A troponina (Tn) regula a contração do músculo estriado esquelético de vertebrados. Ela é composta de três subunidades: troponina I (TnI), troponina C (TnC) e troponina T (TnT). A TnI tem a função inibitória que é neutralizada pela ligação de Ca2+ nos sítios regulatórios do N-domínio da TnC, e a TnT posiciona o complexo no filamento fino. Para monitorar o sinal do Ca2+ sendo transmitido da TnC para a TnI as propriedades espectrais únicas do 5-hidroxitriptofano (5HW) foram utilizadas. O 5HW foi incorporado em mutantes pontuais de TnI com um único códon para triptofano. Foram identificadas duas sondas espectrais intrínsecas na TnI capazes de detectar a ligação de Ca2+ na Tn: as TnIs com 5HW nas posições 100 e 121. Complexos troponina reconstituídos com estes mutantes fluorescentes de TnI, Tn-TnIF100HW e Tn-TnIM121HW, apresentaram respectivamente 12 e 70 % de aumento na intensidade do espectro de emissão devido à ligação de Ca2+ na TnC. Nos complexos binários (TnC-TnI) as TnIs com 5HW nas posições 106 e 121 também captam a ligação do Ca2+ na TnC. A análise da fluorescência destas sondas demonstrou que: 1) as regiões da TnI que respondem ao N-domínio regulatório da TnC ocupado com Ca2+ são a região inibitória da TnI, resíduos 96 até 116, e a região vizinha que inclui a posição 121 da TnI; 2) mutações pontuais e a incorporação de 5HW na TnI podem afetar tanto a afinidade como a cooperatividade da ligação de Ca2+ na TnC, confirmando o papel da TnI em modular a afinidade da TnC por Ca2+; 3) as constantes de dissociação de Ca2+ surpreendentemente altas, Kd ~ 10-8 M, calculadas a partir dos sinais das sondas na região inibitória da TnI, sugerem a possibilidade de que os sítios do domínio N-terminal da TnC sejam os sítios de ligação de Ca2+ de maior afinidade no complexo troponina. / Vertebrate striated muscle contraction is regulated by troponin (Tn). Tn is composed of three subunits: troponin I (TnI), troponin C (TnC) and troponin T (TnT). TnI has an inhibitory role that is neutralized by calcium binding to the regulatory sites in the N-domain of TnC, and TnT positions the troponin complex on the thin filament. In order to follow the Ca2+ induced conformational change that is transmitted from TnC to TnI, the unique spectral properties of 5-hydroxytryptophan (5HW) incorporated as point-mutants of TnI were used. It was possible to identify two new TnI intrinsic spectral probes sensitive to Ca2+ binding to Tn: TnI with single 5HW at positions 100 and 121. Trimeric troponin complexes reconstituted with two fluorescent mutants of TnI, Tn-TnIF100HW and Tn-TnIM121HW, showed respectively 12 and 70 % increase in the emission spectra when Ca2+ bound to TnC. In the binary complexes (TnC-TnI) two TnIs with 5HW at positions 106 and 121 were also sensitive to Ca2+ binding to TnC. Fluorescence analysis of these probes showed: 1) the regions in TnI that respond to Ca2+ binding to the regulatory N-domain of TnC are the inhibitory region of TnI (residues 96 to 116), and a neighbor region that includes position 121; 2) point mutations and incorporation of 5HW in TnI can affect both the affinity and the cooperativity of Ca2+ binding to TnC, confirming the role of TnI as a modulator of the Ca2+ affinity of TnC; 3) the high dissociation constant for sites in the N-terminal domain of TnC (Kd ~ 10-8 M), derived from data using probes in the inhibitory region of TnI suggested the possibility that these sites are the high affinity Ca2+ binding sites in the troponin complex.

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.

