La matrice extracellulaire (MEC) intervient dans de nombreux processus biologiques tels que la migration, la différentiation ou l’adhésion cellulaire. Elle est également associée à plusieurs évènements pathologiques. La cohésion de la MEC est assurée par un réseau organisé et complexe de protéines présent au voisinage immédiat des cellules. Ce projet a pour objectif de contribuer à la caractérisation structurale et fonctionnelle de certaines de ces complexes protéiques. Le Gelatin Binding Domain (GBD) (⁶FI¹²FII ⁷⁸⁹FI), localisé dans la région N-terminale de la fibronectine est connu pour interagir avec la transglutaminase 2 (TG2), le collagène de type I, ou encore des protéines d’adhésion bactériennes tel que la FNE (protéine de Streptococcus equi). Mes travaux de thèse portent donc sur la caractérisation fonctionnelle et structurale de ces interactions par des approches biophysiques et biochimiques. Ce travail a permis de cartographier les régions d’interactionentre la TG2 et le GBD d’une part et la FNE et le GBD d’autre part. Nous avons par la suite entrepris une étude par SAXS des complexes TG2/GBD et FNE/GBD et réussi à établir des modèles structuraux d’interaction entre (1) le GBD et le domaine N-terminal de la TG2 et (2) entre la FNE et le sous fragment ⁷⁸⁹FI du GBD. La structure tridimensionnelle de la protéine FNE a été résolue par cristallographie aux rayons X grâce à l’utilisation d’un outil original facilitant l’obtention de cristaux. / The extracellular matrix (ECM) is involved in a number of biological pathways associated with the cell migration, differentiation, adhesion and is also implicated in several pathological events. The cohesion of the ECM is accomplished by a highly organized protein complex network on the cell surface. The Gelatin Binding Domain (GBD) (⁶FI¹²FII ⁷⁸⁹FI) of the N-terminal region of fibronectin is found to interact with the transglutaminase 2 (TG2), collagen type I and the bacterial adhesion protein FNE. In this study, we conducted the structural and functional characterization of the protein complexes involved in the cohesion of ECM. The interactions between either TG2 or FNE and GBD have been characterized and the regions responsible for the interactions have also been mapped. Furthermore, we studied TG2/GBD and FNE/GBD complex by SAXS and built two models underscoring the interactions between (1), the GBD and the Nterminus of TG2 and (2), FNE and the sub-fragment ⁷⁸⁹FI of GBD providing insights on mechanistically elucidating the protein interactions during the cohehsion of ECM. The X-ray structure of the protein FNE of Streptococcus equi has been determined at 1.8 Å, by using an original tool that facilitates obtaining crystals.

Hao, Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (leaves 98-115). / Abstracts in English and Chinese. / Acknowledgement --- p.i / Abstract --- p.iii / 摘要 --- p.v / List of Abbreviations --- p.vii / List of Figures --- p.x / List of Tables --- p.xi / List of Primers --- p.xii / Publications arising from this study --- p.xiii / Chapter Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Alzheimer's disease --- p.1 / Chapter 1.2 --- APP and its functions --- p.4 / Chapter 1.2.1 --- APP processing --- p.7 / Chapter 1.3 --- APPc-interacting proteins --- p.10 / Chapter 1.3.1 --- FE65 --- p.10 / Chapter 1.3.2 --- Xllα and Xl1β --- p.12 / Chapter 1.3.3 --- JIP-1 --- p.13 / Chapter 1.3.4 --- Dabl and Dab2 --- p.15 / Chapter 1.3.5 --- SNX17 --- p.15 / Chapter 1.3.6 --- Numb --- p.15 / Chapter 1.3.7 --- AIDA-1 --- p.16 / Chapter 1.4 --- Objectives of the project --- p.18 / Chapter 1.4.1 --- Engulfment adaptor protein 1 (GULP1) --- p.19 / Chapter 1.4.2 --- Specific aims of my study --- p.20 / Chapter Chapter 2 --- General Methodology --- p.22 / Chapter 2.1 --- Bacterial culture --- p.22 / Chapter 2.2 --- Mini-preparation/Midi-preparation of plasmid DNA --- p.22 / Chapter 2.3 --- Spectrophotometric analysis of DNA --- p.22 / Chapter 2.4 --- Agarose gel electrophoresis for DNA --- p.23 / Chapter 2.5 --- Preparation of competent E. coli --- p.23 / Chapter 2.6 --- Transformation of competent E. coli --- p.24 / Chapter 2.7 --- Molecular cloning --- p.24 / Chapter 2.7.1 --- Preparation of the cloning vector and insert --- p.25 / Chapter 2.7.2 --- Isolation of DNA from agarose gel --- p.25 / Chapter 2.7.3 --- DNA ligation and transformation --- p.25 / Chapter 2.7.4 --- Rapid screening for ligated plasmid --- p.26 / Chapter 2.8 --- Site-directed mutagenesis --- p.26 / Chapter 2.9 --- Cell culture and transfection --- p.27 / Chapter 2.10 --- Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS/PAGE) --- p.28 / Chapter 2.11 --- Western blotting --- p.29 / Chapter Chapter 3 --- Investigation of the GULP1-APP interaction and the effect of GULP1 on APP processing --- p.31 / Chapter 3.1 --- Introduction --- p.31 / Chapter 3.2 --- Materials and methods --- p.34 / Chapter 3.2.1 --- DNA