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PubMed1994

Redefining the goals of protein secondary structure prediction.

Secondary structure prediction recently has surpassed the 70% level of average accuracy, evaluated on the single residue states helix, strand and loop (Q3). But the ultimate goal is reliable prediction of tertiary (three-dimensional, 3D) structure, not 100% single residue accuracy for secondary structure. A comparison of pairs of structurally homologous proteins with divergent sequences reveals that considerable variation in the position and length of secondary structure segments can be accommodated within the same 3D fold. It is therefore sufficient to predict the approximate location of helix, strand, turn and loop segments, provided they are compatible with the formation of 3D structure. Accordingly, we define here a measure of segment overlap (Sov) that is somewhat insensitive to small variations in secondary structure assignments. The new segment overlap measure ranges from an ignorance level of 37% (random protein pairs) via a current level of 72% for a prediction method based on sequence profile input to neural networks (PHD) to an average 90% level for homologous protein pairs. We conclude that the highest scores one can reasonably expect for secondary structure prediction are a single residue accuracy of Q3 > 85% and a fractional segment overlap of Sov > 90%.

Amino Acid Sequence↗
PubMed1997

Conformational analysis of peptides corresponding to all the secondary structure elements of protein L B1 domain: secondary structure propensities are not conserved in proteins with the same fold.

The solution conformation of three peptides corresponding to the two beta-hairpins and the alpha-helix of the protein L B1 domain have been analyzed by circular dichroism (CD) and nuclear magnetic resonance spectroscopy (NMR). In aqueous solution, the three peptides show low populations of native and non-native locally folded structures, but no well-defined hairpin or helix structures are formed. In 30% aqueous trifluoroethanol (TFE), the peptide corresponding to the alpha-helix adopts a high populated helical conformation three residues longer than in the protein. The hairpin peptides aggregate in TFE, and no significant conformational change occurs in the NMR observable fraction of molecules. These results indicate that the helical peptide has a significant intrinsic tendency to adopt its native structure and that the hairpin sequences seem to be selected as non-helical. This suggests that these sequences favor the structure finally attained in the protein, but the contribution of the local interactions alone is not enough to drive the formation of a detectable population of native secondary structures. This pattern of secondary structure tendencies is different to those observed in two structurally related proteins: ubiquitin and the protein G B1 domain. The only common feature is a certain propensity of the helical segments to form the native structure. These results indicate that for a protein to fold, there is no need for large native-like secondary structure propensities, although a minimum tendency to avoid non-native structures and to favor native ones could be required.

Amino Acid Sequence↗
PubMed1995

A new approach to the evaluation of protein secondary structure predictions at the level of the elements of secondary structure.

For many purposes, such as the prediction of the class of protein folds, the existence of an element of secondary structure rather than its precise position and length must be defined correctly. However, most methods for the evaluation of secondary structure prediction consider success in terms of the percentage of individual amino acids predicted correctly. In this paper the success in predicting elements of secondary structure is discussed. The number of overlapping residues in the predicted and observed secondary structures were considered as a function of the total number of amino acids in the observed and predicted secondary structures. A matrix search procedure was used to remove the ambiguity which similar studies may have had in defining the equivalent secondary structures between predicted and observed structures. In this study a loop was treated in the same way as an alpha-helix and a beta-strand. To describe the accuracy at the level of elements of secondary structure, a set of parameters was defined, similar to those used commonly at the level of individual amino acids. This approach was used to assess the methods of Chou and Fasman (1974b, Biochemistry, 13, 222-245), Lim (1974b, J. Mol. Biol., 88, 873-894) and Garnier et al. (1978, J. Mol. Biol., 120, 97-120). It was found that these methods were much poorer at the secondary structure level than at the amino acid level. This approach can be used generally for secondary structure prediction methods.

Amino Acids↗
PubMed2002

Combinatorial properties of RNA secondary structures.

