Secondary Structure Class Research Articles

A hydrogen bond surrogate approach for stabilization of short peptide sequences in alpha-helical conformation.

Alpha-helices constitute the largest class of protein secondary structures and play a major role in mediating protein-protein interactions. Development of stable mimics of short alpha-helices would be invaluable for inhibition of protein-protein interactions. This Account describes our efforts in developing a general approach for constraining short peptides in alpha-helical conformations by a main-chain hydrogen bond surrogate (HBS) strategy. The HBS alpha-helices feature a carbon-carbon bond derived from a ring-closing metathesis reaction in place of an N-terminal intramolecular hydrogen bond between the peptide i and i + 4 residues. Our approach is centered on the helix-coil transition theory in peptides, which suggests that the energetically demanding organization of three consecutive amino acids into the helical orientation inherently limits the stability of short alpha-helices. The HBS method affords preorganized alpha-turns to overcome this intrinsic nucleation barrier and initiate helix formation. The HBS approach is an attractive strategy for generation of ligands for protein receptors because placement of the cross-link on the inside of the helix does not block solvent-exposed molecular recognition surfaces of the molecule. Our metathesis-based synthetic strategy utilizes standard Fmoc solid phase peptide synthesis methodology, resins, and reagents and provides HBS helices in sufficient amounts for subsequent biophysical and biological analyses. Extensive conformational analysis of HBS alpha-helices with 2D NMR, circular dichroism spectroscopies and X-ray crystallography confirms the alpha-helical structure in these compounds. The crystal structure indicates that all i and i + 4 C=O and NH hydrogen-bonding partners fall within distances and angles expected for a fully hydrogen-bonded alpha-helix. The backbone conformation of HBS alpha-helix in the crystal structure superimposes with an rms difference of 0.75 A onto the backbone conformation of a model alpha-helix. Significantly, the backbone torsion angles for the HBS helix residues fall within the range expected for a canonical alpha-helix. Thermal and chemical denaturation studies suggest that the HBS approach provides exceptionally stable alpha-helices from a variety of short sequences, which retain their helical conformation in aqueous buffers at exceptionally high temperatures. The high degree of thermal stability observed for HBS helices is consistent with the theoretical predictions for a nucleated helix. The HBS approach was devised to afford internally constrained helices so that the molecular recognition surface of the helix and its protein binding properties are not compromised by the constraining moiety. Notably, our preliminary studies illustrate that HBS helices can target their expected protein receptors with high affinity.

Condensed Representations of Protein Secondary Structure Sequences and Their Application

In this paper, we propose a nongraphical representation for protein secondary structures. By counting the frequency of occurrence of all possible four-tuples (i.e., four-letter words) of a protein secondary structure sequence, we construct a set of 3 × 3 matrices for the corresponding protein secondary structure sequence. Furthermore, the leading eigenvalues of these matrices are computed and considered as invariants for the protein secondary structure sequences. To illustrate the utility of our approach, we apply it to a set of real data to distinguish protein structural classes. The result indicates that it can be used to complement the classification of protein secondary structures.

Topological Classification of RNA Structures

We present a novel topological classification of RNA secondary structures with pseudoknots. It is based on the topological genus of the circular diagram associated to the RNA base-pair structure. The genus is a positive integer number whose value quantifies the topological complexity of the folded RNA structure. In such a representation, planar diagrams correspond to pure RNA secondary structures and have zero genus, whereas non-planar diagrams correspond to pseudoknotted structures and have higher genus. The topological genus allows for the definition of topological folding motifs, similar in spirit to those introduced and commonly used in protein folding. We analyze real RNA structures from the databases Worldwide Protein Data Bank and Pseudobase and classify them according to their topological genus. For simplicity, we limit our analysis by considering only Watson–Crick complementary base pairs and G–U wobble base pairs. We compare the results of our statistical survey with existing theoretical and numerical models. We also discuss possible applications of this classification and show how it can be used for identifying new RNA structural motifs.

