A genetic programming method for protein motif discovery and protein classification

Abstract

Proteins can be grouped into families according to some features such as hydrophobicity, composition or structure, aiming to establish common biological functions. This paper presents MAHATMA -Memetic Algorithm-based Highly Adapted Tool for Motif Ascertainment -a system that was conceived to discover features (particular sequences of amino acids, or motifs) that occur very often in proteins of a given family but rarely occur in proteins of other families. These features can be used for the classification of unknown proteins, that is, to predict their function by analyzing their primary structure. Experiments were done with a set of enzymes extracted from the Protein Data Bank. The heuristic method used was based on Genetic Programming using operators specially tailored for the target problem. The final performance was measured using sensitivity (Se), specificity (Sp) and hit rate. The best results obtained for the enzyme dataset suggest that the proposed evolutionary computation method is effective in finding predictive features (motifs) for protein classification.

Determinação Da Função De Transferência De Um Motor Ciclo Otto Utilizando Programação Genética

“…Muitos problemas semelhantes em aprendizado de máquina, inteligência artificial e processamento simbólico podem ser vistos como uma busca ou descoberta de um programa de computador que produza determinadas saídas a partir de um conjunto específico de entradas. Este tipo de problema está presente tanto na engenharia quanto na computação e a PG pode prover soluções interessantes [6][7][8][9] A partir da hipótese de que um programa correto possa ser induzido a partir de um conjunto de casos de testes, pode-se considerar esta atividade como uma tarefa de indução, que por sua vez está sujeita a todos os méritos e problemas do raciocínio indutivo. Um dos principais problemasé que uma hipótese indutiva está ligada diretamenteà capacidade que casos de teste tem de representar o problema inteiro.…”

A Comparative Study of Machine Learning and Evolutionary Computation Approaches for Protein Secondary Structure Classification

“…For instance, (Tsunoda & Lopes, 2006) present a system based on a genetic algorithm that was conceived to discover motifs that occur very often in proteins of a given family but rarely occur in proteins of other families. Also, Tsunoda et al (2011) present an evolutionary approach for motif discovery and transmembrane protein classification, named GAMBIT. Techniques of clustering for sequences analysis were presented by (Manning et al, 1997).…”

“…Therefore, computer science has an important role here, proposing models and methods for studying the Protein Structure Prediction problem (PSP). There are two basic approaches that are used to the prediction of protein functions: prediction of the protein structure and then prediction of function from the structure, or else, classifying proteins and supposing that similar sequences will have similar functions (Tsunoda et al, 2011). Several approaches to solve the PSP exist, each addressing the problem by using a computational method to obtain optimal or quasi-optimal solutions, such as Molecular Dynamics with ab initio model (Hardin et al, 2002;Lee et al, 2001), neural nets (Yanikoglu & Erman, 2002) and evolutionary computation methods with lattice (Benítez & Lopes, 2010;Lopes, 2008;Scapin & Lopes, 2007;Shmygelska & Hoos, 2005) and off-lattice (Kalegari & Lopes, 2010) models.…”