constructs --- p.34 / Chapter 3.2.2 --- Antibodies --- p.34 / Chapter 3.2.3 --- GST pull-down assays --- p.35 / Chapter 3.2.4 --- Rat tissues preparation --- p.36 / Chapter 3.2.5 --- Immunostaining --- p.36 / Chapter 3.2.6 --- "siRNA knockdown of GULPl in CHO, HEK293 and SHSY5Y cells" --- p.37 / Chapter 3.2.7 --- Luciferase assays --- p.37 / Chapter 3.2.9 --- Tricine-SDS/PAGE analysis for APP CTFs --- p.38 / Chapter 3.2.9 --- Aβ enzyme-linked immunosorbent assay (ELISA) --- p.39 / Chapter 3.2.10 --- Statistical analysis --- p.40 / Chapter 3.3 --- Results --- p.40 / Chapter 3.3.1 --- GULP1 F145V mutant abandons the GULP1-APP interaction --- p.40 / Chapter 3.3.2 --- GULP1 and APP colocalize in neurons --- p.45 / Chapter 3.3.3 --- "siRNA mediated knockdown of GULPl in CHO, HEK293 and SHSY5Y cells" --- p.48 / Chapter 3.3.4 --- GULP1 enhances the cleavage of APP in APP-GAL4 cleavage system --- p.49 / Chapter 3.3.5 --- GULP1 alters APP processing by increasing the secretion of APP CTFs --- p.52 / Chapter 3.3.6 --- GULP1 stimulates Aβ secretion --- p.55 / Chapter 3.4 --- Discussion --- p.57 / Chapter Chapter 4 --- Identification and characterization of GULPl phosphorylation sites --- p.60 / Chapter 4.1 --- Introduction --- p.60 / Chapter 4.2 --- Materials and Methods --- p.60 / Chapter 4.2.1 --- DNA constructs --- p.61 / Chapter 4.2.2 --- Antibodies --- p.61 / Chapter 4.2.3 --- Expression and purification of GST fusion proteins --- p.61 / Chapter 4.2.4 --- In vitro phosphorylation of GULP1 by cdk5/p35 --- p.62 / Chapter 4.3 --- Results --- p.62 / Chapter 4.3.1 --- GULP1 Ser223 can be phosphorylated by cdk5/p35 in vivo --- p.62 / Chapter 4.3.2 --- The phosphorylation ofGULPl Thr35 completely abolished the GULP1-APP interaction --- p.67 / Chapter 4.4 --- Discussion --- p.70 / Chapter Chapter 5 --- Crystallization of the PTB domains of GULPl and GULP1t35d…… --- p.72 / Chapter 5.1 --- Introduction --- p.72 / Chapter 5.2 --- Materials and Methods --- p.72 / Chapter 5.2.1 --- DNA constructs --- p.72 / Chapter 5.2.2 --- Small-scale protein expression and purification --- p.73 / Chapter 5.2.3 --- Large-scale protein expression and purification --- p.73 / Chapter 5.2.4 --- Dynamic light scattering measurement --- p.76 / Chapter 5.2.5 --- Crystallization screening GULP1-PTB --- p.76 / Chapter 5.2.6 --- Optimization of GULP1-PTB crystals by grid screen --- p.76 / Chapter 5.2.7 --- Optimization of GULPl -PTB crystals by additive screen and detergent screen --- p.79 / Chapter 5.3 --- Results --- p.79 / Chapter 5.3.1 --- Large-scale expression and purification of GULP 1-PTB --- p.79 / Chapter 5.3.2 --- Small-scale expression and purification of GULP1T35d-PTB --- p.86 / Chapter 5.3.3 --- Crystallization screening and optimization --- p.88 / Chapter 5.4 --- Discussion --- p.91 / Chapter Chapter 6 --- Conclusion and future perspective --- p.94 / Chapter 6.1 --- Conclusion --- p.94 / Chapter 6.2 --- Future perspective --- p.95 / References --- p.98

<p>The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented.</p><p>For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions.</p><p>A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands.</p><p>A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding.</p><p>The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.</p>

Im Mittelpunkt dieser Arbeit standen Signaltransduktionsprozesse in den Strukturen der Kraftübertragung quergestreifter Muskelzellen, d. h. in den Costameren (Zell-Matrix-Kontakten) und den Glanzstreifen (Zell-Zell-Kontakten der Kardiomyozyten).<br><br>Es ließ sich zeigen, dass sich die Morphologie der Zell-Matrix-Kontakte während der Differenzierung von Skelettmuskelzellen dramatisch ändert, was mit einer veränderten Proteinzusammensetzung einhergeht. Immunfluoreszenz-Analysen von Skelettmuskelzellen verschiedener Differenzierungsstadien implizieren, dass die Signalwege, welche die Dynamik der Fokalkontakte in Nichtmuskelzellen bestimmen, nur für frühe Stadien der Muskeldifferenzierung Relevanz haben können. Ausgehend von diesem Befund wurde begonnen, noch unbekannte Signalwege zu identifizieren, welche die Ausbildung von Costameren kontrollieren: In den Vorläuferstrukturen der Costamere gelang es, eine transiente Interaktion der Proteine Paxillin und Ponsin zu identifizieren. Biochemische Untersuchungen legen nahe, dass Ponsin über eine Skelettmuskel-spezifische Insertion im Carboxyterminus das Adapterprotein Nck2 in diesen Komplex rekrutiert. Es wird vorgeschlagen, dass die drei Proteine einen ternären Signalkomplex bilden, der die Umbauvorgänge der Zell-Matrix-Kontakte kontrolliert und dessen Aktivität von mitogen activated protein kinases (MAPK) reguliert wird.