The secondary structure of an RNA molecule is of great importance and possesses influence, e.g., on the interaction of tRNA molecules with proteins or on the stabilization of mRNA molecules. The classification of secondary structures by means of their order proved useful with respect to numerous applications. In 1978, Waterman, who gave the first precise formal framework for the topic, suggested to determine the number a(n,p) of secondary structures of size n and given order p. Since then, no satisfactory result has been found. Based on an observation due to Viennot et al., we will derive generating functions for the secondary structures of order p from generating functions for binary tree structures with Horton-Strahler number p. These generating functions enable us to compute a precise asymptotic equivalent for a(n,p). Furthermore, we will determine the related number of structures when the number of unpaired bases shows up as an additional parameter. Our approach proves to be general enough to compute the average order of a secondary structure together with all the r-th moments and to enumerate substructures such as hairpins or bulges in dependence on the order of the secondary structures considered.

Algorithms↗
PubMed2000

Targeting DNA secondary structures.

DNA secondary structures containing regions of single-stranded DNA have now been identified in the genomic DNA of a number of prokaryotic and eukaryotic species, including humans. Many of these secondary structures are associated with regions of DNA involved in regulation of transcription: promoters or upstream elements. The secondary structures involved appear likely to be hairpin or cruciform structures that may be recognition sites for binding of transcription factors. In the case of the coliphage N4 virion RNA polymerase, a defined hairpin in the polymerase promoter necessary for binding of the polymerase and regulation of transcription has been shown to be extruded under physiological conditions in plasmid DNA. The presence of single-stranded DNA in the promoters of several species suggests that regulatory hairpins may be involved in transcription of a number of genes. In support of this, hairpin- or cruciform-binding proteins have been identified from several species. These results imply that secondary structures in regulatory regions may be targets for drugs that bind and either block or enhance binding of proteins involved in transcription. In this review, we discuss the evidence for DNA secondary structures, particularly hairpins and cruciforms, in genomic DNA and review the studies to date of development of small molecules that can selectively bind these structures.

Animals↗
PubMed1996

Prediction of secondary structural content of proteins from their amino acid composition alone. II. The paradox with secondary structural class.

The success rates reported for secondary structural class prediction with different methods are contradictory. On one side, the problem of recognizing the secondary structural class of a protein knowing only its amino acid composition appears completely solved by simply applying jury decision with an elliptically scaled distance function. Chou and coworkers repeatedly (see Crit. Rev. Biochem. Mol. Biol. 30:275-349, 1995) published prediction accuracies near 100%. On the other hand, traditional secondary structure prediction techniques achieve success rates of about 70% for the secondary structural state per residue and about 75% for structural class only with extensive input information (full sequence of the query protein, its amino acid composition and length, multiple alignments with homologous sequences). In this article, we resolve the paradox and consider (1) the question of the secondary structural class definition, (2) the role of the representativity of the test set of protein tertiary structure for the current state of the Protein Data Bank (PDB); and (3) we estimate the real impact of amino acid composition on secondary structural class. We formulate three objective criteria for a reasonable definition of secondary structural classes and show that only the criterion of Nakashima et al. (J. Biochem. 99:153-162, 1986) complies with all of them. Only this definition matches the distribution of secondary structural content in representative PDB subsets, whereas other criteria leave many proteins (up to 65% of all PDB entries) simply unassigned. We review critically specialized secondary-structural class prediction methods, especially those of Chou and coworkers, which claim almost 100% accuracy using only amino acid composition, and resolve the paradox that these prediction accuracies are better than those from secondary structure predictions from multiple alignments. We show (i) that these techniques rely on a preselection of test sets which removes irregular proteins and other proteins without any class assignment (about 35% of all PDB entries); and (ii) that even for preselected representative test sets, the success rate drops to 60% and lower for a 4-type classification (alpha, beta, alpha + beta, alpha/beta). The prediction accuracies fall to about 50% if the secondary structural class definition of Nakashima et al. is applied and only few irregular proteins are preselected and removed from automatically generated, representative subsets of the PDB. We have applied two new vector decomposition methods for secondary structural content prediction from amino acid composition alone, with and without consideration of amino acid compositional coupling in the learning set of tertiary structures respectively, to the problem of class prediction and achieve about 60% correct assignment among four classes (alpha, beta, mixed, irregular) as well as single sequence-based secondary structure prediction methods like GORIII and COMBI. Our results demonstrate that 60% correctness is the upper limit for a 4-type class prediction from amino acid composition alone for an unknown query protein and that consideration of compositional coupling does not improve the prediction success. The prediction program SSCP offering secondary structural class assignment for query compositions and sequences has been made available as a World Wide Web and E-mail service.

