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Hvidsten, Torgeir R. "Predicting Function of Genes and Proteins from Sequence, Structure and Expression Data." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis : Univ.-bibl. [distributör], 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-4490.
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Muhammad, Ashfaq. "Design and Development of a Database for the Classification of Corynebacterium glutamicum Genes, Proteins, Mutants and Experimental Protocols." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-23.
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<p>Coryneform bacteria are largely distributed in nature and are rod like, aerobic soil bacteria capable of growing on a variety of sugars and organic acids. Corynebacterium glutamicum is a nonpathogenic species of Coryneform bacteria used for industrial production of amino acids. There are three main publicly available genome annotations, Cg, Cgl and NCgl for C. glutamicum. All these three annotations have different numbers of protein coding genes and varying numbers of overlaps of similar genes. The original data is only available in text files. In this format of genome data, it was not easy to search and compare the data among different annotations and it was impossible to make an extensive multidimensional customized formal search against different protein parameters. Comparison of all genome annotations for construction deletion, over-expression mutants, graphical representation of genome information, such as gene locations, neighboring genes, orientation (direct or complementary strand), overlapping genes, gene lengths, graphical output for structure function relation by comparison of predicted trans-membrane domains (TMD) and functional protein domains protein motifs was not possible when data is inconsistent and redundant on various publicly available biological database servers. There was therefore a need for a system of managing the data for mutants and experimental setups. In spite of the fact that the genome sequence is known, until now no databank providing such a complete set of information has been available. We solved these problems by developing a standalone relational database software application covering data processing, protein-DNA sequence extraction and</p><p>management of lab data. The result of the study is an application named, CORYNEBASE, which is a software that meets our aims and objectives.</p>
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Reddy, Joseph. "Identification and Analysis of Important Proteins in Protein Interaction Networks Using Functional and Topological Information." Thesis, University of Skövde, School of Life Sciences, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-2395.
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<p>Studying protein interaction networks using functional and topological information is important for understanding cellular organization and functionality. This study deals with identifying important proteins in protein interaction networks using SWEMODE (Lubovac, et al, 2006) and analyzing topological and functional properties of these proteins with the help of information derived from modular organization in protein interaction networks as well as information available in public resources, in this case, annotation sources describing the functionality of proteins. Multi-modular proteins are short-listed from the modules generated by SWEMODE. Properties of these short-listed proteins are then analyzed using functional information from SGD Gene Ontology(GO) (Dwight, et al., 2002) and MIPS functional categories (Ruepp, et al., 2004). Topological features such as lethality and centrality of these proteins are also investigated, using graph theoretic properties and information on lethal genes from Yeast Hub (Kei-Hoi, et al., 2005). The findings of the study based on GO terms reveal that these important proteins are mostly involved in the biological process of “organelle organization and biogenesis” and a majority of these proteins belong to MIPS “cellular organization” and “transcription” functional categories. A study of lethality reveals that multi-modular proteins are more likely to be lethal than proteins present only in a single module. An examination of centrality (degree of connectivity of proteins) in the network reveals that the ratio of number of important proteins to number of hubs at different hub sizes increases with the hub size (degree).</p>
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Callahan, Nicholas. "Bioinformatics-Driven Enzyme Engineering: Work On Adenylate Kinase." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1420802270.
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Tsirigos, Konstantinos. "Bioinformatics Methods for Topology Prediction of Membrane Proteins." Doctoral thesis, Stockholms universitet, Institutionen för biokemi och biofysik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-138479.
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Membrane proteins are key elements of the cell since they are associated with a variety of very important biological functions crucial to its survival. They are implicated in cellular recognition and adhesion, act as molecular receptors, transport substrates through membranes and exhibit specific enzymatic activity.This thesis is focused on integral membrane proteins, most of which contain transmembrane segments that form an alpha helix and are composed of mainly hydrophobic residues, spanning the lipid bilayer. A more specialized and less well-studied case, is the case of integral membrane proteins found in the outer membrane of Gram-negative bacteria and (presumably) in the outer envelope of mitochondria and chloroplasts, proteins whose transmembrane segments are formed by amphipathic beta strands that create a closed barrel (beta-barrels). The importance of transmembrane proteins, as well as the inherent difficulties in crystallizing and obtaining three-dimensional structures of these, dictates the need for developing computational algorithms and tools that will allow for a reliable and fast prediction of their structural and functional features. In order to elucidate their function, we must acquire knowledge about their structure and topology with relation to the membrane. Therefore, a large number of computational methods have been developed in order to predict the transmembrane segments and the overall topology of transmembrane proteins. In this thesis, I initially describe a large-scale benchmark of many topology prediction tools in order to devise a strategy that will allow for better detection of alpha-helical membrane proteins in a proteome. Then, I give a description of construction of improved machine-learning algorithms and computer software for accurate topology prediction of transmembrane proteins and discrimination of such proteins from non-transmembrane proteins. Finally, I introduce a fast way to obtain a position-specific scoring matrix, which is essential for modern topology prediction methods.<br><p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.</p>
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Viklund, Håkan. "Formalizing life : Towards an improved understanding of the sequence-structure relationship in alpha-helical transmembrane proteins." Doctoral thesis, Stockholm University, Department of Biochemistry and Biophysics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-7144.
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<p>Genes coding for alpha-helical transmembrane proteins constitute roughly 25% of the total number of genes in a typical organism. As these proteins are vital parts of many biological processes, an improved understanding of them is important for achieving a better understanding of the mechanisms that constitute life.</p><p>All proteins consist of an amino acid sequence that fold into a three-dimensional structure in order to perform its biological function. The work presented in this thesis is directed towards improving the understanding of the relationship between sequence and structure for alpha-helical transmembrane proteins. Specifically, five original methods for predicting the topology of alpha-helical transmembrane proteins have been developed: PRO-TMHMM, PRODIV-TMHMM, OCTOPUS, Toppred III and SCAMPI. </p><p>A general conclusion from these studies is that approaches that use multiple sequence information achive the best prediction accuracy. Further, the properties of reentrant regions have been studied, both with respect to sequence and structure. One result of this study is an improved definition of the topological grammar of transmembrane proteins, which is used in OCTOPUS and shown to further improve topology prediction. Finally, Z-coordinates, an alternative system for representation of topological information for transmembrane proteins that is based on distance to the membrane center has been introduced, and a method for predicting Z-coordinates from amino acid sequence, Z-PRED, has been developed.</p>
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Repo, Susanna. "Structural bioinformatics in the study of protein function and evolution /." Turku, Finland : Dept. of Biochemistry and Pharmacy, Abo Akademi University, 2008. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=017048818&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.
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Adeyemi, Samson Adebowale. "Structural bioinformatics analysis of the Hsp40 and Hsp70 molecular chaperones from humans." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1020962.
