Protein-protein interactions (PPIs) are key drug targets and recent breakthroughs in this area are providing insight into the types of molecules needed to selectively and potently inhibit a target traditionally seen as untractable. The rules that have been used to design classic substratecompetitive drugs (for example Lipinski's rule of five) may not apply in this new field in the same way. Here I present work performed in three systems that are well-validated drug targets for oncogenesis: the CDK2/cyclin A complex, the PLK1 Polobox domain and MDM2. In each case the site of the protein-protein interaction is defined and understood and the rationale for pharmaceutical intervention is clear. I use these as a model system to evaluate the characteristics of drugs that target protein-protein interaction sites and present work on the development of inhibitors as potential leads for subsequent drug development. In Chapter 1 I introduce the problems, challenges and rewards of PPI drug development; in Chapter 2 I present co-crystal structures of MDM2 with isoindolinone inhibitors; in Chapter 3 I detail attempts to co-crystallise the Plk1 Polobox with inhibitors and screen potential inhibitors; in Chapter 4 I present the results of screening to identify inhibitors of Cyclin A recruitment; and in Chapter 5 I discuss other strategies for inhibition of the CDK2/cyclin A complex, including results with a covalent inhibitor. Through these projects I have been able to demonstrate the wide applicability of the PPI inhibition approach, identify key features of drugs able to inhibit PPIs and contribute to drug design in each system.

Successful bacterial circular chromosome segregation requires that any dimeric chromosomes, which arise by crossing over during homologous recombination, are converted to monomers. Resolution of dimers to monomers requires the action of the XerCD site-specific recombinase at dif in the chromosome replication terminus region. This reaction requires the DNA translocase, FtsK(C), which activates dimer resolution by catalysing an ATP hydrolysis-dependent switch in the catalytic state of the nucleoprotein recombination complex. We show that a 62-amino-acid fragment of FtsK(C) interacts directly with the XerD C-terminus in order to stimulate the cleavage by XerD of BSN, a dif-DNA suicide substrate containing a nick in the 'bottom' strand. The resulting recombinase-DNA covalent complex can undergo strand exchange with intact duplex dif in the absence of ATP. FtsK(C)-mediated stimulation of BSN cleavage by XerD requires synaptic complex formation. Mutational impairment of the XerD-FtsK(C) interaction leads to reduction in the in vitro stimulation of BSN cleavage by XerD and a concomitant deficiency in the resolution of chromosomal dimers at dif in vivo, although other XerD functions are not affected.

L'objectif de cette thèse est de développer des outils de prédiction d'interactions entre protéines qui puissent être appliqués en particulier sur le réseau d’interaction autour de la protéine CFTR, qui est impliquée dans la mucoviscidose. Le développement de méthodes de prédiction in silico peut s'avérer utile pour suggérer aux biologistes de nouvelles cibles d'interaction. Nous proposons une nouvelle méthode pour la prédiction de liens dans un réseau. Afin de bénéficier de l'information des données non étiquetées, nous nous plaçons dans le cadre de l'apprentissage semi-supervisé. Nous abordons ce problème de prédiction comme une tâche d'apprentissage d'un noyau de sortie. Un noyau de sortie est supposé coder les proximités existantes entres les nœuds du graphe et l'objectif est d'approcher ce noyau à partir de descriptions appropriées en entrée. L'utilisation de l'astuce du noyau dans l'ensemble de sortie permet de réduire le problème d'apprentissage à celui d'une fonction d'une seule variable à valeurs dans un espace de Hilbert. En choisissant les fonctions candidates pour la régression dans un espace de Hilbert à noyau reproduisant à valeur opérateur, nous développons, comme dans le cas de fonctions à valeurs scalaires, des outils de régularisation. Nous établissons en particulier des théorèmes de représentation, qui permettent de définir de nouveaux modèles de régression. Nous avons testé l'approche développée sur des données artificielles, des problèmes test ainsi que sur un réseau d'interaction chez la levure et obtenu de très bons résultats. Puis nous l'avons appliquée à la prédiction d'interactions entre protéines dans le cas d'un réseau construit autour de CFTR. / The aim of this thesis is to develop tools for predicting interactions between proteins that can be applied to the human proteins forming a network with the CFTR protein. This protein, when defective, is involved in cystic fibrosis. The development of in silico prediction methods can be useful for biologists to suggest new interaction targets. We propose a new method to solve the link prediction problem. To benefit from the information of unlabeled data, we place ourselves in the semi-supervised learning framework. Link prediction is addressed as an output kernel learning task, referred as Output Kernel Regression. An output kernel is assumed to encode the proximities of nodes in the target graph and the goal is to approximate this kernel by using appropriate input features. Using the kernel trick in the output space allows one to reduce the problem of learning from pairs to learning a single variable function with output values in a Hilbert space. By choosing candidates for regression functions in a reproducing kernel Hilbert space with operator valued kernels, we develop tools for regularization as for scalar-valued functions. We establish representer theorems in the supervised and semi-supervised cases and use them to define new regression models for different cost functions. We first tested the developed approach on transductive link prediction using artificial data, benchmark data as well as a protein-protein interaction network of the yeast and we obtained very good results. Then we applied it to the prediction of protein interactions in a network built around the CFTR protein.