Amino Acids↗
PubMed1992

Evaluation of the predicted secondary structure of bacteriorhodopsin. Prediction of the bovine rhodopsin secondary structure and its sequence similarity with bacteriorhodopsin.

The secondary structure of bacteriorhodopsin (bacR) is predicted using the Chou and Fasman method in conjunction with the hydropathic index of Kyte and Doolittle. The predicted bacR structure was compared with the structure determined by Henderson et al. (1990) using electron diffraction and was found to correlate extremely well. The secondary structure of bovine rhodopsin (bovR) was then predicted using the same techniques. The proposed transmembrane regions of bovR were then examined and found to have sequence similarity with those transmembrane regions of bacR.

Amino Acid Sequence↗
PubMed1995

Pseudotorsional OCCO backbone angle as a single descriptor of protein secondary structure.

Protein secondary structure is conventionally identified using characteristic ranges of two backbone torsional angles phi and psi. We suggest that the secondary structure can be adequately characterized by a single descriptor, the Oi-1Ci-1CiOi (where i is the residue number) pseudotorsional backbone angle. A set of 102 structurally distinct protein chains from the Protein Data Bank was used to evaluate the adequacy of this descriptor. We find that a specific range of OCCO angles corresponds to each major secondary structure. The complete range of OCCO angles (-180 degrees to 179 degrees) was broken into 18 consecutive subranges of 20 degrees each, and each subrange was assigned a letter. Thus, the OCCO profiles for each protein in the database were "translated" into a sequence of letters. The Needleman-Wunsch primary sequence alignment algorithm was then used for secondary/tertiary structure comparison and alignment. Preliminary results indicate that this new approach has a significant potential for rapid identification of fold families in the Protein Data Bank.

Amino Acid Sequence↗
PubMed1995

Aging of Dry Desiccation-Tolerant Pollen Does Not Affect Protein Secondary Structure.

Protein secondary structure and membrane phase behavior in aging Typha latifolia pollen were studied by means of Fourier transform infrared microspectroscopy (FTIR). Membranes isolated from fresh pollen occurred mainly in the liquid crystalline phase at room temperature, whereas the membrane fluidity of aged pollen was drastically decreased. This decrease did not result in large-scale irreversible protein aggregation, as was concluded from in situ FTIR assessment of the amide-1 bands. Curve-fitting on the infrared absorbance spectra enabled estimation of the proportion of different classes of protein secondary structure. Membrane proteins had a relatively large amount of [alpha]-helical structure (48%; band at 1658 cm-1), and turn-like structures (at 1637 and 1680 cm-1) were also detected. The secondary protein structure of isolated cytoplasmic proteins resembled that of proteins in whole pollen and was conserved upon drying in the absence of sucrose. The isolated cytoplasmic proteins had a large amount of [alpha]-helical structure (43%), and also [beta]-sheet (at 1637 and 1692 cm-1) and turn structures were detected. Heat-denaturing experiments with intact hydrated pollen showed low (1627 cm-1) and high (1692 cm-1) wave number bands indicating irreversible protein aggregates. The results presented in this paper show that FTIR is an extremely suitable technique to study protein secondary structure in intact plant cells of different hydration levels and developmental stages.

