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 "fuzzy oil drop" model assuming the structure of the hydrophobic core of the form of 3-D Gauss function appeared to be verified positively. The protein 1NMF belonging to downhill proteins was found to represent the hydrophobic density distribution accordant with the assumed model. The accordance of the protein structure with the assumed model was measured using elements of theory information. This observation opens the possibility to simulate the folding process as influenced by external force field of hydrophobic character.
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.
The proteins composed of short polypeptides (about 70 amino acid residues) representing the following functional groups (according to PDB notation): growth hormones, serine protease inhibitors, antifreeze proteins, chaperones and proteins of unknown function, were selected for structural and functional analysis. Classification based on the distribution of hydrophobicity in terms of deficiency/excess as the measure of structural and functional specificity is presented. The experimentally observed distribution of hydrophobicity in the protein body is compared to the idealized one expressed by a three-dimensional Gauss function. The differences between these two distributions reveal the specificity of structural/functional characteristics of the protein. The residues of hydrophobicity deficiency versus the idealized distribution are assumed to indicate cavities with the potential to bind ligands, while the residues of hydrophobicity excess are interpreted as potentially participating in protein-protein complexation. The distribution of hydrophobicity irregularity seems to be specific for particular structures and functions of proteins. A comparative analysis of such profiles is carried out to identify the potential biological activity of proteins of unknown function.
The three-dimensional structures of a set of never born proteins (NBP, random amino acid sequence proteins with no significant homology with known proteins) were predicted using two methods: Rosetta and the one based on the fuzzy-oil-drop (FOD) model. More than 3000 different random amino acid sequences have been generated, filtered against the non redundant protein sequence data base, to remove sequences with significant homology with known proteins, and subjected to three-dimensional structure prediction. Comparison between Rosetta and FOD predictions allowed to select the ten top (highest structural similarity) and the ten bottom (the lowest structural similarity) structures from the ranking list organized according to the RMS-D value. The selected structures were taken for detailed analysis to define the scale of structural accordance and discrepancy between the two methods. The structural similarity measurements revealed discrepancies between structures generated on the basis of the two methods. Their potential biological function appeared to be quite different as well. The ten bottom structures appeared to be unfoldable for the FOD model. Some aspects of the general characteristics of the NBPs are also discussed. The calculations were performed on the EUChinaGRID grid platform to test the performance of this infrastructure for massive protein structure predictions.
The proteins composed of short polypeptides (about 70 amino acid residues) participating in large complexes (ribosome) and proteins interacting with DNA/RNA were taken for analysis and classified according to the hydrophobicity excess/deficiency distribution as a measure of structural and functional specificity and similarity. The characterization of this group of proteins is the introductory part to the analysis of the so called "Never Born Proteins" (NBP) in search for protein compounds exhibiting biological activity that may be valuable in pharmacological research. The entropy scale (classification between random and deterministic limits) organized in ranking list allows the comparative analysis of the proteins under consideration. The comparison of the hydrophobicity deficiency appeared to be useful for similarity recognition, the examples of which are shown in the paper. The specificity of proteins participating in large protein-nucleic acid complexes generation is presented.
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