Literature DB >> 23715893

IS-Dom: a dataset of independent structural domains automatically delineated from protein structures.

Teppei Ebina1, Yuki Umezawa, Yutaka Kuroda.   

Abstract

Protein domains that can fold in isolation are significant targets in diverse area of proteomics research as they are often readily analyzed by high-throughput methods. Here, we report IS-Dom, a dataset of Independent Structural Domains (ISDs) that are most likely to fold in isolation. IS-Dom was constructed by filtering domains from SCOP, CATH, and DomainParser using quantitative structural measures, which were calculated by estimating inter-domain hydrophobic clusters and hydrogen bonds from the full length protein's atomic coordinates. The ISD detection protocol is fully automated, and all of the computed interactions are stored in the server which enables rapid update of IS-Dom. We also prepared a standard IS-Dom using parameters optimized by maximizing the Youden's index. The standard IS-Dom, contained 54,860 ISDs, of which 25.5 % had high sequence identity and termini overlap with a Protein Data Bank (PDB) cataloged sequence and are thus experimentally shown to fold in isolation [coined autonomously folded domain (AFDs)]. Furthermore, our ISD detection protocol missed less than 10 % of the AFDs, which corroborated our protocol's ability to define structural domains that are able to fold independently. IS-Dom is available through the web server ( http://domserv.lab.tuat.ac.jp/IS-Dom.html ), and users can either, download the standard IS-Dom dataset, construct their own IS-Dom by interactively varying the parameters, or assess the structural independence of newly defined putative domains.

Entities:  

Mesh:

Substances:

Year:  2013        PMID: 23715893     DOI: 10.1007/s10822-013-9654-6

Source DB:  PubMed          Journal:  J Comput Aided Mol Des        ISSN: 0920-654X            Impact factor:   3.686


  26 in total

1.  Automated search of natively folded protein fragments for high-throughput structure determination in structural genomics.

Authors:  Y Kuroda; K Tani; Y Matsuo; S Yokoyama
Journal:  Protein Sci       Date:  2000-12       Impact factor: 6.725

2.  Twilight zone of protein sequence alignments.

Authors:  B Rost
Journal:  Protein Eng       Date:  1999-02

3.  Protein structural domain identification.

Authors:  W R Taylor
Journal:  Protein Eng       Date:  1999-03

4.  Improving the performance of DomainParser for structural domain partition using neural network.

Authors:  Jun-tao Guo; Dong Xu; Dongsup Kim; Ying Xu
Journal:  Nucleic Acids Res       Date:  2003-02-01       Impact factor: 16.971

5.  Crystal structure of the essential N-terminal domain of telomerase reverse transcriptase.

Authors:  Steven A Jacobs; Elaine R Podell; Thomas R Cech
Journal:  Nat Struct Mol Biol       Date:  2006-02-05       Impact factor: 15.369

6.  A procedure for detecting structural domains in proteins.

Authors:  M B Swindells
Journal:  Protein Sci       Date:  1995-01       Impact factor: 6.725

7.  Satisfying hydrogen bonding potential in proteins.

Authors:  I K McDonald; J M Thornton
Journal:  J Mol Biol       Date:  1994-05-20       Impact factor: 5.469

8.  Mathematical model for empirically optimizing large scale production of soluble protein domains.

Authors:  Eisuke Chikayama; Atsushi Kurotani; Takanori Tanaka; Takashi Yabuki; Satoshi Miyazaki; Shigeyuki Yokoyama; Yutaka Kuroda
Journal:  BMC Bioinformatics       Date:  2010-03-01       Impact factor: 3.169

9.  The Pfam protein families database.

Authors:  Marco Punta; Penny C Coggill; Ruth Y Eberhardt; Jaina Mistry; John Tate; Chris Boursnell; Ningze Pang; Kristoffer Forslund; Goran Ceric; Jody Clements; Andreas Heger; Liisa Holm; Erik L L Sonnhammer; Sean R Eddy; Alex Bateman; Robert D Finn
Journal:  Nucleic Acids Res       Date:  2011-11-29       Impact factor: 16.971

10.  Identification of putative domain linkers by a neural network - application to a large sequence database.

Authors:  Satoshi Miyazaki; Yutaka Kuroda; Shigeyuki Yokoyama
Journal:  BMC Bioinformatics       Date:  2006-06-27       Impact factor: 3.169

View more
  2 in total

1.  Fast H-DROP: A thirty times accelerated version of H-DROP for interactive SVM-based prediction of helical domain linkers.

Authors:  Tambi Richa; Soichiro Ide; Ryosuke Suzuki; Teppei Ebina; Yutaka Kuroda
Journal:  J Comput Aided Mol Des       Date:  2016-12-27       Impact factor: 3.686

2.  H-DROP: an SVM based helical domain linker predictor trained with features optimized by combining random forest and stepwise selection.

Authors:  Teppei Ebina; Ryosuke Suzuki; Ryotaro Tsuji; Yutaka Kuroda
Journal:  J Comput Aided Mol Des       Date:  2014-06-26       Impact factor: 3.686

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.