der Datenbank und wird somit nach und nach den Synthese-Erfolg erhöhen. Des Weiteren stellen alle synthetisierten Produkte neuartige Verbindungen dar, was umso mehr den möglichen Einfluss SCUBIDOOs bei der Entdeckung neuer chemischer Strukturen hervorhebt

In silico strategies to study polypharmacology of G-protein-coupled receptors

The development of drugs that simultaneously target multiple receptors in a rational way (i.e., 'magic shotguns') is regarded as a promising approach for drug discovery to treat complex, multi-factorial and multi-pathogenic diseases. My major goal is to develop and employ different computational approaches towards the rational design of drugs with selective polypharmacology towards guanine nucleotide-binding protein (G-protein)-coupled receptors (GPCRs) to treat central nervous system diseases. Our methodologies rely on the advances in chemocentric informatics and chemogenomics to generate experimentally testable hypotheses that are derived by fusing independent lines of evidence. We posit that such hypothesis fusion approach allows us to improve the overall success rates of in silico lead identification efforts. We have developed an integrated computational approach that combines Quantitative Structure-Activity Relationships (QSAR) modeling, model-based virtual screening (VS), gene expression analysis and mining of the biological literature for drug discovery. The dissertation research described herein is focused on: (1) The development of robust data-driven Quantitative Structure-Activity Relationship (QSAR) models of single target GPCR datasets that will amount to the compendium of GPCR predictors: the GPCR QSARome; (2) The development of robust data-driven QSAR models for families of GPCRs and other trans-membrane molecular targets (i.e., sigma receptors) and the application of models as virtual screening tools for the quick prioritization of compounds for biological testing across receptor families; (3) The development of novel integrative chemocentric informatics approaches to predict receptor-mediated clinical effects of chemicals. Results indicated that our computational efforts to establish a compendium of computational predictors and devise an integrative chemocentric informatics approach to study polypharmacology in silico will eventually lead to useful and reliable tools aimed at identifying and enriching chemical libraries with compounds that have the desired activities for more than one molecular target of interest

Bioaccumulation potential of 'Meeker' and 'Willamette' raspberry (Rubus idaeus L.) fruits towards macro- and microelements and their nutritional evaluation

Raspberry (Rubus idaeus L.) is the most important type of berry fruit in the Republic of Serbia. The bioaccumulation factor (BF) for the elements detected in the fruits of the raspberry cultivars 'Willamette' and 'Meeker' was calculated to determine their bioaccumulation potential. In addition, the nutritional quality of fruits in relation to nutritionally essential elements was evaluated and compared with the recommended daily intake. For determining the concentrations of 19 macro- and microelements in fruits and the soil, the analytical technique of optical emission spectrometry with inductively coupled plasma was used. Among the analyzed elements, As, Cd, Co, Cr, Li and Mo were below the limit of detection in the fruits of both raspberry cultivars, whereas Na and Ni were detected only in fruits of the 'Meeker' cultivar. All analyzed elements were detected in the soil. The results of the work indicated the high potential of the studied cultivars to accumulate nutritional elements K and Ca. In both raspberry cultivars, there were no substantial differences in the bioaccumulation of most elements. However, two elements (B and Mn) can be singled out; the BF for B in the 'Willamette' fruit was 3 times lower compared to the BF in the 'Meeker' fruit, whereas, the BF value for Mn in the 'Willamette' fruit was almost 8 times higher compared to the BF value for the 'Meeker' fruit. Furthermore, the cultivars did not tend to accumulate potentially toxic elements such as Ba, Co, Cu and Ni. The nutritional evaluation revealed that the studied raspberry fruits are a good source of K, Ca, Mg, Fe, Mn and Cu. Based on the BF values, differences observed in the accumulation of B, Ba, Na, Ni and Mn may be attributed to the characteristics of the cultivars