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Changes in protein secondary structure during gluten deformation studied by dynamic fourier transform infrared spectroscopy.

Fourier transform infrared (FT-IR) spectroscopy was used to monitor changes in the secondary structure of wheat prolamins, the main components of gluten, during mechanical deformation in a series of cycles of extension and relaxation. A sample derived from protein bodies isolated from developing grain showed a buildup of persistent beta-sheet structure. In gluten, the ratio of beta-sheet to random and beta-turn structures changed on extension. After the applied force was released, the sample recovered some of its original shape and structure, but the material became stiffer in consecutive extension cycles. The relationship between gluten structure and mechanical properties is discussed in terms of a model in which conversion of beta-turn to beta-sheet structure is a response to extension and a means by which elastic energy is stored in the system.

Glutens↗
PubMed1994

Principal component analysis of Fourier transform infrared and/or circular dichroism spectra of proteins applied in a calibration of protein secondary structure.

Gaining information on the secondary structure of a protein from its spectra is presented as a calibration problem. The secondary structures known from X-ray studies and the spectra of 21 proteins are represented by a linear model. Fourier transform infrared (FTIR) spectra from 1700 to 1600 cm-1, circular dichroism (CD) spectra from 178 to 260 nm, and combined spectra are used; the secondary structure classes of interest are alpha-helices, antiparallel beta-sheets, parallel beta-sheets, beta-turns, and "other." The calibration is solved in two steps: (i) the dependencies between the structures and the spectra of reference proteins are found using the least-squares estimator, and (ii) the secondary structure of a protein is predicted from its spectra using the information gained in the first step and principal component analysis. The problem of information content of the reference spectra is analyzed using the linearly independent pieces of information, the so-called principal components, provided by singular value decomposition. Attention is paid to a number of the principal components sufficient for the prediction, which may be less than the total number. A relative estimable parameter is used to determine unambiguously the number of the components corresponding to the minimum mean square error of the predictor. The analysis gives the solutions to this linear calibration relevant to the underlying protein problem, thus reducing subjective assessments as well as computations.

Calibration↗
PubMed1997

Seventy-five percent accuracy in protein secondary structure prediction.

In this study we present an accurate secondary structure prediction procedure by using an query and related sequences. The most novel aspect of our approach is its reliance on local pairwise alignment of the sequence to be predicted with each related sequence rather than utilization of a multiple alignment. The residue-by-residue accuracy of the method is 75% in three structural states after jack-knife tests. The gain in prediction accuracy compared with the existing techniques, which are at best 72%, is achieved by secondary structure propensities based on both local and long-range effects, utilization of similar sequence information in the form of carefully selected pairwise alignment fragments, and reliance on a large collection of known protein primary structures. The method is especially appropriate for large-scale sequence analysis of efforts such as genome characterization, where precise and significant multiple sequence alignments are not available or achievable.

Algorithms↗
PubMed2004

The optimization of protein secondary structure determination with infrared and circular dichroism spectra.

We have used the circular dichroism and infrared spectra of a specially designed 50 protein database [Oberg, K.A., Ruysschaert, J.M. & Goormaghtigh, E. (2003) Protein Sci. 12, 2015-2031] in order to optimize the accuracy of spectroscopic protein secondary structure determination using multivariate statistical analysis methods. The results demonstrate that when the proteins are carefully selected for the diversity in their structure, no smaller subset of the database contains the necessary information to describe the entire set. One conclusion of the paper is therefore that large protein databases, observing stringent selection criteria, are necessary for the prediction of unknown proteins. A second important conclusion is that only the comparison of analyses run on circular dichroism and infrared spectra independently is able to identify failed solutions in the absence of known structure. Interestingly, it was also found in the course of this study that the amide II band has high information content and could be used alone for secondary structure prediction in place of amide I.

Algorithms↗
PubMed1993

The limits of protein secondary structure prediction accuracy from multiple sequence alignment.

