Pluripotenz wird durch viele Stammzell-spezifische Transkriptionsfaktoren wie Oct3/4, Nanog und Sox2 sowie deren Funktion in ihrem regulatorischen Netzwerk etabliert und aufrechterhalten. Viele Studien haben gezeigt, wie diese Pluripotenz-assoziierten Faktoren ihre Zielgene regulieren. Dies geschieht durch die Interaktion mit bekannten und unbekannten Interaktionspartnern. In der vorliegenden Arbeit haben wir Zfp819 als einen neuen Pluripotenz-assoziierten Faktor beschrieben und dessen Funktion in pluripotenten Stammzellen untersucht. Im ersten Teil der vorliegenden Arbeit haben wir zwei cDNA-Banken für Yeast two Hybdrid (Y2H)-Assays aus unterschiedlichen pluripotenten Stammzelltypen generiert. Dies hatte zum Ziel, potentielle Interaktionspartner eines Kandidatenproteins zu identifizieren um dadurch Eindrücke über die Funktion des Proteins zu gewinnen. Für die Identifizierung von potentiellen Interaktionspartnern von Zfp819 haben wir die cDNA-Bank aus embryonalen Stammzellen benutzt. Wir konnten 17 putative Interaktionspartner identifizieren und daraus ein hypothetisches „Interaktom“ von Zfp819 generieren. Die Einordnung der putativen Interaktionspartner nach ihrer Gen-Ontologie (GO) ließ vermuten, dass Zfp819 eine Rolle in der Regulation der Transkription, der Aufrechterhaltung der genetischen Integrität und im Zellzyklus bzw. bei der Apoptose spielt. Im zweiten Teil der vorliegenden Arbeit wurde die sehr intensive Expression von Zfp819 in undifferenzierten pluripotenten Zelllinien gezeigt. Desweiteren konnte die Promotorregion von Zfp819 identifiziert werden, und es wurde gezeigt, dass diese mit epigenetischen Mustern ausgestattet ist. Zusätzlich konnten wir Regionen im Zfp819-Gen identifizieren, die für die nukleäre Lokalisation von Zfp819 verantwortlich sind. Desweiteren konnten wir zeigen, dass Zfp819 in der transkriptionellen Repression von spezifischen endogenen, retroviralen Elementen (ERVs) in pluripotenten Zellen eine Rolle spielt. Durch zelluläre und biochemische Studien konnten wir zeigen, dass Zfp819 mit vielen Proteinen interagiert (z.B. Kap1 und Chd4), welche für die Aufrechterhaltung der genomischen Integrität von Bedeutung sind. Tatsächlich resultierte der Verlust von Zfp819 in embryonalen Stammzellen in einer erhöhten Anfälligkeit für DNA-Schäden und in einer verminderten DNA-Reparatur. Zusammenfassend lassen die Identifizierung der Interaktionspartner sowie die Ergebnisse der molekularen und der funktionellen Studien vermuten, dass Zfp819 durch die Unterdrückung von ausgewählten ERVs eine Rolle in der Regulation der genomischen Stabilität von pluripotenten Zellen spielt.

