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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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A visual depiction of Algorithm 2, for training a classifier to recognize the environment surrounding a specific atom, given a corpus of examples of that atom’s binding.
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Figure 4: A visual depiction of Algorithm 2, for training a classifier to recognize the environment surrounding a specific atom, given a corpus of examples of that atom’s binding.

Mentions: Refer to Algorithm 2 for a detailed description of this process, and to Figure 4 for a visual depiction.


Local functional descriptors for surface comparison based binding prediction.

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

A visual depiction of Algorithm 2, for training a classifier to recognize the environment surrounding a specific atom, given a corpus of examples of that atom’s binding.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: A visual depiction of Algorithm 2, for training a classifier to recognize the environment surrounding a specific atom, given a corpus of examples of that atom’s binding.
Mentions: Refer to Algorithm 2 for a detailed description of this process, and to Figure 4 for a visual depiction.

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