The expected best residue-by-residue accuracies for secondary structure prediction from multiple protein sequence alignment have been determined by an analysis of known protein structural families. The results show substantial variation is possible among homologous protein structures, and that 100% agreement is unlikely between a consensus prediction and one member of a protein structural family. The study provides the range of agreement to be expected between a perfect secondary structure prediction from a multiple alignment and each protein within the alignment. The results of this study overcome the difficulties inherent in the use of residue-by-residue accuracy for assessing the quality of consensus secondary structure predictions. The accuracies of recent consensus predictions for the annexins, SH2 domains and SH3 domains fall within the expected range for a perfect prediction.

Amino Acid Sequence↗
PubMed1994

Destabilization of the complete protein secondary structure on binding to the chaperone GroEL.

Protein folding in vivo is mediated by helper proteins, the molecular chaperones, of which Hsp60 and its Escherichia coli variant GroEL are some of the best characterized. GroEL is an oligomeric protein with 14 subunits each of M(r) 60K, which possesses weak, co-operative ATPase activity and high plasticity. GroEL seems to interact with non-native proteins, binding one or two molecules per 14-mer in a 'central cavity', but little is known about the conformational state of the bound polypeptides. Here we use nuclear magnetic resonance techniques to show that the interaction of the small protein cyclophilin with GroEL is reversible by temperature changes, and all amide protons in GroEL-bound cyclophilin are exchanged with the solvent, although this exchange does not occur in free cyclophilin. The complete secondary structure of cyclophilin must be disrupted when bound to GroEL.

Amino Acid Isomerases↗
PubMed2004

Protein secondary structure assignment through Voronoï tessellation.

We present a new automatic algorithm, named VoTAP (Voronoï Tessellation Assignment Procedure), which assigns secondary structures of a polypeptide chain using the list of alpha-carbon coordinates. This program uses three-dimensional Voronoï tessellation. This geometrical tool associates with each amino acid a Voronoï polyhedron, the faces of which unambiguously define contacts between residues. Thanks to the face area, for the contacts close together along the primary structure (low-order contacts) a distinction is made between strong and normal ones. This new definition yields new contact matrices, which are analyzed and used to assign secondary structures. This assignment is performed in two stages. The first one uses contacts between residues close together along the primary structure and is based on data collected on a bank of 282 well-refined nonredundant structures. In this bank, associations were made between the prints defined by these low-order contacts and the assignments performed by different automatic methods. The second step focuses on the strand assignment and uses contacts between distant residues. Comparison with several other automatic assignment methods are presented, and the influence of resolution on the assignment is investigated.

Algorithms↗
PubMed2002

Thermal folding and mechanical unfolding pathways of protein secondary structures.

Mechanical stretching of secondary structures is studied through molecular dynamics simulations of a Go-like model. Force versus displacement curves are studied as a function of the stiffness and velocity of the pulling device. The succession of stretching events, as measured by the order in which contacts are ruptured, is compared to the sequencing of events during thermal folding and unfolding. Opposite cross-correlations are found for an alpha-helix and a beta-hairpin structure. In a tandem of two alpha-helices, the two constituent helices unravel nearly simultaneously. A simple condition for simultaneous versus sequential unraveling of repeat units is presented.

Computer Simulation↗
PubMed2006

Water penetration into protein secondary structure revealed by hydrogen-deuterium exchange two-dimensional infrared spectroscopy.

Two-dimensional infrared spectroscopy in conjunction with hydrogen-deuterium exchange experiments provides detailed information about solvent penetration into protein structure. Correlating the secondary-structure sensitivity of the amide I vibration and the solvent-exposure sensitivity of amide II provides a direct probe of solvent-inaccessible residues of proteins embedded in the hydrophobic core or those involved in strong hydrogen bonds in secondary structures. Distinct spectral signatures of the cross-peak region arising from the coupling of the amide I and II modes imply a significant degree of structural stability of hydrogen-bonded contacts in alpha-helices and beta-sheets in a series of proteins. Ubiquitin, an alpha/beta-protein, exhibits strong alpha-helical signatures and lacks those of the beta-sheet in the cross-peak region, demonstrating that ubiquitin's beta-sheet exchanges protons with the surrounding solvent and is conformationally unstable.