Proteins are indispensable players in virtually all biological events. The functions of proteins are determined by their three dimensional (3D) structure and coordinated through intricate networks of protein-protein interactions (PPIs). Hence, a deep comprehension of such networks turns out to be crucial for understanding the cellular biology. Computational approaches have become critical tools for analysing PPI networks. In silico methods take advantage of the existing PPI knowledge to both predict new interactions and predict the function of proteins. Regarding the task of predicting PPIs, several methods have been already developed. However, recent findings demonstrate that such methods could take advantage of the knowledge on non-interacting protein pairs (NIPs). On the task of predicting the function of proteins,the Guilt-by-Association (GBA) principle can be exploited to extend the functional annotation of proteins over PPI networks. In this thesis, a new algorithm for PPI prediction and a protocol to complete cell signalling networks are presented. iLoops is a method that uses NIP data and structural information of proteins to predict the binding fate of protein pairs. A novel protocol for completing signalling networks –a task related to predicting the function of a protein, has also been developed. The protocol is based on the application of GBA principle in PPI networks. / Les proteïnes tenen un paper indispensable en virtualment qualsevol procés biològic. Les funcions de les proteïnes estan determinades per la seva estructura tridimensional (3D) i són coordinades per mitjà d’una complexa xarxa d’interaccions protiques (en anglès, protein-protein interactions, PPIs). Axí doncs, una comprensió en profunditat d’aquestes xarxes és fonamental per entendre la biologia cel•lular. Per a l’anàlisi de les xarxes d’interacció de proteïnes, l’ús de tècniques computacionals ha esdevingut fonamental als darrers temps. Els mètodes in silico aprofiten el coneixement actual sobre les interaccions proteiques per fer prediccions de noves interaccions o de les funcions de les proteïnes. Actualment existeixen diferents mètodes per a la predicció de noves interaccions de proteines. De tota manera, resultats recents demostren que aquests mètodes poden beneficiar-se del coneixement sobre parelles de proteïnes no interaccionants (en anglès, non-interacting pairs, NIPs). Per a la tasca de predir la funció de les proteïnes, el principi de “culpable per associació” (en anglès, guilt by association, GBA) és usat per extendre l’anotació de proteïnes de funció coneguda a través de xarxes d’interacció de proteïnes. En aquesta tesi es presenta un nou mètode pre a la predicció d’interaccions proteiques i un nou protocol basat per a completar xarxes de senyalització cel•lular. iLoops és un mètode que utilitza dades de parells no interaccionants i coneixement de l’estructura 3D de les proteïnes per a predir interaccions de proteïnes. També s’ha desenvolupat un nou protocol per a completar xarxes de senyalització cel•lular, una tasca relacionada amb la predicció de les funcions de les proteïnes. Aquest protocol es basa en aplicar el principi GBA a xarxes d’interaccions proteiques.

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Proteins have an indispensable role in the cell. They carry out a wide variety of structural, catalytic and signaling functions in all known biological systems. To perform their biological functions, proteins establish interactions with other bioorganic molecules including other proteins. Therefore, protein-protein interactions is one of the central topics in molecular biology. My thesis is devoted to three different topics in the field of protein-protein interactions. The first one focuses on solvent contribution to protein interfaces as it is an important component of protein complexes. The second topic discloses the structural and functional potential of fluorine's unique properties, which are attractive for protein design and engineering not feasible within the scope of canonical amino acids. The last part of this thesis is a study of the impact of charged amino acid residues within the hydrophobic interface of a coiled-coil system, which is one of the well-established model systems for protein-protein interactions studies. I. The majority of proteins interact in vivo in solution, thus studies of solvent impact on protein-protein interactions could be crucial for understanding many processes in the cell. However, though solvent is known to be very important for protein-protein interactions in terms of structure, dynamics and energetics, its effects are often disregarded in computational studies because a detailed solvent description requires complex and computationally demanding approaches. As a consequence, many protein residues, which establish water-mediated interactions, are neither considered in an interface definition. In the previous work carried out in our group the protein interfaces database (SCOWLP) has been developed. This database takes into account interfacial solvent and based on this classifies all interfacial protein residues of the PDB into three classes based on their interacting properties: dry (direct interaction), dual (direct and water-mediated interactions), and wet spots (residues interacting only through one water molecule). To define an interaction SCOWLP considers a donor–acceptor distance for hydrogen bonds of 3.2 Å, for salt bridges of 4 Å, and for van der Waals contacts the sum of the van der Waals radii of the interacting atoms. In previous studies of the group, statistical analysis of a non-redundant protein structure dataset showed that 