A identificação e caracterização geométrica e físico-química de espaços vazios na estrutura tridimensional de proteínas é capaz de agregar informações importantes para guiar o desenho racional de drogas e a caracterização funcional de sítios de ligação e sítios catalíticos. Dessa forma, algumas ferramentas computacionais foram desenvolvidas nas últimas duas décadas, visando efetuar essas caracterizações. Contudo, as ferramentas existentes lidam com uma série de limitações, dais quais merecem destaque a falta de precisão, falta de capacidade de integração em protocolos de larga escala, falta de capacidade de customização e a falta de uma caracterização eletrostática . Tendo em mente estas limitações, desenvolvemos uma nova ferramenta, denominada KV-Finder, com o objetivo de estender as funcionalidades dos programas existentes, fornecendo assim uma caracterização sistemática mais eficiente e mais informativa dos espaços vazios da estrutura tridimensional de proteínas. Através de uma modelagem matricial baseada em um direcionamento realizado pelo usuário, nossa ferramenta identifica e caracteriza espaços vazios em topologias proteicas. O utilitário é capaz de quantificar o volume, a forma, a extensão de sua superfície, os resíduos proteicos que interagem com os espaços vazios e um mapa de cargas parciais da superfície encontrada. Nossa rotina foi integrada com ferramentas gráficas de modelagem molecular, fornecendo uma interação fácil e eficiente com o usuário. A validação de nosso algoritmo foi realizada em um conjunto de proteínas cujos diversos tipos de espaços vazios englobam os mais variados sítios de ligação e sítios catalíticos. O cálculo do volume de cavidades enzimáticas foi efetuado em larga escala, acompanhando a evolução do tamanho de bolsões na superfamília ALDH. Com relação aos outros softwares existentes, nossa ferramenta apresenta uma série de vantagens das quais merecem destaque menor tempo de execução, maior precisão, maior acessibilidade e facilidade de integração com outros programas, além das características únicas de permitir que a busca ocorra em regiões específicas dentro da proteína e de realizar um mapeamento parcial de cargas da superfície encontrada. / The identification and characterization of geometrical and physical-chemical properties in protein vacant spaces aggregates important information for steering rational drug designing and functional characterization of binding and catalytic sites. Therefore, several softwares have been develop during the past two decades in order to perform such characterization. Nevertheless, the existing tools still present a series of limitations such as lack of precision, lack of integrability in large scale protocols, lack of customization capacity and the lack of a proper electrostatic depiction. We developed a new software, dubbed KV-Finder, in order to complement and extend the functionality of existing softwares, providing a systematic and more descriptive portrayal of protein vacant spaces. By employing a user-driven matrix modeling, our tool identifies and characterizes empty spaces in all sorts of protein topologies. The software quantifies the volume, the area and the shape of the surface, the residues that interact with the vacant spaces and a partial charge map of the computed surface. Our routine was integrated with a graphical molecular modeling software, providing the user with a simple and easy-to-use interface. KV-Finder has been validated with a distinct set of proteins and binding sites. The volume computation was carried in large scale, accompanying the evolution of the pocket volume in the ALDH superfamily. Compared with existing software, KV-Finder presents greater precision, greater accessibility and ease of integration in large scale protocols and visualization softwares. Also, the software possesses unique and innovative features such as the ability to segment and subsegment the empty spaces, a electrostatic depiction and a ligand interaction highlight feature.

