Protein docking GPU acceleration

• Autor(a) principal: Ribeiro, Ricardo Alexandre do Rosário
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/82980
• topic: proteins; docking; high performance computing; GPU; Bioinformatics; BiGGER; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

In Bioinformatics, finding the complex resulting from an interaction between a pair of proteins is a computationally demanding task. There are methods and algorithms that simulate the binding between two proteins. However, the computation related to docking simulation has many extensive and repeating steps. Thus, the execution of the simulation if the program is CPU-only can last for hours, making the option of using these programs a very inefficient one in terms of work/time. One of the methods used is BiGGER, created by prof. Nuno Palma and others. This algorithm has features that give it a lower time complexity compared to others, therefore execution times in BiGGER can be lower than most of the algorithms fitted for execution of docking simulations. Studying protein interactions has medical aplications, contributing to the development of ways to protect, diagnose and heal humanity from neuronal diseases. It also contributes to computer assisted drug design and development. To improve the execution time of docking programs, these were optimized for GPU execution, reducing the execution time in docking scenarios that can last for hours to minutes or even seconds. This document presents an aproach to the implementation of optimizations to BiGGER, running it with GPU assistance. This implementation is to be done via high performance computing techniques, so that the machine’s GPU assists the CPU on parallelizing those required computations. By having more resources at disposal, it should be expected that the execution time of BiGGER is reduced due to the improvement of BiGGER performance in relation to the sequential version. If the implementations succeed, there will be additional advantages for BiGGER in relation to other algorithms. Thus, a value proposition is given for those who intend to use BiGGER as a method for efficiently studying interactions between proteins in a personal ou professional computer.