De-novo protein function prediction using DNA binding and RNA binding proteins as a test case

Sapir Peled, Olga Leiderman, Rotem Charar, Gilat Efroni, Yaron Shav-Tal and Yanay Ofran ()
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Sapir Peled: The Goodman Faculty of Life Sciences, Nanotechnology building, Bar Ilan University
Olga Leiderman: The Goodman Faculty of Life Sciences, Nanotechnology building, Bar Ilan University
Rotem Charar: The Goodman Faculty of Life Sciences, Nanotechnology building, Bar Ilan University
Gilat Efroni: The Goodman Faculty of Life Sciences, Nanotechnology building, Bar Ilan University
Yaron Shav-Tal: The Goodman Faculty of Life Sciences, Nanotechnology building, Bar Ilan University
Yanay Ofran: The Goodman Faculty of Life Sciences, Nanotechnology building, Bar Ilan University

Nature Communications, 2016, vol. 7, issue 1, 1-9

Abstract: Abstract Of the currently identified protein sequences, 99.6% have never been observed in the laboratory as proteins and their molecular function has not been established experimentally. Predicting the function of such proteins relies mostly on annotated homologs. However, this has resulted in some erroneous annotations, and many proteins have no annotated homologs. Here we propose a de-novo function prediction approach based on identifying biophysical features that underlie function. Using our approach, we discover DNA and RNA binding proteins that cannot be identified based on homology and validate these predictions experimentally. For example, FGF14, which belongs to a family of secreted growth factors was predicted to bind DNA. We verify this experimentally and also show that FGF14 is localized to the nucleus. Mutating the predicted binding site on FGF14 abrogated DNA binding. These results demonstrate the feasibility of automated de-novo function prediction based on identifying function-related biophysical features.

Date: 2016
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DOI: 10.1038/ncomms13424

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