O exossomo é um complexo multiproteico conservado evolutivamente de archaea a eucariotos superiores que desempenha funções celulares essenciais tais como: atividade exoribonucleolítica 3\'→5\', regulação dos níveis de mRNA, maturação de RNAs estruturais e controle de qualidade de RNAs durante os vários estágios do mecanismo de expressão gênica. Em Archaea, o exossomo é composto por até quatro subunidades diferentes, duas com domínios de RNase PH, aRrp41 e aRrp42, e duas com domínios de ligação a RNAs, aCsl4 e aRrp4. Três cópias das proteínas aRrp4 e/ou aCsl4 se associam com o núcleo hexamérico catalítico do anel de RNase PH e completam a formação do complexo. A proteína PaNip7 é um cofator de regulação do exossomo da archaea Pyrococcus abyssi e atua na inibição do complexo enzimático ligando-se simultaneamente ao exossomo e a RNAs. Neste projeto, a reconstituição in vitro do exossomo da archaea Pyrococcus abyssi formado pela proteína de topo PaCsl4 foi obtida. Para tanto foram realizadas análises de interação proteica usando as técnicas de cromatografia de afinidade, gel filtração e SDS-PAGE. Em adição à formação da isoforma PaCsl4-exossomo, um fragmento peptídico correspondente à região C-terminal da PaNip7 foi sintetizado pelo método da fase sólida, purificado por RP-HPLC e o purificado foi caracterizado por LC/ESI-MS almejando realizar futuros experimentos de interação com o exossomo. / The exosome is a multiprotein complex evolutionarily conserved from archaea to higher eukaryotes that performs essential cellular functions such as: 3\'→5\' exoribonucleolytic activity, regulation of mRNA levels, maturation of structural RNAs and quality control of RNAs during the various stages of the gene expression mechanism. In Archaea, the exosome is composed of up to four different subunits, two with RNase PH domains, aRrp41 and aRrp42, and two with RNAs binding domains, aCsl4 and aRrp4. Three copies of the aRrp4 and/or aCsl4 proteins associate with the hexameric catalytic core of the RNase PH ring and complete the formation of the complex. The PaNip7 protein is a regulating cofactor of the Pyrococcus abyssi archaeal exosome and acts in the inhibition of the enzyme complex by binding simultaneously to the exosome and RNAs. In this project, the reconstitution in vitro of the Pyrococcus abyssi archaeal exosome formed by the PaCsl4 top protein was achieved. To this end protein interaction analyses were performed using affinity chromatography, gel filtration and SDS-PAGE techniques. In addition to the formation of the PaCsl4-exosome isoform, a peptide fragment corresponding to the C-terminal region of PaNip7 was synthesized by solid-phase method, purified by RP-HPLC and the purified peptide was characterized by LC/ESI-MS aiming to perform future binding experiments with the exosome.

No description available.

An increasing number of proteins with unknown function have their three-dimensional structure solved at high resolution. This situation, largely due to structural genomics initiatives, has been stimulating the development of automated structure-based function prediction methods. Knowledge of residues important for function – and more particularly – for binding can help automated prediction of function in different ways. The properties of a binding site such as its shape or amino acid composition can provide clues on the ligand that may bind to it. Also, having information on functionally important regions in similar proteins can refine the process of annotation transfer between homologues.<p>Experimental results indicate that functional residues often have an unfavourable contribution to the stability of the folded state of a protein. This observation is the underlying principle of several computational methods for predicting the location of functional sites in protein structures. These methods search protein structures for destabilising residues, with the assumption that these are likely to be important for function.<p>We have developed a method to detect clusters of destabilising residues which are in close spatial proximity within a protein structure. Individual residue contributions to protein stability are evaluated using detailed atomic models and an energy function based on fundamental physico-chemical principles.<p>Our overall aim in this work was to evaluate the overlap between these clusters of destabilising residues and known binding sites in proteins.<p>Unfortunately, reliable benchmark datasets of known binding sites in proteins are sorely lacking. Therefore, we have undertaken a comprehensive approach to define binding sites unambiguously from structural data. We have rigorously identified seven issues which should be considered when constructing datasets of binding sites to validate prediction methods, and we present the construction of two new datasets in which these problems are handled. In this regard, our work constitute a major improvement over previous studies in the field.<p>Our first dataset consists of 70 proteins with binding sites for diverse types of ligands (e.g. nucleic acids, metal ions) and was constructed using all available data, including literature curation. The second dataset contains 192 proteins with binding sites for small ligands and polysaccharides, does not require literature curation, and can therefore be automatically updated.