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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↗

PubMed1998

Interaction of cisplatin with human serum albumin. Drug binding mode and protein secondary structure.

Cis-diamminedichloroplatinum(II) (cisplatin) is an antitumor drug, which forms intrastrand cross-links DNA adducts. Protein interaction with cisplatin-DNA complexes induces DNA bending and biopolymer structural changes. This study is designed to examined the interaction of cisplatin with human serum albumin (HSA) in aqueous solution at physiological pH with drug concentrations of 0.0001 mM to 0.1 mM, and HSA (fatty acid free) concentration of 2% w/v. Absorption spectra and Fourier transform infrared (FTIR) spectroscopy with its self-deconvolution and second derivative resolution enhancement, as well as curve-fitting procedures, were used to determine the drug binding mode, drug binding constant and the protein secondary structure in aqueous solution. Spectroscopic evidence showed that at low drug concentration (0.0001 mM), minor cisplatin-protein interaction occurs, while at higher drug content (0.001 mM), major Pt-HSA complexation takes place via protein C=O, C-N and S-H donor groups with overall binding constant K = 8.52 x 10(2) M-1. At high drug concentration, cisplatin binding results in major protein secondary structural changes from that of the alpha-helix 55% (free HSA) to 45% and beta-sheet 22% (free HSA) to 32%, in the cisplatin-HSA complexes. The observed spectral changes indicate a partial unfolding of the protein structure, in the presence of cisplatin at high drug concentrations.

Binding Sites↗

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↗

PubMed1995

Prediction of protein secondary structures from their hydrophobic characteristics.

Deciphering the native conformation of proteins from their amino acid sequences is one of the greatest challenges in the field of molecular biology. The successful prediction of structural class may help to improve the accuracy levels of structure (secondary and tertiary) predictive schemes in globular proteins. In our earlier works we developed a new surrounding hydrophobicity scale for the 20 amino acid residues applicable for both globular and membrane proteins and used it successfully to predict the transmembrane helical and strand segments in membrane proteins. In this article we propose (i) rules to predict the structural class of proteins and (ii) a new predictive scheme for forecasting secondary structures of globular proteins, with the use of the new hydrophobicity scale. This scheme predicts the structural class and secondary structures of globular proteins to 92 and 82% levels of accuracy, respectively, far better than the levels from other existing methods.

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↗

PubMed2005

Consolidating critical binding determinants by noncyclic rearrangement of protein secondary structure.

We designed a single-chain variant of the Arc repressor homodimer in which the beta strands that contact operator DNA are connected by a hairpin turn and the alpha helices that form the tetrahelical scaffold of the dimer are attached by a short linker. The designed protein represents a noncyclic permutation of secondary structural elements in another single-chain Arc molecule (Arc-L1-Arc), in which the two subunits are fused by a single linker. The permuted protein binds operator DNA with nanomolar affinity, refolds on the sub-millisecond time scale, and is as stable as Arc-L1-Arc. The crystal structure of the permuted protein reveals an essentially wild-type fold, demonstrating that crucial folding information is not encoded in the wild-type order of secondary structure. Noncyclic rearrangement of secondary structure may allow grouping of critical active-site residues in other proteins and could be a useful tool for protein design and minimization.

Bacteriophage P22↗

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↗

PubMed1993

Protein secondary structure prediction with partially recurrent neural networks.

Partially recurrent neural networks with different topologies are applied for secondary structure prediction of proteins. The state of some activations in the network is available after a pattern presentation via feedback connections as additional input during the processing of the next pattern in a sequence. A reference data set containing 91 proteins in the training set and 15 non-homologous proteins in the test set is used for training and testing a network with a modified, hierarchical Elman architecture. The network predicts the secondary structures alpha-helix, beta-sheet, and "coil" for each amino acid. The percentage of correctly classified amino acids is 67.83% on the training set and 63.98% on the test set. The best performance of a three-layer feedforward network is 62.7% on the same test set. A cascaded network, where the outputs of the recurrent network are processed by a second net with 13 x 3 inputs, four hidden and three output units has a predictive performance of 64.49%. The best corresponding feedforward net has a performance of 64.3%.

