Proteins and their function play one of the most essential roles in various biological processes. The study of PPI is of considerable importance. PPI network data are of great scientific value, however, they are incomplete and experimental identification is time and money consuming. Available computational methods perform well on model organisms’ PPI prediction but perform poorly for a novel organism. Due to the incompleteness of interaction data, it is challenging to train a model for a novel organism. Also, millions to billions of interactions need to be verified which is extremely compute-intensive. We aim to improve the performance of predicting whether a pair of proteins will interact, with only two sequences as input. And also efficiently predict a PPI network with a proteome of sequences as input. We hypothesize that information about cellular locations where proteins are active and proteins' 3D structures can help us to significantly improve predict performance. To overcome the lack of experimental data, we use predicted structures by AlphaFold2 and cellular locations by DeepGoPlus. We believe that proteins belonging to disjoint biological components have very little chance to interact. We manually choose several disjoint pairs and further confirmed it by experimental PPI. We generate new no-interaction pairs with disjoint classes to update the D-SCRIPT dataset. As result, the AUPR has improved by 10% compared to the D-SCRIPT dataset. Besides, we pre-filter the negatives instead of enumerating all the potential PPI for de-novo PPI network prediction. For E.coli, we can pass around a million negative interactions. To combine the structure and sequence information, we generate a graph for each protein. A graph convolution network using Self-Attention Graph Pooling in Siamese architecture is used to learn these graphs for PPI prediction. In this way, we can improve around 20% in AUPR compared to our baseline model D-SCRIPT.

In the common fruit fly, Drosophila melanogaster, neural cell fate specification is triggered by a group of conserved transcriptional regulators known as proneural factors. Proneural factors induce neural fate in uncommitted neuroectodermal progenitor cells, in a process that culminates in sensory neuron differentiation. While the role of proneural factors in early fate specification has been described, less is known about the transition between neural specification and neural differentiation. The aim of this thesis is to use computational methods to improve the understanding of terminal neural differentiation in the Peripheral Nervous System (PNS) of Drosophila. To provide an insight into how proneural factors coordinate the developmental programme leading to neural differentiation, expression profiling covering the first 3 hours of PNS development in Drosophila embryos had been previously carried out by Cachero et al. [2011]. The study revealed a time-course of gene expression changes from specification to differentiation and suggested a cascade model, whereby proneural factors regulate a group of intermediate transcriptional regulators which are in turn responsible for the activation of specific differentiation target genes. In this thesis, I propose to select potentially important differentiation genes from the transcriptional data in Cachero et al. [2011] using a novel approach centred on protein interaction network-driven prioritisation. This is based on the insight that biological hypotheses supported by diverse data sources can represent stronger candidates for follow-up studies. Specifically, I propose the usage of protein interaction network data because of documented transcriptome-interactome correlations, which suggest that differentially expressed genes encode products that tend to belong to functionally related protein interaction clusters. Experimental protein interaction data is, however, remarkably sparse. To increase the informative power of protein-level analyses, I develop a novel approach to augment publicly available protein interaction datasets using functional conservation between orthologous proteins across different genomes, to predict interologs (interacting orthologs). I implement this interolog retrieval methodology in a collection of open-source software modules called Bio:: Homology::InterologWalk, the first generalised framework using web-services for “on-the- fly” interolog projection. Bio::Homology::InterologWalk works with homology data for any of the hundreds of genomes in Ensembl and Ensembgenomes Metazoa, and with experimental protein interaction data curated by EBI Intact. It generates putative protein interactions and optionally collates meta-data into a prioritisation index that can be used to help select interologs with high experimental support. The methodology proposed represents a significant advance over existing interolog data sources, which are restricted to specific biological domains with fixed underlying data sources often only accessible through basic web-interfaces. Using Bio::Homology::InterologWalk, I build interolog models in Drosophila sensory neurons and, guided by the transcriptome data, find evidence implicating a small set of genes in a conserved sensory neuronal specialisation dynamic, the assembly of the ciliary dendrite in mechanosensory neurons. Using network community-finding algorithms I obtain functionally enriched communities, which I analyse using an array of novel computational techniques. The ensuing datasets lead to the elucidation of a cluster of interacting proteins encoded by the target genes of one of the intermediate transcriptional regulators of neurogenesis and ciliogenesis, fd3F. These targets are validated in vivo and result in improved knowledge of the important target genes activated by the transcriptional cascade, suggesting a scenario for the mechanisms orchestrating the ordered assembly of the cilium during differentiation.

