Use of ligand based models for protein domains to predict novel molecular targets and applications to triage affinity chromatography data.
The elucidation of drug targets is important both to optimize desired compound action and to understand drug side-effects. In this study, we created statistical models which link chemical substructures of ligands to protein domains in a probabilistic manner and employ the model to triage the results of affinity chromatography experiments. By annotating targets with their InterPro domains, general rules of ligand-protein domain associations were derived and successfully employed to predict protein targets outside the scope of the training set. This methodology was then tested on a proteomics affinity chromatography data set containing 699 compounds. The domain prediction model correctly detected 31.6% of the experimental targets at a specificity of 46.8%. This is striking since 86% of the predicted targets are not part of them (but share InterPro domains with them), and thus could not have been predicted by conventional target prediction approaches. Target predictions improve drastically when significance (FDR) scores for target pulldowns are employed, emphasizing their importance for eliminating artifacts. Filament proteins (such as actin and tubulin) are detected to be 'frequent hitters' in proteomics experiments and their presence in pulldowns is not supported by the target predictions. On the other hand, membrane-bound receptors such as serotonin and dopamine receptors are noticeably absent in the affinity chromatography sets, although their presence would be expected from the predicted targets of compounds. While this can partly be explained by the experimental setup, we suggest the computational methods employed here as a complementary step of identifying protein targets of small molecules. Affinity chromatography results for gefitinib are discussed in detail and while two out of the three kinases with the highest affinity to gefitinib in biochemical assays are detected by affinity chromatography, also the possible involvement of NSF as a target for modulating cancer progressions via beta-arrestin can be proposed by this method.
Bender
Andreas
A
Center for Proteomic Chemistry, Lead Discovery Informatics, Developmental and Molecular Pathways, and Global Discovery Chemistry, Chemogenetics and Proteomics, Novartis Institutes for BioMedical Research, Inc., Cambridge, Massachusetts 02139, USA.
Mikhailov
Dmitri
D
Glick
Meir
M
Scheiber
Josef
J
Davies
John W
JW
Cleaver
Stephen
S
Marshall
Stephen
S
Tallarico
John A
JA
Harrington
Edmund
E
Cornella-Taracido
Ivan
I
Jenkins
Jeremy L
JL
D001665:Binding Sites; D002846:Chromatography, Affinity / Q000379:methods*; D016503:Drug Delivery Systems / Q000379:methods; D000077156:Gefitinib; D006801:Humans; D008024:Ligands; D008954:Models, Biological; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000008:administration & dosage / Q000737:chemistry / Q000378:metabolism*; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000378:metabolism; D011494:Protein Kinases / Q000378:metabolism; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D040901:Proteomics / Q000379:methods*; D011799:Quinazolines / Q000737:chemistry / Q000378:metabolism; D015203:Reproducibility of Results
D008024:Ligands; D004364:Pharmaceutical Preparations; D047428:Protein Kinase Inhibitors; D011506:Proteins; D011799:Quinazolines; D011494:Protein Kinases; D000077156:Gefitinib