<br><br>Die Anpassungsvorgänge der Strukturen der Kraftübertragung an pathologische Situtation (Kardiomyopathien) in der adulten quergestreiften Muskulatur wurden ausgehend von einem zweiten Protein, dem muscle LIM protein (MLP), untersucht. Es konnte gezeigt werden, dass ein mutiertes MLP-Protein, das im Menschen eine hypertrophe Kardiomyopathie (HCM) auslöst, strukturelle Defekte aufweist und weniger stabil ist. Weiterhin zeigte dieses mutierte Protein eine verringerte Bindungsfähigkeit an die beiden Liganden N-RAP und alpha-Actinin. Die molekulare Grundlage der HCM-verursachenden Mutationen im MLP-Gen könnte folglich eine Veränderung der Homöostase im ternären Komplex MLP &ndash; N-RAP &ndash; alpha-Actinin sein. Die Expressionsdaten eines neu generierten monoklonalen MLP-Antikörpers deuten darauf hin, dass die Funktionen des MLP nicht nur für die Integrität des Myokards, sondern auch für die der Skelettmuskulatur notwendig sind. / The cell-matrix-contacts (costameres) and cell-cell-contacts (intercalated discs of cardiomyocytes) of cross-striated muscle cells transmit mechanical forces to the exterior. On top of this mechanical function, both structures have been implied to be involved in signal transduction processes.<br><br>Dramatic morphological changes in the overall structure of cell-matrix-contacts of skeletal muscle cells were revealed during differentiation. Moreover, this reorganisation was accompanied by alterations in protein composition. Immunofluorescence microscopy indicated that signalling pathways which control the dynamics of focal contacts in non-muscle cells seem to be important only for early differentiation stages of skeletal muscle cells. To explore novel signalling pathways involved in regulating the formation of costameres, signalling molecules engaged were identified. Thus, paxillin and ponsin transiently interact at the precursors of costameres during muscle development. In addition, biochemical data indicate that a skeletal muscle specific module in the carboxyterminal part of ponsin can recruit the adapter protein Nck2 to this complex. Hence, the three proteins might form a ternary signalling complex involved in controlling the reorganisation of cell-matrix-contacts. Apparently, the activity of this signalling complex is regulated by mitogen activated protein kinases (MAPK).<br><br>A second approach has focussed on adaptational processes of the same structures observed in pathological situations. In particular, the role of muscle LIM protein (MLP) in hypertrophic cardiomyopathy (HCM) was investigated. It was shown that a HCM-causing mutant MLP protein fails to fold properly and that the consequent loss of stability is reflected in altered binding properties: the mutant MLP protein shows decreased binding to both N-RAP and alpha-actinin. Hence, the molecular basis for HCM-causing mutations in the MLP gene might be an altered homeostasis of the ternary complex MLP &ndash; N-RAP &ndash; alpha-actinin. Increasing evidence indicates that the functions of MLP are required not only for the integrity of the myocardium. In addition, MLP seems to have regulatory functions in skeletal muscle tissues.

The ability to accurately predict binding free energies from computer simulations is an invaluable resource in understanding biochemical processes and drug action. Several methods based on microscopic molecular dynamics simulations exist, and in this thesis the validation, application, and development of the linear interaction energy (LIE) method is presented. For a test case of several hydrophobic ligands binding to P450cam it is found that the LIE parameters do not change when simulations are performed with three different force fields. The nonpolar contribution to binding of these ligands is best reproduced with a constant offset and a previously determined scaling of the van der Waals interactions. A new methodology for prediction of binding free energies of protein-protein complexes is investigated and found to give excellent agreement with experimental results. In order to reproduce the nonpolar contribution to binding, a different scaling of the van der Waals interactions is neccesary (compared to small ligand binding) and found to be, in part, due to an electrostatic preorganization effect not present when binding small ligands. A new treatment of the electrostatic contribution to binding is also proposed. In this new scheme, the chemical makeup of the ligand determines the scaling of the electrostatic ligand interaction energies. These scaling factors are calibrated using the electrostatic contribution to hydration free energies and proposed to be applicable to ligand binding. The issue of codon-anticodon recognition on the ribosome is adressed using LIE. The calculated binding free energies are in excellent agreement with experimental results, and further predict that the Leu2 anticodon stem loop is about 10 times more stable than the Ser stem loop in complex with a ribosome loaded with the Phe UUU codon. The simulations also support the previously suggested roles of A1492, A1493, and G530 in the codon-anticodon recognition process.

