Literature DB >> 10451550

Importance of anchor group positioning in protein loop prediction.

U Lessel1, D Schomburg.   

Abstract

The aim of loop prediction in protein homology modeling is to connect the main chain ends of two successive regions, conserved in template and target structures by protein fragments that are as similar to the target as possible. For the development of a new loop prediction method, examples of insertions and deletions were searched automatically in data sets of structurally aligned protein pairs. Three different criteria were applied for the determination of the positions where the main chain conformations of the proteins begin to differ, i.e., the anchoring groups of the insertions and deletions, giving three test data sets. The target structures in these data sets were predicted by inserting fragments from different fragment data banks between the anchoring groups of the templates. The proposals of matching fragments were sorted with decreasing correspondence in the geometry of the anchoring groups. For assessment of the prediction quality, the template loops were substituted by the proposed ones, and their root mean square deviations to the target structures were determined. In addition, the best 20 fragments in the whole loop data bank used-those with the lowest deviations from the target structures after insertion into the templates-were determined and compared with the proposals. The analysis of the results shows limitations of knowledge-based loop prediction. It is demonstrated that the selection of the anchoring groups is the most important step in the whole procedure. Proteins 1999;37:56-64. Copyright 1999 Wiley-Liss, Inc.

Mesh:

Year:  1999        PMID: 10451550

Source DB:  PubMed          Journal:  Proteins        ISSN: 0887-3585


  9 in total

1.  Ab initio computational modeling of long loops in G-protein coupled receptors.

Authors:  Sandhya Kortagere; Amitava Roy; Ernest L Mehler
Journal:  J Comput Aided Mol Des       Date:  2006-09-14       Impact factor: 3.686

2.  Analysis of loop boundaries using different local structure assignment methods.

Authors:  Manoj Tyagi; Aurélie Bornot; Bernard Offmann; Alexandre G de Brevern
Journal:  Protein Sci       Date:  2009-09       Impact factor: 6.725

3.  CODA: a combined algorithm for predicting the structurally variable regions of protein models.

Authors:  C M Deane; T L Blundell
Journal:  Protein Sci       Date:  2001-03       Impact factor: 6.725

4.  Structure prediction of loops with fixed and flexible stems.

Authors:  A Subramani; C A Floudas
Journal:  J Phys Chem B       Date:  2012-03-02       Impact factor: 2.991

5.  A supersecondary structure library and search algorithm for modeling loops in protein structures.

Authors:  Narcis Fernandez-Fuentes; Baldomero Oliva; András Fiser
Journal:  Nucleic Acids Res       Date:  2006-04-14       Impact factor: 16.971

6.  SL2: an interactive webtool for modeling of missing segments in proteins.

Authors:  Jochen Ismer; Alexander S Rose; Johanna K S Tiemann; Andrean Goede; Robert Preissner; Peter W Hildebrand
Journal:  Nucleic Acids Res       Date:  2016-04-21       Impact factor: 16.971

7.  LoopIng: a template-based tool for predicting the structure of protein loops.

Authors:  Mario Abdel Messih; Rosalba Lepore; Anna Tramontano
Journal:  Bioinformatics       Date:  2015-08-06       Impact factor: 6.937

8.  A self-organizing algorithm for modeling protein loops.

Authors:  Pu Liu; Fangqiang Zhu; Dmitrii N Rassokhin; Dimitris K Agrafiotis
Journal:  PLoS Comput Biol       Date:  2009-08-21       Impact factor: 4.475

9.  Loop modeling: Sampling, filtering, and scoring.

Authors:  Cinque S Soto; Marc Fasnacht; Jiang Zhu; Lucy Forrest; Barry Honig
Journal:  Proteins       Date:  2008-02-15
  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.