Literature DB >> 17565494

A reduced amino acid alphabet for understanding and designing protein adaptation to mutation.

C Etchebest1, C Benros, A Bornot, A-C Camproux, A G de Brevern.   

Abstract

Protein sequence world is considerably larger than structure world. In consequence, numerous non-related sequences may adopt similar 3D folds and different kinds of amino acids may thus be found in similar 3D structures. By grouping together the 20 amino acids into a smaller number of representative residues with similar features, sequence world simplification may be achieved. This clustering hence defines a reduced amino acid alphabet (reduced AAA). Numerous works have shown that protein 3D structures are composed of a limited number of building blocks, defining a structural alphabet. We previously identified such an alphabet composed of 16 representative structural motifs (5-residues length) called Protein Blocks (PBs). This alphabet permits to translate the structure (3D) in sequence of PBs (1D). Based on these two concepts, reduced AAA and PBs, we analyzed the distributions of the different kinds of amino acids and their equivalences in the structural context. Different reduced sets were considered. Recurrent amino acid associations were found in all the local structures while other were specific of some local structures (PBs) (e.g Cysteine, Histidine, Threonine and Serine for the alpha-helix Ncap). Some similar associations are found in other reduced AAAs, e.g Ile with Val, or hydrophobic aromatic residues Trp with Phe and Tyr. We put into evidence interesting alternative associations. This highlights the dependence on the information considered (sequence or structure). This approach, equivalent to a substitution matrix, could be useful for designing protein sequence with different features (for instance adaptation to environment) while preserving mainly the 3D fold.

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Year:  2007        PMID: 17565494     DOI: 10.1007/s00249-007-0188-5

Source DB:  PubMed          Journal:  Eur Biophys J        ISSN: 0175-7571            Impact factor:   1.733


  71 in total

1.  The Protein Data Bank.

Authors:  H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  Bayesian probabilistic approach for predicting backbone structures in terms of protein blocks.

Authors:  A G de Brevern; C Etchebest; S Hazout
Journal:  Proteins       Date:  2000-11-15

3.  A metric model of amino acid substitution.

Authors:  Weijia Xu; Daniel P Miranker
Journal:  Bioinformatics       Date:  2004-02-10       Impact factor: 6.937

4.  A substitution matrix for structural alphabet based on structural alignment of homologous proteins and its applications.

Authors:  Manoj Tyagi; Venkataraman S Gowri; Narayanaswamy Srinivasan; Alexandre G de Brevern; Bernard Offmann
Journal:  Proteins       Date:  2006-10-01

Review 5.  Studies of folding and misfolding using simplified models.

Authors:  Nikolay V Dokholyan
Journal:  Curr Opin Struct Biol       Date:  2006-01-18       Impact factor: 6.809

6.  A 3D building blocks approach to analyzing and predicting structure of proteins.

Authors:  R Unger; D Harel; S Wherland; J L Sussman
Journal:  Proteins       Date:  1989

7.  Origins of structure in globular proteins.

Authors:  H S Chan; K A Dill
Journal:  Proc Natl Acad Sci U S A       Date:  1990-08       Impact factor: 11.205

8.  Local protein structure prediction using discriminative models.

Authors:  Oliver Sander; Ingolf Sommer; Thomas Lengauer
Journal:  BMC Bioinformatics       Date:  2006-01-11       Impact factor: 3.169

9.  Protein Block Expert (PBE): a web-based protein structure analysis server using a structural alphabet.

Authors:  M Tyagi; P Sharma; C S Swamy; F Cadet; N Srinivasan; A G de Brevern; B Offmann
Journal:  Nucleic Acids Res       Date:  2006-07-01       Impact factor: 16.971

10.  Use of a structural alphabet for analysis of short loops connecting repetitive structures.

Authors:  Laurent Fourrier; Cristina Benros; Alexandre G de Brevern
Journal:  BMC Bioinformatics       Date:  2004-05-12       Impact factor: 3.169

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  26 in total

1.  Prediction of ketoacyl synthase family using reduced amino acid alphabets.

Authors:  Wei Chen; Pengmian Feng; Hao Lin
Journal:  J Ind Microbiol Biotechnol       Date:  2011-10-26       Impact factor: 3.346

2.  A comparison of genotype-phenotype maps for RNA and proteins.

Authors:  Evandro Ferrada; Andreas Wagner
Journal:  Biophys J       Date:  2012-04-18       Impact factor: 4.033

3.  A new prediction strategy for long local protein structures using an original description.

Authors:  Aurélie Bornot; Catherine Etchebest; Alexandre G de Brevern
Journal:  Proteins       Date:  2009-08-15

4.  β-Bulges: extensive structural analyses of β-sheets irregularities.

Authors:  Pierrick Craveur; Agnel Praveen Joseph; Joseph Rebehmed; Alexandre G de Brevern
Journal:  Protein Sci       Date:  2013-09-14       Impact factor: 6.725

5.  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

Review 6.  In silico studies on DARC.

Authors:  Alexandre G de Brevern; Ludovic Autin; Yves Colin; Olivier Bertrand; Catherine Etchebest
Journal:  Infect Disord Drug Targets       Date:  2009-06

7.  A short survey on protein blocks.

Authors:  Agnel Praveen Joseph; Garima Agarwal; Swapnil Mahajan; Jean-Christophe Gelly; Lakshmipuram S Swapna; Bernard Offmann; Frédéric Cadet; Aurélie Bornot; Manoj Tyagi; Hélène Valadié; Bohdan Schneider; Catherine Etchebest; Narayanaswamy Srinivasan; Alexandre G De Brevern
Journal:  Biophys Rev       Date:  2010-08-05

8.  Clustering of protein families into functional subtypes using Relative Complexity Measure with reduced amino acid alphabets.

Authors:  Aydin Albayrak; Hasan H Otu; Ugur O Sezerman
Journal:  BMC Bioinformatics       Date:  2010-08-18       Impact factor: 3.169

9.  Novel hydrophobins from Trichoderma define a new hydrophobin subclass: protein properties, evolution, regulation and processing.

Authors:  Verena Seidl-Seiboth; Sabine Gruber; Ugur Sezerman; Torsten Schwecke; Aydin Albayrak; Torsten Neuhof; Hans von Döhren; Scott E Baker; Christian P Kubicek
Journal:  J Mol Evol       Date:  2011-03-22       Impact factor: 2.395

Review 10.  Folding by numbers: primary sequence statistics and their use in studying protein folding.

Authors:  Brent Wathen; Zongchao Jia
Journal:  Int J Mol Sci       Date:  2009-04-08       Impact factor: 6.208

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