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

Integrative Systems Approaches Towards Brain Pharmacology and Polypharmacology

Polypharmacology is considered as the future of drug discovery and emerges as the next paradigm of drug discovery. The traditional drug design is primarily based on a “one target-one drug” paradigm. In polypharmacology, drug molecules always interact with multiple targets, and therefore it imposes new challenges in developing and designing new and effective drugs that are less toxic by eliminating the unexpected drug-target interactions. Although still in its infancy, the use of polypharmacology ideas appears to already have a remarkable impact on modern drug development. The current thesis is a detailed study on various pharmacology approaches at systems level to understand polypharmacology in complex brain and neurodegnerative disorders. The research work in this thesis focuses on the design and construction of a dedicated knowledge base for human brain pharmacology. This pharmacology knowledge base, referred to as the Human Brain Pharmacome (HBP) is a unique and comprehensive resource that aggregates data and knowledge around current drug treatments that are available for major brain and neurodegenerative disorders. The HBP knowledge base provides data at a single place for building models and supporting hypotheses. The HBP also incorporates new data obtained from similarity computations over drugs and proteins structures, which was analyzed from various aspects including network pharmacology and application of in-silico computational methods for the discovery of novel multi-target drug candidates. Computational tools and machine learning models were developed to characterize protein targets for their polypharmacological profiles and to distinguish indications specific or target specific drugs from other drugs. Systems pharmacology approaches towards drug property predictions provided a highly enriched compound library that was virtually screened against an array of network pharmacology based derived protein targets by combined docking and molecular dynamics simulation workflows. The developed approaches in this work resulted in the identification of novel multi-target drug candidates that are backed up by existing experimental knowledge, and propose repositioning of existing drugs, that are undergoing further experimental validations

Interactions between curcumin and human salt-induced kinase 3 elucidated from computational tools and experimental methods

Natural products are widely used for treating mitochondrial dysfunction-related diseases and cancers. Curcumin, a well-known natural product, can be potentially used to treat cancer. Human salt-induced kinase 3 (SIK3) is one of the target proteins for curcumin. However, the interactions between curcumin and human SIK3 have not yet been investigated in detail. In this study, we studied the binding models for the interactions between curcumin and human SIK3 using computational tools such as homology modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations. The open activity loop conformation of SIK3 with the ketoenol form of curcumin was the optimal binding model. The I72, V80, A93, Y144, A145, and L195 residues played a key role for curcumin binding with human SIK3. The interactions between curcumin and human SIK3 were also investigated using the kinase assay. Moreover, curcumin exhibited an IC50 (half-maximal inhibitory concentration) value of 131 nM, and it showed significant antiproliferative activities of 9.62 ± 0.33 µM and 72.37 ± 0.37 µM against the MCF-7 and MDA-MB-23 cell lines, respectively. This study provides detailed information on the binding of curcumin with human SIK3 and may facilitate the design of novel salt-inducible kinases inhibitors

une approche computationnelle pour le développement d'agents thérapeutiques

Over the last few decades, computer-aided drug design (CADD) has established as a strong tool for developing novel therapeutic compounds. In computer-aided drug design, two methodologies are typically used: structure-based drug design and ligand-based drug design. Molecular docking combined with molecular dynamics is one of the most important tools of drug discovery and drug design, which it used to examine the type of binding between the ligand and its protein enzyme. Global reactivity has important properties, which enable chemists to understand the chemical reactivity and kinetic stability of compounds. The recent new contagion coronavirus 2019 (COVID-19) disease is a new generation of severe acute respiratory syndrome coronavirus-2 SARS-CoV-2 which infected millions confirmed cases and hundreds of thousands death cases around the world so far. In this study, molecular docking and reactivity were applied for eighteen drugs, which are similar in structure to chloroquine and hydroxychloroquine, the potential inhibitors to angiotensinconverting enzyme (ACE2). Those drugs were selected from DrugBank. The reactivity, molecular docking and molecular dynamics were performed for two receptors ACE2 and Crystal structure SARS-CoV-2 spike receptor-binding with ACE2 complex receptor in two active sites to find a ligand, which may inhibit COVID-19. The results obtained from this study showed that Ramipril, Delapril and Lisinopril could bind with ACE2 receptor andCrystal structure SARS-CoV-2 spike receptor-binding with ACE2 complex better than chloroquine and hydroxychloroquine. The tyrosine kinase inhibitors gefitinib and erlotinib activated mutations of the epidermal growth factor receptor (EGFR) in non-small cell lung cancer. Quinazolines and pyridopyrimidines are antibacterial, antifungal, and cancer-fighting compounds. The goal of this study is to look into the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of a series of quinazolines and pyrido[3,4-d]pyrimidines as irreversible inhibitors of wild-type (WT) and L858R and T790M EGFR kinase domain mutants, as well as their reactivity, molecular docking, and molecular dynamics simulation. The 27 heterocycles under examination show a wide range of affinities for WT, L858R, and T790M, as well as strong chemical reactivity and kinetic stability. The compounds were found to have high ADMET characteristics, and pyrido[3,4-d]pyrimidines had good reactivity and affinity towards WT, L858R, and T790M mutations. New, powerful, irreversible tyrosine kinase inhibitors have been discovered