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HSP70 is one of the most important families of molecular chaperone that regulate the folding and transport of client proteins in an ATP dependent manner. The ATPase activity of HSP70 is stimulated through an interaction with its family of HSP40 co-chaperones. There is evidence to suggest that specific partnerships occur between the different HSP40 and HSP70 isoforms. While some of the residues involved in the interaction are known, many of the residues governing the specificity of HSP40-HSP70 partnerships are not precisely defined. It is not currently possible to predict which HSP40 and HSP70 isoforms will interact. We attempted to use bioinformatics to identify residues involved in the specificity of the interaction between the J domain from HSP40 and the ATPase domain from the HSP70 isoforms from humans. A total of 49 HSP40 and 13 HSP70 sequences from humans were retrieved and used for subsequent analyses. The HSP40 J domains and HSP70 ATPase domains were extracted using python scripts and classified according to the subcellular localization of the proteins using localization prediction programs. Motif analysis was carried out using the full length HSP40 proteins and Multiple Sequence Alignment (MSA) was performed to identify conserved residues that may contribute to the J domain – ATPase domain interactions. Phylogenetic inference of the proteins was also performed in order to study their evolutionary relationship. Homology models of the J domains and ATPase domains were generated. The corresponding models were docked using HADDOCK server in order to analyze possible putative interactions between the partner proteins using the Protein Interactions Calculator (PIC). The level of residue conservation was found to be higher in Type I and II HSP40 than in Type III J proteins. While highly conserved residues on helixes II and III could play critical roles in J domain interactions with corresponding HSP70s, conserved residues on helixes I and IV seemed to be significant in keeping the J domain in its right orientation for functional interactions with HSP70s. Our results also showed that helixes II and III formed the interaction interface for binding to HSP70 ATPase domain as well as the linker residues. Finally, data based docking procedures, such as applied in this study, could be an effective method to investigate protein-protein interactions complex of biomolecules.
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Dickens, Nicholas J. "Comparisons of proteins and genomes by integrating bioinformatics data." Thesis, University of Oxford, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.496848.
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Nordström, Karl J. V. "Characterization and Evolution of Transmembrane Proteins with Focus on G-protein coupled receptors in Pre-vertebrate Species." Doctoral thesis, Uppsala universitet, Funktionell farmakologi, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-121696.
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G protein-coupled receptors (GPCRs) are one of the largest protein families in mammals. GPCRs are instrumental for hormonal and neurotransmitter signalling and are important in all major physiological systems of the body. Paper I describes the repertoire of GPCRs in Branchiostoma floridae, which is one of the species most closely related species to vertebrates. Mining and phylogenetic analysis of the amphioxus genome showed the presence of at least 664 distinct GPCRs distributed among all the main families of GPCRs; Glutamate (18), Rhodopsin (570), Adhesion (37), Frizzled (6) and Secretin (16). Paper II contains studies of the Adhesion, Methuselah and Secretin GPCR families in nine genomes. The Adhesion GPCRs are the most complex gene family among GPCRs with large genomic size, multiple introns and a fascinating flora of functional domains. Phylogenetic analysis showed Adhesion group V (that contains GPR133 and GPR144) to be the closest relative to the Secretin family among the groups in the Adhesion family, which was also supported by splice site setup and conserved motifs. Paper III examines the repertoire of human transmembrane proteins. These form key nodes in mediating the cell’s interaction with the surroundings, which is one of the main reasons why the majority of drug targets are membrane proteins. We identified 6,718 human membrane proteins and classified the majority of them into 234 families of which 151 belong to the three major functional groups; Receptors (63 groups, 1,352 members), Transporters (89 groups, 817 members) or Enzymes (7 groups, 533 members). In addition, 74 Miscellaneous groups were shown to include 697 members. Paper IV clarifies the hierarchy of the main families and evolutionary origin of majority of the metazoan GPCR families. Overall, it suggests common decent of at least 97% of the GPCRs sequences found in humans, including all the main families.
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Hedin, Linnea E., Kristoffer Illergård, and Arne Elofsson. "An Introduction to Membrane Proteins." Stockholms universitet, Institutionen för biokemi och biofysik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-69241.
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alpha-Helical membrane proteins are important for many biological functions. Due to physicochemical constraints, the structures of membrane proteins differ from the structure of soluble proteins. Historically, membrane protein structures were assumed to be more or less two-dimensional, consisting of long, straight, membrane-spanning parallel helices packed against each other. However, during the past decade, a number of the new membrane protein structures cast doubt on this notion. Today, it is evident that the structures of many membrane proteins are equally complex as for many soluble proteins. Here, we review this development and discuss the consequences for our understanding of membrane protein biogenesis, folding, evolution, and bioinformatics.<br><p>authorCount :3</p>
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Sheth, Vrunda. "Visualization of protein 3D structures in reduced represetnation with simultaneous display of intra and inter-molecular interactions /." Online version of thesis, 2009. http://hdl.handle.net/1850/10857.
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Cuthbertson, Jonathan M. "Structural bioinformatics and simulation studies of α-helical membrane proteins." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.420449.
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Shafiq, Muhammad Imtiaz. "Molecular modelling and bioinformatics studies of CDK4 and related proteins." Thesis, University of Leicester, 2011. http://hdl.handle.net/2381/10295.
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Cyclin-dependent kinases play a key role in the regulation of the eukaryotic cell cycle. CDK4 regulates the G1/S phase transition and the entry into the S-phase of the cell cycle. The activity of CDK4 is misregulated in many human cancers. The natural product fascaplysin inhibits CDK4 specifically, and is considered as a lead compound for specific CDK4 inhibitors. In the present work the structural features of the active sites of CDKs are compared, the evolution of CDKs is studied and homology models of CDK4 are generated and used to gain insights into its sequential and structural features. Also the CDK4-ligand interactions of fascaplysin and its tryptamine based derivatives are predicted and the fascaplysin specificity for CDK4 is at least partially explained using thermodynamic integration. CDK4 homology models were generated based on CDK2 templates. However, after the availability of experimentally determined X-ray structures of CDK4 in an inactive form, CDK4 models were built in a putative active form by incorporating the structural information from both CDK4 and CDK2 for its later use in molecular modelling. Docking studies on fascaplysin with CDK4 predict a polar contact between His95CDK4 and fascaplysin in addition to bidentate hydrogen bonds with Val96. This interaction partly explains the selectivity for CDK4 compared to CDK2. The effect of the positive charge of fascaplysin on specificity is studied in thermodynamic integration MD simulations by the isoelectronic substitution of the positively charged nitrogen into a carbon atom. From these thermodynamics integration calculations it is concluded that fascaplysin shows a preference for CDK4 due to better stabilization of the positive charge. ChemScores for tryptamine based derivatives docked into CDK4 show a weak correlation with experimental IC50 values. This indicates that the ChemScores can be used as a weak predictor for relative affinities of CDK4 inhibitors. A new class of α- carboline based inhibitors is proposed, and based on docking studies, predicted to have improved binding affinities for CDK4.
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Sakhawalkar, Neha. "Hub Proteins, Paralogs, and Unknown Proteins in Bacterial Interaction Networks." VCU Scholars Compass, 2017. http://scholarscompass.vcu.edu/etd/4730.
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Proteins are the functional units of cells. However, a major portion of the proteome does not have a known functional annotation. This dissertation explores protein -protein interactions, involving these uncharacterized or unknown function proteins. Initially, protein – protein interactions were tested and analyzed for paralogous proteins in Escherichia coli. To expand this concept further and to get an overview, protein – protein interactions were analyzed using ‘comparative interactomics’ for four pathogenic bacterial species including Escherichia coli, Yersinia pestis, Vibrio cholerae and Staphylococcus aureus. This approach was used to study unknown function protein pairs as well as to focus on uncharacterized hub proteins. The dissertation aims at using protein – protein interactions along with other research data about proteins as a possible approach to narrow down on functions of proteins.
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Siu, Wing-yan. "Multiple structural alignment for proteins." Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B4068748X.
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Mthombeni, Jabulani S. "A comparative bioinformatic analysis of zinc binuclear cluster proteins." Thesis, Rhodes University, 2005. http://hdl.handle.net/10962/d1004064.
