Fig 6 - uploaded by Juan Cortés
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Two views of the voxel map representing geometrically feasible motions of POUh with respect to POUs. The voxels display the relative position of the centers of mass of both domains. Voxels resolution is 2 ˚ A, and colors have been assigned depending on the energies of the associated conformations. The black voxel indicates the initial conformation.
Source publication
This paper builds on the combination of robotic path planning algorithms and molecular modeling methods for computing large-amplitude molecular motions, and introduces voxel maps as a computational tool to encode and to represent such motions. We investigate several applications and show results that illustrate the interest of such representation.
Citations
... The method was further extended for flexible ligands [3]. Moreover, the high-dimensional space roadmap can be projected back to 3D space [2]. ...
The paper introduces a new randomized sampling-based method of motion planning suitable for the problem of narrow passages. The proposed method was inspired by the method of exit points for cavities in protein models and is based on the Rapidly Exploring Random Tree (RRT). Unlike other methods, it can also provide locations of the exact positions of narrow passages. This information is extremely important as it helps to solve this part of space in more detail and even to decide whether a path through this bottleneck exists or not. For data with narrow passages, the proposed method finds more paths in a shorter time, for data without narrow passages, the proposed method is slower but still provides correct paths.
... Lee and Varshney [LV02] depicted thermal vibrations of atoms through multi-layered semitransparent surfaces. Selected flexible elements such as loops or domains in proteins can be represented by voxel maps [CBES11]. Figure 13: Visualizing cellular signalling processes with a volumetric representation obtained from discrete signal proteins [FKRE10]. ...
Structural properties of molecules are of primary concern in many fields. This report provides a comprehensive overview on techniques that have been developed in the fields of molecular graphics and visualization with a focus on applications in structural biology. The field heavily relies on computerized geometric and visual representations of three-dimensional, complex, large and time-varying molecular structures. The report presents a taxonomy that demonstrates which areas of molecular visualization have already been extensively investigated and where the field is currently heading. It discusses visualizations for molecular structures, strategies for efficient display regarding image quality and frame rate, covers different aspects of level of detail and reviews visualizations illustrating the dynamic aspects of molecular simulation data. The survey concludes with an outlook on promising and important research topics to foster further success in the development of tools that help to reveal molecular secrets.
... In a subsequent work [28], we extended this approach to dynamic molecular paths computed from molecular trajectories. Two of the few approaches that compute possible molecular paths based on the correct geometry of a ligand are the methods by Cortes et al. [7,8] and Haranczyk & Sethian [14]. While Cortes et al. use rapidly exploring random trees, Haranczyk & Sethian sample the ligand orientation space to compute possible molecular paths. ...
The most popular molecular surface in molecular visualization is the solvent excluded surface (SES). It provides information about the accessibility of a biomolecule for a solvent molecule that is geometrically approximated by a sphere. During a period of almost four decades, the SES has served for many purposes - including visualization, analysis of molecular interactions and the study of cavities in molecular structures. However, if one is interested in the surface that is accessible to a molecule whose shape differs significantly from a sphere, a different concept is necessary. To address this problem, we generalize the definition of the SES by replacing the probe sphere with the full geometry of the ligand defined by the arrangement of its van der Waals spheres. We call the new surface ligand excluded surface (LES) and present an efficient, grid-based algorithm for its computation. Furthermore, we show that this algorithm can also be used to compute molecular cavities that could host the ligand molecule. We provide a detailed description of its implementation on CPU and GPU. Furthermore, we present a performance and convergence analysis and compare the LES for several molecules, using as ligands either water or small organic molecules.
... Combined with methods in computational physics, such as normal-mode analysis, or by using appropriate multiscale molecular models, 267 robot path-planning algorithms relying on a mechanistic modeling of (macro)molecules are able to compute large-amplitude conformational transitions in proteins with several orders of magnitude faster than such standard simulation methods as MD. 268,269 These robotics-inspired methods have also been developed with very low computational cost to simulate ligand displacement inside an active-site pocket of a protein, considering both partners as flexible molecular models. 268,270,271 Another novel feature lies in the possibility for estimating the "escape path" and "escape time" for the ligand to escape from the "funnel of attraction" at the binding site. . ...
... 268,269 These robotics-inspired methods have also been developed with very low computational cost to simulate ligand displacement inside an active-site pocket of a protein, considering both partners as flexible molecular models. 268,270,271 Another novel feature lies in the possibility for estimating the "escape path" and "escape time" for the ligand to escape from the "funnel of attraction" at the binding site. . The distribution obtained for the R-enantiomer (blue) clearly appears larger and less constrained than for the S-enantiomer (white). ...
