| Attributs | Valeurs |
|---|
| type
| |
| Is Part Of
| |
| Subject
| |
| Title
| - Chemometric Analysis of Ligand Receptor Complementarity: IdentifyingComplementary Ligands Based on Receptor Information (CoLiBRI)
|
| has manifestation of work
| |
| related by
| |
| Author
| |
| Abstract
| - We have developed a novel structure-based chemoinformatics approach to search for Complimentary LigandsBased on Receptor Information (CoLiBRI). CoLiBRI is based on the representation of both receptor bindingsites and their respective ligands in a space of universal chemical descriptors. The binding site atoms involvedin the interaction with ligands are identified by the means of a computational geometry technique known asDelaunay tessellation as applied to X-ray characterized ligand−receptor complexes. TAE/RECON multiplechemical descriptors are calculated independently for each ligand as well as for its active site atoms. Therepresentation of both ligands and active sites using chemical descriptors allows the application of well-known chemometric techniques in order to correlate chemical similarities between active sites and theirrespective ligands. We have established a protocol to map patterns of nearest neighbor active site vectorsin a multidimensional TAE/RECON space onto those of their complementary ligands and vice versa. Thisprotocol affords the prediction of a virtual complementary ligand vector in the ligand chemical space fromthe position of a known active site vector. This prediction is followed by chemical similarity calculationsbetween this virtual ligand vector and those calculated for molecules in a chemical database to identify realcompounds most similar to the virtual ligand. Consequently, the knowledge of the receptor active site structureaffords straightforward and efficient identification of its complementary ligands in large databases of chemicalcompounds using rapid chemical similarity searches. Conversely, starting from the ligand chemical structure,one may identify possible complementary receptor cavities as well. We have applied the CoLiBRI approachto a data set of 800 X-ray characterized ligand−receptor complexes in the PDBbind database. Using a knearest neighbor (kNN) pattern recognition approach and variable selection, we have shown that knowledgeof the active site structure affords identification of its complimentary ligand among the top 1% of a largechemical database in over 90% of all test active sites when a binding site of the same protein family waspresent in the training set. In the case where test receptors are highly dissimilar and not present among thereceptor families in the training set, the prediction accuracy is decreased; however, CoLiBRI was still ableto quickly eliminate 75% of the chemical database as improbable ligands. CoLiBRI affords rapid prefilteringof a large chemical database to eliminate compounds that have little chance of binding to a receptor activesite.
|
| article type
| |
| is part of this journal
| |
