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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

Computational methods in diagnostics of chronic hepatitis C

Kędziora P., Figlerowicz M., Formanowicz P., Alejska M., Jackowiak P., Malinowska N., Frątczak A., Błażewicz J.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2005

Vol. 53, nr 3

273-281

Despite the considerable progress that has recently been made in medicine, the treatment of viral infections is still a problem remaining to be solved. This especially concerns infections caused by newly emerging patogenes such as: human immunodeficiency virus, hepatitis C virus or SARS-coronavirus. There are several lines of evidence that the unusual genetic polymorphism of these viruses is responsible for the observed therapeutic difficulties. In order to determine whether some parameters describing a very complex and variable viral population can be used as prognostic factors during antiviral treatment computational methods were applied. To this end, the structure of the viral population and virus evolution in the organisms of two patients suffering from chronic hepatitis C were analyzed. Here we demonstrated that phylogenetic trees and Hamming distances best reflect the differences between virus populations present in the organisms of patients who responded positively and negatively to the applied therapy. Interestingly, the obtained results suggest that based on the elaborated method df virus population analysis one can predict the final outcome of the treatment even before it has started.