Relevant bibliographies by topics / "Protein Data Bank" (PDB)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic '"Protein Data Bank" (PDB)'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic ""Protein Data Bank" (PDB)"

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Battle, Gary, Christine Zardecki, Nahoko Haruki, Gerard Kleywegt, Helen Berman, Haruki Nakamura, and Matthew Conroy. "Educational Outreach and User Training at the Worldwide Protein Data Bank." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1271. http://dx.doi.org/10.1107/s2053273314087282.

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The Protein Data Bank (PDB) contains a wealth of structural and functional knowledge about proteins, RNA, DNA, and other macromolecules, and their assemblies and complexes with small molecules. The challenge faced by the providers of PDB data is to make this knowledge accessible to an increasingly large and diverse audience, ranging from expert structural biologists to non-specialist consumers of structural information. Educators, students, and general audiences will have their own specific interests and expectations from molecular structure data. For a general user, a 2D image of hemoglobin illustrates how a protein looks at a microscopic level. For high school students and educators, 3D models or computer graphics can show how one or a few specific proteins can assemble into an icosahedral virus. In contrast, PhD and post-doc level researchers require expert guidance on how to critically assess the quality of structural data, and in-depth training on the use of specialist tools and resources for the comparison and analysis of structures. The PDB archive is managed by members of the Worldwide Protein Data Bank (wwPDB): the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB; rcsb.org), Protein Data Bank in Europe (PDBe; pdbe.org), Protein Data Bank Japan (PDBj), and BioMagResBank (BMRB, bmrb.wisc.edu). In addition to managing and distributing structural data, the wwPDB partners are engaged in numerous outreach initiatives and user training programs. These efforts are vital to ensuring that these uniquely valuable data can be effectively accessed and used by research scientists, students, and educators alike. This talk will describe on-going wwPDB outreach efforts and highlight exciting new initiatives at the RCSB PDB, PDBe and PDBj.
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Berman, Helen M., Tammy Battistuz, T. N. Bhat, Wolfgang F. Bluhm, Philip E. Bourne, Kyle Burkhardt, Zukang Feng, et al. "The Protein Data Bank." Acta Crystallographica Section D Biological Crystallography 58, no. 6 (May 29, 2002): 899–907. http://dx.doi.org/10.1107/s0907444902003451.

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The Protein Data Bank [PDB; Berman, Westbrooket al.(2000),Nucleic Acids Res.28, 235–242; http://www.pdb.org/] is the single worldwide archive of primary structural data of biological macromolecules. Many secondary sources of information are derived from PDB data. It is the starting point for studies in structural bioinformatics. This article describes the goals of the PDB, the systems in place for data deposition and access, how to obtain further information and plans for the future development of the resource. The reader should come away with an understanding of the scope of the PDB and what is provided by the resource.
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Mukhopadhyay, Abhik, Neera Borkakoti, Lukáš Pravda, Jonathan D. Tyzack, Janet M. Thornton, and Sameer Velankar. "Finding enzyme cofactors in Protein Data Bank." Bioinformatics 35, no. 18 (February 13, 2019): 3510–11. http://dx.doi.org/10.1093/bioinformatics/btz115.

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Abstract Motivation Cofactors are essential for many enzyme reactions. The Protein Data Bank (PDB) contains >67 000 entries containing enzyme structures, many with bound cofactor or cofactor-like molecules. This work aims to identify and categorize these small molecules in the PDB and make it easier to find them. Results The Protein Data Bank in Europe (PDBe; pdbe.org) has implemented a pipeline to identify enzyme cofactor and cofactor-like molecules, which are now part of the PDBe weekly release process. Availability and implementation Information is made available on the individual PDBe entry pages at pdbe.org and programmatically through the PDBe REST API (pdbe.org/api). Supplementary information Supplementary data are available at Bioinformatics online.
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NAKAMURA, Haruki. "Protein Data Bank (PDB) for Big Data Era." Seibutsu Butsuri 53, no. 1 (2013): 044–46. http://dx.doi.org/10.2142/biophys.53.044.

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Kirkwood, Jobie, David Hargreaves, Simon O'Keefe, and Julie Wilson. "Analysis of crystallization data in the Protein Data Bank." Acta Crystallographica Section F Structural Biology Communications 71, no. 10 (September 23, 2015): 1228–34. http://dx.doi.org/10.1107/s2053230x15014892.

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The Protein Data Bank (PDB) is the largest available repository of solved protein structures and contains a wealth of information on successful crystallization. Many centres have used their own experimental data to draw conclusions about proteins and the conditions in which they crystallize. Here, data from the PDB were used to reanalyse some of these results. The most successful crystallization reagents were identified, the link between solution pH and the isoelectric point of the protein was investigated and the possibility of predicting whether a protein will crystallize was explored.
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Berman, Helen. "The history of the PDB as a public resource for enabling science." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C934. http://dx.doi.org/10.1107/s2053273314090652.

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As the crystal structures of biological macromolecules were being determined, a new field of structural biology was born. Inspired by these new structures, the scientific community worked to establish a home to archive and share the data emerging from these experiments. The Protein Data Bank (PDB) was established in 1971 with seven structures. The PDB provides a repository for scientists who generate the data, and an access point for researchers and students to find the information needed to drive additional studies. Today, the PDB contains and supports online access to ~100,000 biomacromolecules that help researchers understand aspects of biology, including medicine, agriculture, and biological energy. The ways in which the interrelationships among science, technology, and community have driven the evolution of the PDB resource for more than forty years will be discussed. The PDB archive is managed by the Worldwide Protein Data Bank (wwpdb.org), whose members are the RCSB PDB, PDBe, PDBj and BMRB.
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Faezov, Bulat, and Roland L. Dunbrack. "PDBrenum: A webserver and program providing Protein Data Bank files renumbered according to their UniProt sequences." PLOS ONE 16, no. 7 (July 6, 2021): e0253411. http://dx.doi.org/10.1371/journal.pone.0253411.