In the present thesis we have explored different factors that impede accurate quantitative description of non-covalent protein-protein and protein-ligand interactions and design of new potent and specific binders from the scratch. Firstly, we addressed the role of solvent in the mechanism of non-covalent interactions. Secondly, we tackled the question about the intrinsic conformational flexibility of the protein molecules and the part it plays in weak interactions between proteins. In the first part of the thesis we studied the interactions of vascular endothelial growth factor (VEGF) protein with five cyclic peptides in solution and gas phase. The results showed that affinities of five ligands to VEGF in solution and gas phase are ranked in inversed order. That is, the that has the highest affinity in solution (as shown by chemical shift perturbation NMR and isothermal titration calorimetry) forms the weakest complex with VEGF in gas phase, and vice versa. We compared gas-phase and solution binding affinities of of five peptides and made qualitative conclusions about the role of the solvent in protein-ligand interactions. In order to obtain more quantitative information about the gas-phase behavior of non-covalent complexes we have developed a combined experimental/theoretical approach to study the energetics of collisional activation of the ion prior to dissociation. We applied developed strategy to model CID in traveling wave ion guide (TWIG) collision cell. We validated the model on the CID of leu-enkephalin peptide and then applied developed strategy to five non-covalent protein-peptide complexes and found activation energies of their dissociation reactions. Next we applied ESI native MS to study the allosteric interactions between the molecular chaperonin GroEL and ATP. The obtained data allowed to construct a scheme of conformational transition of GroEL upon binding of ATP and distinguish between two different cooperativity models, providing strong arguments in favor of Monod-Wyman-Changeux (MWC) model. Finally, be studied the backbone dynamics of VEGF with a combination of NMR relaxation and all-atom force-field based normal mode analysis (NMA). We showed that combination of experimental and computational approach allows to identify flexible zones with higher level of confidence. We also found out that residues, that are involved VEGF-receptor interactions, reside in or close to the flexible zones, suggesting the critical role conformational plasticity plays in the non-covalent protein-protein interactions. / Las biomoléculas de los organismos vivos realizan sus funciones principalmente a través de interacciones débiles reversibles entre ellas. La transducción de señal, la replicación de ADN/ARN, otros procesos enzimáticos y, virtualmente, cualquier otro proceso involucrado en las funciones vitales de cualquier organismo vivo (de las simples amebas, al complejo ser humano), requiere que las moléculas “hablen” entre ellas. Dicho lenguaje se basa en interacciones no covalentes. La flexibilidad conformacional es una propiedad esencial de las grandes biomoléculas, y muchas de las funciones desempeñadas por proteínas se basan en su capacidad para cambiar de conformación en respuesta a un factor externo. Geométricamente hablando, la presencia de flexibilidad en una proteína obstaculiza el diseño racional de medicamentos porque posibilita la existencia de un número muy elevado de conformaciones de dicha proteína. Por este motivo, cualquier información sobre la flexibilidad de una proteína es sumamente valiosa para la comprensión de PPI y PLI y para el diseño racional de medicamentos. Los capítulos 1-3 de la presente tesis versan sobre la solvatación, mientras que la flexibilidad se estudiara en el capitulo 4.

Transport across the membrane of a cell is of crucial importance for cellular functions. The solute carrier family,SLC38 is a family of membrane proteins that transports various substances through the membrane and thusperforms many physiologically important functions, for example, transport of glutamine from astrocyte toneurons in the central nervous system. In this paper, we demonstrate that one of the transporters in this familynamed SLC38A6 forms several protein complexes with a variety of proteins in the membrane and in synapticvesicles, suggesting that SLC38A6 is involved in the synaptic release of neurotransmitters in synapses. Weperformed sensitive protein interaction analysis between the protein of interest and a variety of proteinsexpressed at different sites in the neuronal cell. We showed that SLC38A6 interacts with proteins in the cellmembrane as well as in the membrane of synaptic vesicles. The current theory is that SLC38A6 interact withthese proteins when the synaptic vesicles are in close proximity with the cell membrane during the release of theneurotransmitters.

Systems biology adopts an integrated approach to study and understand the function of biological systems, particularly, the response of such systems to perturbations, such as the inhibition of a reaction in a pathway, or the administration of a drug. The complexity and large scale of biological systems make modelling and simulation an essential and critical part of systems-level studies. Systems-level modelling of pathogenic organisms has the potential to significantly enhance drug discovery programmes. In this thesis, we show how systems--level models can positively impact anti-tubercular drug target identification. *Mycobacterium tuberculosis*, the principal aetiological agent of tuberculosis in humans, is estimated to cause two million deaths every year. The existing drugs, although of immense value in controlling the disease to some extent, have several shortcomings, the most important of them being the emergence of drug resistance rendering even the front-line drugs inactive. As drug discovery efforts are increasingly becoming rational, focussing at a molecular level, the identification of appropriate targets becomes a fundamental pre-requisite. We have constructed many system-level models, to identify drug targets for tuberculosis. We construct a constraint-based stoichiometric model of mycolic acid biosynthesis, and simulate it using flux balance analysis, to identify critical points in mycobacterial metabolism for targeting drugs. We then analyse protein--protein functional linkage networks to identify influential hubs, which can be targeted to disrupt bacterial metabolism. An important aspect of tuberculosis is the emergence of drug resistance. A network analysis of potential information pathways in the cell helps to identify important proteins as co-targets, targeting which could counter the emergence of resistance. We integrate analyses of metabolism, protein--protein interactions and protein structures to develop a generic drug target identification pipeline, for identifying most suitable drug targets. Finally, we model the interplay between the pathogen and the human immune system, using Boolean networks, to elucidate critical factors influencing the outcome of infection. The strategies described can be applied to understand various pathogens and can impact many drug discovery programmes.

