Analysis of Positional Interdependencies of Mutations in Reverse Transcriptase
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ABSTRACT:
Mutations in one position may depend on mutations in other positions, therefore it is important to quantify, i.e.
measure mutational influence between various positions. For this purpose a new methodological tool is used,
which is based on information- and graph theory. Data about the Reverse Transcriptase (RT) available from the
EuResist project's (www.euresist.org) integrated database are represented as a weighted directed graph. Positions
in the RT sequence are represented as nodes, while the weight of the edge connecting nodes i and j is calculated
from the Thiel's entropy coefficient k(i, j) = Ii,j / Si, where Si is the entropy of amino-acid probability density at
position i, whilst Ii,j is the mutual information of amino-acid occurrence between positions i and j. The weight
measures how much the knowledge of the amino-acid at position j statistically determines the knowledge of the
amino-acid at position i. Graph is clustered using the Szemerédi's regularity lemma. Because of graph's
directedness, to each position two cluster indices were assigned. The outcome of the clustering procedure is a
grouping of RT positions into groups which influence each other's mutations in the strongest possible way.
Simulations showed that best clustering results (without vacuous categories) were obtained when edges with
weights 0.9 were considered, and the number of clusters was fixed to 15. The method can be applied to the
analysis of other enzymes and genetic data. The clustering results give new perspective on positions responsible
for the development of antiviral drug resistance, and also suggest new targets suitable for future drug- and
vaccine design.
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STATISTICS
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