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The Protein Data Bank (PDB) was established at Brookhaven National Laboratories in 1971 as an archive for biological macromolecular crystal structures. In mid 2021, the database has almost 180,000 structures solved by X-ray crystallography, nuclear magnetic resonance, cryo-electron microscopy, and other methods. Many proteins have been studied under different conditions, including binding partners such as ligands, nucleic acids, or other proteins; mutations, and post-translational modifications, thus enabling extensive comparative structure-function studies. However, these studies are made more difficult because authors are allowed by the PDB to number the amino acids in each protein sequence in any manner they wish. This results in the same protein being numbered differently in the available PDB entries. For instance, some authors may include N-terminal signal peptides or the N-terminal methionine in the sequence numbering and others may not. In addition to the coordinates, there are many fields that contain structural and functional information regarding specific residues numbered according to the author. Here we provide a webserver and Python3 application that fixes the PDB sequence numbering problem by replacing the author numbering with numbering derived from the corresponding UniProt sequences. We obtain this correspondence from the SIFTS database from PDBe. The server and program can take a list of PDB entries or a list of UniProt identifiers (e.g., “P04637” or “P53_HUMAN”) and provide renumbered files in mmCIF format and the legacy PDB format for both asymmetric unit files and biological assembly files provided by PDBe.
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Mezei, Mihaly. "Revisiting Chameleon Sequences in the Protein Data Bank." Algorithms 11, no. 8 (July 28, 2018): 114. http://dx.doi.org/10.3390/a11080114.

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The steady growth of the Protein Data Bank (PDB) suggests the periodic repetition of searches for sequences that form different secondary structures in different protein structures; these are called chameleon sequences. This paper presents a fast (nlog(n)) algorithm for such searches and presents the results on all protein structures in the PDB. The longest such sequence found consists of 20 residues.
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Mara Fischer Günther, Tânia, Valdelúcia M.A.S. Grinevicius, and Rozangela Curi Pedrosa. "Active Learning Using Protein Data Bank (PDB) Biochemical Data by Undergraduate Students of Nutrition Course at UFSC." Revista de Ensino de Bioquímica 16 (November 21, 2018): 11. http://dx.doi.org/10.16923/reb.v16i0.833.

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NTRODUCTION: Many biochemistry internet sites lacking scientific accuracy dismiss their use. However PDB provides macromolecules structures that are experimentally very accurately determined. Besides, PDB provides biochemistry of nutritional chronic/metabolic diseases very useful to students and professionals. In addition, the PDB provides biochemical knowledge of chronic and nutritional metabolic diseases very useful for students and professionals. However, PDB database idiom, sophisticate search tools and technical terms can be obstacles to active learning. OBJECTIVES: Incentive students to develop and improve their knowledge/learning network and skills needed to practice as professionals based in active learning of protein structures using PDB as a tool and scientific source of biochemical data obtained using computer structural models. MATERIALS AND METHODS. Firstly, traditional lectures showed basics concepts of the proteins biochemistry, accordingly to curricular content. Then, PDB protein categories showed (http://www.rcsb.org/pdb/home/home.do) using myoglobin as model (https://pdb101.rcsb.org/motm/1). Finally, each pair of students select a protein to be described using Powerpoint™ format. Questions about pedagogic strategy and PDB aspects and all presentations were available at Moodle-UFSC (interactive virtual environment). DISCUSSION AND RESULTS: Students answers confirmed PDB structures as scientifically based (86%). PDB was considered a good pedagogical strategy (44%) rooted in scientific theory and experiment-based (48%) with attractive computational molecular models (57%). Students highlighted PDB give free/easy/fast access (53%) and considered it as good to spread knowledge for all countries (61%). PDB beneficiates Professors/Health professionals including Nutritionists (57%) and Academy (74%). CONCLUSION: Active learning process increase opportunities to access scientific curated PDB information capable to improve Biochemistry skills of future nutritionists.
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KURISU, Genji. "Modifications to the Protein Data Bank : A new PDB format, Data Deposition, and Validation Report(PDBj: Protein Data Bank Japan,The 52nd Annual Meeting of the Biophysical Society of Japan(BSJ2014))." Seibutsu Butsuri 54, supplement1-2 (2014): S330. http://dx.doi.org/10.2142/biophys.54.s330_1.

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Dissertations / Theses on the topic ""Protein Data Bank" (PDB)"

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Ramraj, Varun. "Exploiting whole-PDB analysis in novel bioinformatics applications." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:6c59c813-2a4c-440c-940b-d334c02dd075.

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The Protein Data Bank (PDB) is the definitive electronic repository for experimentally-derived protein structures, composed mainly of those determined by X-ray crystallography. Approximately 200 new structures are added weekly to the PDB, and at the time of writing, it contains approximately 97,000 structures. This represents an expanding wealth of high-quality information but there seem to be few bioinformatics tools that consider and analyse these data as an ensemble. This thesis explores the development of three efficient, fast algorithms and software implementations to study protein structure using the entire PDB. The first project is a crystal-form matching tool that takes a unit cell and quickly (< 1 second) retrieves the most related matches from the PDB. The unit cell matches are combined with sequence alignments using a novel Family Clustering Algorithm to display the results in a user-friendly way. The software tool, Nearest-cell, has been incorporated into the X-ray data collection pipeline at the Diamond Light Source, and is also available as a public web service. The bulk of the thesis is devoted to the study and prediction of protein disorder. Initially, trying to update and extend an existing predictor, RONN, the limitations of the method were exposed and a novel predictor (called MoreRONN) was developed that incorporates a novel sequence-based clustering approach to disorder data inferred from the PDB and DisProt. MoreRONN is now clearly the best-in-class disorder predictor and will soon be offered as a public web service. The third project explores the development of a clustering algorithm for protein structural fragments that can work on the scale of the whole PDB. While protein structures have long been clustered into loose families, there has to date been no comprehensive analytical clustering of short (~6 residue) fragments. A novel fragment clustering tool was built that is now leading to a public database of fragment families and representative structural fragments that should prove extremely helpful for both basic understanding and experimentation. Together, these three projects exemplify how cutting-edge computational approaches applied to extensive protein structure libraries can provide user-friendly tools that address critical everyday issues for structural biologists.
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Sekhi, Ikram. "Développement d'un alphabet structural intégrant la flexibilité des structures protéiques." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCC084/document.

