Aminoacyl-tRNA synthetases (aaRSs) are at the center of the question of the origin of life and are essential proteins found in all living organisms. AARSs arose early in evolution to interpret genetic code and are believed to be a group of ancient proteins. They constitute a family of enzymes integrating the two levels of cellular organization: nucleic acids and proteins. These enzymes ensure the fidelity of transfer of genetic information from the DNA to the protein. They are responsible for attaching amino acid residues to their cognate tRNA molecules by virtue of matching the nucleotide triplet, which is the first step in the protein synthesis. The translation of genetic code into protein sequence is mediated by tRNA, which accurately picks up the cognate amino acids. The attachment of the cognate amino acid to tRNA is catalyzed by aaRSs, which have binding sites for the anticodon region of tRNA and for the amino acid to be attached. The two binding sites are separated by ≈ 76 Å and experiments have shown that the communication does not go through tRNA (Gale et al., 1996). The problem addressed here is how the information of binding of tRNA anticodon near the anticodon binding site is communicated to the active site through the protein structure. These enzymes are modular with distinct domains on which extensive kinetic and mutational experiments and supported by structural data are available, highlighting the role of inter-domain communication (Alexander and Schimmel, 2001). Hence these proteins present themselves as excellent systems for in-silico studies. Various methods involved for the construction of protein structure networks are well established and analyzed in a variety of ways to gain insights into different aspects of protein structure, stability and function (Kannan and Vishveshwara, 1999; Brinda and Vishveshwara, 2005). In the present study, we have incorporated network parameters for the analysis of molecular dynamics (MD) simulation data, representing the global dynamic behavior of protein in a more elegant way. MD simulations have been performed on the available (and modeled) structures of aaRSs bound to a variety of ligands, and the protein structure networks (PSN) of non-covalent interactions have been characterized in dynamical equilibrium. The changes in the structure networks are used to understand the mode of communication, and the paths between the two sites of interest identified by the analysis of the shortest path. The allosteric concept has played a key role in understanding the biological functions of aaRSs. The rigidity/plasticity and the conformational population are the two important ideas invoked in explaining the allosteric effect. We have explored the conformational changes in the complexes of aaRSs through novel parameters such as cliques and communities (Palla et al., 2005), which identify the rigid regions in the protein structure networks (PSNs) constructed from the non-covalent interactions of amino acid side chains. The thesis consists of 7 chapters. The first chapter constitutes the survey of the literature and also provides suitable background for this study. The aims of the thesis are presented in this chapter. Chapter 2 describes various techniques employed and the new techniques developed for the analysis of PSNs. It includes a brief description of well -known methods of molecular dynamics simulations, essential dynamics, and cross correlation maps. The method used for the construction of graphs and networks is also described in detail. The incorporation of network parameters for the analysis of MD simulation data are done for the first time and has been applied on a well studied protein lysozyme, as described in chapter 3. Chapter 3 focuses on the dynamical behavior of protein structure networks, examined by considering the example of T4-lysozyme. The equilibrium dynamics and the process of unfolding are followed by simulating the protein with explicit water molecules at 300K and at higher temperatures (400K, 500K) respectively. Three simulations of 10ns duration have been performed at 500K to ensure the validity of the results. The snapshots of the protein structure from the simulations are represented as Protein Structure Networks (PSN) of non-covalent interactions. The strength of the non-covalent interaction is evaluated and used as an important criterion in the construction of edges. The profiles of the network parameters such as the degree distribution and the size of the largest cluster (giant component) have been examined as a function of interaction strength (Ghosh et al., 2007). We observe a critical strength of interaction (Icritical) at which there is a transition in the size of the largest cluster. Although the transition profiles at all temperatures show behavior similar to those found in the crystal structures, the 500K simulations show that the non-native structures have lower Icritical values. Based on the interactions evaluated at Icritical value, the folding/unfolding transition region has been identified from the 500K simulation trajectories. Furthermore, the residues in the largest cluster obtained at interaction strength higher than Icritical have been identified to be important for folding. Thus, the compositions of the top largest clusters in the 500K simulations have been monitored to understand the dynamical processes such as folding/unfolding and domain formation/disruption. The results correlate well with experimental findings. In addition, the highly connected residues in the network have