Statistically significant dependence of the Xaa-Pro peptide bond conformation on secondary structure and amino acid sequence

Background  

A reliable prediction of the Xaa-Pro peptide bond conformation would be a useful tool for many protein structure calculation methods. We have analyzed the Protein Data Bank and show that the combined use of sequential and structural information has a predictive value for the assessment of the cis versus trans peptide bond conformation of Xaa-Pro within proteins. For the analysis of the data sets different statistical methods such as the calculation of the Chou-Fasman parameters and occurrence matrices were used. Furthermore we analyzed the relationship between the relative solvent accessibility and the relative occurrence of prolines in the cis and in the trans conformation.     

Clustering of amino acids for protein secondary structure prediction

Simple hidden Markov models are proposed for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of length distribution, and focus on inclusion of short range correlations of residues and of conformation states in the models. Conformation-independent and -dependent amino acid coarse-graining schemes are designed for the models by means of proper mutual information. We compare models of different level of complexity, and establish a practical model with a high prediction accuracy.     

The influence of the peptide bond on the conformation of amino acids: a theoretical and FT-IR matrix-isolation study of N-acetylproline

A combined experimental matrix-isolation FT-IR and theoretical study has been performed to investigate the conformational behavior of N-acetylproline. The conformational landscape of N-acetylproline was explored using successively higher computational methods, i.e. HF, DFT(B3LYP) and finally MP2. The exploration resulted in 10 conformations with a relative energy difference smaller than 22 kJ.mol(-1) at the HF/3-21G level of theory. These conformations led to six different conformations after DFT(B3LYP) optimizations. Further optimization at the MP2/6-31++G** level of theory resulted in the same six conformations, all of them with an energy difference smaller than 11.5kJ.mol(-1). One conformation with an intramolecular H-bond was found which was energetically the most favorable conformation. The vibrational and thermodynamical features were calculated using the DFT and MP2 methodologies. In the experimental matrix-isolation FT-IR spectrum, the most stable conformation was dominant and at least two non-H-bonded conformations could be identified. An experimental rotamerization constant between the H-bonded and the other non-H-bonded conformations was estimated and appeared to agree reasonably well with the theoretical MP2 predictions. Some new spectral features of N-acetylproline compared to proline were discovered which might be used to discriminate between the acetylated and non-acetylated form.     

SVM-based method for protein structural class prediction using secondary structural content and structural information of amino acids

The knowledge collated from the known protein structures has revealed that the proteins are usually folded into the four structural classes: all-α, all-β, α/β and α + β. A number of methods have been proposed to predict the protein's structural class from its primary structure; however, it has been observed that these methods fail or perform poorly in the cases of distantly related sequences. In this paper, we propose a new method for protein structural class prediction using low homology (twilight-zone) protein sequences dataset. Since protein structural class prediction is a typical classification problem, we have developed a Support Vector Machine (SVM)-based method for protein structural class prediction that uses features derived from the predicted secondary structure and predicted burial information of amino acid residues. The examination of different individual as well as feature combinations revealed that the combination of secondary structural content, secondary structural and solvent accessibility state frequencies of amino acids gave rise to the best leave-one-out cross-validation accuracy of ~81% which is comparable to the best accuracy reported in the literature so far.     

The prediction of the conformation of membrane proteins from the sequence of amino acids.

The methods of Chou & Fasman [Biochemistry (1974) 13, 211-222, 222-245] and of Lim [J. Mol. Biol. (1974)88, 857-872, 873-894] for predicting secondary structure from amino acid sequence have been applied to five predominantly helical membrane-associated peptides. The predictions from the method of Lim (1974a,b) are consistent with the experimental observations, whereas those from Chou & Fasman (1974a,b), although not inconsistent with alpha-helix, favour a beta-structure for several very hydrophobic regions. The results may be rationalized in terms of the effect of the solvent on the conformation of a polypeptide.     

Combining the GOR V algorithm with evolutionary information for protein secondary structure prediction from amino acid sequence

We have modified and improved the GOR algorithm for the protein secondary structure prediction by using the evolutionary information provided by multiple sequence alignments, adding triplet statistics, and optimizing various parameters. We have expanded the database used to include the 513 non-redundant domains collected recently by Cuff and Barton (Proteins 1999;34:508-519; Proteins 2000;40:502-511). We have introduced a variable size window that allowed us to include sequences as short as 20-30 residues. A significant improvement over the previous versions of GOR algorithm was obtained by combining the PSI-BLAST multiple sequence alignments with the GOR method. The new algorithm will form the basis for the future GOR V release on an online prediction server. The average accuracy of the prediction of secondary structure with multiple sequence alignment and full jack-knife procedure was 73.5%. The accuracy of the prediction increases to 74.2% by limiting the prediction to 375 (of 513) sequences having at least 50 PSI-BLAST alignments. The average accuracy of the prediction of the new improved program without using multiple sequence alignments was 67.5%. This is approximately a 3% improvement over the preceding GOR IV algorithm (Garnier J, Gibrat JF, Robson B. Methods Enzymol 1996;266:540-553; Kloczkowski A, Ting K-L, Jernigan RL, Garnier J. Polymer 2002;43:441-449). We have discussed alternatives to the segment overlap (Sov) coefficient proposed by Zemla et al. (Proteins 1999;34:220-223).     