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Members of the zinc binuclear cluster family are important fungal transcriptional regulators sharing a common DNA binding domain. Da181p is a pleotropic zinc binuclear cluster protein involved in the induction of the UGA genes required for the γ-aminobutyrate nitrogen catabolic pathway in Saccharomyces cerevisiae. The zinc binuclear cluster domain is indispensable for function in Da181p and little is known about other domains in this protein. The aim of the study was to explore the zinc binuclear cluster protein family using comparative bioinformatics as a complement to biochemical and structural approaches. A database of all zinc binuclear cluster proteins was composed. A total of 118 zinc binuclear proteins are reported in this work. Thirty nine previously unidentified zinc binuclear cluster proteins were found. Four homologues of Da181p were identified by homology searching. Important sequence motifs were identified in the aligned sequences of Da181p and its homologues. The coiled coil motif found in the Ga14p zinc binuclear cluster protein could not be identified in Da181p and its homologues. This suggested that Da181p did not dimerise through this structural motif as other zinc binuclear cluster proteins. Solvent accessible site that could be phosphorylated by protein kinase C or casein kinase II and the role of such sites in the possible regulation of Da181p function were discussed.
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Peterson, Mark Erik. "Evolutionary constraints on the structural similarity of proteins and applications to comparative protein structure modeling." Diss., Search in ProQuest Dissertations & Theses. UC Only, 2008. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3339202.
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Bray, Tracey. "From structure to function in proteins : a computational study." Thesis, University of Manchester, 2010. https://www.research.manchester.ac.uk/portal/en/theses/from-structure-to-function-a-computational-study(5a78c88c-f890-4c2f-9122-a7adec5d2ca0).html.
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The study of proteins and their function is key to understanding how the cell works in normal and disease states. Historically, the study of protein function was limited to biochemical characterisation, but as computing power and the number of available protein sequences and structures increased this allowed the relationship between sequence, structure and function to be explored. As the number of sequences and structures grows beyond the capacity for experimental groups to study them, computational approaches to inferring function become more important. Enzymes make up approximately half of the known protein sequences and structures, and most of the work in this thesis focuses on the relationship between the sequence, structure and function in enzymes.Firstly, the differences in sequence and structural features between enzymes of the six main functional classes are explored. Features that exhibited the most significant differences between the six classes were further studied to explore their link with function. This study suggested reasons as to why groups of functionally similar but non-homologous enzymes share similar sequence and structural features. A computational tool to predict EC class was then developed in an attempt to exploit the differences in these features. In order to calculate features relating to a particular active site to be used in the EC class prediction method, it was first necessary to predict the active site location. A comprehensive analysis of currently-available functional site prediction tools identified an approach previously developed by this group as amongst the best-performing methods. Here, a tool was created to deliver this approach via a publicly-available web-server, which was subsequently used in the attempt to predict EC class. The study of differences in sequence and structural features between classes revealed differences in oligomeric status between functions. High-order oligomers were linked to an increase in metabolic control in the lyases, possibly via mechanisms such as cooperativity. To further test this idea, it was necessary to be able to computationally identify oligomeric enzymes that act cooperatively. Since no such method currently exists, the degree of coupling of dynamic fluctuations between subunits was explored as a possible way of detecting cooperativity. Whilst this was unsuccessful, the study highlighted the existence of a pattern of correlated motions that were conserved over a wide range of non-homologous and functionally diverse proteins. These observations shed further light on the link between sequence, structure and function and highlight the functional importance of dynamics in protein structures.
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Siu, Wing-yan, and 蕭穎欣. "Multiple structural alignment for proteins." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B4068748X.
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Jüttemann, Thomas. "Adding 3D-structural context to protein-protein interaction data from high-throughput experiments." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5666.
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In the past decade, automatisation has led to an immense increase of data in biology. Next generation sequencing techniques will produce a vast amount of sequences across all species in the coming years. In many cases, identifying the function and biological role of a protein from its sequence can be a complicated and time-intensive task. The identification of a protein's interaction partners is a tremendous help for understanding the biological context in which it is involved. In order to fully characterise a protein-protein interaction (PPIs), it is necessary to know the three-dimensional structure of the interacting partners. Despite optimisation efforts from projects such as the Protein Structure Initivative, determining the structure of a protein through crystallography remains a time- and cost-intensive procedure. The primary aim of the research described in this dissertation was to produce a World Wide Web resource that facilitates visual exploration and validation (or questioning) of data derived from functional genomics experiments, by building upon existing structural information about direct physical PPIs. Secondary aims were (i) to demonstrate the utility of the new resource, and (ii) its application in biological research. We created a database that emphasises specifically the intersection between the PPIs-results emerging from the structural biology and functional genomics communities. The BISC database holds BInary SubComplexes and Modellable Interactions in current functional genomics databases (BICS-MI). It is publicly available at hyyp://bisc.cse.ucsc.edu. BISC is divided in three sections that deliver three types of information of interest to users seeking to investigate or browse PPIs. The template section (BISCHom and BISCHet) is devoted to those PPIs that are characterised in structural detail, i.e. binary SCs extracted from experimentally determined three-dimensional structures. BISCHom and BISCHet contain the homodimeric (13,583 records) and heterodimeric (5612 records) portions of these, respectively. Besides interactive, embedded Jmol displays emphasising the interface, standard information and links are provided, e.g. sequence information and SPOP classification for both partners, interface size and energy scores (PISA). An automated launch of the MolSurfer program enables the user to investigate electrostatic and hydrophobic correlation between the partners, at the inter-molecular interface. The modellable interactions section (BISC0MI) identifies potentially modellable interactions in three major functional genomics interaction databases (BioGRID), IntAct, HPRD). To create BISC-MI all PPIs that are amenable to automated homology modelling based on conservative similarity cut-offs and whose partner protein sequences have recrods in the UniProt database, have been extracted. The modellable interaction services (BISC-MI Services) section offers, upon user request, modelled SC-structures for any PPIs in BISC-MI. This is enabled through an untomated template-based (homology) modelling protocol using the popular MODELLER program. First, a multiple sequence alignment (MSA) is generated using MUSCLE, between the target and homologous proteins collected from UniProt (only reviewed proteins from organisms whose genome has been completely sequenced are included to find putative orthologs). Then a sequence-to-profile alignment is generated to integrate the template structure in the MSA. All models are produced upon user request to ensure that the most recent sequence data for the MSAs are used. Models generated through this protocol are expected to be more accurate generally than models offered by other automated resources that rely on pairwise alignments, e.g. ModBase. Two small studies were carried out to demonstrate the usability and utility of BISC in biological research. (1) Interaction data in functional genomics databases often suffers from insufficient experimental and reporting standards. For example, multiple protein complexes are typically recorded as an inferred set of binary interactions. Using the 20S core particle of the yeast proteasome as an example, we demonstrate how the BISC Web resource can be used as a starting point for further investigation of such inferred interactions. (2) Malaria, a mosquito-borne disease, affects 3500-500 million people worldwide. Still very little is known about the malarial parasites' genes and their protein functions. For Plasmodium falciparum, the most lethal among the malaria parasites, only one experimentally derived medium scale PPIs set is available. The validity of this set has been doubted in the the malarial biologist community. We modelled and investigated eleven binary interactions from this set using the BISC modelling pipeline. Alongside we compared the BISC models of the individual partners to those obtained from ModBase.
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Ma, Chun-Wai. "Aboav-Weaire law in complex network and its applications in bioinformatics /." View abstract or full-text, 2005. http://library.ust.hk/cgi/db/thesis.pl?PHYS%202005%20MA.