<p>We have used our dataset of 70 proteins to evaluate the overlap between destabilising regions and binding sites (the second dataset of 192 proteins was not used for that evaluation as it constitutes a later improvement). The overlap is on average limited but significantly larger than random. The extent of the overlap varies with the type of bound ligand. Significant overlap is obtained for most polysaccharide- and small ligand-binding sites, whereas no overlap is observed for nucleic acid-binding sites. These differences are rationalised in terms of the geometry and energetics of the binding sites.<p>Although destabilising regions, as detected in this work, can in general not be used to predict all types of binding sites in protein structures, they can provide useful information, particularly on the location of binding sites for polysaccharides and small ligands.<p>In addition, our datasets of binding sites in proteins should help other researchers to derive and validate new function prediction methods. We also hope that the criteria which we use to define binding sites may be useful in setting future standards in other analyses. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

The 2OG oxygenases comprise a superfamily of ferrous iron dependent dioxygenases with multiple biological roles, including in hypoxia sensing, transcriptional control, and splicing control. It was recently proposed that 2OG oxygenases catalyse the hydroxylation of ribosomal proteins in prokaryotes (ycfD) and in humans (NO66 and MINA53), raising the possibility that 2OG oxygenases also control translation. The work described in this thesis concerned investigations on the biochemical and functional aspects of prokaryotic and mammalian ribosomal protein hydroxylases (ROX) in vitro and in cells. An efficient chromatographic system linked to mass spectrometric analysis (LC-MS) was developed for studying the masses of individual ribosomal proteins (>90% coverage of ribosomal proteome) to ±1 Da accuracy. It was demonstrated that ycfD catalyses the hydroxylation of R81 on L16 in E. coli, in a manner dependent on atmospheric oxygen levels. YcfD deletion results in growth phenotype at low temperatures and in minimal medium, and in decreased global translation rates in minimal medium; ycfD deletion does not affect translational accuracy and ribosome assembly. Furthermore, ycfD-deletion results in increased sensitivity to the antibiotics chloramphenicol and lincomycin. Consistent with a 2OG-oxygenase mediated mechanism of antibiotic resistance, chloramphenicol sensitivity of the E. coli wild-type strain could be increased by inhibiting the activity of ycfD by removing co-factors required for catalytic activity (Fe(II) and O2), and, at least in part, by using a ycfD inhibitor, IOX1, which inhibits ycfD with IC₅₀ of 38 μM in vitro. The therapeutic potential of a post-translational modification mediating antibiotic resistance provides an opportunity for medicinal targeting of ribosome-modifying enzymes, for example ycfD, which may be more ‘druggable’ than the ribosome itself. In co-treatment with an existing antibiotic, such as chloramphenicol, a small molecule inhibitor would achieve a potentiated antibiotic effect. Structural aspects of ROX hydroxylation were pursued by characterising a thermophilic ROX-substrate complex; a ycfD homologue was identified in the thermophilic bacterium Rhodothermus marinus and shown to be a thermophilic 2OG oxygenase ycfD_RM, acting on R82 of ribosomal protein L16_RM. The activity of ycfD_RM in cells was limited at high growth temperature and oxygen solubility was demonstrated as a likely limiting factor of ycfD_RM activity, thus identifiying a potential 2OG oxygenase oxygen sensor in prokaryotes. A crystal structure of ycfD_RM in complex with L16RM substrate fragment was determined to 3.0 Å resolution. Structural analyses suggested that ycfD_RM contains 30% more hydrophobic interactions and 100% more salt-bridge interactions than ycfD_EC, suggesting that these interactions are important for thermal stabilisation of ycfD_RM. The structures reveal key interactions required for binding of ribosomal proteins. Substantial structural changes were observed in the presence of the substrate fragment, which implies induced-fit binding of the L16_RM substrate. The work has informed further structural studies on the evolutionarily related human ROX, NO66 and MINA53, for which substrate structures have been obtained since the completion of the work. The LC-MS analysis of ribosomal proteins was extended to mouse and human cells to demonstrate that the human ROX homologue of ycfD, MINA53, hydroxylates the 60S ribosomal protein rpL27a in cells. It was demonstrated that rpL27a hydroxylation is widespread and found in all mouse organs analysed, as well as in cancer cell lines and in clinical cancer tissues. A partial or complete reduction of rpL27a hydroxylation was observed in a number of clinically identified MINA53 mutations from the COSMIC database of cancer mutations. Structural analysis suggested that mutations occur more frequently at structurally important regions of MINA53, including the βIV-βV insert in the core fold of MINA53. The identification of inhibiting clinical mutations suggests