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rcsb-api: Python Toolkit for Streamlining Access to RCSB protein data bank APIs: rcsb-api: Python Toolkit to Access *** APIs

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Journal of Molecular Biology 2025年 168970页

作者： Piehl, Dennis W. Vallat, Brinda Truong, Ivana Morsy, Habiba Bhatt, Rusham Blaumann, Santiago Biswas, Pratyoy Rose, Yana Bittrich, Sebastian Duarte, Jose M. Segura, Joan Bi, Chunxiao Myers-Turnbull, Douglas Hudson, Brian P. Zardecki, Christine Burley, Stephen K. Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States

The protein data bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB protein data bank *** research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support *** users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api, that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (***/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences. © 2025 The Author(s)

关键词： bioinformatics FAIR principles RCSB protein data bank *** structural biology

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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

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Journal of Molecular Biology 2025年 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide protein data bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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BPS2025 - Enhancing exploration and visualization of biomolecular 3D information at ***

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Biophysical Journal 2025年第3期124卷 642a-642a页

作者： Joan Segura Yana Rose Sebastian Bittrich Chunxiao Bi Jose Duarte Stephen K. Burley Research Collaboratory for Structural Bioinformatics Protein Data Bank University of California San Diego La Jolla CA USA Rutgers The State University of New Jersey Piscataway NJ USA

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Resources for structural Genomics from the RCSB PDB

Resources for Structural Genomics from the RCSB PDB

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The 4th ISGO International Conference on structural Genomics

作者： Helen M.Berman Philip E.Bourne Andrei Kouranov John Westbrook Protein Data Bank Research Collaboratory for Structural Bioinformatics Rutgers the State University of New Jersey Department of Chemistry 610 Taylor Road Piscataway NJ 08854-8087 USA Protein Data Bank Research Collaboratory for Structural Bioinformatics San Diego Supercomputer Center/Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego 9500 Gilman Drive La Jolla CA 92093-0537 Protein Data Bank Research Collaboratory for Structural Bioinformatics Rutgers the State University of New Jersey Department of Chemistry 610 Taylor Road Piscataway NJ 08854-8087 USA Protein Data Bank Research Collaboratory for Structural Bioinformatics Rutgers the State University of New Jersey Department of Chemistry 610 Taylor Road Piscataway NJ 08854-8087 USA

The RCSB protein data bank has developed a portal for structural genomics resources at http://***. Reports about the worldwide contributing centers are available, including summary reports for target lists, target sta... 详细信息

关键词： Resources for structural Genomics from the RCSB PDB

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Impact of the protein data bank across scientific disciplines

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data Science Journal 2020年第1期19卷 1-14页

作者： Feng, Zukang Verdiguel, Natalie Di Costanzo, Luigi Goodsell, David S. Westbrook, John D. Burley, Stephen K. Zardecki, Christine Research Collaboratory for Structural Bioinformatics Protein Data Bank Rutgers The State University of New Jersey PiscatawayNJ United States Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ United States University of Central Florida OrlandoFL United States Department of Agricultural Sciences University of Naples Federico II Portici Italy Department of Integrative Structural and Computational Biology The Scripps Research Institute La JollaCA United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La JollaCA United States Rutgers Cancer Institute of New Jersey Rutgers The State University of New Jersey New BrunswickNJ United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La JollaCA United States

The protein data bank archive (PDB) was established in 1971 as the 1st open access digital data resource for biology and medicine. Today, the PDB contains >160,000 atomic-level, experimentally-determined 3D biomolecular structures. PDB data are freely and publicly available for download, without restrictions. Each entry contains summary information about the structure and experiment, atomic coordinates, and in most cases, a citation to a corresponding scientific publication. Individually and in bulk, PDB structures can be downloaded and/or analyzed and visualized online using tools at ***. As such, it is challenging to understand and monitor reuse of data. Citations of the scientific publications describing PDB structures provide one way of understanding which structures are being used, and in which research areas. Our analysis highlights frequently-cited structures and identifies milestone structures that have demonstrated impact across scientific fields. © 2020 The Author(s).

