Protein-protein docking based on shape complementarity and Voronoi fingerprint

National audiencePredicting the three-dimensional structures of protein-protein complexes is a major challenge for computational biology. Using a Voronoi tessellation model of protein structure, we showed previously that it was possible to use an evolutionary algorithm to train a scoring function to distinguish reliably between native and non-native docking conformations. Here, we show that this approach can be further improved by combining it with rigid body docking predictions generated by the Hex docking algorithm. This new approach is able to rank an acceptable or better conformation within the top 10 predictions for 7 out of the 9 targets available from rounds 8 to 18 of the CAPRI docking experiment.La prédiction de la structure tri-dimensionnelle des complexes protéine-protéine est un enjeu majeur pour la bioinformatique. Nous avions montré dans des travaux précédents que grâce à la modélisation par un diagramme de Voronoï de la structure des protéines, et à l'utilisation d'algorithmes génétiques, il était possible d'optimiser des fonctions de score permettant de distinguer avec une bonne fiabilité les conformations natives des conformations non-natives. Nous montrons dans cet article que cette approche peut être sensiblement améliorée en combinant celle-ci avec des modèles en corps rigide générés par l'algorithme de docking Hex. Cette nouvelle approche, testée sur les cibles CAPRI des rounds 8 à 18, permet de classer dans les 10 meilleures, une conformation quasi-native pour 7 cibles sur les 9 disponibles

Unraveling the molecular architecture of a G protein-coupled receptor/β-arrestin/Erk module complex

International audienceβ-arrestins serve as signaling scaffolds downstream of G protein-coupled receptors, and thus play a crucial role in a plethora of cellular processes. Although it is largely accepted that the ability of β-arrestins to interact simultaneously with many protein partners is key in G protein-independent signaling of GPCRs, only the precise knowledge of these multimeric arrangements will allow a full understanding of the dynamics of these interactions and their functional consequences. However, current experimental procedures for the determination of the three-dimensional structures of protein-protein complexes are not well adapted to analyze these short-lived, multi-component assemblies. We propose a model of the receptor/β-arrestin/Erk1 signaling module, which is consistent with most of the available experimental data. Moreover, for the β-arrestin/Raf1 and the β-arrestin/ERK interactions, we have used the model to design interfering peptides and shown that they compete with both partners, hereby demonstrating the validity of the predicted interaction regions

A Hybrid Classification Approach based on FCA and Emerging Patterns - An application for the classification of biological inhibitors

International audienceClassification is an important task in data analysis and learning. Classification can be performed using supervised or unsupervised methods. From the unsupervised point of view, Formal Concept Analysis (FCA) can be used for such a task in an efficient and well-founded way. From the supervised point of view, emerging patterns rely on pattern mining and can be used to characterize classes of objects w.r.t. a priori labels. In this paper, we present a hybrid classification method which is based both on supervised and unsupervised aspects. This method relies on FCA for building a concept lattice and then detects the concepts whose extents determines classes of objects sharing the same labels. These classes can then be used as reference classes for classifying unknown objects. This hybrid approach has been used in an experiment in chemistry for classifying inhibitors of the c-Met protein which plays an important role in protein interactions and in the development of cancer

A Collaborative Filtering Approach for Protein-Protein Docking Scoring Functions

A protein-protein docking procedure traditionally consists in two successive tasks: a search algorithm generates a large number of candidate conformations mimicking the complex existing in vivo between two proteins, and a scoring function is used to rank them in order to extract a native-like one. We have already shown that using Voronoi constructions and a well chosen set of parameters, an accurate scoring function could be designed and optimized. However to be able to perform large-scale in silico exploration of the interactome, a near-native solution has to be found in the ten best-ranked solutions. This cannot yet be guaranteed by any of the existing scoring functions

Community-Wide Assessment of Protein-Interface Modeling Suggests Improvements to Design Methodology

The CAPRI and CASP prediction experiments have demonstrated the power of community wide tests of methodology in assessing the current state of the art and spurring progress in the very challenging areas of protein docking and structure prediction. We sought to bring the power of community wide experiments to bear on a very challenging protein design problem that provides a complementary but equally fundamental test of current understanding of protein-binding thermodynamics. We have generated a number of designed protein-protein interfaces with very favorable computed binding energies but which do not appear to be formed in experiments, suggesting there may be important physical chemistry missing in the energy calculations. 28 research groups took up the challenge of determining what is missing: we provided structures of 87 designed complexes and 120 naturally occurring complexes and asked participants to identify energetic contributions and/or structural features that distinguish between the two sets. The community found that electrostatics and solvation terms partially distinguish the designs from the natural complexes, largely due to the non-polar character of the designed interactions. Beyond this polarity difference, the community found that the designed binding surfaces were on average structurally less embedded in the designed monomers, suggesting that backbone conformational rigidity at the designed surface is important for realization of the designed function. These results can be used to improve computational design strategies, but there is still much to be learned; for example, one designed complex, which does form in experiments, was classified by all metrics as a non-binder

Exploitation des algorithmes génétiques pour la prédiction de structure de complexe protéine-protéine

