MOTIVATION: Chemical cross-linking of proteins or protein complexes and the mass spectrometry-based localization of the cross-linked amino acids in peptide sequences is a powerful method for generating distance restraints on the substrate's topology. RESULTS: Here, we introduce the algorithm Xwalk for predicting and validating these cross-links on existing protein structures. Xwalk calculates and displays non-linear distances between chemically cross-linked amino acids on protein surfaces, while mimicking the flexibility and non-linearity of cross-linker molecules. It returns a 'solvent accessible surface distance', which corresponds to the length of the shortest path between two amino acids, where the path leads through solvent occupied space without penetrating the protein surface. AVAILABILITY: Xwalk is freely available as a web server or stand-alone JAVA application at http://www.xwalk.org.

Physicochemical complementarity is commonly believed to be the driving force for molecular binding. The complementarity for example of electrostatic potentials is regarded as the force that draws the ligand from the solvent into the binding site [ 1 ]. If this hypothesis is true, the same ligand should encounter complementarity environmental properties in all proteins to which it binds. We have used our recently published ligand and binding pocket matching algorithm [ 2 ] to test this common assumption by searching for property distributions that are similar for the same ligand bound to different proteins. The algorithm bases on real spherical harmonic functions, which are applicable to approximate any property function on a unit sphere. These property functions can either be of geometrical or physicochemical nature. For our current analysis we used the shape of binding pockets to test their geometrical similarity and mapped electrostatic, van der Waals and hydrophobicity potentials of the protein on the ligand surface to simulate the physicochemical forces that a ligand may feel in its binding site. It was discovered that, of these properties the two that vary least for a given ligand are the binding conformation of the ligand followed by the shape of the binding pocket. Conversely, the same ligand encountered very different electrostatic and van der Waals potential environments in the different proteins to which it is bound. These properties were often found not to be complementary to the ligand's properties, which is in conflict with the general assumption stated above. However, the hydrophobicity of the binding pocket did seem to correlate with the properties of the ligand bound to the protein. Hydrophobic parts of the ligand are often confronted with hydrophobic parts of the protein, giving rise to similar hydrophobicity distributions within different binding pockets binding the same ligand (see Figure 1 ). A set of Adenosine-mono-phosphate (AMP) ligands bound to non-homologous binding sites is shown . Each row displays different geometrical and physicochemical properties of the binding site and ligand respectively. From top to bottom are shown the variation of the ligand shape, the binding pocket shape, the hydrophobicity of the protein mapped on the ligand shape, the van der Waals potential and the electrostatic potential both again mapped on the ligand shape. The properties were ordered according to their average degree of similarity among the different binding pockets from highest to lowest from top to bottom. In addition the AMP binding pockets were ranked according to the similarity of their bound ligand to the AMP ligand of the Protein Data Bank [3] structure 1amu [4]. These results demonstrate that binding sites that bind the same ligand can exhibit a large variation of properties by facing different physicochemical forces within different binding sites. The results urge a re-evaluation of the total contribution of some physicochemical properties to molecular recognition and the factors that drive molecular binding.

UNLABELLED: We have created PhenomicDB, a multi-species genotype/phenotype database by merging public genotype/phenotype data from a wide range of model organisms and Homo sapiens. Until now these data were available in distinct organism-specific databases (e.g. WormBase, OMIM, FlyBase and MGI). We compiled this wealth of data into a single integrated resource by coarse-grained semantic mapping of the phenotypic data fields, by including common gene indices (NCBI Gene), and by the use of associated orthology relationships. With its use-case-oriented user interface, PhenomicDB allows scientists to compare and browse known phenotypes for a given gene or a set of genes from different organisms simultaneously. AVAILABILITY: PhenomicDB has been implemented at Schering AG as described below. A PhenomicDB implementation differing in some technical details has been set up for the public at Metalife AG http://www.phenomicDB.de SUPPLEMENTARY INFORMATION: database model, semantic mapping table.

MOTIVATION: An increasing number of protein structures are being determined for which no biochemical characterization is available. The analysis of protein structure and function assignment is becoming an unexpected challenge and a major bottleneck towards the goal of well-annotated genomes. As shape plays a crucial role in biomolecular recognition and function, the examination and development of shape description and comparison techniques is likely to be of prime importance for understanding protein structure-function relationships. RESULTS: A novel technique is presented for the comparison of protein binding pockets. The method uses the coefficients of a real spherical harmonics expansion to describe the shape of a protein's binding pocket. Shape similarity is computed as the L2 distance in coefficient space. Such comparisons in several thousands per second can be carried out on a standard linux PC. Other properties such as the electrostatic potential fit seamlessly into the same framework. The method can also be used directly for describing the shape of proteins and other molecules. AVAILABILITY: A limited version of the software for the real spherical harmonics expansion of a set of points in PDB format is freely available upon request from the authors. Binding pocket comparisons and ligand prediction will be made available through the protein structure annotation pipeline Profunc (written by Roman Laskowski) which will be accessible from the EBI website shortly.