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Lambert, Caroline L. "Identification and Description of Burkholderia pseudomallei Proteins that Bind HostComplement-Regulatory Proteins via in silico and in vitro Analyses." University of Toledo Health Science Campus / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=mco1533315186098586.
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Jin, Shuangshuang. "Integrated data modeling in high-throughput proteomices." Online access for everyone, 2007. http://www.dissertations.wsu.edu/Dissertations/Fall2007/S_Jin_111907.pdf.
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Sisu, Cristina Smaranda Domnica. "Computational studies on protein similarity, specificity and design." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609407.
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Daniels, Noah Manus. "Remote Homology Detection in Proteins Using Graphical Models." Thesis, Tufts University, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3563611.
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<p> Given the amino acid sequence of a protein, researchers often infer its structure and function by finding homologous, or evolutionarily-related, proteins of known structure and function. Since structure is typically more conserved than sequence over long evolutionary distances, recognizing remote protein homologs from their sequence poses a challenge. </p><p> We first consider all proteins of known three-dimensional structure, and explore how they cluster according to different levels of homology. An automatic computational method reasonably approximates a human-curated hierarchical organization of proteins according to their degree of homology. </p><p> Next, we return to homology prediction, based only on the one-dimensional amino acid sequence of a protein. Menke, Berger, and Cowen proposed a Markov random field model to predict remote homology for beta-structural proteins, but their formulation was computationally intractable on many beta-strand topologies. </p><p> We show two different approaches to approximate this random field, both of which make it computationally tractable, for the first time, on all protein folds. One method simplifies the random field itself, while the other retains the full random field, but approximates the solution through stochastic search. Both methods achieve improvements over the state of the art in remote homology detection for beta-structural protein folds.</p>
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Gendoo, Deena. "Bioinformatic sequence and structural analysis for Amyloidogenicity in Prions and other proteins." Thesis, McGill University, 2012. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=110518.
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Detection of amyloidogenic peptides or domains in proteins is of paramount importance towards understanding their role in amyloidosis in conformational diseases. This thesis explores different methods towards detection and prediction of amyloidogenic peptides using a variety of bioinformatic analytical methods. Bioinformatic analysis of secondary structural changes is employed to determine whether classes of structurally ambivalent peptides, mainly discordant and chameleon sequences, are efficient predictors of amyloidogenic segments. This analysis elucidates statistical relationships between discordance, chameleonism, and amyloidogenicity across a database of protein domains (SCOP), a subset of amyloid-forming proteins, and the prion family. The presented results stress upon the limitations of these peptides as predictors of amyloidogenicity, and raise issues on the predictive power that can be reaped from secondary structure prediction methods. In another bioinformatic approach, detection of conformationally variable segments in tertiary structures of PrP globular domains has been performed using Principal Component Analysis. This technique succeeded in identifying five conformationally variable domains within PrP, and ranking these subdomains by their ability to differentiate PrPs based on non-local structural response to pathogenic mutation and prion disease susceptibility. The presented results are corroborated by previous observations from experimental methods and molecular dynamic simulations, suggesting that this approach serves as a fast and reliable method for detection of potential amyloidogenic segments in amyloid-forming proteins. Finally, a structural, functional, and evolutionary bioinformatic analysis is conducted to assess the prevalence of the first experimentally verified amyloid fibril fold in nature, and whether this fold can serve as a prototype for other amyloid-forming proteins. The results indicate a limited scope of this fold in amyloid-forming proteins and across the protein universe, and have implications on future identification of amyloid-forming proteins that share this fold. Collectively, the presented thesis compares these different methods and discusses their efficacy in detection of amyloidogenic segments.<br>La détection de peptides ou de domaines amyloïdogéniques dans les protéines est d'une importance primordiale dans la compréhension de leur rôle dans l'amylose dans les maladies conformationnelles. Cette thèse explore différentes méthodes en vue de la détection et la prédiction des peptides amyloïdogéniques utilisant une variété de méthodes d'analyse bio-informatique. L'analyse bio-informatique des changements structurels secondaires est employé afin de déterminer si les classes des peptides structurellement ambivalentes, principalement des séquences discordantes et caméléons, sont des prédicteurs efficaces de segments amyloïdogéniques. Cette analyse élucide des relations statistiques entre la discordance, la chameleonism et l'amyloïdogénicité à travers une base de données de domaines protéiques (SCOP), un sous-ensemble de protéines formées d'amyloïdes, et de la famille prion. Les résultats présentés soulignent les limites de ces peptides en tant que prédicteurs d'amyloïdogénicité, et soulèvent des questions sur le pouvoir prédictif qui peut être récolté de méthodes de prédiction de structure secondaire. Dans une autre approche bio-informatique, la détection de segments de conformation variables dans les structures tertiaires de domaines globulaires PrP a été effectuée utilisant « Principal Component Analysis ». Cette technique a réussi à identifier cinq domaines de conformation variables au sein de la protéine PrP, et à classer ces sous-domaines par leur capacité à différencier les PrP fondés sur des réponses structurelles non-locales à la mutation pathogène et la susceptibilité aux maladies prion. Les résultats présentés sont corroborés par des observations antérieures à partir de méthodes expérimentales et de simulations de dynamique moléculaire, ce qui suggère que cette approche sert comme une méthode rapide et fiable pour la détection de segments amyloïdogéniques potentiels dans les protéines formées d'amyloïdes. Finalement, une analyse structurelle, fonctionnelle et évolutive bio-informatique est menée afin d'évaluer la prévalence du premier pli de fibrille amyloïde dans la nature vérifié expérimentalement, et si ce pli peut servir de prototype pour d'autres protéines formées d'amyloïdes. Les résultats indiquent une portée limitée de ce pli dans les protéines formées d'amyloïdes et à travers l'univers des protéines, et ont des répercussions sur l'identification future de protéines formées d'amyloïdes qui partagent ce pli. Collectivement, la thèse présentée compare ces différentes méthodes et discute leur efficacité dans la détection de segments amyloïdogéniques.
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Szekeres, Ferenc. "Bioinformatics applied to chlorophyll a/b binding proteins in Avena sativa (oat)." Thesis, University of Skövde, Department of Computer Science, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-820.
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<p>The chlorophyll a/b binding (CAB) genes play a very central role in all photosynthetic systems and are for Avena sativa (oat) totally unexplored. This dissertation investigates a large number of EST sequences and this investigation characterises the CAB genes in oat, with help from the evolutionary background of oat and the comparison to a reference organism and similar species.</p>
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Bailey, Christopher Michael. "A Bioinformatics Analysis of Bacterial Type-III Secretion System Genes and Proteins." Thesis, University of Birmingham, 2010. http://etheses.bham.ac.uk//id/eprint/1300/.
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Type-III secretion systems (T3SSs) are responsible for the biosynthesis of flagella, and the interaction of many animal and plant pathogens with eukaryotic cells. T3SSs consist of multiple proteins which assemble to form an apparatus capable of exporting proteins through both membranes of Gram-negative bacteria in one step. Proteins conserved amongst T3SSS can be used for analysis of these systems using computational homology searching. By using tools including BLAST and HMMER in conjunction phylogenetic analysis this thesis examines the range of T3SSs, both in terms of the proteins they contain, and also the bacteria which contain them. In silico analysis of several of the conserved components of T3SSs shows similarities between them and other secretion systems, as well as components of ATPases. Use of conserved components allows for identification of T3SS loci in diverse bacteria, in order to assess in the different proteins used by different T3SSs, and to see where, in evolutionary space, these differences arose. Analysis of homology data also allows for comprehensive re-annotation of T3SS loci within Desulfovibrio, Lawsonia and Hahella, and subsequent comparison of these T3SSs with related Yersinial T3SSs, and also (in conjunction with in vitro assays) for identification of many novel effectors in E. coli.