关键词： proteins

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'EMDB-the Electron Microscopy data bank' (vol 1, gkad1019, 2023)

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NUCLEIC ACIDS research 2023年第D1期52卷 D1701-D1701页

作者： Turner, Jack Abbott, Sanja Fonseca, Neli Pye, Ryan Carrijo, Lucas Duraisamy, Amudha Kumari Salih, Osman Wang, Zhe Kleywegt, Gerard J. Morris, Kyle L. Patwardhan, Ardan Cellular Structure and 3D Bioimaging European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute of New Jersey Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States Protein Data Bank in Europe European Molecular Biology Laboratory (EMBL) European Bioinformatics Institute (EBI) Wellcome Genome Campus Hinxton Cambridgeshire CB10 1SD United Kingdom CEITEC Central European Institute of Technology Masaryk University Kamenice 5 Brno 62500 Czech Republic Molecular Biophysics Unit Indian Institute of Science Bangalore India Protein Data Bank Japan Protein Research Foundation Minoh Osaka 562-8686 Japan Protein Data Bank Japan Institute for Protein Research Osaka University Suita Osaka 565-0871 Japan Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics UConn Health 263 Farmington Ave. Farmington 06030-3305 CT United States National Facility for Protein Science Shanghai Advanced Research Institute Chinese Academy of Sciences Shanghai 201210 China School of Life Science and Technology Shanghai Tech University Shanghai 201210 China

The Electron Microscopy data bank (EMDB) is the global public archive of three-dimensional electron microscopy (3DEM) maps of biological specimens derived from transmission electron microscopy experiments. As of 2021, EMDB is managed by the Worldwide protein data bank consortium (wwPDB; ***) as a wwPDB Core Archive, and the EMDB team is a core member of the consortium. Today, EMDB houses over 30 000 entries with maps containing macromolecules, complexes, viruses, organelles and cells. Herein, we provide an overview of the rapidly growing EMDB archive, including its current holdings, recent updates, and future plans.

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Assessment of protein assembly prediction in CASP13

arXiv

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arXiv 2019年

作者： Guzenko, Dmytro Lafita, Aleix Monastyrskyy, Bohdan Kryshtafovych, Andriy Duarte, Jose M. Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La JollaCA92093 United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Protein Structure Prediction Center Genome and Biomedical Sciences Facilities University of California DavisCA95616 United States

We present the assembly category assessment in the 13th edition of the CASP community-wide experiment. For the second time, protein assemblies constitute an independent assessment category. Compared to the last edition we see a clear uptake in participation, more oligomeric targets released, and consistent, albeit modest, improvement of the predictions quality. Looking at the tertiary structure predictions we observe that ignoring the oligomeric state of the targets hinders modelling success. We also note that some contact prediction groups successfully predicted homomeric interfacial contacts, though it appears that these predictions were not used for assembly modelling. Homology modelling with sizeable human intervention appears to form the basis of the assembly prediction techniques in this round of CASP. Future developments should see more integrated approaches to modelling where multiple subunits are a natural part of the modelling process, which would benefit the structure prediction field as a whole. K E Y W O R D S CASP, protein assembly, protein interfaces, structure prediction Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Restraint Validation of Biomolecular Structures Determined By NMR in the protein data bank

SSRN

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SSRN 2023年

作者： Baskaran, Kumaran Ploskon, Eliza Tejero, Roberto Yokochi, Masashi Harrus, Deborah Liang, Yuhe Peisach, Ezra Persikova, Irina Ramelot, Theresa A. Sekharan, Monica Tolchard, James Westbrook, John D. Bardiaux, Benjamin Schwieters, Charles D. Patwardnhan, Ardan Venankar, Sameer Burley, Stephen K. Kurisu, Genji Hoch, Jeffrey C. Montelione, Gaetano T. Vuister, Geerten W. Young, Jasmine Y. Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics UConn Health FarmingtonCT06030 United States Department of Molecular and Cell Biology Leicester Institute of Structural and Chemical Biology University of Leicester LeicesterLE1 7RH United Kingdom Department of Chemistry and Chemical Biology Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita565-0871 Japan Protein Data Bank Japan Protein Research Foundation Osaka Minoh562-8686 Japan Protein Data Bank in Europe EMBL-EBI Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ08854 United States Dept of Chemistry and Chemical Biology Center for Biotechnology and Interdisciplinary Sciences Rensselaer Polytechnic Institute TroyNY12180 United States Department of Structural Biology and Chemistry Institut Pasteur Université Paris Cité CNRS UMR3528 Paris75015 France Computational Biomolecular Magnetic Resonance Core National Institute of Diabetes and Digestive and Kidney Diseases BethesdaMD20892 United States The Electron Microscopy Data Bank EMBL-EBI Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla CA United States Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ08854 United States Cancer Institute of New Jersey Rutgers The State University of New Jersey New BrunswickNJ08901 United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey PiscatawayNJ08854 United States

