The development of hybridoma technology nearly forty years ago set the foundation for the use of antibodies in the life sciences. Subsequent advances in recombinant DNA technology have allowed us to adapt antibody genes to various screening systems, greatly increasing the throughput and specialized applications for which these complex biomolecules can be adapted. While selection systems are a powerful tool for discovery and evolution, they can be slow and prone to unintended biases. We see computational approaches as an efficient process for rapid discovery and engineering of antibodies. This is particularly relevant for biodefense and emerging infectious disease applications, for which time is a valuable commodity. In the first chapter of this work, we examine computational protocols for ‘supercharging’ proteins. This process resurfaces the target protein, adding charged moieties to impart specialized functions such as thermoresistance and cell penetration. Current algorithms for resurfacing proteins are static, treating each mutation as an event within a vacuum. The net result is that while several variants can be created, each must be tested experimentally to ensure the resultant protein is functional. In many cases, the designed proteins were severely impaired or incapable of folding. We hypothesize that a more dynamic approach, keeping an eye on energetics and the consequences of mutations will yield a more efficient and robust method for supercharging, successfully adding charges to proteins while minimizing deleterious effects. We continue on this theme applying the successful algorithm to supercharging antibodies for increased function. Utilizing the MS2 model biosensor system, we rationally engineer charges onto the surface of an antibody fragment, increasing thermoresistance, minimizing destabilizing effects, and in some cases actually increasing affinity. Finally, we apply next-generation sequencing approaches to the rapid discovery of antibodies directed against the Zaire Ebolavirus species. We utilize a local immunization strategy to generate a polarized antibody repertoire that is then sequenced to provide a database of antigen-specific variants. This repertoire is probed in silico and individual antibodies selected for analysis, bypassing time- and resource-consuming selection experiments. / text

Kinetic target-guided synthesis (TGS) and in situ click chemistry are among unconventional discovery strategies having the potential to streamline the development of protein-protein interaction modulators (PPIMs). In kinetic TGS and in situ click chemistry, the target is directly involved in the assembly of its own potent, bidentate ligand from a pool of reactive fragments. Herein, we report the use and validation of kinetic TGS based on the sulfo-click reaction between thio acids and sulfonyl azides as a screening and synthesis platform for the identification of high-quality PPIMs. Starting from a randomly designed library consisting of nine thio acids and nine sulfonyl azides leading to eighty one potential acylsulfonamides, the target protein, Bcl-XL selectively assembled four PPIMs, acylsulfonamides SZ4TA2, SZ7TA2, SZ9TA1, and SZ9TA5, which have been shown to modulate Bcl-XL/BH3 interactions. To further investigate the Bcl-XL templation effect, control experiments were carried out using two mutants of Bcl-XL. In one mutant, phenylalanine Phe131 and aspartic acid Asp133, which are critical for the BH3 domain binding, have been substituted by alanines, while arginine Arg139, a residue identified to play a crucial role in the binding of ABT-737, a BH3 mimetic, has been replaced by an alanine in the other mutant. Incubation of these mutants with the reactive fragments and subsequent LC/MS-SIM analysis confirmed that these building block combinations yield the corresponding acylsulfonamides at the BH3 binding site, the actual "hot spot" of Bcl-XL. These results validate kinetic TGS using the sulfo-click reaction as a valuable tool for the straightforward identification of high-quality PPIMs. Protein-protein interactions of the Bcl-2 family have been extensively investigated and the anti-apoptotic proteins (Bcl-2, Bcl-XL, and Mcl-1) have been validated as crucial targets for the discovery of potential anti-cancer agents. At the outset, Bcl-2 and Bcl-XL were considered to play an important role in the regulation of apoptosis. Accordingly, several small molecule inhibitors targeting Bcl-2 and/or Bcl-XL proteins were primarily designed. A series of acylsulfonamides targeting these proteins were reported by Abbott laboratories, ABT-737 and ABT-263 being the most potent candidates. Remarkably, these molecules were found to exhibit weaker binding affinities against Mcl-1, another anti-apoptotic protein. Further experimental evidence suggests that, inhibitors targeting Mcl-1 selectively or in combination with other anti-apoptotic proteins would lead to desired therapeutic effect. As a result, numerous small molecules displaying activity against Mcl-1 have been identified so far. Specifically, acylsulfonamides derived from structure activity relationship by interligand nuclear overhauser effect (SAR by ILOEs), a fragment-based approach, have been recently reported with binding affinities in the nanomolar range. In the meantime, we have reported that the kinetic TGS approach can also be applied to identify acylsulfonamides as PPIMs targeting Bcl-XL. Taken together, structurally novel acylsulfonamides can be potentially discovered as Mcl-1 inhibitors using the kinetic TGS approach. Thus, a library of thirty one sulfonyl azides and ten thio acids providing three hundred and ten potential products was screened against Mcl-1 and the kinetic TGS hits were identified. Subsequently, control experiments involving Bim BH3 peptide were conducted to confirm that the fragments are assembled at the binding site of the protein. The kinetic TGS hits were then synthesized and subjected to the fluorescence polarization assay. Gratifyingly, activities in single digit micromolar range were detected, demonstrating that the sulfo-click kinetic TGS approach can also be used for screening and identification of acylsulfonamides as PPIMs targeting Mcl-1. The amide bond serves as one of nature's most fundamental functional group and is observed in a large number of organic and biological molecules. Traditionally, the amide functionality is introduced in a molecule through coupling of an amine and an activated carboxylic acid. Recently, various alternative methods have been reported wherein, the aldehydes or alcohols are oxidized using transition metal catalysts and are treated with amines to transform into the corresponding amides. These transformations however, require specially designed catalysts, long reaction times and high temperatures. We herein describe a practical and efficient amidation reaction involving aromatic aldehydes and various azides under mild basic conditions. A broad spectrum of functional groups was tolerated, demonstrating the scope of the reaction. Consequently, the amides were synthesized in moderate to excellent yields, presenting an attractive alternative to the currently available synthetic methods.

