Quoc Trong Nguyen

Multi-GPU-based detection of protein cavities using critical points

Abstract Protein cavities are specific regions on the protein surface where ligands (small molecules) may bind. Such cavities are putative binding sites of proteins for ligands. Usually, cavities correspond to voids, pockets, and depressions of molecular surfaces. The location of such cavities is important to better understand protein functions, as needed in, for example, structure-based drug design. This article introduces a geometric method to detecting cavities on the molecular surface based on the theory of critical points. The method, called CriticalFinder, differs from other surface-based methods found in the literature because it directly uses the curvature of the scalar field (or function) that represents the molecular surface, instead of evaluating the curvature of the Connolly function over the molecular surface. To evaluate the accuracy of CriticalFinder, we compare it to other seven geometric methods (i.e., LIGSITE C S , GHECOM, ConCavity, POCASA, SURFNET, PASS, and Fpocket). The benchmark results show that CriticalFinder outperforms those methods in terms of accuracy. In addition, the performance analysis of the GPU implementation of CriticalFinder in terms of time consumption and memory space occupancy was carried out.

GPU-based detection of protein cavities using Gaussian surfaces

BackgroundProtein cavities play a key role in biomolecular recognition and function, particularly in protein-ligand interactions, as usual in drug discovery and design. Grid-based cavity detection methods aim at finding cavities as aggregates of grid nodes outside the molecule, under the condition that such cavities are bracketed by nodes on the molecule surface along a set of directions (not necessarily aligned with coordinate axes). Therefore, these methods are sensitive to scanning directions, a problem that we call cavity ground-and-walls ambiguity, i.e., they depend on the position and orientation of the protein in the discretized domain. Also, it is hard to distinguish grid nodes belonging to protein cavities amongst all those outside the protein, a problem that we call cavity ceiling ambiguity.ResultsWe solve those two ambiguity problems using two implicit isosurfaces of the protein, the protein surface itself (called inner isosurface) that excludes all its interior nodes from any cavity, and the outer isosurface that excludes most of its exterior nodes from any cavity. Summing up, the cavities are formed from nodes located between these two isosurfaces. It is worth noting that these two surfaces do not need to be evaluated (i.e., sampled), triangulated, and rendered on the screen to find the cavities in between; their defining analytic functions are enough to determine which grid nodes are in the empty space between them.ConclusionThis article introduces a novel geometric algorithm to detect cavities on the protein surface that takes advantage of the real analytic functions describing two Gaussian surfaces of a given protein.

uPATO—Overview of the Application

This chapter presents an overview of uPATO application, which was designed mainly for network analysis applied to team sports. However, this tool can be used for any network that can be represented by an adjacency matrix (e.g., a computer network, a telecommunication network, or even a social network, etc.). Thus, a first module was developed to allow codify the network, which, in the case of team sports, is given by a matrix with the sequences of interactions between teammates (i.e., a digraph). But this tool was designed to support graphs and digraphs, weighted and unweighted. Team sports are a good example of the necessity for calculating metrics on weighted networks. uPATO was developed with the main objective of analyzing team sports, where weights represent the frequency of the interactions between players, providing fundamental information on the analysis of the team factor. It calculates metrics in both weighted and unweighted networks and separates metrics into three major categories: individual metrics, subgroup metrics, and team metrics. Beyond metrics that uPATO allows calculating, a representation module that allows visualizing the network was also developed (i.e., the digraph or graph, weighted or not) and some charts for the data were calculated. Besides, the uPATO tool has an additional module for processing geolocation data. Currently, some teams use GPS devices to have the position of the players during the match (e.g., FieldWiz and TraXports formats are supported). Thus, uPATO has a set of metrics based on geolocation data of the players. This new functionality extends the uPATO capacities for team sports analysis but also for other activities where GPS data is available. But, it does not consider yet the possibility of the ball with a GPS device. However, this additional module is out of the scope of this book but the metrics implemented are described in a previous publication [3].

Network Analysis Tools

This chapter introduces the concept of network analysis and presents a series of software tools developed with the objective of analyzing networks. This analysis is usually performed on a set of metrics calculated on the network or on a representation of the network. Most of the existing tools are not free or are limited to unweighted networks, ignoring, in the case of weighted networks, the weight of the edges that connect the nodes. For example, in team sports, we have weighted networks, where edges represent the interactions between team players and their weight its frequency.

uPATO—A Case Study

This chapter introduces an analysis performed on real data. All described metrics were calculated for both teams and representations were created, using the Web application uPATO. The conclusions obtained from the analysis are then presented.

uPATO—Individual Measures

This chapter contains a set of individual metrics that can be used to analyze the importance of each player in a team sport. The metrics were divided into two main categories: Centrality (Sect. 3.1) and Prestige (Sect. 3.2). Each metric includes a description of a possible interpretation of the metric, and the pseudocode to implement it. Each pseudocode describes the cases (unweighted graphs, unweighted digraphs, weighted graphs, or weighted digraphs) for which it can be used. When the description simply contains graph (or graphs), without any other specifier, it means that the pseudocode is valid for any of the four types of graphs. The included interpretation considers that the connections between the players are the passes performed between them.

uPATO—Collective Measures

A collection of metrics is presented in this chapter. This collection is categorized as meso-level (subgroup) or team metrics, depending on the scope. Each metric includes a description of a possible interpretation of the metric, and the pseudocode to implement it. Each pseudocode describes the cases for which it can be used (unweighted graphs, unweighted digraphs, weighted graphs, or weighted digraphs). When the description simply contains graph (or graphs), without any other specifier, it means that the pseudocode is valid for any of the four types of graphs. The included interpretation considers that the connections between the players are the passes performed between them.

Computational Efficiency Improvement for Analyzing Bending and Tensile Behavior of Woven Fabric Using Strain Smoothing Method.

The tensile and bending behavior of woven fabrics are among the most important characteristics in complex deformation analysis and modelling of textile fabrics and they govern many aesthetics and performance aspects such as wrinkle/buckle, hand and drape. In this paper, a numerical method for analyzing of the tensile and bending behavior of plain-woven fabric structure was developed. The formulated model is based on the first-order shear deformation theory (FSDT) for a four-node quadrilateral element (Q4) and a strain smoothing method in finite elements, referred as a cell-based smoothed finite element method (CS-FEM). The physical and low-stress mechanical parameters of the fabric were obtained through the fabric objective measurement technology (FOM) using the Kawabata evaluation system for fabrics (KES-FB). The results show that the applied numerical method provides higher efficiency in computation in terms of central processing unit (CPU) time than the conventional finite element method (FEM) because the evaluation of compatible strain fields of Q4 element in CS-FEM model is constants, and it was also appropriated for numerical modelling and simulation of mechanical deformation behavior such as tensile and bending of woven fabric.

Healed marching cubes algorithm for non-manifold implicit surfaces

We present an algorithm, called Healed Marching Cubes (HMC), which is capable of triangulating non-manifold implicit surfaces with the help of healing techniques based on differential geometry tools. The leading idea of HMC algorithm is to leverage data generated by the standard Marching Cubes (MC) algorithm for manifold surfaces, healing the sub-triangulation inside each critical cube, i.e., a cube inside which the surface self-intersects along a (straight or curved) line. The healing process starts with the identification of such critical cubes, continues with the identification of endpoints of the intersection line segment across each critical cube, and terminates with re-triangulation inside each critical cube. As far as we know, this geometric healing process has never been used as an extension of MC algorithm for non-manifold implicit surfaces.