Protein short loop prediction in terms of a structural alphabet

Tyagi, Manoj; Bornot, Aurélie; Offmann, Bernard; Brevern, Alexandre G. de

doi:10.1016/j.compbiolchem.2009.06.002

Cited by 22 publications

(22 citation statements)

References 37 publications

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“…-PBs prediction and influence of specie context. 100 independent simulations have been carried out to predict PBs using a Bayesian prediction procedure (de Brevern et al, 2000;Etchebest et al, 2005;Tyagi et al, 2009b). From left to right (x axis), each label (NR, Sc, At and Pf) indicates databank on which the training is carried out.…”

Section: Resultsmentioning

confidence: 99%

“…The three specialized databanks (Sc, At and Pf) represents 202,410, 173,450 and 36,175 amino acid residues respectively. With regards to our previous studies de Brevern, 2005;de Brevern et al, 2000;de Brevern et al, 2002;de Brevern et al, 2004;Etchebest et al, 2005;Fourrier et al, 2004), the number of amino acids in each databank is sufficient to analyze the sequence -structure relationships in terms of secondary structure and Protein Blocks (Tyagi et al, 2009b).…”

Section: Analysis Of the Databanksmentioning

confidence: 99%

“…It gives an idea of the influence of species specific preferences on prediction efficiency. 100 independent simulations have been carried out, as in one of our earlier works (Tyagi et al, 2009b). Figure 6 summarizes the results of this analysis.…”

Section: Pilot Studymentioning

confidence: 99%

“…They have been used to describe the 3D protein backbones (de Brevern, 2005) and to perform local structure prediction (Benros et al, 2009;de Brevern et al, 2000;de Brevern et al, 2004;Dong et al, 2008;Etchebest et al, 2005;Rangwala et al, 2009;Zimmermann and Hansmann, 2008). PB based approaches have been proved to be effective in prediction based on protein sequence-structure relationships Li et al, 2009;Tyagi et al, 2009b). Several other applications based on the PB structural alphabet have also given successful results Dudev and Lim, 2007;Faure et al, 2009;Thomas et al, 2006;Tyagi et al, 2008).…”

Section: Protein Blocksmentioning

confidence: 99%

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Species specific amino acid sequence–protein local structure relationships: An analysis in the light of a structural alphabet

Brevern

Joseph

2011

Journal of Theoretical Biology

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To cite this version:Alexandre De Brevern, Agnel Praveen Joseph. Species specific amino acid sequence-protein local structure relationships: An analysis in the light of a structural alphabet.: species sequence -structure relationship. Journal of Theoretical Biology, Elsevier, 2011, 276 (1) AbstractProtein structure analysis and prediction methods are based on non-redundant data extracted from the available protein structures, regardless of the species from which the protein originates. Hence, these datasets represent the global knowledge on protein folds, which constitutes a generic distribution of amino acid sequence -protein structure (AAS-PS) relationships. In this study, we try to elucidate whether the AAS-PS relationship could possess specificities depending on the specie.For this purpose, we have chosen three different species: Saccharomyces cerevisiae, Plasmodium falciparum and Arabidopsis thaliana. We analyzed the AAS-PS behaviors of the proteins from these three species and compared it to the "expected" distribution of a classical non-redundant databank. With the classical secondary structure description, only slight differences in amino acid preferences could be observed. With a more precise description of local protein structures (Protein Blocks), significant changes could be highlighted.Saccharomyces cerevisiae's AAS-PS relationship is close to the general distribution, while striking differences are observed in the case of Arabidopsis thaliana. Plasmodium falciparum is the most distant one.This study presents some interesting view-points on AAS-PS relationship. Certain species exhibit unique preferences for amino acids to be associated with protein local structural elements. Thus, AAS-PS relationships are species dependant. These results can give useful insights for improving prediction methodologies which take the species specific information into account.

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Section: Resultsmentioning

confidence: 99%

Section: Analysis Of the Databanksmentioning

confidence: 99%

Section: Pilot Studymentioning

confidence: 99%

Section: Protein Blocksmentioning

confidence: 99%

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Species specific amino acid sequence–protein local structure relationships: An analysis in the light of a structural alphabet

Brevern

Joseph

2011

Journal of Theoretical Biology

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“…They are labeled from a to p (cf. Figure 1 of (Tyagi et al 2009b)). They were obtained by an unsupervised classifier similar to Kohonen Maps (Kohonen 1982;2001) and Hidden Markov Models (Rabiner 1989).…”

Section: Protein Blocksmentioning

confidence: 99%

Influence of assignment on the prediction of transmembrane helices in protein structures

et al. 2010

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Abstract-helical transmembrane proteins (TMP ) are composed of series of helices embedded in the lipid bilayer. Due to technical difficulties, few 3D structures are available. Therefore the design of structural models of TMP is of major interest. In this work, we study secondary structures of TMP by analyzing the influence of secondary structures assignment methods (SSAMs). For this purpose, a published and updated benchmark databank of TMP is used and several SSAMs (9) are evaluated.The analysis of the results points out significant differences in SSA depending on methods used.Pairwise comparisons between SSAMs led to more than 10% of disagreement. Helical regions corresponding to transmembrane zones are often correctly characterized. The study of the sequencestructure relationship shows very limited differences with regards to the structural disagreement.Secondary structure prediction based on Bayes' rule and using only a single sequence give correct prediction rates ranging from 78 to 81%. A structural alphabet approach gives a slightly better prediction, i.e., only 2% less than the best equivalent approach whereas the prediction rate with a very different assignment bypasses 86%. This last result highlights the importance of the correct assignment choice to evaluate the prediction assessment.

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