Conventional drug discovery focuses on single protein targets and follows a “sequence, structure, and function” paradigm for selecting best protein targets to screen lead chemical compounds. This established paradigm simply avoids addressing directly the challenge of evaluating chemical toxicity and side effects until a later stage of drug discovery, resulting in inefficiencies and increased time and cost. We developed a new “network biology” perspective to assess proteins as potential drug targets using emerging biomolecular network data sets. To do so, we integrated several types of biological data for current drug targets from DrugBank, protein interaction data from the HAPPI and HPRD databases, literature co-citation data from PubMed, and side effects data from FDA-approved drug usage warnings. We used the Bayes factor and Positive Predictive Values to examine the use of certain network properties, such as network node degrees and essentiality, to predict candidate drug targets. We also developed a metric to evaluate a protein target’s overall side effects by taking into account aggregated side effect scores of all FDA-approved drugs targeting the protein. We discovered that non-essential protein with lower-to-medium network node degree could better serve as drug targets when combined with conventional protein function information. Integrated biomolecular associations, instead of physical interactions, are better sources for predicting drug targets with network biology methods. Our network biology framework presents exciting promises in developing better drug targets that lower the side-effects at later stages of drug development and help establish the field of “network pharmacology.”

Thesis advisor: Marc-Jan Gubbels / Apicomplexan parasites like Toxoplasma gondii have a complex life cycle comprising of transitions between different hosts, different organ systems and between the extracellular and intracellular milieu. The parasite must thus adjust itself and its cellular processes in accordance with its environment. In this dissertation, I have focused on such stage specific behaviors of three distinct intermediate filament-like proteins as well as a glycolytic enzyme, glyceraldehyde-3-phosphate dehydrogenase 1 (GAPDH1). These proteins relocate from the cytosol to the unique cortical membrane skeleton of non-dividing parasites. The intermediate filament-like proteins IMC7, 12 and 14, localize exclusively to the mature cytoskeleton. One model of function was that these proteins differentially stabilized mother and budding daughter cytoskeletons in the division process, but we ruled out this role for the individual proteins, as they are not essential for the lytic cycle of the parasite. However, we determined that IMC7 and IMC14 are contributing to the maintenance of rigidity of the cytoskeleton under osmotic stress conditions in extracellular parasites. In addition, IMC14 is critical in cell cycle progression as its depletion results in the formation of multiple daughters per division round. When the parasite egresses from the host cell, glycolytic enzyme GAPDH1 translocates to the cortex. The functional role of GAPDH1 in the parasite and the mechanism of its cortical translocation are deciphered based on the 2.25Å resolution crystal structure of the GAPDH1 holoenzyme in a quaternary complex. These studies identified that GAPDH1’s enzymatic function is essential for intracellular replication but we confirmed the previous reports that glycolysis is not strictly essential in presence of excess L-glutamine. We identify, for the first time, S-loop phosphorylation as a novel, critical regulator of enzymatic activity that is consistent with the notion that the S-loop is critical for cofactor binding, allosteric activation and oligomerization. We show that neither enzymatic activity nor phosphorylation state correlate with the ability to translocate to the cortex. However, we demonstrate that association of GAPDH1 with the cortex is mediated by Cysteine 3 in the N-terminus, likely by palmitoylation. Overall, glycolysis and cortical translocation are functionally decoupled by post-translational modifications. Collectively, the discoveries made in this dissertation reveal unprecedented detail in mechanism and function of cortical protein translocation and thereby identifying new drug targets. / Thesis (PhD) — Boston College, 2017. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Biology.

Brugia malayi is a filarial nematode causing lymphatic filariasis in humans characterized by swelling of the lower extremities. The aim of this study was to conduct a real time PCR (qRT-PCR) to verify gene expression levels of Brugia malayi nematodes treated with 20 hydroxyecdysone. Transcriptome analysis was previously performed resulting in the identification of 44 genes that were upregulated by exposure to 20-hydroxyecdysone. Based on transcriptome results, known GO Terms and functions, four genes and one endogenous housekeeping gene were chosen for validation by RT-PCR. Induced samples showed a mean increase of microfilarie by 2.2 fold. Induced wells exhibited a 2.8 fold increase of pre- microfilarie production. On day two adult females treated with 20-HE displayed 3.8-fold increase of microfilaria production as compared to uninduced controls. Overall, all four genes showed upregulation with treatment of 20-hydroxyecdysone at levels that corresponded to the results obtained from the transcriptome analysis. Findings in this experiment expand on the understanding of the ecdysone response system in Brugia malayi, which could serve as a potential drug target against filarial disease.