A defining characteristic of neurodegenerative disease is the accumulation of mutant or misfolded proteins within neurons. Selective macroautophagy of aggregates, or aggrephagy, is a lysosome-mediated protein degradation pathway implicated in the turnover of disease-relevant accumulated proteins, but its specific function in vivo in the mammalian nervous system is poorly understood. The large PI3P-binding protein Alfy (Autophagy-linked FYVE protein) is an adaptor required for selective macroautophagy of aggregated proteins in cellular model systems. We sought to address Alfy-mediated aggrephagy in the mammalian brain in mouse models of Huntington’s disease (HD). HD is a neurodegenerative disorder caused by autosomal dominant inheritance of an expanded CAG repeat within the IT15, or huntingtin (htt) gene. The mutation causes an expansion of a polyglutamine (polyQ) tract in the protein Huntingtin (Htt), which results in psychiatric, cognitive, and motor symptomology. A pathological hallmark of HD is the accumulation of intracellular deposits of mutant Htt and ubiquitin. The exact relevance of these deposits remains unclear, but their elimination, hypothesized to occur via macroautophagy, correlates with behavioral improvements in mouse models of HD. The selective mechanisms of this phenomenon are largely unexplored in vivo. We have created two mouse models to address the role of Alfy-mediated selective macroautophagy in mammalian HD brain. First, we created tamoxifen-inducible Alfy knockout mice (Alfy iKO) and crossed them with a redesigned inducible HD mouse (HD103Q) that uses a tetracycline-regulated system to control reversible expression of mutant exon-1 Htt. Western blot, in situ, and PCR analysis confirm that Alfy can be eliminated from brain in adult Alfy iKO mice. A timecourse of Htt aggregation and clearance reveals that HD103Q mice accumulate huntingtin deposits, which clear in a linear manner upon transgene suppression over the course of four months. The loss of Alfy significantly impedes the removal of these deposits. Second, an Alfy knockout mouse was created using gene-trap technology, and mice hemizygous for Alfy knockout were crossed with BACHD mice expressing full-length human mutant Htt. We find that 50% Alfy depletion in the BACHD leads to increased insoluble Htt aggregate deposition along with accelerated decline in motor behavioral performance. Furthermore, inducible knockout of Alfy alone has a severe and age-dependent motor behavioral phenotype. This work reveals an in vivo role for Alfy in turnover of mutant Htt deposits, suggests that the accumulation of detergent-insoluble mutant Htt species contributes to behavioral pathogenesis, and supports an important function for Alfy at the intersection of HD and aging.

