Unexpected features of the dark proteome

Nelson Perdigão, Julian Heinrich, Christian Stolte, Kenneth S. Sabir, Michael J. Buckley, Bruce Tabor, Beth Signal, Brian S. Gloss, Christopher J. Hammang, Burkhard Rost, Andrea Schafferhans, Seán I. O'Donoghue
<span title="2015-11-17">2015</span> <i title="Proceedings of the National Academy of Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/nvtuoas5pbdsllkntnhizy4f4q" style="color: black;">Proceedings of the National Academy of Sciences of the United States of America</a> </i> &nbsp;
We surveyed the "dark" proteome-that is, regions of proteins never observed by experimental structure determination and inaccessible to homology modeling. For 546,000 Swiss-Prot proteins, we found that 44-54% of the proteome in eukaryotes and viruses was dark, compared with only ∼14% in archaea and bacteria. Surprisingly, most of the dark proteome could not be accounted for by conventional explanations, such as intrinsic disorder or transmembrane regions. Nearly half of the dark proteome
more &raquo; ... ed dark proteins, in which the entire sequence lacked similarity to any known structure. Dark proteins fulfill a wide variety of functions, but a subset showed distinct and largely unexpected features, such as association with secretion, specific tissues, the endoplasmic reticulum, disulfide bonding, and proteolytic cleavage. Dark proteins also had short sequence length, low evolutionary reuse, and few known interactions with other proteins. These results suggest new research directions in structural and computational biology. structure prediction | protein disorder | transmembrane proteins | secreted proteins | unknown unknowns T he Protein Data Bank (PDB) (1) of experimentally determined macromolecular structures recently surpassed 110,000 entries-a landmark in understanding the molecular machinery of life. Structure determination lags far behind DNA sequencing, but high-throughput computational modeling (2, 3) can leverage the PDB to provide accurate structural predictions for a large fraction of protein sequences. Thus, structural data now scale with se-
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