Amino Acid Sequence↗

PubMed1998

Predictions of protein secondary structures using factor analysis on Fourier transform infrared spectra: effect of Fourier self-deconvolution of the amide I and amide II bands.

Fourier self-deconvolution (FSD) was performed on protein amide I and II Fourier transform infrared (FTIR) spectra to test if the resultant increased band shape variation would lead to improvements in protein secondary structure prediction with our factor analysis based restricted multiple regression (RMR) methods. FTIR spectra of 23 proteins dissolved in H2O were measured and normalized to a constant amide I peak absorbance. The deconvolved spectra were renormalized by area so that the deconvolved spectra sets had the same area as before. Principal component analysis of the deconvolved spectra sets was carried out, which was followed by a selective multiple linear regression (RMR) analysis of the principal component loadings with regard to the fractional components (FC) of secondary structure. As compared to analyses based on the original spectra set, helix and sheet predictions were not noticeably improved by FSD; but, if a very large number of component spectra (16) were retained in the pool to select which loadings to be used in the RMR optimization, better predictions of turn and "other" resulted. The prediction quality varied depending on the deconvolution parameters used.

Animals↗

PubMed1997

Protein secondary structure prediction using local alignments.

The accuracy of secondary structure prediction methods has been improved significantly by the use of aligned protein sequences. The PHD method and the NNSSP method reach 71 to 72% of sustained overall three-state accuracy when multiple sequence alignments are with neural networks and nearest-neighbor algorithms, respectively. We introduce a variant of the nearest-neighbor approach that can achieve similar accuracy using a single sequence as the query input. We compute the 50 best non-intersecting local alignments of the query sequence with each sequence from a set of proteins with known 3D structures. Each position of the query sequence is aligned with the database amino acids in alpha-helical, beta-strand or coil states. The prediction type of secondary structure is selected as the type of aligned position with the maximal total score. On the dataset of 124 non-membrane non-homologous proteins, used earlier as a benchmark for secondary structure predictions, our method reaches an overall three-state accuracy of 71.2%. The performance accuracy is verified by an additional test on 461 non-homologous proteins giving an accuracy of 71.0%. The main strength of the method is the high level of prediction accuracy for proteins without any known homolog. Using multiple sequence alignments as input the method has a prediction accuracy of 73.5%. Prediction of secondary structure by the SSPAL method is available via Baylor College of Medicine World Wide Web server.

Algorithms↗

PubMed1993

Thermal stability of protein secondary structure in Langmuir-Blodgett films.

The temperature dependence of the secondary structure of photosynthetic reaction centres from Rhodobacter sphaeroides in solution and in Langmuir-Blodgett film was studied by circular dichroism. It was shown that the secondary structure of the protein was not affected in Langmuir-Blodgett films by heating up to 200 degrees C, while in solution it was completely lost at 55 degrees C. Molecular order rather than decreased hydration degree was held responsible.

Circular Dichroism↗

PubMedDate not supplied

A protein folding degree measure and its dependence on crystal packing, protein size, secondary structure, and domain structural class.

Comparing two or more protein structures with respect to their degree of folding is common practice in structural biology despite the fact that there is no scale for a folding degree. Here we introduce a formal definition of a folding degree, capable of quantitative characterization. This enables ordering among protein chains based on their degree of folding. The folding degree of a data set of 152 representative nonhomologous proteins is then studied. We demonstrate that the variation in the folding degree seen for this data set is not due to crystallization artifacts or experimental conditions, such as resolution, refinement protocol, pH, or temperature. A good linear relationship is observed between the folding degree and the percentages of secondary structures in the protein. The folding degree is able to account for the small changes produced in the structure due to crystal packing and temperature. Automating the classification of proteins into their respective structural domain classes, namely mainly-alpha, mainly-beta, and alpha-beta, is also possible.

Computational Biology↗