An optimized self-organizing map algorithm has been used to obtain protein topological (proteinotopic) maps. A neural network is able to arrange a set of proteins depending on their ultraviolet circular dichroism spectra in a completely unsupervised learning process. Analysis of the proteinotopic map reveals that the network extracts the main secondary structure features even with the small number of examples used. Some methods to use the proteinotopic map for protein secondary structure prediction are tested showing a good performance in the 200–240 nm wavelength range that is likely to increase as new protein structures are known.

The increasing number of experimentally detected interactions between proteins makes it difficult for researchers to extract the interactions relevant for specific biological processes or diseases. This makes it necessary to accompany the large-scale detection of protein–protein interactions (PPIs) with strategies and tools to generate meaningful PPI subnetworks. To this end, we generated the Human Integrated Protein–Protein Interaction rEference or HIPPIE (http://cbdm.uni-mainz.de/hippie/). HIPPIE is a one-stop resource for the generation and interpretation of PPI networks relevant to a specific research question. We provide means to generate highly reliable, context-specific PPI networks and to make sense out of them. We just released the second major update of HIPPIE, implementing various new features. HIPPIE grew substantially over the last years and now contains more than 270 000 confidence scored and annotated PPIs. We integrated different types of experimental information for the confidence scoring and the construction of context-specific networks. We implemented basic graph algorithms that highlight important proteins and interactions. HIPPIE's graphical interface implements several ways for wet lab and computational scientists alike to access the PPI data.