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de Brevern, A. G.

Publications and source records attributed to de Brevern, A. G..

2 recordsLinked to original sources

PBxplore: A Tool To Analyze Local Protein Structure And Deformability With Protein Blocks

Proteins are highly dynamic macromolecules. A classical way to analyze their inner flexibility is to perform molecular dynamics simulations. In this context, we present the advantage to use small structural prototypes, namely the Protein Blocks (PBs). PBs give a good approximation of the local structure of the protein backbone. More importantly, by reducing the conformational complexity of protein structures, they allow analyzes of local protein deformability which cannot be done with other methods and had been used efficiently in different applications. PBxplore is a suite of tools to analyze the dynamics and deformability of protein structures using PBs. It is able to process large amount of data such as those produced by molecular dynamics simulations. It produces various outputs with text and graphics, such as frequencies, entropy and information logo. PBxplore is available at https://github.com/pierrepo/PBxplore and is released under the open-source MIT license.

bioinformatics

PB-kPRED: Knowledge-Based Prediction Of Protein Backbone Conformation Using A Structural Alphabet

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks (PBs), is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of PBs. Thus, predicting the local structure of a protein in terms of protein blocks is a step towards the objective of predicting its 3-D structure. Here a new approach, kPred, is proposed towards this aim that is independent of the evolutionary information available. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) apply a purely knowledge-based algorithm, not relying on secondary structure predictions or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures.\n\nBased on the strategy used for scanning the database, the method was able to achieve efficient mean Q16 accuracies between 40.8% and 66.3% for a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. The impact of these scanning strategies on the prediction was evaluated and is discussed. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

bioinformatics