Current home networks typically involve two or more machines sharing network resources. The vision for the home network has grown from a simple computer network, to every day appliances embedded with network capabilities. In this environment devices and services within the home can interoperate, regardless of protocol or platform. Network clients can discover required resources by performing network discovery over component descriptions. Common approaches to this discovery process involve simple matching of keywords or attribute/value pairings. Interest emerging from the Semantic Web community has led to ontology languages being applied to network domains, providing a logical and semantically rich approach to both describing and discovering network components. In much of the existing work within this domain, developers have focused on defining new description frameworks in isolation from existing protocol frameworks and vocabularies. This work proposes an ontology-based description framework which takes the ontology approach to the next step, where existing description frameworks are in- corporated into the ontology-based framework, allowing discovery mechanisms to cover multiple existing domains. In this manner, existing protocols and networking approaches can participate in semantically-rich discovery processes. This framework also includes a system architecture developed for the purpose of reconciling existing home network solutions with the ontology-based discovery process. This work also describes an implementation of the approach and is deployed within a home-network environment. This implementation involves existing home networking frameworks, protocols and components, allowing the claims of this work to be examined and evaluated from a ‘real-world’ perspective.

In efforts to search for tool compounds that are capable of probing normal and disease-associated biological processes, both quality and identity of the screening collection are very important. Towards this goal, diversity-oriented synthesis (DOS) has been explored for a decade, which aims to populate the chemical space with diverse sets of small molecules distinct from the traditional ones obtained via combinatorial chemistry. In the practice of DOS, macrocyclic ring-closing metathesis (RCM) reactions have been widely used. However, the prediction and control of stereoselectivity of the reaction is often challenging; chemical transformation of the olefin moiety within the product is in general limited. Chapter I of this thesis describes a methodology that addresses both problems simultaneously and thus extends the utility of the RCM reactions. By installing a silyl group at the internal position of one of the olefin termini, the RCM reaction could proceed with high stereoselectivity to afford the (E)-alkenylsiloxane regardless of the intrinsic selectivity of the substrate. The resulting alkenylsiloxane can be transformed to a variety of functionalities in a regiospecific fashion. The conversion of the (E)-alkenylsiloxanes to alkenyl bromides could proceed with inversion of stereochemistry for some substrates allowing the selective access of both the E- and Z-trisubstituted macrocyclic alkenes. It was also found that the silyl group could trap the desired mono-cyclized product by suppressing nonproductive pathways. Chapter II of this thesis describes the application of the concept of DOS in the area of fragment-based drug discovery. Most fragment libraries used to date have been limited to aromatic heterocycles with an underrepresentation of chiral, enantiopure, \(sp^3\)-rich compounds. In order to create a more diverse fragment collection, the build/couple/pair algorithm was adopted. Starting from proline derivatives, a series of bicyclic compounds were obtained with complete sets of stereoisomers and high \(sp^3\) ratio. Efforts are also described toward the generation of diverse fragments using methodology described in Chapter I. The glycogen synthase kinase \((GSK3\beta)\) was selected as the proof-of-concept target for screening the DOS fragments. / Chemistry and Chemical Biology