Le docking moléculaire est une méthode permettant de prédire l'orientation d'une molécule donnée relativement à une autre lorsque celles-ci forment un complexe. Le premier algorithme de docking moléculaire a vu jour en 1990 afin de trouver de nouveaux candidats face à la protéase du VIH-1. Depuis, l'utilisation de protocoles de docking est devenue une pratique standard dans le domaine de la conception de nouveaux médicaments. Typiquement, un protocole de docking comporte plusieurs phases. Il requiert l'échantillonnage exhaustif du site d'interaction où les éléments impliqués sont considérées rigides. Des algorithmes de clustering sont utilisés afin de regrouper les candidats à l'appariement similaires. Des méthodes d'affinage sont appliquées pour prendre en compte la flexibilité au sein complexe moléculaire et afin d'éliminer de possibles artefacts de docking. Enfin, des algorithmes d'évaluation sont utilisés pour sélectionner les meilleurs candidats pour le docking. Cette thèse présente de nouveaux algorithmes de protocoles de docking qui facilitent la prédiction des structures de complexes protéinaires, une des cibles les plus importantes parmi les cibles visées par les méthodes de conception de médicaments. Une première contribution concerne l‘algorithme Docktrina qui permet de prédire les conformations de trimères protéinaires triangulaires. Celui-ci prend en entrée des prédictions de contacts paire-à-paire à partir d'hypothèse de corps rigides. Ensuite toutes les combinaisons possibles de paires de monomères sont évalués à l'aide d'un test de distance RMSD efficace. Cette méthode à la fois rapide et efficace améliore l'état de l'art sur les protéines trimères. Deuxièmement, nous présentons RigidRMSD une librairie C++ qui évalue en temps constant les distances RMSD entre conformations moléculaires correspondant à des transformations rigides. Cette librairie est en pratique utile lors du clustering de positions de docking, conduisant à des temps de calcul améliorés d'un facteur dix, comparé aux temps de calcul des algorithmes standards. Une troisième contribution concerne KSENIA, une fonction d'évaluation à base de connaissance pour l'étude des interactions protéine-protéine. Le problème de la reconstruction de fonction d'évaluation est alors formulé et résolu comme un problème d'optimisation convexe. Quatrièmement, CARBON, un nouvel algorithme pour l'affinage des candidats au docking basés sur des modèles corps-rigides est proposé. Le problème d'optimisation de corps-rigides est vu comme le calcul de trajectoires quasi-statiques de corps rigides influencés par la fonction énergie. CARBON fonctionne aussi bien avec un champ de force classique qu'avec une fonction d'évaluation à base de connaissance. CARBON est aussi utile pour l'affinage de complexes moléculaires qui comportent des clashes stériques modérés à importants. Finalement, une nouvelle méthode permet d'estimer les capacités de prédiction des fonctions d'évaluation. Celle-ci permet d‘évaluer de façon rigoureuse la performance de la fonction d'évaluation concernée sur des benchmarks de complexes moléculaires. La méthode manipule la distribution des scores attribués et non pas directement les scores de conformations particulières, ce qui la rend avantageuse au regard des critères standard basés sur le score le plus élevé. Les méthodes décrites au sein de la thèse sont testées et validées sur différents benchmarks protéines-protéines. Les algorithmes implémentés ont été utilisés avec succès pour la compétition CAPRI concernant la prédiction de complexes protéine-protéine. La méthodologie développée peut facilement être adaptée pour de la reconnaissance d'autres types d'interactions moléculaires impliquant par exemple des ligands, de l'ARN… Les implémentations en C++ des différents algorithmes présentés seront mises à disposition comme SAMSON Elements de la plateforme logicielle SAMSON sur http://www.samson-connect.net ou sur http://nano-d.inrialpes.fr/software. / Molecular docking is a method that predicts orientation of one molecule with respect to another one when forming a complex. The first computational method of molecular docking was applied to find new candidates against HIV-1 protease in 1990. Since then, using of docking pipelines has become a standard practice in drug discovery. Typically, a docking protocol comprises different phases. The exhaustive sampling of the binding site upon rigid-body approximation of the docking subunits is required. Clustering algorithms are used to group similar binding candidates. Refinement methods are applied to take into account flexibility of the molecular complex and to eliminate possible docking