Mycobacterium tuberculosis (Mtb) causes tuberculosis (TB) and poses a global threat to human health. A third of the world’s population is infected with Mtb. Multi-drug resistant and extensively drug resistant Mtb strains are widespread and development of new drugs is urgently needed to treat drug resistant TB. This thesis focuses on the Mtb cytochrome P450 (P450) enzymes CYP126A1 and CYP143A1. P450s are heme-binding enzymes that catalyse activation of molecular oxygen and the oxidation of substrates bound close to the heme. CYP126A1 and CYP143A1 are “orphans” in terms of their functional characterization, but potential drug targets in view of ability of azole-based P450 inhibitors to inhibit growth and viability of Mtb. The CYP126A1 and CYP143A1 genes were cloned and expressed in Escherichia coli. Expression conditions and strains were optimised to maximise soluble protein production and methods were developed to purify the P450s using affinity, ion exchange and size exclusion chromatography. Both P450s were shown to bind heme b, and heme was shown to be axially coordinated by a cysteine thiolate and a water molecule in both cases using UV-visible and electron paramagnetic resonance (EPR) spectroscopy. Both P450s bound carbon monoxide (CO) in their reduced forms to produce heme Fe2+-CO complexes with absorption maxima at ~450 nm – characteristic of P450s. CYP126A1 and CYP143A1 bound avidly to a range of inhibitors, including several azole drugs. As examples, binding constant (Kd) values of 13.8 µM and 21.9 µM were determined for clotrimazole and econazole with CYP143A1; while ketoconazole bound CYP126A1 with a Kd of 0.20 µM. Each of these drugs is very effective in inhibiting Mtb growth. EPR confirmed inhibitory coordination of both P450s by azole drug nitrogen atoms; though indirect coordination via a retained axial water ligand may also occur in some cases. Extinction coefficients were determined as έ420 = 125 mM-1 cm-1 (CYP126A1) and έ415 = 105 mM-1 cm-1 (CYP143A1). CYP126A1’s heme iron redox potential was shown to be unusually positive (E°’ = -80 mV). Light scattering studies showed CYP126A1 to be a monodisperse, monomeric protein. CYP143A1 is also mainly a monomer, but with a small proportion of an oligomeric form. Despite its polydispersity, CYP143A1 was crystallized and its structure solved by X-ray diffraction to a resolution of 1.9 Å, using molecular replacement with the Mtb P450 CYP142A1. A limited compound screen of typical P450 substrates failed to provide “hits” to identify CYP143A1 substrate selectivity, but the presence of polyethylene glycol in the CYP143A1 active site in crystals suggests fatty acids as potential substrates. CYP126A1 was crystallized for studies to identify binding modes of small molecules (“fragments”) identified to interact with CYP126A1 by NMR. Crystal structures of CYP126A1 in complex with two such fragments (NMR401 and NMR343) were determined to ~2.0 Å resolution in ongoing research to build Mtb P450 isoform-specific inhibitors. Compounds identified as CYP126A1 substrates/inhibitors identified by high-throughput screening were validated by UV-visible titrations with the P450, and binding modes and affinity established. In conclusion, this thesis provides novel insights into the biochemical, biophysical and structural properties of two novel Mtb P450s that are potential targets for new anti-TB drugs.