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Bull, Simon. "Predicting drug target proteins and their properties." Thesis, University of Manchester, 2015. https://www.research.manchester.ac.uk/portal/en/theses/predicting-drug-target-proteins-and-their-properties(4a57420f-ba76-4b24-bb3a-f8f8627aac75).html.
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The discovery of drug targets is a vital component in the development of therapeutic treatments, as it is only through the modulation of a target’s activity that a drug can alleviate symptoms or cure. Accurate identification of drug targets is therefore an important part of any development program, and has an outsized impact on the program’s success due to its position as the first step in the pipeline. This makes the stringent selection of potential targets all the more vital when attempting to control the increasing cost and time needed to successfully complete a development program, and in order to increase the throughput of the entire drug discovery pipeline. In this work, a computational approach was taken to the investigation of protein drug targets. First, a new heuristic, Leaf, for the approximation of a maximum independent set was developed, and evaluated in terms of its ability to remove redundancy from protein datasets, the goal being to generate the largest possible non-redundant dataset. The ability of Leaf to remove redundancy was compared to that of pre-existing heuristics and an optimal algorithm, Cliquer. Not only did Leaf find unbiased non-redundant sets that were around 10% larger than the commonly used PISCES algorithm, it found ones that were no more than one protein smaller than the maximum possible found by Cliquer. Following this, the human proteome was mined to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein’s sequence, post-translational modifications, secondary structure, germline variants, expression profile and target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all GPCRs, ion channels, kinases and proteases, as well as for a subset consisting of all proteins that are implicated in cancer. Next, machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. For each dataset, this was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily found to be those that are directly related to a protein’s sequence (e.g. secondary structure). Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins’ hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and are therefore likely to produce the best results if used as the basis for building a drug development programme.
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Yerardi, Jason T. "The Implementation and Evaluation of Bioinformatics Algorithms for the Classification of Arabinogalactan-Proteins in Arabidopsis thaliana." Ohio University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1301069861.
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Stahl, Morgan A. "The Perilipin Family of Proteins: Structural and Bioinformatic Analysis." Otterbein University Honors Theses / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=otbnhonors1620460421392971.
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Aken, Bronwen Louise. "Stress-inducible protein 1 : a bioinformatic analysis of the human, mouse and yeast STI1 gene structure /." Thesis, Rhodes University, 2005. http://eprints.ru.ac.za/160/.
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Thesis (M. Sc. (Biochemistry, Microbiology and Biotechnology))--Rhodes University, 2005.<br>A research report submitted in partial fulfilment of the requirements for the degree of Master of Science (in Bioinformatics and Computational Molecular Biology).
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Söderquist, Fredrik. "Proteus : A new predictor for protean segments." Thesis, Linköpings universitet, Teknisk biologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-121260.
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The discovery of intrinsically disordered proteins has led to a paradigm shift in protein science. Many disordered proteins have regions that can transform from a disordered state to an ordered. Those regions are called protean segments. Many intrinsically disordered proteins are involved in diseases, including Alzheimer's disease, Parkinson's disease and Down's syndrome, which makes them prime targets for medical research. As protean segments often are the functional part of the proteins, it is of great importance to identify those regions. This report presents Proteus, a new predictor for protean segments. The predictor uses Random Forest (a decision tree ensemble classifier) and is trained on features derived from amino acid sequence and conservation data. Proteus compares favourably to state of the art predictors and performs better than the competition on all four metrics: precision, recall, F1 and MCC. The report also looks at the differences between protean and non-protean regions and how they differ between the two datasets that were used to train the predictor.
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Sontheimer, Jana. "Functional characterization of proteins involved in cell cycle by structure-based computational methods." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-86778.
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In the recent years, a rapidly increasing amount of experimental data has been generated by high-throughput technologies. Despite of these large quantities of protein-related data and the development of computational prediction methods, the function of many proteins is still unknown. In the human proteome, at least 20% of the annotated proteins are not characterized. Thus, the question, how to predict protein function from its amino acid sequence, remains to be answered for many proteins. Classical bioinformatics approaches for function prediction are based on inferring function from well-characterized homologs, which are identified based on sequence similarity. However, these methods fail to identify distant homologs with low sequence similarity. As protein structure is more conserved than sequence in protein families, structure-based methods (e.g. fold recognition) may recognize possible structural similarities even at low sequence similarity and therefore provide information for function inference. These fold recognition methods have already been proven to be successful for individual proteins, but their automation for high-throughput application is difficult due to intrinsic challenges of these techniques, mainly caused by a high false positive rate. Automated identification of remote homologs based on fold recognition methods would allow a signi cant improvement in functional annotation of proteins. My approach was to combine structure-based computational prediction methods with experimental data from genome-wide RNAi screens to support the establishment of functional hypotheses by improving the analysis of protein structure prediction results.
In the first part of my thesis, I characterized proteins from the Ska complex by computational methods. I showed the benefit of including experimental information to identify remote homologs: Integration of functional data helped to reduce the number of false positives in fold recognition results and made it possible to establish interesting functional hypotheses based on high con dence structural predictions. Based on the structural hypothesis of a GLEBS motif in c13orf3 (Ska3), I could derive a potential molecular mechanism that could explain the observed phenotype.
In the second part of my thesis, my goal was to develop computational tools and automated analysis techniques to be able to perform structure-based functional annotation in a high-throughput way. I designed and implemented key tools that were successfully integrated into a computational platform, called StrAnno, which I set up together with my colleagues. These novel computational modules include a domain prediction algorithm and a graphical overview that facilitates and accelerates the analysis of results.
StrAnno can be seen as a first step towards automatic functional annotation of proteins by structure-based methods. First, the analysis of long hit lists to identify promising candidates for further analysis is substantially facilitated by integration and combination of various sequence-based computational tools and data from functional databases. Second, the developed post-processing tools accelerate the evaluation of structural and functional hypotheses. False positives from the threading result lists are removed by various filters, and analysis of the possible true positives is greatly enhanced by the graphical overview. With these two essential benefits, fold recognition techniques are applicable to large-scale approaches. By applying this developed methodology to hits from a genome-wide cell cycle RNAi screen and evaluating structural hypotheses by molecular modeling techniques, I aimed to associate biological functions to human proteins and link the RNAi phenotype to a molecular function. For two selected human proteins, c20orf43 and HJURP, I could establish interesting structural and functional hypotheses. These predictions were based on templates with low sequence identity (10-20%). The uncharacterized human protein c20orf43 might be a E3 SUMO-ligase that could be involved either in DNA repair or rRNA regulatory processes. Based on the structural hypotheses of two domains of HJURP, I predicted a potential link to ubiquitylation processes and direct DNA binding. In addition, I substantiated the cell cycle arrest phenotype of these two genes upon RNAi knockdown.
Fold recognition methods are a promising alternative for functional annotation of proteins that escape sequence-based annotation due to their low sequence identity to well-characterized protein families. The structural and functional hypotheses I established in my thesis open the door to investigate the molecular mechanisms of previously uncharacterized proteins, which may provide new insights into cellular mechanisms.