40.1% of the interfacial residues participate in water-mediated interactions, and that 14.5% of the total residues in interfaces are wet spots. Moreover, wet spots have been shown to display similar characteristics to residues contacting water molecules in cores or cavities of proteins. The goals of this part of the thesis were: 1. to characterize the impact of solvent in protein-protein interactions 2. to elucidate possible effects of solvent inclusion into the correlated mutations approach for protein contacts prediction To study solvent impact on protein interfaces a molecular dynamics (MD) approach has been used. This part of the work is elaborated in section 2.1 of this thesis. We have characterized properties of water-mediated protein interactions at residue and solvent level. For this purpose, an MD analysis of 17 representative complexes from SH3 and immunoglobulin protein families has been performed. We have shown that the interfacial residues interacting through a single water molecule (wet spots) are energetically and dynamically very similar to other interfacial residues. At the same time, water molecules mediating protein interactions have been found to be significantly less mobile than surface solvent in terms of residence time. Calculated free energies indicate that these water molecules should significantly affect formation and stability of a protein-protein complex. The results obtained in this part of the work also suggest that water molecules in protein interfaces contribute to the conservation of protein interactions by allowing more sequence variability in the interacting partners, which has important implications for the use of the correlated mutations concept in protein interactions studies. This concept is based on the assumption that interacting protein residues co-evolve, so that a mutation in one of the interacting counterparts is compensated by a mutation in the other. The study presented in section 2.2 has been carried out to prove that an explicit introduction of solvent into the correlated mutations concept indeed yields qualitative improvement of existing approaches. For this, we have used the data on interfacial solvent obtained from the SCOWLP database (the whole PDB) to construct a “wet” similarity matrix. This matrix has been used for prediction of protein contacts together with a well-established “dry” matrix. We have analyzed two datasets containing 50 domains and 10 domain pairs, and have compared the results obtained by using several combinations of both “dry” and “wet” matrices. We have found that for predictions for both intra- and interdomain contacts the introduction of a combination of a “dry” and a “wet” similarity matrix improves the predictions in comparison to the “dry” one alone. Our analysis opens up the idea that the consideration of water may have an impact on the improvement of the contact predictions obtained by correlated mutations approaches. There are two principally novel aspects in this study in the context of the used correlated mutations methodology : i) the first introduction of solvent explicitly into the correlated mutations approach; ii) the use of the definition of protein-protein interfaces, which is essentially different from many other works in the field because of taking into account physico-chemical properties of amino acids and not being exclusively based on distance cut-offs. II. The second part of the thesis is focused on properties of fluorinated amino acids in protein environments. In general, non-canonical amino acids with newly designed side-chain functionalities are powerful tools that can be used to improve structural, catalytic, kinetic and thermodynamic properties of peptides and proteins, which otherwise are not feasible within the use of canonical amino acids. In this context fluorinated amino acids have increasingly gained in importance in protein chemistry because of fluorine's unique properties: high electronegativity and a small atomic size. Despite the wide use of fluorine in drug design, properties of fluorine in protein environments have not been yet extensively studied. The aims of this part of the dissertation were: 1. to analyze the basic properties of fluorinated amino acids such as electrostatic and geometric characteristics, hydrogen bonding abilities, hydration properties and conformational preferences (section 3.1) 2. to describe the behavior of fluorinated amino acids in systems emulating protein environments (section 3.2, section 3.3) First, to characterize fluorinated amino acids side chains we have used fluorinated ethane derivatives as their simplified models and applied a quantum mechanics approach. Properties such as charge distribution, dipole moments, volumes and size of the fluoromethylated groups within the model have been characterized. Hydrogen bonding properties of these groups have been compared with the groups typically presented in natural protein environments. We have shown that hydrogen and fluorine atoms within these fluoromethylated groups are weak hydrogen bond donors and acceptors. Nevertheless they should not be disregarded for applications in protein engineering. Then, we have implemented four fluorinated L-amino acids for the AMBER force field and characterized their conformational and hydration properties at the MD level. We have found that hydrophobicity of fluorinated side chains grows with the number of fluorine atoms and could be explained in terms of high electronegativity of fluorine atoms and spacial demand of fluorinated side-chains. These data on hydration agrees with the results obtained in the experimental work performed by our collaborators. We