BMC bioinformatics · 2015
Identifying representative drug resistant mutants of HIV.
СтатьяПоддержка NIH: внешняя программаПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Yu Xiaxia, Weber Irene T, Harrison Robert W
Аннотация
BACKGROUND: Drug resistance is one of the most important causes for failure of anti-AIDS treatment. During therapy, multiple mutations accumulate in the HIV genome, eventually rendering the drugs ineffective in blocking replication of the mutant virus. The huge number of possible mutants precludes experimental analysis to explore the molecular mechanisms of resistance and develop improved antiviral drugs.
RESULTS: In order to solve this problem, we have developed a new algorithm to reveal the most representative mutants from the whole drug resistant mutant database based on our newly proposed unified protein sequence and 3D structure encoding method. Mean shift clustering and multiple regression analysis were applied on genotype-resistance data for mutants of HIV protease and reverse transcriptase. This approach successfully chooses less than 100 mutants with the highest resistance to each drug out of about 10K in the whole database. When considering high level resistance to multiple drugs, the numbers reduce to one or two representative mutants.
CONCLUSION: This approach for predicting the most representative mutants for each drug has major importance for experimental verification since the results provide a small number of representative sequences, which will be amenable for in vitro testing and characterization of the expressed mutant proteins.
RESULTS: In order to solve this problem, we have developed a new algorithm to reveal the most representative mutants from the whole drug resistant mutant database based on our newly proposed unified protein sequence and 3D structure encoding method. Mean shift clustering and multiple regression analysis were applied on genotype-resistance data for mutants of HIV protease and reverse transcriptase. This approach successfully chooses less than 100 mutants with the highest resistance to each drug out of about 10K in the whole database. When considering high level resistance to multiple drugs, the numbers reduce to one or two representative mutants.
CONCLUSION: This approach for predicting the most representative mutants for each drug has major importance for experimental verification since the results provide a small number of representative sequences, which will be amenable for in vitro testing and characterization of the expressed mutant proteins.