that rpL27a hydroxylation level could be used as a cancer mark, and in the future for selective inhibition by ribosomal antibiotics. The work presented in this thesis demonstrates that it is possible to selectively inhibit modified ribosomes; an inhibitor of unhydroxylated rpL27a could therefore, at least in principle, be active against the sub-set of tumours with inactivating mutation(s) of MINA53, but not normal tissue. Future work should therefore focus on identifying a selective inhibitor of unhydroxylated eukaryotic ribosomes which could be applied for treatment of cancers harbouring deactivating MINA53 mutations. The same approach could be applied to other ribosome modifications (to rRNA, ribosomal proteins, and ribosome-associate factors) that are different in cancer compared to normal cells.

Proteins, which regulate most of the biological activities, perform their functions through their unique three-dimensional structures. The folding process of this three dimensional structure from one dimensional sequence is not well understood. The available facts infer that the protein structures are mostly conserved while sequences are more tolerant to mutations i.e. a number of sequences can adopt the same fold. These arch of optimal sequences for a chosen conformation is known as inverse protein folding and this thesis takes this approach to solve the enigmatic problem. This thesis presents a protein sequence design method based on the native state topology of protein structure. The structural importance of the amino acid positions has been converted into the topological parameter of the protein conformation. This scheme of extraction of topology of structures has been successfully applied on three dimensional lattice structures and in turn sequences with minimum energy for a given structure are obtained. This technique along with the reduced amino cid alphabet(A reduced amino acid alphabet is any clustering of twenty amino acids based on some measure of the irrelative similarity) has been applied on the protein structures and hence designed optimal amino acid sequences for a given structure. These designed sequences are energetically much better than the native amino acid sequence. The utility of this method is further confirmed by showing the similarity between naturally occurring and the designed sequences. In summary, a computationally efficient method of designing optimal sequences for a given structure is given. The physical interaction energy between the amino acids is an important part of study of protein-protein interaction, structure prediction, modeling and docking etc. The local environment of amino acids makes a difference between the same amino acid pairs in the protein structure and so the pair-wise interaction energy of amino acid residues should depend on the irrespective environment. A local environment depended knowledge based potential energy function is developed in this thesis. Two different environments, one of these is the local degree (number of contacts) and the other is the secondary structural element of amino acids, have been considered. The investigations have shown that the environment-based interaction preferences for amino acids is able to provide good potential energy functions which perform exceedingly well in discriminating the native structure from the structures with random interactions. Further, the membrane proteins are located in a completely different physico-chemical environment with different amino acid composition than the water soluble proteins. This work provides reliable potential energy functions which take care of different environment for the investigation(model/predict) of the structure of helical membrane proteins. Three different environments, parallel and perpendicular to the lipid bilayer and number of amino acid contacts, are explored to analyze the environmental effects on the potential functions. These environment dependent scoring functions perform exceedingly well indiscriminating the native sequence from a set of random sequences. Hydrophobicity of amino acids is a measure of buriedness or exposure to the aqueous environment. The lack of uniformity within the protein environment gives rise to the different values of hydrophobicity for the same amino acids, which completely depends on its location inside the protein.The contact based environment dependent hydrophobicity values of all amino acids, separately for globular and membrane proteins, have also been evaluated in this thesis. Apart from developing scoring functions, the packing of helices in membrane proteins is investigated by an approach based on the local backbone geometry and side chain atom-atom contacts of amino acids. A parameter defined in this study is able to capture the essential features of inter-helical packing, which may prove to be useful in modeling of helical membrane proteins. In conclusion, this thesis has described a novel technique to design the energetically minimized amino acid sequences which can fold in to a given conformation. Also the environment dependent interaction preference of amino acids in globular proteins is captured an efficient manner. Specially, the environment dependent scoring function for helical membrane proteins is a first successful attempt in this direction.