In the past decade, automatisation has led to an immense increase of data in biology. Next generation sequencing techniques will produce a vast amount of sequences across all species in the coming years. In many cases, identifying the function and biological role of a protein from its sequence can be a complicated and time-intensive task. The identification of a protein's interaction partners is a tremendous help for understanding the biological context in which it is involved. In order to fully characterise a protein-protein interaction (PPIs), it is necessary to know the three-dimensional structure of the interacting partners. Despite optimisation efforts from projects such as the Protein Structure Initivative, determining the structure of a protein through crystallography remains a time- and cost-intensive procedure. The primary aim of the research described in this dissertation was to produce a World Wide Web resource that facilitates visual exploration and validation (or questioning) of data derived from functional genomics experiments, by building upon existing structural information about direct physical PPIs. Secondary aims were (i) to demonstrate the utility of the new resource, and (ii) its application in biological research. We created a database that emphasises specifically the intersection between the PPIs-results emerging from the structural biology and functional genomics communities. The BISC database holds BInary SubComplexes and Modellable Interactions in current functional genomics databases (BICS-MI). It is publicly available at hyyp://bisc.cse.ucsc.edu. BISC is divided in three sections that deliver three types of information of interest to users seeking to investigate or browse PPIs. The template section (BISCHom and BISCHet) is devoted to those PPIs that are characterised in structural detail, i.e. binary SCs extracted from experimentally determined three-dimensional structures. BISCHom and BISCHet contain the homodimeric (13,583 records) and heterodimeric (5612 records) portions of these, respectively. Besides interactive, embedded Jmol displays emphasising the interface, standard information and links are provided, e.g. sequence information and SPOP classification for both partners, interface size and energy scores (PISA). An automated launch of the MolSurfer program enables the user to investigate electrostatic and hydrophobic correlation between the partners, at the inter-molecular interface. The modellable interactions section (BISC0MI) identifies potentially modellable interactions in three major functional genomics interaction databases (BioGRID), IntAct, HPRD). To create BISC-MI all PPIs that are amenable to automated homology modelling based on conservative similarity cut-offs and whose partner protein sequences have recrods in the UniProt database, have been extracted. The modellable interaction services (BISC-MI Services) section offers, upon user request, modelled SC-structures for any PPIs in BISC-MI. This is enabled through an untomated template-based (homology) modelling protocol using the popular MODELLER program. First, a multiple sequence alignment (MSA) is generated using MUSCLE, between the target and homologous proteins collected from UniProt (only reviewed proteins from organisms whose genome has been completely sequenced are included to find putative orthologs). Then a sequence-to-profile alignment is generated to integrate the template structure in the MSA. All models are produced upon user request to ensure that the most recent sequence data for the MSAs are used. Models generated through this protocol are expected to be more accurate generally than models offered by other automated resources that rely on pairwise alignments, e.g. ModBase. Two small studies were carried out to demonstrate the usability and utility of BISC in biological research. (1) Interaction data in functional genomics databases often suffers from insufficient experimental and reporting standards. For example, multiple protein complexes are typically recorded as an inferred set of binary interactions. Using the 20S core particle of the yeast proteasome as an example, we demonstrate how the BISC Web resource can be used as a starting point for further investigation of such inferred interactions. (2) Malaria, a mosquito-borne disease, affects 3500-500 million people worldwide. Still very little is known about the malarial parasites' genes and their protein functions. For Plasmodium falciparum, the most lethal among the malaria parasites, only one experimentally derived medium scale PPIs set is available. The validity of this set has been doubted in the the malarial biologist community. We modelled and investigated eleven binary interactions from this set using the BISC modelling pipeline. Alongside we compared the BISC models of the individual partners to those obtained from ModBase.