Journal Article↗
PubMed1984

Xenopus laevis 18S ribosomal RNA: experimental determination of secondary structural elements, and locations of methyl groups in the secondary structure model.

18S ribosomal RNA from X. laevis was subjected to partial digestion with ribonucleases A or T1 under a variety of conditions, and base-paired fragments were isolated. Sequence analysis of the fragments enabled five base-paired secondary structural elements of the 18S RNA to be established. Four of these elements (covering bases 221-256, 713-757, 1494-1555 and 1669-1779) confirm our previous secondary structure predictions, whereas the fifth (comprising bases 1103-1125) represents a phylogenetically conserved "switch" structure, which can also form in prokaryotic 16S RNA. The results are incorporated into a refined model of the 18S RNA secondary structure, which also includes the locations of the many methyl groups in X. laevis 18S RNA. In general the methyl groups occur in non-helical regions, at hairpin loop ends, or at helix boundaries and imperfections. One large cluster of 2'-O-methyl groups occurs in a region of complicated secondary structure in the 5'-one third of the molecule.

Animals↗
PubMed1992

Secondary structural changes of metmyoglobin and apomyoglobin in anionic and cationic surfactant solutions: effect of the hydrophobic chain length of the surfactants on the structural changes.

Secondary structural changes of metmyoglobin and apomyoglobin were examined in solutions of sodium alkylsulfates with hydrocarbon numbers of 8 and 12, and alkyltrimethylammonium bromides with hydrocarbon numbers of 10, 12, 14, and 16. The relative proportion of alpha-helical structure was estimated by the curve-fitting method of circular dichroic spectrum. The helical proportions of metmyoglobin and apomyoglobin were 82 and 63%, respectively. The shorter the hydrocarbon chain the surfactant had, the higher the concentration necessary to disrupt the secondary structures of these proteins. However, the helical proportion had a tendency to decrease down to lower values in solutions of the cationic surfactants with short hydrophobic groups. On the other hand, the alpha-helical structure of apomyoglobin was disrupted in lower concentrations of each cationic surfactant than that of metmyoglobin, although the disruptions of the same structures in both the proteins occurred in the same concentration range of each anionic surfactant. It appeared likely that the removal of the heme group unstabilized the myoglobin conformation only in the cationic surfactant solutions.

Anions↗
PubMed2006

Importance of context in protein folding: secondary structural propensities versus tertiary contact-assisted secondary structure formation.

Molecular dynamics simulations can be used to reveal the detailed conformational behaviors of peptides and proteins. By comparing fragment and full-length protein simulations, we can investigate the role of each peptide segment in the folding process. Here, we take advantage of information regarding the helix formation process from our previous simulations of barnase and protein A as well as new simulations of four helical fragments from these proteins at three different temperatures, starting with both helical and extended structures. Segments with high helical propensity began the folding process by tethering the chain through side chain interactions involving either polar interactions, such as salt bridges, or hydrophobic staples. These tethers were frequently nonnative (i.e., not i --> i + 4 spacing) and provided a scaffold for other residues, thereby limiting the conformational search. The helical structure then propagated on both sides of the tether. Segments with low stability and propensity formed later in the folding process and utilized contacts with other portions of the protein when folding. These helices formed via a tertiary contact-assisted mechanism, primarily via hydrophobic contacts between residues distant in sequence. Thus, segments with different helical propensities appear to play different roles during protein folding. Furthermore, the active role of nonlocal side chains in helix formation highlights why we must move beyond simple hierarchical models of protein folding.

Amino Acid Sequence↗
PubMed1984

Equilibria in 5-S ribosomal RNA secondary structure. Bulges and interior loops in 5-S RNA secondary structure may serve as articulations for a flexible molecule.

