Relation between the spectral NMR parameters and conformation characteristics of the amino acid residue backbone. Analysis of elements of the secondary structure of proteins

Based on the analysis of the proton-proton distance dependences from the conformational characteristics of the L-amino acid residues, the correlation diagram of the NOE cross peak intensity waited values with the regions of the sterically allowed space (phi, psi) was proposed. The method for determining the dihedral angles phi, psi values using the information about NOE cross peak intensities was elaborated. By the model spectral NMR parameters of the bovine pancreatic trypsin inhibitor, it is shown that the accuracy of the angles phi, psi determination exceed the corresponding accuracy provided by other methods of the structural interpretation of the two-dimensional NMR spectroscopy data. The analysis of the waited spectral NMR parameters for the different types of protein regular secondary structures and beta-turns was performed.     

Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimentional structures of proteins and its implications for secondary structure prediction from amino acid sequences

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected non-homologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for a helix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For b-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.     

Generation of deviation parameters for amino acid singlets, doublets and triplets from three-dimensional structures of proteins and its implications for secondary structure prediction from amino acid sequences

We present a new method, secondary structure prediction by deviation parameter (SSPDP) for predicting the secondary structure of proteins from amino acid sequence. Deviation parameters (DP) for amino acid singlets, doublets and triplets were computed with respect to secondary structural elements of proteins based on the dictionary of secondary structure prediction (DSSP)-generated secondary structure for 408 selected nonhomologous proteins. To the amino acid triplets which are not found in the selected dataset, a DP value of zero is assigned with respect to the secondary structural elements of proteins. The total number of parameters generated is 15,432, in the possible parameters of 25,260. Deviation parameter is complete with respect to amino acid singlets, doublets, and partially complete with respect to amino acid triplets. These generated parameters were used to predict secondary structural elements from amino acid sequence. The secondary structure predicted by our method (SSPDP) was compared with that of single sequence (NNPREDICT) and multiple sequence (PHD) methods. The average value of the percentage of prediction accuracy for αhelix by SSPDP, NNPREDICT and PHD methods was found to be 57%, 44% and 69% respectively for the proteins in the selected dataset. For Β-strand the prediction accuracy is found to be 69%, 21% and 53% respectively by SSPDP, NNPREDICT and PHD methods. This clearly indicates that the secondary structure prediction by our method is as good as PHD method but much better than NNPREDICT method.     

Relation between the structure of oligopeptides and the amino acid sequence

The present paper deals with determination of the relationship between the order of the arrangement of amino acids in comparatively short-range oligopeptides (tetrapeptides) and their conformational potentialities. It is shown that the spatial and conformational possibilities of the tetrapeptides composed of the same amino acid residues exhibit high sensibility to their mutual arrangement, i. e. to the amino acid sequence. A detailed conformation analysis vividly demonstrated that the difference in conformational possibilities is manly determined by different conditions of realization of residual interactions. It is shown convincingly that energetic differences of the fragments are due to different interaction contributions for each of the considered fragments.     

Discriminating acidic and alkaline enzymes using a random forest model with secondary structure amino acid composition

Understating the adaptation mechanism of enzymes to pH extremes and discriminating them is a challenging task and would help to design stable enzymes. In this work, we have systematically analyzed the secondary structure amino acid compositions of 105 acidic and 111 alkaline enzymes, respectively. We found that the propensity of the individual residues to participate in different secondary structures might be a general stability mechanism for their adaptation to pH extremes. Based on it, we present a secondary structure amino acid composition method for extracting useful features from sequence, and a novel ensemble classifier named random forest was used. The overall prediction accuracy evaluated by the 10-fold cross-validation reached 90.7%. Comparing our method with other feature extraction methods, the improvement of the overall prediction accuracy ranged from 5.5% to 21.2%. The random forests algorithm also outperformed other machine learning techniques with an improvement ranging from 3.2% to 19.9%.     