SMARTS Approach to Chemical Data Mining and Physicochemical Property Prediction.

The calculation of physicochemical and biological properties is essential in order to facilitate modern drug discovery. Chemical spaces dimensionalized by these descriptors have been used to scaffold-hop in order to discover new lead and drug-like molecules. Broadening the boundaries of structure based drug design, these molecules are expected to share the same physiological target and have similar efficacy, as do known drug molecules sharing the same region in chemical property space. In the past few decades physicochemical and ADMET (absorption, distribution, metabolism, elimination, and toxicity) property predictors have been the subject of increased focus in academia and the pharmaceutical industry. Due to the ever increasing attention given to data mining and property predictions, we first discuss the sources of experimental pKa values and current methodologies used for pKa prediction in proteins and small molecules. Of particular concern is an analysis of the scope, statistical validity, overall accuracy, and predictive power of these methods. The expressed concerns are not limited to predicting pKa, but apply to all empirical predictive methodologies. In a bottom-up approach, we explored the influence of freely generated SMARTS string representations of molecular fragments on chelation and cytotoxicity. Later investigations, involving the derivation of predictive models, use stepwise regression to determine the optimal pool of SMARTS strings having the greatest influence over the property of interest. By applying a unique scoring system to sets of highly generalized SMARTS strings, we have constructed well balanced regression trees with predictive accuracy exceeding that of many published and commercially available models for cytotoxicity, pKa, and aqueous solubility. The methodology is robust, extremely adaptable, and can handle any molecular dataset with experimental data. This story details our struggles of data gathering, curation, and the development of a machine learning methodology able to derive and validate highly accurate regression trees capable of extremely fast property predictions. Regression trees created by our method are well suited to calculate descriptors for large in silico molecular libraries, facilitating data mining of chemical spaces in search of new lead molecules in drug discovery.Ph.D.Medicinal ChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/64627/1/adamclee_1.pd

Modeling the Binding of Inhibitors/Drugs to the Human Serotonin Transporter

Human serotonin transporter (hSERT), a membrane protein from the neurotransmitter sodium symporter family, is implicated in depression disorder and has been the primary target of antidepressant discovery research for several decades. Since the currently available antidepressants may cause adverse effects and have several limitations, novel drugs are highly desired. However, the efforts to develop better therapeutics are hampered by the lack of a crystal structure of hSERT. Knowledge of the binding site of the drug and its orientation in the protein is crucial in structure-based drug discovery. We employed a novel computational protocol comprised of active site detection, docking, scoring, molecular dynamics simulations, and absolute binding free energy (ABFE) calculations to elucidate the binding site and the binding mode of a dual hSERT/5HT-1A blocker SSA-426 and our in-house hSERT inhibitor DJLDU-3-79 in hSERT. Through this approach, we propose that both of these inhibitors bind in the S1 pocket of hSERT and in a similar orientation. This disproves the earlier hypothesis that both these inhibitors bind in the S2 site; however, we are in agreement with the earlier hypothesis that both of the ligands orient similarly. Further, we resolved the ambiguity in binding energies and binding trends of the tricyclic antidepressant drugs clomipramine, imipramine, and desipramine with leucine transporter (LeuT) (a bacterial homologue of hSERT) through relative binding free energy (RBFE) calculations. Based on our RBFE results, we proposed that clomipramine should have the highest affinity for LeuT, followed by imipramine and desipramine. Finally, to achieve accuracy in binding energy estimations and to perform all CHARMM simulations, we developed CHARMM general force field parameters (CGenFF) for fifteen monoamine transporter ligands

