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.

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis.

In this paper we introduce a technique for applying textual labels to 3D surfaces. An effective labeling must balance the conflicting goals of conveying the shape of the surface while being legible from a range of viewing directions. Shape can be conveyed by placing the text as a texture directly on the surface, providing shape cues, meaningful landmarks and minimally obstructing the rest of the model. But rendering such surface text is problematic both in regions of high curvature, where text would be warped, and in highly occluded regions, where it would be hidden. Our approach achieves both labeling goals by applying surface labels to a 'text scaffold', a surface explicitly constructed to hold the labels. Text scaffolds conform to the underlying surface whenever possible, but can also float above problem regions, allowing them to be smooth while still conveying the overall shape. This paper provides methods for constructing scaffolds from a variety of input sources, including meshes, constructive solid geometry, and scalar fields. These sources are first mapped into a distance transform, which is then filtered and used to construct a new mesh on which labels are either manually or automatically placed. In the latter case, annotated regions of the input surface are associated with proximal regions on the new mesh, and labels placed using cartographic principles.

In this paper we introduce a visualization technique that provides an abstracted view of the shape and spatio-physico-chemical properties of complex molecules. Unlike existing molecular viewing methods, our approach suppresses small details to facilitate rapid comprehension, yet marks the location of significant features so they remain visible. Our approach uses a combination of filters and mesh restructuring to generate a simplified representation that conveys the overall shape and spatio-physico-chemical properties (e.g. electrostatic charge). Surface markings are then used in the place of important removed details, as well as to supply additional information. These simplified representations are amenable to display using stylized rendering algorithms to further enhance comprehension. Our initial experience suggests that our approach is particularly useful in browsing collections of large molecules and in readily making comparisons between them.