Biomolecular structure analysis from experimental NMR generally relies on empirical (residual dipolar couplings, scalar couplings, paramagnetic relaxation enhancements, nuclear Overhauser effects) data and derived geometrical restraints (distance, dihedral angle). A challenge for the structural biology community has been a lack of standards for representing these restraints, preventing establishment of uniform methods of model-vs-data structure validation and limiting interoperability between restraint-based structure modeling programs. The NMR exchange (NEF) and NMR-STAR formats provide a standardized approach for representing commonly used NMR restraints. Using these restraint formats, a standardized validation system for assessing structural models of biopolymers against restraints has been developed and implemented in the wwPDB OneDep data harvesting system. The resulting wwPDB Restraint Violation Report provides a model vs. data assessment of biomolecule structures determined using distance and dihedral restraints, with extensions to other restraint types currently being implemented. These tools are useful for assessing NMR models, as well as for assessing biomolecular structure predictions based on distance restraints. © 2023, The Authors. All rights reserved.

关键词： Chemical shift

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BIPSPI+: Mining Type-Specific datasets of protein Complexes to Improve protein Binding Site Prediction

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Journal of Molecular Biology 2022年第11期434卷 167556-167556页

作者： Sanchez-Garcia, R. Macias, J.R. Sorzano, C.O.S. Carazo, J.M. Segura, J. Biocomputing Unit National Center for Biotechnology (CSIC) Darwin 3 Campus Univ. Autónoma de Madrid Cantoblanco Madrid 28049 Spain Oxford Protein Informatics Group Department of Statistics University of Oxford 29 St Giles' Oxford OX1 3LB UK United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La Jolla 92093 CA United States

Computational approaches for predicting protein-protein interfaces are extremely useful for understanding and modelling the quaternary structure of protein assemblies. In particular, partner-specific binding site prediction methods allow delineating the specific residues that compose the interface of protein complexes. In recent years, new machine learning and other algorithmic approaches have been proposed to solve this problem. However, little effort has been made in finding better training datasets to improve the performance of these methods. With the aim of vindicating the importance of the training set compilation procedure, in this work we present BIPSPI+, a new version of our original server trained on carefully curated datasets that outperforms our original predictor. We show how prediction performance can be improved by selecting specific datasets that better describe particular types of protein interactions and interfaces (e.g. homo/hetero). In addition, our upgraded web server offers a new set of functionalities such as the sequence-structure prediction mode, hetero- or homo-complex specialization and the guided docking tool that allows to compute 3D quaternary structure poses using the predicted interfaces. BIPSPI+ is freely available at https://***. © 2022 The Authors

关键词： binding site machine learning protein interactions web server

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Crystallography and databases

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data Science Journal 2017年 16卷

作者： Bruno, Ian Gražulis, Saulius Helliwell, John R. Kabekkodu, Soorya N. McMahon, Brian Westbrook, John Cambridge Crystallographic Data Centre 12 Union Road CambridgeCB2 1EZ United Kingdom Vilnius University Institute of Biotechnology Sauletekio al. 7 VilniusLT-10257 Lithuania School of Chemistry University of Manchester M13 9PL United Kingdom International Centre for Diffraction Data 12 Campus Boulevard Newtown SquarePA19073 United States International Union of Crystallography 5 Abbey Square ChesterCH1 2HU United Kingdom Department of Chemistry and Chemical Biology Research Collaboratory for Structural Bioinformatics Protein Data Bank Center for Integrative Proteomics Research Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ08854 United States

Crystallographic databases have existed as electronic resources for over 50 years, and have provided comprehensive archives of crystal structures of inorganic, organic, metal–organic and biological macromolecular compounds of immense value to a wide range of structural sciences. They thus serve a variety of scientific disciplines, but are all driven by considerations of accuracy, precise characterization, and potential for search, analysis and reuse. They also serve a variety of end-users in academia and industry, and have evolved through different funding and licensing models. The diversity of their operational mechanisms combined with their undisputed value as scientific research tools gives rise to a rich ecosystem. A session at ScidataCon2016 gave an overview of the largest extant crystallographic databases and their current activities and plans for the future. This review summarizes these presentations and considers them alongside other players in the field, demonstrating their variety, versatility and focus on quality and usefulness. © 2017 The Author(s).

关键词： database systems

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