20 research outputs found
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 cavitĂ©s sont le lieu privilĂ©giĂ© des interactions dâune protĂ©ine avec ses ligands, et sont donc dĂ©terminantes pour sa fonction, elle-mĂȘme aussi influencĂ©e par la dynamique de la protĂ©ine. Peu de mĂ©thodes permettent dâĂ©tudier en dĂ©tail la dynamique des cavitĂ©s malgrĂ© leur intĂ©rĂȘt notamment pour le criblage virtuel. Les cavitĂ©s dâune protĂ©ine dĂ©finissent un ensemble trĂšs labile. Ainsi, suivre une cavitĂ© le long dâune trajectoire est ardu car elle peut ĂȘtre sujette Ă des divisions, fusions, disparitions et apparitions. Je propose une mĂ©thode pour rĂ©soudre cette question afin dâexploiter la dynamique des cavitĂ©s de façon systĂ©matique et rationnelle, en classifiant les cavitĂ©s selon les groupes dâatomes les entourant. Jâai identifiĂ© les paramĂštres procurant les meilleurs critĂšres de suivi des cavitĂ©s. Pour caractĂ©riser les Ă©volutions principales de la gĂ©omĂ©trie des cavitĂ©s en relation avec la dynamique de la protĂ©ine, jâai dĂ©veloppĂ© une mĂ©thode basĂ©e sur lâAnalyse en Composantes Principales. Cette mĂ©thode peut ĂȘtre utilisĂ©e pour sĂ©lectionner ou construire des conformations Ă partir de la forme de leurs cavitĂ©s. Deux exemples dâapplications sont traitĂ©es : la sĂ©lection de conformations ayant des cavitĂ©s de gĂ©omĂ©tries diverses et lâĂ©tude de lâĂ©volution des cavitĂ©s de la myoglobine lors de la diffusion du monoxyde de carbone. Ces deux mĂ©thodes ont Ă©tĂ© utilisĂ©es pour trois projets de criblage virtuel ciblant lâADN-gyrase de M tuberculosis, la subtilisine 1 de P vivax et GLIC, homologue procaryote des rĂ©cepteurs pentamĂ©riques humains. Les molĂ©cules sĂ©lectionnĂ©es Ă lâaide de ces mĂ©thodes ont permis dâidentifier une molĂ©cule 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 cavitĂ©s sont le lieu privilĂ©giĂ© des interactions dâune protĂ©ine avec ses ligands, et sont donc dĂ©terminantes pour sa fonction, elle-mĂȘme aussi influencĂ©e par la dynamique de la protĂ©ine. Peu de mĂ©thodes permettent dâĂ©tudier en dĂ©tail la dynamique des cavitĂ©s malgrĂ© leur intĂ©rĂȘt notamment pour le criblage virtuel. Les cavitĂ©s dâune protĂ©ine dĂ©finissent un ensemble trĂšs labile. Ainsi, suivre une cavitĂ© le long dâune trajectoire est ardu car elle peut ĂȘtre sujette Ă des divisions, fusions, disparitions et apparitions. Je propose une mĂ©thode pour rĂ©soudre cette question afin dâexploiter la dynamique des cavitĂ©s de façon systĂ©matique et rationnelle, en classifiant les cavitĂ©s selon les groupes dâatomes les entourant. Jâai identifiĂ© les paramĂštres procurant les meilleurs critĂšres de suivi des cavitĂ©s. Pour caractĂ©riser les Ă©volutions principales de la gĂ©omĂ©trie des cavitĂ©s en relation avec la dynamique de la protĂ©ine, jâai dĂ©veloppĂ© une mĂ©thode basĂ©e sur lâAnalyse en Composantes Principales. Cette mĂ©thode peut ĂȘtre utilisĂ©e pour sĂ©lectionner ou construire des conformations Ă partir de la forme de leurs cavitĂ©s. Deux exemples dâapplications sont traitĂ©es : la sĂ©lection de conformations ayant des cavitĂ©s de gĂ©omĂ©tries diverses et lâĂ©tude de lâĂ©volution des cavitĂ©s de la myoglobine lors de la diffusion du monoxyde de carbone. Ces deux mĂ©thodes ont Ă©tĂ© utilisĂ©es pour trois projets de criblage virtuel ciblant lâADN-gyrase de M tuberculosis, la subtilisine 1 de P vivax et GLIC, homologue procaryote des rĂ©cepteurs pentamĂ©riques humains. Les molĂ©cules sĂ©lectionnĂ©es Ă lâaide de ces mĂ©thodes ont permis dâidentifier une molĂ©cule 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.
International audienceProtein 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 (âŒ10 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
An automatic tool to analyze and cluster macromolecular conformations based on self-organizing maps.