Membrane proteins roughly constitute 30% of open reading frames in a genome and form 70% of current drug targets. They are classified as integral, peripheral membrane proteins and polypeptide toxins. α-helices and β -strands are the principal secondary structures observed in integral membrane proteins. This thesis presents the results of studies on analysis and correlation of sequence and structure of helices constituting integral helical membrane proteins. The aim of this work is to understand the helix stabilization, distortion as well as packing in terms of amino acid sequences and the correlated structures they adopt. To this end, analyses of datasets of X-ray crystal structures of integral helical membrane proteins and their comparison with a dataset of representative folds of globular proteins was carried out. Initial analysis was carried out using a non-redundant dataset of 75 membrane proteins to understand sequence and structural preferences for stabilization of helix termini. The subsequent analysis of helix distortions in membrane proteins was carried out using an updated dataset of 90 membrane proteins. Chapter 1 of the thesis reviews experimental as well as theoretical studies that have provided insights into understanding the structure of helical membrane proteins. Chapter 2 details the methods used during the course of the present investigations. These include the protocol used for creation of the non-redundant database of membrane and globular proteins. Various statistical methods used to test significance of the position-wise representation of amino acids in helical regions and the differences in globular and membrane protein datasets have been listed. Based on the tests of significance, a methodology to identify differences in propensity values that are statistically significant among two datasets has been devised. Programs used for secondary structure identification of membrane proteins namely Structure Identification (STRIDE) and Assignment of Secondary Structure in Proteins (ASSP) as well as those used for characterization of helical geometry (Helanal-Plus) have also been enlisted. In Chapter 3, datasets of 865 α-helices in 75 membrane proteins and 2680 α- helices from 626 representative folds in globular proteins defined by the STRIDE program have been analyzed to study the sequence determinants at fifteen positions within and around the α-helix. The amino acid propensities have been studied for positions that are important for the process of helix initiation, propagation, stabilization and termination. Each of the 15 positions has unique sequence characteristics reflecting their role and contribution towards the stability of the α-helix. A comparison of the sequence preferences in membrane and globular proteins revealed common residue preferences in both these datasets confirming the importance of these positions and the strict residue preferences therein. However, short/medium length α-helices that initiated/terminated within the membrane showed distinct amino acid preferences at the N-terminus (Ncap, N1, N2) as well as the C-terminus ( Ccap, Ct) when compared to α-helices belonging to membrane and globular proteins. The sequence preferences in membrane proteins were governed by the helix initiating and terminating property of the amino acids as well as the external environment of the helix. Results from our analysis also conformed well with experimentally tested amino acid preferences in a position-specific amino acid preference library of the rat neurotensin receptor (Schlinkmann et al (2012) Proc Natl Acad Sci USA 109(25):1890-5) as well as crystal structures of GPCR proteins. In the light of the environment dependent amino acid preferences found at α- helix termini, a survey was carried out to find various helix capping motifs adopted at both termini of α-helices in globular and membrane proteins to stabilize these helix termini. The results from these findings have been reported in Chapter 4. A sequence dependent structural preference is found for capping motifs at helix termini embedded inside and protruding outside