Using genetic algorithms to predict structural protein-protein interactions. Most proteins fulfill their functions through the interaction with one or many partners as nucleic acids, other proteins... Because most of these interactions are transitory, they are difficult to detect experimentally and obtaining the structure of the complex is generally not possible. Consequently, "in silico prediction" of the existence of these interactions and of the structure of the resulting complex has received a lot of attention in the last decade. However, proteins are very complex objects, and classical computing approaches have lead to computer-time consuming methods, whose accuracy is not sufficient for large scale exploration of the so-called "interactome" of different organisms. In this context development of high-throughput prediction methods for protein-protein docking is needed. We present here the implementation of a new method based on: Two types of formalisms: The Voronoi and Laguerre tessellations Two simplified geometric models for coarse-grained modeling of complexes. This leads to computation time more reasonable than in atomic representation. The use and optimization of learning algorithms (genetic algorithms) to isolate the most relevant conformations between two protein partners. An evaluation method based on clustering of meta-attributes calculated at the interface to sort the best subset of candidate conformations.Les fonctions de la majorité des protéines sont subordonnées à l'interaction avec un ou plusieurs partenaires : acides nucléiques, autres protéines... La plupart de ces interactions sont transitoires, difficiles à détecter expérimentalement et leurs structures sont souvent impossible à obtenir. C'est pourquoi la prédiction in silico de l'existence de ces interactions et la structure du complexe résultant ont été l'objet de nombreuses études depuis plus d'une décennie maintenant. Pour autant les protéines sont des objets complexes et les méthodes informatiques classiques sont trop "gourmandes" en temps pour l'exploration à grande échelle de l'interactome des différents organismes. Dans ce contexte de développement d'une méthode de docking protéine-protéine haut débit nous présenterons ici l'implémentation d'une nouvelle méthode d'amarrage, celle-ci est basée sur : l'utilisation de deux types de formalismes : Les tessellations de Voronoï et Laguerre permettant la manipulation de modèles géométriques simplifiés permettant une bonne modélisation des complexes et des temps de calcul plus raisonnable qu'en représentation atomique. L'utilisation et l'optimisation d'algorithmes d'apprentissage (algorithmes génétiques) permettant d'isoler les conformations les plus pertinentes entre deux partenaires protéiques. Une méthode d'évaluation basée sur le clustering de méta-attributs calculés au niveau de l'interface permettant de trier au mieux ce sous-ensemble de conformations candidates

Exploitation des algorithmes génétiques pour la prédiction de structures protéine-protéine

ORSAY-PARIS 11-BU Sciences (914712101) / SudocSudocFranceF

Identification de complexes protéine-protéine par combinaison de classifieurs. Application à Escherichia coli

National audienceWe propose an approach to predict complexes with three proteins (trimers) by using classifiers learnt on protein-protein complexes (dimers). The prediction of trimers relies on two strong biological hypotheses: (i) two orthologous proteins share similar functional characteristics; (ii) two proteins interact as a complex to ensure an essential biological function for the studied species. These two hypotheses are used to describe each pair of proteins with the set of species for which they share an ortholog. A set of quality measures, initially developed for the evaluation of the interest of association rules, is used to evaluate the strength of the link between the two proteins. Our approach has been tested on Eschericha coliNous proposons une approche permettant de prédire des complexes impliquant trois protéines (appelés trimères) à partir de combinaison de classifieurs appris sur des complexes n'impliquant que deux protéines (dimères). La prédiction de ces trimères repose sur deux hypothèses biologiques : (i) deux protéines orthologues présentent des caractéristiques fonctionnelles similaires; (ii) deux protéines interagissant sous la forme d'un complexe sous-tendent une fonction biologique essentielle à l'espèce concernée. Ces deux hypothèses sont exploitées pour décrire chaque paire de protéines par l'ensemble des espèces pour lesquelles elles possèdent un orthologue. Un ensemble de mesures de qualité classiquement utilisées pour évaluer l'intérêt des règles d'association est utilisé pour évaluer la force du lien entre les deux protéines. L'organisme modèle Escherichia coli a été utilisé pour évaluer notre approche

Identification de complexes protéine-protéine par combinaison de classifieurs. Application à Escherichia coli

National audienceWe propose an approach to predict complexes with three proteins (trimers) by using classifiers learnt on protein-protein complexes (dimers). The prediction of trimers relies on two strong biological hypotheses: (i) two orthologous proteins share similar functional characteristics; (ii) two proteins interact as a complex to ensure an essential biological function for the studied species. These two hypotheses are used to describe each pair of proteins with the set of species for which they share an ortholog. A set of quality measures, initially developed for the evaluation of the interest of association rules, is used to evaluate the strength of the link between the two proteins. Our approach has been tested on Eschericha coliNous proposons une approche permettant de prédire des complexes impliquant trois protéines (appelés trimères) à partir de combinaison de classifieurs appris sur des complexes n'impliquant que deux protéines (dimères). La prédiction de ces trimères repose sur deux hypothèses biologiques : (i) deux protéines orthologues présentent des caractéristiques fonctionnelles similaires; (ii) deux protéines interagissant sous la forme d'un complexe sous-tendent une fonction biologique essentielle à l'espèce concernée. Ces deux hypothèses sont exploitées pour décrire chaque paire de protéines par l'ensemble des espèces pour lesquelles elles possèdent un orthologue. Un ensemble de mesures de qualité classiquement utilisées pour évaluer l'intérêt des règles d'association est utilisé pour évaluer la force du lien entre les deux protéines. L'organisme modèle Escherichia coli a été utilisé pour évaluer notre approche