been identified from the 300K and 400K simulations and have been correlated with the protein stability as determined from mutation experiments. Based on these analyses, certain residues, on which experimental data is not available, have been predicted to be important for the folding and the stability of the protein. The method can also be employed as a valuable tool in the analysis of MD simulation data, since it captures the details at a global level, which may elude conventional pair-wise interaction analysis. After standardizing the concept of dynamical network analysis using Lysozyme, it was applied to our system of interest, the aaRSs. The investigations carried out on Methionyl-tRNA synthetases (MetRS) are presented in chapter 4. This chapter is divided into three parts: Chapter 4A deals with the introduction to aminoacyl tRNA synthetases (aaRS). Classification and functional insights of aaRSs obtained through various studies are presented. Chapter 4B is again divided into parts: BI and BII. Chapter 4BI elucidates a new technique developed for finding communication pathways essential for proper functioning of aaRS. The enzymes of the family of tRNA synthetases perform their functions with high precision, by synchronously recognizing the anticodon region and the amino acylation region, which is separated by about 70Å in space. This precision in function is brought about by establishing good communication paths between the two regions. We have modelled the structure of E.coli Methionyl tRNA synthetase, which is complexed with tRNA and activated methionine. Molecular dynamics simulations have been performed on the modeled structure to obtain the equilibrated structure of the complex and the cross correlations between the residues in MetRS. Furthermore, the network analysis on these structures has been carried out to elucidate the paths of communication between the aminoacyl activation site and the anticodon recognition site (Ghosh and Vishveshwara, 2007). This study has provided the detailed paths of communication, which are consistent with experimental results. A similar study on the (MetRS + activated methionine) and (MetRS+tRNA) complexes along with ligand free-native enzyme has also been carried out. A comparison of the paths derived from the four simulations has clearly shown that the communication path is strongly correlated and unique to the enzyme complex, which is bound to both the tRNA and the activated methionine. The method developed here could also be utilized to investigate any protein system where the function takes place through long distance communication. The details of the method of our investigation and the biological implications of the results are presented in this chapter. In chapter 4BII, we have explored the conformational changes in the complexes of E.coli Methionyl tRNA synthetase (MetRS) through novel parameters such as cliques and communities, which identify the rigid regions in the protein structure networks (PSNs). The rigidity/plasticity and the conformational population are the two important ideas invoked in explaining the allosteric effect. MetRS belongs to the aminoacyl tRNA Synthetases (aaRSs) family that play a crucial role in initiating the protein synthesis process. The network parameters evaluated here on the conformational ensembles of MetRS complexes, generated from molecular dynamics simulations, have enabled us to understand the inter-domain communication in detail. Additionally, the characterization of conformational changes in terms of cliques/communities has also become possible, which had eluded conventional analyses. Furthermore, we find that most of the residues participating in clique/communities are strikingly different from those that take part in long-range communication. The cliques/communities evaluated here for the first time on PSNs have beautifully captured the local geometries in their detail within the framework of global topology. Here the allosteric effect is revealed at the residue level by identifying the important residues specific for structural rigidity and functional flexibility in MetRS. Chapter 4C focuses on MD simulations of Methionyl tRNA synthetase (AmetRS) from a thermophilic bacterium, Aquifex aeolicus. As describe in Chapter 4B, we have explored the communication pathways between the anticodon binding region and the aminoacylation site, and the conformational changes in the complexes through cliques and communities. The two MetRSs from E.coli and Aquifex aeolicus are structurally and sequentially very close to each other. But the communication pathways between anticodon binding region and the aminoacylation site from A. aeolicus have differed significantly with the communication paths obtained from E.coli. The residue composition and cliques/communities structure participating in communication are not similar in the MetRSs of both these organisms. Furthermore the formation of cliques/communities and hubs in the communication paths are more in A. aeolicus compared to E.coli. The participation of structurally homologous linker peptide, essential for orienting the two domains for efficient communication is same in both the organisms although, the residues composition near domain interface regions including the linker peptide is different. Thus, the diversity in the functioning of two different MetRS has been brought out, by comparing the E.coli and Aquifex aeolicus systems. Protein Structure network analysis of MD simulated trajectories of various ligand bound complexes of Escherichia coli