A reexamination of the propensities of amino acids towards a particular secondary structure: classification of amino acids based on their chemical structure

The correlation between the primary and secondary structures of proteins was analysed using a large data set from the Protein Data Bank. Clear preferences of amino acids towards certain secondary structures classify amino acids into four groups: α-helix preferrers, strand preferrers, turn and bend preferrers, and His and Cys (the latter two amino acids show no clear preference for any secondary structure). Amino acids in the same group have similar structural characteristics at their Cβ and Cγ atoms that predicts their preference for a particular secondary structure. All α-helix preferrers have neither polar heteroatoms on Cβ and Cγ atoms, nor branching or aromatic group on the Cβ atom. All strand preferrers have aromatic groups or branching groups on the Cβ atom. All turn and bend preferrers have a polar heteroatom on the Cβ or Cγ atoms or do not have a Cβ atom at all. These new rules could be helpful in making predictions about non-natural amino acids.

Intramolecular cyclization of isothiocyanyl amino acids/peptide: arrival at unnatural thioxoimidazolidinyl/thioxooxazolidinyl amino acids

Amino Acids - A novel intramolecular cyclization of isothiocyanyl amino acids/peptide is reported to arrive at unnatural thioxoimidazolidinyl (TOI)/thioxooxazolidinyl (TOO) amino acids for the...     

An information measure of the quality of protein secondary structure prediction.

We describe an information-theory-based measure of the quality of secondary structure prediction (RELINFO). RELINFO has a simple yet intuitive interpretation: it represents the factor by which secondary structure choice at a residue has been restricted by a prediction scheme. As an alternative interpretation of secondary structure prediction, RELINFO complements currently used methods by providing an information-based view as to why a prediction succeeds and fails. To demonstrate this score's capabilities, we applied RELINFO to an analysis of a large set of secondary structure predictions obtained from the first five rounds of the Critical Assessment of Structure Prediction (CASP) experiment. RELINFO is compared with two other common measures: percent correct (Q3) and secondary structure overlap (SOV). While the correlation between Q3 and RELINFO is approximately 0.85, RELINFO avoids certain disadvantages of Q3, including overestimating the quality of a prediction. The correlation between SOV and RELINFO is approximately 0.75. The valuable SOV measure unfortunately suffers from a saturation problem, and perhaps has unfairly given the general impression that secondary structure prediction has reached its limit since SOV hasn't improved much over the recent rounds of CASP. Although not a replacement for SOV, RELINFO has greater dispersion. Over the five rounds of CASP assessed here, RELINFO shows that predictions targets have been more difficult in successive CASP experiments, yet the predictions quality has continued to improve measurably over each round. In terms of information, the secondary structure prediction quality has almost doubled from CASP1 to CASP5. Therefore, as a different perspective of accuracy, RELINFO can help to improve prediction of protein secondary structure by providing a measure of difficulty as well as final quality of a prediction.     

Exploiting intrastructure information for secondary structure prediction with multifaceted pipelines

Predicting the secondary structure of proteins is still a typical step in several bioinformatic tasks, in particular, for tertiary structure prediction. Notwithstanding the impressive results obtained so far, mostly due to the advent of sequence encoding schemes based on multiple alignment, in our view the problem should be studied from a novel perspective, in which understanding how available information sources are dealt with plays a central role. After revisiting a well-known secondary structure predictor viewed from this perspective (with the goal of identifying which sources of information have been considered and which have not), we propose a generic software architecture designed to account for all relevant information sources. To demonstrate the validity of the approach, a predictor compliant with the proposed generic architecture has been implemented and compared with several state-of-the-art secondary structure predictors. Experiments have been carried out on standard data sets, and the corresponding results confirm the validity of the approach. The predictor is available at http://iasc.diee.unica.it/ssp2/ through the corresponding web application or as downloadable stand-alone portable unpack-and-run bundle.     

Disulfide connectivity prediction using secondary structure information and diresidue frequencies

MOTIVATION: We describe a stand-alone algorithm to predict disulfide bond partners in a protein given only the amino acid sequence, using a novel neural network architecture (the diresidue neural network), and given input of symmetric flanking regions of N-terminus and C-terminus half-cystines augmented with residue secondary structure (helix, coil, sheet) as well as evolutionary information. The approach is motivated by the observation of a bias in the secondary structure preferences of free cysteines and half-cystines, and by promising preliminary results we obtained using diresidue position-specific scoring matrices. RESULTS: As calibrated by receiver operating characteristic curves from 4-fold cross-validation, our conditioning on secondary structure allows our novel diresidue neural network to perform as well as, and in some cases better than, the current state-of-the-art method. A slight drop in performance is seen when secondary structure is predicted rather than being derived from three-dimensional protein structures.     

