wCReF : uma interface web para o método CReF de predição da estrutura 3D aproximada de proteínas

Abstract

The prediction of protein tertiary structure is a problem of Structural Bioinformatics still unsolved by science. The challenge is to understand the relationship between the amino acid sequence of a protein and a three dimensional structure, which is related to the function of these macromolecules. Among the methods related to protein structure prediction is CREF (Central Residue Fragment-based Method) proposed by Dorn & Norbert Souza (2008) for prediction of proteins’ or polypeptide’s approximate 3D structure. In this thesis we present the wCReF, the Web interface for the CREF method developed with a focus on usability. With this tool the users can enter the amino acid sequence of its target protein, and get as a result the approximate 3D structure of a protein in an automated manner without the need to install all the necessary tools for their use. To define the requirements for its development were conducted usability evaluations, guided by experts on both Human-Computer Interaction and bioinformatics domain areas, in three protein structures prediction servers - I-TASSER, QUARK and Robetta - all participants at CASP (Critical Assessment of Protein Structure Prediction) competition. The inspections were conducted through the Heuristic Evaluation method using Nielsens’ 10 heuristics. As a result, violations were found in all heuristics resulting in 89 usability problems. They were classified into 5 severities, 29 scored as being of high priority solution and 25 as problems to be solved immediately. Assessment results serve as guiding orientation of the key features that wCReF must have compiled in a document software requirements for its implementation. From this step was carried out prototyping and glimpsing the detection of new usability problems with end users by adapting the Ssemugabi satisfaction questionnaire. As a final product we present the wCReF server, protein structure prediction server rooted in concern about the usability and interaction with its users. Furthermore, this study can contribute to improvement of usability of existing bioinformatics applications, the prediction servers analyzed and the development of new scientific tools.