Browse
Submit Data
Databases
API
Help

Dataset Information

44 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Automated protein crystal structure determination using ELVES.

ABSTRACT: Efficient determination of protein crystal structures requires automated x-ray data analysis. Here, we describe the expert system ELVES and its use to determine automatically the structure of a 12-kDa protein. Multiwavelength anomalous diffraction analysis of a selenomethionyl derivative was used to image the Asn-16-Ala variant of the GCN4 leucine zipper. In contrast to the parallel, dimeric coiled coil formed by the WT sequence, the mutant unexpectedly formed an antiparallel trimer. This structural switch reveals how avoidance of core cavities at a single site can select the native fold of a protein. All structure calculations, including indexing, data processing, locating heavy atoms, phasing by multiwavelength anomalous diffraction, model building, and refinement, were completed without human intervention. The results demonstrate the feasibility of automated methods for determining high-resolution, x-ray crystal structures of proteins.

SUBMITTER: Holton J

PROVIDER: S-EPMC341770 | biostudies-literature | 2004 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Automated protein crystal structure determination using ELVES.

Holton James J Alber Tom T

Proceedings of the National Academy of Sciences of the United States of America 20040129 6

Efficient determination of protein crystal structures requires automated x-ray data analysis. Here, we describe the expert system ELVES and its use to determine automatically the structure of a 12-kDa protein. Multiwavelength anomalous diffraction analysis of a selenomethionyl derivative was used to image the Asn-16-Ala variant of the GCN4 leucine zipper. In contrast to the parallel, dimeric coiled coil formed by the WT sequence, the mutant unexpectedly formed an antiparallel trimer. This struct ...[more]

PMID: 14752198

Similar Datasets

Accurate automated protein NMR structure determination using unassigned NOESY data.

Project description:Conventional NMR structure determination requires nearly complete assignment of the cross peaks of a refined NOESY peak list. Depending on the size of the protein and quality of the spectral data, this can be a time-consuming manual process requiring several rounds of peak list refinement and structure determination. Programs such as Aria, CYANA, and AutoStructure can generate models using unassigned NOESY data but are very sensitive to the quality of the input peak lists and can converge to inaccurate structures if the signal-to-noise of the peak lists is low. Here, we show that models with high accuracy and reliability can be produced by combining the strengths of the high-resolution structure prediction program Rosetta with global measures of the agreement between structure models and experimental data. A first round of models generated using CS-Rosetta (Rosetta supplemented with backbone chemical shift information) are filtered on the basis of their goodness-of-fit with unassigned NOESY peak lists using the DP-score, and the best fitting models are subjected to high resolution refinement with the Rosetta rebuild-and-refine protocol. This hybrid approach uses both local backbone chemical shift and the unassigned NOESY data to direct Rosetta trajectories toward the native structure and produces more accurate models than AutoStructure/CYANA or CS-Rosetta alone, particularly when using raw unedited NOESY peak lists. We also show that when accurate manually refined NOESY peak lists are available, Rosetta refinement can consistently increase the accuracy of models generated using CYANA and AutoStructure.

| S-EPMC2841443 | biostudies-literature

Automated NMR Assignment and Protein Structure Determination using Sparse Dipolar Coupling Constraints.

Project description: Not available

| S-EPMC2755298 | biostudies-literature

Automated Crystal Structure Determination Has its Pitfalls: Correction to the Crystal Structures of Iodine Azide.

Project description:Previously published crystal structure determinations of two modifications of iodine azide (IN3 ) are corrected. In the original determinations, the very weak X-ray reflections with odd k Miller indices had been discarded, resulting in too small unit cells and models with misordered, partly occupied atomic positions. Using the original diffraction data, refinements with the correct unit cells yield structures of polymeric (-I-N3 -)n chains that are interlocked to layers. A skilled look at the primary X-ray data is always recommended to overcome the lack of crystallographic expertise of computers at automated structure determinations.

| S-EPMC8361933 | biostudies-literature

PONDEROSA, an automated 3D-NOESY peak picking program, enables automated protein structure determination.