The article reviews the significant contributions to, and the present status of, applications of computational methods for the characterization and prediction of protein-carbohydrate interactions. After a presentation of the specific features of carbohydrate modeling, along with a brief description of the experimental data and general features of carbohydrate-protein interactions, the survey provides a thorough coverage of the available computational methods and tools. At the quantum-mechanical level, the use of both molecular orbitals and density-functional theory is critically assessed. These are followed by a presentation and critical evaluation of the applications of semiempirical and empirical methods: QM/MM, molecular dynamics, free-energy calculations, metadynamics, molecular robotics, and others. The usefulness of molecular docking in structural glycobiology is evaluated by considering recent docking- validation studies on a range of protein targets. The range of applications of these theoretical methods provides insights into the structural, energetic, and mechanistic facets that occur in the course of the recognition processes. Selected examples are provided to exemplify the usefulness and the present limitations of these computational methods in their ability to assist in elucidation of the structural basis underlying the diverse function and biological roles of carbohydrates in their dialogue with proteins. These test cases cover the field of both carbohydrate biosynthesis and glycosyltransferases, as well as glycoside hydrolases. The phenomenon of (macro)molecular recognition is illustrated for the interactions of carbohydrates with such proteins as lectins, monoclonal antibodies, GAG-binding proteins, porins, and viruses.
© 2014 Elsevier Inc. All rights reserved.
... VICE Tripathi and Kellogg, 2010 Grid (with integer arithmetic); pockets are lists of protein atoms; paths are lists of voxels. T- RRT Jaillet et al., 2010;Cortés et al., 2011 Grid; Monte-Carlo path planning through voxels. PROPORES Lee and Helms, 2012 Grid and voxels; hybrid variant of POCKET and SURFNET; shortest paths in voxels with arbitrary cost functions. ...
Identifying protein cavities, channels and pockets accessible to ligands is a major step to predict potential protein-ligands complexes. It is also essential for preparation of protein-ligand docking experiments in the context of enzymatic activity mechanism and structure-based drug design.
We introduce a new method, implemented in a program named CCCPP, which computes the void parts of the proteins, i.e. cavities, channels and pockets. The present approach is a variant of the alpha shapes method, with the advantage of taking into account the size and the shape of the ligand. We show that the widely used spherical model of ligands is most of the time inadequate and that cylindrical shapes are more realistic. The analysis of the void parts of the protein is done via a network of channels depending on the ligand. The performance of CCCPP is tested with known substrates of cytochromes P450 (CYP) 1A2 and 3A4 involved in xenobiotics metabolism. The test results indicate that CCCPP is able to find pathways to the buried heminic P450 active site even for high molecular weight CYP 3A4 substrates such as two ketoconazoles together, an experimentally observed situation.
Free binaries are available through a software repository at http://petitjeanmichel.free.fr/itoweb.petitjean.freeware.html CONTACT: michel.petitjean@univ-paris-diderot.fr.
... Based on robotics background, computationally efficient methods have been developed in recent years for sampling and exploring conformational space of biological macromolecules. Combined with methods in computational physics such as normal mode analysis [85], or using appropriate multi-scale molecular models [86], robot path-planning algorithms relying on a mechanistic modeling of (macro)molecules are able to compute large-amplitude conformational transitions in proteins with several orders of magnitude faster than standard simulation methods such as MD [84,87]. These robotics-inspired methods have also been developed to simulate ligand displacement inside an active-site pocket of a protein considering both partners as flexible molecular models with very low computational cost [87][88][89] and provide information about the interactions between the ligand and the protein and about the required conformational changes that are important for understanding the complex biochemical processes. ...
... Combined with methods in computational physics such as normal mode analysis [85], or using appropriate multi-scale molecular models [86], robot path-planning algorithms relying on a mechanistic modeling of (macro)molecules are able to compute large-amplitude conformational transitions in proteins with several orders of magnitude faster than standard simulation methods such as MD [84,87]. These robotics-inspired methods have also been developed to simulate ligand displacement inside an active-site pocket of a protein considering both partners as flexible molecular models with very low computational cost [87][88][89] and provide information about the interactions between the ligand and the protein and about the required conformational changes that are important for understanding the complex biochemical processes. Such methods have already been successfully applied for rational enzyme engineering [90,91], showing the efficiency and the potential of molecular robotics methods to guide the engineering of enzyme mutants with improved activity, selectivity and specificity. ...
This thesis presents an account of two important facets of glycobioinformatics, comprising database development and molecular modeling of 3D structures of carbohydrates alongside the simulation of protein-carbohydrate interactions. Classical molecular modeling techniques were used to reconstruct 3D polysaccharide structures from experimentally determined atomic coordinates, or known starting points about their structures were used as guidelines to model them. A genetic algorithm search was employed as a high-throughput technique to characterize low energy conformers of bioactive oligosaccharides. The data generated were organized into two open-access relational databases, namely, PolySac3DB and BiOligo, for use by the scientific community. The validation of the molecular techniques used were performed using solution phase NMR experiments on four entero aggregative pathogenic E. coli strains, and were found to be robust and realistic. Further, the impact of the presentation of human fucosylated oligosaccharide epitopes to lectins from opportunistic gram negative bacteria, was investigated in a screening study using molecular docking studies, which could help in evaluating the feasibility of using automated docking procedures in such instances as well as deciphering binding data from glycan array experiments and also correlated to isothermal calorimetry data. On comparison with high-resolution experimental crystal complexes, automated docking was found to delineate the present level of applicability, while emphasizing the need of constant monitoring and possible filtering of the results obtained. Finally, a review of the present status of the computational aspects of protein-carbohydrate interaction studies is discussed in the perspectives of using molecular modeling and simulation studies to probe this aspect of molecular and structural glycobiology.