the membrane. The N-terminus of α-helices was capped by hydrogen bonds involving free main chain amide groups of the first helical turn as donors and amino acid side chains as acceptors, as against the C-terminus which showed position-dependent characteristic backbone conformations to cap the helix. Overall helix termini inside the membrane did not show a very high number of capping motifs; instead these termini were stabilized by helix- helix interactions contributed by the neighboring helices of the helical bundle. In Chapter 5, we examine transmembrane helical (TMH) regions to identify as well as characterize the various types of helix perturbations in membrane proteins using ASSP and Helanal-Plus. A survey of literature shows that the term ‘helix kink’ has been used rather loosely when in fact helical regions show significant amounts of variation and transitions in helical parameters. Hence a systematic analysis of TMH regions was undertaken to quantify different types of helix perturbations, based on geometric parameters such as helical twist, rise per residue and local bending angle. Results from this analysis indicated that helices are not only kinked but undergo transitions to form interspersed stretches of 310 helices and π-bulges within the bilayer. These interspersed 310 and π-helices showed unique sequence preferences within and around their helical body, and also assisted in main- taining the helical structure within the bilayer. We found that Proline not only kinked the helical regions in a characteristic manner but also caused a tightening or unwinding in a helical region to form 310 and π-helix fragments respectively. The helix distortions also resulted in backbone hydrogen bonds to be missed which were stabilized by hydrogen bonds from neighboring residues mediated by their side chain atoms. Furthermore, a packing analysis showed that helical regions with distortions were able to establish inter-helical interactions with more number of transmembrane segments in the helical bundle. The study on helix perturbations presented in the previous chapter, brought to light a previously unreported 19 amino acid π-helix fragment interspersed between α-helices in the functionally important transmembrane helix 2 (TM2) belonging to Mitochondrial cytochrome-c-oxidase (1v55). Chapter 6 describes a case study of the structurally similar but functionally different members within the Heme-Copper- Superoxidases (HCO) superfamily that were considered for a comparative analysis of TM2. An analysis of 7 family members revealed that the π-helix shortens, fragments in two shorter π-helices or was even absent in some family members. The long π-helix significantly decreased the total twist and rise of the entire helical fragment thus accommodating more hydrophobic amino acids within the bilayer to avoid hydrophobic mismatch with the bilayer. The increased radius of the TM2 helical fragment also assisted in helix packing interactions by increasing the number of residues involved in helix-helix interactions and hydrogen bonds. Chapter 7 documents the conclusions from the different analyses presented in each of the above chapters. Overall, it is found that membrane proteins optimize the biophysical and chemical constraints of the external environment to strategically place select amino acids at helix termini to ‘start’ and ‘stop’ α-helices. The stabilization of these helix termini is a consequence of sequence dependent structural preferences to form helix capping motifs. The studies on helix transitions and distortions highlight that membrane proteins are not only packed as α-helices but also accomodate 310- and π-helical fragments. These transitions and distortions help in harboring more hydrophobic amino acids and aiding inter-helical interactions important for maintaining the fold of the membrane protein. Appendix A describes a comparison of α-helix assignments in globular and membrane proteins by two algorithms, one based on Cα trace (ASSP) and the other using a combination of hydrogen bond pattern along with backbone torsion angles φ and ψ (STRIDE).

