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Local functional descriptors for surface comparison based binding prediction.

Cipriano GM, Phillips GN, Gleicher M - BMC Bioinformatics (2012)

Bottom Line: Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface.Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand.The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.

ABSTRACT

Background: Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface.

Results: We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods.

Conclusions: Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications.

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Shown here are, for one sample point, the disc-shaped patches of each radii used in the functional surface descriptor: 1.6Å, 3.2Å, 4.8Å, 6.4Å and 8Å.
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Figure 3: Shown here are, for one sample point, the disc-shaped patches of each radii used in the functional surface descriptor: 1.6Å, 3.2Å, 4.8Å, 6.4Å and 8Å.

Mentions: Each of the sampled, spatially varying properties (curvature, anisotropy, curvature variance, hydropathy, and electrostatic charge) are sampled at several different scales. A scale is specified as a geodesic disc around the sample point. For example, electrostatic charge at the 8Å scale is the weighted average of all sample points within 8Å geodesic distance of the sample point. Each property is sampled at 5 scales: 1.6Å, 3.2Å, 4.8Å, 6.4Å and 8Å. These also map to the scale of important biological features, the first to the size of an atom, the next two to the size of a residue, and the last two to the size of small pockets (see Figure 3 for a depiction of these sizes).


Local functional descriptors for surface comparison based binding prediction.

Cipriano GM, Phillips GN, Gleicher M - BMC Bioinformatics (2012)

Shown here are, for one sample point, the disc-shaped patches of each radii used in the functional surface descriptor: 1.6Å, 3.2Å, 4.8Å, 6.4Å and 8Å.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3585919&req=5

Figure 3: Shown here are, for one sample point, the disc-shaped patches of each radii used in the functional surface descriptor: 1.6Å, 3.2Å, 4.8Å, 6.4Å and 8Å.
Mentions: Each of the sampled, spatially varying properties (curvature, anisotropy, curvature variance, hydropathy, and electrostatic charge) are sampled at several different scales. A scale is specified as a geodesic disc around the sample point. For example, electrostatic charge at the 8Å scale is the weighted average of all sample points within 8Å geodesic distance of the sample point. Each property is sampled at 5 scales: 1.6Å, 3.2Å, 4.8Å, 6.4Å and 8Å. These also map to the scale of important biological features, the first to the size of an atom, the next two to the size of a residue, and the last two to the size of small pockets (see Figure 3 for a depiction of these sizes).

Bottom Line: Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface.Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand.The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.

ABSTRACT

Background: Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface.

Results: We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods.

Conclusions: Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications.

Show MeSH