Fuzzy oil drop model to interpret the structure of antifreeze proteins and their mutants

Mutations in proteins introduce structural changes and influence biological activity: the specific effects depend on the location of the mutation. The simple method proposed in the present paper is based on a two-step model of in silico protein folding. The structure of the first intermediate is assumed to be determined solely by backbone conformation. The structure of the second one is assumed to be determined by the presence of a hydrophobic center. The comparable structural analysis of the set of mutants is performed to identify the mutant-induced structural changes. The changes of the hydrophobic core organization measured by the divergence entropy allows quantitative comparison estimating the relative structural changes upon mutation. The set of antifreeze proteins, which appeared to represent the hydrophobic core structure accordant with “fuzzy oil drop” model was selected for analysis

The use of supramolecular structures as protein ligands

Congo red dye as well as other eagerly self-assembling organic molecules which form rod-like or ribbon-like supramolecular structures in water solutions, appears to represent a new class of protein ligands with possible wide-ranging medical applications. Such molecules associate with proteins as integral clusters and preferentially penetrate into areas of low molecular stability. Abnormal, partly unfolded proteins are the main binding target for such ligands, while well packed molecules are generally inaccessible. Of particular interest is the observation that local susceptibility for binding supramolecular ligands may be promoted in some proteins as a consequence of function-derived structural changes, and that such complexation may alter the activity profile of target proteins. Examples are presented in this paper

Inoculation of Scrapie with the Self-Assembling RADA-Peptide Disrupts Prion Accumulation and Extends Hamster Survival

Intracerebral inoculation of 263K Scrapie brain homogenate (PrPsc) with a self-assembling RADA-peptide (RADA) significantly delayed disease onset and increased hamster survival. Time of survival was dependent on the dose of RADA and pre-incubation with PrPsc prior to inoculation. RADA treatment resulted in the absence of detectable PrPsc at 40 d followed by an increased rate of PrPsc accumulation at 75 d up to sacrifice. In all PrPsc inoculated animals, clinical symptoms were observed ∼10 d prior to sacrifice and brains showed spongiform degeneration with Congo red positive plaques. A time-dependent increase in reactive gliosis was observed in both groups with more GFAP detected in RADA treated animals at all time points. The PrP protein showed dose-dependent binding to RADA and this binding was competitively inhibited by Congo Red. We conclude that RADA disrupts the efficacy of prion transmission by altering the rate of PrPsc accumulation. This is the first demonstration that a self-assembling biomolecular peptide can interact with PrPsc, disrupt the course of Scrapie disease process, and extend survival

Catalytic residues in hydrolases: analysis of methods designed for ligand-binding site prediction

The comparison of eight tools applicable to ligand-binding site prediction is presented. The methods examined cover three types of approaches: the geometrical (CASTp, PASS, Pocket-Finder), the physicochemical (Q-SiteFinder, FOD) and the knowledge-based (ConSurf, SuMo, WebFEATURE). The accuracy of predictions was measured in reference to the catalytic residues documented in the Catalytic Site Atlas. The test was performed on a set comprising selected chains of hydrolases. The results were analysed with regard to size, polarity, secondary structure, accessible solvent area of predicted sites as well as parameters commonly used in machine learning (F-measure, MCC). The relative accuracies of predictions are presented in the ROC space, allowing determination of the optimal methods by means of the ROC convex hull. Additionally the minimum expected cost analysis was performed. Both advantages and disadvantages of the eight methods are presented. Characterization of protein chains in respect to the level of difficulty in the active site prediction is introduced. The main reasons for failures are discussed. Overall, the best performance offers SuMo followed by FOD, while Pocket-Finder is the best method among the geometrical approaches

Structural similarity of CheY-like proteins

The problem of structural similarity of polypeptide chains of low sequence similarity representing a similar 3D structural form has been the object of analysis of researchers engaged in the protein folding problem. Three homologous proteins of similar biological function with low sequence similarity are the objects of analysis presented in this paper. The structure of a hydrophobic core is used as the criterion for structural similarity assessment of these three proteins. The applied method allows recognition of differentiati on in topologically similar structures