Journal articles: '"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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Samuel, Selvaraj, and Mary Rajathei. "A Web Database IR-PDB for Sequence Repeats of Proteins in the Protein Data Bank." International Journal of Knowledge Discovery in Bioinformatics 7, no. 2 (July 2017): 1–10. http://dx.doi.org/10.4018/ijkdb.2017070101.

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Amino acid repeats play significant roles in the evolution of structure and function of many large proteins. Analysis of internal repeats of protein with known structure helps to understand the importance of repeats of the protein. A database IR-PDB for repeats in sequence of the proteins in the PDB has been developed for the analysis of impact of repeats in proteins. Using the state of the art repeat detection method RADAR, internal repeats in 148202 sequences out of 285714 sequences belonging to 115031 PDB structures were detected. The identified sequence repeats were annotated with secondary structural information with a view to analyze the structural consequence and conservation of the repeats. The tertiary structure of the repeats and their functional involvements can be found out through web links to PDB, PDBsum and Pfam. IR-PDB is systematically annotated for the the proteins in the PDB with sequence repeats and their structure with the possibility to access the dataset interactively through web services.

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Noguchi, T. "PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB)." Nucleic Acids Research 29, no. 1 (January 1, 2001): 219–20. http://dx.doi.org/10.1093/nar/29.1.219.

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Sussman, Joel L., Dawei Lin, Jiansheng Jiang, Nancy O. Manning, Jaime Prilusky, Otto Ritter, and Enrique E. Abola. "Protein Data Bank (PDB): Database of Three-Dimensional Structural Information of Biological Macromolecules." Acta Crystallographica Section D Biological Crystallography 54, no. 6 (November 1, 1998): 1078–84. http://dx.doi.org/10.1107/s0907444998009378.

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The Protein Data Bank (PDB) at Brookhaven National Laboratory, is a database containing experimentally determined three-dimensional structures of proteins, nucleic acids and other biological macromolecules, with approximately 8000 entries. Data are easily submittedviaPDB's WWW-based toolAutoDep, in either mmCIF or PDB format, and are most conveniently examinedviaPDB's WWW-based tool3DB Browser.

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Voet, Judith, and Shuchismita Dutta. "The Protein Data Bank and Its Uses in Structural Biology Education." Revista de Ensino de Bioquímica 2, no. 2 (May 15, 2004): 21. http://dx.doi.org/10.16923/reb.v2i2.153.

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The Protein Data Bank (PDB) is a repository for the structures of proteins and nucleic acids. Itcontains les of their 3-dimensional coordinates, information on how these structures were determinedand references to the journal articles describing them. The PDB was established in 1971 by HelenBerman (it s present director) and has grown exponentially so that it now contains 25,000 data lesrepresenting X-ray crystallographic, NMR and other structure determinations. Database queryingand data miningtools and resources at the PDB make it possible to search, compare and infer orpredict the function of newly identied proteins. Computer graphics capabilities make it possible foranyone to easily visualize and study the structural data. The capability to present beautiful graphicrepresentations of the 3-dimesnional structures of proteins and nucleic acids has been a boon to theeducation community. Communicating an understanding of these structures and the chemical forcesdetermining them and their interactions is one of the major aims of biochemistry and molecular biologyeducation. The ability to teach these principles visually has made a great dierence in our abilityto excite our students and provide them with physical interpretations for some abstract concepts inbiochemistry and molecular biology. In this talk we will explore some of the ways that the education community uses the PDB.

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Gore, Swanand, Pieter Hendrickx, Eduardo Sanz-Garcia, Sameer Velankar, and Gerard Kleywegt. "New wwPDB validation pipelines for X-ray, NMR and 3DEM structures." Acta Crystallographica Section A Foundations and Advances 70, a1 (August 5, 2014): C1478. http://dx.doi.org/10.1107/s2053273314085210.

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The Protein Data Bank (PDB) is the single global archive of 3D biomacromolecular structure data. The archive is managed by the Worldwide Protein Data Bank (wwPDB; wwpdb.org) organisation through its partners, the Research Collaboratory for Structural Bioinformatics (RCSB PDB), the Protein Data Bank Japan (PDBj), the Protein Data Bank in Europe and the Biological Magnetic Resonance Bank (BMRB). Analogously, the Electron Microscopy Data Bank (EMDB) is managed by the EMDataBank (emdatabank.org) organisation. A few years ago, realising the needs and opportunities to assess the quality of biomacromolecular structures deposited in the PDB, the wwPDB and EMDataBank partners established Validation Task Forces (VTFs) to advice them on up-to-date and community-agreed methods and standards to validate X-ray, NMR and 3DEM structures and data. All three VTFs have now published their recommendations (1, 2, 3) and these are getting implemented as validation-software pipelines . The pipelines are integrated in the new joint wwPDB deposition and annotation system (http://deposit.wwpdb.org/deposition/). In addition, stand-alone servers are provided to allow practising structural biologists to validate models prior to publication and deposition (http://wwpdb.org/validation-servers.html). The validation pipelines and the output they produce (human-readable PDF reports and machine-readable XML files) will be described.

