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.

"Fuzzy oil drop" model verified positively.

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.

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.

"Fuzzy oil drop" model applied to individual small proteins built of 70 amino acids.

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.

In silico structural study of random amino acid sequence proteins not present in nature.

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.

Functional characteristics of small proteins (70 amino acid residues) forming protein-nucleic acid complexes.

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.

Never born proteins as a test case for ab initio protein structures prediction.

The number of natural proteins although large is significantly smaller than the theoretical number of proteins that can be obtained combining the 20 natural amino acids, the so-called "never born proteins" (NBPs). The study of the structure and properties of these proteins allows to investigate the sources of the natural proteins being of unique characteristics or special properties. However the structural study of NPBs can also been intended as an ideal test for evaluating the efficiency of software packages for the ab initio protein structure prediction.

Prediction of functional sites based on the fuzzy oil drop model.

A description of many biological processes requires knowledge of the 3-D structure of proteins and, in particular, the defined active site responsible for biological function. Many proteins, the genes of which have been identified as the result of human genome sequencing, and which were synthesized experimentally, await identification of their biological activity. Currently used methods do not always yield satisfactory results, and new algorithms need to be developed to recognize the localization of active sites in proteins.