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Hetti, Arachchilage Madara Dilhani. "Coevolution of epitopes in HIV-1 pre-integration complex proteins: protein-protein interaction insights." Kent State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=kent1530646538935895.
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Midic, Uros. "Genome-Wide Prediction of Intrinsic Disorder; Sequence Alignment of Intrinsically Disordered Proteins." Diss., Temple University Libraries, 2012. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/159800.
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Computer and Information Science<br>Ph.D.<br>Intrinsic disorder (ID) is defined as a lack of stable tertiary and/or secondary structure under physiological conditions in vitro. Intrinsically disordered proteins (IDPs) are highly abundant in nature. IDPs possess a number of crucial biological functions, being involved in regulation, recognition, signaling and control, e.g. their functional repertoire complements the functions of ordered proteins. Intrinsically disordered regions (IDRs) of IDPs have a different amino-acid composition than structured regions and proteins. This fact has been exploited for development of predictors of ID; the best predictors currently achieve around 80% per-residue accuracy. Earlier studies revealed that some IDPs are associated with various human diseases, including cancer, cardiovascular disease, amyloidoses, neurodegenerative diseases, diabetes and others. We developed a methodology for prediction and analysis of abundance of intrinsic disorder on the genome scale, which combines data from various gene and protein databases, and utilizes several ID prediction tools. We used this methodology to perform a large-scale computational analysis of the abundance of (predicted) ID in transcripts of various classes of disease-related genes. We further analyzed the relationships between ID and the occurrence of alternative splicing and Molecular Recognition Features (MoRFs) in human disease classes. An important, never before addressed issue with such genome-wide applications of ID predictors is that - for less-studied organisms - in addition to the experimentally confirmed protein sequences, there is a large number of putative sequences, which have been predicted with automated annotation procedures and lack experimental confirmation. In the human genome, these predicted sequences have significantly higher predicted disorder content. I investigated a hypothesis that this discrepancy is not correct, and that it is due to incorrectly annotated parts of the putative protein sequences that exhibit some similarities to confirmed IDRs, which lead to high predicted ID content. I developed a procedure to create synthetic nonsense peptide sequences by translation of non-coding regions of genomic sequences and translation of coding regions with incorrect codon alignment. I further trained several classifiers to discriminate between confirmed sequences and synthetic nonsense sequences, and used these predictors to estimate the abundance of incorrectly annotated regions in putative sequences, as well as to explore the link between such regions and intrinsic disorder. Sequence alignment is an essential tool in modern bioinformatics. Substitution matrices - such as the BLOSUM family - contain 20x20 parameters which are related to the evolutionary rates of amino acid substitutions. I explored various strategies for extension of sequence alignment to utilize the (predicted) disorder/structure information about the sequences being aligned. These strategies employ an extended 40 symbol alphabet which contains 20 symbols for amino acids in ordered regions and 20 symbols for amino acids in IDRs, as well as expanded 40x40 and 40x20 matrices. The new matrices exhibit significant and substantial differences in the substitution scores for IDRs and structured regions. Tests on a reference dataset show that 40x40 matrices perform worse than the standard 20x20 matrices, while 40x20 matrices - used in a scenario where ID is predicted for a query sequence but not for the target sequences - have at least comparable performance. However, I also demonstrate that the variations in performance between 20x20 and 20x40 matrices are insignificant compared to the variation in obtained matrices that occurs when the underlying algorithm for calculation of substitution matrices is changed.<br>Temple University--Theses
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Garcia, Krystine. "Bioinformatics Pipeline for Improving Identification of Modified Proteins by Neutral Loss Peak Filtering." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1440157843.
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Togawa, Roberto Coiti. "Development of a suite of bioinformatics tools for the analysis and prediction of membrane protein structure." Thesis, University of Bedfordshire, 2006. http://hdl.handle.net/10547/614881.
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This thesis describes the development of a novel approach for prediction of the three-dimensional structure of transmembrane regions of membrane proteins directly from amino acid sequence and basic transmembrane region topology. The development rationale employed involved a knowledge-based approach. Based on determined membrane protein structures, 20x20 association matrices were generated to summarise the distance associations between amino acid side chains on different alpha helical transmembrane regions of membrane proteins. Using these association matrices, combined with a knowledge-based scale for propensity for residue orientation in transmembrane segments (kPROT) (Pilpel et al., 1999), the software predicts the optimal orientations and associations of transmembrane regions and generates a 3D structural model of a gi ven membrane protein, based on the amino acid sequence composition of its transmembrane regions. During the development, several structural and biostatistical analyses of determined membrane protein structures were undertaken with the aim of ensuring a consistent and reliable association matrix upon which to base the predictions. Evaluation of the model structures obtained for the protein sequences of a dataset of 17 membrane proteins of detennined structure based on cross-validated leave-one-out testing revealed generally high accuracy of prediction, with over 80% of associations between transmembrane regions being correctly predicted. These results provide a promising basis for future development and refinement of the algorithm, and to this end, work is underway using evolutionary computing approaches. As it stands, the approach gives scope for significant immediate benefit to researchers as a valuable starting point in the prediction of structure for membrane proteins of hitherto unknown structure.
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Yao, Jianchao, and 姚劍超. "Predicting the 3D structure of human aquaporin-0 protein in eye lens using computational tools." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2003. http://hub.hku.hk/bib/B2948540X.
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Mudenda, Lwiindi. "Identification of Dermacentor andersoni saliva proteins that modulate mammalian phagocyte function." Thesis, Washington State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3717421.
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<p> Ticks are obligate blood sucking parasites which transmit a wide range of pathogens worldwide including protozoa, bacteria and viruses. Additionally, tick feeding alone may result in anemia, dermatosis and toxin-induced paralysis. <i> Dermacentor andersoni</i> is a species of tick found in the western United States that transmits pathogens of public health importance including <i> Rickettsia rickettsii, Francisella tularensis,</i> and Colorado Tick Fever Virus, as well as <i>Anaplasma marginale</i>, a rickettsial pathogen that causes economic losses in both the dairy and beef industries worldwide. <i>D. andersoni</i> ticks are obligate blood sucking parasites that require a blood meal through all stages of their lifecycle. During feeding, ticks secrete factors that modulate both innate and acquired immune responses in the host which enables them to feed for several days without detection. The pathogens transmitted by ticks exploit these immunomodulatory properties to facilitate invasion of and replication in the host. Molecular characterization of these immunomodulatory proteins secreted in tick saliva offers an opportunity to develop novel anti-tick vaccines as well as anti-inflammatory drug targets. To this end we performed deep sequence analysis on unfed ticks and ticks fed for 2 or 5 days. The pooled data generated a database of 21,797 consensus sequences. Salivary gland gene expression levels of unfed ticks were compared to 2- and 5-day fed ticks to identify genes upregulated early during tick feeding. Next we performed mass spectrometry on saliva from 2- and 5-day fed ticks and used the database to identify 677 proteins. We cross referenced the protein data with the transcriptome data to identify 157 proteins of interest for immunomodulation and blood feeding. Both proteins of unknown function and known immunomodulators were identified. We expressed four of these proteins and tested them for inhibition of macrophage activation and/or cytokine expression in vitro. The results showed diverse effects of the various test proteins on the inflammatory response of mouse macrophage cell lines. The proteins upregulated some cytokines while downregulating others. However, all the proteins upregulated the regulatory cytokine IL-10.</p>
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Quan, Jie. "The Roles of RNA-binding Proteins in the Developing Nervous System." Thesis, Harvard University, 2013. http://dissertations.umi.com/gsas.harvard:11249.