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L’objectif de cette thèse est de proposer un Alphabet Structural (AS) permettant une caractérisation fine et précise des structures tridimensionnelles (3D) des protéines, à l’aide des chaînes de Markov cachées (HMM) qui permettent de prendre en compte la logique issue de l’enchaînement des fragments structuraux en intégrant l’augmentation des conformations 3D des structures protéiques désormais disponibles dans la banque de données de la Protein Data Bank (PDB). Nous proposons dans cette thèse un nouvel alphabet, améliorant l’alphabet structural HMM-SA27,appelé SAFlex (Structural Alphabet Flexibility), dans le but de prendre en compte l’incertitude des données (données manquantes dans les fichiers PDB) et la redondance des structures protéiques. Le nouvel alphabet structural SAFlex obtenu propose donc un nouveau modèle d’encodage rigoureux et robuste. Cet encodage permet de prendre en compte l’incertitude des données en proposant trois options d’encodages : le Maximum a posteriori (MAP), la distribution marginale a posteriori (POST)et le nombre effectif de lettres à chaque position donnée (NEFF). SAFlex fournit également un encodage consensus à partir de différentes réplications (chaînes multiples, monomères et homomères) d’une même protéine. Il permet ainsi la détection de la variabilité structurale entre celles-ci. Les avancées méthodologiques ainsi que l’obtention de l’alphabet SAFlex constituent les contributions principales de ce travail de thèse. Nous présentons aussi le nouveau parser de la PDB (SAFlex-PDB) et nous démontrons que notre parser a un intérêt aussi bien sur le plan qualitatif (détection de diverses erreurs)que quantitatif (rapidité et parallélisation) en le comparant avec deux autres parsers très connus dans le domaine (Biopython et BioJava). Nous proposons également à la communauté scientifique un site web mettant en ligne ce nouvel alphabet structural SAFlex. Ce site web représente la contribution concrète de cette thèse alors que le parser SAFlex-PDB représente une contribution importante pour le fonctionnement du site web proposé. Cette caractérisation précise des conformations 3D et la prise en compte de la redondance des informations 3D disponibles, fournies par SAFlex, a en effet un impact très important pour la modélisation de la conformation et de la variabilité des structures 3D, des boucles protéiques et des régions d’interface avec différents partenaires, impliqués dans la fonction des protéines
The purpose of this PhD is to provide a Structural Alphabet (SA) for more accurate characterization of protein three-dimensional (3D) structures as well as integrating the increasing protein 3D structure information currently available in the Protein Data Bank (PDB). The SA also takes into consideration the logic behind the structural fragments sequence by using the hidden Markov Model (HMM). In this PhD, we describe a new structural alphabet, improving the existing HMM-SA27 structural alphabet, called SAFlex (Structural Alphabet Flexibility), in order to take into account the uncertainty of data (missing data in PDB files) and the redundancy of protein structures. The new SAFlex structural alphabet obtained therefore offers a new, rigorous and robust encoding model. This encoding takes into account the encoding uncertainty by providing three encoding options: the maximum a posteriori (MAP), the marginal posterior distribution (POST), and the effective number of letters at each given position (NEFF). SAFlex also provides and builds a consensus encoding from different replicates (multiple chains, monomers and several homomers) of a single protein. It thus allows the detection of structural variability between different chains. The methodological advances and the achievement of the SAFlex alphabet are the main contributions of this PhD. We also present the new PDB parser(SAFlex-PDB) and we demonstrate that our parser is therefore interesting both qualitative (detection of various errors) and quantitative terms (program optimization and parallelization) by comparing it with two other parsers well-known in the area of Bioinformatics (Biopython and BioJava). The SAFlex structural alphabet is being made available to the scientific community by providing a website. The SAFlex web server represents the concrete contribution of this PhD while the SAFlex-PDB parser represents an important contribution to the proper function of the proposed website. Here, we describe the functions and the interfaces of the SAFlex web server. The SAFlex can be used in various fashions for a protein tertiary structure of a given PDB format file; it can be used for encoding the 3D structure, identifying and predicting missing data. Hence, it is the only alphabet able to encode and predict the missing data in a 3D protein structure to date. Finally, these improvements; are promising to explore increasing protein redundancy data and obtain useful quantification of their flexibility
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Gomes, Benros Cristina. "Analyse et prédiction des structures tridimensionnelles locales des protéines." Paris 7, 2005. http://www.theses.fr/2005PA077090.

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Nolin, Loïc. "Outils d'aide à l'étude des protéines: modélisation surfacique et visualisation sémantique des feuillets béta." Reims, 2010. http://theses.univ-reims.fr/sciences/2010REIMS008.pdf.