... These robotics-inspired methods have also been developed to simulate ligand displace- ment inside an active-site pocket of a protein considering both partners as flexible K12506_C004.indd 86 5/15/12 1:00 AM molecular models with very low computational cost ( Cortés et al. 2011Cortés et al. , 2008Cortés et al. , 2010 and provide information about the interactions between the ligand and the protein and about the required conformational changes that are important for understanding the complex biochemical processes. Such methods have already been successfully applied for rational enzyme engineering ( Guieysse et al. 2008, Lafaquière et al. 2009, showing the efficiency and the potential of molecular robotics methods to guide the engineering of enzyme mutants with improved activity, selectivity, and specificity. ...
In nature, carbohydrates form an important family of biomolecules, as simple or complex carbohydrates, either alone or covalently linked to proteins or lipids. Most of the earlier studies on carbohydrates focused on plant polysaccharides, such as cellulose, starch, pectins, and so on, largely because of their wide range of applications. More recently, the role of carbohydrates in biological events has been recognized, and glycobiology has emerged as a new and challenging research area at the interface of biology and chemistry. Of special interest are the carbohydrate-mediated recognition events that are important in biological phenomena, which give a pivotal role to the study of protein-carbohydrate interactions. Actually, the binding protein partners of carbohydrates encompass a wide variety of macromolecules involved in functions such as recognition, biosynthesis, modification, hydrolysis, and so on (Figure 4.1).
... Roll [49] is a program that also detects pockets based on the comparison of molecular surfaces with different probe radii. Molecular path planning employing rapidly-exploring random trees (RRTs) [29] was done by Cortes et al. [13,12]. This technique allows one to detect paths depending on the geometry and dynamic of a given substrate, but the algorithm does not compute all paths of a protein. ...
Visual analysis is widely used to study the behavior of molecules. Of particular interest are the analysis of molecular interactions and the investigation of binding sites. For large molecules, however, it is difficult to detect possible binding sites and paths leading to these sites by pure visual inspection. In this paper, we present new methods for the computation and visualization of potential molecular paths. Using a novel filtering method, we extract the significant paths from the Voronoi diagram of spheres. For the interactive visualization of molecules and their paths, we present several methods using deferred shading and other state-of-the-art techniques. To allow for a fast overview of reachable regions of the molecule, we illuminate the molecular surface using a large number of light sources placed on the extracted paths. We also provide a method to compute the extension surface of selected paths and visualize it using the skin surface. Furthermore, we use the extension surface to clip the molecule to allow easy visual tracking of even deeply buried paths. The methods are applied to several proteins to demonstrate their usefulness.
... Each voxel has a unique position and associated scalar value from the Houndsfield scale [4]. Due to its importance, various visualization, examination, and simulation techniques, based on voxels have been developed to date [5][6][7][8][9]. ...
This paper introduces a new algorithm for losslessly compressing voxel-based 3D CT medical images. Firstly, medical data is segmented according to selected ranges of the Hounsfield scale. The data is then arranged into two data streams. The first stream identifies the positions of the remaining data after segmentation. This information is compressed by the JBIG standard. The second stream contains the exact data values and it is losslessly compressed by our algorithm. The efficiency of this approach has been evaluated by a prototype application. This approach represents an interesting alternative for the long-term storage of medical 2D and 3D images, and for applications in telemedicine. The compression method can be used either for 2D or 3D medical data.
A complete dataset is essential for most bioinformatics analytical techniques, including gene expression data categorization, prognosis, and prediction. Due to sensor malfunction, software inability, or human error, the gene sample value may be missing. In gene expression experiments, missing data has a massive effect on analyzing the data obtained. Consequently, this has become a crucial issue requiring an efficient imputation technique to address. This research provided a technique for predicting missing values by using clustering and top K closest neighbor techniques that consider the local similarity pattern. The K‐means method is integrated with a spectral clustering methodology. After optimizing the clustering parameters, cluster size, and weighting criteria, missed gene sample values are estimated. The top K closest neighbor method uses weighted distance to predict the missed gene sample value falling in a specific cluster. Experimental outcomes show that the suggested imputation methodology generates efficient predictions compared to existing imputation techniques. In this research, microarray datasets comprising information from various cancers and tumors are used to experiment with the imputation performance. The primary contribution of this work is that even if the microarray dataset has varied dimensions and features, local similarity‐based approaches may be employed for missing value prediction.