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RNA-binding proteins are key players in post-transcriptional regulation of gene expression by orchestrating RNA fate from synthesis to decay. Hundreds of proteins with RNA-binding capacity have been identified so far, yet only a small fraction has been functionally characterized and presumably many more RNA-binding proteins await discovery. The roles of RNA-binding proteins in the nervous system are of particular interest because accumulative evidence has linked RNA-based mechanisms to neural development, maintenance and repair. Here, the three RNA-binding proteins under study are IGF-II mRNA binding proteins IMP-1 and IMP-2, known to be involved in mRNA localization, translational control and stability, and adenomatous polyposis coli (APC), identified as a novel RNA-binding protein. To systematically identify their RNA binding profiles, a high-throughput approach combining protein-RNA crosslinking and immunoprecipitation with next-generation sequencing (HITS-CLIP) was applied in embryonic mouse brain. A nonparametric method was developed to computationally analyze the CLIP sequencing data, mapping transcriptome-wide protein-RNA interactions. The identified target mRNAs of IMP-1 and IMP-2 were highly enriched for functions related to neural development, especially neuron projection morphogenesis and axon guidance signaling. Moreover, these target mRNAs were associated with a variety of neurological diseases, including neurodevelopmental and neurodegenerative disorders. Supporting roles in axon development, knockdown of IMP-1 or IMP-2 caused aberrant trajectories of commissural axons in chicken spinal cord. APC mRNA targets were highly enriched for APC-related functions, including microtubule organization, cell and axon motility, Wnt signaling, cancer and neurological disease. Among the APC targets was Tubulin β-2B (Tubb2b), previously known to be required for neuronal migration. It was found that Tubb2b was synthesized in axons, and localized preferentially to dynamic microtubules in the peripheral domain of the growth cone. Blocking the APC binding site in the Tubb2b mRNA 3'UTR caused reduction in its expression in axons and loss of the growth cone peripheral area, and impaired cortical neuron migration in vivo. These findings offer an informative snapshot of the protein-RNA interactome, which can provide a basis to better understand the roles of RNA-binding proteins in the nervous system.
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Choudhury, Salimur Rashid, and University of Lethbridge Faculty of Arts and Science. "Approximation algorithms for a graph-cut problem with applications to a clustering problem in bioinformatics." Thesis, Lethbridge, Alta. : University of Lethbridge, Deptartment of Mathematics and Computer Science, 2008, 2008. http://hdl.handle.net/10133/774.
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Clusters in protein interaction networks can potentially help identify functional relationships
among proteins. We study the clustering problem by modeling it as graph cut problems.
Given an edge weighted graph, the goal is to partition the graph into a prescribed
number of subsets obeying some capacity constraints, so as to maximize the total weight
of the edges that are within a subset. Identification of a dense subset might shed some light
on the biological function of all the proteins in the subset.
We study integer programming formulations and exhibit large integrality gaps for various
formulations. This is indicative of the difficulty in obtaining constant factor approximation
algorithms using the primal-dual schema. We propose three approximation algorithms for
the problem. We evaluate the algorithms on the database of interacting proteins and on
randomly generated graphs. Our experiments show that the algorithms are fast and have
good performance ratio in practice.<br>xiii, 71 leaves : ill. ; 29 cm.
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Durani, Venuka. "The Cycle of Protein Engineering: Bioinformatics Design of Two Dimeric Proteins and Computational Design of a Small Globular Domain." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1338311626.
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Cham, Jennifer A. "Bioinformatics solutions for confident identification and targeted quantification of proteins using tandem mass spectrometry." Thesis, Cranfield University, 2009. http://dspace.lib.cranfield.ac.uk/handle/1826/4630.
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Proteins are the structural supports, signal messengers and molecular workhorses that underpin living processes in every cell. Understanding when and where proteins are expressed, and their structure and functions, is the realm of proteomics. Mass spectrometry (MS) is a powerful method for identifying and quantifying proteins, however, very large datasets are produced, so researchers rely on computational approaches to transform raw data into protein information. This project develops new bioinformatics solutions to support the next generation of proteomic MS research. Part I introduces the state of the art in proteomic bioinformatics in industry and academia. The business history and funding mechanisms are examined to fill a notable gap in management research literature, and to explain events at the sponsor, GlaxoSmithKline. It reveals that public funding of proteomic science has yet to come to fruition and exclusively high-tech niche bioinformatics businesses can succeed in the current climate. Next, a comprehensive review of repositories for proteomic MS is performed, to locate and compile a summary of sources of datasets for research activities in this project, and as a novel summary for the community. Part II addresses the issue of false positive protein identifications produced by automated analysis with a proteomics pipeline. The work shows that by selecting a suitable decoy database design, a statistically significant improvement in identification accuracy can be made. Part III describes development of computational resources for selecting multiple reaction monitoring (MRM) assays for quantifying proteins using MS. A tool for transition design, MRMaid (pronounced „mermaid‟), and database of pre-published transitions, MRMaid-DB, are developed, saving practitioners time and leveraging existing resources for superior transition selection. By improving the quality of identifications, and providing support for quantitative approaches, this project brings the field a small step closer to achieving the goal of systems biology.
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Francis, Ore. "Bioinformatics, phylogenetic and biochemical analyses of the proteins of the muskelin/RanBP9/CTLH complex." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.665153.
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Ubiquitination is an essential post-translational modification that regulates signalling and protein turnover in eukaryotic cells. However, many ubiquitin E3 ligases remain poorly understood. The mammalian muskelin/RanBP9/CTLH complex contains eight proteins, five of which, RanBP9 (RanBPM), TWA1, Maea, Rmnd5a and muskelin, share striking similarities of domain organisation. In Saccharomyces cerevisiae, the related GID complex includes the Rmnd5a homologue GID2 which has E3 ubiquitin ligase activity and down-regulates gluconeogenesis. E3 ubiquitin ligase activity of mammalian Rmnd5a has not been reported. To better understand the large mammalian complex a major goal of this thesis was to analyse its evolution as a multi-protein system. Bioinformatic studies identify that TWA1, Rmnd5 and Maea are conserved throughout five eukaryotic supergroups. RanBPM is absent from excavates and from some lineages within other super-groups, and muskelin is present only in opisthokonts. Phylogenetic analysis based on the shared sequence regions that correspond to the lissencephaly-l homology (LisH) and C-terminal to LisH (CTLH) domains revealed closer relationships between Rmnd5 and MAEA, and TWAl and RanBPM, respectively. In-depth sequence analyses confirmed the greater similarity of the LisH/ CTLH domains of Rmnd5 and MAEA vs. TWAl and RanBPM, respectively, and id~ntified unique signatures of conserved residues within the LisH and CTLH domains of each protein. ~ further goal was to purify and express Rmnd5a and TWAl for laboratory experiments. Bacterially expressed Rmnd5a exhibits E3 ubiquitin ligase activity in Escherichia coli BL21lysates but not as a purified protein. Bacterial expression and purification of TWAl enabled biophysical characterisation of TWAl as an all a-helical, natively-dimerised protein. TWAl crystals were produced. When optimized, crystals diffracted to 3.5A, though a 3D structure was not resolved. Threaded structure predictions of Rmnd5a and TWAl agreed with secondary structure prediction algorithms. These studies advance knowledge of structural! functional relationships of proteins in this poorly-understood complex.
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Moses, Vuyani. "The investigation of type-specific features of the copper coordinating AA9 proteins and their effect on the interaction with crystalline cellulose using molecular dynamics studies." Thesis, Rhodes University, 2018. http://hdl.handle.net/10962/58327.