Knowledge of protein-protein interactions (PPIs) is essential to understanding regulatory processes in a cell. High-throughput experimental methods have made significant contributions to PPI determination, but they are known to have many false positives and fail to identify a signification portion of bona fide interactions. The same is true for the many computational tools that have been developed. Significantly, although protein structures provide atomic details of PPIs, they have had relatively little impact in large-scale PPI predictions and there has been only limited overlap between structural and systems biology. Here in this thesis, I present our progress in combining structural biology and systems biology in the context of studies analyzing, coarse-grained modeling and prediction of protein-protein interactions. I first report a comprehensive analysis of the degree to which the location of a protein interface is conserved in sets of proteins that share different levels of similarities. Our results show that while, in general, the interface conservation is most significant among close neighbors, it is still significant even for remote structural neighbors. Based on this finding, we designed PredUs, a method to predict protein interface simply by "mapping" the interface information from its structural neighbors (i.e., "templates") to the target structure. We developed the PredUs web server to predict protein interfaces using this "template-based" method and a support vector machine (SVM) to further improve predictions. The PredUs webserver outperforms other state-of-the-art methods that are typically based on amino acid properties in terms of both prediction precision and recall. Meanwhile, PredUs runs very fast and can be used to study protein interfaces in a high throughput fashion. Maybe more importantly, it is not sensitive to local conformational changes and small errors in structures and thus can be applied to predict interface of protein homology models, when experimental structures are not available. I then describe a novel structural modeling method that uses geometric relationships between protein structures, including both PDB structures and homology models, to accurately predict PPIs on a genome-wide scale. We applied the method with considerable success to both the yeast and the human genomes. We found that the accuracy and the coverage of our structure-based prediction compare favorably with the methods derived from sequence and functional clues, e.g. sequence similarity, co-expression, phylogenetic similarity, etc. Results further improve when using a naive Bayesian classifier to combine structural information with non-structural clues (PREPPI), yielding predictions of comparable quality to high-throughput experiments. Our data further suggests that PREPPI predictions are substantially complementary to those by experimental methods thus providing a way to dissect interactions that would be hard to identify on a purely high-throughput experimental basis. We have for the first time designed a "template-based" method that predicts protein interface with high precision and recall. We have also for the first time used 3D structure as part of the repertoire of experimental and computational information and find a way to accurately infer PPIs on a large scale. The success of PredUs and PREPPI can be attributed to the exploitation of both the information contained in imperfect models and the remote structure-function relationships between proteins that have been usually considered to be unrelated. Our results constitute a significant paradigm shift in both structural and systems biology and suggest that they can be integrated to an extent that has not been possible in the past.

This thesis focuses on an important aspect of protein functionality – protein-protein interactions (PPI). Three physical chemistry techniques for or derived from protein-protein interaction investigation are discussed. First, in Chapter 2, we demonstrate a new fluorescent imaging technique that creates high-order nonlinear signals by harnessing the frustrated fluorescence resonance energy transfer (FRET) – energy transfer between certain proteins close in proximity which is commonly used in PPI studies. In Chapter 3, we combine fluorescence resonance energy transfer (FRET) and bioluminescence resonance energy transfer (BRET), two most commonly used approaches to monitor protein-protein interactions in vivo, to create a novel hybrid strategy, bioluminescence assisted switching and fluorescence imaging (BASFI), which integrates the advantages of FRET and BRET. We demonstrate BASFI with Dronpa-RLuc8 fusion constructs and drug-inducible intermolecular FKBP-FRB protein-protein interactions in live cells with high sensitivity, resolution, and specificity. Finally, in Chapter 4, we propose a systematic free energy perturbation (FEP) protocol to computationally calculate the binding affinities between proteins. We demonstrate our protocol with the gp120 envelope glycoprotein of HIV-1 and three broadly neutralizing antibodies (bNAbs) of the VRC01 class and analyze antibody residues’ contributions to the binding which further provides insights for antibody design.

