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Protein structures are highly dynamic macromolecules. This dynamics is often analysed through experimental and/or computational methods only for an isolated or a limited number of proteins. Here, we explore large-scale protein dynamics simulation to observe dynamics of local protein conformations using different perspectives. We analysed molecular dynamics to investigate protein flexibility locally, using classical approaches such as RMSf, solvent accessibility, but also innovative approaches such as local entropy. First, we focussed on classical secondary structures and analysed specifically how β-strand, β-turns, and bends evolve during molecular simulations. We underlined interesting specific bias between β-turns and bends, which are considered as the same category, while their dynamics show differences. Second, we used a structural alphabet that is able to approximate every part of the protein structures conformations, namely protein blocks (PBs) to analyse (i) how each initial local protein conformations evolve during dynamics and (ii) if some exchange can exist among these PBs. Interestingly, the results are largely complex than simple regular/rigid and coil/flexible exchange. AbbreviationsNeqnumber of equivalentPBProtein BlocksPDBProtein DataBankRMSfroot mean square fluctuationsCommunicated by Ramaswamy H. Sarma.

Knowing the position of protein structures within the membrane is crucial for fundamental and applied research in the field of molecular biology. Only few web resources propose coordinate files of oriented transmembrane proteins, and these exclude predicted structures, although they represent the largest part of the available models. In this article, we present TMPL (http://www.dsimb.inserm.fr/TMPL/), a database of transmembrane protein structures (α-helical and β-sheet) positioned in the lipid bilayer. It is the first database to include theoretical models of transmembrane protein structures, making it a large repository with more than 11 000 entries. The TMPL database also contains experimentally solved protein structures, which are available as either atomistic or coarse-grained models. A unique feature of TMPL is the possibility for users to update the database by uploading, through an intuitive web interface, the membrane assignments they can obtain with our recent OREMPRO web server.

International audience; Brain glycogen metabolism plays a critical role in major brain functions such as learning or memory consolidation. However, alteration of glycogen metabolism and glycogen accumulation in the brain contributes to neurodegeneration as observed in Lafora disease. Glycogen phosphorylase (GP), a key enzyme in glycogen metabolism, catalyzes the rate-limiting step of glyco-gen mobilization. Moreover, the allosteric regulation of the three GP isozymes (muscle, liver, and brain) by metabolites and phosphorylation, in response to hormonal signaling, fine-tunes glycogenolysis to fulfill energetic and metabolic requirements. Whereas the structures of muscle and liver GPs have been known for decades, the structure of brain GP (bGP) has remained elusive despite its critical role in brain glycogen metabolism. Here, we report the crystal structure of human bGP in complex with PEG 400 (2.5 A ˚) and in complex with its allos-teric activator AMP (3.4 A ˚). These structures demonstrate that bGP has a closer structural relationship with muscle GP, which is also activated by AMP, contrary to liver GP, which is not. Importantly, despite the structural similarities between human bGP and the two other mammalian isozymes, the bGP structures reveal molecular features unique to the brain isozyme that provide a deeper understanding of the differences in the activation properties of these allosteric enzymes by the allosteric effector AMP. Overall, our study further supports that the distinct structural and regulatory properties of GP isozymes contribute to the different functions of muscle, liver, and brain glycogen.

International audience; Brain glycogen metabolism plays a critical role in major brain functions such as learning or memory consolidation. However, alteration of glycogen metabolism and glycogen accumulation in the brain contributes to neurodegeneration as observed in Lafora disease. Glycogen phosphorylase (GP), a key enzyme in glycogen metabolism, catalyzes the rate-limiting step of glyco-gen mobilization. Moreover, the allosteric regulation of the three GP isozymes (muscle, liver, and brain) by metabolites and phosphorylation, in response to hormonal signaling, fine-tunes glycogenolysis to fulfill energetic and metabolic requirements. Whereas the structures of muscle and liver GPs have been known for decades, the structure of brain GP (bGP) has remained elusive despite its critical role in brain glycogen metabolism. Here, we report the crystal structure of human bGP in complex with PEG 400 (2.5 A ˚) and in complex with its allos-teric activator AMP (3.4 A ˚). These structures demonstrate that bGP has a closer structural relationship with muscle GP, which is also activated by AMP, contrary to liver GP, which is not. Importantly, despite the structural similarities between human bGP and the two other mammalian isozymes, the bGP structures reveal molecular features unique to the brain isozyme that provide a deeper understanding of the differences in the activation properties of these allosteric enzymes by the allosteric effector AMP. Overall, our study further supports that the distinct structural and regulatory properties of GP isozymes contribute to the different functions of muscle, liver, and brain glycogen.

International audience

International audience; A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions. Proteins 2009. (c) 2009 Wiley-Liss, Inc.

