Metal ions are constituents of many metalloproteins, in which they have either catalytic (metalloenzymes) or structural functions. In this work, the characteristics of various metals were studied (Cu, Zn, Mg, Mn, Fe, Co, Ni, Cd and Ca in proteins with known crystal structure) as well as the specificity of their environments. The analysis was performed on two data sets: the set of protein structures in the Protein Data Bank (PDB) determined with resolution <1.5 A and the set of nonredundant protein structures from the PDB. The former was used to determine the distances between each metal ion and its electron donors and the latter was used to assess the preferred coordination numbers and common combinations of amino-acid residues in the neighbourhood of each metal. Although the metal ions considered predominantly had a valence of two, their preferred coordination number and the type of amino-acid residues that participate in the coordination differed significantly from one metal ion to the next. This study concentrates on finding the specificities of a metal-ion environment, namely the distribution of coordination numbers and the amino-acid residue types that frequently take part in coordination. Furthermore, the correlation between the coordination number and the occurrence of certain amino-acid residues (quartets and triplets) in a metal-ion coordination sphere was analysed. The results obtained are of particular value for the identification and modelling of metal-binding sites in protein structures derived by homology modelling. Knowledge of the geometry and characteristics of the metal-binding sites in metalloproteins of known function can help to more closely determine the biological activity of proteins of unknown function and to aid in design of proteins with specific affinity for certain metals.

Background: PSAIA (Protein Structure and Interaction Analyzer) was developed to compute geometric parameters for large sets of protein structures in order to predict and investigate protein-protein interaction sites.

The extracellular ribonuclease barnase and its intracellular inhibitor barstar bind fast and with high affinity. Although extensive experimental and theoretical studies have been carried out on this system, it is unclear what the relative importance of different contributions to the high affinity is and whether binding can be improved through point mutations. In this work, we first applied Poisson-Boltzmann electrostatic calculations to 65 barnase-barstar complexes with mutations in both barnase and barstar. The continuum electrostatic calculations with a van der Waals surface dielectric boundary definition result in the electrostatic interaction free energy providing the dominant contribution favoring barnase-barstar binding. The results show that the computed electrostatic binding free energy can be improved through mutations at W44/barstar and E73/barnase. Furthermore, the determinants of binding affinity were quantified by applying COMparative BINding Energy (COMBINE) analysis to derive quantitative structure-activity relationships (QSARs) for the 65 complexes. The COMBINE QSAR model highlights approximately 20 interfacial residue pairs as responsible for most of the differences in binding affinity between the mutant complexes, mainly due to electrostatic interactions. Based on the COMBINE model, together with Brownian dynamics simulations to compute diffusional association rate constants, several mutants were designed to have higher binding affinities than the wild-type proteins.

Identifying interaction sites in proteins provides important clues to the function of a protein and is becoming increasingly relevant in topics such as systems biology and drug discovery. Although there are numerous papers on the prediction of interaction sites using information derived from structure, there are only a few case reports on the prediction of interaction residues based solely on protein sequence. Here, a sliding window approach is combined with the Random Forests method to predict protein interaction sites using (i) a combination of sequence- and structure-derived parameters and (ii) sequence information alone. For sequence-based prediction we achieved a precision of 84% with a 26% recall and an F-measure of 40%. When combined with structural information, the prediction performance increases to a precision of 76% and a recall of 38% with an F-measure of 51%. We also present an attempt to rationalize the sliding window size and demonstrate that a nine-residue window is the most suitable for predictor construction. Finally, we demonstrate the applicability of our prediction methods by modeling the Ras–Raf complex using predicted interaction sites as target binding interfaces. Our results suggest that it is possible to predict protein interaction sites with quite a high accuracy using only sequence information.

Human dipeptidyl peptidase III (DPP III) is a two domain metallo-peptidase from the M49 family. The wide interdomain cleft and broad substrate specificity suggest that this enzyme could experience significant conformational change. Long (>100 ns) molecular dynamics (MD) simulations of DPP III revealed large range conformational changes of the protein, suggesting the pre-existing equilibrium model for a substrate binding. The binding free energy calculations revealed tighter binding of the preferred synthetic substrate Arg-Arg-2-naphtylamide to the "closed" than to the "open" DPP III conformation. Our assumption that Asp372 plays a crucial role in the large scale interdomain closure was proved by the MD simulations of the Asp372Ala variant. During the same simulation time, the variant remained more "open" than the wild type protein. Apparently, Ala was not as efficient as Asp in establishing the interdomain interactions. According to the MM-PBSA calculations, the electrostatic component of the free energy of solvation turned out to be higher for the "closed" protein than for its less compact form. However, the gain in entropy due to water released from the interdomain cleft nicely balanced this negative effect.