Research into the relationship between the structure and function of proteins has been ongoing now for several decades. More recently, there has been an explosion in the investigation of the dynamic properties of proteins, and how their dynamic propensity relates to their function. This new direction in protein research requires new techniques to analyse protein dynamics, since most traditional techniques are biased towards a fixed tertiary structure. Mass spectrometry (MS) is emerging as a powerful tool to probe protein dynamics since it can provide information on interconverting conformations and has no preference towards the folded state. Furthermore, its low sample consumption, rapid data acquisition and low data processing positions MS as an attractive tool in protein structure research. The hybrid technique of ion mobility-mass spectrometry provides further insight into the range of conformations adopted by proteins and protein complexes, by providing information on the size in terms of rotationally averaged collision cross section. The work presented in this thesis considers proteins with a range of structural characteristics. We use ion mobility mass spectrometry to investigate proteins of different extents of disorder, protein complexes with dynamic entities and a system that undergoes structural rearrangement upon ligand binding. First, a framework of mass spectrometry experiments is described which allows identification of the extent of structure and disorder within proteins. This framework is tested on a range of different systems throughout the thesis. Differences in the gas-phase properties of two conformationally dynamic proteins which behave similarly in solution are investigated and from this research we postulate a new ionisation mechanism for partially folded proteins. The dynamic propensity of C-terminal p27 is investigated and compared to two permutants which allows us to delineate how the location of charged residues in a primary sequence affects the structure of a protein. We monitor the 'folding-upon-binding' behaviour of p27 upon association with its binding partners, and how this differs with the order of charged residues in the linear sequence. Finally, we describe the structural rearrangement of Fdc1 upon the binding of its cofactor; a prenylated FMN molecule. This thesis demonstrates the suitability of ion mobility-mass spectrometry for the investigation of dynamic properties of proteins and protein complexes.

Protein aggregation is a frequently cited problem during the development of liquid protein formulations, which is especially problematic since each protein exhibits different aggregation behaviour. Aggregation can be controlled by judicious choice of solution conditions, such as salt and buffer type and concentration, pH, and small molecule additives. However, finding conditions is still a trial and error process. In order to improve formulation development, a fundamental understanding of how excipients impact upon protein aggregation would significantly contribute to the development of stable protein therapeutics. The underlying mechanisms that control effects of excipients on protein behaviour are poorly understood. This dissertation is directed at understanding how excipients alter the conformational and colloidal stability of proteins and the link to aggregation. This knowledge can be used for finding novel ways of either predicting or preventing/inhibiting protein aggregation. Experiments using static and dynamic light scattering, intrinsic fluorescence, turbidity and electrophoretic light scattering were conducted to study the effect of solution conditions such as pH, salt type and concentration on protein aggregation behaviour for three model systems: lysozyme, insulin and a monoclonal antibody. Emphasis is placed on understanding the effects of solution additives on protein-protein interactions and the link to aggregation. This understanding has allowed the rational development of stable formulations with novel additives, such as arginine containing dipeptides and polycations.