artefacts. Finally, scoring algorithms are employed to select the best binding candidates. The current thesis presents novel algorithms of docking protocols that facilitate structure prediction of protein complexes, which belong to one of the most important target classes in the structure-based drug design. First, DockTrina - a new algorithm to predict conformations of triangular protein trimers (i.e. trimers with pair-wise contacts between all three pairs of proteins) is presented. The method takes as input pair-wise contact predictions from a rigid-body docking program. It then scans and scores all possible combinations of pairs of monomers using a very fast root mean square deviation (RMSD) test. Being fast and efficient, DockTrina outperforms state-of-the-art computational methods dedicated to predict structure of protein oligomers on the collected benchmark of protein trimers. Second, RigidRMSD - a C++ library that in constant time computes RMSDs between molecular poses corresponding to rigid-body transformations is presented. The library is practically useful for clustering docking poses, resulting in ten times speed up compared to standard RMSD-based clustering algorithms. Third, KSENIA - a novel knowledge-based scoring function for protein-protein interactions is developed. The problem of scoring function reconstruction is formulated and solved as a convex optimization problem. As a result, KSENIA is a smooth function and, thus, is suitable for the gradient-base refinement of molecular structures. Remarkably, it is shown that native interfaces of protein complexes provide sufficient information to reconstruct a well-discriminative scoring function. Fourth, CARBON - a new algorithm for the rigid-body refinement of docking candidates is proposed. The rigid-body optimization problem is viewed as the calculation of quasi-static trajectories of rigid bodies influenced by the energy function. To circumvent the typical problem of incorrect stepsizes for rotation and translation movements of molecular complexes, the concept of controlled advancement is introduced. CARBON works well both in combination with a classical force-field and a knowledge-based scoring function. CARBON is also suitable for refinement of molecular complexes with moderate and large steric clashes between its subunits. Finally, a novel method to evaluate prediction capability of scoring functions is introduced. It allows to rigorously assess the performance of the scoring function of interest on benchmarks of molecular complexes. The method manipulates with the score distributions rather than with scores of particular conformations, which makes it advantageous compared to the standard hit-rate criteria. The methods described in the thesis are tested and validated on various protein-protein benchmarks. The implemented algorithms are successfully used in the CAPRI contest for structure prediction of protein-protein complexes. The developed methodology can be easily adapted to the recognition of other types of molecular interactions, involving ligands, polysaccharides, RNAs, etc. The C++ versions of the presented algorithms will be made available as SAMSON Elements for the SAMSON software platform at http://www.samson-connect.net or at http://nano-d.inrialpes.fr/software.

H παρούσα διατριβή εκπονήθηκε στο Εργαστήριο Αναγνώρισης Προτύπων, του Τμήματος Μηχανικών Ηλεκτρονικών Υπολογιστών και Πληροφορικής του Πανεπιστημίου Πατρών. Αποτελεί μέρος της ευρύτερης ερευνητικής δραστηριότητας του Εργαστηρίου στον τομέα του σχεδιασμού και της εφαρμογής των τεχνολογιών Υπολογιστικής Νοημοσύνης στην ανάλυση βιολογικών δεδομένων. Η διδακτορική αυτή διατριβή χρηματοδοτήθηκε από το πρόγραμμα Ηράκλειτος ΙΙ. Ο τομέας της πρωτεωμικής είναι ένα σχετικά καινούργιο και γρήγορα αναπτυσσόμενο ερευνητικό πεδίο. Μια από τις μεγαλύτερες προκλήσεις στον τομέα της πρωτεωμικής είναι η αναδόμηση του πλήρους πρωτεϊνικού αλληλεπιδραστικού δικτύου μέσα στα κύτταρα. Εξαιτίας του γεγονότος, ότι οι πρωτεϊνικές αλληλεπιδράσεις παίζουν πολύ σημαντικό ρόλο στις βασικές λειτουργίες ενός κυττάρου, η ανάλυση αυτών των δικτύων μπορεί να αποκαλύψει τον ρόλο αυτών των αλληλεπιδράσεων στις ασθένειες καθώς και τον τρόπο με τον οποίο οι τελευταίες αναπτύσσονται. Παρόλα αυτά, είναι αρκετά δύσκολο να καταγραφούν και να μελετηθούν οι πρωτεϊνικές αλληλεπιδράσεις ενός οργανισμού, καθώς το πρωτέωμα διαφοροποιείται από κύτταρο σε κύτταρο και αλλάζει συνεχώς μέσα από τις βιοχημικές του αλληλεπιδράσεις με το γονιδίωμα και το περιβάλλον. Ένας οργανισμός έχει ριζικά διαφορετική πρωτεϊνική έκφραση στα διάφορα σημεία του σώματός του, σε διαφορετικά στάδια του κύκλου ζωής του και υπό