have rationally engineered systems that allow us to study fluorine properties and extract results that could be extrapolated to proteins. For this, we have emulated protein environments by introducing fluorinated amino acids into a parallel coiled-coil and enzyme-ligand chymotrypsin systems. The results on fluorination effect on coiled-coil dimerization and substrate affinities in the chymotrypsin active site obtained by MD, molecular docking and free energy calculations are in strong agreement with experimental data obtained by our collaborators. In particular, we have shown that fluorine content and position of fluorination can considerably change the polarity and steric properties of an amino acid side chain and, thus, can influence the properties that a fluorinated amino acid reveals within a native protein environment. III. Coiled-coils typically consist of two to five right-handed α-helices that wrap around each other to form a left-handed superhelix. The interface of two α-helices is usually represented by hydrophobic residues. However, the analysis of protein databases revealed that in natural occurring proteins up to 20% of these positions are populated by polar and charged residues. The impact of these residues on stability of coiled-coil system is not clear. MD simulations together with free energy calculations have been utilized to estimate favourable interaction partners for uncommon amino acids within the hydrophobic core of coiled-coils (Chapter 4). Based on these data, the best hits among binding partners for one strand of a coiled-coil bearing a charged amino acid in a central hydrophobic core position have been selected. Computational data have been in agreement with the results obtained by our collaborators, who applied phage display technology and CD spectroscopy. This combination of theoretical and experimental approaches allowed to get a deeper insight into the stability of the coiled-coil system. To conclude, this thesis widens existing concepts of protein structural biology in three areas of its current importance. We expand on the role of solvent in protein interfaces, which contributes to the knowledge of physico-chemical properties underlying protein-protein interactions. We develop a deeper insight into the understanding of the fluorine's impact upon its introduction into protein environments, which may assist in exploiting the full potential of fluorine's unique properties for applications in the field of protein engineering and drug design. Finally we investigate the mechanisms underlying coiled-coil system folding. The results presented in the thesis are of definite importance for possible applications (e.g. introduction of solvent explicitly into the scoring function) into protein folding, docking and rational design methods. The dissertation consists of four chapters: ● Chapter 1 contains an introduction to the topic of protein-protein interactions including basic concepts and an overview of the present state of research in the field. ● Chapter 2 focuses on the studies of the role of solvent in protein interfaces. ● Chapter 3 is devoted to the work on fluorinated amino acids in protein environments. ● Chapter 4 describes the study of coiled-coils folding properties. The experimental parts presented in Chapters 3 and 4 of this thesis have been performed by our collaborators at FU Berlin. Sections 2.1, 2.2, 3.1, 3.2 and Chapter 4 have been submitted/published in peer-reviewed international journals. Their organization follows a standard research article structure: Abstract, Introduction, Methodology, Results and discussion, and Conclusions. Section 3.3, though not published yet, is also organized in the same way. The literature references are summed up together at the end of the thesis to avoid redundancy within different chapters.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are the building blocks in a biological system. They interact with other proteins to make unique biological phenomenon. Protein-protein interactions play a valuable role in understanding the molecular mechanisms occurring in any biological system. Protein interaction databases are a rich source on protein interaction related information. They gather large amounts of information from published literature to enrich their data. Expert curators put in most of these efforts manually. The amount of accessible and publicly available literature is growing very rapidly. Manual annotation is a time consuming process. And with the rate at which available information is growing, it cannot be dealt with only manual curation. There need to be tools to process this huge amounts of data to bring out valuable gist than can help curators proceed faster. In case of extracting protein-protein interaction evidences from literature, just a mere mention of a certain protein by look-up approaches cannot help validate the interaction. Supporting protein interaction information with experimental evidence can help this cause. In this study, we are applying machine learning based classification techniques to classify and given protein interaction related document into an interaction detection method. We use biological attributes and experimental factors, different combination of which define any particular interaction detection method. Then using predicted detection methods, proteins identified using named entity recognition techniques and decomposing the parts-of-speech composition we search for sentences with experimental evidence for a protein-protein interaction. We report an accuracy of 75.1% with a F-score of 47.6% on a dataset containing 2035 training documents and 300 test documents.