La RMN à l’état solide est une méthode de choix pour l’étude des protéines insolubles et des complexes protéiques de haut poids moléculaire. L’insolubilité intrinsèque des protéines fibrillaires, ainsi que leur architecture complexe, rendent difficile leur caractérisation structurale par la cristallographie et par la RMN en solution. La RMN à l‘état solide n’est pas limitée par le poids moléculaire et constitue donc un outil puissant pour l’étude des protéines fibrillaires. L’attribution des résonances RMN est le prérequis pour obtenir des informations structurales à résolution atomique. La première partie de ce travail de thèse décrit le développement de méthodes en RMN à l’état solide pour l’attribution des résonances. Nous avons appliqué ces méthodes afin d’attribuer le domaine C-terminal du prion Ure2 (33 kDa), qui est à ce jour la plus grande protéine attribuée par RMN à l’état solide. Nos résultats fournissent les bases pour l’étude de protéines à haut poids moléculaire à l’échelle atomique. Ceci est démontré dans la seconde partie de ce travail de thèse avec les premières études RMN à l’état solide des fibrilles des prions Ure2 et Sup35. Nous avons caractérisé la structure de ces prions pour les fibrilles entières ainsi que pour les domaines isolés. La troisième fibrille étudiée est l’α- synuclein, fibrille associée à la maladie de Parkinson, pour laquelle nous présentons l’attribution des résonances RMN ainsi que la structure secondaire d’un nouveau polymorphe. Les études présentées ici fournissent de nouvelles clés pour comprendre la diversité des architectures de fibrilles, en considérant les fibrilles comme entités individuelles d’un point de vue structural / Solid-state NMR is the method of choice for studies on insoluble proteins and other high molecular weight protein complexes. The inherent insolubility of fibrillar proteins, as well as their complex architecture, makes the application of x-ray crystallography and solution state NMR difficult. Solid-state NMR is not limited by the molecular weight or by the absence of long-range structural order, and is thus a powerful tool for the 3D structural investigation of fibrillar proteins. The assignment of the NMR resonances is a prerequisite to obtain structural information at atomic level. The first part of this thesis describes the development of solid-state NMR methods to assign the resonances in large proteins. We apply these methods to assign the 33 kDa C-terminal domain of the Ure2p prion which is up to now the largest protein assigned by solid-state NMR. Our results provide the basis to study high molecular weight proteins at atomic level. This is demonstrated in the second part with the first high-resolution solid-state NMR study of Ure2 and Sup35 prion fibrils. We describe the conformation of the functional domains and prion domains in the full-length fibrils and in isolation. The third fibrillar protein addressed in this work is the Parkinson’s disease related α-synuclein whereof we demonstrate the NMR resonance assignment and the secondary structure determination of a new polymorph. Thus, the studies described here provide new insights in the structural diversity of fibril architectures, and plead to view fibrils as individuals from a structural point of view, rather than a homogenous protein family

As proteínas são moléculas que desempenham funções essenciais para a vida. Para entender a função de uma proteína é preciso conhecer sua estrutura tridimensional. No entanto, encontrar a estrutura da proteína pode ser um processo caro e demorado, exigindo profissionais altamente qualificados. Neste sentido, métodos computacionais têm sido investigados buscando predizer a estrutura de uma proteína a partir de uma sequência de aminoácidos. Em geral, tais métodos computacionais utilizam conhecimentos de estruturas de proteínas já determinadas por métodos experimentais, para tentar predizer proteínas com estrutura desconhecida. Embora métodos computacionais como, por exemplo, o Rosetta, I-Tasser e Quark tenham apresentado sucesso em suas predições, são apenas capazes de produzir estruturas significativamente semelhantes às já determinadas experimentalmente. Com isso, por utilizarem conhecimento a priori de outras estruturas pode haver certa tendência em suas predições. Buscando elaborar um algoritmo eficiente para Predição de Estruturas de Proteínas livre de tendência foi desenvolvido um Algoritmo de Estimação de Distribuição (EDA) específico para esse problema, com modelagens full-atom e algoritmos ab initio. O fato do algoritmo proposto ser ab initio é mais interessante para aplicação envolvendo proteínas com baixa similaridade, com relação às estruturas já conhecidas. Três tipos de modelos probabilísticos foram desenvolvidos: univariado, bivariado e hierárquico. O univariado trata o aspecto de multi-modalidade de uma variável, o bivariado trata os ângulos diedrais (&Phi; &Psi;) de um mesmo aminoácido como variáveis correlacionadas. O hierárquico divide o