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L'enjeu de ces travaux consiste en la représentation de motifs structuraux réguliers des protéines : les feuillets β. Les représentations classiques de la modélisation moléculaire n'étant pas satisfaisantes, étant donne qu'elles ne représentent pas les feuillets β dans leur ensemble, nous proposons nos modèles représentant ces structures sous forme de surfaces. Nous utilisons le logiciel open source ≪ BALLView ≫ pour créer nos propres modèles de feuillets β. La première approche utilise la description des feuillets β présente dans les fichiers issus de la ≪ Protein Data Bank ≫, la banque de données mondiale de structures protéiques, pour calculer une interpolation bidimensionnelle basée sur les splines de Catmull-Rom. La seconde approche utilise des carreaux de Bézier, construits a partir des résultats issus d'un algorithme d'attribution des structures secondaires des protéines, dont les feuillets β font partie. Ces approches sont les premières à représenter les feuillets β dans leur ensemble. Les modèles classiques ne représentent que les brins β. Pour visualiser leur orientation nous plaquons cette information par le biais de textures. Cela nous amène à considérer nos surfaces comme de nouveaux medias sur lesquels nous pouvons dépeindre des données supplémentaires par l'intermédiaire de méthodes de coloration (≪ Hydrophobic Cluster Analysis ≫, ≪ Molecular Hydrophobicity Potential ≫…). Nos modèles sont utilisables sur l'ensemble des fichiers au format PDB, en statique, mais également sur des fichiers de simulation de dynamique moléculaire. Nous pouvons alors constater l'évolution des feuillets β, leurs déformations, l'apparition de trous, d'invaginations ou de déchirures. Ces constatations nous amènent à baptiser nos modèles SheHeRASADe pour ≪ Sheets Helper for RepresentAtion of SurfAce Descriptors ≫. Nous nous intéressons, entre autres, à l'application de ces modèles sur les divers repliements protéiques des feuillets β repertoriés dans la classification CATH, ainsi qu'aux fibres amyloides, impliquées dans de nombreuses pathologies
The aim of this work consists in the representation of common structural motifs of proteins: the β sheets. The classical visualization modes are not satisfying, considering that they don't represent the whole β sheets. We propose innovative models materializing those structures using surfaces. We use the open source software "BALLView" to create our own β sheet models. The first one uses the β sheets description stored in files from the Protein Data Bank, the worldwide data bank of proteic structures, to compute a bidimensionnal interpolated surface based on Catmull-Rom splines. The second one uses Bezier patches defined from β sheets produced by a secondary structure prediction algorithm. Those models are the first ones to fully represent β sheets. Previous methods only represent β strands. In order to visualize their orientation, we map these important data to our surfaces by using textures. It leads us to consider our surfaces as a new medium on which we can depict additional information using coloring methods (Hydrophobic Cluster Analysis, Molecular Hydrophobicity Potential. . . ). Our models are available for any PDB formatted file, in both static and dynamic ways, using molecular dynamics simulations. We can observe the evolution of β sheets, deformations, holes appearances, invaginations or splits. Those observations lead us to call our models SheHeRASADe for "Sheets Helper for RepresentAtion of SurfAce Descriptors". We apply those models to the different proteic folds of β sheets listed in the CATH classification, and on amyloid fibrils involved in many diseases
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Rojas, Macias Miguel Angel [Verfasser]. "Deciphering the molecular basis of the specificity of protein-carbohydrate interactions by statistical analysis of 3D structural data from the Protein Data Bank / Miguel Angel Rojas Macias." Gießen : Universitätsbibliothek, 2016. http://d-nb.info/1097168662/34.

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Deforte, Shelly. "Intrinsic Disorder Where You Least Expect It: The Incidence and Functional Relevance of Intrinsic Disorder in Enzymes and the Protein Data Bank." Scholar Commons, 2016. http://scholarcommons.usf.edu/etd/6219.

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Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) exist as interconverting conformational ensembles, without a single fixed three-dimensional structure in vivo. The focus in the literature up to this point has been primarily on IDPs that are mostly or entirely disordered. Therefore, we have an incomplete understanding of the incidence and functional relevance of IDPRs in proteins that have regions of both order and disorder. This work explores these populations, by examining IDPRs in the Protein Data Bank (PDB) and in enzymes. By applying disorder prediction methods combined with an analysis of missing regions in crystal structure data, this work shows that enzymes have a similar incidence and length of IDPRs as do non-enzymes, and that these IDPRs are correlated with functions related to macromolecular metabolism, signaling, and regulation. Furthermore, extensive analyses of missing regions with conflicting information between multiple structures in the PDB show that, rather than experimental artifacts, this ambiguity most likely arises due to partially or conditionally disordered regions. This work documents the first proteome level study of protein intrinsic disorder in enzyme populations and demonstrates a novel way of analyzing missing regions in the PDB. Furthermore, an extensive literature search as part of this work provides information for 1127 IDPs with experimental evidence documented in the literature, 96 of which are enzymes. The results contained herein present a new model of the protein universe, where disorder is directed by evolution in both non-enzymes and enzymes to make the most of limited proteomes in complex organisms through complicated signaling networks and tightly controlled regulation.
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Sladký, Roman. "Techniky pro porovnávání biologických sekvencí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235890.

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This work presents the building up of basic biological units DNA, RNA and proteins as well as their function. Provided data are kept in biological databases which are connected worldwide to supply preferable communication along with all kinds of available information to be used in the scientific research. The secret of alive is hidden in genes coded in sequences of nucleotides. Genes enable the creation of proteins which are made of sequences of amino-acids. The wide-spread methods of comparing these sequences are FASTA and BLAST algorithms. Their base is used for the PSProt program which is described in this work. PSProt program is the tool for comparing the sequences of proteins. First it is necessary to synthesise the protein from the DNA oligonucleotide because it codes the surveyed protein. The most similar proteins are searched out by heuristic of hitpoints, then their final score that is essential for aligning is modified by semiglobal alignment algorithm.
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Calixto, Tulio Marcus Ribeiro. "Análises de propriedades eletrostáticas e estruturais de complexos de proteínas para o desenvolvimento de preditores de complexação em larga escala." Universidade de São Paulo, 2010. http://www.teses.usp.br/teses/disponiveis/60/60136/tde-17112010-093652/.

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Estudos teóricos dos mecanismos moleculares responsáveis pela formação e estabilidade de complexos moleculares vêm ganhando relevância pelas possibilidades práticas que oferecem, por exemplo, na compreensão de diversas doenças e no desenho racional de fármacos. Neste projeto, nossa ênfase está no estudo de complexos de proteínas, extraídos do banco de dados de proteínas (PDB), onde desenvolvemos ferramentas computacionais as quais permitem efetuar análises em duas direções: 1) efetuar previsões básicas, através do emprego de propriedades eletrostáticas de proteínas, em diferentes condições e níveis preditivos e 2) realização de um conjunto de análises estatísticas, como freqüência de contato, em busca de preditores de complexos de proteínas e identificar padrões de interação entre seus aminoácidos em função da distância de separação. Com base nos resultados obtidos por ambos os estudos, objetivamos quantificar as forças físicas envolvidas na formação dos complexos protéicos. O foco do projeto, a longo prazo, é prever o fenômeno da complexação através da fusão dessas duas linhas de estudos: preditor básico de complexos protéicos e análise do potencial estatístico entre os aminoácidos que formam o complexo. O presente projeto é concluído com a construção de portais web que disponibilizarão os resultados obtidos por nossos trabalhos bem como a possibilidade de qualquer usuário, efetuar consultas por propriedades de proteínas e/ou grupo de proteínas.
Theoretical studies of the molecular mechanisms responsible for the formation and stability of molecular complexes are gaining relevance for the practical possibilities that they offer, for example, in the understanding of diverse diseases and rational drug design. In this project, our emphasis is on the study of protein complexes, extracted from protein data bank (PDB). We have developed computational tools which allow to perform analyses in two directions: 1) to make basic complexation forecasts, through the use of electrostatics properties of proteins, in different conditions and predictive levels, and 2) to carry out a set of statistical analyses, as contacts frequency, in order to build up predictor of protein complexes and to identify patters of interactions between the amino acids as a function of their separation distance. Based on the results obtained on both studies, we aim quantify the physical forces involved in the formation of protein complexes. The focus of the project, in the long run, is to foresee the phenomenon of the protein complexes through the fusing of these two study lines: a coarse-grained predictor of protein complexes and analysis of the statistical potentials between the amino acids that form the complex. The present project is concluded with the construction of web services where we make available the results obtained on our works. This server also has the possibility to be used by any computer user, that wishes to perform search on protein and/or protein group properties
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Tikah, Marrakchi Mohamed. "Helix Explorer : une nouvelle base de données de structures de protéines." Thèse, 2006. http://hdl.handle.net/1866/15675.