Unique amino acid composition of proteins in halophilic bacteria

The amino acid compositions of proteins from halophilic archaea were compared with those from non-halophilic mesophiles and thermophiles, in terms of the protein surface and interior, on a genome-wide scale. As we previously reported for proteins from thermophiles, a biased amino acid composition also exists in halophiles, in which an abundance of acidic residues was found on the protein surface as compared to the interior. This general feature did not seem to depend on the individual protein structures, but was applicable to all proteins encoded within the entire genome. Unique protein surface compositions are common in both halophiles and thermophiles. Statistical tests have shown that significant surface compositional differences exist among halophiles, non-halophiles, and thermophiles, while the interior composition within each of the three types of organisms does not significantly differ. Although thermophilic proteins have an almost equal abundance of both acidic and basic residues, a large excess of acidic residues in halophilic proteins seems to be compensated by fewer basic residues. Aspartic acid, lysine, asparagine, alanine, and threonine significantly contributed to the compositional differences of halophiles from meso- and thermophiles. Among them, however, only aspartic acid deviated largely from the expected amount estimated from the dinucleotide composition of the genomic DNA sequence of the halophile, which has an extremely high G+C content (68%). Thus, the other residues with large deviations (Lys, Ala, etc.) from their non-halophilic frequencies could have arisen merely as "dragging effects" caused by the compositional shift of the DNA, which would have changed to increase principally the fraction of aspartic acid alone.     

Adaptation of model proteins from cold to hot environments involves continuous and small adjustments of average parameters related to amino acid composition

The growth temperature adaptation of six model proteins has been studied in 42 microorganisms belonging to eubacterial and archaeal kingdoms, covering optimum growth temperatures from 7 to 103 degrees C. The selected proteins include three elongation factors involved in translation, the enzymes glyceraldehyde-3-phosphate dehydrogenase and superoxide dismutase, the cell division protein FtsZ. The common strategy of protein adaptation from cold to hot environments implies the occurrence of small changes in the amino acid composition, without altering the overall structure of the macromolecule. These continuous adjustments were investigated through parameters related to the amino acid composition of each protein. The average value per residue of mass, volume and accessible surface area allowed an evaluation of the usage of bulky residues, whereas the average hydrophobicity reflected that of hydrophobic residues. The specific proportion of bulky and hydrophobic residues in each protein almost linearly increased with the temperature of the host microorganism. This finding agrees with the structural and functional properties exhibited by proteins in differently adapted sources, thus explaining the great compactness or the high flexibility exhibited by (hyper)thermophilic or psychrophilic proteins, respectively. Indeed, heat-adapted proteins incline toward the usage of heavier-size and more hydrophobic residues with respect to mesophiles, whereas the cold-adapted macromolecules show the opposite behavior with a certain preference for smaller-size and less hydrophobic residues. An investigation on the different increase of bulky residues along with the growth temperature observed in the six model proteins suggests the relevance of the possible different role and/or structure organization played by protein domains. The significance of the linear correlations between growth temperature and parameters related to the amino acid composition improved when the analysis was collectively carried out on all model proteins.     

Prediction of protein secondary structure from amino acid sequence

The conformational parametersP _k for each amino acid species (j=1–20) of sequential peptides in proteins are presented as the product ofP _i,k , wherei is the number of the sequential residues in thekth conformational state (k=-helix,-sheet,-turn, or unordered structure). Since the average parameter for ann-residue segment is related to the average probability of finding the segment in the kth state, it becomes a geometric mean of (P _k )_av=(P _i,k ) ^1/n with amino acid residuei increasing from 1 ton. We then used ln(P_k)_av to convert a multiplicative process to a summation, i.e., ln(P _k ) _av =(1/n)P _i,k (i=1 ton) for ease of operation. However, this is unlike the popular Chou-Fasman algorithm, which has the flaw of using the arithmetic mean for relative probabilities. The Chou-Fasman algorithm happens to be close to our calculations in many cases mainly because the difference between theirP _k and our InP _k is nearly constant for about one-half of the 20 amino acids. When stronger conformation formers and breakers exist, the difference become larger and the prediction at the N- and C-terminal-helix or-sheet could differ. If the average conformational parameters of the overlapping segments of any two states are too close for a unique solution, our calculations could lead to a different prediction.     

Identify DNA-binding proteins with optimal Chou's amino acid composition

DNA-binding proteins play an important role in most cellular processes, such as gene regulation, recombination, repair, replication, and DNA modification. In this article, an optimal Chou's pseudo amino acid composition (PseAAC) based on physicochemical characters of amino acid is proposed to represent proteins for identifying DNAbinding proteins. Six physicochemical characters of amino acids are utilized to generate the sequence features via the web server PseAAC. The optimal values of two important parameters (correlation factor δ and weighting factor w) about PseAAC are determined to get the appropriate representation of proteins, which ultimately result in better prediction performance. Experimental results on the benchmark datasets using random forest show that our method is really promising to predict DNA-binding proteins and may at least be a useful supplement tool to existing methods.     

