Molecular surfaces are one of the key players in processes of bimolecular recognition and interaction. Nowadays, state of the art methods exist for visualizing molecule surface and surface distributed properties in three-dimensional space. However, such visual information could only be analyzed by human eye and therefore prompt to be biased and onerous in case of large sets of objects. Here we present a method to create 2D projections or ”earth maps” of whole protein surface – protein surface topography (PST). Representing complex molecule surfaces as an array of data gives the advantage of simple and pictorial visualization of surface properties. PST can be used to easy visualize conformational changes between different states of molecules, perform group analysis, and reveal common patterns or dissimilarities. It is useful tool to add to docking experiments, illustrating complementary features between ligand and receptor surfaces.
This study is dedicated to the introduction of a novel method that automatically extracts potential structural alerts from a data set of molecules. These triggering structures can be further used for knowledge discovery and classification purposes. Computation of the structural alerts results from an implementation of a sophisticated workflow that integrates a graph mining tool guided by growth rate and stability. The growth rate is a well-established measurement of contrast between classes. Moreover, the extracted patterns correspond to formal concepts; the most robust patterns, named the stable emerging patterns (SEPs), can then be identified thanks to their stability, a new notion originating from the domain of formal concept analysis. All of these elements are explained in the paper from the point of view of computation. The method was applied to a molecular data set on mutagenicity. The experimental results demonstrate its efficiency: it automatically outputs a manageable number of structural patterns that are strongly related to mutagenicity. Moreover, a part of the resulting structures corresponds to already known structural alerts. Finally, an in-depth chemical analysis relying on these structures demonstrates how the method can initiate promising processes of chemical knowledge discovery. © 2015 American Chemical Society.