διαφορετικές περιβαλλοντικές συνθήκες. Δημιουργούνται, λοιπόν, δύο πάρα πολύ σημαντικοί τομείς έρευνας, που είναι, πρώτον, η εύρεση των πραγματικών πρωτεϊνικών αλληλεπιδράσεων ενός οργανισμού που θα συνθέσουν το πρωτεϊνικό δίκτυο αλληλεπιδράσεων και, δεύτερον, η περαιτέρω ανάλυση του πρωτεϊνικού δικτύου για εξόρυξη πληροφορίας (εύρεση πρωτεϊνικών συμπλεγμάτων, καθορισμός λειτουργίας πρωτεϊνών κτλ). Στην παρούσα διδακτορική διατριβή παρουσιάζονται καινοτόμες αλγοριθμικές τεχνικές Υπολογιστικής Νοημοσύνης για την πρόβλεψη πρωτεϊνικών αλληλεπιδράσεων, τον υπολογισμό ενός βαθμού εμπιστοσύνης για κάθε προβλεφθείσα αλληλεπίδραση, την πρόβλεψη πρωτεϊνικών συμπλόκων από δίκτυα πρωτεϊνικών αλληλεπιδράσεων και την πρόβλεψη της λειτουργίας πρωτεϊνών. Συγκεκριμένα, στο κομμάτι της πρόβλεψης και βαθμολόγησης πρωτεϊνικών αλληλεπιδράσεων αναπτύχθηκε μια πληθώρα καινοτόμων τεχνικών ταξινόμησης. Αυτές κυμαίνονται από υβριδικούς συνδυασμούς μετα-ευρετικών μεθόδων και ταξινομητών μηχανικής μάθησης, μέχρι μεθόδους γενετικού προγραμματισμού και υβριδικές μεθοδολογίες ασαφών συστημάτων. Στο κομμάτι της πρόβλεψης πρωτεϊνικών συμπλόκων υλοποιήθηκαν δύο βασικές καινοτόμες μεθοδολογίες μη επιβλεπόμενης μάθησης, οι οποίες θεωρητικά και πειραματικά ξεπερνούν τα μειονεκτήματα των υπαρχόντων αλγορίθμων. Για τις περισσότερες από αυτές τις υλοποιηθείσες μεθοδολογίες υλοποιήθηκαν φιλικές προς τον χρήστη διεπαφές. Οι περισσότερες από αυτές τις μεθοδολογίες μπορούν να χρησιμοποιηθούν και σε άλλους τομείς. Αυτό πραγματοποιήθηκε με μεγάλη επιτυχία σε προβλήματα βιοπληροφορικής όπως η πρόβλεψη microRNA γονιδίων και mRNA στόχων τους και η μοντελοποίηση - πρόβλεψη οικονομικών χρονοσειρών. Πειραματικά, η μελέτη αρχικά επικεντρώθηκε στον οργανισμό της ζύμης (Saccharomyces cerevisiae), έτσι ώστε να αξιολογηθούν οι αλγόριθμοι, που υλοποιήθηκαν και να συγκριθούν με τις υπάρχουσες αλγοριθμικές μεθοδολογίες. Στη συνέχεια, δόθηκε ιδιαίτερη έμφαση στις πρωτεΐνες του ανθρώπινου οργανισμού. Συγκεκριμένα, οι καλύτερες αλγοριθμικές τεχνικές για την ανάλυση δεδομένων πρωτεϊνικών αλληλεπιδράσεων εφαρμόστηκαν σε ένα σύνολο δεδομένων που δημιουργήθηκε για τον ανθρώπινο οργανισμό. Αυτό είχε σαν αποτέλεσμα την δημιουργία ενός πλήρους, σταθμισμένου δικτύου πρωτεϊνικών αλληλεπιδράσεων για τον άνθρωπο και την εξαγωγή των πρωτεϊνικών συμπλόκων, που υπάρχουν σε αυτό καθώς και τον λειτουργικό χαρακτηρισμό πολλών αχαρακτήριστων πρωτεϊνών. Τα αποτελέσματα της ανάλυσης των δεδομένων πρωτεϊνικών αλληλεπιδράσεων για τον άνθρωπο είναι διαθέσιμα μέσω μίας διαδικτυακής βάσης γνώσης HINT-KB (http://hintkb.ceid.upatras.gr), που υλοποιήθηκε στα πλαίσια αυτής της διδακτορικής διατριβής. Σε αυτή την βάση γνώσης ενσωματώνεται, από διάφορες πηγές, ακολουθιακή, δομική και λειτουργική πληροφορία για ένα τεράστιο πλήθος ζευγών πρωτεϊνών του ανθρώπινου οργανισμού. Επίσης, οι χρήστες μπορούν να έχουν προσβαση στις προβλεφθείσες πρωτεϊνικές αλληλεπιδράσεις και στον βαθμό εμπιστοσύνης τους. Τέλος, παρέχονται εργαλεία οπτικοποίησης του δικτύου πρωτεϊνικών αλληλεπιδράσεων, αλλά και εργαλεία ανάκτησης των πρωτεϊνικών συμπλόκων που υπάρχουν σε αυτό και της λειτουργίας πρωτεϊνών και συμπλόκων. Το προβλήματα με τα οποία καταπιάνεται η παρούσα διδακτορική διατριβή έχουν σημαντικό ερευνητικό ενδιαφέρον, όπως τεκμηριώνεται και από την παρατιθέμενη στη διατριβή εκτενή βιβλιογραφία. Μάλιστα, βασικός στόχος είναι οι παρεχόμενοι αλγόριθμοι και υπολογιστικά εργαλεία να αποτελέσουν ένα οπλοστάσιο στα χέρια των βιοπληροφορικάριων για την επίτευξη της κατανόησης των κυτταρικών λειτουργιών και την χρησιμοποίηση αυτής της γνώσης για γονιδιακή θεραπεία διαφόρων πολύπλοκων πολυπαραγοντικών ασθενειών όπως ο καρκίνος. Τα σημαντικόταρα επιτεύγματα της παρούσας διατριβής μπορούν να συνοψισθούν στα ακόλουθα σημεία: • Παροχή ολοκληρωμένης υπολογιστικής διαδικασίας ανάλυσης δεδομένων πρωτεϊνικών αλληλεπιδράσεων • Σχεδιασμός και υλοποίηση ευφυών τεχνικών πρόβλεψης και βαθμολόγησης πρωτεϊνικών αλληλεπιδράσεων, που θα παρέχουν αποδοτικά και ερμηνεύσιμα μοντέλα πρόβλεψης. • Σχεδιασμός και υλοποίηση αποδοτικών αλγορίθμων μη επιβλεπόμενης μάθησης για την εξόρυξη πρωτεϊνικών συμπλόκων από δίκτυα πρωτεϊνικών αλληλλεπιδράσεων. • Δημιουργία μιας βάσης γνώσης που θα παρέχει στην επιστημονική κοινότητα όλα τα ευρήματα της ανάλυσης των δεδομένων πρωτεϊνικών αλληλεπιδράσεων για τον ανθρώπινο οργανισμό. / The present dissertation was conducted in the Pattern Recognition Laboratory, of the Department of Computer Engineering and Informatics at the University of Patras. It is a part of the wide research activity of the Pattern Recognition Laboratory in the domain of designing, implementing and applying Computational Intelligence