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Fine, Jonathan, and Gaurav Chopra. "Lemon: a framework for rapidly mining structural information from the Protein Data Bank." Bioinformatics 35, no. 20 (March 14, 2019): 4165–67. http://dx.doi.org/10.1093/bioinformatics/btz178.

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Abstract Motivation The Protein Data Bank (PDB) currently holds over 140 000 biomolecular structures and continues to release new structures on a weekly basis. The PDB is an essential resource to the structural bioinformatics community to develop software that mine, use, categorize and analyze such data. New computational biology methods are evaluated using custom benchmarking sets derived as subsets of 3D experimentally determined structures and structural features from the PDB. Currently, such benchmarking features are manually curated with custom scripts in a non-standardized manner that results in slow distribution and updates with new experimental structures. Finally, there is a scarcity of standardized tools to rapidly query 3D descriptors of the entire PDB. Results Our solution is the Lemon framework, a C++11 library with Python bindings, which provides a consistent workflow methodology for selecting biomolecular interactions based on user criterion and computing desired 3D structural features. This framework can parse and characterize the entire PDB in <10 min on modern, multithreaded hardware. The speed in parsing is obtained by using the recently developed MacroMolecule Transmission Format to reduce the computational cost of reading text-based PDB files. The use of C++ lambda functions and Python bindings provide extensive flexibility for analysis and categorization of the PDB by allowing the user to write custom functions to suite their objective. We think Lemon will become a one-stop-shop to quickly mine the entire PDB to generate desired structural biology features. Availability and implementation The Lemon software is available as a C++ header library along with a PyPI package and example functions at https://github.com/chopralab/lemon. Supplementary information Supplementary data are available at Bioinformatics online.

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SAKAE, YOSHITAKE, and YUKO OKAMOTO. "PROTEIN FORCE-FIELD PARAMETERS OPTIMIZED WITH THE PROTEIN DATA BANK I: FORCE-FIELD OPTIMIZATIONS." Journal of Theoretical and Computational Chemistry 03, no. 03 (September 2004): 339–58. http://dx.doi.org/10.1142/s0219633604001082.

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We optimized five existing sets of force-field parameters for protein systems by our recently proposed method. The five force fields are AMBER parm94, AMBER parm96, AMBER parm99, CHARMM version 22, and OPLS-AA. The method consists of minimizing the sum of the square of the force acting on each atom in the proteins with the structures from the Protein Data Bank (PDB). We selected the partial-charge and backbone torsion-energy parameters for this optimization, and 100 molecules from the PDB were used. We gave detailed comparisons of the optimized force fields and found that there is a tendency of convergence towards the same function for the torsion-energy term.

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Goodsell, David S., and Stephen K. Burley. "RCSB Protein Data Bank tools for 3D structure-guided cancer research: human papillomavirus (HPV) case study." Oncogene 39, no. 43 (September 16, 2020): 6623–32. http://dx.doi.org/10.1038/s41388-020-01461-2.

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Abstract Atomic-level three-dimensional (3D) structure data for biological macromolecules often prove critical to dissecting and understanding the precise mechanisms of action of cancer-related proteins and their diverse roles in oncogenic transformation, proliferation, and metastasis. They are also used extensively to identify potentially druggable targets and facilitate discovery and development of both small-molecule and biologic drugs that are today benefiting individuals diagnosed with cancer around the world. 3D structures of biomolecules (including proteins, DNA, RNA, and their complexes with one another, drugs, and other small molecules) are freely distributed by the open-access Protein Data Bank (PDB). This global data repository is used by millions of scientists and educators working in the areas of drug discovery, vaccine design, and biomedical and biotechnology research. The US Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB) provides an integrated portal to the PDB archive that streamlines access for millions of worldwide PDB data consumers worldwide. Herein, we review online resources made available free of charge by the RCSB PDB to basic and applied researchers, healthcare providers, educators and their students, patients and their families, and the curious public. We exemplify the value of understanding cancer-related proteins in 3D with a case study focused on human papillomavirus.

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Noguchi, T. "PDB-REPRDB: a database of representative protein chains from the Protein Data Bank (PDB) in 2003." Nucleic Acids Research 31, no. 1 (January 1, 2003): 492–93. http://dx.doi.org/10.1093/nar/gkg022.

