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Immunological Computation for Protein Function Prediction

Zekri M., Alem K., Souici-Meslati L.

Fundamenta Informaticae

2015

Vol. 139, nr 1

91--114

Immunological computation is one of the largest recent bio-inspired approaches of artificial intelligence. Artificial immune systems (AIS) are inspired by the processes of the biological immune systems like the learning and memory characteristics which are used for solving complex problems. During the last two decades, AIS have been applied in various fields such as optimization, network security and data mining. In this article, we focus on the application of AIS to data mining in bioinformatics, more specifically, the classification task. For this purpose, we suggest three immune models based on clonal selection theory for the identification of G-protein coupled receptors (GPCRs) to predict their function. Our three classifiers are the artificial immune recognition system (AIRS), the clonal selection algorithm (CLONALG) and the clonal selection classification algorithm (CSCA). The GPCRs represent one of the largest and most important families of multifunctional proteins and are a significant target for bioactive and drug discovery programs. It is estimated that more than half of the drugs on the market currently target GPCRs. However, although thousands of GPCRs sequences are known, many of them remain orphans, have unknown function. Our experiments show that the three immunological classifiers have provided interesting results, however, AIRS obtained the best ones. Therefore, it is, for us, the most suitable immune model for the GPCRs identification problem.

Sequence similarity based method for protein function prediction

Blazewicz J., Lukasiak P., Milostan M., Krasnogor N., Jackowiak P.

Foundations of Computing and Decision Sciences

2009

Vol. 34, No. 3

173-191

Motivation: Proteins are the main building blocks of life. They catalyze biological processes in living cells to sustain life and improve metabolism. They also act as biological scaffolds and are cell's workhorses. As a matter of fact, knowing their function is one of the most important milestones for understanding life.The function depends on the tertiary structure of the protein, but only for a fraction of amino acid sequences gathered in databases the structure is known. Thus, creation of efficient and accurate methods that predict function from sequences, based on already known function-sequence assignments, is a fundamental challenge in computational biology. Results: First, we show a detailed analysis of a usability of similarity search engines in the context of function prediction. Then we propose a simple and effective method for assigning function to sequences based on the results of similarity searches and information gathered from gene ontology annotation graphs. Availability: All data used for the analysis presented in this paper as well as raw result are available at the site: http://bio.cs.put.poznan.pl/funcpred/data/ Suplementary Material: Suplementary materials with additional charts are available at: http://bio.es.put.poznan.pl/funcpred/suplement/ Contact: protbio@cs.put.poznan.pl