Rapid and accurate prediction and scoring of water molecules in protein binding sites

Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential dru...

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Bibliographic Details
Published in:PloS one 2012-03, Vol.7 (3), p.e32036
Main Authors: Ross, Gregory A, Morris, Garrett M, Biggin, Philip C
Format: Article
Language:English
Subjects:
Accuracy
Affinity
Algorithms
Amino acids
Automation
Binding Sites
Biochemistry
Bioinformatics
Biology
Chemistry
Computational Biology - methods
Computer simulation
Coordination compounds
Crystallography
Crystallography, X-Ray - methods
Data mining
Data processing
Displacement
Drug development
Energy
Genetic algorithms
Hydration
Hydrogen Bonding
Hydrogen bonds
Learning algorithms
Ligands
Machine learning
Medicine
Methods
Model accuracy
Models, Statistical
Molecular Conformation
Molecular dynamics
Monte Carlo Method
Neutron diffraction
Predictions
Probability
Protein binding
Protein interaction
Proteins
Proteins - chemistry
Reproducibility of Results
Simulation
Software
Solvents
Solvents - chemistry
Water - chemistry
X-ray crystallography
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Summary:Water plays a critical role in ligand-protein interactions. However, it is still challenging to predict accurately not only where water molecules prefer to bind, but also which of those water molecules might be displaceable. The latter is often seen as a route to optimizing affinity of potential drug candidates. Using a protocol we call WaterDock, we show that the freely available AutoDock Vina tool can be used to predict accurately the binding sites of water molecules. WaterDock was validated using data from X-ray crystallography, neutron diffraction and molecular dynamics simulations and correctly predicted 97% of the water molecules in the test set. In addition, we combined data-mining, heuristic and machine learning techniques to develop probabilistic water molecule classifiers. When applied to WaterDock predictions in the Astex Diverse Set of protein ligand complexes, we could identify whether a water molecule was conserved or displaced to an accuracy of 75%. A second model predicted whether water molecules were displaced by polar groups or by non-polar groups to an accuracy of 80%. These results should prove useful for anyone wishing to undertake rational design of new compounds where the displacement of water molecules is being considered as a route to improved affinity.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0032036