SCOP: A structural classification of proteins database for the investigation of sequences and structures

Murzin, Alexey G.; Brenner, Steven E.; Hubbard, Tim; Chothia, Cyrus

doi:10.1016/s0022-2836(05)80134-2

Cited by 5,022 publications

(3,924 citation statements)

References 13 publications

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“…The EM density corresponding to the modeled TPR density is colored yellow. The fits (except τ131, which has not been present in the databases) were obtained by fitting of all TPR domains from CATH (Orengo et al , 1997) and SCOP (Murzin et al , 1995) databases using PowerFit (van Zundert et al , 2016). τ131, which is a TPR domain containing protein from transcription factor IIIC of Pol III, was fitted using Chimera to assess its similarity to Rrn11.…”

Section: Resultsmentioning

confidence: 99%

“…Nine helices comprising residues 204–442 could be assigned to Rrn11. The tracing of the Rrn11 TPR domain was additionally supported by fits of unrelated TPR domains derived from CATH (Orengo et al , 1997) and SCOP database (Murzin et al , 1995), automatically fitted to the EM map using PowerFit (van Zundert et al , 2016). In addition, the cross‐links from Lys333 of Rrn11 to Lys30 of A135 and Lys406 of Rrn11 to Lys174 of A135 supported the placement of the TPR domain.…”

Section: Methodsmentioning

confidence: 99%

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Structural insights into transcription initiation by yeast RNA polymerase I

et al. 2017

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In eukaryotic cells, RNA polymerase I (Pol I) synthesizes precursor ribosomal RNA (pre‐rRNA) that is subsequently processed into mature rRNA. To initiate transcription, Pol I requires the assembly of a multi‐subunit pre‐initiation complex (PIC) at the ribosomal RNA promoter. In yeast, the minimal PIC includes Pol I, the transcription factor Rrn3, and Core Factor (CF) composed of subunits Rrn6, Rrn7, and Rrn11. Here, we present the cryo‐EM structure of the 18‐subunit yeast Pol I PIC bound to a transcription scaffold. The cryo‐EM map reveals an unexpected arrangement of the DNA and CF subunits relative to Pol I. The upstream DNA is positioned differently than in any previous structures of the Pol II PIC. Furthermore, the TFIIB‐related subunit Rrn7 also occupies a different location compared to the Pol II PIC although it uses similar interfaces as TFIIB to contact DNA. Our results show that although general features of eukaryotic transcription initiation are conserved, Pol I and Pol II use them differently in their respective transcription initiation complexes.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Structural insights into transcription initiation by yeast RNA polymerase I

et al. 2017

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“…The 1189 benchmark was introduced by [8] consisting of 1189 proteins with less than 40% sequence similarities. This benchmark was modified in later studies to address further corrections of Structural Classification of Proteins (SCOP) [47] and 97 of its proteins were removed [45]. Therefore, later version of this benchmark consists of 1092 proteins.…”

Section: Benchmarksmentioning

confidence: 99%

Proposing a highly accurate protein structural class predictor using segmentation-based features

et al. 2014

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BackgroundPrediction of the structural classes of proteins can provide important information about their functionalities as well as their major tertiary structures. It is also considered as an important step towards protein structure prediction problem. Despite all the efforts have been made so far, finding a fast and accurate computational approach to solve protein structural class prediction problem still remains a challenging problem in bioinformatics and computational biology.ResultsIn this study we propose segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins. By applying SVM to our extracted features, for the first time we enhance the protein structural class prediction accuracy to over 90% and 85% for two popular low-homology benchmarks that have been widely used in the literature. We report 92.2% and 86.3% prediction accuracies for 25PDB and 1189 benchmarks which are respectively up to 7.9% and 2.8% better than previously reported results for these two benchmarks.ConclusionBy proposing segmented distribution and segmented auto covariance feature extraction methods to capture local and global discriminatory information from evolutionary profiles and predicted secondary structure of the proteins, we are able to enhance the protein structural class prediction performance significantly.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-S1-S2) contains supplementary material, which is available to authorized users.

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“…The growth rate of structures deposited in the PDB is slowing down since 2004, along with the number of new superfamilies or folds discovered [8,9]. One possible explanation is that many proteins still evade the structural biology pipelines at this time because of the technical difficulties described above.…”

Section: Introductionmentioning

confidence: 99%

“…One possible explanation is that many proteins still evade the structural biology pipelines at this time because of the technical difficulties described above. In 1992 Chothia hypothesized that the number of protein folds in nature is probably finite and around 1,000 [9,10]. The latest analysis of the PDB and of the structural classification of proteins (SCOP) showed that we have not yet reached a plateau (currently estimated to be around 1,500) [8].…”

Section: Introductionmentioning

confidence: 99%

A quality metric for homology modeling: the H-factor

Luccio

Koehl

2011

BMC Bioinformatics

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BackgroundThe analysis of protein structures provides fundamental insight into most biochemical functions and consequently into the cause and possible treatment of diseases. As the structures of most known proteins cannot be solved experimentally for technical or sometimes simply for time constraints, in silico protein structure prediction is expected to step in and generate a more complete picture of the protein structure universe. Molecular modeling of protein structures is a fast growing field and tremendous works have been done since the publication of the very first model. The growth of modeling techniques and more specifically of those that rely on the existing experimental knowledge of protein structures is intimately linked to the developments of high resolution, experimental techniques such as NMR, X-ray crystallography and electron microscopy. This strong connection between experimental and in silico methods is however not devoid of criticisms and concerns among modelers as well as among experimentalists.ResultsIn this paper, we focus on homology-modeling and more specifically, we review how it is perceived by the structural biology community and what can be done to impress on the experimentalists that it can be a valuable resource to them. We review the common practices and provide a set of guidelines for building better models. For that purpose, we introduce the H-factor, a new indicator for assessing the quality of homology models, mimicking the R-factor in X-ray crystallography. The methods for computing the H-factor is fully described and validated on a series of test cases.ConclusionsWe have developed a web service for computing the H-factor for models of a protein structure. This service is freely accessible at http://koehllab.genomecenter.ucdavis.edu/toolkit/h-factor.

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SCOP: A structural classification of proteins database for the investigation of sequences and structures

Cited by 5,022 publications

References 13 publications

Structural insights into transcription initiation by yeast RNA polymerase I

Structural insights into transcription initiation by yeast RNA polymerase I

Proposing a highly accurate protein structural class predictor using segmentation-based features

A quality metric for homology modeling: the H-factor

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