The main aim of this thesis was to discover and design new ligands for difficult, under-explored and clinically relevant protein targets. A number of protein-protein interaction complexes (PPIs) are introduced as the target focus for the methods employed and developed herein. This thesis is separated into two sections to independently address both peptides and small molecules as screening agents. The project examines both approaches through comprehensive library design strategies and screening by NMR spectroscopic methods. ATAD2 is the first PPI investigated and was expressed and purified in good yield and was also isotopically labelled with Nitrogen-15 for enhanced sensitivity and orthogonal ligand and protein-observed NMR methods. A known pentapeptide was synthesised by solid-phase peptide synthesis (SPPS) using Fmoc chemistry for target validation and tool compound development. A one-bead one-compound (OBOC) tripeptide library was synthesised by SPPS in good yield and purity, determined using single-bead labelling techniques with a fluorescent dye (TMR) and HPLC analysis. This library contained 3072 unique tripeptides with 12 central non-natural, lysine derivatives flanked by 16 natural L amino acids. The library screening technique was based on using a fluorescently labelled protein and Confocal Nanoscanning to detect binding. However, fluorescent labelling of ATAD2 was unsuccessful due to difficult protein handling conditions, therefore this library was not screened. The advent of small molecule, high affinity inhibitors of this target protein generated by GSK shifted focus to a different PPI target, the ubiquitin conjugating enzyme, UbE2L3. A novel 'on-protein peptide building' approach was introduced with the aim of screening a library of fluorinated dipeptides and extending the most potent via the 'N' and 'C' terminus to increase the affinity. A proof-of-concept tetrapeptide to survivin was synthesised by SPPS by incorporation of a non-natural, fluorinated amino acid in the known tetrapeptide sequence. This fluorinated derivative showed target binding activity by 19F NMR spectroscopy. The tripeptide and dipeptide truncates were synthesised by SPPS and binding was still observable by 19F NMR. This method was extended to screening a library of synthesised fluorinated dipeptides by 19F NMR against UbE2L3. A single dipeptide was identified with low affinity and the dipeptide was extended C and N terminally by SPPS to increase affinity. However, there were no tripeptides identified for this protein using this method. The proof of concept tetrapeptide was a success, therefore further protein targets are required to conclusively assess the viability of the approach. Fragment based screening is then introduced as a second approach to novel ligand discovery. Coupled with cheminformatics analysis and in silico library design, we created an in-house fluorinated fragment library consisting of 109 fluorinated fragments using three parallel methods. Compounds were purchased and quality checked by LCMS, HPLC and 19F-NMR. These fragment libraries were screened in a 19F NMR assay against the UbE2L3 and NusE/NusB protein targets. In a primary mixture screen, two fragment hits were identified against the NusE/NusB PPI and there were no fragment hits identified against the UbE2L3 protein. The two fragments against NusE/NusB were validated using orthogonal ligand-binding NMR methods. A mini-series, consisting of six commercially available analogues, were purchased and two fragment analogues showed increased affinity and were active against E. coli in a bacterial inhibition assay. The dissociation constants of the six active compounds were determined by 15N-HSQC NMR titration experiments and shown to be in 100-500 μM range. The binding sites of each compound were also determined by 15N-HSQC chemical shift mapping. These fragment hits represent a novel chemical scaffold identified against the NusE/NusB PPI and demonstrate the potential druggability of this new, complex target. The use of fluorine as a sensor for binding detection is evaluated by incorporating into both peptides and fragments. Through the use of novel library design strategies, a campaign to discover novel ligands of difficult protein targets is presented.

Protein-protein interactions are essential for the survival of all living cells, allowing for processes such as cell signalling, metabolism and cell division to occur. Yet in humans there are only >38k annotated interactions of an interactome estimated to range between 150k to 600k interactions and out of a potential 300M protein pairs.Experimental methods to define the human interactome generate high quality results, but are expensive and slow. Computational methods play an important role to fill the gap.To further this goal, the prediction of human protein-protein interactions was investigated by the development of new predictive modules and the analysis of diverse datasets within the framework of the previously established PIPs protein-protein interaction predictor Scott and Barton 2007. New features considered include the semantic similarity of Gene Ontology annotating terms, clustering of interaction networks, primary sequences and gene co-expression. Integrating the new features in a naive Bayesian manner as part of the PIPs 2 predictor resulted in two sets of predictions. With a conservative threshold, the union of both sets is >300k predicted human interactions with an intersect of >94k interactions, of which a subset have been experimentally validated. The PIPs 2 predictor is also capable of making predictions in organisms that have no annotated interactions. This is achieved by training the PIPs 2 predictor based on a set of evidence and annotated interactions in another organism resulting in a ranking of protein pairs in the original organism of interest. Such an approach allows for predictions to be made across the whole proteome of poorly characterised organism, rather than being limited only to proteins with known orthologues. The work described here has increased the coverage of the human interactome and introduced a method to predict interactions in organisms that have previously had limited or no annotated interactions. The thesis aims to provide a stepping stone towards the completion of the human interactome and a way of predicting interactions in organisms that have been less well studied, but are often clinically relevant.