DEPICTER: Intrinsic Disorder and Disorder Function Prediction Server

被引:45
|
作者
Barik, Amita [1 ,2 ]
Katuwawala, Akila [1 ]
Hanson, Jack [3 ]
Paliwal, Kuldip [3 ]
Zhou, Yaoqi [4 ,5 ]
Kurgan, Lukasz [1 ]
机构
[1] Virginia Commonwealth Univ, Dept Comp Sci, Richmond, VA 23284 USA
[2] Natl Inst Technol, Dept Biotechnol, Durgapur, India
[3] Griffith Univ, Signal Proc Lab, Brisbane, Qld 4122, Australia
[4] Griffith Univ, Sch Informat & Commun Technol, Gold Coast, Qld 4222, Australia
[5] Griffith Univ, Inst Glyc, Gold Coast, Qld 4222, Australia
基金
美国国家科学基金会; 澳大利亚研究理事会;
关键词
webserver; prediction center; intrinsically disordered proteins; intrinsically disordered region; protein-nucleic acids interactions; PROTEIN-BINDING REGIONS; 3; DOMAINS; DNA; RECOGNITION; DISPROT; ORDER; MORFS; SIR3; RNA;
D O I
10.1016/j.jmb.2019.12.030
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Computational predictions of the intrinsic disorder and its functions are instrumental to facilitate annotation for the millions of unannotated proteins. However, access to these predictors is fragmented and requires substantial effort to find them and to collect and combine their results. The DEPICTER (DisorderEd PredictIon CenTER) server provides first-of-its-kind centralized access to 10 popular disorder and disorder function predictions that cover protein and nucleic acids binding, linkers, and moonlighting regions. It automates the prediction process, runs user-selected methods on the server side, visualizes the results, and outputs all predictions in a consistent and easy-to-parse format. DEPICTER also includes two accurate consensus predictors of disorder and disordered protein binding. Empirical tests on an independent (low similarity) benchmark dataset reveal that the computational tools included in DEPICTER generate accurate predictions that are significantly better than the results secured using sequence alignment. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3379 / 3387
页数:9
相关论文
共 50 条
  • [21] Protein intrinsic disorder and structure-function continuum
    Uversky, Vladimir N.
    DANCING PROTEIN CLOUDS: INTRINSICALLY DISORDERED PROTEINS IN HEALTH AND DISEASE, PT A, 2019, 166 : 1 - 17
  • [22] IDP-LM: Prediction of protein intrinsic disorder and disorder functions based on language models
    Pang, Yihe
    Liu, Bin
    PLOS COMPUTATIONAL BIOLOGY, 2023, 19 (11)
  • [23] SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning
    Jack Hanson
    Kuldip K.Paliwal
    Thomas Litfin
    Yaoqi Zhou
    Genomics,Proteomics & Bioinformatics, 2019, 17 (06) : 645 - 656
  • [24] SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning
    Hanson, Jack
    Paliwal, Kuldip K.
    Litfin, Thomas
    Zhou, Yaoqi
    GENOMICS PROTEOMICS & BIOINFORMATICS, 2019, 17 (06) : 645 - 656
  • [25] SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning
    Jack Hanson
    Kuldip KPaliwal
    Thomas Litfin
    Yaoqi Zhou
    Genomics,Proteomics & Bioinformatics, 2019, (06) : 645 - 656
  • [26] DisorderUnetLM: Validating ProteinUnet for efficient protein intrinsic disorder prediction
    Kotowski, Krzysztof
    Roterman, Irena
    Stapor, Katarzyna
    Computers in Biology and Medicine, 2025, 185
  • [27] Intrinsic Disorder in Transmembrane Proteins: Roles in Signaling and Topology Prediction
    Burgi, Jerome
    Xue, Bin
    Uversky, Vladimir N.
    van der Goot, F. Gisou
    PLOS ONE, 2016, 11 (07):
  • [28] Intrinsic disorder and overcrowding
    Uversky, Vladimir
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2017, 253
  • [29] Concentrating on intrinsic disorder
    Strzyz P.
    Nature Reviews Genetics, 2018, 19 (9) : 534 - 534
  • [30] Concentrating on intrinsic disorder
    Paulina Strzyz
    Nature Reviews Molecular Cell Biology, 2018, 19 (9) : 544 - 544