published_or_final_version / Education / Master / Master of Education

Pleckstrin homology (PH) domains are structurally conserved domains, which generally bind to phosphatidylinositol phosphate (PtdInsP) lipids. They are present in a variety of proteins, including those that are upregulated in cancer growth and metastasis, and represent a crucial component of intracellular signaling cascades and membrane translocation. Thus, they may be considered as attractive targets for cancer drug therapy. AKT (protein kinase B), a pleckstrin homology lipid binding domain and a serine/threonine kinase-containing protein, is a key component of the phophatidylinositol-3-kinase (PI3K)/AKT cell survival signaling pathway which is activated in a variety of cancers, including prostate, pancreatic, and skin cancers. In this study, I report the finding of a novel inhibitor of AKT; PH-427. I describe its effects on binding to the PH domain of AKT thus preventing its binding to PtdIns3-P at the plasma membrane and subsequent activation. In vivo testing of the drug led to reduction of tumor size and numbers in a mouse pancreatic cancer model. Additional testing of PH-427 on squamous cell carcinomas revealed that the drug is able to reduce tumor burden and multiplicity in vivo when topically applied. Thus, we demonstrate proof-of-principle in targeting PH domains as a viable cancer drug therapy option. The effects of PH-427 raised the intriguing possibility that targeting PH domains may have beneficial effects in other signaling pathways with PH domain-containing proteins. Guanine exchange factors (GEFs) contain a Dbl homology (DH) domain and a PH domain and have been shown to be involved in the process of metastasis. More specifically, RacGEFs activate Rac1 GTPase by facilitating the exchange of GDP to GTP. Over-expression of certain GEFs has been shown to contribute to increased malignancy in a variety of cancers. T-lymphoma invasion and metastasis-inducing protein-1 (Tiam1) is a highly conserved GEF and contains an N-terminal pleckstrin homology domain (nPH) and a DH/C-terminal PH domain (cPH). Tiam1 has been found to be over-expressed in several cancers, including breast, colon and prostate cancers. In this study, I describe the identification, development, experimental testing, and potential mechanism of action of novel small molecule inhibitors targeting the RacGEF Tiam1 to inhibit prostate cancer bone metastasis.

Η παρούσα διατριβή ασχολήθηκε με την εφαρμογή τεχνικών data mining σε συστήματα ηλεκτρονικού εμπορίου. Για να είμαστε πιο ακριβείς επικεντρωθήκαμε στην εύρεση κανόνων συσχετίσεων από δεδομένα, και κύρια δεδομένα που είχαν να κάνουν με βάσεις συναλλαγών. Η βασική ιδέα ενός κανόνα συσχετίσεως είναι να αναπτύξει μια συστηματική μέθοδο με την οποία ένας χρήστης μπορεί να προβλέψει την εμφάνιση κάποιων αντικειμένων, δοσμένης της ύπαρξης κάποιων άλλων σε μια συναλλαγή, και συνήθως αποτελούν συνεπαγωγές της μορφής Χ=>Y. Παράδειγμα ενός τέτοιου κανόνα είναι: “οι πελάτες που αγοράζουν κινητά τηλέφωνα και handsfree αγοράζουν και θήκη για το κινητό τους”. Τα τελευταία χρόνια είχε γίνει κοινός τόπος όλων των μελετών και των ερευνητών οι αδυναμίες και τα μειονεκτήματα του μοντέλου εύρεσης κανόνων συσχετίσεων. Στόχος μας ήταν να επιλύσουμε υπάρχοντα προβλήματα αλλά και να εκθέσουμε και να αντιμετωπίσουμε κάποια νέα. Σαν σύγγραμμα η παρούσα διατριβή μπορεί να χωριστεί σε τρία κομμάτια. Το πρώτο είναι τα τρία πρώτα κεφάλαια, τα οποία και αποτελούν εισαγωγικά κεφάλαια απαραίτητα για την υποστήριξη και κατανόηση της δουλειάς μας. Ακολούθως τα κεφάλαια 4 έως 8 αποτελούν το δεύτερο και κύριο κομμάτι της παρούσας διατριβής, και περιγράφουν διάφορες τεχνικές και προτάσεις μας, αποτελέσματα της ερευνάς μας. Το τρίτο και τελευταίο κομμάτι της διατριβής, αναφορικά το Κεφάλαιο 9, αποτελεί την σύνοψη ολόκληρης της εργασίας μας όπου παραθέτουμε εν συντομία την τελική προσφορά μας στο χώρο, δίνουμε πιθανές εφαρμογές των προτάσεων μας, και τέλος προτείνουμε μελλοντικές κατευθύνσεις της έρευνας σε ανοιχτά πεδία – προβλήματα. / -

