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DescribePROT: database of amino acid-level protein structure and function predictions

Cited 37 time in Web of Science Cited 42 time in Scopus
Authors

Zhao, Bi; Katuwawala, Akila; Oldfield, Christopher J.; Dunker, A. Keith; Faraggi, Eshel; Gsponer, Jorg; Kloczkowski, Andrzej; Malhis, Nawar; Mirdita, Milot; Obradovic, Zoran; Soding, Johannes; Steinegger, Martin; Zhou, Yaoqi; Kurgan, Lukasz

Issue Date
2021-01
Publisher
Oxford University Press
Citation
Nucleic Acids Research, Vol.49 No.D1, pp.D298-D308
Abstract
We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.8 billion predictions for close to 600 million amino acids in 1.4 million proteins. The descriptors encompass sequence conservation, position specific scoring matrix, secondary structure, solvent accessibility, intrinsic disorder, disordered linkers, signal peptides, MoRFs and interactions with proteins, DNA and RNAs. Users can search DescribePROT by the amino acid sequence and the UniProt accession number and entry name. The pre-computed results are made available instantaneously. The predictions can be accesses via an interactive graphical interface that allows simultaneous analysis of multiple descriptors and can be also downloaded in structured formats at the protein, proteome and whole database scale. The putative annotations included by DescriPROT are useful for a broad range of studies, including: investigations of protein function, applied projects focusing on therapeutics and diseases, and in the development of predictors for other protein sequence descriptors. Future releases will expand the coverage of DescribePROT.
ISSN
0305-1048
URI
https://hdl.handle.net/10371/202561
DOI
https://doi.org/10.1093/nar/gkaa931
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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