Prediction of Protein Pairs Sharing Common Active Ligands Using Protein Sequence, Structure, and Ligand Similarity

We benchmarked the ability of comparative computational approaches to correctly discriminate protein pairs sharing a common active ligand (positive protein pairs) from protein pairs with no common active ligands (negative protein pairs). Since the target and the off-targets of a drug share at least...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2016-09, Vol.56 (9), p.1734-1745
Main Authors: Chen, Yu-Chen, Tolbert, Robert, Aronov, Alex M, McGaughey, Georgia, Walters, W. Patrick, Meireles, Lidio
Format: Article
Language:English
Subjects:
Amino Acid Sequence
Benchmarking
Binding sites
Computational Biology
Correlation analysis
Ligands
Models, Molecular
Protein Conformation
Proteins
Proteins - chemistry
Proteins - metabolism
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Summary:We benchmarked the ability of comparative computational approaches to correctly discriminate protein pairs sharing a common active ligand (positive protein pairs) from protein pairs with no common active ligands (negative protein pairs). Since the target and the off-targets of a drug share at least a common ligand, i.e., the drug itself, the prediction of positive protein pairs may help identify off-targets. We evaluated representative protein-centric and ligand-centric approaches, including (1) 2D and 3D ligand similarity, (2) several measures of protein sequence similarity in conjunction with different sequence sources (e.g., full protein sequence versus binding site residues), and (3) a newly described pocket shape similarity and alignment program called SiteHopper. While the sequence-based alignment of pocket residues achieved the best overall performance, SiteHopper outperformed sequence-based approaches for unrelated proteins with only 20–30% pocket residue identity. Analogously, among ligand-centric approaches, path-based fingerprints achieved the best overall performance, but ROCS-based ligand shape similarity outperformed path-based fingerprints for structurally dissimilar ligands (Tanimoto 25%–40%). A significant drop in recognition performance was observed for ligand-centric approaches when PDB ligands were used instead of ChEMBL ligands. Finally, we analyzed the relationship between pocket shape and ligand shape in our data set and found that similar ligands tend to bind to similar pockets while similar pockets may accept a range of different-shaped ligands.
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.6b00118