Biomedical subjects

A G de Brevern

Publications and source records attributed to A G de Brevern.

5 recordsLinked to original sources

PubMed2006

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

Encoding protein 3D structures into 1D string using short structural prototypes or structural alphabets opens a new front for structure comparison and analysis. Using the well-documented 16 motifs of Protein Blocks (PBs) as structural alphabet, we have developed a methodology to compare protein structures that are encoded as sequences of PBs by aligning them using dynamic programming which uses a substitution matrix for PBs. This methodology is implemented in the applications available in Protein Block Expert (PBE) server. PBE addresses common issues in the field of protein structure analysis such as comparison of proteins structures and identification of protein structures in structural databanks that resemble a given structure. PBE-T provides facility to transform any PDB file into sequences of PBs. PBE-ALIGNc performs comparison of two protein structures based on the alignment of their corresponding PB sequences. PBE-ALIGNm is a facility for mining SCOP database for similar structures based on the alignment of PBs. Besides, PBE provides an interface to a database (PBE-SAdb) of preprocessed PB sequences from SCOP culled at 95% and of all-against-all pairwise PB alignments at family and superfamily levels. PBE server is freely available at http://bioinformatics.univ-reunion.fr/PBE/.

Amino Acid Motifs↗

PubMed2006

Protein Peeling 2: a web server to convert protein structures into series of protein units.

Protein Peeling 2 (PP2) is a web server for the automatic identification of protein units (PUs) given the 3D coordinates of a protein. PUs are an intermediate level of protein structure description between protein domains and secondary structures. It is a new tool to better understand and analyze the organization of protein structures. PP2 uses only the matrices of protein contact probabilities and cuts the protein structures optimally using Matthews' coefficient correlation. An index assesses the compactness quality of each PU. Results are given both textually and graphically using JMol and PyMol softwares. The server can be accessed from http://www.ebgm.jussieu.fr/~gelly/index.html.

Computer Graphics↗

PubMed2005

A structural model of a seven-transmembrane helix receptor: the Duffy antigen/receptor for chemokine (DARC).

The Duffy antigen/receptor for chemokine (DARC) is an erythrocyte receptor for malaria parasites (Plasmodium vivax and Plasmodium knowlesi) and for chemokines. In contrast to other chemokine receptors, DARC is a promiscuous receptor that binds chemokines of both CC and CXC classes. The four extracellular domains (ECDs) of DARC are essential for its interaction with chemokines, whilst the first (ECD1) is sufficient for the interaction with malaria erythrocyte-binding protein. In this study, we elaborate and analyze structural models of the DARC. The construction of the 3D models is based on a comparative modeling process and on the use of many procedures to predict transmembrane segments and to detect far homologous proteins with known structures. Threading, ab initio, secondary structure and Protein Blocks approaches are used to build a very large number of models. The conformational exploration of the ECDs is performed with simulated annealing. The second and fourth ECDs are strongly constrained. On the contrary, the ECD1 is highly flexible, but seems composed of three consecutive regions: a small beta-sheet, a linker region and a structured loop. The chosen structural models encompass most of the biochemical features and reflect the known experimental data. They may be used to analyze functional interaction properties.

Amino Acid Sequence↗

PubMed2003

'Hybrid protein model' for optimally defining 3D protein structure fragments.

MOTIVATION: Our aim is to develop a process that automatically defines a repertory of contiguous 3D protein structure fragments and can be used in homology modeling. We present here improvements to the method we introduced previously: the 'hybrid protein model' (de Brevern and Hazout, THEOR: Chem. Acc., 106, 36-47, (2001)) The hybrid protein learns a non-redundant databank encoded in a structural alphabet composed of 16 Protein Blocks (PBs; de Brevern et al., Proteins, 41, 271-287, (2000)). Every local fold is learned by looking for the most similar pattern present in the hybrid protein and modifying it slightly. Finally each position corresponds to a cluster of similar 3D local folds. RESULTS: In this paper, we describe improvements to our method for building an optimal hybrid protein: (i) 'baby training,' which is defined as the introduction of large structure fragments and the progressive reduction in the size of training fragments; and (ii) the deletion of the redundant parts of the hybrid protein. This repertory of contiguous 3D protein structure fragments should be a useful tool for molecular modeling.

Algorithms↗

PubMed2000

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

By using an unsupervised cluster analyzer, we have identified a local structural alphabet composed of 16 folding patterns of five consecutive C(alpha) ("protein blocks"). The dependence that exists between successive blocks is explicitly taken into account. A Bayesian approach based on the relation protein block-amino acid propensity is used for prediction and leads to a success rate close to 35%. Sharing sequence windows associated with certain blocks into "sequence families" improves the prediction accuracy by 6%. This prediction accuracy exceeds 75% when keeping the first four predicted protein blocks at each site of the protein. In addition, two different strategies are proposed: the first one defines the number of protein blocks in each site needed for respecting a user-fixed prediction accuracy, and alternatively, the second one defines the different protein sites to be predicted with a user-fixed number of blocks and a chosen accuracy. This last strategy applied to the ubiquitin conjugating enzyme (alpha/beta protein) shows that 91% of the sites may be predicted with a prediction accuracy larger than 77% considering only three blocks per site. The prediction strategies proposed improve our knowledge about sequence-structure dependence and should be very useful in ab initio protein modelling.

Artificial Intelligence↗