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Selvan, Joshua. "Parallel algorithms for querying spatial properties in the Protein Data Bank." Thesis, 2019. https://hdl.handle.net/10539/30383.

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A research report submitted to the Faculty of Engineering and the Built Environ- ment, University of the Witwatersrand, Johannesburg, in partial ful lment of the requirements for the degree of Master of Science in Engineering.
Searching large protein databases for proteins with certain structural properties is expensive. This research explored the use of GPGPUs (General Purpose Graphical Processing Units) in speeding up such structural queries. Brute force and kd-tree spatial data structure algorithms were compared and benchmarked against non-GPU parallel algorithms to assess the e ectiveness of using GPGPUs. This was done with the aim of increasing the speed at which queries against large protein databases can be completed to help mitigate the e ect of increasing data set sizes of current protein databases [56]. A set of parallel variations of range search algorithms were developed and imple- mented in the GPU programming language CUDA and their performances times in completing batch range search jobs were compared against other parallel approach types such as multi-threading and message passing to see if the GPU approaches completed notably faster or slower than more traditional parallelised approaches. The results showed GPGPUs can construct kd-trees far faster than other parallelised implementations can achieve and that in most scenarios (excluding speci c cases such as very low or zero result searches) the GPGPU approaches either matched or performed far better than the other parallelised approaches. While comparing di erent GPU algorithms, the complex GPU based kd-tree algo- rithm performed similarly to a simple GPU brute force range search. This high- lighted the bene ts of writing code which made the most of the GPU's parallel architecture as opposed to modifying e cient (recursive) algorithms to adequately t into those same GPU architectures. This implied that even though spatial data structures are e ective ways of dealing with protein data, there are better returns on e ort in writing code speci cally for the GPU's inherently parallel architecture for initiatives which require algorithms to be developed from scratch.
PH2021
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Books on the topic ""Protein Data Bank" (PDB)"

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Laboratory, Brookhaven National. Protein Data Bank CD-ROM: Version August 1990. Springer, 1992.

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Eleanor, Mitchell, and University of Sheffield. Dept. of Information Studies, eds. Three-dimensional substructure searching in the protein data bank. Sheffield, [England]: Department of Information Studies and Biochemistry, University of Sheffield, 1988.

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Eleanor, Mitchell, ed. Three-dimensional substructure searching in the Protein Data Bank: Final report for the period October 1986 to September 1988 to the British Library Research and Development Department on Project SI/G/760. Sheffield: Dept. of Information Studies and Biochemistry, University of Sheffield, 1990.

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Giacovazzo, Carmelo. Phasing in Crystallography. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199686995.001.0001.

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Modern crystallographic methods originate from the synergy of two main research streams, the small-molecule and the macro-molecular streams. The first stream was able to definitively solve the phase problem for molecules up to 200 atoms in the asymmetric unit. The achievements obtained by the macromolecular stream are also impressive. A huge number of protein structures have been deposited in the Protein Data Bank. The solution of them is no longer reserved to an elite group of scientists, but may be attained in a large number of laboratories around the world, even by young scientists. New probabilistic approaches have been tailored to deal with larger structures, errors in the experimental data, and modest data resolution. Traditional phasing techniques like ab initio, molecular replacement, isomorphous replacement, and anomalous dispersion techniques have been revisited. The new approaches have been implemented in robust phasing programs, which have been organized in automatic pipelines usable even by non-experts. Protein structures, which 50 years ago could take months or even years to solve, can now be solved in a matter of hours, partly also due to technological advances in computer science. This book describes all modern crystallographic phasing methods, and introduces a new rational classification of them. A didactic approach is used, with the techniques described simply and logically in the main text, and further mathematical details confined to the Appendices for motivated readers. Numerous figures and applicative details illustrate the text.
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Meurig Thomas, John. Architects of Structural Biology. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198854500.001.0001.

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Designed for the non-specialist, the explanations and illustrations used here describe the work, personalities, collaborations, and idiosyncrasies of four of the most distinguished Nobel Laureates of the twentieth century. They exploited a discovery made over a century ago about the nature of X-rays, and thereby created a new branch of science. This enabled them to elucidate, in atomic detail, the structure and mode of action of molecules of the living world: enzymes, vitamins, and viruses, as well as antibiotics. Perutz and Kendrew, from their pioneering work using X-ray diffraction on haemoglobin and myoglobin, the proteins that transport and store oxygen in all animals, led them to establish in 1962 one of the most successful research centres ever—the Laboratory of Molecular Biology (LMB) in Cambridge. Medicines discovered there are used worldwide to treat leukaemia, arthritis, and other diseases. Their work also led to the creation in the United States of the Protein Data Bank that guides scientists in understanding the misfolding of proteins, which cause Alzheimer’s disease, Parkinson’s disease, and other neurodegenerative diseases. This book is first a memoir of these scientists and their contemporaries, many of them friends of the author. Second, it is an insight into the great excitement associated with structural molecular biology, which directly informs our understanding of ourselves. Third, it describes how two renowned research centres in the United Kingdom—the LMB and the Davy-Faraday Research Laboratory—achieved iconic status. It also highlights the importance of the popularization of science, of which Bragg, Perutz, and Kendrew, as well as Dorothy Hodgkin (who solved the structures of penicillin and vitamin B12) were experts.
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Book chapters on the topic ""Protein Data Bank" (PDB)"

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Burley, Stephen K., Helen M. Berman, Gerard J. Kleywegt, John L. Markley, Haruki Nakamura, and Sameer Velankar. "Protein Data Bank (PDB): The Single Global Macromolecular Structure Archive." In Methods in Molecular Biology, 627–41. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-7000-1_26.

