Prediction of calcium-binding sites by combining loop-modeling with machine learning.
BACKGROUND
Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information.


RESULTS
We report a hybrid approach for recognizing calcium-binding sites in disordered regions. Our approach combines loop modeling with a machine learning method (FEATURE) for structure-based site recognition. For validation, we compared the performance of our method on known calcium-binding sites for which there are both holo and apo structures. When loops in the apo structures are rebuilt using modeling methods, FEATURE identifies 14 out of 20 crystallographically proven calcium-binding sites. It only recognizes 7 out of 20 calcium-binding sites in the initial apo crystal structures.We applied our method to unstructured loops in proteins from SCOP families known to bind calcium in order to discover potential cryptic calcium binding sites. We built 2745 missing loops and evaluated them for potential calcium binding. We made 102 predictions of calcium-binding sites. Ten predictions are consistent with independent experimental verifications. We found indirect experimental evidence for 14 other predictions. The remaining 78 predictions are novel predictions, some with intriguing potential biological significance. In particular, we see an enrichment of beta-sheet folds with predicted calcium binding sites in the connecting loops on the surface that may be important for calcium-mediated function switches.


CONCLUSIONS
Protein crystal structures are a potentially rich source of functional information. When loops are missing in these structures, we may be losing important information about binding sites and active sites. We have shown that limited loop modeling (e.g. loops less than 17 residues) combined with pattern matching algorithms can recover functions and propose putative conformations associated with these functions.
Liu
Tianyun
T
Department of Genetics, Stanford University, Stanford, CA, USA. tianyunl@stanford.edu
Altman
Russ B
RB
D018171:Agrin / Q000737:chemistry / Q000378:metabolism; D000595:Amino Acid Sequence; D000818:Animals; D000942:Antigens, Bacterial / Q000737:chemistry / Q000378:metabolism; D001185:Artificial Intelligence; D001408:Bacillus anthracis / Q000737:chemistry; D001427:Bacterial Toxins / Q000737:chemistry / Q000378:metabolism; D001665:Binding Sites; D002118:Calcium / Q000378:metabolism*; D032449:Canavalia / Q000737:chemistry; D002645:Chickens; D003208:Concanavalin A / Q000737:chemistry / Q000378:metabolism; D006801:Humans; D008958:Models, Molecular; D008969:Molecular Sequence Data; D011485:Protein Binding; D011487:Protein Conformation; D011494:Protein Kinases / Q000737:chemistry; D011506:Proteins / Q000737:chemistry* / Q000378:metabolism
D018171:Agrin; D000942:Antigens, Bacterial; D001427:Bacterial Toxins; D011506:Proteins; C030325:anthrax toxin; D003208:Concanavalin A; D011494:Protein Kinases; D002118:Calcium