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Dive into the research topics where Eldon L. Ulrich is active.

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Featured researches published by Eldon L. Ulrich.


Proteins | 2005

The CCPN data model for NMR spectroscopy: Development of a software pipeline

Wim F. Vranken; Wayne Boucher; Tim J. Stevens; Rasmus H. Fogh; Anne Pajon; Miguel Llinás; Eldon L. Ulrich; John L. Markley; John Ionides; Ernest D. Laue

To address data management and data exchange problems in the nuclear magnetic resonance (NMR) community, the Collaborative Computing Project for the NMR community (CCPN) created a “Data Model” that describes all the different types of information needed in an NMR structural study, from molecular structure and NMR parameters to coordinates. This paper describes the development of a set of software applications that use the Data Model and its associated libraries, thus validating the approach. These applications are freely available and provide a pipeline for high‐throughput analysis of NMR data. Three programs work directly with the Data Model: CcpNmr Analysis, an entirely new analysis and interactive display program, the CcpNmr FormatConverter, which allows transfer of data from programs commonly used in NMR to and from the Data Model, and the CLOUDS software for automated structure calculation and assignment (Carnegie Mellon University), which was rewritten to interact directly with the Data Model. The ARIA 2.0 software for structure calculation (Institut Pasteur) and the QUEEN program for validation of restraints (University of Nijmegen) were extended to provide conversion of their data to the Data Model. During these developments the Data Model has been thoroughly tested and used, demonstrating that applications can successfully exchange data via the Data Model. The software architecture developed by CCPN is now ready for new developments, such as integration with additional software applications and extensions of the Data Model into other areas of research. Proteins 2005.


Proteins | 2005

RECOORD: A Recalculated Coordinate Database of 500 Proteins from the PDB Using Restraints from the BioMagResBank

Aart J. Nederveen; Jurgen F. Doreleijers; Wim F. Vranken; Zachary Miller; Chris A. E. M. Spronk; Sander B. Nabuurs; Peter Güntert; Miron Livny; John L. Markley; Michael Nilges; Eldon L. Ulrich; Robert Kaptein; Alexandre M. J. J. Bonvin

State‐of‐the‐art methods based on CNS and CYANA were used to recalculate the nuclear magnetic resonance (NMR) solution structures of 500+ proteins for which coordinates and NMR restraints are available from the Protein Data Bank. Curated restraints were obtained from the BioMagResBank FRED database. Although the original NMR structures were determined by various methods, they all were recalculated by CNS and CYANA and refined subsequently by restrained molecular dynamics (CNS) in a hydrated environment. We present an extensive analysis of the results, in terms of various quality indicators generated by PROCHECK and WHAT_CHECK. On average, the quality indicators for packing and Ramachandran appearance moved one standard deviation closer to the mean of the reference database. The structural quality of the recalculated structures is discussed in relation to various parameters, including number of restraints per residue, NOE completeness and positional root mean square deviation (RMSD). Correlations between pairs of these quality indicators were generally low; for example, there is a weak correlation between the number of restraints per residue and the Ramachandran appearance according to WHAT_CHECK (r = 0.31). The set of recalculated coordinates constitutes a unified database of protein structures in which potential user‐ and software‐dependent biases have been kept as small as possible. The database can be used by the structural biology community for further development of calculation protocols, validation tools, structure‐based statistical approaches and modeling. The RECOORD database of recalculated structures is publicly available from http://www.ebi.ac.uk/msd/recoord. Proteins 2005.


Nucleic Acids Research | 2007

Remediation of the protein data bank archive

Kim Henrick; Zukang Feng; Wolfgang F. Bluhm; Dimitris Dimitropoulos; Jurgen F. Doreleijers; Shuchismita Dutta; Judith L. Flippen-Anderson; John Ionides; Chisa Kamada; Eugene Krissinel; Catherine L. Lawson; John L. Markley; Haruki Nakamura; Richard Newman; Yukiko Shimizu; Jawahar Swaminathan; Sameer Velankar; Jeramia Ory; Eldon L. Ulrich; Wim F. Vranken; John D. Westbrook; Reiko Yamashita; Huanwang Yang; Jasmine Young; Muhammed Yousufuddin; Helen M. Berman

The Worldwide Protein Data Bank (wwPDB; wwpdb.org) is the international collaboration that manages the deposition, processing and distribution of the PDB archive. The online PDB archive at ftp://ftp.wwpdb.org is the repository for the coordinates and related information for more than 47 000 structures, including proteins, nucleic acids and large macromolecular complexes that have been determined using X-ray crystallography, NMR and electron microscopy techniques. The members of the wwPDB–RCSB PDB (USA), MSD-EBI (Europe), PDBj (Japan) and BMRB (USA)–have remediated this archive to address inconsistencies that have been introduced over the years. The scope and methods used in this project are presented.


