Concepts et méthodes d'analyse numérique de la dynamique des cavités au sein des protéines et applications à l'élaboration de stratégies novatrices d'inhibition

Cavities are the prime location of the interactions between a protein and its ligands, and thus are crucial for its functions, together with its dynamics. Although cavities have been studied since the seventies, specific studies on their dynamical behavior only appeared recently. Few methods can tackle this aspect, despite its interest for virtual screening and drug design. Protein cavities define an extremely labile ensemble. Following one cavity along a trajectory is therefore an arduous task, because it can be subjected to several events of fusions, divisions, apparitions and disappearances. I propose a method to resolve this question, thus enabling systematic and rational dynamical exploitation of protein cavities. This method classify cavities using the atom groups around them, using algorithms and parameters that I identified as giving best results for cavity tracking. To characterize the main directions of evolution of cavity geometry, and to relate them with the dynamics of the underlying structure, I developed a method based on Principal Component Analysis (PCA). This method can be used to select or build conformations with given cavity shapes. Two examples of applications have been treated: the selection of conformations with diverse cavity geometries, and the analysis of the myoglobin cavity network evolution during the diffusion of carbon monoxide in it. These two methods have been used in three projects involving virtual screening, targeting M. tuberculosis DNA-gyrase, P vivax subtilisin 1 and GLIC, an procaryotic model of human pentameric ligand-gated ion channel. These methods allowed us to identify an inhibitor of subtilisin 1 and four effectors of GLIC.Les cavités sont le lieu privilégié des interactions d’une protéine avec ses ligands, et sont donc déterminantes pour sa fonction, elle-même aussi influencée par la dynamique de la protéine. Peu de méthodes permettent d’étudier en détail la dynamique des cavités malgré leur intérêt notamment pour le criblage virtuel. Les cavités d’une protéine définissent un ensemble très labile. Ainsi, suivre une cavité le long d’une trajectoire est ardu car elle peut être sujette à des divisions, fusions, disparitions et apparitions. Je propose une méthode pour résoudre cette question afin d’exploiter la dynamique des cavités de façon systématique et rationnelle, en classifiant les cavités selon les groupes d’atomes les entourant. J’ai identifié les paramètres procurant les meilleurs critères de suivi des cavités. Pour caractériser les évolutions principales de la géométrie des cavités en relation avec la dynamique de la protéine, j’ai développé une méthode basée sur l’Analyse en Composantes Principales. Cette méthode peut être utilisée pour sélectionner ou construire des conformations à partir de la forme de leurs cavités. Deux exemples d’applications sont traitées : la sélection de conformations ayant des cavités de géométries diverses et l’étude de l’évolution des cavités de la myoglobine lors de la diffusion du monoxyde de carbone. Ces deux méthodes ont été utilisées pour trois projets de criblage virtuel ciblant l’ADN-gyrase de M tuberculosis, la subtilisine 1 de P vivax et GLIC, homologue procaryote des récepteurs pentamériques humains. Les molécules sélectionnées à l’aide de ces méthodes ont permis d’identifier une molécule active contre la subtilisine et quatre effecteurs de GLIC

Concepts and methods of numerical analysis of protein cavities dynamics and application to the design of innovative inhibition strategies

