Taxonomic diversity-based domain interaction prediction
Identification of protein domain-domain interactions (DDIs) is an essential step in understanding proteins’ functional and structural roles. MirrorTree is a DDI prediction method that is based on the principle of interacting proteins’ co-evolution. However, this method is sensitive to taxonomic dive...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
Pamukkale University
2019-04-01
|
Series: | Pamukkale University Journal of Engineering Sciences |
Subjects: | |
Online Access: | https://dergipark.org.tr/tr/pub/pajes/issue/44767/556919 |
Summary: | Identification of protein domain-domain
interactions (DDIs) is an essential step in understanding proteins’ functional
and structural roles. MirrorTree is a DDI prediction method that is based on
the principle of interacting proteins’ co-evolution. However, this method is
sensitive to taxonomic diversity and evolutionary span within the two protein
homolog sets compared to predict DDI. In this work, we propose a new
MirrorTree-based DDI prediction method, namely Taxonomic Diversity-based Domain
Interaction Prediction (TAXDIP). TAXDIP improves the MirrorTree method by
adding a sampling step that favors representation of higher-level taxonomic
ranks (e.g. family over species) in two protein homolog sets prior to their
comparison. This additional step ensures increased evolutionary span within
protein homolog sets. TAXDIP is first assessed using a set containing 6,514
positive (interacting) domain pairs and a negative (non-interacting) set of
equal size containing randomly generated domain pairs with no known
interactions. TAXDIP achieved 71.0% sensitivity and 63.0% specificity on this
set. Next, a benchmark-set containing
500 interacting and 500 non-interacting domain pairs is used to compare the
performance of TAXDIP against DDI prediction methods ME and RDFF. TAXDIP showed better sensitivity and
specificity than RDFF. While TAXDIP’s sensitivity is better than ME, its
specificity remained below ME. In conclusion, TAXDIP, with its performance, is
a viable alternative to existing prediction methods. Furthermore, given
TAXDIP’s true predictions are overlapping with, and furthermore, complementing
other DDI prediction methods, TAXDIP has a strong position in becoming part of
a meta-DDI prediction method that combines multiple methods to build a
consensus prediction. |
---|---|
ISSN: | 1300-7009 2147-5881 |