Nature Structural & Molecular Biology | 2002

The CCPN project: an interim report on a data model for the NMR community.

Rasmus H. Fogh; John Ionides; Eldon L. Ulrich; Wayne Boucher; Wim F. Vranken; Jens P. Linge; Michael Habeck; Wolfgang Rieping; Talapady N. Bhat; John D. Westbrook; Kim Henrick; Gary L. Gilliland; Helen M. Berman; Janet M. Thornton; Michael Nilges; John L. Markley; Ernest D. Laue

A recent workshop discusses the progress toward integrating NMR data into a unifying data model.


Journal of Structural and Functional Genomics | 2003

Project management system for structural and functional proteomics: Sesame

Zsolt Zolnai; Peter T. Lee; Jing Li; Michael R. Chapman; Craig S. Newman; N George PhillipsJr.; Ivan Rayment; Eldon L. Ulrich; Brian F. Volkman; John L. Markley

A computing infrastructure (Sesame) has been designed to manage and link individual steps in complex projects. Sesame is being developed to support a large-scale structural proteomics pilot project. When complete, the system is expected to manage all steps from target selection to data-bank deposition and report writing. We report here on the design criteria of the Sesame system and on results demonstrating successful achievement of the basic goals of its architecture. The Sesame software package, which follows the client/server paradigm, consists of a framework, which supports secure interactions among the three tiers of the system (the client, server, and database tiers), and application modules that carry out specific tasks. The framework utilizes industry standards. The client tier is written in Java2 and can be accessed anywhere through the Internet. All the development on the server tier is also carried out in Java2 so as to accommodate a wide variety of computer platforms. The database tier employs a commercial database management system. Each Sesame application module consists of a simple user interface in the client tier, corresponding objects in the server tier, and relevant data stored in the centralized database. For security, access to stored data is controlled by access privileges. The system facilitates both local and remote collaborations. Because users interact with the system using Java Web Start or through a web browser, access is limited only by the availability of an Internet connection. We describe several Sesame modules that have been developed to the point where they are being utilized routinely to support steps involved in structural and functional proteomics. This software is available to parties interested in using it and assisting to guide its further development.


Journal of Biomolecular NMR | 2008

BioMagResBank (BMRB) as a partner in the Worldwide Protein Data Bank (wwPDB): new policies affecting biomolecular NMR depositions

John L. Markley; Eldon L. Ulrich; Helen M. Berman; Kim Henrick; Haruki Nakamura; Hideo Akutsu

We describe the role of the BioMagResBank (BMRB) within the Worldwide Protein Data Bank (wwPDB) and recent policies affecting the deposition of biomolecular NMR data. All PDB depositions of structures based on NMR data must now be accompanied by experimental restraints. A scheme has been devised that allows depositors to specify a representative structure and to define residues within that structure found experimentally to be largely unstructured. The BMRB now accepts coordinate sets representing three-dimensional structural models based on experimental NMR data of molecules of biological interest that fall outside the guidelines of the Protein Data Bank (i.e., the molecule is a peptide with 23 or fewer residues, a polynucleotide with 3 or fewer residues, a polysaccharide with 3 or fewer sugar residues, or a natural product), provided that the coordinates are accompanied by representation of the covalent structure of the molecule (atom connectivity), assigned NMR chemical shifts, and the structural restraints used in generating model. The BMRB now contains an archive of NMR data for metabolites and other small molecules found in biological systems.


Structure | 2015

Outcome of the First wwPDB Hybrid/Integrative Methods Task Force Workshop

Andrej Sali; Helen M. Berman; Torsten Schwede; Jill Trewhella; Gerard J. Kleywegt; Stephen K. Burley; John L. Markley; Haruki Nakamura; Paul D. Adams; Alexandre M. J. J. Bonvin; Wah Chiu; Matteo Dal Peraro; Frank Di Maio; Thomas E. Ferrin; Kay Grünewald; Aleksandras Gutmanas; Richard Henderson; Gerhard Hummer; Kenji Iwasaki; Graham Johnson; Catherine L. Lawson; Jens Meiler; Marc A. Marti-Renom; Gaetano T. Montelione; Michael Nilges; Ruth Nussinov; Ardan Patwardhan; Juri Rappsilber; Randy J. Read; Helen R. Saibil

Structures of biomolecular systems are increasingly computed by integrative modeling that relies on varied types of experimental data and theoretical information. We describe here the proceedings and conclusions from the first wwPDB Hybrid/Integrative Methods Task Force Workshop held at the European Bioinformatics Institute in Hinxton, UK, on October 6 and 7, 2014. At the workshop, experts in various experimental fields of structural biology, experts in integrative modeling and visualization, and experts in data archiving addressed a series of questions central to the future of structural biology. How should integrative models be represented? How should the data and integrative models be validated? What data should be archived? How should the data and models be archived? What information should accompany the publication of integrative models?