problema em subproblemas e tenta tratá-los separadamente. Os resultados desta pesquisa mostraram que é possível obter melhores resultados quando considerado a relação bivariada (&Phi; &Psi;). O hierárquico também mostrou melhorias nos resultados obtidos, principalmente para proteínas com mais de 50 resíduos. Além disso, foi realiza uma comparação com algumas heurísticas da literatura, como: Busca Aleatória, Monte Carlo, Algoritmo Genético e Evolução Diferencial. Os resultados mostraram que mesmo uma metaheurística pouco eficiente, como a Busca Aleatória, pode encontrar a solução correta, porém utilizando muito conhecimento a priori (predição que pode ser tendenciosa). Por outro lado, o algoritmo proposto neste trabalho foi capaz de obter a estrutura da proteína esperada sem utilizar conhecimento a priori, caracterizando uma predição puramente ab initio (livre de tendência). / Proteins are molecules that perform critical roles in the living organism and they are essential for their lifes. To understand the function of a protein, its 3D structure should be known. However, to find the protein structure is an expensive and a time-consuming task, requiring highly skilled professionals. Aiming to overcome such a limitation, computational methods for Protein Structure Prediction (PSP) have been investigated, in order to predict the protein structure from its amino acid sequence. Most of computational methods require knowledge from already determined structures from experimental methods in order to predict an unknown protein. Although computational methods such as Rosetta, I-Tasser and Quark have showed success in their predictions, they are only capable to predict quite similar structures to already known proteins obtained experimentally. The use of such a prior knowledge in the predictions of Rosetta, I-Tasser and Quark may lead to biased predictions. In order to develop a computational algorithm for PSP free of bias, we developed an Estimation of Distribution Algorithm applied to PSP with full-atom and ab initio model. A computational algorithm with ab initio model is mainly interesting when dealing with proteins with low similarity with the known proteins. In this work, we developed an Estimation of Distribution Algorithm with three probabilistic models: univariate, bivariate and hierarchical. The univariate deals with multi-modality of the distribution of the data of a single variable. The bivariate treats the dihedral angles (Proteins are molecules that perform critical roles in the living organism and they are essential for their lifes. To understand the function of a protein, its 3D structure should be known. However, to find the protein structure is an expensive and a time-consuming task, requiring highly skilled professionals. Aiming to overcome such a limitation, computational methods for Protein Structure Prediction (PSP) have been investigated, in order to predict the protein structure from its amino acid sequence. Most of computational methods require knowledge from already determined structures from experimental methods in order to predict an unknown protein. Although computational methods such as Rosetta, I-Tasser and Quark have showed success in their predictions, they are only capable to predict quite similar structures to already known proteins obtained experimentally. The use of such a prior knowledge in the predictions of Rosetta, I-Tasser and Quark may lead to biased predictions. In order to develop a computational algorithm for PSP free of bias, we developed an Estimation of Distribution Algorithm applied to PSP with full-atom and ab initio model. A computational algorithm with ab initio model is mainly interesting when dealing with proteins with low similarity with the known proteins. In this work, we developed an Estimation of Distribution Algorithm with three probabilistic models: univariate, bivariate and hierarchical. The univariate deals with multi-modality of the distribution of the data of a single variable. The bivariate treats the dihedral angles (&Phi; &Psi;) within an amino acid as correlated variables. The hierarchical approach splits the original problem into subproblems and attempts to treat these problems in a separated manner. The experiments show that, indeed, it is possible to achieve better results when modeling the correlation (&Phi; &Psi;). The hierarchical model also showed that is possible to improve the quality of results, mainly for proteins above 50 residues. Besides, we compared our proposed techniques among other metaheuristics from literatures such as: Random Walk, Monte Carlo, Genetic Algorithm and Differential Evolution. The results show that even a less efficient metaheuristic such as Random Walk managed to find the correct structure, however using many prior knowledge (prediction that may be biased). On the other hand, our proposed EDA for PSP was able to find the correct structure with no prior knowledge at all, so we can call this prediction as pure ab initio (biased-free).