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Goodsell, David S. "The Protein Data Bank." In Atomic Evidence, 1–4. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32510-1_1.

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Sussman, J. L., E. E. Abola, D. Lin, J. Jiang, N. O. Manning, and J. Prilusky. "The Protein Data Bank." In Structural Biology and Functional Genomics, 251–64. Dordrecht: Springer Netherlands, 1999. http://dx.doi.org/10.1007/978-94-011-4631-9_16.

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Janes, Robert W. "Protein Circular Dichroism Data Bank." In Encyclopedia of Biophysics, 1961–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-16712-6_645.

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Berman, H. M., J. Westbrook, Z. Feng, G. Gilliland, T. N. Bhat, H. Weissig, I. N. Shindyalov, and P. E. Bourne. "The Protein Data Bank, 1999–." In International Tables for Crystallography, 675–84. Chester, England: International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000722.

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Berman, H. M., K. Henrick, G. Kleywegt, H. Nakamura, and J. Markley. "The Worldwide Protein Data Bank." In International Tables for Crystallography, 827–32. Chester, England: International Union of Crystallography, 2012. http://dx.doi.org/10.1107/97809553602060000896.

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Velankar, Sameer, Stephen K. Burley, Genji Kurisu, Jeffrey C. Hoch, and John L. Markley. "The Protein Data Bank Archive." In Methods in Molecular Biology, 3–21. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1406-8_1.

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Sussman, J. L., D. Lin, J. Jiang, N. O. Manning, J. Prilusky, and E. E. Abola. "The Protein Data Bank at Brookhaven." In International Tables for Crystallography, 649–56. Chester, England: International Union of Crystallography, 2006. http://dx.doi.org/10.1107/97809553602060000718.

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Dutta, Shuchismita, Kyle Burkhardt, Ganesh J. Swaminathan, Takashi Kosada, Kim Henrick, Haruki Nakamura, and Helen M. Berman. "Data Deposition and Annotation at the Worldwide Protein Data Bank." In Methods in Molecular Biology, 81–101. Totowa, NJ: Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-058-8_5.

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Stanek, Dawid, Dariusz Mrozek, and Bożena Małysiak-Mrozek. "MViewer: Visualization of Protein Molecular Structures Stored in the PDB, mmCIF and PDBML Data Formats." In Computer Networks, 323–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38865-1_33.

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Conference papers on the topic ""Protein Data Bank" (PDB)"

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Ghani, Nur Syatila Ab, and Mohd Firdaus-Raih. "Computational mining for hypothetical patterns of amino acid side chains in protein data bank (PDB)." In THE 2017 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the University Kebangsaan Malaysia, Faculty of Science and Technology 2017 Postgraduate Colloquium. Author(s), 2018. http://dx.doi.org/10.1063/1.5027994.

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Arikawa, Keisuke. "Extension of the Kinematics-Based Method for Predicting the Motion of Proteins." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34576.

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On the basis of an analogy between the kinematic structures of proteins and robotic mechanisms, we have so far developed methods for predicting the internal motion of proteins from three-dimensional structural data in the protein data bank (PDB). With these methods, we model proteins as serial manipulators constrained by springs, and calculate the structural compliance of the protein model. In this study, toward more practical purposes, we reformulate and extend the existing methods by broadening the definition of structural compliance and reducing the number of variables for expressing the conformation of the model. The broadening is performed by separating the parts whose deformations are evaluated from those where forces are applied. This separation allows the calculation of the effective forces causing deformation in other specified parts. We also reduce the number of conformation variables from the consideration based on the algebraic structure of the basic equations. The size of the matrix whose inverse must be calculated is thus minimized, and the computational cost is reduced. We verify the effectiveness of these extensions by analyzing the PDB data of some proteins.
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Arikawa, Keisuke. "Analyzing Motion Properties of Proteins Affected by Localized Structures From a Robot Kinematics Perspective." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-47010.

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On the basis of robot kinematics, we have thus far developed a method for predicting the motion of proteins from their 3D structural data given in the Protein Data Bank (PDB data). In this method, proteins are modeled as serial manipulators constrained by springs and the structural compliance properties of the models are evaluated. We focus on localized instead of whole structures of proteins. Employing the same model used in our method of motion prediction, the motion properties of the localized structures and the relation between the motion properties of localized and whole structures are analyzed. First, we present a method for graphically expressing the deformation of objects with a complex shape, such as proteins, by approximating the shape as a rectangular prism with a mesh on its surface. We then formulate a method for comparing the motion properties of localized structures cleaved from the whole structure and those remaining in it by expressing the motion of the latter using the decomposed motion modes of the former according to the structural compliance. Finally, we show a method for evaluating the effect of a localized structure on the motion properties of proteins by applying forces to localized structures. In the formulations, we demonstrate applications as illustrative examples using the PDB data of a real protein.
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Arikawa, Keisuke. "A Computational Framework for Predicting the Motions of a Protein System From a Robot Kinematics Viewpoint." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12527.

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There is an analogy between the kinematic structures of proteins and robotic mechanisms. On the basis of this analogy, we have so far developed some methods for predicting the internal motions of proteins from their three-dimensional structural data in protein data bank (PDB). However, these methods are basically applicable to a single protein molecule. In this study, we extended these methods to apply them to systems that consist of multiple molecules including proteins (protein systems), and developed a computational framework for predicting the motions of the molecules. The model used in this method is a type of elastic network model. In particular, proteins are modeled as a robot manipulator constrained by the springs (the dihedral angles on the main chains correspond to the joint angles). The interactions between molecules are also modeled as springs. The basic concept for predicting the motions is based on the analysis of structural compliance. By applying statically balanced forces to the model in various directions, we extracted those motions with larger structural compliance. To reduce the computational time, we formulated the method with the prospect of efficient computation including parallel computation. In addition, we developed a preparatory computer program implementing the proposed algorithms, and analyzed some protein systems. The results showed that the proposed computational framework can efficiently analyze large protein systems.
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Subramanian, Raghavendran, and Kazem Kazerounian. "Residue Level Inverse Kinematics of Peptide Chains in the Presence of Observation Inaccuracies and Bond Length Changes." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84352.

