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One of the most difficult problems in modern biochemistry is that of accurately predicting a protein's three dimensional structure from its sequence (the <I>protein folding problem</I>). This structure is essential for a proper understanding of how a protein functions. As experimental derivation of a protein's structure is far more time consuming than deriving a protein's sequence, prediction of structure from sequence is an important goal for many protein biochemists; several methods have been suggested for this. Given a protein of known structure of similar sequence to the protein you wish to model homology modelling is the method most likely to produce a fairly good model. In this work a tool was produced for examining the various stages of homology modelling and analysing how well various method for carrying out these stages perform. The tool produced consists of an object-oriented database of protein structures and testbed software written in a mixture of PROLOG and DAPLEX. Tests were carried out using this software to examine the predictivity of various guidelines suggested in the literature for the loop selection stage of cut and paste homology modelling. The results of these tests produced surprising new information on the relative importance of different factors which may be used to choose between candidate fragments for the variable regions of a protein being modelled. The results of the application of these automated modelling methods were then compared with a short series of modelling tests using human modellers in an attempt to measure how the usual modelling procedures using 'hand and eye' compare with automated measures. Finally the results of the tests carried out were used to guide the production of a model of a previously unmodelled serine proteinase.

Submitted by isabela.moljf@hotmail.com (isabela.moljf@hotmail.com) on 2017-06-21T11:13:33Z No. of bitstreams: 1 arturduquerossi.pdf: 11420528 bytes, checksum: 07d7635a64ff2d13fe27216b526f4f72 (MD5) / Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2017-08-07T19:03:40Z (GMT) No. of bitstreams: 1 arturduquerossi.pdf: 11420528 bytes, checksum: 07d7635a64ff2d13fe27216b526f4f72 (MD5) / Made available in DSpace on 2017-08-07T19:03:40Z (GMT). No. of bitstreams: 1 arturduquerossi.pdf: 11420528 bytes, checksum: 07d7635a64ff2d13fe27216b526f4f72 (MD5) Previous issue date: 2017-02-24 / O MHOLline é um workflow científico voltado para a modelagem e análise de proteínas, atendendo a pesquisadores de diversas áreas, como Bioinformática, Biofísica, Químicos Computacionais e Biólogos Computacionais. Este projeto, iniciado em 2004 como um software de uso local, tornou-se um serviço web em 2010, através da parceria da Universidade Federal do Rio de Janeiro (UFRJ) com o Laboratório Nacional de Computação Científica (LNCC), o qual pode ser acessado pelo endereço web http : //www.mholline . lncc . br. Em 2013, uma parceria com a Universidade Federal de Juiz de Fora deu início ao projeto do MHOLline 2.0, disponível no endereço web http : //www.mholline2 . lncc .br, que conta com adições de softwares, uma interface completamente nova e uma área de refinamento de resultados para usuários logados. A área do refinamento de resultados oferece a possibilidade aos usuários de adicionar ou trocar o molde da proteína modelada, criar restrições de estrutura secundária no Modeller, clivar regiões de peptídeo sinal e otimizar loops no Modeller, tudo de forma automática, dispensando a necessidade do usuário gerar qualquer script manualmente. Caso o usuário deseje é possível refinar a proteína automaticamente, através do uso de ferramentas de inteligência artificial para classificar os resultados gerados com as opções de restrição modeladas, em grupos, visando reduzir o trabalho de analisar os resultados finais do refinamento. Neste trabalho, apresentamos também uma nova proposta para agrupamento de modelos de proteínas baseado em um conjunto de atributos relacionados com a sua qualidade (e.g. energia e estrutural). Ao usuário, além dos grupos de estruturas com qualidades similares, também é retornada a estrutura representativa de cada grupo, com o objetivo de auxiliar na tomada de decisão de qual ou quais modelos seguirão para os próximos estudos. / MHOLline is a scientific workflow designed to model and analyze proteins, reaching researchers in domains of Bioinformatics, Biophysics, Computational Chemists and Computational Biologists. This project started in 2004 as a local software and became a web service in 2010 (available at http : //www .mholline . lncc . br), through the partnership between the Universidade Federal do Rio de Janeiro (UFRJ) and Laboratório Nacional de Computação Científica (LNCC). In 2013, a new partnership with Universidade Federal de Juiz de Fora started the development of MHOLline 2.0, now available at http : //www .mholline2 . lncc . br. This version presents a new interface and a refinement ama to logged users, offering the possibility to add or modify the template of the protein, remove signal peptides and restrict secondary structures and optimize protein loops on Modeller. All can be done in an automatic way, dispensing the user to manually generate any script. The user can also refine the protein automatically trough the use of artificial intelligence tools classifying the generated results with a set of restrictions in groups, aiming to reduce the effort to analyze the final refinement results. In this work, we also present a new proposal for clustering protein models based on a set of attributes related to their quality (i.e., energy and structural quality). To the user, in addition to the groups of structures with similar qualities, is also returned the representative structure of each group, in order to assist in the decision making of which model or models will follow for the future studies.

Using computational methods to identify protein-protein interactions (PPIs) supports experimental techniques by using less time and less resources. Identifying PPIs can be made through a template-based approach that describes how unstudied proteins interact by aligning a common structural template that exists in both interacting proteins. A pipeline that uses this is InterPred, that combines homology modelling and massive template comparison to construct coarse interaction models. These models are reviewed by a machine learning classifier that classifies models that shows traits of being true, which can be further refined with a docking technique. However, InterPred is dependent on using complex structural information, that might not be available from unstudied proteins, while it is suggested that PPIs are dependent of the shape and interface of proteins. A method that aligns structures based on the interface attributes is InterComp, which uses topological and sequence-order independent structural comparison. Implementing this method into InterPred will lead to restricting structural information to the interface of proteins, which could lead to discovery of undetected PPI models. The result showed that the modified pipeline was not comparable based on the receiver operating characteristic (ROC) performance. However, the modified pipeline could identify new potential PPIs that were undetected by InterPred.

