The Protein Ensemble Database

Váradi, Mihály; Tompa, Péter

doi:10.1007/978-3-319-20164-1_11

Cited by 26 publications

(17 citation statements)

References 38 publications

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“…The experiments were complemented with various computational approaches to generate a set of conformations that conformed to the experimental constraints . The PED database was launched to systematically collect the generated conformational ensembles and to initiate their comparisons and analyses and to promote standardization and further improvements in this field . However, currently only a limited number of proteins have been characterized in such a detailed way.…”

Section: Future Directionsmentioning

confidence: 99%

Prediction of protein disorder based on IUPred

2017

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Many proteins contain intrinsically disordered regions (IDRs), functional polypeptide segments that in isolation adopt a highly flexible conformational ensemble instead of a single, well-defined structure. Disorder prediction methods, which can discriminate ordered and disordered regions from the amino acid sequence, have contributed significantly to our current understanding of the distinct properties of intrinsically disordered proteins by enabling the characterization of individual examples as well as large-scale analyses of these protein regions. One popular method, IUPred provides a robust prediction of protein disorder based on an energy estimation approach that captures the fundamental difference between the biophysical properties of ordered and disordered regions. This paper reviews the energy estimation method underlying IUPred and the basic properties of the web server. Through an example, it also illustrates how the prediction output can be interpreted in a more complex case by taking into account the heterogeneous nature of IDRs. Various applications that benefited from IUPred to provide improved disorder predictions, complementing domain annotations and aiding the identification of functional short linear motifs are also described here. IUPred is freely available for noncommercial users through the web server (http://iupred.enzim.hu and http://iupred.elte.hu) . The program can also be downloaded and installed locally for large-scale analyses.

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Section: Future Directionsmentioning

confidence: 99%

Prediction of protein disorder based on IUPred

2017

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“…A resource that is somewhat related to the previous two is PED 3 , the Protein Ensemble Database [23], which holds ensembles of structural models of intrinsically disordered proteins based on NMR and/or SAXS data. It has 24 entries (encompassing over 25 000 individual models) but this number has been constant for some time as data submissions have been suspended since January 2016.…”

Section: Related Archivesmentioning

confidence: 99%

Structural biology data archiving – where we are and what lies ahead

2018

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For almost 50 years, structural biology has endeavoured to conserve and share its experimental data and their interpretations (usually, atomistic models) through global public archives such as the Protein Data Bank, Electron Microscopy Data Bank and Biological Magnetic Resonance Data Bank (BMRB). These archives are treasure troves of freely accessible data that document our quest for molecular or atomic understanding of biological function and processes in health and disease. They have prepared the field to tackle new archiving challenges as more and more (combinations of) techniques are being utilized to elucidate structure at ever increasing length scales. Furthermore, the field has made substantial efforts to develop validation methods that help users to assess the reliability of structures and to identify the most appropriate data for their needs. In this Review, we present an overview of public data archives in structural biology and discuss the importance of validation for users and producers of structural data. Finally, we sketch our efforts to integrate structural data with bioimaging data and with other sources of biological data. This will make relevant structural information available and more easily discoverable for a wide range of scientists.

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“…For cellular localization purposes, immunofluorescence, immunohistochemistry, and immunoelectron microscopy are used (McDonough et al, 2015). Structure prediction and simulation can be achieved with the use of crystallography and cryo-electron microscopy, but it can be also studied indirectly by using computational methods, such as homology modeling, molecular docking or molecular mechanics (Varadi & Tompa, 2015;Shen et al, 2013). Protein fragment complementation assays are often used to detect protein-protein interactions (Waadt et al, 2014).…”

Section: Proteomementioning

confidence: 99%

Deciphering soybean molecular stress response via high-throughput approach

et al. 2017

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As a result of thousands of years of agriculture, humans had created many crop varieties that became the basis of our daily diet, animal feed and also carry industrial application. Soybean is one of the most important crops worldwide and because of its high economic value the demand for soybean products is constantly growing. In Europe, due to unfavorable climate conditions, soybean cultivation is restricted and we are forced to rely on imported plant material. The development of agriculture requires continuous improvements in quality and yield of crop varieties under changing or adverse conditions, namely stresses. To achieve this goal we need to recognize and understand the molecular dependencies underlying plant stress responses. With the advent of new technologies in studies of plant transcriptomes and proteomes, now we have the tools necessary for fast and precise elucidation of desirable crop traits. Here, we present an overview of high-throughput techniques used to analyze soybean responses to different abiotic (drought, flooding, cold stress, salinity, phosphate deficiency) and biotic (infections by F. oxysporum, cyst nematode, SMV) stress conditions at the level of the transcriptome (mRNAs and miRNAs) and the proteome.

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The Protein Ensemble Database

Cited by 26 publications

References 38 publications

Prediction of protein disorder based on IUPred

Prediction of protein disorder based on IUPred

Structural biology data archiving – where we are and what lies ahead

Deciphering soybean molecular stress response via high-throughput approach

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