Abstract

The metabolic and regulatory capabilities of an organism are implicit in its protein content. This is often hard to estimate, however, due to ascertainment biases inherent in the available genome annotations. Its complement of recognizable functional protein domains and their combinations convey essentially the same information and at the same time are much more readily accessible, although protein domain models trained for one phylogenetic group frequently fail on distantly related sequences. Pooling related domain models based on their GO-annotation in combination with de novo gene prediction methods provides estimates that seem to be less affected by phylogenetic biases. We show here for 18 diverse representatives from all eukaryotic kingdoms that a pooled analysis of the tendencies for co-occurrence or avoidance of protein domains is indeed feasible. This type of analysis can reveal general large-scale patterns in the domain co-occurrence and helps to identify lineage-specific variations in the evolution of protein domains. Somewhat surprisingly, we do not find strong ubiquitous patterns governing the evolutionary behavior of specific functional classes. Instead, there are strong variations between the major groups of Eukaryotes, pointing at systematic differences in their evolutionary constraints.

Highlights

  • The protein repertoire of an organism provides summary information on its metabolic and regulatory capabilities

  • With the exception of the functional classes regulation of enzymatic activity (rE) and regulation of catalytic activity (rC) there is no pattern of conserved avoidance

  • We find that most of the domain GO-classes are at least weakly positively correlated, in part reflecting the fact that the protein domains can have promiscuous functions, in part possibly because the domains investigated here are mostly involved in binding and regulatory processes

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Summary

Introduction

The protein repertoire of an organism provides summary information on its metabolic and regulatory capabilities. It should be possible at least in principle to identify large-scale trends in evolution such as the increased complexity of transcriptional regulation, chromatin-related mechanisms, or post-transcriptional silencing, by comparing the proteomes among species. In a recent study of chromatin evolution, we demonstrated that it is feasible to determine large-scale trends in regulatory capabilities based on domain content [7]

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