Full textAbstract:
AA9 proteins are metallo-enzymes which are crucial for the early stages of cellulose degradation. AA9 proteins have been suggested to cleave glycosidic bonds linking cellulose through the use of their Cu2+ coordinating active site. AA9 proteins possess different regioselectivities depending on the resulting cleavage they form and as result, are grouped accordingly. Type 1 AA9 proteins cleave the C1 carbon of cellulose while Type 2 AA9 proteins cleave the C4 carbon and Type 3 AA9 proteins cleave either C1 or C4 carbons. The steric congestion of the AA9 active site has been proposed to be a contributor to the observed regioselectivity. As such, a bioinformatics characterisation of type-specific sequence and structural features was performed. Initially AA9 protein sequences were obtained from the Pfam database and multiple sequence alignment was performed. The sequences were phylogenetically characterised and sequences were grouped into their respective types and sub-groups were identified. A selection analysis was performed on AA9 LPMO types to determine the selective pressure acting on AA9 protein residues. Motif discovery was then performed to identify conserved sequence motifs in AA9 proteins. Once type-specific sequence features were identified structural mapping was performed to assess possible effects on substrate interaction. Physicochemical property analysis was also performed to assess biochemical differences between AA9 LPMO types. Molecular dynamics (MD) simulations were then employed to dynamically assess the consequences of the discovered type-specific features on AA9-cellulose interaction. Due to the absence of AA9 specific force field parameters MD simulations were not readily applicable. As a result, Potential Energy Surface (PES) scans were performed to evaluate the force field parameters for the AA9 active site using the PM6 semi empirical approach and least squares fitting. A Type 1 AA9 active site was constructed from the crystal structure 4B5Q, encompassing only the Cu2+ coordinating residues, the Cu2+ ion and two water residues. Due to the similarity in AA9 active sites, the Type force field parameters were validated on all three AA9 LPMO types. Two MD simulations for each AA9 LPMO types were conducted using two separate Lennard-Jones parameter sets. Once completed, the MD trajectories were analysed for various features including the RMSD, RMSF, radius of gyration, coordination during simulation, hydrogen bonding, secondary structure conservation and overall protein movement. Force field parameters were successfully evaluated and validated for AA9 proteins. MD simulations of AA9 proteins were able to reveal the presence of unique type-specific binding modes of AA9 active sites to cellulose. These binding modes were characterised by the presence of unique type-specific loops which were present in Type 2 and 3 AA9 proteins but not in Type 1 AA9 proteins. The loops were found to result in steric congestion that affects how the Cu2+ ion interacts with cellulose. As a result, Cu2+ binding to cellulose was observed for Type 1 and not Type 2 and 3 AA9 proteins. In this study force field parameters have been evaluated for the Type 1 active site of AA9 proteins and this parameters were evaluated on all three types and binding. Future work will focus on identifying the nature of the reactive oxygen species and performing QM/MM calculations to elucidate the reactive mechanism of all three AA9 LPMO types.
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Habtemariam, Mesay. "Bioinformatics Approach to Probe Protein-Protein Interactions: Understanding the Role of Interfacial Solvent in the Binding Sites of Protein-Protein Complexes;Network Based Predictions and Analysis of Human Proteins that Play Critical Roles in HIV Pathogenesis." VCU Scholars Compass, 2013. http://scholarscompass.vcu.edu/etd/2997.
Full textAbstract:
The thesis work contains two projects under the same umbrella. The first project is to provide a detailed analysis on the behavior of interfacial water molecules at protein-protein complexes, in this case focusing on homodimeric complexes, and to investigate their effect with respect to different residue types. For that reason the homodimeric data-set, which includes high-resolution (≤ 2.30 Å) X-ray crystal structures of 252 (140 Biological & 112 Non-biological) protein complexes was chosen to explore fundamental differences between interfaces that Nature has “engineered” vs. compared to interfaces found under man-made conditions. The data set was comprised of 5391 water molecules where a maximum of 4 Å from both interfacing proteins. Our analysis is applied a suite of modeling tools based on HINT, a program for hydropathic analysis developed in our laboratory. HINT is based on the experimental measurement of the hydrophobic effect. The second project is designed to explore various means of suppressing the expression of human genes that play critical role in HIV pathogenesis. To achieve this aim, a data set of Affymetrix Human HG Focus Target Array, which measures the expression levels of HIV seronegative and seropositive individuals in human PBMCs, was analyzed with Pathway Studio 9.0 software. This work gives insight into the elucidation of the important mechanisms of human proteins interactions in HIV seropositive individuals and their implications. Hence, we found the kind and types of microRNAs that are suppressing the human genes which have great role for HIV replication in a cell.
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Bahena, Silvia. "Computational Methods for the structural and dynamical understanding of GPCR-RAMP interactions." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-416790.
Full textAbstract:
Protein-protein interaction dominates all major biology processes in living cells. Recent studies suggestthat the surface expression and activity of G protein-coupled receptors (GPCRs), which are the largestfamily of receptors in human cells, can be modulated by receptor activity–modifying proteins (RAMPs). Computational tools are essential to complement experimental approaches for the understanding ofmolecular activity of living cells and molecular dynamics simulations are well suited to providemolecular details of proteins function and structure. The classical atom-level molecular modeling ofbiological systems is limited to small systems and short time scales. Therefore, its application iscomplicated for systems such as protein-protein interaction in cell-surface membrane. For this reason, coarse-grained (CG) models have become widely used and they represent an importantstep in the study of large biomolecular systems. CG models are computationally more effective becausethey simplify the complexity of the protein structure allowing simulations to have longer timescales. The aim of this degree project was to determine if the applications of coarse-grained molecularsimulations were suitable for the understanding of the dynamics and structural basis of the GPCRRAMP interactions in a membrane environment. Results indicate that the study of protein-proteininteractions using CG needs further improvement with a more accurate parameterization that will allowthe study of complex systems.
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Soares, Dinesh Christopher. "Bioinformatics studies on sequence, structure and functional relationships of proteins involved in the complement system." Thesis, University of Edinburgh, 2007. http://hdl.handle.net/1842/11424.
Full textAbstract:
Regulators of complement activation (RCA) ensure that a complement-mediated immune response is proportionate and targeted against infection. RCA proteins are characterised by numerous occurrences of a single module-type; the complement control protein (CCP) module. In this work, comprehensive bioinformatics analyses of sequence and structure of CCP modules was undertaken. Through extensive database and literature searches, CCP module sequences and structures were assembled and large-scale <i>all-against-all </i>sequence and structure comparisons performed, along with analysis of intermodular orientations for pairs of modules and larger fragments. Based upon optimal use of experimentally determined CCP module structures as templates, an automated large-scale protein structure comparative modelling procedure was implemented for a large set of CCP-module sequences. The models are publicly available online at “The CCP module model database”, which also serves as a comprehensive resource for information on CCP modules. The models are shown to serve as a rich vein of information for design of mutants, interpretation of phenotypic consequences of polymorphisms, and prediction of function. For example, the models proved useful for inferring the consequences of several disease-associated sequence variations of complement proteins, CR1, factor H, MCP; and another CCP-containing protein, SRPX2. Finally, homology models of C5 and C5b were created on the basis of the recent landmark publication of C3 and C3b structures. This exercise revealed the existence of a novel putative disulfide bond specific to C5. Additionally it helped revisit previous peptide and mutant-based studies and provided insight into the latter stages of complement assembly.