Cysteinyl-tRNA synthetase (CysRS) have been discussed in Chapter 5. The modeling of the complex is done by docking the ligand CysAMP into the tRNA bound structure of E.coli Cysteinyl tRNA synthetase. Molecular dynamics simulations have been performed on the modeled structure and the paths of communications were evaluated using a similar method as used in finding communication paths for MetRS enzymes. Compared to MetRS the evaluation of communication paths in CysRS is complicated due to presence of both direct and indirect readouts. The direct and indirect readouts (DR/IR) involve interaction of protein residues with base-specific functional group and sugar-phosphate backbone of nucleic acids respectively. Two paths of communication between the anticodon region and the activation site has been identified by combining the cross correlation information with the protein structure network constructed on the basis of non-covalent interaction. The complete paths include DR/IR interactions with tRNA. Cliques/communities of non-covalently interacting residues imparting structural rigidity are present along the paths. The reduction of cooperative fluctuation due to the presence of community is compensated by IR/DR interaction and thus plays a crucial role in communication of CysRS. Chapter 6 focuses on free energy calculations of aminoacyl tRNA synthetases with various ligands. The free energy contributions to the binding of the substrates are calculated using a method called MM-PBSA (Massova and Kollman, 2000). The binding free energies were calculated as the difference between the free energy of the enzyme-ligand complex, and the free ligand and protein. The ligand unbinding energy values obtained from the umbrella sampling MD correlates well with the ligand binding energies obtained from MM-PBSA method. Furthermore the essential dynamics was captured from MD simulations trajectories performed on E.coli MetRS, A. aeolius MetRS and E.coli CysRS in terms of the eigenvalues. The top two modes account for more than 50% of the motion in essential space for systems E.coli MetRS, A. aeolius MetRS and E.coli CysRS. Population distribution of protein conformation states are looked at the essential plane defined by the two principal components with highest eigenvalues. This shows how aaRSs existed as a population of conformational states and the variation with the addition of ligands. The population of conformational states is converted into Free energy contour surface. From free energy surfaces, it is evident that the E.coli tRNAMet bound MetRS conformational fluctuations are more, which attributes to less rigidity in the complex. Whereas E.coli tRNACys bound CysRS conformational fluctuations are less and this is reflected in the increase in rigidity of the complex as confirmed by its entropic contribution. Future directions have been discussed in the final chapter (Chapter 7). Specifically, it deals with the ab-initio QM/MM study of the enzymatic reaction involved in the active site of E.coli Methionyl tRNA synthetase. To achieve this, two softwares are integrated: the Quantum Mechanics (QM) part includes small ligands and the Molecular Mechanics (MM) part as protein MetRS are handled using CPMD and Gromacs respectively. The inputs for two reactions pathways are prepared. First reaction involves cyclization reaction of homocysteine in the active site of MetRS and the second reaction deals with charging of methionine in the presence of ATP and magnesium ion. These simulations require very high power computing systems and also time of computation is also very large. With the available computational power we could simulate up to 10ps and it is insufficient for analysis. The future direction will involve the simulations of these systems for longer time, followed by the analysis for reaction pathways.

Electron crystallography of two-dimensional crystals is a structure-determination method well suited to the study of membrane protein structure-function. Two-dimensional crystals consist of ordered arrays of protein within reconstituted lipid bilayers, an arrangement that mimics the natural membrane environment. In this work we describe our recent progress in the use of this method with three different proteins, each providing a window into a separate paradigm in the electron crystallographic pipeline. Specific crystallization conditions for human leukotriene C₄ synthase (LTC₄S) have previously been determined, but our continued refinement of purification and crystallization has identified a number of additional parameters that greatly affect crystal size and quality, and we have developed a protocol to rapidly and reproducibly grow large, non-mosaic crystals of LTC₄S. The human gamma-glutamyl carboxylase (GGCX) has also been crystallized, but is sensitive to cryo-EM sample preparation conditions and we present here the successful reproduction of crystallization and refinement of cryo-EM sample preparation conditions. Lastly, we describe our crystallization screens with the Vibrio cholerae sodium-pumping NADH:ubiquinone reductase complex (Na⁺-NQR), and identify the factors critical to membrane reconstitution of the complex, a necessary first step towards crystallization. We also describe a semi-quantitative crystal screening protocol we have developed that provides quick and accurate method to assess two- dimensional crystallization trials, and discuss some general observations in optimization of membrane protein purification and two-dimensional crystallization for electron crystallography.

