Manual Annotation of Protein Interactions

Abstract

Protein interactions are the basic building blocks for assembly of pathways and networks. Almost any biologically meaningful functionality (for instance, linear signaling pathways, chains of metabolic reactions, transcription factor dimmers, protein complexes of transcriptosome, gene–disease associations) can be represented as a combination of binary relationships between “network objects” (genes, proteins, RNA species, bioactive compounds). Naturally, the assembled pathways and networks are only as good as their “weakest” link (i.e., a wrongly assigned interaction), and the errors multiply in multi-step pathways. Therefore, the utility of “systems biology” is fundamentally dependent on quality and relevance of protein interactions. The second important parameter is the sheer number of interactions assembled in the database. One needs a “critical mass” of species-specific interactions in order to build cohesive networks for a gene list, not a constellation of non-connected proteins and protein pairs. The third issue is semantic consistency between interactions of different types. Transient physical signal transduction interactions, reactions of endogenous metabolism, transcription factor–promoter binding, and kinetic drug–target interactions are all very different in nature. Yet, they have to fit well into one database format and be consistent in order to be useful in reconstruction of cellular processes.

High-quality protein interactions are available in peer-reviewed “small experiment” literature and, to a much smaller extent, patents. However, it is very challenging to find the interactions, annotate with searchable (and computable) parameters, catalogue in the database format in computer readable form, and assemble into a database. There are hundreds of thousands of mammalian interactions scattered in tens of thousands of papers in a few thousands of scientific journals. There are no widely used standards for reporting the interactions in scientific texts and, therefore, text-mining tools have only limited applicability. In order to generate a meaningful database of protein interactions, one needs a well-developed technology of manual curation, equipped with computational solutions, managerial procedures, quality control, and users’ feedback. Here we describe our ever-evolving annotation approach, the important annotation issues and our solutions, and the mammalian protein interactions database MetaBase™ which we have been working on for over 8 years.