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Smart, Oliver S., Vladimír Horský, Swanand Gore, Radka Svobodová Vařeková, Veronika Bendová, Gerard J. Kleywegt, and Sameer Velankar. "Worldwide Protein Data Bank validation information: usage and trends." Acta Crystallographica Section D Structural Biology 74, no. 3 (March 1, 2018): 237–44. http://dx.doi.org/10.1107/s2059798318003303.

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Realising the importance of assessing the quality of the biomolecular structures deposited in the Protein Data Bank (PDB), the Worldwide Protein Data Bank (wwPDB) partners established Validation Task Forces to obtain advice on the methods and standards to be used to validate structures determined by X-ray crystallography, nuclear magnetic resonance spectroscopy and three-dimensional electron cryo-microscopy. The resulting wwPDB validation pipeline is an integral part of the wwPDB OneDep deposition, biocuration and validation system. The wwPDB Validation Service webserver (https://validate.wwpdb.org) can be used to perform checks prior to deposition. Here, it is shown how validation metrics can be combined to produce an overall score that allows the ranking of macromolecular structures and domains in search results. The ValTrendsDBdatabase provides users with a convenient way to access and analyse validation information and other properties of X-ray crystal structures in the PDB, including investigating trends in and correlations between different structure properties and validation metrics.

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Horský, Vladimír, Veronika Bendová, Dominik Toušek, Jaroslav Koča, and Radka Svobodová. "ValTrendsDB: bringing Protein Data Bank validation information closer to the user." Bioinformatics 35, no. 24 (July 2, 2019): 5389–90. http://dx.doi.org/10.1093/bioinformatics/btz532.

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Abstract Summary Structures in PDB tend to contain errors. This is a very serious issue for authors that rely on such potentially problematic data. The community of structural biologists develops validation methods as countermeasures, which are also included in the PDB deposition system. But how are these validation efforts influencing the structure quality of subsequently published data? Which quality aspects are improving, and which remain problematic? We developed ValTrendsDB, a database that provides the results of an extensive exploratory analysis of relationships between quality criteria, size and metadata of biomacromolecules. Key input data are sourced from PDB. The discovered trends are presented via precomputed information-rich plots. ValTrendsDB also supports the visualization of a set of user-defined structures on top of general quality trends. Therefore, ValTrendsDB enables users to see the quality of structures published by selected author, laboratory or journal, discover quality outliers, etc. ValTrendsDB is updated weekly. Availability and implementation Freely accessible at http://ncbr.muni.cz/ValTrendsDB. The web interface was implemented in JavaScript. The database was implemented in C++. Supplementary information Supplementary data are available at Bioinformatics online.

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Peter, Emanuel, and Jiří Černý. "Enriched Conformational Sampling of DNA and Proteins with a Hybrid Hamiltonian Derived from the Protein Data Bank." International Journal of Molecular Sciences 19, no. 11 (October 30, 2018): 3405. http://dx.doi.org/10.3390/ijms19113405.

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In this article, we present a method for the enhanced molecular dynamics simulation of protein and DNA systems called potential of mean force (PMF)-enriched sampling. The method uses partitions derived from the potentials of mean force, which we determined from DNA and protein structures in the Protein Data Bank (PDB). We define a partition function from a set of PDB-derived PMFs, which efficiently compensates for the error introduced by the assumption of a homogeneous partition function from the PDB datasets. The bias based on the PDB-derived partitions is added in the form of a hybrid Hamiltonian using a renormalization method, which adds the PMF-enriched gradient to the system depending on a linear weighting factor and the underlying force field. We validated the method using simulations of dialanine, the folding of TrpCage, and the conformational sampling of the Dickerson–Drew DNA dodecamer. Our results show the potential for the PMF-enriched simulation technique to enrich the conformational space of biomolecules along their order parameters, while we also observe a considerable speed increase in the sampling by factors ranging from 13.1 to 82. The novel method can effectively be combined with enhanced sampling or coarse-graining methods to enrich conformational sampling with a partition derived from the PDB.

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Sim, K. L., T. Uchida, and S. Miyano. "ProDDO: a database of disordered proteins from the Protein Data Bank (PDB)." Bioinformatics 17, no. 4 (April 1, 2001): 379–80. http://dx.doi.org/10.1093/bioinformatics/17.4.379.

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Lobanov, Mikhail Yu, Ilya V. Likhachev, and Oxana V. Galzitskaya. "Disordered Residues and Patterns in the Protein Data Bank." Molecules 25, no. 7 (March 27, 2020): 1522. http://dx.doi.org/10.3390/molecules25071522.

