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Towards a Realistic Representation in Surface-Based Pseudoreceptor Modeling: a PDB-Wide Analysis of Binding Pockets
Gregory L Wilson1, Markus A Lill2
1Department of Medicinal Chemistry and Molecular Pharmacology, College of Pharmacy, Purdue University, 575 Stadium Mall Drive, West Lafayette, IN 47907, USA phone: ++1 (765) 496-9375; fax: ++1 (765) 494-1414.
Molecular Informatics
|August 2, 2016
Summary
New surface descriptors based on 2D Gaussian functions improve protein binding site modeling in quantitative structure-activity relationship (QSAR) studies. This approach reduces overfitting and enhances the realistic representation of binding pockets for drug discovery.
Area of Science:
- Computational chemistry
- Structural biology
- Drug discovery
Background:
- Surface-based pseudoreceptor methods expand 3D-QSAR by mapping physicochemical properties onto a 3D surface around aligned compounds.
- These methods model protein binding sites but are prone to overfitting due to independent surface point descriptors.
Purpose of the Study:
- To develop novel surface descriptors for modeling protein binding site physicochemical properties.
- To improve the accuracy and reduce overfitting in pseudoreceptor-based QSAR models.
Main Methods:
- Analysis of binding pocket surfaces from experimentally determined protein-ligand complexes.
- Fitting surface properties using 2D Gaussian functions.
- Development of surface descriptors based on these fitted Gaussian functions.
Main Results:
- Developed surface descriptors based on 2D Gaussian functions to model protein binding site properties.
- Fitted property values showed an average deviation of 15-25% from original values.
- An average of six Gaussian functions were required to model each surface property.
Conclusions:
- The new 2D Gaussian-based descriptors offer a realistic representation of protein binding sites.
- This method effectively limits the number of descriptors, mitigating overfitting in QSAR optimization.
- Enables more robust and accurate modeling of protein-ligand interactions.
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