Design of drug-like hepsin inhibitors against prostate cancer and kidney stones

[[abstract]]Hepsin, a transmembrane serine protease abundant in renal endothelial cells, is a promising therapeutic target against several cancers, particularly prostate cancer. It is involved in the release and polymerization of uromodulin in the urine, which plays a role in kidney stone formation. In this work, we design new potential hepsin inhibitors for high activity, improved specificity towards hepsin, and promising ADMET properties. The ligands were developed in silico through a novel hierarchical pipeline. This pipeline explicitly accounts for off-target binding to the related serine proteases matriptase and HGFA (human hepatocyte growth factor activator). We completed the pipeline incorporating ADMET properties of the candidate inhibitors into custom multi-objective optimization functions. The ligands designed show excellent prospects for targeting hepsin via the blood stream and the urine and thus enable key experimental studies. The computational pipeline proposed is remarkably cost-efficient and can be easily adapted for designing inhibitors against new drug targets

Docking Study to Predict the Efficacy of Phosphatidylinositol 3-Kinase α Inhibitors

The phosphatidylinositol 3-kinase (PI3K) family comprises lipid kinases that cross-link signals between living cells and their surroundings. PI3Ks are classified into several groups and isoforms with specific characteristics and functions. Genes encoding PI3Ks are mutated in several types of cancer, and their isoforms have varying capacity in promoting cell signaling and cancer progression. Many compounds have been introduced as PI3Kα inhibitors, but not all of them have the same inhibitory effects. For successful PI3K-related biomedical experiments, it is vital to select the most specific and potent compounds with the highest inhibitory effects for targeting this kinase. In this study, we investigate 28 well-recognized PI3Kα inhibitors through predicting their specificity and potency using the docking software AutoDock Vina. Our data showed that PF 05212384 had the highest docking score (−9.2 kcal/mol), and 3-methyladenine had the lowest docking score (−4.8 kcal/mol). Our data also showed different types of interactions and bonds formed between the inhibitors and protein residues. In conclusion, PF 05212384 and AZD 6482 compounds are the best candidates for targeting PI3Kα. In addition to hydrophobic interactions in the PI3Kα binding pocket, the formation of hydrogen bonds between these inhibitors and binding pocket residues was confirmed

Doctor of Philosophy

dissertationThe dysregulation of proteinâ€"protein interaction (PPI) networks has been implicated in many diseases. Designing therapeutic small-molecule inhibitors of these interactions is a challenging field for medicinal chemistry. This work advances the techniques for discovering more potent PPI inhibitors through integration of computational and biochemical techniques. High-throughput screening using fluorescence polarization and AlphaScreen assays identified an acyl hydrazone-containing inhibitor of the β-catenin/Tcf4 PPI, a key mediator of the canonical Wnt signaling pathway. By removing the undesirable acyl hydrazone moiety, a new compound, 4-(5H-[1,2,5]oxadiazolo[3',4':5,6]pyrazino[2,3-b]indol-5-yl)butanoic acid, was developed to selectively inhibit the β-catenin/Tcf4 interaction. The ethyl ester of this compound was tested in zebrafish embryos and shown to inhibit Wnt signaling in vivo at 2 and 10 μM concentrations. Differences between the PPI interface and the active site of traditional targets add to the difficulty of discovering PPI inhibitors. Herein, the relationship between inhibitor potency and ligand burialâ€"defined as the fraction of the solvent accessible surface areas of the bound over unbound ligand, θlâ€"in the PPI surface was evaluated. A positive correlation between θl and inhibitor potency was discovered. However, this correlation was secondary to the strong nonbonding interactions. A study of five PPI targets with corresponding inhibitor-bound crystal structures also revealed that empirical scoring functions were slightly better at identifying known inhibitors out of the putatively inactive test set, and the Lamarckian genetic algorithm was more successful at pose prediction. Due to the nature of the PPI surface, directly targeting the binding site may be difficult. A novel combination of computational methods explored the druggability, selectivity, and potential allosteric regulation of PPIs. Solvent mapping confirmed that Tcf4, E-cadherin, APC and axin use the same binding site on β-catenin in different ways. Evolutionary trace analysis indicated that the region surrounding W504 of β-catenin might be a potentially allosteric site. Site-directed mutagenesis testing results for a W504I β-catenin mutant resulted in three-fold increased binding of Tcf4 to β-catenin over the wild-type. This new site is promising for the discovery of future allosteric inhibitors of the β-catenin/Tcf4 PPI. The combined results from these studies reveals ways to better design PPI inhibitors

KAJIAN SENYAWA DERIVAT FLOROTANIN Ecklonia cava SEBAGAI KANDIDAT ANTI SARS-CoV-2 BERDASARKAN PENDEKATAN MOLECULAR DOCKING