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We created a new library of disordered patterns and disordered residues in the Protein Data Bank (PDB). To obtain such datasets, we clustered the PDB and obtained the groups of chains with different identities and marked disordered residues. We elaborated a new procedure for finding disordered patterns and created a new version of the library. This library includes three sets of patterns: unique patterns, patterns consisting of two kinds of amino acids, and homo-repeats. Using this database, the user can: (1) find homologues in the entire Protein Data Bank; (2) perform a statistical analysis of disordered residues in protein structures; (3) search for disordered patterns and homo-repeats; (4) search for disordered regions in different chains of the same protein; (5) download clusters of protein chains with different identity from our database and library of disordered patterns; and (6) observe 3D structure interactively using MView. A new library of disordered patterns will help improve the accuracy of predictions for residues that will be structured or unstructured in a given region.

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Burley, Stephen K., Charmi Bhikadiya, Chunxiao Bi, Sebastian Bittrich, Li Chen, Gregg V. Crichlow, Cole H. Christie, et al. "RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences." Nucleic Acids Research 49, no. D1 (November 19, 2020): D437—D451. http://dx.doi.org/10.1093/nar/gkaa1038.

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Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

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Gore, Swanand, Sameer Velankar, and Gerard J. Kleywegt. "Implementing an X-ray validation pipeline for the Protein Data Bank." Acta Crystallographica Section D Biological Crystallography 68, no. 4 (March 16, 2012): 478–83. http://dx.doi.org/10.1107/s0907444911050359.

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There is an increasing realisation that the quality of the biomacromolecular structures deposited in the Protein Data Bank (PDB) archive needs to be assessed critically using established and powerful validation methods. The Worldwide Protein Data Bank (wwPDB) organization has convened several Validation Task Forces (VTFs) to advise on the methods and standards that should be used to validate all of the entries already in the PDB as well as all structures that will be deposited in the future. The recommendations of the X-ray VTF are currently being implemented in a software pipeline. Here, ongoing work on this pipeline is briefly described as well as ways in which validation-related information could be presented to users of structural data.

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SAKAE, Yoshitake, and Yuko OKAMOTO. "Optimizations of Protein Force-Field Parameters with Protein Data Bank." Seibutsu Butsuri 45, no. 3 (2005): 145–48. http://dx.doi.org/10.2142/biophys.45.145.

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Berman, Helen M., Brinda Vallat, and Catherine L. Lawson. "The data universe of structural biology." IUCrJ 7, no. 4 (May 28, 2020): 630–38. http://dx.doi.org/10.1107/s205225252000562x.

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The Protein Data Bank (PDB) has grown from a small data resource for crystallographers to a worldwide resource serving structural biology. The history of the growth of the PDB and the role that the community has played in developing standards and policies are described. This article also illustrates how other biophysics communities are collaborating with the worldwide PDB to create a network of interoperating data resources. This network will expand the capabilities of structural biology and enable the determination and archiving of increasingly complex structures.

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Shinada, Nicolas K., Peter Schmidtke, and Alexandre G. de Brevern. "Accurate Representation of Protein-Ligand Structural Diversity in the Protein Data Bank (PDB)." International Journal of Molecular Sciences 21, no. 6 (March 24, 2020): 2243. http://dx.doi.org/10.3390/ijms21062243.

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The number of available protein structures in the Protein Data Bank (PDB) has considerably increased in recent years. Thanks to the growth of structures and complexes, numerous large-scale studies have been done in various research areas, e.g., protein–protein, protein–DNA, or in drug discovery. While protein redundancy was only simply managed using simple protein sequence identity threshold, the similarity of protein-ligand complexes should also be considered from a structural perspective. Hence, the protein-ligand duplicates in the PDB are widely known, but were never quantitatively assessed, as they are quite complex to analyze and compare. Here, we present a specific clustering of protein-ligand structures to avoid bias found in different studies. The methodology is based on binding site superposition, and a combination of weighted Root Mean Square Deviation (RMSD) assessment and hierarchical clustering. Repeated structures of proteins of interest are highlighted and only representative conformations were conserved for a non-biased view of protein distribution. Three types of cases are described based on the number of distinct conformations identified for each complex. Defining these categories decreases by 3.84-fold the number of complexes, and offers more refined results compared to a protein sequence-based method. Widely distinct conformations were analyzed using normalized B-factors. Furthermore, a non-redundant dataset was generated for future molecular interactions analysis or virtual screening studies.

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Ito, N., K. Kobayashi, H. Sakamoto, and H. Nakamura. "PDB-ML, an XML for Protein Data Bank, and a search system with it." Seibutsu Butsuri 43, supplement (2003): S112. http://dx.doi.org/10.2142/biophys.43.s112_2.

