Exploring the "dark matter" of a mammalian proteome by protein structure and function modeling

A growing body of evidence shows that gene products encoded by short open reading frames play key roles in numerous cellular processes. Yet, they are generally overlooked in genome assembly, escaping annotation because small protein-coding genes are difficult to predict computationally. Consequently, there are still a considerable number of small proteins whose functions are yet to be characterized.

To address this issue, we apply a collection of structural bioinformatics algorithms to infer molecular function of putative small proteins from the mouse proteome. Specifically, we construct 1,743 confident structure models of small proteins, which reveal a significant structural diversity with a noticeably high helical content. A subsequent structure-based function annotation of small protein models exposes 178,745 putative protein-protein interactions with the remaining gene products in the mouse proteome, 1,100 potential binding sites for small organic molecules and 987 metal-binding signatures.

These results strongly indicate that many small proteins adopt three-dimensional structures and are fully functional, playing important roles in transcriptional regulation, cell signaling and metabolism. Data collected through this work is freely available to the academic community at http://www.brylinski.org/content/databases to support future studies oriented on elucidating the functions of hypothetical small proteins.