No description available.

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.

Septinas pertencem a uma família de proteínas de ligação a GTP e são encontradas em diversos eucariontes, participando de diferentes processos celulares citoesqueléticos. As septinas apresentam um domínio central de ligação a GTP (domínio G) flanqueado por uma região amino-terminal e outra carboxi-terminal. As septinas se caracterizam por interagirem entre si formando heterocomplexos que se polimerizam, constituindo filamentos. A única estrutura resolvida de um complexo de septinas é de um hexâmero, composto por duas subunidades de três septinas humanas diferentes: SEPT7-SEPT6-SEPT2-SEPT2-SEPT6-SEPT7. Esta estrutura revelou que a formação do filamento envolve interações conservadas entre os domínios G, estando o restante da estrutura desordenado no cristal. Além disso, mostrou que dois tipos de interface se alternam ao longo do filamento, as chamadas interfaces G (que incluem a região de ligação do nucleotídeo de duas subunidades) e interfaces NC (que incluem as regiões N e C-terminais do domínio G). Várias evidências sugerem que as regiões C-terminais da proteína sejam as principais responsáveis pela seleção do parceiro correto de interação para montagem dos heterocomplexos. Nesse contexto, buscou-se avaliar a importância das regiões C-terminais na seleção das parceiras SEPT6 e SEPT7 para formar a interface NC, frente ao domínio G. Inicialmente, uma septina quimérica foi produzida de forma a conter o domínio G de SEPT2 e o C-terminal de SEPT6, gerando SEPT2G6C. As proteínas SEPT7GC, SEPT6GC, SEPT2GC e SEPT2G6C foram expressas e purificadas separadamente. Análises de estabilidade térmica e de afinidade proteína-proteína dos pares indicou que a quimera foi capaz de interagir com SEPT7GC, gerando o heterodímero SEPT7GC-SEPT2G6C, mas este não se mostrou tão estável quanto o heterodímero fisiológico. Foi também avaliada a importância da ligação do nucleotídeo para formação da interface G e, para isso, foram construídos os mutantes SEPT2GT78M e SEPT2GD185N, cujos resíduos importantes para hidrólise e ligação do nucleotídeo, respectivamente, foram alterados. A análise de oligomerização por cromatografia de exclusão molecular mostrou deslocamento no volume de eluição das proteínas expressas sozinhas e coexpressas com SEPT6G, indicando que a formação do dímero via interface G depende da ligação do nucleotídeo, mas não da sua hidrólise. Finalmente, foi avaliada a estabilidade térmica e estrutural e a propensão à formação de amilóides do heterodímero SEPT6G-SEPT2G, o qual apresentou maior estabilidade estrutural quando comparado ao homodímero de SEPT2G, mas ainda exibiu alteração de sua estrutura para um estado capaz de ligar Thioflavina-T, sugerindo a formação de amilóides. Entretanto, isso foi observado em temperaturas cerca de 30 ºC acima daquela observada para o homodímero, confirmando a maior estabilidade do heterodímero e sugerindo que a formação da interface G com o parceiro correto pode ser um fator importante na prevenção da formação de estruturas amilóides à temperaturas fisiológicas. / Septins belong to a family of GTP binding proteins and are found in several eucaryotes, participating in different cytoskeletal cell processes. The septins have a central GTP binding domain (G domain) flanked by an amino-terminal and a carboxy-terminal regions. The septins are characterized by the ability to interact with each other forming heterocomplexes which polymerize themselves, forming filaments. The only solved structure of a septin complex is a hexamer, formed by two subunits of three different human septins: SEPT7-SEPT6-SEPT2- SEPT2-SEPT6-SEPT7. This structure revealed that the filament arrangement involves conserved interaction between G domains, being the remainder of the structure disordered in the crystal. Moreover, two types of interface alternate along the filament were shown, socalled G interfaces (which include the nucleotide binding region of the two subunits) and NC interfaces (which include the N- and C- terminal regions of G domain). Plenty of evidences suggest that C-terminal regions of the protein are the main responsible for the selection of the correct interaction partner to assembly of heterocomplexes. In this context, it was sought to evaluate the importance of the C-terminal regions in the selection of the partnerships SEPT6 and SEPT7 to form the NC interface, against the G domain. For this, a chimerical septin was designed so that contains the G-domain of SEPT2 and the C-terminal of SEPT6, creating SEPT2G6C. The SEPT7GC, SEPT6GC, SEPT2GC and SEPT2G6C proteins were expressed and purified individually. Thermal stability and protein-protein affinity analysis of the pairs indicated that the chimera was able to interact with SEPT7GC, forming the heterodimer SEPT7GC-SEPT2G6C, which, however, did not show as stable as the physiological heterodimer. The importance of nucleotide binding to the interaction through G interface was also evaluated and, for that, SEPT2 mutants on GTP-domain were constructed, SEPT2T78M and SEPT2D185N, whose important residues in the hydrolysis and linking of nucleotide, respectively, were changed. Oligomerization analysis by size exclusion chromatography showed a shift in the elution volume of proteins expressed alone and coexpressed with SEPT6, indicating that the complexation of proteins to form G interface depends on the nucleotide binding, but not on its hydrolysis. Finally, the thermal and structural stability and the propensity to amyloid formation of heterodimer SEPT6G-SEPT2G were evaluated, which showed greater structural stability when compared to SEPT2 homodimers, but still exhibited alteration of its structure to a state that was able to bind Thioflavin-T, suggesting amyloid formation. However, this was observed at temperatures around 30 ºC above that observed for the homodimer, confirming the greater conformational stability of the heterodimer and suggesting that the formation of G interface with the right partner can be an important factor of the amyloid filament prevention at physiological temperatures.