Les cavités sont le lieu privilégié des interactions d’une protéine avec ses ligands, et sont donc déterminantes pour sa fonction, elle-même aussi influencée par la dynamique de la protéine. Peu de méthodes permettent d’étudier en détail la dynamique des cavités malgré leur intérêt notamment pour le criblage virtuel. Les cavités d’une protéine définissent un ensemble très labile. Ainsi, suivre une cavité le long d’une trajectoire est ardu car elle peut être sujette à des divisions, fusions, disparitions et apparitions. Je propose une méthode pour résoudre cette question afin d’exploiter la dynamique des cavités de façon systématique et rationnelle, en classifiant les cavités selon les groupes d’atomes les entourant. J’ai identifié les paramètres procurant les meilleurs critères de suivi des cavités. Pour caractériser les évolutions principales de la géométrie des cavités en relation avec la dynamique de la protéine, j’ai développé une méthode basée sur l’Analyse en Composantes Principales. Cette méthode peut être utilisée pour sélectionner ou construire des conformations à partir de la forme de leurs cavités. Deux exemples d’applications sont traitées : la sélection de conformations ayant des cavités de géométries diverses et l’étude de l’évolution des cavités de la myoglobine lors de la diffusion du monoxyde de carbone. Ces deux méthodes ont été utilisées pour trois projets de criblage virtuel ciblant l’ADN-gyrase de M tuberculosis, la subtilisine 1 de P vivax et GLIC, homologue procaryote des récepteurs pentamériques humains. Les molécules sélectionnées à l’aide de ces méthodes ont permis d’identifier une molécule active contre la subtilisine et quatre effecteurs de GLIC.Cavities are the prime location of the interactions between a protein and its ligands, and thus are crucial for its functions, together with its dynamics. Although cavities have been studied since the seventies, specific studies on their dynamical behavior only appeared recently. Few methods can tackle this aspect, despite its interest for virtual screening and drug design. Protein cavities define an extremely labile ensemble. Following one cavity along a trajectory is therefore an arduous task, because it can be subjected to several events of fusions, divisions, apparitions and disappearances. I propose a method to resolve this question, thus enabling systematic and rational dynamical exploitation of protein cavities. This method classify cavities using the atom groups around them, using algorithms and parameters that I identified as giving best results for cavity tracking. To characterize the main directions of evolution of cavity geometry, and to relate them with the dynamics of the underlying structure, I developed a method based on Principal Component Analysis (PCA). This method can be used to select or build conformations with given cavity shapes. Two examples of applications have been treated: the selection of conformations with diverse cavity geometries, and the analysis of the myoglobin cavity network evolution during the diffusion of carbon monoxide in it. These two methods have been used in three projects involving virtual screening, targeting M. tuberculosis DNA-gyrase, P vivax subtilisin 1 and GLIC, an procaryotic model of human pentameric ligand-gated ion channel. These methods allowed us to identify an inhibitor of subtilisin 1 and four effectors of GLIC

Principal Component Analysis reveals correlation of cavities evolution and functional motions in proteins

AbstractProtein conformation has been recognized as the key feature determining biological function, as it determines the position of the essential groups specifically interacting with substrates. Hence, the shape of the cavities or grooves at the protein surface appears to drive those functions. However, only a few studies describe the geometrical evolution of protein cavities during molecular dynamics simulations (MD), usually with a crude representation. To unveil the dynamics of cavity geometry evolution, we developed an approach combining cavity detection and Principal Component Analysis (PCA). This approach was applied to four systems subjected to MD (lysozyme, sperm whale myoglobin, Dengue envelope protein and EF-CaM complex). PCA on cavities allows us to perform efficient analysis and classification of the geometry diversity explored by a cavity. Additionally, it reveals correlations between the evolutions of the cavities and structures, and can even suggest how to modify the protein conformation to induce a given cavity geometry. It also helps to perform fast and consensual clustering of conformations according to cavity geometry. Finally, using this approach, we show that both carbon monoxide (CO) location and transfer among the different xenon sites of myoglobin are correlated with few cavity evolution modes of high amplitude. This correlation illustrates the link between ligand diffusion and the dynamic network of internal cavities

mkgridXf : Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics

International audienceWe describe here a method to identify potential binding sites in ensembles of protein structures as obtained by molecular dynamics simulations. This is a highly important task in the context of structure based drug discovery, and many methods exist for the much simpler case of static structures. However , during molecular dynamics, the cavities and grooves that are used to define binding sites merge, split, appear and disappear, and cover a large volume. Combined with the large number of sites (∼10 5 and more) these characteristics hamper a consistent and comprehensive definition of binding sites. Our method is based on the calculation of instantaneous cavities and of the pockets delineating them. Classification of the pockets over the structure ensemble generates consensus pockets, which define sites. Sites are reported as lists of atoms or residues. This avoids the pitfalls of the classification of cavities by spatial overlap, used in most existing methods, which is bound to fail on non-ordered or unaligned ensembles, or as soon as significant molecular motions are involved. To achieve a robust and consistent classification we thoroughly optimized and benchmarked the method. For this we assembled from the literature a set of reference sites on systems involving significant functional molecular motions. We tested different descriptors, metrics and clustering methods. The resulting method is able to perform a global analysis of potential sites efficiently. Tests on examples show that our approach can make predictions of potential sites on the whole surface of a protein, and identify novel sites absent from static structures

