4.8 Article

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

期刊

NUCLEIC ACIDS RESEARCH
卷 49, 期 D1, 页码 D298-D308

出版社

OXFORD UNIV PRESS
DOI: 10.1093/nar/gkaa931

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资金

  1. National Science Foundation [1617369, 1661391]
  2. National Institutes of Health [R01 GM127701]
  3. Robert J. Mattauch Endowment funds
  4. Div Of Biological Infrastructure
  5. Direct For Biological Sciences [1661391] Funding Source: National Science Foundation

向作者/读者索取更多资源

DescribePROT is a database of predicted amino acid-level descriptors of protein structure and function, offering a comprehensive collection of descriptors predicted using accurate algorithms for key model organisms. Users can search and download pre-computed results for various research purposes. Future releases will expand the coverage of DescribePROT.
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.

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