BINARY QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP ANALYSIS IN RETROSPECTIVE STRUCTURE-BASED VIRTUAL SCREENING CAMPAIGNS TARGETING ESTROGEN RECEPTOR ALPHA

  Objective: The objective of this study is to construct predictive unbiased structure-based virtual screening (SBVS) protocols to identify potent ligands for estrogen receptor alpha by combining molecular docking, protein-ligand interaction fingerprinting (PLIF), and binary quantitative structure-activity relationship (QSAR) analysis using recursive partition and regression tree method.Methods: Employing the enhanced version of a directory of useful decoys, SBVS protocols using molecular docking simulations, and PLIF were constructed and retrospectively validated. To avoid bias, SMILES format of the compounds was used. The predictive abilities of the SBVS protocols were then compared based on the enrichment factor (EF) and the F-measure values.Results: The SBVS protocols resulted in this research were SBVS_1 (employing docking scores of the best pose on every compound to rank the results and selecting compounds within 1% false positives as positive), SBVS_2 (employing decision tree resulted from the binary QSAR analysis using docking scores and PLIF bitstrings of the best pose of every compound as descriptors), and SBVS_3 (employing decision tree resulted from the binary QSAR analysis using ensemble PLIF of the selected poses from optimized docking score as the cutoff). The EF values of SBVS_1, SBVS_2, and SBVS_3 are 28.315, 576.084, and 713.472, respectively, while their F-measure values are 0.310, 0.573, and 0.769, respectively.Conclusion: Highly predictive unbiased SBVS protocols to identify potent estrogen receptor alpha ligands were constructed. Further application in prospective screening is therefore highly suggested

Computer-aided Design of Chalcone Derivatives as Lead Compounds Targeting Acetylcholinesterase

One of well-established biological activities for chalcone derivatives is as acetylcholinesterase inhibitors, which can be developed for the therapy of Alzheimer’s disease. Assisted byretrospectively validated structure-based virtual screening (SBVS) protocol to identify potent acetylcholinesterase inhibitors, 80chalcone derivatives were designed and virtually screened. The F-measure value as the parameter of the predictive ability of the SBVS protocol developed in the research presented in this article was 0.413, which was considerably better than the original SBVS protocol (F-measure = 0.226). Among the screened chalcone derivatives two were selected as potential lead compounds to designpotent inhibitors for acetylcholinesterase: 3-[4-(benzyloxy)-3-methoxyphenyl]-1-(4-hydroxy-3-methoxyphenyl)prop-2-en-1-one(3k) and 3-[4-(benzyloxy)-3-methoxyphenyl]-1-(4-hydroxyphenyl)prop-2-en-1-one (4k)

OPTIMIZING STRUCTURE-BASED VIRTUAL SCREENING PROTOCOL TO IDENTIFY PHYTOCHEMICALS AS CYCLOOXYGENASE-2 INHIBITORS

By employing Databases of Useful Decoys (DUD) and its enhanced version (DUD-E), several attempts to construct validated Structure-based Virtual Screening (SBVS) protocols to identify cyclooxygenase-2 (COX-2) inhibitors have been performed. Both databases tagged active COX-2 inhibitors for compounds with IC50 values < 1mM. In the search for phytochemicals as natural COX-2 inhibitors, however, most of their IC50 values are in the micromolar range, which will likely be identified as non-inhibitors for COX-2 by the available SBVS protocols. In this article, validation of an SBVS protocol by adding marginal active COX-2 inhibitors from DUD-E as active compounds is presented. Binary quantitative-structure activity relationship analysis by using recursive partition and regression tree method was performed subsequently to optimize the predictive ability of the protocol. The enrichment factor and the F-measure values of the optimized protocol could reach 44.78 and 0.47, respectively. The optimized protocol could identify 1 out of 9 phytochemicals as COX-2 inhibitors

In silico search for novel bacterial inhibitors targeting RNA polymerase switch region