Variable predictive model based classification algorithm for effective separation of protein structural classes

Variable predictive model based class discrimination (VPMCD) algorithm is proposed as an effective protein secondary structure classification tool. The algorithm mathematically represents the characteristics amino acid interactions specific to each protein structure and exploits them further to distinguish different structures. The new concept and the VPMCD classifier are established using well-studied datasets containing four protein classes as benchmark. The protein samples selected from SCOP and PDB databases with varying homology (25–100%) and non-uniform distribution of class samples provide challenging classification problem. The performance of the new method is compared with advanced classification algorithms like component coupled, SVM and neural networks. VPMCD provides superior performance for high homology datasets. 100% classification is achieved for self-consistency test and an improvement of 5% prediction accuracy is obtained during Jackknife test. The sensitivity of the new algorithm is investigated by varying model structures/types and sequence homology. Simpler to implement VPMCD algorithm is observed to be a robust classification technique and shows potential for effective extensions to other clinical diagnosis and data mining applications in biological systems.

Assessing Side-Chain Perturbations of the Protein Backbone: A Knowledge-Based Classification of Residue Ramachandran Space

Grouping the 20 residues is a classic strategy to discover ordered patterns and insights about the fundamental nature of proteins, their structure, and how they fold. Usually, this categorization is based on the biophysical and/or structural properties of a residue's side-chain group. We extend this approach to understand the effects of side chains on backbone conformation and to perform a knowledge-based classification of amino acids by comparing their backbone ϕ,ψ distributions in different types of secondary structure. At this finer, more specific resolution, torsion angle data are often sparse and discontinuous (especially for nonhelical classes) even though a comprehensive set of protein structures is used. To ensure the precision of Ramachandran plot comparisons, we applied a rigorous Bayesian density estimation method that produces continuous estimates of the backbone ϕ,ψ distributions. Based on this statistical modeling, a robust hierarchical clustering was performed using a divergence score to measure the similarity between plots. There were seven general groups based on the clusters from the complete Ramachandran data: nonpolar/β-branched (Ile and Val), AsX (Asn and Asp), long (Met, Gln, Arg, Glu, Lys, and Leu), aromatic (Phe, Tyr, His, and Cys), small (Ala and Ser), bulky (Thr and Trp), and, lastly, the singletons of Gly and Pro. At the level of secondary structure (helix, sheet, turn, and coil), these groups remain somewhat consistent, although there are a few significant variations. Besides the expected uniqueness of the Gly and Pro distributions, the nonpolar/β-branched and AsX clusters were very consistent across all types of secondary structure. Effectively, this consistency across the secondary structure classes implies that side-chain steric effects strongly influence a residue's backbone torsion angle conformation. These results help to explain the plasticity of amino acid substitutions on protein structure and should help in protein design and structure evaluation.

Roles of beta-turns in protein folding: from peptide models to protein engineering.

Reverse turns are a major class of protein secondary structure; they represent sites of chain reversal and thus sites where the globular character of a protein is created. It has been speculated for many years that turns may nucleate the formation of structure in protein folding, as their propensity to occur will favor the approximation of their flanking regions and their general tendency to be hydrophilic will favor their disposition at the solvent-accessible surface. Reverse turns are local features, and it is therefore not surprising that their structural properties have been extensively studied using peptide models. In this article, we review research on peptide models of turns to test the hypothesis that the propensities of turns to form in short peptides will relate to the roles of corresponding sequences in protein folding. Turns with significant stability as isolated entities should actively promote the folding of a protein, and by contrast, turn sequences that merely allow the chain to adopt conformations required for chain reversal are predicted to be passive in the folding mechanism. We discuss results of protein engineering studies of the roles of turn residues in folding mechanisms. Factors that correlate with the importance of turns in folding indeed include their intrinsic stability, as well as their topological context and their participation in hydrophobic networks within the protein's structure.

Determination of Peptide Backbone Torsion Angles Using Double-Quantum Dipolar Recoupling Solid-State NMR Spectroscopy

Several approaches for utilizing dipolar recoupling solid-state NMR (ssNMR) techniques to determine local structure at high resolution in peptides and proteins have been developed. However, many of these techniques measure only one torsion angle or are accurate for only certain classes of secondary structure. Additionally, the efficiency with which these dipolar recoupling experiments suppress the deleterious effects of chemical shift anisotropy (CSA) at high magnetic field strengths varies. Dipolar recoupling with a windowless sequence (DRAWS) has proven to be an effective pulse sequence for exciting double-quantum (DQ) coherences between adjacent carbonyl carbons along the peptide backbone. By allowing this DQ coherence to evolve, it is possible to measure the relative orientations of the CSA tensors and subsequently use this information to determine the Ramachandran torsion angles phi and psi. Here, we explore the accuracies of the assumptions made in interpreting DQ-DRAWS data and demonstrate their fidelity in measuring torsion angles corresponding to a variety of secondary structures irrespective of hydrogen-bonding patterns. It is shown how a simple choice of isotopic labels and experimental conditions allows accurate measurement of backbone secondary structures without any prior knowledge. This approach is considerably more sensitive for determining structure in helices and has comparable accuracy for beta-sheet and extended conformations relative to other methods. We also illustrate the ability of DQ-DRAWS to distinguish between structures in heterogeneous samples.