Biochimica et Biophysica Acta | 1979

Isolation of photosynthetic catalysts from cyanobacteria

Kwok Ki Ho; Eldon L. Ulrich; David W. Krogmann; Carlos Gómez-Lojero

Methods are described for the isolation of ferredoxins I and II, cytochrome c-553, cytochrome f, cytochrome c-550 and plastocyanin from large quantities of various cyanobacteria. The amino acid composition of cytochrome c-550 is reported. There is a variation in the relative amounts of these proteins in different batches of cells which may relate to the nutritional status of the organisms.


pacific symposium on biocomputing | 2006

New bioinformatics resources for metabolomics.

John L. Markley; Mark E. Anderson; Qiu Cui; Hamid R. Eghbalnia; Ian A. Lewis; Adrian D. Hegeman; Jing Li; Christopher F. Schulte; Michael R. Sussman; William M. Westler; Eldon L. Ulrich; Zsolt Zolnai

We recently developed two databases and a laboratory information system as resources for the metabolomics community. These tools are freely available and are intended to ease data analysis in both MS and NMR based metabolomics studies. The first database is a metabolomics extension to the BioMagResBank (BMRB, http://www.bmrb.wisc.edu), which currently contains experimental spectral data on over 270 pure compounds. Each small molecule entry consists of five or six one- and two-dimensional NMR data sets, along with information about the source of the compound, solution conditions, data collection protocol and the NMR pulse sequences. Users have free access to peak lists, spectra, and original time-domain data. The BMRB database can be queried by name, monoisotopic mass and chemical shift. We are currently developing a deposition tool that will enable people in the community to add their own data to this resource. Our second database, the Madison Metabolomics Consortium Database (MMCD, available from http://mmcd.nmrfam.wisc.edu/), is a hub for information on over 10,000 metabolites. These data were collected from a variety of sites with an emphasis on metabolites found in Arabidopsis. The MMC database supports extensive search functions and allows users to make bulk queries using experimental MS and/or NMR data. In addition to these databases, we have developed a new module for the Sesame laboratory information management system (http://www.sesame.wisc.edu) that captures all of the experimental protocols, background information, and experimental data associated with metabolomics samples. Sesame was designed to help coordinate research efforts in laboratories with high sample throughput and multiple investigators and to track all of the actions that have taken place in a particular study.


Proteins | 2005

Addressing the intrinsic disorder bottleneck in structural proteomics

Christopher J. Oldfield; Eldon L. Ulrich; Yugong Cheng; A. Keith Dunker; John L. Markley

The Center for Eukaryotic Structural Genomics (CESG), as part of the Protein Structure Initiative (PSI), has established a high‐throughput structure determination pipeline focused on eukaryotic proteins. NMR spectroscopy is an integral part of this pipeline, both as a method for structure determinations and as a means for screening proteins for stable structure. Because computational approaches have estimated that many eukaryotic proteins are highly disordered, about 1 year into the project, CESG began to use an algorithm (the Predictor of Naturally Disordered Regions, PONDR®) to avoid proteins that were likely to be disordered. We report a retrospective analysis of the effect of this filtering on the yield of viable structure determination candidates. In addition, we have used our current database of results on 70 protein targets from Arabidopsis thaliana and 1 from Caenorhabditis elegans, which were labeled uniformly with nitrogen‐15 and screened for disorder by NMR spectroscopy, to compare the original algorithm with 13 other approaches for predicting disorder from sequence. Our study indicates that the efficiency of structural proteomics of eukaryotes can be improved significantly by removing targets predicted to be disordered by an algorithm chosen to provide optimal performance. Proteins 2005.

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John L. Markley

University of Wisconsin-Madison

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William M. Westler

University of Wisconsin-Madison

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Brian F. Volkman

Medical College of Wisconsin

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Craig S. Newman

University of Wisconsin-Madison

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Wim F. Vranken

Radboud University Nijmegen Medical Centre

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