The arise of antibiotic-resistant bacterial strains in an alarming rate has increased the interest in the discovery of novel antibiotics. The rifamycins are a valuable class of antibiotics that target bacterial Ribonucleic Acid polymerase (RNAP) and are considered the first-line treatment for tuberculosis. Consequently, bacterial strains resistant to rifamycin constitute a public health threat. RNAP switch region is an attractive target for the development of new antibacterial agents as it lies away from the rifamycin binding region and thus the compounds that target the switch region would not show cross-resistance with rifamycins. In this work, we developed a virtual screening pipeline to identify new bacterial RNAP inhibitors that target the enzyme switch region. The screening pipeline involved docking of the designated libraries using the Maestro Glide docking tool, and the compounds with the best docking scores were submitted for binding free energy calculations using the molecular mechanics-generalised born surface area (MM-GBSA)-based method. Moreover, a quantitative structure-activity relationship (QSAR) model was developed, and it was applied to predict the biological activity of the compounds with the most favourable calculated binding free energies. Based on the results of docking, MM-GBSA binding free energies and the activities predicted by the QSAR model, the most promising compounds were chosen to be evaluated by molecular dynamics (MD) simulations. The results of the MD simulation of each docked candidate compound in the RNAP binding site were compared with the MD simulations carried out with the apo protein and with a reference co-crystallized ligand in the RNAP binding site. The candidate compounds showing comparable binding to the RNAP site to the reference ligand were selected for further biological testing

Field-based Proteochemometric Models Derived from 3D Protein Structures : A Novel Approach to Visualize Affinity and Selectivity Features