The rapidly increasing number of existing drugs with genomic, biomedical, and pharmacological data make computational analyses possible, which reduces the search space for drugs and facilitates drug repositioning (DR). Thus, artificial intelligence, machine learning, and data mining have been used to identify biological interactions such as drug-target interactions (DTI), drug-disease associations, and drug-response. The prediction of these biological interactions is seen as a critical phase needed to make drug development more sustainable. Furthermore, late-stage drug development failures are usually a consequence of ineffective targets. Thus, proper target identification is needed. In this dissertation, we tried to address three crucial problems associated with the DR pipeline and presents several novel computational methods developed for DR. First, we developed three network-based DTI prediction methods using machine learning, graph embedding, and graph mining. These methods significantly improved prediction performance, and the best-performing method reduces the error rate by more than 33% across all datasets compared to the best state-of-the-art method. Second, because it is more insightful to predict continuous values that indicate how tightly the drug binds to a specific target, we conducted a comparison study of current regression-based methods that predict drug-target binding affinities (DTBA). We discussed how to develop more robust DTBA methods and subsequently developed Affinity2Vec, the first regression-based method that formulates the entire task as a graph-based method and combines several computational techniques from feature representation learning, graph mining, and machine learning with no 3D structural data of proteins. Affinity2Vec outperforms the state-of-the-art methods. Finally, since drug development failure is associated with sub-optimal target identification, we developed the first DL-based computational method (OncoRTT) to identify cancer-specific therapeutic targets for the ten most common cancers worldwide. Implementing our approach required creating a suitable dataset that could be used by the computational method to identify oncology-related DTIs. Thus, we created the OncologyTT datasets to build and evaluate our OncoRTT method. Our methods demonstrated their efficiency by achieving high prediction performance and identifying therapeutic targets for several cancer types. Overall, in this dissertation, we developed several computational methods to solve biomedical domain problems, specifically drug repositioning, and demonstrated their efficiencies and capabilities.

The amount of biomedical literature has been increasing rapidly during the last decade. Text mining techniques can harness this large-scale data, shed light onto complex drug mechanisms, and extract relation information that can support computational polypharmacology. In this work, we introduce CASSANDRA, a fully corpus-based and unsupervised algorithm which uses the MEDLINE indexed titles and abstracts to infer drug gene associations and assist drug repositioning. CASSANDRA measures the Pointwise Mutual Information (PMI) between biomedical terms derived from Gene Ontology (GO) and Medical Subject Headings (MeSH). Based on the PMI scores, drug and gene profiles are generated and candidate drug gene associations are inferred when computing the relatedness of their profiles. Results show that an Area Under the Curve (AUC) of up to 0.88 can be achieved. The algorithm can successfully identify direct drug gene associations with high precision and prioritize them over indirect drug gene associations. Validation shows that the statistically derived profiles from literature perform as good as (and at times better than) the manually curated profiles. In addition, we examine CASSANDRA’s potential towards drug repositioning. For all FDA-approved drugs repositioned over the last 5 years, we generate profiles from publications before 2009 and show that the new indications rank high in these profiles. In summary, co-occurrence based profiles derived from the biomedical literature can accurately predict drug gene associations and provide insights onto potential repositioning cases.

Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interactions and categorize them into protein structure-based and similarity-based methods. We evaluate three representative structure-based predictors and develop a Protein-Drug Interaction Database (PDID) that includes the putative drug targets generated by these three methods for the entire structural human proteome. To address the fact that only a limited set of proteins has known structures, we study the similarity-based methods that do not require this information. We review a comprehensive set of 35 high-impact similarity-based predictors and develop a novel, high-quality benchmark database. We group these predictors based on three types of similarities and their combinations that they use. We discuss and compare key architectural aspects of these methods including their source databases, internal databases and predictive models. Using our novel benchmark database, we perform comparative empirical analysis of predictive performance of seven types of representative predictors that utilize each type of similarity individually or in all possible combinations. We assess predictive quality at the database-wide drug-protein interaction level and we are the first to also include evaluation across individual drugs. Our comprehensive analysis shows that predictors that use more similarity types outperform methods that employ fewer similarities, and that the model combining all three types of similarities secures AUC of 0.93. We offer a first-of-its-kind analysis of sensitivity of predictive performance to intrinsic and extrinsic characteristics of the considered predictors. We find that predictive performance is sensitive to low levels of similarities between sequences of the drug targets and several extrinsic properties of the input drug structures, drug profiles and drug targets.