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Bond, Charles S. "Easy editing of Protein Data Bank formatted files withEMACS." Journal of Applied Crystallography 36, no. 2 (March 15, 2003): 350–51. http://dx.doi.org/10.1107/s0021889803001651.

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A protein crystallographer typically spends a large proportion of time manipulating Protein Data Bank (PDB) formatted coordinate files. Of the many situations where such coordinate data must be altered for a particular purpose, a combination of a simple text editor and a number of programs is required.pdb-modeforEMACSprovides a set of commonly used commands that allow many of these repetitive or trivial computational tasks to be performed within the environment of theEMACSeditor, without recourse to writing temporary files and running external programs.

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Yao, Sen, and Hunter N. B. Moseley. "Finding High-Quality Metal Ion-Centric Regions Across the Worldwide Protein Data Bank." Molecules 24, no. 17 (September 1, 2019): 3179. http://dx.doi.org/10.3390/molecules24173179.

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As the number of macromolecular structures in the worldwide Protein Data Bank (wwPDB) continues to grow rapidly, more attention is being paid to the quality of its data, especially for use in aggregated structural and dynamics analyses. In this study, we systematically analyzed 3.5 Å regions around all metal ions across all PDB entries with supporting electron density maps available from the PDB in Europe. All resulting metal ion-centric regions were evaluated with respect to four quality-control criteria involving electron density resolution, atom occupancy, symmetry atom exclusion, and regional electron density discrepancy. The resulting list of metal binding sites passing all four criteria possess high regional structural quality and should be beneficial to a wide variety of downstream analyses. This study demonstrates an approach for the pan-PDB evaluation of metal binding site structural quality with respect to underlying X-ray crystallographic experimental data represented in the available electron density maps of proteins. For non-crystallographers in particular, we hope to change the focus and discussion of structural quality from a global evaluation to a regional evaluation, since all structural entries in the wwPDB appear to have both regions of high and low structural quality.

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Nakamura, Haruki. "Activities of PDBj and wwPDB : A new PDB format, Data Deposition, Validation, and Data Integration(PDBj: Protein Data Bank Japan,The 51st Annual Meeting of the Biophysical Society of Japan)." Seibutsu Butsuri 53, supplement1-2 (2013): S286. http://dx.doi.org/10.2142/biophys.53.s286_1.

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SZABADKA, ZOLTÁN, RAFAEL ÖRDÖG, and VINCE GROLMUSZ. "THE RAMACHANDRAN MAP OF MORE THAN 6,500 PERFECT POLYPEPTIDE CHAINS." Biophysical Reviews and Letters 02, no. 03n04 (October 2007): 267–71. http://dx.doi.org/10.1142/s1793048007000519.

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The Protein Data Bank (PDB) is the most important depository of protein structural information, containing more than 45,000 deposited entries today. Because of its inhomogeneous structure, its fully automated processing is almost impossible. In a previous work, we cleaned and re-structured the entries in the Protein Data Bank, and from the result we have built the RS-PDB database. Using the RS-PDB database, we draw a Ramachandran-plot from 6,593 "perfect" polypeptide chains found in the PDB, containing 1,192,689 residues. This is a more than tenfold increase in the size of data analyzed before this work. The density of the data points makes it possible to draw a logarithmic heat map enhanced Ramachandran map, showing the fine inner structure of the right-handed α-helix region.

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Suresh, V., K. Ganesan, and S. Parthasarathy. "PDB-2-PB: a curated online protein block sequence database." Journal of Applied Crystallography 45, no. 1 (December 22, 2011): 127–29. http://dx.doi.org/10.1107/s0021889811052356.

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This article describes the development of a curated online protein block sequence database, PDB-2-PB. The protein block sequences for protein structures with complete backbone coordinates have been encoded using the encoding procedure of de Brevern, Etchebest & Hazout [Proteins(2000),41, 271–287]. In the current release of the PDB-2-PB database (version 1.0), the protein entries from a recent release of the World Wide Protein Data Bank (wwPDB), which has 74 297 solved PDB entries as of 7 July 2011, have been used as a primary source. The PDB-2-PB database stores the protein block sequences for all the chains present in a protein structure. PDB-2-PB version 1.0 has the curated protein block sequences for 103 252 PDB chain entries (93 547 X-ray, 7033 NMR and 2672 other experimental chain entries). From the PDB-2-PB database, users can extract the curated protein block sequence and its corresponding amino acid sequence, which is extracted from the PDB ATOM records. Users can download these sequences either by using the PDB code or by using various parameters listed in the database. The PDB-2-PB database is freely available at http://bioinfo.bdu.ac.in/~pb/.