In 2011, over 1.5 million new book titles were published in the United States, a 400% increase in just five years compared to 2006. In the same time period, the market share for eBooks increased dramatically and now comprises 20% or more of sales from many of the biggest publishing companies. This hyper-abundance of titles in an increasingly heterogeneous market place has made it difficult for consumers to connect to books they might want to read. This is the discovery problem. It is compounded by the continuing decline of traditional gatekeepers and sources of discovery such as mass media reviews and advertising, as well as the decline of traditional bookstores where people often find books through browse. Authors and publishers therefore have turned to social media to spread the word about their titles. Social gatekeeping, an extension of traditional gatekeeping theory, is proposed as the framework for understanding how author participation in social networks initiates a flow of the diffusion of information over the web and other computer mediated communication channels, and through individuals and social networks to potential readers. Serendipitous browse and discovery is a key strategy for readers to find titles of interest, and the serendipitous tie is proposed as a social mechanism through which individuals discover new titles and bring it back to their social networks to share. To explore these concepts, a random sample of new eBook titles published during the first week of April, 2012 was generated and analyzed in three phases. The first phase of research classified books and authors according to facets such as traditional or self-published, use of social media and other factors. The second phase used multiple regression to establish an association between the use of social media by authors and a title's sales and presence on the Web. The third phase reviewed selected titles for new approaches to social media use and evidence of the serendipitous tie. The results are consistent with the hypothesis that author web presence predicts discoverability and sales.

The advance of information technologies (IT) makes it possible to collect a massive amount of data in business applications and information systems. The increasing data volumes require more effective knowledge discovery techniques to make the best use of the data. This dissertation focuses on knowledge discovery on graph-structured data, i.e., graph-based learning. Graph-structured data refers to data instances with relational information indicating their interactions in this study. Graph-structured data exist in a variety of application areas related to information systems, such as business intelligence, knowledge management, e-commerce, medical informatics, etc. Developing knowledge discovery techniques on graph-structured data is critical to decision making and the reuse of knowledge in business applications.In this dissertation, I propose a graph-based learning framework and identify four major knowledge discovery tasks using graph-structured data: topology description, node classification, link prediction, and community detection. I present a series of studies to illustrate the knowledge discovery tasks and propose solutions for these example applications. As to the topology description task, in Chapter 2 I examine the global characteristics of relations extracted from documents. Such relations are extracted using different information processing techniques and aggregated to different analytical unit levels. As to the node classification task, Chapter 3 and Chapter 4 study the patent classification problem and the gene function prediction problem, respectively. In Chapter 3, I model knowledge diffusion and evolution with patent citation networks for patent classification. In Chapter 4, I extend the context assumption in previous research and model context graphs in gene interaction networks for gene function prediction. As to the link prediction task, Chapter 5 presents an example application in recommendation systems. I frame the recommendation problem as link prediction on user-item interaction graphs, and propose capturing graph-related features to tackle this problem. Chapter 6 examines the community detection task in the context of online interactions. In this study, I propose to take advantage of the sentiments (agreements and disagreements) expressed in users' interactions to improve community detection effectiveness. All these examples show that the graph representation allows the graph structure and node/link information to be more effectively utilized in addressing the four knowledge discovery tasks.In general, the graph-based learning framework contributes to the domain of information systems by categorizing related knowledge discovery tasks, promoting the further use of the graph representation, and suggesting approaches for knowledge discovery on graph-structured data. In practice, the proposed graph-based learning framework can be used to develop a variety of IT artifacts that address critical problems in business applications.

Given the vast amount of online information covering almost all aspects of human endeavor, the Internet, especially the Web, is clearly a fertile ground for data mining research from which to extract valuable knowledge. Web mining is the application of data mining techniques to extract knowledge from Web data, including Web documents, Web hyperlink structure, and Web usage logs.Traditional Web mining research has been mainly focused on addressing the information overload problem. Many information retrieval (IR) and artificial intelligence (AI) techniques have been adopted or developed to identify relevant information from the Web to meet users' specific information needs. However, most existing studies do not fully explore the social and behavioral aspects of the Web. Thus, the primary goal of this dissertation is to develop an integrated research framework that extends traditional Web mining methodologies to fully explore the technical, social, and behavioral aspects of Web knowledge discovery.My dissertation framework is composed of technical and social/behavioral components. In the technical component of my dissertation, a set of domain specific Web collection building, Web content and link structure mining, and Web knowledge presentation techniques were developed. These techniques were tested in a series of case studies to demonstrate their effectiveness and efficiency in facilitating knowledge discovery in various domains.The social/behavioral component of my dissertation is to explore the application of Web mining technology as a new means to study the social interactions and behavior of Web content providers and users. Several case studies were conducted to extract knowledge on covert organizations' resource allocation plans, information management policies, and technical sophistication using Web mining techniques. Such knowledge would be very difficult to obtain through other means.The major contributions of this dissertation are twofold. First, it proposed a set of new Web mining techniques that can help facilitate knowledge discovery in various domains. Second, it demonstrated the effectiveness and efficiency of applying Web mining techniques in extracting social and behavioral knowledge in different contexts.