Predicting proteolytic sites in extracellular proteins: only halfway there.
BACKGROUND
Many secretory proteins are synthesized as inactive precursors that must undergo post-translational proteolysis in order to mature and become active. In the current study, we address the challenge of sequence-based discovery of proteolytic sites in secreted proteins using machine learning.


RESULTS
The results revealed that only half of the extracellular proteolytic sites are currently annotated, leaving over 3600 unannotated ones. Furthermore, we have found that only 6% of the unannotated sites are similar to known proteolytic sites, whereas the remaining 94% do not share significant similarity with any annotated proteolytic site. The computational challenges in these two cases are very different. While the precision in detecting the former group is close to perfect, only a mere 22% of the latter group were detected with a precision of 80%. The applicability of the classifier is demonstrated through members of the FGF family, in which we verified the conservation of physiologically-relevant proteolytic sites in homologous proteins.
Kliger
Yossef
Y
Compugen Ltd, 72 Pinchas Rosen, Tel Aviv, Israel. kliger@compugen.co.il
Gofer
Eyal
E
Wool
Assaf
A
Toporik
Amir
A
Apatoff
Avihay
A
Olshansky
Moshe
M
D000595:Amino Acid Sequence; D001665:Binding Sites; D003198:Computer Simulation; D016326:Extracellular Matrix Proteins / Q000737:chemistry*; D008956:Models, Chemical; D008969:Molecular Sequence Data; D011485:Protein Binding; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
D016326:Extracellular Matrix Proteins