Cette thèse se place dans le cadre de l'étude in silico, c'est-à-dire assistée par ordinateur, des liens qui unissent la séquence d'une protéine à la (ou aux) structure(s) tri-dimensionnelle(s) qu'elle adopte. Le décryptage de ces liens présente de nombreuses applications dans divers domaines et constitue sans doute l'une des problématiques les plus fascinantes de la recherche en biologie moléculaire. Le premier aspect de notre travail concerne le développement de potentiels statistiques dérivés de bases de données de protéines dont les structures sont connues. Ces potentiels présentent plusieurs avantages: ils peuvent être aisément adaptés à des représentations structurales simplifiées, et permettent de définir un nombre limité de fonctions énergétiques qui incarnent l'ensemble complexe d'interactions gouvernant la structure et la stabilité des protéines, et qui incluent également certaines contributions entropiques. Cependant, leur signification physique reste assez nébuleuse, car l'impact des diverses hypothèses nécessaires à leur dérivation est loin d'être clairement établi. Nous nous sommes attachés à l'étude de certaines limitations des ces potentiels: leur dépendance en la taille des protéines incluses dans la base de données, la non-additivité des termes de potentiels, et l'importance souvent négligée de l'environnement protéique spécifique ressenti par chaque résidu. Nous avons ainsi mis en évidence que l'influence de la taille des protéines de la base de données sur les potentiels de distance entre résidus est spécifique à chaque paire d'acides aminés, peut être relativement importante, et résulte essentiellement de la répartition inhomogène des résidus hydrophobes et hydrophiles entre le coeur et la surface des protéines. Ces résultats ont guidé la mise au point de fonctions correctives qui permettent de tenir compte de cette influence lors de la dérivation des potentiels. Par ailleurs, la définition d'une procédure générale de dérivation de potentiels et de termes de couplage a rendu possible la création d'une fonction énergétique qui tient compte simultanément de plusieurs descripteurs de séquence et de structure (la nature des résidus, leurs conformations, leurs accessibilités au solvant, ainsi que les distances qui les séparent dans l'espace et le long de la séquence). Cette fonction énergétique présente des performances nettement améliorées par rapport aux potentiels originaux, et par rapport à d'autres potentiels décrits dans la littérature. Le deuxième aspect de notre travail concerne l'application de programmes basés sur des potentiels statistiques à l'étude de protéines qui adoptent des structures alternatives. La permutation de domaines est un phénomène qui affecte diverses protéines et qui implique la génération d'un oligomère suite à l'échange de fragments structuraux entre monomères identiques. Nos résultats suggèrent que la présence de "faiblesses structurales", c'est-à-dire de régions qui ne sont pas optimales vis-à-vis de la stabilité de la structure native ou qui présentent une préférence marquée pour une conformation non-native en absence d'interactions tertiaires, est intimement liée aux mécanismes de permutation. Nous avons également mis en évidence l'importance des interactions de type cation-{pi}, qui sont fréquemment observées dans certaines zones clés de la permutation. Finalement, nous avons sélectionné un ensemble de mutations susceptibles de modifier sensiblement la propension de diverses protéines à permuter. L'étude expérimentale de ces mutations devrait permettre de valider, ou de raffiner, les hypothèses que nous avons proposées quant au rôle joué par les faiblesses structurales et les interactions de type cation-{pi}. Nous avons également analysé une autre protéine soumise à d'importants réarrangements conformationnels: l'{alpha}1-antitrypsine. Dans le cas de cette protéine, les modifications structurales sont indispensables à l'exécution de l'activité biologique normale, mais peuvent sous certaines conditions mener à la formation de polymères insolubles et au développement de maladies. Afin de contribuer à une meilleure compréhension des mécanismes responsables de la polymérisation, nous avons cherché à concevoir rationnellement des protéines mutantes qui présentent une propension à polymériser contrôlée. Des tests expérimentaux ont été réalisés par le groupe australien du Professeur S.P. Bottomley, et ont permis de valider nos prédictions de manière assez remarquable. ---------------------------------------------------------------------------------------------------- The work presented in this thesis concerns the computational study of the relationships between the sequence of a protein and its three-dimensional structure(s). The unravelling of these relationships has many applications in different domains and is probably one of the most fascinating issues in molecular biology. The first part of our work is devoted to the development of statistical potentials derived from databases of known protein structures. These potentials allow to define a limited number of energetic functions embodying the complex ensemble of interactions that rule protein folding and stability (including some entropic contributions), and can be easily adapted to simplified representations of protein structures. However, their physical meaning remains unclear since several hypotheses and approximations are necessary, whose impact is far from clearly understood. We studied some of the limitations of these potentials: their dependence on the size of the proteins included in the database, the non-additivity of the different potential terms, and the importance of the specific environment of each residue. Our results show that residue-based distance potentials are affected by the size of the database proteins, and that this effect can be quite strong, is residue-specific, and seems to result mostly from the inhomogeneous partition of hydrophobic and hydrophilic residues between the surface and the core of proteins. On the basis of these observations, we defined a set of corrective functions in order to take protein size into account while deriving the potentials. On the other hand, we developed a general procedure of derivation of potentials and coupling terms and consequently created an energetic function describing the correlations between several sequence and structure descriptors (the nature of each residue, the conformation of its main chain, its solvent accessibility, and the distances that separate it from other residues, in space and along the sequence). This energetic function presents a strongly improved predictive power, in comparison with the original potentials and with other potentials described in the literature. The second part describes the application of different programs, based on statistical potentials, to the study of proteins that adopt alternative structures. Domain swapping involves the exchange of a structural element between identical proteins, and leads to the generation of an oligomeric unit. We showed that the presence of “structural weaknesses”, regions that are not optimal with respect to the folding mechanisms or to the stability of the native structure, seems to be intimately linked with the swapping mechanisms. In addition, cation-{pi} interactions were frequently detected in some key locations and might also play an important role. Finally, we designed a set of mutations that are likely to affect the swapping propensities of different proteins. The experimental study of these mutations should allow to validate, or refine, our hypotheses concerning the importance of structural weaknesses and cation-{pi} interactions. We also analysed another protein that undergoes large conformational changes: {alpha}1-antitrypsin. In this case, the structural modifications are necessary to the proper execution of the biological activity. However, under certain circumstances, they lead to the formation of insoluble polymers and the development of diseases. With the aim of reaching a better understanding of the mechanisms that are responsible for this polymerisation, we tried to design mutant proteins that display a controlled polymerisation propensity. An experimental study of these mutants was conducted by the group of Prof. S.P. Bottomley, and remarkably confirmed our predictions.

No description available.

Characterising and understanding factors that affect the rate of molecular evolution in proteins has played a major part in the development of evolutionary theory. The early analyses of amino acid substitutions stimulated the development of the neutral theory of molecular evolution, which later evolved into the nearly neutral theory. More recent work has lead to a better understanding of the role selection plays at the molecular level, but there is still limited understanding of how higher levels of protein organisation affect the way natural selection acts. The investigation of this question is the central aim of this thesis, which is addressed via the analysis of selective pressures in secondary protein structures in insects, mammals and fungi. The analyses for the first two groups were conducted using publically available datasets. To conduct the analyses in fungi, genome sequence data from the fungal genus Microbotryum (sequenced in our laboratory) was assembled and annotated, resulting in the development of a number of bioinformatics tools which are described here. The fungal, insect and mammalian datasets were interrogated with regard to a number of structural features, such as protein secondary structure, position of a site with regard to adaptively evolving sites, hydropathy and solvent-accessibility. These features were correlated with the signals of positive and purifying selection detected using phylogenetic maximum likelihood and Bayesian approaches. I conclude that all of the factors examined can have an effect on the rate of molecular evolution. In particular, disordered and hydrophilic regions of the protein are found to experience fewer physiochemical constraints and contain a higher proportion of adaptively evolving sites. It is also revealed that positively selected residues are ‘clustered’ together spatially, and these trends persist in the three taxa. Finally, I show that this variation in adaptive evolution is a result of both selective events and physiochemical constraint.

