Literature DB >> 15320732

Integrating protein secondary structure prediction and multiple sequence alignment.

V A Simossis1, J Heringa.   

Abstract

Modern protein secondary structure prediction methods are based on exploiting evolutionary information contained in multiple sequence alignments. Critical steps in the secondary structure prediction process are (i) the selection of a set of sequences that are homologous to a given query sequence, (ii) the choice of the multiple sequence alignment method, and (iii) the choice of the secondary structure prediction method. Because of the close relationship between these three steps and their critical influence on the prediction results, secondary structure prediction has received increased attention from the bioinformatics community over the last few years. In this treatise, we discuss recent developments in computational methods for protein secondary structure prediction and multiple sequence alignment, focus on the integration of these methods, and provide some recommendations for state-of-the-art secondary structure prediction in practice.

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Year:  2004        PMID: 15320732     DOI: 10.2174/1389203043379675

Source DB:  PubMed          Journal:  Curr Protein Pept Sci        ISSN: 1389-2037            Impact factor:   3.272


  13 in total

1.  A Consensus Data Mining secondary structure prediction by combining GOR V and Fragment Database Mining.

Authors:  Taner Z Sen; Haitao Cheng; Andrzej Kloczkowski; Robert L Jernigan
Journal:  Protein Sci       Date:  2006-09-25       Impact factor: 6.725

2.  Evolution of new function through a single amino acid change in the yeast repressor Sum1p.

Authors:  Alexias Safi; Kelley A Wallace; Laura N Rusche
Journal:  Mol Cell Biol       Date:  2008-02-11       Impact factor: 4.272

3.  PRALINE: a multiple sequence alignment toolbox that integrates homology-extended and secondary structure information.

Authors:  V A Simossis; J Heringa
Journal:  Nucleic Acids Res       Date:  2005-07-01       Impact factor: 16.971

4.  MUMMALS: multiple sequence alignment improved by using hidden Markov models with local structural information.

Authors:  Jimin Pei; Nick V Grishin
Journal:  Nucleic Acids Res       Date:  2006-08-26       Impact factor: 16.971

5.  Detailed estimation of bioinformatics prediction reliability through the Fragmented Prediction Performance Plots.

Authors:  Oliviero Carugo
Journal:  BMC Bioinformatics       Date:  2007-10-11       Impact factor: 3.169

6.  MAO: a Multiple Alignment Ontology for nucleic acid and protein sequences.

Authors:  Julie D Thompson; Stephen R Holbrook; Kazutaka Katoh; Patrice Koehl; Dino Moras; Eric Westhof; Olivier Poch
Journal:  Nucleic Acids Res       Date:  2005-07-25       Impact factor: 16.971

7.  New structural insights into Golgi Reassembly and Stacking Protein (GRASP) in solution.

Authors:  Luís F S Mendes; Assuero F Garcia; Patricia S Kumagai; Fabio R de Morais; Fernando A Melo; Livia Kmetzsch; Marilene H Vainstein; Marcio L Rodrigues; Antonio J Costa-Filho
Journal:  Sci Rep       Date:  2016-07-20       Impact factor: 4.379

8.  MARS: improving multiple circular sequence alignment using refined sequences.

Authors:  Lorraine A K Ayad; Solon P Pissis
Journal:  BMC Genomics       Date:  2017-01-14       Impact factor: 3.969

9.  PROMALS3D: a tool for multiple protein sequence and structure alignments.

Authors:  Jimin Pei; Bong-Hyun Kim; Nick V Grishin
Journal:  Nucleic Acids Res       Date:  2008-02-20       Impact factor: 16.971

10.  Systematic mutational analysis of the putative hydrolase PqsE: toward a deeper molecular understanding of virulence acquisition in Pseudomonas aeruginosa.

Authors:  Benjamin Folch; Eric Déziel; Nicolas Doucet
Journal:  PLoS One       Date:  2013-09-10       Impact factor: 3.240

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