Please use this identifier to cite or link to this item: https://research.matf.bg.ac.rs/handle/123456789/333
Title: Data set of intrinsically disordered proteins analysed at a local protein conformation level
Authors: Melarkode Vattekatte, Akhila
Narwani, Tarun Jairaj
Floch, Aline
Maljković, Mirjana
Bisoo, Soubika
Shinada, Nicolas K
Kranjc, Agata
Gelly, Jean-Christophe
Srinivasan, Narayanaswamy
Mitić, Nenad 
de Brevern, Alexandre G
Affiliations: Informatics and Computer Science 
Keywords: Ensembles;Entropy;Local protein conformation;PDB;Protein disorder;Structural alphabet
Issue Date: 2020
Journal: Data in brief
Abstract: 
Intrinsic Disorder Proteins (IDPs) have become a hot topic since their characterisation in the 90s. The data presented in this article are related to our research entitled "A structural entropy index to analyse local conformations in Intrinsically Disordered Proteins" published in Journal of Structural Biology [1]. In this study, we quantified, for the first time, continuum from rigidity to flexibility and finally disorder. Non-disordered regions were also highlighted in the ensemble of disordered proteins. This work was done using the Protein Ensemble Database (PED), which is a useful database collecting series of protein structures considered as IDPs. The data set consists of a collection of cleaned protein files in classical pdb format that can be readily used as an input with most automatic analysis software. The accompanying data include the coding of all structural information in terms of a structural alphabet, namely Protein Blocks (PBs). An entropy index derived from PBs that allows apprehending the continuum between protein rigidity to flexibility to disorder is included, with information from secondary structure assignment, protein accessibility and prediction of disorder from the sequences. The data may be used for further structural bioinformatics studies of IDPs. It can also be used as a benchmark for evaluating disorder prediction methods.
URI: https://research.matf.bg.ac.rs/handle/123456789/333
DOI: 10.1016/j.dib.2020.105383
Appears in Collections:Research outputs

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