Predicting protein domain interactions from coevolution of conserved regions.
The knowledge of protein and domain interactions provide crucial insights into their function within a cell. Several computational methods have been proposed to detect interactions between proteins and their constitutive domains. In this work, we focus on approaches based on correlated evolution (coevolution) of sequences of interacting proteins. In this type of approach, often referred to as the mirrortree method, a high correlation of evolutionary histories of two proteins is used as an indicator to predict protein interactions. Recently, it has been observed that subtracting the underlying speciation process by separating coevolution due to common speciation divergence from that due to common function of interacting pairs greatly improves the predictive power of the mirrortree approach. In this article, we investigate possible improvements and limitations of this method. In particular, we demonstrate that the performance of the mirrortree method that can be further improved by restricting the coevolution analysis to the relatively conserved regions in the protein domain sequences (disregarding highly divergent regions). We provide a theoretical validation of our results leading to new insights into the interplay between coevolution and speciation of interacting proteins.
Kann
Maricel G
MG
Department of Health and Human Services, National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, Maryland 20894, USA.
Jothi
Raja
R
Cherukuri
Praveen F
PF
Przytycka
Teresa M
TM
D000595:Amino Acid Sequence; D019295:Computational Biology; D019277:Entropy; D019143:Evolution, Molecular; D008958:Models, Molecular; D017434:Protein Structure, Tertiary; D011506:Proteins / Q000737:chemistry* / Q000235:genetics / Q000378:metabolism*
D011506:Proteins