COVID-19 (Coronavirus Disease 2019) yang disebabkan oleh SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) telah mendorong banyak peneliti untuk melakukan eksplorasi senyawa kandidat anti-SARS-CoV-2. Senyawa derivat florotanin, terutama diekol dari makroalga coklat Ecklonia cava dilaporkan memiliki potensi sebagai anti-SARS-CoV yang secara genomik memiliki kemiripan hingga 86% dengan SARS CoV-2. Pada penelitian ini dilakukan screening potensi senyawa derivat florotanin dari Ecklonia cava sebagai kandidat anti-SARS-CoV-2, menggunakan simulasi molecular docking terhadap tiga reseptor yang berperan penting pada COVID-19, yaitu Mpro SARS-CoV-2, RBD SARS-CoV-2 dan ACE2. Tahapan penelitian yang dilakukan meliputi preparasi protein menggunakan Autodock Tools 1.5.6, validasi metode docking, optimasi dan preparasi ligan menggunakan MOPAC dan Autodock Tools 1.5.6, molecular docking menggunakan Autodock Vina 4.2, serta visualisasi sisi pengikatan dan interaksi molekuler dengan menggunakan Biovia Discovery Studio 2020. Hasil penelitian menunjukkan bahwa diekol memiliki afinitas pengikatan tertinggi jika dibandingkan dengan senyawa derivat florotanin lainnya dan kandidat obat komersial. Afinitas pengikatan diekol dengan Mpro, RBD SARS-CoV-2 dan ACE2, berurut-turut sebesar 9,0 kkal/mol, 7,5 kkal/mol dan -7,9 kkal/mol. Afinitas pengikatan diekol dengan Mpro, RBD SARS-CoV-2 dan ACE2 berturut-turut 1,07; 1,17; 1,27 kali lipat lebih tinggi dibandingkan nelfinavir dan mencapai 1,50; 1,49; 1,47 kali lipat lebih tinggi dibandingkan klorokuin, serta 1,48; 1,40; 1,44 kali lipat lebih tinggi dibandingkan hidroksiklorokuin. Semua senyawa derivat florotanin dan kandidat obat komersial berinteraksi dengan Mpro dan RBD SARS-CoV-2 pada sisi pengikatan yang sama, sedangkan pada ACE2 tidak semua berikatan pada sisi yang sama. Interaksi diekol dengan ketiga reseptor tersebut melibatkan ikatan hidrogen, interaksi hidrofobik dan gaya Van der Waals. Berdasarkan hasil simulasi ditunjukkan bahwa senyawa derivat florotanin, terutama diekol diprediksi memiliki potensi sebagai senyawa anti-SARS-CoV-2. Uji in vitro dan in vivo perlu dilakukan untuk mengetahui efektivitas senyawa derivat florotanin sebagai anti-SARS-CoV-2 secara empirik. COVID-19 (Coronavirus Disease 2019) which caused by SARS-CoV-2 (Severe Acute Respiratory Syndrome Coronavirus 2) has been growing interests among researchers to explore functional compounds for anti-SARS-CoV-2. Phlorotannin derivatives, particularly dieckol derived from brown macroalgae Ecklonia cava has been reported to exhibit the potential as anti-SARS-CoV, which has 86% of genomic similarity with SARS-CoV-2. In this study, a molecular docking simulation was carried out to screen the potential of phlorotannin derivatives derived from Ecklonia cava as anti-SARS-CoV-2 candidates. Molecular docking simulation was performed between Phlorotannin derivatives derived from Ecklonia cava with three significant receptors of COVID-19 i.e. Mpro SARS-CoV-2, RBD SARS-CoV-2, and ACE2. Research stages include preparation of protein using Autodock Tools 1.5.6, docking methods validation, optimization and preparation of ligand using MOPAC and Autodock Tools 1.5.6, molecular docking using Autodock Vina 4.2, and visualization of the binding site and molecular interactions using Biovia Discovery Studio. The results show that dieckol has the highest binding affinity compared to either phlorotannin derivatives and commercial drug candidates. The binding affinity of dieckol with Mpro SARS-CoV-2, RBD SARS-CoV-2, and ACE2 were 9.0 kcal/mol; 7.5 kcal/mol; and -7.9 kcal/mol, respectively. The binding affinity of dieckol with Mpro, RBD SARS-CoV-2, and ACE2 were 1.07; 1.17; 1.27 times higher than nelfinavir, 1.50; 1.49; 1.47-times higher than those of the chloroquine, and 1.48; 1.40; 1.44 times higher compared to hydroxychloroquine. Phlorotannin derivatives and commercial drugs binds to the same active site of Mpro and RBD SARS-CoV-2, but on a different binding site of ACE2. Molecular interactions of dieckol with three receptors involves hydrogen bonds, hydrophobic interactions and van der Waals forces. Molecular docking simulation suggested that phlorotannin derivatives, particularly dieckol, has potential as anti-SARS-CoV-2. In vitro and in vivo study has to be conducted in order to explore the efficacy of phlorotannin derivatives as anti-SARS-CoV-2 empirically. Keywords: COVID-19, Ecklonia cava, phlorotannin, molecular docking, SARS-CoV-2

Docking Studies on the Effects of Some Bioactive Compounds from Pistacia atlantica Desf. against Main Protease SARS-CoV2

