In:
PLOS ONE, Public Library of Science (PLoS), Vol. 16, No. 8 ( 2021-8-4), p. e0255693-
Abstract:
A method for predicting HIV drug resistance by using genotypes would greatly assist in selecting appropriate combinations of antiviral drugs. Models reported previously have had two major problems: lack of information on the 3D protein structure and processing of incomplete sequencing data in the modeling procedure. We propose obtaining the 3D structural information of viral proteins by using homology modeling and molecular field mapping, instead of just their primary amino acid sequences. The molecular field potential parameters reflect the physicochemical characteristics associated with the 3D structure of the proteins. We also introduce the Bayesian conditional mutual information theory to estimate the probabilities of occurrence of all possible protein candidates from an incomplete sequencing sample. This approach allows for the effective use of uncertain information for the modeling process. We applied these data analysis techniques to the HIV-1 protease inhibitor dataset and developed drug resistance prediction models with reasonable performance.
Type of Medium:
Online Resource
ISSN:
1932-6203
DOI:
10.1371/journal.pone.0255693
DOI:
10.1371/journal.pone.0255693.g001
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10.1371/journal.pone.0255693.g002
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10.1371/journal.pone.0255693.g003
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10.1371/journal.pone.0255693.g004
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10.1371/journal.pone.0255693.t001
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10.1371/journal.pone.0255693.t002
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10.1371/journal.pone.0255693.t003
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10.1371/journal.pone.0255693.s001
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10.1371/journal.pone.0255693.s002
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10.1371/journal.pone.0255693.s003
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10.1371/journal.pone.0255693.s014
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10.1371/journal.pone.0255693.s015
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10.1371/journal.pone.0255693.s016
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10.1371/journal.pone.0255693.s017
DOI:
10.1371/journal.pone.0255693.s018
Language:
English
Publisher:
Public Library of Science (PLoS)
Publication Date:
2021
detail.hit.zdb_id:
2267670-3
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