The unfoldomics decade: an update on intrinsically disordered proteins.

BackgroundOur first predictor of protein disorder was published just over a decade ago in the Proceedings of the IEEE International Conference on Neural Networks (Romero P, Obradovic Z, Kissinger C, Villafranca JE, Dunker AK (1997) Identifying disordered regions in proteins from amino acid sequence. Proceedings of the IEEE International Conference on Neural Networks, 1: 90–95). By now more than twenty other laboratory groups have joined the efforts to improve the prediction of protein disorder. While the various prediction methodologies used for protein intrinsic disorder resemble those methodologies used for secondary structure prediction, the two types of structures are entirely different. For example, the two structural classes have very different dynamic properties, with the irregular secondary structure class being much less mobile than the disorder class. The prediction of secondary structure has been useful. On the other hand, the prediction of intrinsic disorder has been revolutionary, leading to major modifications of the more than 100 year-old views relating protein structure and function. Experimentalists have been providing evidence over many decades that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. In addition, experimentalists are also showing that, for many proteins, their functions depend on the unstructured rather than structured state; such results are in marked contrast to the greater than hundred year old views such as the lock and key hypothesis. Despite extensive data on many important examples, including disease-associated proteins, the importance of disorder for protein function has been largely ignored. Indeed, to our knowledge, current biochemistry books don't present even one acknowledged example of a disorder-dependent function, even though some reports of disorder-dependent functions are more than 50 years old. The results from genome-wide predictions of intrinsic disorder and the results from other bioinformatics studies of intrinsic disorder are demanding attention for these proteins.ResultsDisorder prediction has been important for showing that the relatively few experimentally characterized examples are members of a very large collection of related disordered proteins that are wide-spread over all three domains of life. Many significant biological functions are now known to depend directly on, or are importantly associated with, the unfolded or partially folded state. Here our goal is to review the key discoveries and to weave these discoveries together to support novel approaches for understanding sequence-function relationships.ConclusionIntrinsically disordered protein is common across the three domains of life, but especially common among the eukaryotic proteomes. Signaling sequences and sites of posttranslational modifications are frequently, or very likely most often, located within regions of intrinsic disorder. Disorder-to-order transitions are coupled with the adoption of different structures with different partners. Also, the flexibility of intrinsic disorder helps different disordered regions to bind to a common binding site on a common partner. Such capacity for binding diversity plays important roles in both protein-protein interaction networks and likely also in gene regulation networks. Such disorder-based signaling is further modulated in multicellular eukaryotes by alternative splicing, for which such splicing events map to regions of disorder much more often than to regions of structure. Associating alternative splicing with disorder rather than structure alleviates theoretical and experimentally observed problems associated with the folding of different length, isomeric amino acid sequences. The combination of disorder and alternative splicing is proposed to provide a mechanism for easily "trying out" different signaling pathways, thereby providing the mechanism for generating signaling diversity and enabling the evolution of cell differentiation and multicellularity. Finally, several recent small molecules of interest as potential drugs have been shown to act by blocking protein-protein interactions based on intrinsic disorder of one of the partners. Study of these examples has led to a new approach for drug discovery, and bioinformatics analysis of the human proteome suggests that various disease-associated proteins are very rich in such disorder-based drug discovery targets.

Clustering structures of large proteins using multifractal analyses based on a 6-letter model and hydrophobicity scale of amino acids

The Schneider and Wrede hydrophobicity scale of amino acids and the 6-letter model of protein are proposed to study the relationship between the primary structure and the secondary structural classification of proteins. Two kinds of multifractal analyses are performed on the two measures obtained from these two kinds of data on large proteins. Nine parameters from the multifractal analyses are considered to construct the parameter spaces. Each protein is represented by one point in these spaces. A procedure is proposed to separate large proteins in the α, β, α + β and α/β structural classes in these parameter spaces. Fisher’s linear discriminant algorithm is used to assess our clustering accuracy on the 49 selected large proteins. Numerical results indicate that the discriminant accuracies are satisfactory. In particular, they reach 100.00% and 84.21% in separating the α proteins from the {β, α + β, α/β} proteins in a parameter space; 92.86% and 86.96% in separating the β proteins from the {α + β, α/β} proteins in another parameter space; 91.67% and 83.33% in separating the α/β proteins from the α + β proteins in the last parameter space.