Amides↗
PubMed2002

Protein secondary structure and stability determined by combining exoproteolysis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

In the post-genomic era, several projects focused on the massive experimental resolution of the three-dimensional structures of all the proteins of different organisms have been initiated. Simultaneously, significant progress has been made in the ab initio prediction of protein three-dimensional structure. One of the keys to the success of such a prediction is the use of local information (i.e. secondary structure). Here we describe a new limited proteolysis methodology, based on the use of unspecific exoproteases coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), to map quickly secondary structure elements of a protein from both ends, the N- and C-termini. We show that the proteolytic patterns (mass spectra series) obtained can be interpreted in the light of the conformation and local stability of the analyzed proteins, a direct correlation being observed between the predicted and the experimentally derived protein secondary structure. Further, this methodology can be easily applied to check rapidly the folding state of a protein and characterize mutational effects on protein conformation and stability. Moreover, given global stability information, this methodology allows one to locate the protein regions of increased or decreased conformational stability. All of this can be done with a small fraction of the amount of protein required by most of the other methods for conformational analysis. Thus limited exoproteolysis, together with MALDI-TOF MS, can be a useful tool to achieve quickly the elucidation of protein structure and stability.

Carboxypeptidases↗
PubMed2005

Application of the local regression method interval partial least-squares to the elucidation of protein secondary structure.

The infrared amide bands are sensitive to the conformation of the polypeptide backbone of proteins. Since the backbone of proteins folds in complex spatial arrangements, the amide bands of these proteins result from the superimposition of vibration modes corresponding to the different types of structural motifs (alpha helices, beta sheets, etc.). Initially, band deconvolution techniques were applied to determine the secondary structure of proteins, i.e., the abundance of each structural motif in the polypeptide chain was directly related to the area of the suitable deconvolved vibration modes under the amide I band (1700-1600 cm(-1)). Recently, several multivariate regression methods have been used to predict the secondary structure of proteins as an alternative to the previous methods. They are based on establishing a relationship between a matrix of infrared protein spectra and another that includes their secondary structure, expressed as the fractions of the different structural motifs, determined from X-ray analysis. In this study, we investigated the use of the local regression method interval partial least-squares (iPLS) to seek improvements to the full-spectrum PLS and other regression methods. The local character of iPLS avoids the use of spectral regions that can introduce noise or that can be irrelevant for prediction and focuses on finding specific spectral ranges related to each secondary structure motif in all the proteins. This study has been applied to a representative protein data set with infrared spectra covering a large wavenumber range, including amides I-III bands (1700-1200 cm(-1)). iPLS has revealed new structural mode assignments related to less explored amide bands and has offered a satisfactory predictive ability using a small amount of selected specific spectral information.

Amides↗
PubMed2004

An evaluation of chemical shift index-based secondary structure determination in proteins: influence of random coil chemical shifts.

Random coil chemical shifts are commonly used to detect protein secondary structural elements in chemical shift index (CSI) calculations. Though this technique is widely used and seems reliable for folded proteins, the choice of reference random coil chemical shift values can significantly alter the outcome of secondary structure estimation. In order to evaluate these effects, we present a comparison of secondary structure content calculated using CSI, based on five different reference random coil chemical shift value sets, to that derived from three-dimensional structures. Our results show that none of the reference random coil data sets chosen for evaluation fully reproduces the actual secondary structures. Among the reference values generally available to date, most tend to be good estimators only of helices. Based on our evaluation, we recommend the experimental values measured by Schwarzinger et al.(2000), and statistical values obtained by Lukin et al. (1997), as good estimators of both helical and sheet content.

Nuclear Magnetic Resonance, Biomolecular↗
PubMed1999

Interaction of RNase A with VO3- and VO2+ ions. Metal ion binding mode and protein secondary structure.