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Mrozek, Dariusz, Tomasz Dąbek, and Bożena Małysiak-Mrozek. "Scalable Extraction of Big Macromolecular Data in Azure Data Lake Environment." Molecules 24, no. 1 (January 5, 2019): 179. http://dx.doi.org/10.3390/molecules24010179.

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Calculation of structural features of proteins, nucleic acids, and nucleic acid-protein complexes on the basis of their geometries and studying various interactions within these macromolecules, for which high-resolution structures are stored in Protein Data Bank (PDB), require parsing and extraction of suitable data stored in text files. To perform these operations on large scale in the face of the growing amount of macromolecular data in public repositories, we propose to perform them in the distributed environment of Azure Data Lake and scale the calculations on the Cloud. In this paper, we present dedicated data extractors for PDB files that can be used in various types of calculations performed over protein and nucleic acids structures in the Azure Data Lake. Results of our tests show that the Cloud storage space occupied by the macromolecular data can be successfully reduced by using compression of PDB files without significant loss of data processing efficiency. Moreover, our experiments show that the performed calculations can be significantly accelerated when using large sequential files for storing macromolecular data and by parallelizing the calculations and data extractions that precede them. Finally, the paper shows how all the calculations can be performed in a declarative way in U-SQL scripts for Data Lake Analytics.

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Varadi, Mihaly, John Berrisford, Mandar Deshpande, Sreenath S. Nair, Aleksandras Gutmanas, David Armstrong, Lukas Pravda, et al. "PDBe-KB: a community-driven resource for structural and functional annotations." Nucleic Acids Research 48, no. D1 (October 4, 2019): D344—D353. http://dx.doi.org/10.1093/nar/gkz853.

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Abstract The Protein Data Bank in Europe-Knowledge Base (PDBe-KB, https://pdbe-kb.org) is a community-driven, collaborative resource for literature-derived, manually curated and computationally predicted structural and functional annotations of macromolecular structure data, contained in the Protein Data Bank (PDB). The goal of PDBe-KB is two-fold: (i) to increase the visibility and reduce the fragmentation of annotations contributed by specialist data resources, and to make these data more findable, accessible, interoperable and reusable (FAIR) and (ii) to place macromolecular structure data in their biological context, thus facilitating their use by the broader scientific community in fundamental and applied research. Here, we describe the guidelines of this collaborative effort, the current status of contributed data, and the PDBe-KB infrastructure, which includes the data exchange format, the deposition system for added value annotations, the distributable database containing the assembled data, and programmatic access endpoints. We also describe a series of novel web-pages—the PDBe-KB aggregated views of structure data—which combine information on macromolecular structures from many PDB entries. We have recently released the first set of pages in this series, which provide an overview of available structural and functional information for a protein of interest, referenced by a UniProtKB accession.

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Hooft, R. W. W., C. Sander, and G. Vriend. "Reconstruction of symmetry-related molecules from protein data bank (PDB) files." Journal of Applied Crystallography 27, no. 6 (December 1, 1994): 1006–9. http://dx.doi.org/10.1107/s0021889894007764.

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Dauter, Zbigniew, and Alexander Wlodawer. "Crystallographically correct but confusing presentation of structural models deposited in the Protein Data Bank." Acta Crystallographica Section D Structural Biology 74, no. 9 (September 1, 2018): 939–45. http://dx.doi.org/10.1107/s2059798318009828.

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The Protein Data Bank (PDB) constitutes a collection of the available atomic models of macromolecules and their complexes obtained by various methods used in structural biology, but chiefly by crystallography. It is an indispensable resource for all branches of science that deal with the structures of biologically active molecules, such as structural biology, bioinformatics, the design of novel drugs etc. Since not all users of the PDB are familiar with the methods of crystallography, it is important to present the results of crystallographic analyses in a form that is easy to interpret by nonspecialists. It is advisable during the submission of structures to the PDB to pay attention to the optimal placement of molecules within the crystal unit cell, to the correct representation of oligomeric assemblies and to the proper selection of the space-group symmetry. Examples of significant departures from these principles illustrate the potential for the misinterpretation of such suboptimally presented crystal structures.

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Karuppasamy, Muthuvel Prasath, Suresh Venkateswaran, and Parthasarathy Subbiah. "PDB-2-PBv3.0: An updated protein block database." Journal of Bioinformatics and Computational Biology 18, no. 02 (April 2020): 2050009. http://dx.doi.org/10.1142/s0219720020500092.

