Improved protein–ligand binding affinity prediction by using a curvature-dependent surface-area model

Motivation: Hydrophobic effect plays a pivotal role in most protein–ligand binding. State-of-the-art protein–ligand scoring methods usually treat hydrophobic free energy as surface tension, which is proportional to interfacial surface area for simplicity and efficiency. However, this treatment ignor...

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Vydáno v:Bioinformatics (Oxford, England) Ročník 30; číslo 12; s. 1674 - 1680
Hlavní autoři: Cao, Yang, Li, Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 15.06.2014
Témata:
Entropy
Hydrogen Bonding
Hydrophobic and Hydrophilic Interactions
Ligands
Models, Chemical
Protein Binding
Proteins - chemistry
Software
ISSN:1367-4803, 1367-4811, 1367-4811
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Shrnutí:Motivation: Hydrophobic effect plays a pivotal role in most protein–ligand binding. State-of-the-art protein–ligand scoring methods usually treat hydrophobic free energy as surface tension, which is proportional to interfacial surface area for simplicity and efficiency. However, this treatment ignores the role of molecular shape, which has been found very important by either experimental or theoretical studies. Results: We propose a new empirical scoring function, named Cyscore. Cyscore improves the prediction accuracy by using a novel curvature-dependent surface-area model, which is able to distinguish convex, planar and concave surface in hydrophobic free energy calculation. Benchmark tests show that this model significantly improves the protein–ligand scoring and Cyscore outperforms a variety of well established scoring functions using PDBbind benchmark sets for binding affinity correlation and ranking tests. We expect the curvature-dependent surface-area model and Cyscore would contribute to the study of protein–ligand interactions. Availability: Cyscore is available to non-commercial users at http://clab.labshare.cn/software/cyscore.html. Contact:  cao@scu.edu.cn Supplementary information:  Supplementary Data is available at Bioinformatics online.
Bibliografie:ObjectType-Article-1
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ISSN:1367-4803
1367-4811
1367-4811
DOI:10.1093/bioinformatics/btu104