The basic assumption in this paper is that the secondary structure of a 5-S ribosomal RNA cannot be represented by a single model. We propose that the molecule can adopt, at least within the ribosome, a series of slightly different structures of nearly equal stability. The different structures arise from the existence of ambiguous base-pairing opportunities in bulged helices and the adjacent interior loops. In eubacterial 5-S RNAs there is one such an area, in eukaryotic 5-S RNAs two such areas that can give rise to structural switches. We explain how a change in secondary structure in these areas may influence the relative orientation of the surrounding helices, in other words how bulges and interior loops may serve as articulations and give rise to a flexible tertiary structure.

Animals↗
PubMed2006

Analysis of an optimal hidden Markov model for secondary structure prediction.

BACKGROUND: Secondary structure prediction is a useful first step toward 3D structure prediction. A number of successful secondary structure prediction methods use neural networks, but unfortunately, neural networks are not intuitively interpretable. On the contrary, hidden Markov models are graphical interpretable models. Moreover, they have been successfully used in many bioinformatic applications. Because they offer a strong statistical background and allow model interpretation, we propose a method based on hidden Markov models. RESULTS: Our HMM is designed without prior knowledge. It is chosen within a collection of models of increasing size, using statistical and accuracy criteria. The resulting model has 36 hidden states: 15 that model alpha-helices, 12 that model coil and 9 that model beta-strands. Connections between hidden states and state emission probabilities reflect the organization of protein structures into secondary structure segments. We start by analyzing the model features and see how it offers a new vision of local structures. We then use it for secondary structure prediction. Our model appears to be very efficient on single sequences, with a Q3 score of 68.8%, more than one point above PSIPRED prediction on single sequences. A straightforward extension of the method allows the use of multiple sequence alignments, rising the Q3 score to 75.5%. CONCLUSION: The hidden Markov model presented here achieves valuable prediction results using only a limited number of parameters. It provides an interpretable framework for protein secondary structure architecture. Furthermore, it can be used as a tool for generating protein sequences with a given secondary structure content.

Computational Biology↗
PubMed1993

Effect of neutral and acidic phospholipids on mitochondrial ATP synthase secondary structure.

The secondary structure of delipidated and egg phosphatidylcholine or asolectin reconstituted mitochondrial ATP synthase complex from beef heart was investigated by Fourier transform infrared spectroscopy. Upon reconstitution, the infrared spectra of ATP synthase revealed an increase in turns and a concomitant decrease in beta-sheet content which occurred to a larger extent in the presence of asolectin rather than in the presence of egg phosphatidylcholine. These data correlate with kinetic data showing a higher ATPase activity of the asolectin reconstituted enzyme protein than the egg phosphatidylcholine reconstituted or delipidated enzyme complexes.

Animals↗
PubMed2003

Statistical analysis of pair-wise compatibility of spatially nearest neighbor and adjacent residues in alpha-helix and beta-strands: application to a minimal model for secondary structure prediction.

Secondary structural elements like alpha-helix and beta-strands possess distinctly different structural features and thus the relative positioning of the nearest neighbor residues, and also the sequence-wise adjacent residues is important in determining the structural preference. In the present work we have statistically examined the pair-wise compatibility pattern of physically nearest neighbors and separately the adjacent residue pairs along the sequence in between the nearest neighbor partners in alpha-helices and beta-strands. It has been demonstrated that the patterns and hence, the physical basis of the compatibility of adjacent residue pairs and the spatially nearest neighbors are significantly different in most cases. The influence of tertiary contacts on the pair-wise compatibility is shown to be significant for beta-strands while it is small for alpha-helices. Based on the compatibility of physically nearest neighbors and the sequence-wise adjacent residue pairs, a minimal model has been constructed to predict the alpha-helices, beta-strands and coils of a protein from its sequence. Application of this method to 100 sequences shows that it has a predictive capability comparable to that of other more sophisticated statistical methods.

Base Pairing↗