Abstract
BackgroundMany molecules are flexible and undergo significant shape deformation as part of their function, and yet most existing molecular shape comparison (MSC) methods treat them as rigid bodies, which may lead to incorrect shape recognition.ResultsIn this paper, we present a new shape descriptor, named Diffusion Distance Shape Descriptor (DDSD), for comparing 3D shapes of flexible molecules. The diffusion distance in our work is considered as an average length of paths connecting two landmark points on the molecular shape in a sense of inner distances. The diffusion distance is robust to flexible shape deformation, in particular to topological changes, and it reflects well the molecular structure and deformation without explicit decomposition. Our DDSD is stored as a histogram which is a probability distribution of diffusion distances between all sample point pairs on the molecular surface. Finally, the problem of flexible MSC is reduced to comparison of DDSD histograms.ConclusionsWe illustrate that DDSD is insensitive to shape deformation of flexible molecules and more effective at capturing molecular structures than traditional shape descriptors. The presented algorithm is robust and does not require any prior knowledge of the flexible regions.
Highlights
Many molecules are flexible and undergo significant shape deformation as part of their function, and yet most existing molecular shape comparison (MSC) methods treat them as rigid bodies, which may lead to incorrect shape recognition
Inner distance In order to overcome the disadvantages of Euclidean distance (ED) and geodesic distance (GD), we recently proposed a new shape descriptor based on inner distance (ID) for comparing the shapes of flexible molecules [13]
The ID descriptors is sensitive to topological changes of shapes, while our diffusion distance (DD) descriptors keep largely consistent for the four deformed conformation shapes of the same protein
Summary
Many molecules are flexible and undergo significant shape deformation as part of their function, and yet most existing molecular shape comparison (MSC) methods treat them as rigid bodies, which may lead to incorrect shape recognition. The geometrical shape of a molecule is a key factor for biological activity in computer aided molecular design, rational drug design, molecular docking and function prediction [1]. To exploit the shape similarity between molecules, a useful tool is molecular shape comparison (MSC) that compares the shapes of two or more molecules and identifies common spatial features [1,2,3,4]. In computer aided drug design, for instance, an alternative process of virtual screening takes advantage of such comparison for searching a molecular database for compounds that most closely resemble a given query molecule [2,3,5,6]. The efficient MSC is still a challenge [1,2,3,4] due to the high complexity of 3D molecular shapes
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