Prediction of enzyme function based on 3D templates of evolutionarily important amino acids.
BACKGROUND
Structural genomics projects such as the Protein Structure Initiative (PSI) yield many new structures, but often these have no known molecular functions. One approach to recover this information is to use 3D templates - structure-function motifs that consist of a few functionally critical amino acids and may suggest functional similarity when geometrically matched to other structures. Since experimentally determined functional sites are not common enough to define 3D templates on a large scale, this work tests a computational strategy to select relevant residues for 3D templates.


RESULTS
Based on evolutionary information and heuristics, an Evolutionary Trace Annotation (ETA) pipeline built templates for 98 enzymes, half taken from the PSI, and sought matches in a non-redundant structure database. On average each template matched 2.7 distinct proteins, of which 2.0 share the first three Enzyme Commission digits as the template's enzyme of origin. In many cases (61%) a single most likely function could be predicted as the annotation with the most matches, and in these cases such a plurality vote identified the correct function with 87% accuracy. ETA was also found to be complementary to sequence homology-based annotations. When matches are required to both geometrically match the 3D template and to be sequence homologs found by BLAST or PSI-BLAST, the annotation accuracy is greater than either method alone, especially in the region of lower sequence identity where homology-based annotations are least reliable.


CONCLUSIONS
These data suggest that knowledge of evolutionarily important residues improves functional annotation among distant enzyme homologs. Since, unlike other 3D template approaches, the ETA method bypasses the need for experimental knowledge of the catalytic mechanism, it should prove a useful, large scale, and general adjunct to combine with other methods to decipher protein function in the structural proteome.
Kristensen
David M
DM
Department of Molecular and Human Genetics, Biophysics, Baylor College of Medicine, Houston, TX 77030, USA. dk131363@gmail.com
Ward
R Matthew
RM
Lisewski
Andreas Martin
AM
Erdin
Serkan
S
Chen
Brian Y
BY
Fofanov
Viacheslav Y
VY
Kimmel
Marek
M
Kavraki
Lydia E
LE
Lichtarge
Olivier
O
D020816:Amino Acid Motifs / Q000235:genetics*; D001185:Artificial Intelligence; D030562:Databases, Protein; D004798:Enzymes / Q000737:chemistry / Q000235:genetics / Q000378:metabolism; D019143:Evolution, Molecular; D016013:Likelihood Functions; D008954:Models, Biological; D010363:Pattern Recognition, Automated; D011487:Protein Conformation; D020543:Proteome; D016415:Sequence Alignment; D017386:Sequence Homology, Amino Acid; D040681:Structural Homology, Protein; D013329:Structure-Activity Relationship
D004798:Enzymes; D020543:Proteome