Designing drugs that are selective is crucial in pharmaceutical research to avoid unwanted side effects. To decipher selectivity of drug targets, computational approaches that utilize the sequence and structural information of the protein binding pockets are frequently exploited. In addition to methods that rely only on protein information, quantitative approaches such as proteochemometrics (PCM) use the combination of protein and ligand descriptions to derive quantitative relationships with binding affinity. PCM aims to explain cross-interactions between the different proteins and ligands, hence facilitating our understanding of selectivity. The main goal of this dissertation is to develop and apply field-based PCM to improve the understanding of relevant molecular interactions through visual illustrations. Field-based description that depends on the 3D structural information of proteins enhances visual interpretability of PCM models relative to the frequently used sequence-based descriptors for proteins. In these field-based PCM studies, knowledge-based fields that explain polarity and lipophilicity of the binding pockets and WaterMap-derived fields that elucidate the positions and energetics of water molecules are used together with the various 2D / 3D ligand descriptors to investigate the selectivity profiles of kinases and serine proteases. Field-based PCM is first applied to protein kinases, for which designing selective inhibitors has always been a challenge, owing to their highly similar ATP binding pockets. Our studies show that the method could be successfully applied to pinpoint the regions influencing the binding affinity and selectivity of kinases. As an extension of the initial studies conducted on a set of 50 kinases and 80 inhibitors, field-based PCM was used to build classification models on a large dataset (95 kinases and 1572 inhibitors) to distinguish active from inactive ligands. The prediction of the bioactivities of external test set compounds or kinases with accuracies over 80% (Matthews correlation coefficient, MCC: ~0.50) and area under the ROC curve (AUC) above 0.8 together with the visual inspection of the regions promoting activity demonstrates the ability of field-based PCM to generate both predictive and visually interpretable models. Further, the application of this method to serine proteases provides an overview of the sub-pocket specificities, which is crucial for inhibitor design. Additionally, alignment-independent Zernike descriptors derived from fields were used in PCM models to study the influence of protein superimpositions on field comparisons and subsequent PCM modelling.Lääketutkimuksessa selektiivisten lääkeaineiden suunnittelu on ratkaisevan tärkeää haittavaikutusten välttämiseksi. Kohdeselektiivisyyden selvittämiseen käytetään usein tietokoneavusteisia menetelmiä, jotka hyödyntävät proteiinien sitoutumiskohtien sekvenssi- ja rakennetietoja. Proteiinilähtöisten menetelmien lisäksi kvantitatiiviset menetelmät kuten proteokemometria (proteochemometrics, PCM) yhdistävät sekä proteiinin että ligandin tietoja muodostaessaan kvantitatiivisen suhteen sitoutumisaffiniteettiin. PCM pyrkii selittämään eri proteiinien ja ligandien vuorovaikutuksia ja näin auttaa ymmärtämään selektiivisyyttä. Väitöstutkimuksen tavoitteena oli kehittää ja hyödyntää kenttäpohjaista proteokemometriaa, joka auttaa ymmärtämään relevantteja molekyylitasoisia vuorovaikutuksia visuaalisen esitystavan kautta. Proteiinin kolmiulotteisesta rakenteesta riippuva kenttäpohjainen kuvaus helpottaa PCM-mallien tulkintaa, etenkin usein käytettyihin sekvenssipohjaisiin kuvauksiin verrattuna. Näissä kenttäpohjaisissa PCM-mallinnuksissa käytettiin tietoperustaisia sitoutumistaskun polaarisuutta ja lipofiilisyyttä kuvaavia kenttiä ja WaterMap-ohjelman tuottamia vesimolekyylien sijaintia ja energiaa havainnollistavia kenttiä yhdessä lukuisten ligandia kuvaavien 2D- ja 3D-deskriptorien kanssa. Malleja sovellettiin kinaasien ja seriiniproteaasien selektiivisyysprofiilien tutkimukseen. Tutkimuksen ensimmäisessä osassa kenttäpohjaista PCM-mallinnusta sovellettiin proteiinikinaaseihin, joille selektiivisten inhibiittorien suunnittelu on haastavaa samankaltaisten ATP sitoutumistaskujen takia. Tutkimuksemme osoitti menetelmän soveltuvan kinaasien sitoutumisaffiniteettia ja selektiivisyyttä ohjaavien alueiden osoittamiseen. Jatkona 50 kinaasia ja 80 inhibiittoria käsittäneelle alkuperäiselle tutkimukselle rakensimme kenttäpohjaisia PCM-luokittelumalleja suuremmalle joukolle kinaaseja (95) ja inhibiittoreita (1572) erotellaksemme aktiiviset ja inaktiiviset ligandit toisistaan. Ulkoisen testiyhdiste- tai testikinaasijoukon bioaktiivisuuksien ennustaminen yli 80 % tarkkuudella (Matthews korrelaatiokerroin, MCC noin 0,50) ja ROC-käyrän alle jäävä ala (AUC) yli 0,8 yhdessä aktiivisuutta tukevien alueiden visuaalisen tarkastelun kanssa osoittivat kenttäpohjaisen PCM:n pystyvän tuottamaan sekä ennustavia että visuaalisesti ymmärrettäviä malleja. Tutkimuksen toisessa osassa metodin soveltaminen seriiniproteaaseihin tuotti yleisnäkemyksen sitoutumistaskun eri osien spesifisyyksistä, mikä on ensiarvoisen tärkeää inhibiittorien suunnittelulle. Lisäksi kentistä johdettuja, proteiinien päällekkäinasettelusta riippumattomia Zernike-deskriptoreita hyödynnettiin PCM-malleissa arvioidaksemme proteiinien päällekkäinasettelun vaikutusta kenttien vertailuun ja sen jälkeiseen PCM-mallinnukseen

Benchmarking and Developing Novel Methods for G Protein-coupled Receptor Ligand Discovery