A identificação e caracterização geométrica e físico-química de espaços vazios na estrutura tridimensional de proteínas é capaz de agregar informações importantes para guiar o desenho racional de drogas e a caracterização funcional de sítios de ligação e sítios catalíticos. Dessa forma, algumas ferramentas computacionais foram desenvolvidas nas últimas duas décadas, visando efetuar essas caracterizações. Contudo, as ferramentas existentes lidam com uma série de limitações, dais quais merecem destaque a falta de precisão, falta de capacidade de integração em protocolos de larga escala, falta de capacidade de customização e a falta de uma caracterização eletrostática . Tendo em mente estas limitações, desenvolvemos uma nova ferramenta, denominada KV-Finder, com o objetivo de estender as funcionalidades dos programas existentes, fornecendo assim uma caracterização sistemática mais eficiente e mais informativa dos espaços vazios da estrutura tridimensional de proteínas. Através de uma modelagem matricial baseada em um direcionamento realizado pelo usuário, nossa ferramenta identifica e caracteriza espaços vazios em topologias proteicas. O utilitário é capaz de quantificar o volume, a forma, a extensão de sua superfície, os resíduos proteicos que interagem com os espaços vazios e um mapa de cargas parciais da superfície encontrada. Nossa rotina foi integrada com ferramentas gráficas de modelagem molecular, fornecendo uma interação fácil e eficiente com o usuário. A validação de nosso algoritmo foi realizada em um conjunto de proteínas cujos diversos tipos de espaços vazios englobam os mais variados sítios de ligação e sítios catalíticos. O cálculo do volume de cavidades enzimáticas foi efetuado em larga escala, acompanhando a evolução do tamanho de bolsões na superfamília ALDH. Com relação aos outros softwares existentes, nossa ferramenta apresenta uma série de vantagens das quais merecem destaque menor tempo de execução, maior precisão, maior acessibilidade e facilidade de integração com outros programas, além das características únicas de permitir que a busca ocorra em regiões específicas dentro da proteína e de realizar um mapeamento parcial de cargas da superfície encontrada. / The identification and characterization of geometrical and physical-chemical properties in protein vacant spaces aggregates important information for steering rational drug designing and functional characterization of binding and catalytic sites. Therefore, several softwares have been develop during the past two decades in order to perform such characterization. Nevertheless, the existing tools still present a series of limitations such as lack of precision, lack of integrability in large scale protocols, lack of customization capacity and the lack of a proper electrostatic depiction. We developed a new software, dubbed KV-Finder, in order to complement and extend the functionality of existing softwares, providing a systematic and more descriptive portrayal of protein vacant spaces. By employing a user-driven matrix modeling, our tool identifies and characterizes empty spaces in all sorts of protein topologies. The software quantifies the volume, the area and the shape of the surface, the residues that interact with the vacant spaces and a partial charge map of the computed surface. Our routine was integrated with a graphical molecular modeling software, providing the user with a simple and easy-to-use interface. KV-Finder has been validated with a distinct set of proteins and binding sites. The volume computation was carried in large scale, accompanying the evolution of the pocket volume in the ALDH superfamily. Compared with existing software, KV-Finder presents greater precision, greater accessibility and ease of integration in large scale protocols and visualization softwares. Also, the software possesses unique and innovative features such as the ability to segment and subsegment the empty spaces, a electrostatic depiction and a ligand interaction highlight feature.

Les cavités sont le lieu privilégié des interactions d’une protéine avec ses ligands, et sont donc déterminantes pour sa fonction, elle-même aussi influencée par la dynamique de la protéine. Peu de méthodes permettent d’étudier en détail la dynamique des cavités malgré leur intérêt notamment pour le criblage virtuel. Les cavités d’une protéine définissent un ensemble très labile. Ainsi, suivre une cavité le long d’une trajectoire est ardu car elle peut être sujette à des divisions, fusions, disparitions et apparitions. Je propose une méthode pour résoudre cette question afin d’exploiter la dynamique des cavités de façon systématique et rationnelle, en classifiant les cavités selon les groupes d’atomes les entourant. J’ai identifié les paramètres procurant les meilleurs critères de suivi des cavités. Pour caractériser les évolutions principales de la géométrie des cavités en relation avec la dynamique de la protéine, j’ai développé une méthode basée sur l’Analyse en Composantes Principales. Cette méthode peut être utilisée pour sélectionner ou construire des conformations à partir de la forme de leurs cavités. Deux exemples d’applications sont traitées : la sélection de conformations ayant des cavités de géométries diverses et l’étude de l’évolution des cavités de la myoglobine lors de la diffusion du monoxyde de carbone. Ces deux méthodes ont été utilisées pour trois projets de criblage virtuel ciblant l’ADN-gyrase de M tuberculosis, la subtilisine 1 de P vivax et GLIC, homologue procaryote des récepteurs pentamériques humains. Les molécules sélectionnées à l’aide de ces méthodes ont permis d’identifier une molécule active contre la subtilisine et quatre effecteurs de GLIC. / Cavities are the prime location of the interactions between a protein and its ligands, and thus are crucial for its functions, together with its dynamics. Although cavities have been studied since the seventies, specific studies on their dynamical behavior only appeared recently. Few methods can tackle this aspect, despite its interest for virtual screening and drug design. Protein cavities define an extremely labile ensemble. Following one cavity along a trajectory is therefore an arduous task, because it can be subjected to several events of fusions, divisions, apparitions and disappearances. I propose a method to resolve this question, thus enabling systematic and rational dynamical exploitation of protein cavities. This method classify cavities using the atom groups around them, using algorithms and parameters that I identified as giving best results for cavity tracking. To characterize the main directions of evolution of cavity geometry, and to relate them with the dynamics of the underlying structure, I developed a method based on Principal Component Analysis (PCA). This method can be used to select or build conformations with given cavity shapes. Two examples of applications have been treated: the selection of conformations with diverse cavity geometries, and the analysis of the myoglobin cavity network evolution during the diffusion of carbon monoxide in it. These two methods have been used in three projects involving virtual screening, targeting M. tuberculosis DNA-gyrase, P vivax subtilisin 1 and GLIC, an procaryotic model of human pentameric ligand-gated ion channel. These methods allowed us to identify an inhibitor of subtilisin 1 and four effectors of GLIC.

