Literature DB >> 20525820

Exploring the potential of template-based modelling.

Braddon K Lance1, Charlotte M Deane, Graham R Wood.   

Abstract

MOTIVATION: Template-based modelling can approximate the unknown structure of a target protein using an homologous template structure. The core of the resulting prediction then comprises the structural regions conserved between template and target. Target prediction could be improved by rigidly repositioning such single template, structurally conserved fragment regions. The purpose of this article is to quantify the extent to which such improvements are possible and to relate this extent to properties of the target, the template and their alignment.
RESULTS: The improvement in accuracy achievable when rigid fragments from a single template are optimally positioned was calculated using structure pairs from the HOMSTRAD database, as well as CASP7 and CASP8 target/best template pairs. Over the union of the structurally conserved regions, improvements of 0.7 A in root mean squared deviation (RMSD) and 6% in GDT_HA were commonly observed. A generalized linear model revealed that the extent to which a template can be improved can be predicted using four variables. Templates with the greatest scope for improvement tend to have relatively more fragments, shorter fragments, higher percentage of helical secondary structure and lower sequence identity. Optimal positioning of the template fragments offers the potential for improving loop modelling. These results demonstrate that substantial improvement could be made on many templates if the conserved fragments were to be optimally positioned. They also provide a basis for identifying templates for which modification of fragment positions may yield such improvements.

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Year:  2010        PMID: 20525820     DOI: 10.1093/bioinformatics/btq294

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  6 in total

1.  Novel insights through the integration of structural and functional genomics data with protein networks.

Authors:  Declan Clarke; Nitin Bhardwaj; Mark B Gerstein
Journal:  J Struct Biol       Date:  2012-02-11       Impact factor: 2.867

2.  Protein Structure Refinement through Structure Selection and Averaging from Molecular Dynamics Ensembles.

Authors:  Vahid Mirjalili; Michael Feig
Journal:  J Chem Theory Comput       Date:  2012-12-22       Impact factor: 6.006

Review 3.  Methods for the Refinement of Protein Structure 3D Models.

Authors:  Recep Adiyaman; Liam James McGuffin
Journal:  Int J Mol Sci       Date:  2019-05-09       Impact factor: 5.923

4.  In Silico identification of angiotensin-converting enzyme inhibitory peptides from MRJP1.

Authors:  Rana Adnan Tahir; Afsheen Bashir; Muhammad Noaman Yousaf; Azka Ahmed; Yasmine Dali; Sanaullah Khan; Sheikh Arslan Sehgal
Journal:  PLoS One       Date:  2020-02-03       Impact factor: 3.240

5.  GalaxyTBM: template-based modeling by building a reliable core and refining unreliable local regions.

Authors:  Junsu Ko; Hahnbeom Park; Chaok Seok
Journal:  BMC Bioinformatics       Date:  2012-08-10       Impact factor: 3.169

Review 6.  Molecular dynamics simulations: advances and applications.

Authors:  Adam Hospital; Josep Ramon Goñi; Modesto Orozco; Josep L Gelpí
Journal:  Adv Appl Bioinform Chem       Date:  2015-11-19
  6 in total

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