Some of vanadyl complexes have shown potential to inhibit RNase activity by acting as transition state analogue, while at the same time not inhibiting DNase. To gain an insight into the interaction of protein with vanadate (VO3-) and vanadyl (VO2+) ions, the present study was designed to examine the binding of ribonuclase A (RNase A) with NaVO3 and VOSO4 in aqueous solution at physiological pH with metal ion concentrations of 0.001 mM to 1 mM, and protein concentration of 2% w/v. Absorption spectra and Fourier transform infrared (FTIR) spectroscopy with self-deconvolution and second derivative resolution enhancement were used to determine the cation binding mode, association constant and the protein secondary structure in the presence of vanadate and vanadyl ions in aqueous solution. Spectroscopic results show that an indirect metal ion interaction occurs with the polypeptide C = O, C-N (via H2O) with overall binding constants of K(VO3-) = 3.93x10(2) M(-1) and K(VO2+) = 4.20x10(3) M(-1). At high metal ion concentrations, major protein secondary structural changes occur from that of the alpha-helix 29% (free enzyme) to 23-24%; beta-sheet (pleated and anti) 50% (free enzyme) to 64-66% and turn 21% (free enzyme) to 10-12% in the metal-RNase complexes. The observed structural changes indicate a partial protein unfolding in the presence of high metal ion concentration.

Animals↗
PubMed1994

PHD--an automatic mail server for protein secondary structure prediction.

By the middle of 1993, > 30,000 protein sequences has been listed. For 1000 of these, the three-dimensional (tertiary) structure has been experimentally solved. Another 7000 can be modelled by homology. For the remaining 21,000 sequences, secondary structure prediction provides a rough estimate of structural features. Predictions in three states range between 35% (random) and 88% (homology modelling) overall accuracy. Using information about evolutionary conservation as contained in multiple sequence alignments, the secondary structure of 4700 protein sequences was predicted by the automatic e-mail server PHD. For proteins with at least one known homologue, the method has an expected overall three-state accuracy of 71.4% for proteins with at least one known homologue (evaluated on 126 unique protein chains).

Algorithms↗
PubMed1999

A holistic approach to protein secondary structure characterization using amide I band Raman spectroscopy.

We have developed a holistic protein structure estimation technique using amide I band Raman spectroscopy. This technique combines the superposition of reference spectra for pure secondary structure elements with simultaneous aromatic, fluorescence, and solvent background subtraction, and is applicable to solution, suspension, and solid protein samples. A key component of this technique was the calculation of the reference spectra for ordered helix, unordered helix, and sheet, turns, and unordered structures from a series of well-characterized reference proteins. We accurately account for the overlap between the amide I and non-amide I regions and allow for different scattering efficiencies for different secondary structures. For hydrated samples, we allowed for the possibility that bound water spectra differ from the bulk water spectra. Our computed reference spectra compare well with previous experimental and theoretical results in the literature. We have demonstrated the use of these reference spectra for the estimation of secondary structures of proteins in solution, suspension, and dry solid forms. The agreement between our structure estimates and the corresponding determinations from X-ray crystallography is good.

Algorithms↗
PubMed1993

Protein secondary structure modelling with probabilistic networks.

In this paper we study the performance of probabilistic networks in the context of protein sequence analysis in molecular biology. Specifically, we report the results of our initial experiments applying this framework to the problem of protein secondary structure prediction. One of the main advantages of the probabilistic approach we describe here is our ability to perform detailed experiments where we can experiment with different models. We can easily perform local substitutions (mutations) and measure (probabilistically) their effect on the global structure. Window-based methods do not support such experimentation as readily. Our method is efficient both during training and during prediction, which is important in order to be able to perform many experiments with different networks. We believe that probabilistic methods are comparable to other methods in prediction quality. In addition, the predictions generated by our methods have precise quantitative semantics which is not shared by other classification methods. Specifically, all the causal and statistical independence assumptions are made explicit in our networks thereby allowing biologists to study and experiment with different causal models in a convenient manner.

Algorithms↗