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- Geometric algorithms for cavity detection on protein surfacesPublication . Simões, Tiago Miguel Carrola; Gomes, Abel João PadrãoMacromolecular structures such as proteins heavily empower cellular processes or functions. These biological functions result from interactions between proteins and peptides, catalytic substrates, nucleotides or even human-made chemicals. Thus, several interactions can be distinguished: protein-ligand, protein-protein, protein-DNA, and so on. Furthermore, those interactions only happen under chemical- and shapecomplementarity conditions, and usually take place in regions known as binding sites. Typically, a protein consists of four structural levels. The primary structure of a protein is made up of its amino acid sequences (or chains). Its secondary structure essentially comprises -helices and -sheets, which are sub-sequences (or sub-domains) of amino acids of the primary structure. Its tertiary structure results from the composition of sub-domains into domains, which represent the geometric shape of the protein. Finally, the quaternary structure of a protein results from the aggregate of two or more tertiary structures, usually known as a protein complex. This thesis fits in the scope of structure-based drug design and protein docking. Specifically, one addresses the fundamental problem of detecting and identifying protein cavities, which are often seen as tentative binding sites for ligands in protein-ligand interactions. In general, cavity prediction algorithms split into three main categories: energy-based, geometry-based, and evolution-based. Evolutionary methods build upon evolutionary sequence conservation estimates; that is, these methods allow us to detect functional sites through the computation of the evolutionary conservation of the positions of amino acids in proteins. Energy-based methods build upon the computation of interaction energies between protein and ligand atoms. In turn, geometry-based algorithms build upon the analysis of the geometric shape of the protein (i.e., its tertiary structure) to identify cavities. This thesis focuses on geometric methods. We introduce here three new geometric-based algorithms for protein cavity detection. The main contribution of this thesis lies in the use of computer graphics techniques in the analysis and recognition of cavities in proteins, much in the spirit of molecular graphics and modeling. As seen further ahead, these techniques include field-of-view (FoV), voxel ray casting, back-face culling, shape diameter functions, Morse theory, and critical points. The leading idea is to come up with protein shape segmentation, much like we commonly do in mesh segmentation in computer graphics. In practice, protein cavity algorithms are nothing more than segmentation algorithms designed for proteins.
- New concepts integration on e-learning platformsPublication . Simões, Tiago Miguel Carrola; Rodrigues, Joel José Puga CoelhoThe learning experience has evolved into the virtual world of the Internet, where learners have the possibility to shift from face-to-face learning environments to virtual learning environments supported by technologies. This concept, called e-learning, emerged in the early 1960s where a group of researchers from the Stanford University, USA began experimenting different ways to publish and assign learning content using a computer. These experiments were the beginning that led to the creation of countless learning platforms, initially constructed in standalone environments and later ported to the Internet as Webbased learning platforms. As initial objectives, these learning platforms include a collection of features to support instructors and learners in the learning process. However, some of these platforms continued to be based on an old instructor-centered learning model and created a collection of outdated technologies that, given the current need to a learner-center learning model and the existence of Web 2.0 technologies, become inadequate. As a solution to address and overcome these challenges, a friendly user interface and a correct root incorporation of Web 2.0 services a platform designed to focus the learning experience and environment personalization into the learner is needed to propose. In an operating system (OS) context the graphic user interface (GUI) is guided by a collection of approaches that details how human beings should interact with computers. These are the key ideas to customize, install, and organize virtual desktops. The combination of desktop concepts into a learning platform can be an asset to reduce the learning curve necessary to know how to use the system and also to create a group of flexible learning services. However, due to limitations in hypertext transfer protocol-hypertext markup language (HTTP-HTML) traditional solutions, to shift traditional technologies to a collection of rich Internet application (RIA) technologies and personal learning environments (PLEs) concepts is needed, in order to construct a desktop-like learning platform. RIA technologies will allow the design of powerful Web solutions containing many of the characteristics of desktop-like applications. Additionally, personal learning environments (PLEs) will help learners to manage learning contents. In this dissertation the personal learning environment box (PLEBOX) is presented. The PLEBOX platform is a customizable, desktop-like platform similar to the available operating systems, based on personal learning environments concepts and rich Internet applications technologies that provide a better learning environment for users. PLEBOX developers have a set of tools that allow the creation of learning and management modules that can be installed on the platform. These tools are management learning components and interfaces built as APIs, services, and objects of the software development kit (SDK). A group of prototype modules were build for evaluation of learning and management services, APIs, and SDKs. Furthermore, three case studies were created in order to evaluate and demonstrate the learning service usage in external environments. The PLEBOX deployment and corresponding features confirms that this platform can be seen as a very promising e-learning platform. Exhaustive experiments were driven with success and it is ready for use.
- Geometric Detection Algorithms for Cavities on Protein Surfaces in Molecular Graphics: A SurveyPublication . Simões, Tiago M. C.; Lopes, Daniel Simões; Dias, Sérgio Emanuel Duarte; Fernandes, Francisco; Jorge, Joaquim A; Pereira, João; Bajaj, Chandrajit; Gomes, AbelDetecting and analysing protein cavities provides significant information about active sites for biological processes (e.g. protein–protein or protein–ligand binding) in molecular graphics and modelling. Using the three‐dimensional (3D) structure of a given protein (i.e. atom types and their locations in 3D) as retrieved from a PDB (Protein Data Bank) file, it is now computationally viable to determine a description of these cavities. Such cavities correspond to pockets, clefts, invaginations, voids, tunnels, channels and grooves on the surface of a given protein. In this work, we survey the literature on protein cavity computation and classify algorithmic approaches into three categories: evolution‐based, energy‐based and geometry‐based. Our survey focuses on geometric algorithms, whose taxonomy is extended to include not only sphere‐, grid‐ and tessellation‐based methods, but also surface‐based, hybrid geometric, consensus and time‐varying methods. Finally, we detail those techniques that have been customized for GPU (graphics processing unit) computing.
- CavVis - A field-of-view geometric algorithm for protein cavity detectionPublication . Simões, Tiago M. C.; Gomes, AbelSeveral geometric-based methods have been developed for the last two to three decades to detect and identify cavities (i.e., putative binding sites) on proteins, as needed to study protein–ligand interactions and protein docking. This paper introduces a new protein cavity method, called CavVis, which combines voxelization (i.e., a grid of voxels) and an analytic formulation of Gaussian surfaces that approximates the solvent-excluded surface. This method builds upon visibility of points on protein surface to find its cavities. Specifically, the visibility criterion combines three concepts we borrow from computer graphics, the field-of-view of each surface point, voxel ray casting, and back-face culling.
- CavBench: a benchmark for protein cavity detection methodsPublication . Dias, Sérgio; Simões, Tiago M. C.; Fernandes, Francisco; Martins, Ana Mafalda; Ferreira, Alfredo; Jorge, Joaquim A; Gomes, AbelExtensive research has been applied to discover new techniques and methods to model protein-ligand interactions. In particular, considerable efforts focused on identifying candidate binding sites, which quite often are active sites that correspond to protein pockets or cavities. Thus, these cavities play an important role in molecular docking. However, there is no established benchmark to assess the accuracy of new cavity detection methods. In practice, each new technique is evaluated using a small set of proteins with known binding sites as ground-truth. However, studies supported by large datasets of known cavities and/or binding sites and statistical classification (i.e., false positives, false negatives, true positives, and true negatives) would yield much stronger and reliable assessments. To this end, we propose CavBench, a generic and extensible benchmark to compare different cavity detection methods relative to diverse ground truth datasets (e.g., PDBsum) using statistical classification methods.