Project description:SummaryPONDEROSA (Peak-picking Of Noe Data Enabled by Restriction of Shift Assignments) accepts input information consisting of a protein sequence, backbone and sidechain NMR resonance assignments, and 3D-NOESY ((13)C-edited and/or (15)N-edited) spectra, and returns assignments of NOESY crosspeaks, distance and angle constraints, and a reliable NMR structure represented by a family of conformers. PONDEROSA incorporates and integrates external software packages (TALOS+, STRIDE and CYANA) to carry out different steps in the structure determination. PONDEROSA implements internal functions that identify and validate NOESY peak assignments and assess the quality of the calculated three-dimensional structure of the protein. The robustness of the analysis results from PONDEROSA's hierarchical processing steps that involve iterative interaction among the internal and external modules. PONDEROSA supports a variety of input formats: SPARKY assignment table (.shifts) and spectrum file formats (.ucsf), XEASY proton file format (.prot), and NMR-STAR format (.star). To demonstrate the utility of PONDEROSA, we used the package to determine 3D structures of two proteins: human ubiquitin and Escherichia coli iron-sulfur scaffold protein variant IscU(D39A). The automatically generated structural constraints and ensembles of conformers were as good as or better than those determined previously by much less automated means.AvailabilityThe program, in the form of binary code along with tutorials and reference manuals, is available at http://ponderosa.nmrfam.wisc.edu/.

| S-EPMC3106192 | biostudies-literature

Automated protein fold determination using a minimal NMR constraint strategy.

Project description:Determination of precise and accurate protein structures by NMR generally requires weeks or even months to acquire and interpret all the necessary NMR data. However, even medium-accuracy fold information can often provide key clues about protein evolution and biochemical function(s). In this article we describe a largely automatic strategy for rapid determination of medium-accuracy protein backbone structures. Our strategy derives from ideas originally introduced by other groups for determining medium-accuracy NMR structures of large proteins using deuterated, (13)C-, (15)N-enriched protein samples with selective protonation of side-chain methyl groups ((13)CH(3)). Data collection includes acquiring NMR spectra for automatically determining assignments of backbone and side-chain (15)N, H(N) resonances, and side-chain (13)CH(3) methyl resonances. These assignments are determined automatically by the program AutoAssign using backbone triple resonance NMR data, together with Spin System Type Assignment Constraints (STACs) derived from side-chain triple-resonance experiments. The program AutoStructure then derives conformational constraints using these chemical shifts, amide (1)H/(2)H exchange, nuclear Overhauser effect spectroscopy (NOESY), and residual dipolar coupling data. The total time required for collecting such NMR data can potentially be as short as a few days. Here we demonstrate an integrated set of NMR software which can process these NMR spectra, carry out resonance assignments, interpret NOESY data, and generate medium-accuracy structures within a few days. The feasibility of this combined data collection and analysis strategy starting from raw NMR time domain data was illustrated by automatic analysis of a medium accuracy structure of the Z domain of Staphylococcal protein A.

| S-EPMC2323888 | biostudies-literature

Protein structure determination using metagenome sequence data.

Project description:Despite decades of work by structural biologists, there are still ~5200 protein families with unknown structure outside the range of comparative modeling. We show that Rosetta structure prediction guided by residue-residue contacts inferred from evolutionary information can accurately model proteins that belong to large families and that metagenome sequence data more than triple the number of protein families with sufficient sequences for accurate modeling. We then integrate metagenome data, contact-based structure matching, and Rosetta structure calculations to generate models for 614 protein families with currently unknown structures; 206 are membrane proteins and 137 have folds not represented in the Protein Data Bank. This approach provides the representative models for large protein families originally envisioned as the goal of the Protein Structure Initiative at a fraction of the cost.

| S-EPMC5493203 | biostudies-literature

Feasibility of one-shot-per-crystal structure determination using Laue diffraction.