International audienceSampling the conformational space of biological macromolecules generates large sets of data with considerable complexity. Data-mining techniques, such as clustering, can extract meaningful information. Among them, the self-organizing maps (SOMs) algorithm has shown great promise; in particular since its computation time rises only linearly with the size of the data set. Whereas SOMs are generally used with few neurons, we investigate here their behavior with large numbers of neurons. We present here a python library implementing the full SOM analysis workflow. Large SOMs can readily be applied on heavy data sets. Coupled with visualization tools they have very interesting properties. Descriptors for each conformation of a trajectory are calculated and mapped onto a 3D landscape, the U-matrix, reporting the distance between neighboring neurons. To delineate clusters, we developed the flooding algorithm, which hierarchically identifies local basins of the U-matrix from the global minimum to the maximum. Availability and implementation: The python implementation of the SOM library is freely available on github: https://github.com/bougui505/SOM. [email protected] or [email protected] Supplementary information: Supplementary data are available at Bioinformatics online
An automatic tool to analyze and cluster macromolecular conformations based on self-organizing maps
Identification of binding sites and favorable ligand binding moieties by virtual screening and self-organizing map analysis.
International audienceIdentifying druggable cavities on a protein surface is a crucial step in structure based drug design. The cavities have to present suitable size and shape, as well as appropriate chemical complementarity with ligands. We present a novel cavity prediction method that analyzes results of virtual screening of specific ligands or fragment libraries by means of Self-Organizing Maps. We demonstrate the method with two thoroughly studied proteins where it successfully identified their active sites (AS) and relevant secondary binding sites (BS). Moreover, known active ligands mapped the AS better than inactive ones. Interestingly, docking a naive fragment library brought even more insight. We then systematically applied the method to the 102 targets from the DUD-E database, where it showed a 90% identification rate of the AS among the first three consensual clusters of the SOM, and in 82% of the cases as the first one. Further analysis by chemical decomposition of the fragments improved BS prediction. Chemical substructures that are representative of the active ligands preferentially mapped in the AS. The new approach provides valuable information both on relevant BSs and on chemical features promoting bioactivity
Impact of M36I polymorphism on the interaction of HIV-1 protease with its substrates: insights from molecular dynamics.
International audienceOver the last decades, a vast structural knowledge has been gathered on the HIV-1 protease (PR). Noticeably, most of the studies focused the B-subtype, which has the highest prevalence in developed countries. Accordingly, currently available anti-HIV drugs target this subtype, with considerable benefits for the corresponding patients. However, in developing countries, there is a wide variety of HIV-1 subtypes carrying PR polymorphisms related to reduced drug susceptibility. The non-active site mutation, M36I, is the most frequent polymorphism, and is considered as a non-B subtype marker. Yet, the structural impact of this substitution on the PR structure and on the interaction with natural substrates remains poorly documented
Identification of novel leishmanicidal molecules by virtual and biochemical screenings targeting Leishmania eukaryotic translation initiation factor 4A.
Leishmaniases are neglected parasitic diseases in spite of the major burden they inflict on public health. The identification of novel drugs and targets constitutes a research priority. For that purpose we used Leishmania infantum initiation factor 4A (LieIF), an essential translation initiation factor that belongs to the DEAD-box proteins family, as a potential drug target. We modeled its structure and identified two potential binding sites. A virtual screening of a diverse chemical library was performed for both sites. The results were analyzed with an in-house version of the Self-Organizing Maps algorithm combined with multiple filters, which led to the selection of 305 molecules. Effects of these molecules on the ATPase activity of LieIF permitted the identification of a promising hit (208) having a half maximal inhibitory concentration (IC50) of 150 ± 15 ΌM for 1 ΌM of protein. Ten chemical analogues of compound 208 were identified and two additional inhibitors were selected (20 and 48). These compounds inhibited the mammalian eIF4I with IC50 values within the same range. All three hits affected the viability of the extra-cellular form of L. infantum parasites with IC50 values at low micromolar concentrations. These molecules showed non-significant toxicity toward THP-1 macrophages. Furthermore, their anti-leishmanial activity was validated with experimental assays on L. infantum intramacrophage amastigotes showing IC50 values lower than 4.2 ΌM. Selected compounds exhibited selectivity indexes between 19 to 38, which reflects their potential as promising anti-Leishmania molecules
LieIF models and pockets.
<p>(a) Apo-LieIF corresponds to the open conformation of LieIF and (b) Holo-LieIF corresponds to its closed conformation. The blue to red color gradient goes from the N-terminus to the C-terminus. The dumbbell shape, consisting of the two linked RecA-like domains that are common to the DBPs, is obtained for both models. (c) Apo-LieIF<sub>trunc/MD</sub> model with a representation of the conserved motifs and the identified pockets. Pocket P1 is the orange grid. Pocket P2 is the blue grid. Conserved motifs of the DEAD-box family are shown in different colors: Q-motif in red, motif I in yellow, motif Ia in green, GG doublet in yellow, motif Ib in blue, motif II in magenta, motif III in orange, motif IV in red, QxxR motif in blue, motif V in green and motif VI in yellow. The phosphorylation site (T135) observed in an amastigote version of LieIF is shown in magenta.</p