Identifying representative drug resistant mutants of HIV reverse transcriptase

Author

Xiaxia Yu ; Harrison, Robert W. ; Weber, Irene T.

Author_Institution

Dept. of Comput. Sci., Georgia State Univ., Atlanta, GA, USA

fYear

2014

fDate

2-4 June 2014

Firstpage

1

Lastpage

1

Abstract

Drug resistance is an important cause in the failure of anti-AIDS treatment. Predictions based on genotype data can guide therapy very efficiently compared to phenotype experiments. We have developed a new algorithm using mean shift clustering to reveal the most representative mutants from a drug resistance database based on our unified protein sequence and 3D structure encoding. This algorithm was tested on genotype-resistance data for mutants of HIV reverse transcriptase and successfully chooses around 300 mutants out of 10K from the whole database.

Keywords

bioinformatics; diseases; drugs; medical computing; molecular biophysics; patient treatment; proteins; 3D structure encoding; HIV reverse transcriptase mutants; anti-AIDS treatment; drug resistance database; genotype data; genotype-resistance data; mean shift clustering; phenotype experiments; representative drug resistant mutants; representative mutants; unified protein sequence; whole database; Clustering algorithms; Databases; Drugs; Educational institutions; Encoding; Human immunodeficiency virus; Immune system; Delaunay triangulation; Drug resistance prediction; HIV-1 reverse transcriptase; mean shift;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Advances in Bio and Medical Sciences (ICCABS), 2014 IEEE 4th International Conference on

Conference_Location

Miami, FL

Print_ISBN

978-1-4799-5786-6

Type

conf

DOI

10.1109/ICCABS.2014.6863934

Filename

6863934