Theoretical and computational modeling of rna-ligand interactions

Ribonucleic acid (RNA) is a polymeric nucleic acid that plays a variety of critical roles in gene expression and regulation at the level of transcription and translation. Recently, there has been an enormous interest in the development of therapeutic strategies that target RNA molecules. Instead of modifying the product of gene expression, i.e., proteins, RNAtargeted therapeutics aims to modulate the relevant key RNA elements in the disease-related cellular pathways. Such approaches have two significant advantages. First, diseases with related proteins that are difficult or unable to be drugged become druggable by targeting the corresponding messenger RNAs (mRNAs) that encode the amino acid sequences. Second, besides coding mRNAs, the vast majority of the human genome sequences are transcribed to noncoding RNAs (ncRNAs), which serve as enzymatic, structural, and regulatory elements in cellular pathways of most human diseases. Targeting noncoding RNAs would open up remarkable new opportunities for disease treatment. The first step in modeling the RNA-drug interaction is to understand the 3D structure of the given RNA target. With current theoretical models, accurate prediction of 3D structures for large RNAs from sequence remains computationally infeasible. One of the major challenges comes from the flexibility in the RNA molecule, especially in loop/junction regions, and the resulting rugged energy landscape. However, structure probing techniques, such as the “selective 20-hydroxyl acylation analyzed by primer extension” (SHAPE) experiment, enable the quantitative detection of the relative flexibility and hence structure information of RNA structural elements. Therefore, one may incorporate the SHAPE data into RNA 3D structure prediction. In the first project, we investigate the feasibility of using a machine-learning-based approach to predict the SHAPE reactivity from the 3D RNA structure and compare the machine-learning result to that of a physics-based model. In the second project, in order to provide a user-friendly tool for RNA biologists, we developed a fully automated web interface, “SHAPE predictoR” (SHAPER) for predicting SHAPE profile from any given 3D RNA structure. In a cellular environment, various factors, such as metal ions and small molecules, interact with an RNA molecule to modulate RNA cellular activity. RNA is a highly charged polymer with each backbone phosphate group carrying one unit of negative (electronic) charge. In order to fold into a compact functional tertiary structure, it requires metal ions to reduce Coulombic repulsive electrostatic forces by neutralizing the backbone charges. In particular, Mg2+ ion is essential for the folding and stability of RNA tertiary structures. In the third project, we introduce a machine-learning-based model, the “Magnesium convolutional neural network” (MgNet) model, to predict Mg2+ binding site for a given 3D RNA structure, and show the use of the model in investigating the important coordinating RNA atoms and identifying novel Mg2+ binding motifs. Besides Mg2+ ions, small molecules, such as drug molecules, can also bind to an RNA to modulate its activities. Motivated by the tremendous potential of RNA-targeted drug discovery, in the fourth project, we develop a novel approach to predicting RNA-small molecule binding. Specifically, we develop a statistical potential-based scoring/ranking method (SPRank) to identify the native binding mode of the small molecule from a pool of decoys and estimate the binding affinity for the given RNA-small molecule complex. The results tested on a widely used data set suggest that SPRank can achieve (moderately) better performance than the current state-of-art models

Structural characterization and selective drug targeting of higher-order DNA G-quadruplex systems.