This dissertation concerns the study and prediction of loops in protein structures. Proteins perform crucial functions in living organisms. Despite their importance, we are currently unable to predict their three dimensional structure accurately. Loops are segments that connect regular secondary structures of proteins. They tend to be located on the surface of proteins and often interact with other biological agents. As loops are generally subject to more frequent mutations than the rest of the protein, their sequences and structural conformations can vary significantly even within the same protein family. Although homology modelling is the most accurate computational method for protein structure prediction, difficulties still arise in predicting protein loops. Protein loop structure prediction is therefore a bottleneck in solving the protein structure prediction problem. Reflecting on the success of homology modelling, I implement an improved version of a database search method, FREAD. I show how sequence similarity as quantified by environment specific substitution scores can be used to significantly improve loop prediction. FREAD performs appreciably better for an identifiable subset of loops (two thirds of shorter loops and half of the longer loops tested) than ab initio methods; FREAD's predictive ability is length independent. In general, it produces results within 2Å root mean square deviation (RMSD) from the native conformations, compared to an average of over 10Å for loop length 20 for any of the other tested ab initio methods. I then examine FREAD’s predictive ability on a specific type of loops called complementarity determining regions (CDRs) in antibodies. CDRs consist of six hypervariable loops and form the majority of the antigen binding site. I examine CDR loop structure prediction as a general case of loop structure prediction problem. FREAD achieves accuracy similar to specific CDR predictors. However, it fails to accurately predict CDR-H3, which is known to be the most challenging CDR. Various FREAD versions including FREAD with contact information (ConFREAD) are examined. The FREAD variants improve predictions for CDR-H3 on homology models and docked structures. Lastly, I focus on the local properties of protein loops and demonstrate that the protein loop structure prediction problem is a local protein folding problem. The end-to-end distance of loops (loop span) follows a distinctive frequency distribution, regardless of secondary structure elements connected or the number of residues in the loop. I show that the loop span distribution follows a Maxwell-Boltzmann distribution. Based on my research, I propose future directions in protein loop structure prediction including estimating experimentally undetermined local structures using FREAD, multiple loop structure prediction using contact information and a novel ab initio method which makes use of loop stretch.

Protein structure predication is a field of computational molecular modeling with an enormous potential for improvement. Side-chain geometry prediction is a critical component of this process that is crucial for computational protein structure predication as well as crystallographers in refining experimentally determined protein crystal structures. The cornerstone of side-chain geometry prediction are side-chain rotamer libraries, usually obtained through exhaustive statistical analysis of existing protein structures. Little is known, however, about the driving forces leading to the preference or suitability of one rotamer over another. Construction of 3D hydropathic interaction maps for nearly 30,000 tyrosines extracted from the PDB reveals their environments, in terms of hydrophobic and polar (collectively “hydropathic”) interactions. Using a unique 3D similarity metric, these environments were clustered with k-means. In the ϕ, ψ region (–200° < ϕ < –155°; –205° < ψ < –160°) representing 631 tyrosines, clustering reduced the set to 14 unique hydropathic environments, with most diversity arising from favorable hydrophobic interactions. Polar interactions for tyrosine include ubiquitous hydrogen bonding with the phenolic OH and a handful of unique environments surrounding the backbone. The memberships of all but one of the 14 environments are dominated by a single χ1/χ2 rotamer. Each tyrosine residue attempts to fulfill its hydropathic valence. Structural water molecules are thus used in a variety of roles throughout protein structure. A second project involves elucidating the 3D structure of CRIP1a, a cannabinoid 1 receptor (CB1R) binding protein that could provide information for designing small molecules targeting the CRIP1a-CB1R interaction. The CRIP1a protein was produced in high purity. Crystallization experiments failed, both with and without the last 9 or 12 amino acid peptide of the CB1R C-terminus. Attempts were made to use NMR for structure determination; however, the protein precipitated out during data acquisition. A model was thus built computationally to which the CB1R C-terminus peptide was docked. HINT was used in selecting optimum models and analyzing interactions involved in the CRIP1a-CB1R complex. The final model demonstrated key putative interactions between CRIP1a and CB1R while also predicting highly flexible areas of the CRIP1a possibly contributing to the difficulties faced during crystallization.