Project description:Crystal size is an important factor in determining the number of diffraction patterns which may be obtained from a protein crystal before severe radiation damage sets in. As crystal dimensions decrease this number is reduced, eventually falling to one, at which point a complete data set must be assembled using data from multiple crystals. When only a single exposure is to be collected from each crystal, the polychromatic Laue technique may be preferable to monochromatic methods owing to its simultaneous recording of a large number of fully recorded reflections per image. To assess the feasibility of solving structures using single Laue images from multiple crystals, data were collected using a 'pink' beam at the CHESS D1 station from groups of lysozyme crystals with dimensions of the order of 20-30 microm mounted on MicroMesh grids. Single-shot Laue data were used for structure determination by molecular replacement and correct solutions were obtained even when as few as five crystals were used.

| S-EPMC2803125 | biostudies-literature

Automated serial rotation electron diffraction combined with cluster analysis: an efficient multi-crystal workflow for structure determination.

Project description:Serial rotation electron diffraction (SerialRED) has been developed as a fully automated technique for three-dimensional electron diffraction data collection that can run autonomously without human intervention. It builds on the previously established serial electron diffraction technique, in which submicrometre-sized crystals are detected using image processing algorithms. Continuous rotation electron diffraction (cRED) data are collected on each crystal while dynamically tracking the movement of the crystal during rotation using defocused diffraction patterns and applying a set of deflector changes. A typical data collection screens up to 500 crystals per hour, and cRED data are collected from suitable crystals. A data processing pipeline is developed to process the SerialRED data sets. Hierarchical cluster analysis is implemented to group and identify the different phases present in the sample and to find the best matching data sets to be merged for subsequent structure analysis. This method has been successfully applied to a series of zeolites and a beam-sensitive metal-organic framework sample to study its capability for structure determination and refinement. Two multi-phase samples were tested to show that the individual crystal phases can be identified and their structures determined. The results show that refined structures obtained using automatically collected SerialRED data are indistinguishable from those collected manually using the cRED technique. At the same time, SerialRED has lower requirements of expertise in transmission electron microscopy and is less labor intensive, making it a promising high-throughput crystal screening and structure analysis tool.

| S-EPMC6760450 | biostudies-literature

The J-UNIO protocol for automated protein structure determination by NMR in solution.

Project description:The J-UNIO (JCSG protocol using the software UNIO) procedure for automated protein structure determination by NMR in solution is introduced. In the present implementation, J-UNIO makes use of APSY-NMR spectroscopy, 3D heteronuclear-resolved [(1)H,(1)H]-NOESY experiments, and the software UNIO. Applications with proteins from the JCSG target list with sizes up to 150 residues showed that the procedure is highly robust and efficient. In all instances the correct polypeptide fold was obtained in the first round of automated data analysis and structure calculation. After interactive validation of the data obtained from the automated routine, the quality of the final structures was comparable to results from interactive structure determination. Special advantages are that the NMR data have been recorded with 6-10 days of instrument time per protein, that there is only a single step of chemical shift adjustments to relate the backbone signals in the APSY-NMR spectra with the corresponding backbone signals in the NOESY spectra, and that the NOE-based amino acid side chain chemical shift assignments are automatically focused on those residues that are heavily weighted in the structure calculation. The individual working steps of J-UNIO are illustrated with the structure determination of the protein YP_926445.1 from Shewanella amazonensis, and the results obtained with 17 JCSG targets are critically evaluated.

| S-EPMC3541938 | biostudies-literature

The AUDANA algorithm for automated protein 3D structure determination from NMR NOE data.

Project description:We introduce AUDANA (Automated Database-Assisted NOE Assignment), an algorithm for determining three-dimensional structures of proteins from NMR data that automates the assignment of 3D-NOE spectra, generates distance constraints, and conducts iterative high temperature molecular dynamics and simulated annealing. The protein sequence, chemical shift assignments, and NOE spectra are the only required inputs. Distance constraints generated automatically from ambiguously assigned NOE peaks are validated during the structure calculation against information from an enlarged version of the freely available PACSY database that incorporates information on protein structures deposited in the Protein Data Bank (PDB). This approach yields robust sets of distance constraints and 3D structures. We evaluated the performance of AUDANA with input data for 14 proteins ranging in size from 6 to 25 kDa that had 27-98 % sequence identity to proteins in the database. In all cases, the automatically calculated 3D structures passed stringent validation tests. Structures were determined with and without database support. In 9/14 cases, database support improved the agreement with manually determined structures in the PDB and in 11/14 cases, database support lowered the r.m.s.d. of the family of 20 structural models.

| S-EPMC4921114 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data