Critical amino acids in the active site of meprin metalloproteinases for substrate and peptide bond specificity

The protease domains of the evolutionarily related alpha and beta subunits of meprin metalloproteases are approximately 55% identical at the amino acid level; however, their substrate and peptide bond specificities differ markedly. The meprin beta subunit favors acidic residues proximal to the scissile bond, while the alpha subunit prefers small or aromatic amino acids flanking the scissile bond. Thus gastrin, a peptide that contains a string of five Glu residues, is an excellent substrate for meprin beta, while it is not hydrolyzed by meprin alpha. Work herein aimed to identify critical amino acids in the meprin active sites that determine the substrate specificity differences. Sequence alignments and homology models, based on the crystal structure of the crayfish astacin, showed electrostatic differences within the meprin active sites. Site-directed mutagenesis of active site residues demonstrated that replacement of a hydrophobic residue by a basic amino acid enabled the meprin alpha protease to cleave gastrin. The meprin alphaY199K mutant was most effective; the corresponding mutation of meprin betaK185Y resulted in decreased activity toward gastrin. Peptide cleavage site determinations and kinetic analyses using a variety of peptides extended evidence that meprin alphaTyr-199/betaLys-185 are substrate specificity determinants in meprin active sites. These studies shed light on the molecular basis for the substrate specificity differences of astacin metalloproteinases.     

Cis/trans conformational equilibrium across the N-methylphenylalanine- N-methylphenylalanine peptide bond of arginine vasopressin analogs.

Two cyclic analogs of vasopressin, -Pro-Arg-Gly-NH(2) (1) and -Pro-Arg-Gly-NH(2) (2) were synthesized by the solid phase method. Their structure was determined in aqueous solution by two-dimensional NMR techniques and simulated annealing algorithm from an extended template in X-PLOR. The total chemical shift correlation spectroscopy and rotating-frame Overhauser enhancement spectroscopy of the peptides displayed four distinct sets of residual proton resonances. This suggests that both analogs adopt four families of conformations in H(2)O/D(2)O (9 : 1) (one major and three minor species). In further analysis only signals of major species (M) and of one minor species (m(1)) were considered. The major species of both peptides include a trans peptide bond between the first and second residue, and a cis form between the second and third residue. In the minor species, all peptide bonds were found to exist in trans geometry.     

Influence of amino acid and peptide counterions on the conformation of DNA

We describe fibre diffraction studies on the interaction of DNA with different amino acids and peptides. The B form of DNA, with ten base-pairs per turn, is always found at high levels of humidity. We suggest that this pitch is observed because the DNA molecules are maintained in a straight position. In solution, the DNA molecules are bent and may have a larger pitch. The A form of DNA is never found upon dehydration. Instead, the B form may be either stabilized by the counterions or altered so that the number of base-pairs per helical turn decreases upon dehydration. Alteration is favoured either by small counterions that have a single charge or by large basic polypeptides and proteins. Stabilization is favoured by small counterions that have several charged groups. A third type of behaviour is found with some amino acids that contain hydrophobic groups, which destabilize the secondary structure of DNA, probably due to a modification of its intramolecular interactions. We have not detected any specific effect of amino acid side-chains, although the amino acid sequence has a clear influence on the interaction. We think that these observations are of interest in the pursuit of more detailed crystallographic studies on protein-DNA interactions.     

Fluorometric assay of secondary amino acids

A method is described for the conversion of secondary amino acids to primary amines which can be assayed with fluorescamine (I). Secondary amino acids undergo oxidative decar?ylation when reacted with halogenating agents. The resulting imines are hydrolyzed to primary amines, which are subsequently allowed to react with fluorescamine (I) to yield fluorescent pyrrolinones (II). This reaction sequence provides an efficient fluorometric assay for secondary amino acids. Thus, the fluorescamine procedure is now applicable to the full array of natural amino acids.     

An extension of secondary structure prediction towards the prediction of tertiary structure

Secondary structure prediction parameters and optimised decision constants for use with the method of Garnier et al. [(1978) J. Mol. Biol. 120, 97-120] have been derived for two new and distinct substates of beta-structure. These we term internal and external on the basis of their hydrogen bonding patterns. The profiles of the amino acids for several of the parameters are considerably different in the two substates. Predictions using the new parameters attempt to distinguish the strands at the core of the beta-sheet from those at its edges and so restrict the possible topologies in tertiary structure prediction. The potential application of these parameters is illustrated for the class of beta/alpha proteins.     

Non-coded amino acids as acyl donor substrates for peptide bond formation catalyzed by thermoase in toluene

The peptide bond formation of N-protected non-coded amino acids having different structures as acyl donor substrates that is catalyzed by thermoase in organic media was investigated. In these reactions, N-protected l--non-coded amino acids, including l-Orn, l-Cit, -aminobutyric acid (l--Abu) and phenylalanine homologues, were used as the acyl donors and phenylalanine derivatives were used as the acyl acceptors. This kind of enzymatic reactions cannot be carried out in an aqueous buffer due to the rigid specificity of proteases to coded amino acids in water. The results demonstrated that the substrate specificity of proteases could be broadened in organic solvents. In addition, the factors that influenced these protease-catalyzed reactions, including structures of the substrates, water content and the bases used, were systematically studied. Our work provided important evidence for broadening the application of protease in organic synthesis.