<i>mkgridXf</i>: Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics

We describe here a method to identify potential binding sites in ensembles of protein structures as obtained by molecular dynamics simulations. This is a highly important task in the context of structure-based drug discovery, and many methods exist for the much simpler case of static structures. However, during molecular dynamics, the cavities and grooves that are used to define binding sites merge, split, appear, and disappear, and cover a large volume. Combined with the large number of sites (∼105 and more), these characteristics hamper a consistent and comprehensive definition of binding sites. Our method is based on the calculation of instantaneous cavities and of the pockets delineating them. Classification of the pockets over the structure ensemble generates consensus pockets, which define sites. Sites are reported as lists of atoms or residues. This avoids the pitfalls of the classification of cavities by spatial overlap, used in most existing methods, which is bound to fail on nonordered or unaligned ensembles or as soon as significant molecular motions are involved. To achieve a robust and consistent classification, we thoroughly optimized and benchmarked the method. For this, we assembled from the literature a set of reference sites on systems involving significant functional molecular motions. We tested different descriptors, metrics, and clustering methods. The resulting method is able to perform a global analysis of potential sites efficiently. Tests on examples show that our approach can make predictions of potential sites on the whole surface of a protein and identify novel sites absent from static structures

<i>mkgridXf</i>: Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics

We describe here a method to identify potential binding sites in ensembles of protein structures as obtained by molecular dynamics simulations. This is a highly important task in the context of structure-based drug discovery, and many methods exist for the much simpler case of static structures. However, during molecular dynamics, the cavities and grooves that are used to define binding sites merge, split, appear, and disappear, and cover a large volume. Combined with the large number of sites (∼105 and more), these characteristics hamper a consistent and comprehensive definition of binding sites. Our method is based on the calculation of instantaneous cavities and of the pockets delineating them. Classification of the pockets over the structure ensemble generates consensus pockets, which define sites. Sites are reported as lists of atoms or residues. This avoids the pitfalls of the classification of cavities by spatial overlap, used in most existing methods, which is bound to fail on nonordered or unaligned ensembles or as soon as significant molecular motions are involved. To achieve a robust and consistent classification, we thoroughly optimized and benchmarked the method. For this, we assembled from the literature a set of reference sites on systems involving significant functional molecular motions. We tested different descriptors, metrics, and clustering methods. The resulting method is able to perform a global analysis of potential sites efficiently. Tests on examples show that our approach can make predictions of potential sites on the whole surface of a protein and identify novel sites absent from static structures

<i>mkgridXf</i>: Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics

We describe here a method to identify potential binding sites in ensembles of protein structures as obtained by molecular dynamics simulations. This is a highly important task in the context of structure-based drug discovery, and many methods exist for the much simpler case of static structures. However, during molecular dynamics, the cavities and grooves that are used to define binding sites merge, split, appear, and disappear, and cover a large volume. Combined with the large number of sites (∼105 and more), these characteristics hamper a consistent and comprehensive definition of binding sites. Our method is based on the calculation of instantaneous cavities and of the pockets delineating them. Classification of the pockets over the structure ensemble generates consensus pockets, which define sites. Sites are reported as lists of atoms or residues. This avoids the pitfalls of the classification of cavities by spatial overlap, used in most existing methods, which is bound to fail on nonordered or unaligned ensembles or as soon as significant molecular motions are involved. To achieve a robust and consistent classification, we thoroughly optimized and benchmarked the method. For this, we assembled from the literature a set of reference sites on systems involving significant functional molecular motions. We tested different descriptors, metrics, and clustering methods. The resulting method is able to perform a global analysis of potential sites efficiently. Tests on examples show that our approach can make predictions of potential sites on the whole surface of a protein and identify novel sites absent from static structures

<i>mkgridXf</i>: Consistent Identification of Plausible Binding Sites Despite the Elusive Nature of Cavities and Grooves in Protein Dynamics

We describe here a method to identify potential binding sites in ensembles of protein structures as obtained by molecular dynamics simulations. This is a highly important task in the context of structure-based drug discovery, and many methods exist for the much simpler case of static structures. However, during molecular dynamics, the cavities and grooves that are used to define binding sites merge, split, appear, and disappear, and cover a large volume. Combined with the large number of sites (∼105 and more), these characteristics hamper a consistent and comprehensive definition of binding sites. Our method is based on the calculation of instantaneous cavities and of the pockets delineating them. Classification of the pockets over the structure ensemble generates consensus pockets, which define sites. Sites are reported as lists of atoms or residues. This avoids the pitfalls of the classification of cavities by spatial overlap, used in most existing methods, which is bound to fail on nonordered or unaligned ensembles or as soon as significant molecular motions are involved. To achieve a robust and consistent classification, we thoroughly optimized and benchmarked the method. For this, we assembled from the literature a set of reference sites on systems involving significant functional molecular motions. We tested different descriptors, metrics, and clustering methods. The resulting method is able to perform a global analysis of potential sites efficiently. Tests on examples show that our approach can make predictions of potential sites on the whole surface of a protein and identify novel sites absent from static structures