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

Indiana University-Purdue University Indianapolis (IUPUI) / In mammalian cells DNA double strand breaks (DSBs) are highly variable with respect to sequence and structure all of which are recognized by the DNA- dependent protein kinase (DNA-PK), a critical component for the resolution of these breaks. Previously studies have shown that DNA-PK does not respond the same way to all DSBs but how DNA-PK senses differences in DNA substrate sequence and structure is unknown. Here we explore the enzymatic mechanism by which DNA-PK is activated by various DNA substrates. We provide evidence that recognition of DNA structural variations occur through distinct protein-protein interactions between the carboxy terminal (C-terminal) region of Ku80 and DNA-dependent protein kinase catalytic subunit (DNA-PKcs). Discrimination of terminal DNA sequences, on the other hand, occurs independently of Ku 80 C-terminal interactions and results exclusively from DNA-PKcs interactions with the DNA. We also show that sequence differences in DNA termini can drastically influence DNA repair through altered DNA-PK activation. Our results indicate that even subtle differences in DNA substrates influence DNA-PK activation and ultimately Non-homologous End Joining (NHEJ) efficiency.

Indiana University-Purdue University Indianapolis (IUPUI) / With effective antiretroviral therapy (ART), cardiovascular diseases (CVD), are emerging as a major cause of morbidity and death in the aging population with HIV infection. Although this increase in CVD could be partially explained by the toxic effects of combined anti-retroviral therapy (ART), more recently, HIV infection has emerged as an independent risk factor for CVD. However, it is unclear how HIV can contribute to CVD in patients on ART, when viral titers are low or non-detectable. Here, we provide several lines of evidence that HIV-Nef, produced in infected cells even when virus production is halted by ART, can lead to endothelial activation and dysfunction, and thus may be involved in CVD. We demonstrate that HIV-infected T cell-induced endothelial cell activation requires direct contact as well as functional HIV-Nef. Nef protein from either HIV-infected or Nef-transfected T cells rapidly transfers to endothelial cells while inducing nanotube-like conduits connecting T cells to endothelial cells. This transfer or transfection of endothelial cells results in endothelial apoptosis, ROS generation and release of monocyte attractant protein-1 (MCP-1). A Nef SH3 binding site mutant abolishes Nef-induced apoptosis and ROS formation and reduces MCP-1 production in endothelial cells, suggesting that the Nef SH3 binding site is critical for Nef effects on endothelial cells. Nef induces apoptosis of endothelial cells through both NADPH oxidase- and ROS-dependent mechanisms, while Nef-induced MCP-1 production is NF-kB dependent. Importantly, Nef can be found in CD4 positive and bystander circulating blood cells in patients receiving virally suppressive ART, and in the endothelium of chimeric SIV-infected macaques. Together, these data indicate that Nef could exert pro-atherogenic effects on the endothelium even when HIV infection is controlled and that inhibition of Nef-associated pathways may be promising new therapeutic targets for reducing the risk for cardiovascular disease in the HIV-infected population.

Indiana University-Purdue University Indianapolis (IUPUI) / Traditional treatment strategy development for diseases involves the identification of target proteins related to disease states, and the interference of these proteins with drug molecules. Computational drug discovery and virtual screening from thousands of chemical compounds have accelerated this process. The thesis presents a comprehensive framework of computational drug discovery using system biology approaches. The thesis mainly consists of two parts: disease biomarker identification and disease treatment discoveries. The first part of the thesis focuses on the research in biomarker identification for human diseases in the post-genomic era with an emphasis in system biology approaches such as using the protein interaction networks. There are two major types of biomarkers: Diagnostic Biomarker is expected to detect a given type of disease in an individual with both high sensitivity and specificity; Predictive Biomarker serves to predict drug response before treatment is started. Both are essential before we even start seeking any treatment for the patients. In this part, we first studied how the coverage of the disease genes, the protein interaction quality, and gene ranking strategies can affect the identification of disease genes. Second, we addressed the challenge of constructing a central database to collect the system level data such as protein interaction, pathway, etc. Finally, we built case studies for biomarker identification for using dabetes as a case study. The second part of the thesis mainly addresses how to find treatments after disease identification. It specifically focuses on computational drug repositioning due to its low lost, few translational issues and other benefits. First, we described how to implement literature mining approaches to build the disease-protein-drug connectivity map and demonstrated its superior performances compared to other existing applications. Second, we presented a valuable drug-protein directionality database which filled the research gap of lacking alternatives for the experimental CMAP in computational drug discovery field. We also extended the correlation based ranking algorithms by including the underlying topology among proteins. Finally, we demonstrated how to study drug repositioning beyond genomic level and from one dimension to two dimensions with clinical side effect as prediction features.