There is now substantial evidence that guanine-rich regions of DNA form non-B DNA structures known as G-quadruplexes in cells. G-quadruplexes (G4s) are tetraplex DNA structures that form amid four runs of guanines which are stabilized via Hoogsteen hydrogen bonding to form stacked tetrads. DNA G4s have roles in key genomic functions such as regulating gene expression, replication, and telomere homeostasis. Because of their apparent role in disease, G4s are now viewed as important molecular targets for anticancer therapeutics. To date, the structures of many important G4 systems have been solved by NMR or X-ray crystallographic techniques. Small molecules developed to target these structures have shown promising results in treating cancer in vitro and in vivo, however, these compounds commonly lack the selectivity required for clinical success. There is now evidence that long single-stranded G-rich regions can stack or otherwise interact intramolecularly to form G4-multimers, opening a new avenue for rational drug design. For a variety of reasons, G4 multimers are not amenable to NMR or X-ray crystallography. In the current dissertation, I apply a variety of biophysical techniques in an integrative structural biology (ISB) approach to determine the primary conformation of two disputed higher-order G4 systems: (1) the extended human telomere G-quadruplex and (2) the G4-multimer formed within the human telomerase reverse transcriptase (hTERT) gene core promoter. Using the higher-order human telomere structure in virtual drug discovery approaches I demonstrate that novel small molecule scaffolds can be identified which bind to this sequence in vitro. I subsequently summarize the current state of G-quadruplex focused virtual drug discovery in a review that highlights successes and pitfalls of in silico drug screens. I then present the results of a massive virtual drug discovery campaign targeting the hTERT core promoter G4 multimer and show that discovering selective small molecules that target its loops and grooves is feasible. Lastly, I demonstrate that one of these small molecules is effective in down-regulating hTERT transcription in breast cancer cells. Taken together, I present here a rigorous ISB platform that allows for the characterization of higher-order DNA G-quadruplex structures as unique targets for anticancer therapeutic discovery

Prédictions de complexes protéine-ligand par arrimage moléculaire : développement et applications

Les protéines sont des entités intrinsèquement dynamiques et de nombreuses études ont démontré l’importance de cette propriété à leurs fonctions. Plus particulièrement, la flexibilité protéique est essentielle dans le processus de reconnaissance moléculaire. Lors de tels évènements, les protéines peuvent subir des changements conformationnels mineurs (déplacement de chaînes latérales des acides aminés), majeurs (déplacement de domaines entiers de la protéine) et/ou même se replier. Mes travaux de thèse ont permis de démontrer que de tels réarrangements mineurs sont fréquents et ont aussi permis d’élucider certaines causes potentielles physiques et chimiques. De plus, mes travaux ont démontré l’importance de considérer la flexibilité des chaînes latérales lors de simulations de tels évènements de reconnaissance moléculaire. Plusieurs méthodes computationnelles, dont la dynamique moléculaire et l’arrimage moléculaire, peuvent être utilisées pour prédire la liaison d’un ligand à sa cible. D’un côté, la dynamique moléculaire permet de considérer la flexibilité protéique à toute échelle, mais nécessite un pouvoir computationnel énorme. D’un autre côté, l’arrimage moléculaire restreint le nombre de degrés de liberté considérés, entre autres imposés par la flexibilité protéique. Mes travaux de thèse, en ce qui a attrait au développement de la méthode d’arrimage moléculaire appelée FlexAID, ont permis d’inclure une certaine flexibilité protéique intrinsèque limitant ainsi le nombre de degrés de liberté requis, tout en offrant la possibilité d’ajouter des degrés de liberté supplémentaire pour les mouvements de plus grande envergure ne pouvant être accommodés par cette plasticité protéique. De plus, mes travaux démontrent que FlexAID est compétitive aux autres méthodes dans le domaine et obtient de meilleures performances dans le scénario où les conformations des protéines sous la forme liée sont inconnues. Dans un autre ordre d’idées, les nombreuses simplifications introduites par un logiciel d’arrimage lui permettent d’être une méthode rapide et applicable à la découverte de nouvelles molécules ayant un effet thérapeutique potentiel. Lorsqu’une méthode de repointage est utilisée, les résultats de FlexAID en enrichissement de composés se rapprochent des performances d’autres logiciels couramment utilisés lors de criblage virtuel. Mes travaux portant sur le système biologique de la Matriptase-2 montrent que la méthode FlexAID peut être utilisée à la découverte de nouvelles petites molécules

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