Energy functions are found to be a key of protein structure prediction. In this work, we propose a novel 3-dimensional energy function based on hydrophobic-hydrophilic properties of amino acid where we consider at least three different possible interaction of amino acid in a 3-dimensional sphere categorized as hydrophilic versus hydrophilic, hydrophobic versus hydrophobic and hydrophobic versus hydrophilic. Each of these interactions are governed by a 3-dimensional parameter alpha used to model the interaction and 3-dimensional parameter beta used to model weight of contribution. We use Genetic Algorithm (GA) to optimize the value of alpha, beta and Z-score. We obtain three energy scores libraries from a database of 4332 protein structures obtained from Protein Data Bank (PDB) server. Proposed energy function is found to outperform nearest competitor by 40.9% for the most challenging Rosetta decoy as well as better in terms of the Z-score based on Moulder and Rosetta decoy sets.

This dissertation thesis focuses on creating tools for the analysis and potential therapeutic intervention in the biological processes regulated by proteolysis. I focus on two important proteolytic enzymes: HIV-1 protease, which is indispensable for the polyprotein processing of the nascent virus and thus for the development of infectious viral particle, and glutamate carboxypeptidase II, a tumor marker and a neuropeptidase from the prostate and central nervous system. Rational design of inhibitors of these therapeutically relevant enzymes serves two purposes: firstly, protease inhibitors were shown to be powerful drugs (HIV protease is in fact the example of successful drug development driven by structural biology). Secondly, and in the context of this thesis perhaps more importantly, inhibitors of medicinally relevant proteases might serve as tools for the elucidation of basic biological questions concerning regulation, timing and spatiotemporal control of such key processes as virus maturation or cancer development. The experimental work described in this thesis summarizes my results in both these areas. Human Immunodeficiency Virus Protease Human immunodeficiency virus (HIV), a causative agent of AIDS, has been estimated to kill close to 40 million people during the past four decades with 1.5...

A predição de estruturas de proteínas é um campo de pesquisa que busca simular o enovelamento de cadeias de aminoácidos de forma a descobrir as funções das proteínas na natureza, um processo altamente dispendioso por meio de métodos in vivo. Inserida no contexto da Bioinformática, é uma das tarefas mais computacionalmente custosas e desafiadoras da atualidade. Devido à complexidade, muitas pesquisas se utilizam de gateways científicos para disponibilização de ferramentas de execução e análise desses experimentos, aliado ao uso de workflows científicos para organização de tarefas e disponibilização de informações. No entanto, esses gateways podem enfrentar gargalos de desempenho e falhas estruturais, produzindo resultados de baixa qualidade. Para atuar nesse contexto multifacetado e oferecer alternativas para algumas das limitações, esta tese propõe uma arquitetura modular baseada nos conceitos de Service Oriented Architecture (SOA) para oferta de recursos computacionais em gateways científicos, com foco nos experimentos de Protein Structure Prediction (PSP). A Arquitetura Modular Distribuída para auxílio à Predição de Estruturas de Proteínas (MDAPSP) é descrita conceitualmente e validada em um modelo de simulação computacional, no qual se pode identificar suas capacidades, detalhar o funcionamento de seus módulos e destacar seu potencial. A avaliação experimental demonstra a qualidade dos algoritmos propostos, ampliando a capacidade de atendimento de um gateway científico, reduzindo o tempo necessário para experimentos de predição e lançando as bases para o protótipo de uma arquitetura funcional. Os módulos desenvolvidos alcançam boa capacidade de otimização de experimentos de PSP em ambientes distribuídos e constituem uma novidade no modelo de provisionamento de recursos para gateways científicos. / PSP is a scientific process that simulates the folding of amino acid chains to discover the function of a protein in live organisms, considering that its an expensive process to be done by in vivo methods. PSP is a computationally demanding and challenging effort in the Bioinformatics stateof- the-art. Many works use scientific gateways to provide tools for execution and analysis of such experiments, along with scientific workflows to organize tasks and to share information. However, these gateways can suffer performance bottlenecks and structural failures, producing low quality results. With the goal of offering alternatives to some of the limitations and considering the complexity of the topics involved, this thesis proposes a modular architecture based on SOA concepts to provide computing resources to scientific gateways, with focus on PSP experiments. The Modular Distributed Architecture to support Protein Structure Prediction (MDAPSP) is described conceptually and validated in a computer simulation model that explain its capabilities, detail the modules operation and highlight its potential. The performance evaluation presents the quality of the proposed algorithms, a reduction of response time in PSP experiments and prove the benefits of the novel algorithms, establishing the basis for a prototype. The new modules can optmize the PSP experiments in distributed environments and are a innovation in the resource provisioning model for scientific gateways.