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Dihedral angles as generalized coordinates define the geometric conformation of a peptide chain. Given the exact coordinates of the atoms, it is possible to rigorously calculate the dihedral angles. We will refer to this calculation process as the residue level inverse kinematics of peptide chains. However uncertainties and experimental observation inaccuracies in the atoms’ coordinates handicap this otherwise simple and straightforward process. In this paper, we present three new efficient methodologies to find all the dihedral angles of a peptide chain for a given conformation. Comparison of these results with the dihedral angle values reported in the PDB (Protein Data Bank) indicates significant improvements. While these improvements benefit most modeling methods in protein analysis, it is in particular, very significant in homology modeling where the dihedral angles are the structural variables. The first method presented here fits a best plane through five atoms of each peptide unit. The angle between the successive planes is defined as the dihedral angle. The second method is based on the Zero-Position analysis method. Successive links in this method rotate by the dihedral angles so as to minimize the structural error between respective atoms in the model conformation with given atoms’ coordinates. Dihedral angle final values correspond to the minimum structural error configuration. In this method, singular value decomposition (SVD) technique is used to best fit the atoms in the two conformations. The third method is a variant of the second method. In this instead of rotating all the links successively only three links are matched each time to extract the dihedral angle of the middle link. By doing so, the error accumulation on the successive links is reduced. This paper focuses on the Euclidean norm as the measure of merit (structural error) to compare different methods with the Protein Data Bank (PDB). This Euclidean norm is further, minimized by optimizing the geometrical features of the peptide plane.
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Subramanian, Raghavendran, and Kazem Kazerounian. "Improved Molecular Model of a Peptide Unit for Proteins." In ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2006. http://dx.doi.org/10.1115/detc2006-99315.

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Pauling, Corey and Branson in their seminal paper in 1951 reported numerical values for the bond lengths and bond angles for a peptide unit in proteins. These values became the standard model for several decades after that. This classic peptide model was either confirmed or improved upon by other researchers over the years, by using more advanced X-Ray diffraction equipments. In this paper, we have made an attempt to calibrate the values of these bond lengths and bond angles based on a systematic and deterministic approach applied to a collection of proteins defined structurally in the Protein Data Bank (PDB). Our method is based on the assumption that a peptide chain is a serial chain of identical rigid bodies connected by revolute joints (i.e. dihedral angles). The proposed procedure first computes the best estimate for the dihedral angles in the presence of inaccuracies in the atoms’ coordinates data. Then these values are used to find the conformation of the peptide chain using the calibrated model of the peptide unit. Through an optimization process, the structural error (RMSD of all atoms) between the resultant conformation and the PDB data is minimized to yield the best values for the bond length and bond angles in the calibrated peptide unit. Our numerical experiments indicate that by making small changes in the Pauling-Corey peptide model parameters (0.15% to 8.7%) the structural error is reduced significantly (3.0% to 57.4%). The optimum values for the bond angles and bond lengths are as follow: Bond Lengths: N-C(A): 1.4721Å, C(A)-C: 1.6167Å, C-N: 1.2047Å, C=O: 1.1913Å and N-H: 0.9621Å. Bond Bending Angles: N-C(A)-C: 109.6823°, C(A)-C=0: 119.518°, C(A)-C-N: 114.5553°, O=C-N: 125.9233°, C-N-H: 123.5155°, C-N-C(A): 121.5756°, C(A)-N-H: 114.901°. Peptide bond torsion angle: ω: 179.4432°.
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Menezes, Lis Mariana da Silva, Liliane Rodrigues Garcia, Gabrieli Carolina Favacho Gonçalves, Ronaldo Correia da Silva, and Adonis de Melo Lima. "PROSPECÇÃO DE CANDIDATOS A FÁRMACOS PARA TRATAMENTO DE TUMORES MALIGNOS." In I Congresso Brasileiro de Biotecnologia On-line. Revista Multidisciplinar de Educação e Meio Ambiente, 2021. http://dx.doi.org/10.51189/rema/820.

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Introdução: A principal característica do câncer é o crescimento descontrolado de células neoplásicas, formando tumores. Tecidos tumorais apresentam dependência do aminoácido L-asparagina para se desenvolver, sendo a obtenção desse aminoácido feita do meio extracelular. Alguns medicamentos quimioterápicos hidrolisam a asparagina em amônia e aspartato, eliminando a fonte sérica de asparagina das células tumorais, causando uma apoptose seletiva. Esse efeito é possível pela presença da enzima asparaginase utilizada como princípio ativo dos medicamentos anticancerígenos. Ess molécula é isolada principalmente da bactéria E.coli, porém pacientes têm apresentado efeitos colaterais ao uso de medicamentos que a contem, sendo necessárias novas alternativas farmacológicas. Objetivo: Realizar análise in silico do potencial biotecnológico de asparaginase de Erwinia billingiae, a partir da construção 3D da proteína, identificação do sítio ativo e validação do modelo proposto. Materiais e métodos: Foi realizada uma busca a partir da sequência FASTA da L asparaginase de Erwinia billingiae no Protein Data Bank (PDB), para aquisição de um molde proteíco. Posteriormente foi realizado o alinhamento entre as sequências alvo e molde. Foram construídos cinco modelos proteicos no progama modeller 9v8, e estes foram submetidos à validação através do diagrama de Ramachandran, QMEAN, Prosa e ProQ. Resultados: Foi escolhido o PDB 4O0E para ser utilizado como referência para gerar os candidatos a fármaco, sendo construído diversos modelos, validados e escolhido aquele com melhores resultados. O modelo proposto possui 320 aminoácidos, sendo sua estrutura secundária caracterizada por 11 ẞ-folhas, 8 α-hélices e 20 loops, todos equivalentes às estruturas do modelo de referência (4O0E). Foi observada a presença da tríade catalítica T168, T186 e T219 do modelo de referência em posições equivalentes no modelo construído, sugerindo conservação de sua função. Conclusão: A asparaginase de Erwinia billingiae mantém a tríade catalítica de treoninas conservada sugerindo preservação de sua função proteolítica. Estudos adicionais de acoplamento e dinâmica molecular devem ser realizados para observar se ocorrem interações no complexo enzima-substrato.
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da Silva, Ronaldo Correia, Gabrieli Carolina Favacho Gonçalves, Maria Vitória Nava Moura, Liliane Rodrigues Garcia, and Adonis de Melo Lima. "FERRAMENTAS COMPUTACIONAIS PARA CARACTERIZAÇÃO DE MOLÉCULAS COM POTENCIAL BIOTECNOLÓGICO." In I Congresso Brasileiro de Biotecnologia On-line. Revista Multidisciplinar de Educação e Meio Ambiente, 2021. http://dx.doi.org/10.51189/rema/819.