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Our protein block (PB) sequence database PDB-2-PBv1.0 provides PB sequences and dihedral angles for 74,297 protein structures comprising of 103,252 protein chains of Protein Data Bank (PDB) as on 2011. Since there are a lot of practical applications of PB and also as the size of PDB database increases, it becomes necessary to provide the PB sequences for all PDB protein structures. The current updated PDB-2-PBv3.0 contains PB sequences for 147,602 PDB structures comprising of 400,355 protein chains as on October 2019. When compared to our previous version PDB-2-PBv1.0, the current PDB-2-PBv3.0 contains 2- and 4-fold increase in the number of protein structures and chains, respectively. Notably, it provides PB information for any protein chain, regardless of the missing atom records of protein structure data in PDB. It includes protein interaction information with DNA and RNA along with their corresponding functional classes from Nucleic Acid Database (NDB) and PDB. Now, the updated version allows the user to download multiple PB records by parameter search and/or by a given list. This database is freely accessible at http://bioinfo.bdu.ac.in/pb3 .

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Rodrigues, João P. G. L. M., João M. C. Teixeira, Mikaël Trellet, and Alexandre M. J. J. Bonvin. "pdb-tools: a swiss army knife for molecular structures." F1000Research 7 (December 20, 2018): 1961. http://dx.doi.org/10.12688/f1000research.17456.1.

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The pdb-tools are a collection of Python scripts for working with molecular structure data in the Protein Data Bank (PDB) format. They allow users to edit, convert, and validate PDB files, from the command-line, in a simple but efficient manner. The pdb-tools are implemented in Python, without any external dependencies, and are freely available under the open-source Apache License at https://github.com/haddocking/pdb-tools/ and on PyPI.

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Mehta, Shifali, and Amardeep Singh. "Overcoming the limitations of PDB format." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 2, no. 3 (June 30, 2012): 102–4. http://dx.doi.org/10.24297/ijct.v2i3b.2697.

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The Protein Data Bank is a repository for the 3-D structuraldata of large biological molecules, such as proteins andnucleic acid. The PDB is a key resource in the areas ofstructural biology, structural genomics. Most major scientificjournals, and some funding agencies, such as the NIH in theUSA, now require scientists to submit their structure data tothe PDB. If the contents of the PDB are thought of as primarydata, there are hundreds of derived databases that categorizethe data differently. For example, both SCOP and CATHcategorize structure according to type of structure andassumed evolutionary relations; GO categorize structuresbased on genes. In this paper, we will describe how toovercome the limitations of PDB format.

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Monzon, Alexander Miguel, Marco Necci, Federica Quaglia, Ian Walsh, Giuseppe Zanotti, Damiano Piovesan, and Silvio C. E. Tosatto. "Experimentally Determined Long Intrinsically Disordered Protein Regions Are Now Abundant in the Protein Data Bank." International Journal of Molecular Sciences 21, no. 12 (June 24, 2020): 4496. http://dx.doi.org/10.3390/ijms21124496.

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Intrinsically disordered protein regions are commonly defined from missing electron density in X-ray structures. Experimental evidence for long disorder regions (LDRs) of at least 30 residues was so far limited to manually curated proteins. Here, we describe a comprehensive and large-scale analysis of experimental LDRs for 3133 unique proteins, demonstrating an increasing coverage of intrinsic disorder in the Protein Data Bank (PDB) in the last decade. The results suggest that long missing residue regions are a good quality source to annotate intrinsically disordered regions and perform functional analysis in large data sets. The consensus approach used to define LDRs allows to evaluate context dependent disorder and provide a common definition at the protein level.

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Berman, H., K. Henrick, H. Nakamura, and J. L. Markley. "The worldwide Protein Data Bank (wwPDB): ensuring a single, uniform archive of PDB data." Nucleic Acids Research 35, Database (January 3, 2007): D301—D303. http://dx.doi.org/10.1093/nar/gkl971.

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Chen, Xi, Hao Jiang, Wai Ki Ching, and Li Min Li. "Inferring Functional Annotation for Human Genes from Gene-PDB Structure Mapping." Applied Mechanics and Materials 195-196 (August 2012): 391–96. http://dx.doi.org/10.4028/www.scientific.net/amm.195-196.391.

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Protein 3D structure is one of the main factors in reecting gene functions. The availability of protein structure data in Protein Data Bank (PDB) allows us to conduct gene function analysis based on protein structure data. However, the molecules in PDB, whose structures having been determined, are always not corresponding to a unique gene. That is to say, the mapping from a gene to the PDB is not one-to-one. This feature complicates the situation and increases the difculty of gene function analysis. In this paper, we attempt to tackle this problem and also study the problem of predicting gene function from protein structures based on the gene-PDB mapping. We rst obtain the gene-PDB mapping, which is used to represent a gene by the structure set of all its corresponding PDB molecules. We then dene a new gene-gene similarity measurement based on the structure similarity between PDB molecules, and we further show that this new measurement matches with the gene functional similarity. This means that the measurement we dened here can be used effectively for gene function prediction.