G protein-coupled receptors (GPCR) are integral membrane proteins mediating responses from extracellular effectors that regulate a diverse set of physiological functions. Consequently, GPCR are the targets of ~34% of current FDA-approved drugs.3 Although it is clear that GPCR are therapeutically significant, discovery of novel drugs for these receptors is often impeded by a lack of known ligands and/or experimentally determined structures for potential drug targets. However, computational techniques have provided paths to overcome these obstacles. As such, this work discusses the development and application of novel computational methods and workflows for GPCR ligand discovery. Chapter 1 provides an overview of current obstacles faced in GPCR ligand discovery and defines ligand- and structure-based computational methods of overcoming these obstacles. Furthermore, chapter 1 outlines methods of hit list generation and refinement and provides a GPCR ligand discovery workflow incorporating computational techniques. In chapter 2, a workflow for modeling GPCR structure incorporating template selection via local sequence similarity and refinement of the structurally variable extracellular loop 2 (ECL2) region is benchmarked. Overall, findings in chapter 2 support the use of local template homology modeling in combination with de novo ECL2 modeling in the presence of a ligand from the template crystal structure to generate GPCR models intended to study ligand binding interactions. Chapter 3 details a method of generating structure-based pharmacophore models via the random selection of functional group fragments placed with Multiple Copy Simultaneous Search (MCSS) that is benchmarked in the context of 8 GPCR targets. When pharmacophore model performance was assessed with enrichment factor (EF) and goodness-of-hit (GH) scoring metrics, pharmacophore models possessing the theoretical maximum EF value were produced in both resolved structures (8 of 8 cases) and homology models (7 of 8 cases). Lastly, chapter 4 details a method of structure-based pharmacophore model generation using MCSS that is applicable to targets with no known ligands. Additionally, a method of pharmacophore model selection via machine learning is discussed. Overall, the work in chapter 4 led to the development of pharmacophore models exhibiting high EF values that were able to be accurately selected with machine learning classifiers

The glyoxylate shunt as a target for antibacterial intervention in Pseudomonas aeruginosa

Pseudomonas aeruginosa is an opportunistic human pathogen responsible for a large proportion of drug-resistant, hospital-acquired infections worldwide. These infections are notoriously difficult to treat due to P. aeruginosa’s many intrinsic and acquired resistance mechanisms. Despite the critical need for new antibiotics against P. aeruginosa, the current pipeline is unable to meet the demand for treating these infections. P. aeruginosa can thrive in diverse infection scenarios by rewiring its central metabolism, and an example of this is in the lungs of cystic fibrosis patients, where this bacterium metabolises fatty acids into acetyl coenzyme A for biomass production via the glyoxylate shunt. The glyoxylate shunt is comprised of two enzymes: isocitrate lyase and malate synthase G, and allows the synthesis of cellular constituents from C2 nutrient sources, bypassing the decarboxylation steps of the citric acid cycle when glucose is not available. The glyoxylate shunt has potential as a new antibacterial target in P. aeruginosa as there is evidence to support that, besides carbon fixation, the glyoxylate shunt is implicated in virulence and is essential for establishing pulmonary infections. In this dissertation, the glyoxylate shunt was investigated as an antibacterial target in Pseudomonas aeruginosa. The biochemical characterisation of malate synthase G resulted in the first reported crystal structure solved to 1.6 Å resolution. Computational evaluation of the crystal structure revealed two promising binding sites suitable for future in silico drug design, which confirmed malate synthase as an apt drug target. Two 2-aminopyridine derivatives were found that displayed dual inhibition of the glyoxylate shunt enzyme activities as well as P. aeruginosa growth with low micromolar potencies. Insights into the different mechanisms of action on the target enzymes were explored using kinetic analysis and isothermal titration calorimetry. Hit to lead optimisation experiments of the hit compounds provided awareness of their potential for any future development as antibacterial agents. Although the compounds showed promising efficacies, in vitro drug metabolism and safety profiles, I also found that they displayed possible "off-target" effects and had issues in chemical stability. Together, these data indicate that the compounds warrant further chemical modification to improve these characteristics.European Commission's Marie Skłodowska-Curie Actions, Cambridge Philosophical Society, Lundgren Fund, Gonville & Caius Colleg