Synonymous codon usage in different protein secondary structural classes of human genes: Implication for increased non-randomness of GC3 rich genes towards protein stability

The relationship between the synonymous codon usage and different protein secondary structural classes were investigated using 401 Homo sapiens proteins extracted from Protein Data Bank (PDB). A simple Chi-square test was used to assess the significance of deviation of the observed and expected frequencies of 59 codons at the level of individual synonymous families in the four different protein secondary structural classes. It was observed that synonymous codon families show non-randomness in codon usage in four different secondary structural classes. However,when the genes were classified according to their GC3 levels there was an increase in non-randomness in high GC3 group of genes. The non-randomness in codon usage was further tested among the same protein secondary structures belonging to four different protein folding classes of high GC3 group of genes. The results show that in each of the protein secondary structural unit there exist some synonymous family that shows class specific codon-usage pattern. Moreover, there is an increased non-random behaviour of synonymous codons in sheet structure of all secondary structural classes in high GC3 group of genes. Biological implications of these results have been discussed.

A Protein Folding Degree Measure and Its Dependence on Crystal Packing, Protein Size, Secondary Structure, and Domain Structural Class [J. Chem. Inf. Comput. Sci. 44, 1238−1250 (2004)

<b>A Protein Folding Degree Measure and Its Dependence on Crystal Packing, </b><b>Protein Size, Secondary Structure, and Domain Structural Class</b> [<i>J. Chem. Inf. </i><i>Comput. Sci. 44, </i>1238−1250 (2004)

Solving large protein secondary structure classification problems by a nonlinear complementarity algorithm with {0, 1} variables

The aim of this paper is to present a nonlinear complementarity algorithm, limited to binary variables, to implement a classification algorithm, which will be applied to the determination of the secondary structure of proteins, and to present its statistical and mathematical convergence properties. Extensive application results will be given on the available test data sets of proteins and the results will be compared with those of other implementations. The increase in the recognition accuracy, which will be in evidence, will be shown to be attributable to the adoption of a strict statistical methodology, which will permit to obtain unbiased results, without requiring extraneous assumptions.

A new expertness index for assessment of secondary structure prediction engines

Improvement of prediction accuracy of the protein secondary structure is essential for further developments of the whole field of protein research. In this paper, the expertness of protein secondary structure prediction engines has been studied in three levels and a new criterion has been introduced in the third level. This criterion could be considered as an extension of the previous ones based on amino acid index. Using this new criterion, the expertness of some high score secondary structure prediction engines has been reanalyzed and some hidden facts have been discovered. The results of this new assessment demonstrated that a noticeable harmony has been existed among each amino acid prediction behavior in all engines. This harmony has also been seen between single global propensity and prediction accuracy of amino acid types in each secondary structure class. Moreover, it is shown that Proline and Glycine amino acids have been predicted with less accuracy in alpha helices and beta strands. In addition, regardless of different approaches used in prediction engines, beta strands have been predicted with less accuracy.

Protein structural codes and nucleation sites for protein folding

One of the long-standing controversial arguments in protein folding is Levinthal's paradox. We have recently proposed a new nucleation hypothesis and shown that the nucleation residues are the most conserved sequences in protein. To avoid the complicated effect of tertiary interactions, we limit our search for structural codes to the nucleation residues. Starting with the hypotheses of secondary structure nucleation and conservation of residues important for folding, we have analysed 762 folds classified as unique by SCOP. Segments of 17 residues around the top 20% conserved amino acids are analysed, resulting in approximately 100 clusters each for the main secondary structure classes of helix, sheet and coil. Helical clusters have the longest correlation range, coils the shortest (four residues). Strong specific sequence-structure correlation is observed for coil but not for helix and sheet, suggesting a mapping relationship between the sequence and the structure for coil. We propose that the central sequences in these clusters form `structural codes', a useful basis set for identifying nucleation sites, protein fragments stable in isolation, and secondary structural patterns in proteins (particularly turns and loops).