The sequence of a polypeptide chain determines its structure which in turns determines its function. A protein is stabilized by multiple forces; hydrophobic interaction, electrostatic interactions and hydrogen bond formation between residues. While the above forces are non-covalent in nature the protein structure is also stabilized by disulfide bonds. Structural features such as naturally occurring cavities in proteins also affect its stability. Studying factors which affect a protein’s structural stability helps us understand complex sequence-structure-function relationships, the knowledge of which can be applied in areas such as protein engineering. The work presented in this thesis deals with various and diverse aspects of protein structure. Chapter 1 gives an overall introduction on the topics studied in this thesis. Chapter 2 focuses on a unique, non-regular, structural feature of proteins, viz. protein cavities. Cavities directly affect the packing density of the protein. It has been shown that large to small cavity creating mutations destabilize the protein with the extent of destabilization being proportional to the size of cavity created. On the other hand, small to large cavity filling mutations have been shown to increase protein stability. Tools which analyze protein cavities are thus important in studies pertaining to protein structure and stability. The chapter presents two methods which detect and calculate cavity volumes in proteins. The first method, DEPTH 2.0, focuses on accurate detection and volume calculation of cavities. The second method, ROBUSTCAVITIES, focuses on detection of biologically relevant cavities in proteins. We then study another aspect of protein structure – the disulfide bond. Disulfide bonds confer stability to the protein by decreasing the entropy of the unfolded state. Previous studies which attempted to engineer disulfides in proteins have shown mixed results. Previously, disulfide bonds in individual secondary structures were characterized. Analysis of disulfides in α-helices and antiparallel β-strands yielded important common features of such bonds. In Chapter 3 we present a review of these studies. We then use MODIP; a tool that identifies amino acid pairs which when mutated to cysteines will most likely form a disulfide bond, to analyze disulfide bonds in parallel β-strands. A direct way to analyze sequence-structure relationships is via mutating individual residues, evaluating the effect on stability and activity of the protein and inferring its effect on protein structure. Saturation mutagenesis libraries, where all possible mutations are made at every position in the protein contain a huge amount of information pertaining to the effect of mutations on structure. Making such libraries and screening them has been an extremely resource intensive process. We combine a fast inverse PCR based method to rapidly generate saturation mutagenesis libraries with the power of deep sequencing to derive phenotypes of individual mutants without any large scale screening. In Chapter 4 we present an Illumina data analysis pipeline which analyzes sequencing data from a saturation mutagenesis library, and derives individual mutant phenotypes with high confidence. In Chapter 5 we apply the insights derived from structure-function studies and apply it to the problem of protein engineering, specifically immunogen design. The Human Immunodeficiency Virus adopts various strategies to evade the host immune system. Being able to display the conserved epitopes which elicit a broadly neutralizing response is the first step towards an effective vaccine. Grafting such an epitope onto a foreign scaffold will mitigate some of the key HIV defenses. We develop a computational protocol which grafts the broadly neutralizing antibody b12 epitope on scaffolds selected from the PDB. This chapter also describes the only experimental work presented in this thesis viz. cloning, expressing and screening the epitope-scaffolds using Yeast Surface Display. Our epitope-scaffolds show modest but specific binding. In a bid to improve binding, we make random mutant libraries of the epitope-scaffolds and screen them for better binders using FACS. This work is on-going and we aim to purify our epitope-scaffolds, characterize them biophysically and eventually test their efficacy as immunogens.