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Introdução: Asparaginases são amino-hidrolases que catalisam a hidrólise de asparagina em aspartato e amônia. Desde 1953, estas enzimas são conhecidas por sua atividade anticancerígena devida à dependência, que alguns tecidos tumorais têm de Lasparagina extracelular para sua proliferação. Assim, uma vez injetadas na corrente sanguínea, as L-ASNases reduzem a quantidade de asparagina no corpo, impedindo a sobrevivência das células tumorais. Objetivo: Caracterizar o potencial biotecnológico das L-asparaginases de Escherichia coli por meio de ferramentas de simulação computacional. Material e métodos: A enzima Asparaginase tipo II (ECAII) foi obtida no Protein Data Bank - PDB. Dos PDBs 1IHD, 1JAZ, 1JJA, 1NNS, 1HO3, 4ECA e 3ECA, foi escolhida a enzima tipo selvagem mais antiga depositada sob o código 3ECA. Todas as demais são formas mutantes da molécula. Para o estudo e visualização do sítio da enzima foram utilizados os softwares PYMOLL e Visual Molecular Dynamics em suas versões mais recentes. Resultados e discussão: A estrutura de ativa de ECAII é um tetrâmero com 4 subunidades: A, B, C e D, cada um com um sítio ativo. Consiste de dois domínios α e β, Nt-terminal e C-terminal. O domínio C-terminal vai até o aminoácido Gln190, seguido por alça que conecta a outra extremidade, que vai do resíduo 191 ao 212, seguida da extremidade C-terminal, mais curta, com apenas 113 resíduos. Foram identificadas as cavidades do sítio da enzima e quais resíduos interagem com o substrato asparagina. Conclusão: As caractetísticas moleculares e interação com o substrato observada no sítio ativo da molécula confirmam o potencial biotecnológico dessas enzimas. Tendo isso em vista, sugere-se que as asparaginases sejam otimizadas por meio de mutações em seu sítio ativo, aumentando sua atividade catalítica e, consequentemente, sua atividade anticancerígena.
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Lima, Adonis de Melo, Lis Mariana da Silva Menezes, Liliane Rodrigues Garcia, Maria Vitória Nava Moura, and Ronaldo Correia da Silva. "ANÁLISE COMPUTACIONAL DO SÍTIO ATIVO DA ENZIMA LASPARAGINASE DE Erwinia rhapontici." In I Congresso Brasileiro de Biotecnologia On-line. Revista Multidisciplinar de Educação e Meio Ambiente, 2021. http://dx.doi.org/10.51189/rema/817.

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Introdução: As asparaginases são amplamente distribuídas na natureza, de bactérias a mamíferos, e desempenham uma função central no metabolismo e utilização de aminoácidos. Através desta enzima a asparagina é hidrolisada em aspartato, que então é transaminado em oxaloacetato, um intermediário no ciclo de Krebs. Por isso, as asparaginases são importantes para manter o equilíbrio de nitrogênio e os níveis de aminoácidos dentro células, níveis estes que são indispensáveis para o crescimento do organismo. Objetivo: Realizar análise computacional do sítio ativo de asparaginase tipo I de Erwinia rhapontici. Material e métodos: Foi realizada uma busca a partir da sequência FASTA (genebank id ID: AGB58265.1) da L asparaginase tipo I de Erwinia rhapontici no Protein Data Bank - PDB, para aquisição de um molde proteíco. Posteriormente foi realizado o alinhamento entre as sequências alvo e molde. Foram construídos cinco modelos proteicos no progama modeller 9v8, sendo estes submetidos à validação através do gráfico de Ramachandran, QMEAN, Prosa e ProQ. Resultados: Foi escolhido o PDB 2P2N para ser utilizado como referência para gerar os candidatos a fármaco, sendo construído diversos modelos, validados e escolhido aquele com melhores resultados. O modelo proposto possui 337 aminoácidos, sendo sua estrutura secundária caracterizada por 11 ẞ-folhas, 10 α-hélices e 21 loops, todos equivalentes às estruturas do modelo de referência (2P2N). O sítio ativo é composto de díade de treonina nas posições 14 e 91 no modelo de referência em posições equivalentes no modelo construído. Conclusão: A partir dos estudos computacionais realizados, mostrou-se que a asparaginase tipo I de Erwinia rhapontici se mantém estruturalmente conservada, sugerindo a garantia da sua função no mecanismo catalítico da asparagina, onde participa regulando níveis intracelulares de nitrogênio e aminoácidos celulares. Estudos adicionais de acoplamento e dinâmica molecular devem ser realizados para observar se ocorrem interações no complexo enzima-substrato.
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Tariq, Tayyaba, Javed Frezund, Muhammad Farhan, Rana M. Amir Latif, and Azka Mehmood. "Structure Analysis of Protein Data Bank Using Python Libraries." In 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2020. http://dx.doi.org/10.1109/ibcast47879.2020.9044525.

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Reports on the topic ""Protein Data Bank" (PDB)"

1

Berman, Helen. Protein Data Bank Project at Rutgers University. Office of Scientific and Technical Information (OSTI), July 2002. http://dx.doi.org/10.2172/805813.

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