Structure prediction for CASP7 targets using extensive all-atom refinement with Rosetta@home

We describe predictions made using the Rosetta structure prediction methodology for both template-based modeling and free modeling categories in the Seventh Critical Assessment of Techniques for Protein Structure Prediction. For the first time, aggressive sampling and all-atom refinement could be carried out for the majority of targets, an advance enabled by the Rosetta@home distributed computing network. Template-based modeling predictions using an iterative refinement algorithm improved over the best existing templates for the majority of proteins with less than 200 residues. Free modeling methods gave near-atomic accuracy predictions for several targets under 100 residues from all secondary structure classes. These results indicate that refinement with an all-atom energy function, although computationally expensive, is a powerful method for obtaining accurate structure predictions.

Graphical representations for protein secondary structure sequences and their application

In recent years, some graphical representations for genomic sequences have been studied. They provide an opportunity to inspect genomic sequences intuitively and have become powerful tools for analyzing genomic sequences. In this Letter, we have proposed novel graphical representations for protein secondary structure sequences. By our representations, some important information originally hidden in protein secondary structure sequences can be revealed. To illustrate their availability, we apply them to distinguish protein structural classes. The result indicates that they can be used to complement the classification of protein secondary structures.

The MPI Bioinformatics Toolkit for protein sequence analysis

The MPI Bioinformatics Toolkit is an interactive web service which offers access to a great variety of public and in-house bioinformatics tools. They are grouped into different sections that support sequence searches, multiple alignment, secondary and tertiary structure prediction and classification. Several public tools are offered in customized versions that extend their functionality. For example, PSI-BLAST can be run against regularly updated standard databases, customized user databases or selectable sets of genomes. Another tool, Quick2D, integrates the results of various secondary structure, transmembrane and disorder prediction programs into one view. The Toolkit provides a friendly and intuitive user interface with an online help facility. As a key feature, various tools are interconnected so that the results of one tool can be forwarded to other tools. One could run PSI-BLAST, parse out a multiple alignment of selected hits and send the results to a cluster analysis tool. The Toolkit framework and the tools developed in-house will be packaged and freely available under the GNU Lesser General Public Licence (LGPL). The Toolkit can be accessed at .

2D representation of protein secondary structure sequences and its applications

In terms of the classification of the protein secondary structures, we propose a 2D representation of protein secondary structure sequences. The representation are used to display, analyze, and compare the secondary structure sequences. Based on this representation, we assign the structural class to the protein, and verify the advantage or disadvantage of the methods of predicted protein second structure.

Proteins of the same fold and unrelated sequences have similar amino acid composition

It is well established that there is a relationship between the amino acid composition of a protein and its structural class (i.e., alpha, beta, alpha + beta, or alpha/beta). Several studies have even shown the power of amino acid composition in predicting the secondary structure class of a protein. Herein, we show that significant similarity in amino acid composition exists not only between proteins of the same class, but even between proteins of the same fold. To test conjectural explanations for this phenomenon, we analyzed a set of structurally similar proteins that are dissimilar in sequence. Based on this analysis, we suggest that specific residues that are involved in intramolecular interactions may account for this surprising relationship between composition and structure.

Amino acid propensities for secondary structures are influenced by the protein structural class

Amino acid propensities for secondary structures were used since the 1970s, when Chou and Fasman evaluated them within datasets of few tens of proteins and developed a method to predict secondary structure of proteins, still in use despite prediction methods having evolved to very different approaches and higher reliability. Propensity for secondary structures represents an intrinsic property of amino acid, and it is used for generating new algorithms and prediction methods, therefore our work has been aimed to investigate what is the best protein dataset to evaluate the amino acid propensities, either larger but not homogeneous or smaller but homogeneous sets, i.e., all-α, all-β, α–β proteins. As a first analysis, we evaluated amino acid propensities for helix, β-strand, and coil in more than 2000 proteins from the PDBselect dataset. With these propensities, secondary structure predictions performed with a method very similar to that of Chou and Fasman gave us results better than the original one, based on propensities derived from the few tens of X-ray protein structures available in the 1970s. In a refined analysis, we subdivided the PDBselect dataset of proteins in three secondary structural classes, i.e., all-α, all-β, and α–β proteins. For each class, the amino acid propensities for helix, β-strand, and coil have been calculated and used to predict secondary structure elements for proteins belonging to the same class by using resubstitution and jackknife tests. This second round of predictions further improved the results of the first round. Therefore, amino acid propensities for secondary structures became more reliable depending on the degree of homogeneity of the protein dataset used to evaluate them. Indeed, our results indicate also that all algorithms using propensities for secondary structure can be still improved to obtain better predictive results.