[A search for amyloidogenic regions in protein chain].

We suggest a new method to detect amyloidogenic regions in a protein sequence. In the present work it is shown that regions enriched with amino acid residues which have a high expected packing density are responsible for the amyloid formation. Our predictions are consistent with known disease-related amyloidogenic regions for 8 of 11 amyloid-forming proteins and peptides in which positions of amyloidogenic regions have been revealed experimentally.

[Optimal relationship between average conformational entropy and average energy of interactions between residues for fast protein folding].

Based on the known experimental data and using the theoretical modeling of protein folding, we demonstrate that there exists an optimal relationship between the average conformational entropy and the average energy of contacts per residue, that is an entropy capacity, for fast protein folding. Statistical analysis of conformational entropy and the number of contacts per residue for 5829 protein structures from four general structural classes (all-alpha, all-beta, +/-/beta, alpha+beta) demonstrates that each class of proteins has its own class-specific average number of contacts and average conformational entropy per residue. These class-specific features determine the folding rates: a proteins are the fastest folding proteins, then follow beta and alpha+beta proteins, and finally alpha/beta proteins are the slowest ones.

[Prediction of natively unfolded regions in protein chain].

We have shown that the ability of a protein to be in globular or in natively unfolded state (under native conditions) may be determined (besides low overall hydrophobicity and a large net charge) by such a property as the average environment density, the average number of residues enclosed at the given distance. A statistical scale of the average number of residues enclosed at the given distance for 20 types of amino acid residues in globular state has been created on the basis of 6626 protein structures. Using this scale for separation of 80 globular and 90 natively unfolded proteins we fail only in 11% of proteins (compared with 17% of errors which are observed if to use hydrophobicity scale). The present scale may be used both for prediction of form (folded or unfolded) of the native state of protein and for prediction of natively unfolded regions in protein chains. The results of comparison of our method of predicting natively unfolded regions with the other known methods show that our method has the highest fraction of correctly predicted natively unfolded regions (that is 87% and 77% if to make averaging over residues and over proteins correspondingly).

[Prediction of protein domain boundaries based on statistics of appearance of amino acid residues].

We have created a database of two-domain proteins with homology less than 25% (452 proteins). Based on one half of this set of proteins statistics of appearance of amino acid residues on the domain boundaries of multiple domain proteins has been obtained. Small and hydrophilic amino acids (proline, glycine, asparagine, glutamic acid, arginine and others) appear on the domain boundaries more often than in the whole protein. Opposite, hydrophobic amino acid residues (tryptophane, methionine, phenylalanine and others) appear on the domain boundaries more rarely. The obtained scales of the appearance of amino acid residues on the boundary regions from the statistics have been used for calculation of domain boundaries in the proteins of the second half of the database. The probability scale obtained by averaging the appearance of amino acid residues on the domain boundary region including 8 residues (+/-4 residues from the real domain boundary) gives the best result: for 57% of proteins the predicted boundary was closer than 40 residues to the boundary assigned from three-dimensional structures, for 41% it was closer than 20 residues from the real boundary. The probability scale was used to predict domain boundaries for proteins with unknown three-dimensional structure (international competition CASP6).

[On prediction of folding nuclei in globular proteins].

The approach described in this paper on the prediction of folding nuclei in globular proteins with known three dimensional structures is based on a search of the lowest saddle points through the barrier separating the unfolded state from the native structure on the free-energy landscape of protein chain. This search is performed by a dynamic programming method. Comparison of theoretical results with experimental data on the folding nuclei of two dozen of proteins shows that our model provides good phi value predictions for proteins whose structures have been determined by X-ray analysis, with a less limited success for proteins whose structures have been determined by NMR techniques only. Consideration of a full ensemble of transition states results in more successful prediction than consideration of only the transition states with the minimal free energy. In conclusion we have predicted the localization of folding nuclei for three dimensional protein structures for which kinetics of folding is studied now but the localization of folding nuclei is still unknown.

[The difference between protein structures that are obtained by X-ray analysis and magnetic resonance spectroscopy].

We have analyzed the proteins whose structures were determined both by X-ray analysis (X-ray) and nuclear magnetic resonance (NMR) on condition that these structures do not differ greatly when spatially superimposed on each other (61 pairs of protein structures). Atom-atomic contacts (contact distances varied from 2 to 8 A) have been analyzed and it has been found that NMR structures (in comparison with X-ray ones) have more contacts in the range below 3.5 A and above 5.5 A. In the case of residue-residue contacts NMR structures have more contacts below 3 A and between 4.5 and 6.5 A. At all the other contact distances analyzed the X-ray structures have more contacts. The difference in the number of atom-atomic and residue-residue contacts is greater for internal residues, that are concealed from water, as compared to the surface residues. The other, not less important difference deals with the number of hydrogen bonds in the main chain: it is larger for the X-ray structures. The correlation between the hydrogen bonds identified in the structures obtained by both methods is no more than 32%. The consideration of a complete set of protein structures obtained by NMR results in the fact that the number of hydrogen bonds grows 1.2 times as compared to those obtained with the X-ray analysis, whereas the correlation increases only by 65%. We have also demonstrated that alpha-helices in the NMR structures are more distorted in comparison with the ideal alpha-helix, than alpha-helices in the X-ray structures.