Novel coronavirus which was named later as SARS-CoV2 appeared in Wuhan, China, in the end of December 2019. Actually, no precise drugs are existed and research concerning SARS-CoV2 treatment is deficient. SARS-CoV2 main protease (Mpro) was crystallized by Liu et al. (2020) and represented a crucial drug target. The present work aimed to evaluate some bioactive compounds from Pistacia atlantica as possible SARS-CoV2 Mpro inhibitors, based on molecular docking approach. Molecular docking was carried out using AutoDock Vina software. The results indicated that Beta-Eudesmol, Elemol, Verbenol, Pinocarvone, Myrtenal, Myrtenol and Trans-Carveol have a potential inhibitor activity of SARS-CoV2 Mpro. Nevertheless, further investigations are required to develop and optimize drug process to combat SARS-CoV2

Development and Application of Pseudoreceptor Modeling

Quantitative Structure-Activity Relationship (QSAR) methods are a commonly used tool in the drug discovery process. These methods attempt to form a statistical model that relates descriptor properties of a ligand to the activity of that ligand compound towards a specific desired physiological response. QSAR methods are known as a ligand-based method, as they specifically use information from ligands and not protein structural data. However, a derivation of QSAR methods are pseudoreceptor methods. Pseudoreceptor methods go beyond standard QSAR by building a model representation of the protein pocket. However, the ability of pseudoreceptors to accurately replicate natural protein surfaces has not been studied. The goal of this thesis work is to investigate the necessary descriptors to map a protein binding pocket and a method to accurately recreate the 3-D spatial structure of the binding pocket. In addition, additional applications of existing pseudoreceptor methods are explored

Computational Studies of Mitragynine Analogue Binding at the Human Mu-Opioid Receptor

The mu-opioid receptor (MOR) is a well-characterized G-protein coupled receptor and a major target of opioid pharmaceuticals. Opioid agonists exert their effects by MOR binding through two major downstream pathways: G-protein signaling and beta-arrestin signaling. The design of MOR agonists that favor or exclusively activate G-protein signaling may provide a new class of pharmaceuticals for pain relief with improved side-effect profiles. Mitragynine is the active compound of the herbal supplement kratom. Its distinct molecular structure and potential G-protein bias has gained increasing attention. Mitragynine analogues were designed and tested in-silico using several computer-aided drug design approaches. A homology model of the human MOR was constructed in Swiss-Model using a murine MOR crystal structure (PDB: 5C1M). A compound library of mitragynine analogues was iteratively constructed and passed through the SwissADME web tool to predict pharmacokinetics before molecular docking. Analogues with predicted access to the central nervous system (CNS) underwent water-solvent geometry optimization in Spartan and were simulated in the human MOR active site by a flexible ligand-rigid receptor docking calculation in AutoDock Vina. Analogues with MOR-activity comparable to mitragynine were retained for simulation in a flexible ligand-flexible receptor calculation for improved accuracy. Notably, results revealed several CNS-accessible analogues with similar in-silico activity at the human MOR compared to existing biased and mitragynine-analogue agonists. This computational study provides direction for the rational drug design of mitragynine toward G-protein biased agonists by identifying several analogues with potential activity at the human MOR. These findings prompt follow-up pharmacological testing to establish if these lead compounds act as agonists and possess a bias for G-protein signaling.A one-year embargo was granted for this item.Academic Major: Microbiolog

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

Predicting and Testing Helix-Mimetic Inhibitors of the p53-Mdm2 Interaction

Aberrant protein-protein interactions (PPIs) are found in many disease states. Consequently, there is a need for PPI inhibitors for use as research tools and pharmaceutical lead compounds. Computational methods could greatly assist with the search for new PPIs. Oligobenzamides are novel PPI inhibitors which can theoretically be produced to display any sequence of side chains. Understanding the nature of oligobenzamide binding is important for identification of the most efficient strategy of predicting oligobenzamide inhibitors. The prediction of oligobenzamide affinities using thermodynamic integration and implicit solvent methods is described. Affinities of oligobenzamides for Mdm2 predicted using implicit solvent methods bore a moderate correlation with measured affinities. Examination of MM-PBSA results using analysis of variance revealed that it is not necessary to run simulations with every member of a large combinatorial library in order to predict their relative affinities because within a particular binding site, the degree of interaction between the side chains is small. However, it could be useful to separate molecules based on their predicted binding pose because oligobenzamides can bind to Mdm2 in many different ways, depending on the choice of side chains. This insight will be valuable for future attempts to predict oligobenzamide affinities. The 1H-15N HSQC NMR spectrum peaks of 15N-labelled Mdm2 L33E were assigned to facilitate the future validation of binding poses. An oligoamide was shown using NMR to bind in the correct place. However, NMR testing revealed that oligobenzamides can aggregate in aqueous solution despite being soluble. A novel FRET-based method was also developed which can be used to test potential inhibitors with a low solubility and high absorbance during their development. It was adapted for a microwell plate to facilitate future high throughput screening and an assay involving Cherry-labelled Mdm2 was tested which could be developed into an in vivo assay in the future

Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay

Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein–ligand complexes were finally retained from the 28,656 (36×796) dockings