The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods

Gabanyi, Margaret; Adams, Paul D.; Arnold, Konstantin; Bordoli, Lorenza; Carter, Lester G.; Flippen-Andersen, Judith; Gifford, Lida K.; Haas, Juergen; Kouranov, Andrei; McLaughlin, William A.; Micallef, David; Minor, Władek; Shah, Raship; Schwede, Torsten; Tao, Yi Ping; Westbrook, John D.; Zimmerman, Matthew D.; Berman, Helen M.

doi:10.1007/s10969-011-9106-2

Cited by 66 publications

(65 citation statements)

References 90 publications

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“…We will focus our analysis on the publications from the PSI, which has set up a database of peer-reviewed publications produced and self-reported by its members ([44], [55]). As of August 24, 2015, it contained 2280 publications credited to the PSI.…”

Section: Publicationsmentioning

confidence: 99%

See 1 more Smart Citation

The impact of structural genomics: the first quindecennial

Grabowski

Niedzialkowska

Zimmerman

et al. 2016

J Struct Funct Genomics

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The period 2000–2015 brought the advent of high-throughput approaches to protein structure determination. With the overall funding on the order of $2 billion (in 2010 dollars), the structural genomics (SG) consortia established worldwide have developed pipelines for target selection, protein production, sample preparation, crystallization, and structure determination by X-ray crystallography and NMR. These efforts resulted in the determination of over 13,500 protein structures, mostly from unique protein families, and increased the structural coverage of the expanding protein universe. SG programs contributed over 4,400 publications to the scientific literature. The NIH-funded Protein Structure Initiatives (PSI) alone have produced over 2,000 scientific publications, which to date have attracted more than 93,000 citations. Software and database developments that were necessary to handle high-throughput structure determination workflows have led to structures of better quality and improved integrity of the associated data. Organized and accessible data have a positive impact on the reproducibility of scientific experiments. Most of the experimental data generated by the SG centers are freely available to the community and has been utilized by scientists in various fields of research. SG projects have created, improved, streamlined, and validated many protocols for protein production and crystallization, data collection, and functional analysis, significantly benefiting biological and biomedical research.

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Section: Publicationsmentioning

confidence: 99%

“…The primary mechanism for reporting experimental data in the PSI (and adopted by other SG centers) was the TargetDB database and its successor TargetTrack [61,55]. Many SG centers developed their own systems for aggregating and reporting experimental data to the TargetTrack system via XML-formatted reports.…”

Section: Data Databanks Databases and Other Resourcesmentioning

confidence: 99%

The impact of structural genomics: the first quindecennial

Grabowski

Niedzialkowska

Zimmerman

et al. 2016

J Struct Funct Genomics

Self Cite

View full text Add to dashboard Cite

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“…For this reason, the Structural Biology Knowledgebase (SBKB) was designed as a portal to integrate information about structurally determined proteins (or those targeted for structure determination) from many resources in order to enable new knowledge (Gabanyi et al, 2011). When searching the SBKB by a protein's amino acid sequence, the SBKB runs a BLAST search and returns matching and homologous structures from the PDB, relevant theoretical models from the Protein Model Portal, and biological descriptions (annotations) from over 150 genomics, structural, and function-related databases that hold information about the protein of interest.…”

Section: A Portal To Combined Structural Informationmentioning

confidence: 99%

Structural Databases of Biological Macromolecules

Gabanyi

Berman

2012

Encyclopedia of Life Sciences

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A biological macromolecule's function is determined by the chemical and physical characteristics of its three‐dimensional (3D) shape, or ‘structure’. For this reason, knowing the structure of a biomolecule is very helpful if we want to be able to understand living systems and disease. The Protein Data Bank (PDB) began as an archive of the structural data available about biological macromolecules. The advances made in all technologies have been mirrored in further development of the PDB and in the structural speciality and structural characteristic databases that have also evolved. New resource portals such as the Protein Structure Initiative (PSI) Structural Biology Knowledgebase (SBKB) also collect all available genomic, structural, and functional information together to reduce the time needed to obtain the latest information on structurally determined proteins. This article will describe selected structural databases and resources available to the public today. Key Concepts: Structural information about proteins at the atomic‐level can lead to an explanation of its role in living systems. The Protein Data Bank archive is the sole provider of primary structural data of biological macromolecules worldwide. The four members of the worldwide protein data bank consortium, the RCSB PDB, PDBe, PDBj and BMRB, maintain the PDB archive and provide tools for exploring and understanding the structural entries. Other value‐added databases further classify derived structural information by combining the structural and/or biological aspects of a biomolecule. Meta‐portals such as the Structural Biology Knowledgebase integrate all available genetic, structural, functional, and experimental information for all structurally determined proteins to enable a better understanding of sequence–structure–function relationships.

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“…In other contexts, statistical tools based on sequence have been shown to work; for example, those developed to predict soluble protein expression and/or crystallization propensities (13,24,25). Such predictors are primarily based on available experimental results from the Protein Structure Initiative (26,27). While collectively these methods have supported significant advances in biochemistry, none of the models are able to predict IMP outcomes due to limitations inherent in the model development process.…”

Section: Introductionmentioning

confidence: 99%

A statistical model for improved membrane protein expression using sequence-derived features

Saladi

Javed

Müller

et al. 2018

Journal of Biological Chemistry

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The heterologous expression of integral membrane proteins (IMPs) remains a major bottleneck in the characterization of this important protein class. IMP expression levels are currently unpredictable, which renders the pursuit of IMPs for structural and biophysical characterization challenging and inefficient. Experimental evidence demonstrates that changes within the nucleotide or amino-acid sequence for a given IMP can dramatically affect expression levels; yet these observations have not resulted in generalizable approaches to improve expression levels. Here, we develop a data-driven statistical predictor named IMProve, that, using only sequence information, increases the likelihood of selecting an IMP that expresses in E. coli. The IMProve model, trained on experimental data, combines a set of sequencederived features resulting in an IMProve score, where higher values have a higher probability of success. The model is rigorously validated against a variety of independent datasets that contain a wide range of experimental outcomes from various IMP expression trials. The results demonstrate that use of the model can more than double the number of successfully expressed targets at any experimental scale. IMProve can immediately be used to identify favorable targets for characterization. Most notably, IMProve demonstrates for the first time that IMP expression levels can be predicted directly from sequence.

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The Structural Biology Knowledgebase: a portal to protein structures, sequences, functions, and methods

Cited by 66 publications

References 90 publications

The impact of structural genomics: the first quindecennial

The impact of structural genomics: the first quindecennial

Structural Databases of Biological Macromolecules

A statistical model for improved membrane protein expression using sequence-derived features

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