International audience; A relevant and accurate description of three-dimensional (3D) protein structures can be achieved by characterizing recurrent local structures. In a previous study, we developed a library of 120 3D structural prototypes encompassing all known 11-residues long local protein structures and ensuring a good quality of structural approximation. A local structure prediction method was also proposed. Here, overlapping properties of local protein structures in global ones are taken into account to characterize frequent local networks. At the same time, we propose a new long local structure prediction strategy which involves the use of evolutionary information coupled with Support Vector Machines (SVMs). Our prediction is evaluated by a stringent geometrical assessment. Every local structure prediction with a Calpha RMSD less than 2.5 A from the true local structure is considered as correct. A global prediction rate of 63.1% is then reached, corresponding to an improvement of 7.7 points compared with the previous strategy. In the same way, the prediction of 88.33% of the 120 structural classes is improved with 8.65% mean gain. 85.33% of proteins have better prediction results with a 9.43% average gain. An analysis of prediction rate per local network also supports the global improvement and gives insights into the potential of our method for predicting super local structures. Moreover, a confidence index for the direct estimation of prediction quality is proposed. Finally, our method is proved to be very competitive with cutting-edge strategies encompassing three categories of local structure predictions. Proteins 2009. (c) 2009 Wiley-Liss, Inc.

The description of protein 3D structure usually focuses on the repetitive local folds (alpha-helices and beta-sheets). The remaining class, sometimes called unordered region, has often been considered as random (one also calls it ‘random coil'). However, some interesting local folds are also highly recurrent and definitely more structured than a real random region. One of such particularly interesting motif is tight turn; this latter is characterized by few residues (3 to 5) and by the reversal of the polypeptide chain. This reversal of the protein backbone plays an important role in the topology of peptides or proteins, and can be directly associated to biological functions. Tight turns are characterized by precise dihedral angle values of their central residues and short distance between their extremities (implying thus the reversal). Among all tight turns, gamma-turns are the most important and studied class. They are made of 4 residues and account for 25% of all residues in proteins. According to the values of the dihedral angles of the two central residues, several types are defined. Some specific types have been shown to be involved in specific biological functions. Like for secondary structure, turn prediction has greatly evolved these last decades. Various methods have been used for predicting their presence such as statistical ones, artificial neural networks (ANN) or very recently support vector machines (SVM). In the 1970's, the first methods used only the amino-acid sequence to predict the presence of turns. Some great improvements were made by incorporating evolutionary information and by using predicted “classical” (i.e. alpha-helices, beta-sheets and coil) secondary structure. Some of the latest methods go further and predict not only the presence or absence of turns but also their type. In this paper, we briefly review the discovery and evolution of tight turn definition; we then present the different prediction methods and highlight the importance of the statistical evaluation of the results.

The considerable increase of the protein structural database allows to cross the line from the classical secondary structure description of proteins. While still confronted with numerous problems, defining structural alphabets is an emerging concept in the field of protein structure analysis. It is an attempt to objectively classify the whole set of conformations occurring in protein structures described by small overlapping fragments. It is expected to lead to a better understanding of protein architecture and to open new opportunities for protein structure prediction.

The considerable increase of the protein structural database allows to cross the line from the classical secondary structure description of proteins. While still confronted with numerous problems, defining structural alphabets is an emerging concept in the field of protein structure analysis. It is an attempt to objectively classify the whole set of conformations occurring in protein structures described by small overlapping fragments. It is expected to lead to a better understanding of protein architecture and to open new opportunities for protein structure prediction.

The considerable increase of the protein structural database allows to cross the line from the classical secondary structure description of proteins. While still confronted with numerous problems, defining structural alphabets is an emerging concept in the field of protein structure analysis. It is an attempt to objectively classify the whole set of conformations occurring in protein structures described by small overlapping fragments. It is expected to lead to a better understanding of protein architecture and to open new opportunities for protein structure prediction.

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.

The dopamine D2 receptor (D2R) is a G-proteins coupled receptor (GPCR) localized in the nervous system. The alteration of its activity is involved in psychiatric pathologies such as schizophrenia and studies have shown that the membrane lipid composition had an impact on the pharmacological properties of D2R. Indeed, the presence of polyunsaturated fatty acids (PUFAs) of the omega-3 and omega-6 family influences the affinity of the receptor for some antipsychotics – aspect related to conformational changes. The use of digital simulation technique such as coarse grain molecular dynamics (CGMD) would make it possible to look into these changes since this method, combined with enhanced computer power, currently allows the simulation of systems of biologically relevant size (submicrometric) and timescale (microsecond or millisecond). Through our studies, we have been able to determine the behavior of various omega-3 and omega-6 polyunsaturated lipids (PUL) as well as RD2, and we have identified the helices preferentially contacted by these PUL and cholesterol, which is an important component of the membranes, Le récepteur dopaminergique D2 (RD2) est un récepteur couplé aux protéines G (RCPG) qui est localisé dans le système nerveux. L'altération de son activité est impliquée dans certaines pathologies psychiatriques telles que la schizophrénie et des études ont montré que la composition lipidique de la membrane avait un impact sur les propriétés pharmacologiques du RD2. En effet, la présence d’acides gras polyinsaturés (AGPI) de la famille des oméga 3 et oméga 6 influence l’affinité du récepteur pour certains antipsychotiques, aspect lié aux changements conformationnels. L’utilisation de techniques de simulation numérique telles que la dynamique moléculaire gros grains (DMGG) permettrait d’examiner ces modifications puisque cette méthode, associée à une puissance informatique accrue, permet actuellement de simuler à grande échelle de temps (microseconde ou milliseconde) des systèmes de taille biologiquement pertinente (submicrométrique). Par nos études, nous avons pu déterminer le comportement de divers lipides polyinsaturés (LPI) de type oméga 3 et oméga 6 ainsi que de RD2, et nous avons identifié les hélices préférentiellement contactées par ces LPI et le cholestérol qui est un composant important des membranes.