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Jaskolski, Mariusz. "On the propagation of errors." Acta Crystallographica Section D Biological Crystallography 69, no. 10 (September 20, 2013): 1865–66. http://dx.doi.org/10.1107/s090744491301528x.

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The policy of the Protein Data Bank (PDB) that the first deposition of a small-molecule ligand, even with erroneous atom numbering, sets a precedent over accepted nomenclature rules is disputed. Recommendations regarding ligand molecules in the PDB are suggested.

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Brylinski, Michal. "Is the growth rate of Protein Data Bank sufficient to solve the protein structure prediction problem using template-based modeling?" Bio-Algorithms and Med-Systems 11, no. 1 (January 31, 2015): 1–7. http://dx.doi.org/10.1515/bams-2014-0024.

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AbstractThe Protein Data Bank (PDB) undergoes an exponential expansion in terms of the number of macromolecular structures deposited every year. A pivotal question is how this rapid growth of structural information improves the quality of three-dimensional models constructed by contemporary bioinformatics approaches. To address this problem, we performed a retrospective analysis of the structural coverage of a representative set of proteins using remote homology detected by COMPASS and HHpred. We show that the number of proteins whose structures can be confidently predicted increased during a 9-year period between 2005 and 2014 on account of the PDB growth alone. Nevertheless, this encouraging trend slowed down noticeably around the year 2008 and has yielded insignificant improvements ever since. At the current pace, it is unlikely that the protein structure prediction problem will be solved in the near future using existing template-based modeling techniques. Therefore, further advances in experimental structure determination, qualitatively better approaches in fold recognition, and more accurate template-free structure prediction methods are desperately needed.

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van Beusekom, Bart, Krista Joosten, Maarten L. Hekkelman, Robbie P. Joosten, and Anastassis Perrakis. "Homology-based loop modeling yields more complete crystallographic protein structures." IUCrJ 5, no. 5 (August 8, 2018): 585–94. http://dx.doi.org/10.1107/s2052252518010552.

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Inherent protein flexibility, poor or low-resolution diffraction data or poorly defined electron-density maps often inhibit the building of complete structural models during X-ray structure determination. However, recent advances in crystallographic refinement and model building often allow completion of previously missing parts. This paper presents algorithms that identify regions missing in a certain model but present in homologous structures in the Protein Data Bank (PDB), and `graft' these regions of interest. These new regions are refined and validated in a fully automated procedure. Including these developments in the PDB-REDO pipeline has enabled the building of 24 962 missing loops in the PDB. The models and the automated procedures are publicly available through the PDB-REDO databank and webserver. More complete protein structure models enable a higher quality public archive but also a better understanding of protein function, better comparison between homologous structures and more complete data mining in structural bioinformatics projects.

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Callahan, T., W. B. Gleason, and T. P. Lybrand. "PAP: a protein analysis package." Journal of Applied Crystallography 23, no. 5 (October 1, 1990): 434–36. http://dx.doi.org/10.1107/s0021889890004228.

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A program package has been assembled for the analysis of protein coordinates which are in the Brookhaven Protein Data Bank (PDB) format. These programs can be used to make two types of φ–ψ plots: a Ramachandran-style scatter plot, and a plot of φ and ψ values as a function of the linear sequence. Programs are also available for the display of distance diagonal plots for proteins. Two protein structures can be compared and the resulting r.m.s. differences in the structures plotted as a function of sequence. Temperature factors can be analyzed and plotted as a function of the linear sequence. In addition, various utilities are supplied for splitting PDB files which contain multiple subunits into individual files and also for renumbering PDB files. A utility is also provided for converting Amber-style PDB files into standard PDB files. Priestle's program RIBBON [J. Appl. Cryst. (1988), 21, 572–576] has been converted to run in a stand-alone mode with interactive rotation of the three-dimensional ribbon picture. Programs are Silicon Graphics four-dimensional level and have been tested on 4D70/GT and personal Iris workstations, although programs which give Postscript output have been converted to run on Digital Equipment Corporation VAX computers and Sun workstations.

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Lütteke, Thomas, and Claus-W. Von Der Lieth. "The protein data bank (PDB) as a versatile resource for glycobiology and glycomics." Biocatalysis and Biotransformation 24, no. 1-2 (January 2006): 147–55. http://dx.doi.org/10.1080/10242420600598269.

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

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