Agar is a marine heteropolysaccharide with repeating units consisting of 3,6-α-anhydro-L-galactopyranose and D-galactopyranose linked by α-(1,3) and β-(1,4) linkages. It has been promoted as a prospective replacement for petroleum-based feedstocks and other applications. Enzymatic biotransformation of agar generates high specific products: It is also more environmentally friendly than chemical hydrolysis. In particular, agarolytic bacteria and their agarases are preferred for the processing of agar into sugar derivatives. Agar-producing macroalgae are one of Indonesia's national commodities. However, agar-based products and technology are rarely developed in Indonesia. This research is aimed to explore the potential of an Indonesian marine bacterium and its agarases as bioagents for agar bioprocessing. The research objectives are to identify the novelty of the isolate among known agarolytic bacteria using microbiology and molecular biology approaches, to elucidate the agarolytic system of the bacterium using in silico genome analysis, to express and characterize the recombinant agarases, and to elucidate their potential for producing agar-derived saccharides from Indonesian natural agar. Microbulbifer elongatus PORT2 is a gram-negative marine bacterium that had been isolated from Batu Karas seawater, Pangandaran, West Java Indonesia. PORT2 shows potential as biocatalysts for agar saccharides conversion by showing remarkable agar liquefaction. The annotation of the draft genome identifies six putative β-agarases consist of three GH50, two GH86, and one GH16 in M. elongatus PORT2. Those agarases are clustered at two different contigs. Besides agarases, other genes for D-galactose and 3,6 anhydro-L galactose metabolism, sugar transports and regulatory system are found in the vicinity of the agarases clusters. Despite the ability to utilize agar as a sole carbon sole, PORT2 lacks any putative α-agarase GH117 or GH96. Both are responsible for the cleavage of α-glycosidic bonds in agar. Indeed, several hypothetical proteins are in the neighborhood of the agarase gene clusters in M. elongatus PORT2. They probably could have a function as the alternative machinery or pathway for agar monomerization that needs clarification in future research work. Four recombinant β-agarases from PORT2; AgaA50, AgaB50, AgaC50, and AgaF16A have been successfully overexpressed in E.coli and characterized. The AgaA50 and AgaC50 exhibit metal-dependent activity. They perform exo-agarolytic modes and generates neoagarobiose (NA2). The AgaB50 can act as endo-and exo-β-agarase without any additional activator and produces neoagarohexaose (NA6), neoagarotetraose (NA4), and NA2. AgaF16 produces NA6 and NA4. The enzyme shows pure endo-catalytic action which thiol agents positively affect its activity. The synergetic reaction of AgaF16A and AgaA50 converts Indonesian Gelidium agar into NA2 and Gracilaria agar into modified NA2. The modified NA2 from Gracilaria agar could promise new potential bioactivity that is different from agarose-derived NA2 due to the presence of additional side chains on the saccharide backbone. The NA6, NA4, and NA2 products from agarose have shown potential pharmaceutical applications such as immunomodulator, anti-tumor, antioxidant, anti-diabetic, and moisturizer. Despite being isolated from a mesophilic marine bacterium, the recombinant agarases from M. elongatus PORT2 are active at 50 °C and pH between 6.5 to 8. They maintain more than 75% of their activities even after 1 h preincubation at 50 °C, except for AgaC50. Their thermostability gives advantages for the effective biocatalytic conversion of agar because the substrate is more accessible at mild pH and the temperature above the sol-gel condition (> 40 °C).:Contents 1. Introduction 1 1.1. Motivation and Scientific Goals 1 1.2. Literature Review 3 2. Materials and Methods 12 2.1. Materials 12 2.2. Methods 13 3. Agarolytic Bacterium Microbulbifer elongatus PORT2 22 3.1. Results 22 3.2. Discussion 28 4. Genome Profiling for In Silico Elucidation of the Agarolytic System 32 4.1. Results 32 4.2. Discussion 41 5. Recombinant Agarases from Microbulbifer elongatus PORT2 44 5.1. Results 44 5.2. Discussion 71 6. Conclusions and Outlooks 78 References 81 Appendices 97 Acknowledgements 110

Membrane proteins represent about one third of all known vertebrate proteins and over half of the current drug targets. Knowledge of their three-dimensional (3D) structure is worth millions of pounds to the pharmaceutical industry. Yet experimental structure elucidation of membrane proteins is a slow and expensive process. In the absence of experimental data, computational modelling tools can be used to close the gap between the numbers of known protein sequences and structures. However, currently available structure prediction tools were developed with globular soluble proteins in mind and perform poorly on membrane proteins. This thesis describes the development of a modelling approach able to predict accurately the structure of transmembrane domains of proteins. In this thesis we build a template-based modelling framework especially for membrane proteins, which uses membrane protein-specific information to inform the modelling process.Firstly, we develop a tool to accurately determine a given membrane protein structure's orientation within the membrane. We offer an analysis of the preferred substitution patterns within the membrane, as opposed to non-membrane environments, and how these differences influence the structures observed. This information is then used to build a set of tools that produce better sequence alignments of membrane proteins, compared to previously available methods, as well as more accurate predictions of their 3D structures. Each chapter describes one new piece of software or information and uses the tools and knowledge described in previous chapters to build up to a complete accurate model of a transmembrane domain.