CAID prediction portal: a comprehensive service for predicting intrinsic disorder and binding regions in proteins

被引:20
|
作者
Del Conte, Alessio [1 ]
Bouhraoua, Adel [1 ]
Mehdiabadi, Mahta [1 ]
Clementel, Damiano [1 ]
Monzon, Alexander Miguel [2 ]
CAID Predictors, Damiano [1 ]
Tosatto, Silvio C. E. [1 ]
Piovesan, Damiano [1 ]
机构
[1] Univ Padua, Dept Biomed Sci, Via Ugo Bassi 58b, I-35121 Padua, Italy
[2] Univ Padua, Dept Informat Engn, Via Giovanni Gradenigo 6-B, I-35131 Padua, Italy
关键词
ACCURACY; CHAIN;
D O I
10.1093/nar/gkad430
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
Intrinsic disorder (ID) in proteins is well-established in structural biology, with increasing evidence for its involvement in essential biological processes. As measuring dynamic ID behavior experimentally on a large scale remains difficult, scores of published ID predictors have tried to fill this gap. Unfortunately, their heterogeneity makes it difficult to compare performance, confounding biologists wanting to make an informed choice. To address this issue, the Critical Assessment of protein Intrinsic Disorder (CAID) benchmarks predictors for ID and binding regions as a community blind-test in a standardized computing environment. Here we present the CAID Prediction Portal, a web server executing all CAID methods on user-defined sequences. The server generates standardized output and facilitates comparison between methods, producing a consensus prediction highlighting high-confidence ID regions. The website contains extensive documentation explaining the meaning of different CAID statistics and providing a brief description of all methods. Predictor output is visualized in an interactive feature viewer and made available for download in a single table, with the option to recover previous sessions via a private dashboard. The CAID Prediction Portal is a valuable resource for researchers interested in studying ID in proteins. The server is available at the URL: .
引用
收藏
页码:W62 / W69
页数:8
相关论文
共 50 条
  • [21] The benefit of intrinsic disorder information in neural network prediction of calmodulin binding targets
    O'Connor, TR
    Lawson, JD
    Dunker, AK
    PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3, 2002, : 296 - 299
  • [22] MoRFpred, a computational tool for sequence-based prediction and characterization of short disorder-to-order transitioning binding regions in proteins
    Disfani, Fatemeh Miri
    Hsu, Wei-Lun
    Mizianty, Marcin J.
    Oldfield, Christopher J.
    Xue, Bin
    Dunker, A. Keith
    Uversky, Vladimir N.
    Kurgan, Lukasz
    BIOINFORMATICS, 2012, 28 (12) : I75 - I83
  • [23] Comparative evaluation of AlphaFold2 and disorder predictors for prediction of intrinsic disorder, disorder content and fully disordered proteins
    Zhao, Bi
    Ghadermarzi, Sina
    Kurgan, Lukasz
    COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL, 2023, 21 : 3248 - 3258
  • [24] Intrinsic disorder within the erythrocyte binding-like proteins from Plasmodium falciparum
    Blanc, Manuel
    Coetzer, Theresa L.
    Blackledge, Martin
    Haertlein, Michael
    Mitchell, Edward P.
    Forsyth, V. Trevor
    Jensen, Malene Ringkjobing
    BIOCHIMICA ET BIOPHYSICA ACTA-PROTEINS AND PROTEOMICS, 2014, 1844 (12): : 2306 - 2314
  • [25] Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins
    Lukasz Kurgan
    Gang Hu
    Kui Wang
    Sina Ghadermarzi
    Bi Zhao
    Nawar Malhis
    Gábor Erdős
    Jörg Gsponer
    Vladimir N. Uversky
    Zsuzsanna Dosztányi
    Nature Protocols, 2023, 18 : 3157 - 3172
  • [26] Tutorial: a guide for the selection of fast and accurate computational tools for the prediction of intrinsic disorder in proteins
    Kurgan, Lukasz
    Hu, Gang
    Wang, Kui
    Ghadermarzi, Sina
    Zhao, Bi
    Malhis, Nawar
    Erdos, Gabor
    Gsponer, Joerg
    Uversky, Vladimir N.
    Dosztanyi, Zsuzsanna
    NATURE PROTOCOLS, 2023, 18 (11) : 3157 - 3172
  • [27] MobiDB-lite: fast and highly specific consensus prediction of intrinsic disorder in proteins
    Necci, Marco
    Piovesan, Damiano
    Dosztanyi, Zsuzsanna
    Tosatto, Silvio C. E.
    BIOINFORMATICS, 2017, 33 (09) : 1402 - 1404
  • [28] THE PREDICTION OF HELIX-TURN-HELIX DNA-BINDING REGIONS IN PROTEINS
    YUDKIN, MD
    PROTEIN ENGINEERING, 1987, 1 (05): : 371 - 372
  • [29] PredIDR: Accurate prediction of protein intrinsic disorder regions using deep convolutional neural network
    Han, Kun-Sop
    Song, Se-Ryong
    Pak, Myong-hyon
    Kim, Chol-Song
    Ri, Chol-Pyok
    Del Conte, Alessio
    Piovesan, Damiano
    INTERNATIONAL JOURNAL OF BIOLOGICAL MACROMOLECULES, 2025, 284
  • [30] Predicting DNA-Binding Proteins and Binding Residues by Complex Structure Prediction and Application to Human Proteome
    Zhao, Huiying
    Wang, Jihua
    Zhou, Yaoqi
    Yang, Yuedong
    PLOS ONE, 2014, 9 (05):