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A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors

The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and...

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Autores principales: Tarasova, Olga, Biziukova, Nadezhda, Filimonov, Dmitry, Poroikov, Vladimir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278491/
https://www.ncbi.nlm.nih.gov/pubmed/30355996
http://dx.doi.org/10.3390/molecules23112751
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author Tarasova, Olga
Biziukova, Nadezhda
Filimonov, Dmitry
Poroikov, Vladimir
author_facet Tarasova, Olga
Biziukova, Nadezhda
Filimonov, Dmitry
Poroikov, Vladimir
author_sort Tarasova, Olga
collection PubMed
description The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and successfully used in new drug discovery. An emerging body of data regarding the interactions of small drug-like molecules with their protein targets provides the possibility of building models on “structure-property” relationships and analyzing the performance of various machine-learning techniques. In our research, we analyze several different types of descriptors in order to predict the resistance of HIV reverse transcriptase and protease to the marketed antiretroviral drugs using the Random Forest approach. First, we represented amino acid sequences as a set of short peptide fragments, which included several amino acid residues. Second, we represented nucleotide sequences as a set of fragments, which included several nucleotides. We compared these two approaches using open data from the Stanford HIV Drug Resistance Database. We have determined the factors that modulate the performance of prediction: in particular, we observed that the prediction performance was more sensitive to certain drugs than a type of the descriptor used.
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spelling pubmed-62784912018-12-13 A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors Tarasova, Olga Biziukova, Nadezhda Filimonov, Dmitry Poroikov, Vladimir Molecules Article The high variability of the human immunodeficiency virus (HIV) is an important cause of HIV resistance to reverse transcriptase and protease inhibitors. There are many variants of HIV type 1 (HIV-1) that can be used to model sequence-resistance relationships. Machine learning methods are widely and successfully used in new drug discovery. An emerging body of data regarding the interactions of small drug-like molecules with their protein targets provides the possibility of building models on “structure-property” relationships and analyzing the performance of various machine-learning techniques. In our research, we analyze several different types of descriptors in order to predict the resistance of HIV reverse transcriptase and protease to the marketed antiretroviral drugs using the Random Forest approach. First, we represented amino acid sequences as a set of short peptide fragments, which included several amino acid residues. Second, we represented nucleotide sequences as a set of fragments, which included several nucleotides. We compared these two approaches using open data from the Stanford HIV Drug Resistance Database. We have determined the factors that modulate the performance of prediction: in particular, we observed that the prediction performance was more sensitive to certain drugs than a type of the descriptor used. MDPI 2018-10-24 /pmc/articles/PMC6278491/ /pubmed/30355996 http://dx.doi.org/10.3390/molecules23112751 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Tarasova, Olga
Biziukova, Nadezhda
Filimonov, Dmitry
Poroikov, Vladimir
A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors
title A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors
title_full A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors
title_fullStr A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors
title_full_unstemmed A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors
title_short A Computational Approach for the Prediction of HIV Resistance Based on Amino Acid and Nucleotide Descriptors
title_sort computational approach for the prediction of hiv resistance based on amino acid and nucleotide descriptors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6278491/
https://www.ncbi.nlm.nih.gov/pubmed/30355996
http://dx.doi.org/10.3390/molecules23112751
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