technologies for the analysis of biological data. The present dissertation was co-financed by the research program Hrakleitos II. The proteomics domain is a quite new and fast evolving research domain. One of the great challenges in the domain of proteomics is the reconstruction of the complete protein-protein interaction network within the cells. The analysis of these networks is able to uncover the role of protein-protein interactions in diseases as well as their developmental procedure, as protein-protein interactions play very important roles in the basic cellular functions. However, this is very hard to be accomplished as protein-protein interactions and the whole proteome is differentiated among cells and it constantly changes through the biochemical cellular and environment interactions. An organism has radically different protein expression in different tissues, in different phases of his life and under varying environmental conditions. Two very important domains of research are created. First, the identification of the real protein-protein interactions within an organism which will compose its protein interaction network. Second, the analysis of the protein interaction network to extract knowledge (search for protein complexes, uncovering of proteins functionality e.tc.) In the present dissertation novel algorithmic Computational Intelligent techniques are presented for the prediction of protein-protein interactions, the prediction of a confidence score for each predicted protein-protein interaction, the prediction of protein complexes and the prediction of proteins functionality. In particular, in the task of predicting and scoring protein-protein interactions, a wide range of novel classification techniques was designed and developed. These techniques range from hybrid combinations of meta-heuristic methods and machine learning classifiers, to genetic programming methods and fuzzy systems. For the task of predicting protein complexes, two novel unsupervised methods were designed and developed which theoretically and experimentally surpassed the limitations of existing methodologies. For most of the designed techniques user friendly interfaces were developed to allow their utilizations by other researchers. Moreover, many of the implemented techniques were successfully applied to other research domaines such as the prediction of microRNAs and their targets and the forecastment of financial time series. The experimental procedure, initially focused on the well studied organism of Yeast (Saccharomyces cerevisiae) to validate the performance of the proposed algorithms and compare them with existing computational methodologies. Then, it focuses on the analysis of protein-protein interaction data from the Human organism. In specific, the best algorithmic techniques, from the ones proposed in the present dissertation, were applied to a human protein-protein interaction dataset. This resulted to the construction of a weighted protein-protein interaction network of high coverage, to the extraction of human protein complexes and to the functional characterization of Human proteins and complexes. The results of the analysis of Human protein-protein interaction data are available in the web knowledge base HINT-KB (http://hintkb.ceid.upatras.gr) which was implemented during this dissertation. In this knowledge base, structural, functional and sequential information from various sources were incorporated for every protein pair. Moreover, HINTKB provide access to the predicted and scored protein-protein interactions and to the predicted protein complexes and their functional characterization. The problems which occupied the present dissertation have very significant research interest as it is proved by the provided wide bibliography. The basic goal is the provided algorithms and tools to contribute in the ultimate goal of systems biology to understand the cellular mechanisms and contribute in the development of genomic therapy of complex diseases such as cancer. The most important achievements of the present dissertation are summarized in the next points: • Providing an integrated computational framework for the analysis of protein-protein interaction data. • Designing and implementing intelligent techniques for predicting and scoring protein-protein interactions in an accurate and interpretable manner. • Designing and implementing effective unsupervised algorithmic techniques for extracting protein complexes and predicting their functionality. • Creating a knowledge base which will provide to the scientific community all the findings of the analysis conducted on the Human protein-protein interaction data.