The broken-stick model for amino acid composition in proteins

Summary The relative abundances among the amino acids, which are functionally similar to one another, were explained by random partition of a unit interval.     

Trend of amino acid composition of proteins of different taxa

Archaea, bacteria and eukaryotes represent the main kingdoms of life. Is there any trend for amino acid compositions of proteins found in full genomes of species of different kingdoms? What is the percentage of totally unstructured proteins in various proteomes? We obtained amino acid frequencies for different taxa using 195 known proteomes and all annotated sequences from the Swiss-Prot data base. Investigation of the two data bases (proteomes and Swiss-Prot) shows that the amino acid compositions of proteins differ substantially for different kingdoms of life, and this difference is larger between different proteomes than between different kingdoms of life. Our data demonstrate that there is a surprisingly small selection for the amino acid composition of proteins for higher organisms (eukaryotes) and their viruses in comparison with the "random" frequency following from a uniform usage of codons of the universal genetic code. On the contrary, lower organisms (bacteria and especially archaea) demonstrate an enhanced selection of amino acids. Moreover, according to our estimates, 12%, 3% and 2% of the proteins in eukaryotic, bacterial and archaean proteomes are totally disordered, and long (> 41 residues) disordered segments are found to occur in 16% of arhaean, 20% of eubacterial and 43% of eukaryotic proteins for 19 archaean, 159 bacterial and 17 eukaryotic proteomes, respectively. A correlation between amino acid compositions of proteins of various taxa, show that the highest correlation is observed between eukaryotes and their viruses (the correlation coefficient is 0.98), and bacteria and their viruses (the correlation coefficient is 0.96), while correlation between eukaryotes and archaea is 0.85 only.     

The influence of nearest-neighboring amino acid residues on aspects of secondary structure of proteins. Attempts to locate -helices and -sheets

The influence of nearest-neighbor pairs of amino acids (n ? 1) and (n + 1) on the conformation of amino acid (n) in proteins has been studied. From experimental data on eleven proteins of known three dimensional structures, our definition of an α-helical domain in the Φ,Ψ plot has been reexamined and found to be satisfactory. On the same principle, a regular β-sheet domain has been delineated. We then revised our 20 × 20 table of frequencies of occurrences of various conformations tabulating three values: α-helical, β-sheet, and neither. These frequencies were then used to locate the helixbreaking positions in cytochrome b₅, papain, thermolysin, and calcium-binding protein. In conjuction with the helical wheel method, they were useful for predicting the locations of most α-helical segments. Similarly the β-sheet breaking positions in papain were located and most of the β-sheets found by X-ray diffraction were close to or between these positions. Data on β-sheets are extremely sparse so that extensive tests were not possible. The application of this method to abnormal hemoglobins suggested possible distortions of helices and in several instances correlated with abnormal properties of the hemoglobins and association with disease. The variable region of human immunoglobin heavy chains was found to have a very low α-helical content though β-sheet structures might exist.     

NH2-terminal amino acid distribution and amino acid composition of Streptococcus faecalis R soluble and ribosomal proteins

The NH(2)-terminal amino acid distribution of Streptococcus faecalis R soluble and ribosomal proteins isolated from cells at different stages of growth on either folate-sufficient or folate-deficient medium was determined by the dinitrophenyl method. The NH(2)-terminal residues do not follow the random distribution observed for the total amino acid composition of S. faecalis soluble and ribosomal proteins. Methionine and alanine occur most frequently; serine, threonine, aspartic and glutamic acids, and glycine are also present at the NH(2)-terminal position of S. faecalis R proteins. The absence of folic acid yields cells that are incapable of formylating methionyl-transfer ribonucelic acid tRNA(f) (Met), but does not affect either the qualitative or quantitative NH(2)-terminal distribution of total soluble or total ribosomal proteins compared to cells grown with folate. A small quantitative difference was observed in the frequency of distribution of certain amino acids at the NH(2)-termini between log and stationary phase soluble proteins. The amino acid residues found at the NH(2)-terminal position of S. faecalis proteins are qualitatively similar to those reported for several other organisms.