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.

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.

Embryonic stem (ES) cells are maintained in an undifferentiated state by a gene regulatory network centred on the triumvirate of transcription factors Nanog, Oct4 and Sox2. Genome-wide chromatin immunoprecipitation studies indicate that in many cases target genes contain closely localised binding sites for each of these proteins, as well as additional members of the extended pluripotency transcription factor network. However, the biochemical basis of the interactions between these proteins is largely unknown, as are the mechanisms by which these interactions control ES cell identity. By purifying Nanog from ES cells and identifying co-purified proteins, we determined a Nanog interactome of over 130 proteins including transcription factors, chromatin modifying complexes, phosphorylation and ubiquitination enzymes, basal transcriptional machinery members and RNA processing factors. Validation of interactions was obtained by co-immunoprecipitation of Nanog with putative partners. Sox2 was identified as a robust interacting partner of Nanog and the interaction was investigated further. We show that the interaction is independent of DNA binding and that a region of Nanog known as tryptophan repeat, in which tryptophan is present every 5th residue is necessary and sufficient for the binding of Sox2. Furthermore, mutation of tryptophan residues within the Nanog tryptophan repeat (WR) abolishes the interaction with Sox2. A region of Sox2 known as serine rich region, a triple-repeat motif (S X T/S Y) within a stretch of 21 residues is required for the interaction with Nanog. Mutation of tyrosines to alanine within the three motifs (S X T/S Y) abrogates the Nanog–Sox2 interaction. The disruption of the Nanog-Sox2 interaction results in the alteration of expression of genes associated with the Nanog-Sox2 cognate sequence, and reduces the ability of Sox2 to rescue ES cell differentiation induced by endogenous Sox2 deletion. Substitution of the tyrosines of the motif with phenylalanine rescues both the Sox2–Nanog interaction and efficient self-renewal. These results suggest that aromatic stacking of Nanog tryptophans and Sox2 tyrosines mediates an interaction central to ES cell self-renewal. Together these data shed light on the extent of the interactions of Nanog with protein partners as well as the biochemical nature of the interaction between Nanog and one of the most important partners Sox2, an interaction crucial for maintaining optimal mouse ES cell self-renewal efficiency.

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.