Identification of the unknown targets of drugs, investigative drugs and herbal ingredients is an important task in drug discovery. It can potentially help in several aspects including: (1) determination of unknown therapeutic mechanism of certain drugs and medicinal herbs, (2) prediction of drug toxicity and side effect, and (3) analysis of protein-mediated pharmacokinetic properties of drugs. Here, a computer-aided drug target search method and its validation studies are presented. / Singapore-MIT Alliance (SMA)

Abnormal activation of brain microglial cells is widely implicated in the pathogenesis of schizophrenia. The disrupted balance of microglia phenotypes has been hypothesized to influence the clinical course of the disease and affect symptom severity. Previously, the pathophysiology of microglial activation was considered to be intrinsic to the central nervous system. We hypothesised that due to their perivascular localization, microglia can also be activated by factors present in circulating blood. We applied a high-content functional screening platform, to characterize alterations in microglial intracellular signalling cascades induced by schizophrenia patient serum relative to control serum. Using automated sample preparation, fluorescent cellular barcoding and flow cytometry, the applied platform is capable of detecting multiple parallel cell signalling responses in microglia. First, we exposed a human microglia cell line to serum isolated from first-onset drug-naïve schizophrenia patients (n=60) and healthy controls (n=79). We were able to show that peripheral blood serum obtained from schizophrenia patients induced differential microglial cell signalling network responses in vitro. We subsequently assessed whether antipsychotic drug-treatment can normalise the abnormal microglial signalling responses previously identified by exposing microglia cells to serum from antipsychotic treated schizophrenia patients (n=15) and controls (n=17). In addition, in order to assess microglia activation in vivo, we obtained positron emission tomography (PET) imaging data from collaborators, who used a radiotracer to assess potential altered microglia activation in patients suffering from schizophrenia. Finally, as a proof of concept study, we attempted to validate these findings by assessing the effect of serum collected from first-onset drug-naïve schizophrenia patients (n=9), controls (n=12) as well as serum isolated from the same patients subjected to six weeks of clinical treatment with the antipsychotic olanzapine (n=9). This study aimed to identify normalisation of previously detected differences in microglia signalling pathways based on successful in vivo treatment. We demonstrate that peripheral blood serum isolated from schizophrenia patients, independent of their treatment status, is sufficient to trigger microglial cell signalling network responses in vitro, which are indicative of altered STAT3 signalling. We further explored the composition of the serum for differential expression of analytes, previously associated with neuropsychiatric disorders, and the utility of the detected microglial cellular phenotype as a target for novel drug discovery.

<div>G-quadruplexes (G4s) are non-canonical secondary structures formed in single-stranded guanine-rich nucleic acid sequences, such as those found in oncogene promoters and telomeres. <i>MYC</i>, one of the most critical oncogenes, has a DNA G4 (MycG4) in its proximal promoter region that functions as a transcriptional silencer. MycG4 is very stable and the pathological activation of <i>MYC</i> requires its active unfolding. However, it remains unclear what drives MycG4 unfolding in cancer cells. We have studied the interactions of DDX5 with the MycG4 at both molecular and cellular levels and discovered that DDX5 actively unfolds the MycG4 and is involved in the <i>MYC</i> gene transcriptional regulation, which is described in the first part of this dissertation. DDX5 is extremely proficient at unfolding the MycG4 and ATP hydrolysis is not directly coupled to the G4-unfolding of DDX5. In cancer cells, DDX5 is enriched at the <i>MYC</i> promoter and activates <i>MYC</i> transcription. G4-interactive small molecules inhibit the DDX5 interaction with the <i>MYC</i> promoter and DDX5-mediated <i>MYC</i> activation. The second part of this dissertation describes the study of interactions of indenoisoquinoline anticancer drugs with MycG4. The MycG4 transcriptional silencer is a very attractive therapeutic target. Compounds that bind and stabilize the MycG4 have been shown to repress <i>MYC</i> gene transcription and are antitumorigenic. Indenoisoquinolines are human topoisomerase I inhibitors in clinical testing. However, some indenoisoquinolines with potent anticancer activity do not exhibit strong topoisomerase I inhibition, suggesting a separate mechanism of action. Our studies show that indenoisoquinolines strongly bind and stabilize MycG4 and lower <i>MYC</i> levels in cancer cells. Moreover, the analysis of indenoisoquinoline analogues for their <i>MYC</i> inhibitory activity, topoisomerase I inhibitory activity, and anticancer activity reveals a synergistic effect of <i>MYC</i> inhibition and topoisomerase I inhibition on anticancer activity. Besides the MycG4, human telomeric G4s are also attractive targets for anticancer drugs due to their ability to inhibit telomere extension in cancer cells. The last part of this dissertation reviews two recent solution structural studies on small molecule complexes with the hybrid-2 telomeric G4 and the hybrid-1 telomeric G4. Structural information of those complexes can advance the design of telomeric G4-interactive small molecules in the cancer therapeutic areas.</div>