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At least 19 recordsLinked to original sources
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↗
PubMed2006

Micelle density regulated by a reversible switch of protein secondary structure.

Protein secondary structures may exhibit reversible transitions that occur in an abrupt and controllable manner. In this report, we demonstrate that such transitions may be utilized in the design of a "smart" protein micellar system, in which a stimulus-induced change in protein structure triggers a rapid change in micelle compacticity and size. Specifically, recombinant DNA methods were used to prepare a protein triblock copolymer containing a central hydrophilic block and two hydrophobic end blocks derived from elastin-mimetic peptide sequences. Below the copolymer inverse transition temperature (T(t)), dilute solutions of this amphiphilic protein formed monodispersed micelles in a narrow range of R(H) of approximately 100 nm. When the the temperature was raised above T(t), an abrupt increase in micelle internal density was observed with a concomitant reduction in micelle size. This reversible change in micelle compacticity was triggered by helix-to-sheet protein folding transition. Significantly, these protein polymer-based micelles, which are rapidly responsive to environmental stimuli, establish a new mechanism for the design of controlled drug delivery vehicles.

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↗
PubMed2005

A seqlet-based maximum entropy Markov approach for protein secondary structure prediction.

A novel method for predicting the secondary structures of proteins from amino acid sequence has been presented. The protein secondary structure seqlets that are analogous to the words in natural language have been extracted. These seqlets will capture the relationship between amino acid sequence and the secondary structures of proteins and further form the protein secondary structure dictionary. To be elaborate, the dictionary is organism-specific. Protein secondary structure prediction is formulated as an integrated word segmentation and part of speech tagging problem. The word-lattice is used to represent the results of the word segmentation and the maximum entropy model is used to calculate the probability of a seqlet tagged as a certain secondary structure type. The method is markovian in the seqlets, permitting efficient exact calculation of the posterior probability distribution over all possible word segmentations and their tags by viterbi algorithm. The optimal segmentations and their tags are computed as the results of protein secondary structure prediction. The method is applied to predict the secondary structures of proteins of four organisms respectively and compared with the PHD method. The results show that the performance of this method is higher than that of PHD by about 3.9% Q3 accuracy and 4.6% SOV accuracy. Combining with the local similarity protein sequences that are obtained by BLAST can give better prediction. The method is also tested on the 50 CASP5 target proteins with Q3 accuracy 78.9% and SOV accuracy 77.1%. A web server for protein secondary structure prediction has been constructed which is available at http://www.insun.hit.edu.cn:81/demos/biology/index.html.

Algorithms↗
PubMed2005

Non-parametric classification of protein secondary structures.

Proteins were classified into their families using a classification tree method which is based on the coefficient of variations of physico-chemical and geometrical properties of the secondary structures of proteins. The tree method uses as splitting criterion the increase in purity when a node is split into two subnodes and the size of the tree is controlled by a threshold level for the improvement of the apparent misclassification rate (AMR) of the tree after each splitting step. The classification tree method seems effective in reproducing similar structural groupings as the method of dynamic programming. For comparison, we also used another two methods: neural networks and support vector machines. We could show that the presented classification tree method performs better in classifying proteins into their families. The presented algorithm might be suitable for a rapid preliminary classification of proteins into their corresponding families.

Algorithms↗
PubMed2004

Using synchrotron-based FTIR microspectroscopy to reveal chemical features of feather protein secondary structure: comparison with other feed protein sources.

Studying the secondary structure of proteins leads to an understanding of the components that make up a whole protein. An understanding of the structure of the whole protein is often vital to understanding its digestive behavior in animals and nutritive quality. Usually protein secondary structures include alpha-helix and beta-sheet. The percentages of these two structures in protein secondary structures may influence feed protein quality and digestive behavior. Feathers are widely available as a potential protein supplement. They are very high in protein (84%), but the digestibility of the protein is very low (5%). The objective of this study was to use synchrotron-based Fourier transform infrared (FTIR) microspectroscopy to reveal chemical features of feather protein secondary structure within amide I at ultraspatial resolution (pixel size = 10 x 10 microm), in comparison with other protein sources from easily digested feeds such as barley, oat, and wheat tissue at endosperm regions (without destruction of their inherent structure). This experiment was performed at beamline U2B of the Albert Einstein Center for Synchrotron Biosciences at the National Synchrotron Light Source (NSLS) in Brookhaven National Laboratory (BNL), U.S. Dept of Energy (NSLS-BNL, Upton, NY). The results showed that ultraspatially resolved chemical imaging of feed protein secondary structure in terms of beta-sheet to alpha-helix peak height ratio by stepping in pixel-sized increments was obtained. Using synchrotron FTIR microspectroscopy can distinguish structures of protein amide I among the different feed protein sources. The results show that the secondary structure of feather protein differed from those of other feed protein sources in terms of the line-shape and position of amide I. The feather protein amide I peaked at approximately 1630 cm(-1). However, other feed protein sources showed a peak at approximately 1650 cm(-1). By using multicomponent peak modeling, the relatively quantitative amounts of alpha-helix and beta-sheet in protein secondary structure were obtained, which showed that feather contains 88% beta-sheet and 4% alpha-helix, barley contains 17% beta-sheet and 71% alpha-helix, oat contains 2% beta-sheet and 92% alpha-helix, and wheat contains 42% beta-sheet and 50% alpha-helix. The difference in percentage of protein secondary structure may be part of the reason for different feed protein digestive behaviors. These results demonstrate the potential of highly spatially resolved infrared microspectroscopy to reveal feed protein secondary structure. Information from this study by the infrared probing of feed protein secondary structure may be valuable as a guide for feed breeders to improve and maintain protein quality for animal use.

Amides↗
PubMed2004

Comparison of probabilistic combination methods for protein secondary structure prediction.

MOTIVATION: Protein secondary structure prediction is an important step towards understanding how proteins fold in three dimensions. Recent analysis by information theory indicates that the correlation between neighboring secondary structures are much stronger than that of neighboring amino acids. In this article, we focus on the combination problem for sequences, i.e. combining the scores or assignments from single or multiple prediction systems under the constraint of a whole sequence, as a target for improvement in protein secondary structure prediction. RESULTS: We apply several graphical chain models to solve the combination problem and show that they are consistently more effective than the traditional window-based methods. In particular, conditional random fields (CRFs) moderately improve the predictions for helices and, more importantly, for beta sheets, which are the major bottleneck for protein secondary structure prediction.

Algorithms↗
PubMed2003

Neuro-fuzzy structural classification of proteins for improved protein secondary structure prediction.

Fourier transform infrared (FTIR) spectroscopy is a very flexible technique for characterization of protein secondary structure. Measurements can be carried out rapidly in a number of different environments based on only small quantities of proteins. For this technique to become more widely used for protein secondary structure characterization, however, further developments in methods to accurately quantify protein secondary structure are necessary. Here we propose a structural classification of proteins (SCOP) class specialized neural networks architecture combining an adaptive neuro-fuzzy inference system (ANFIS) with SCOP class specialized backpropagation neural networks for improved protein secondary structure prediction. Our study shows that proteins can be accurately classified into two main classes "all alpha proteins" and "all beta proteins" merely based on the amide I band maximum position of their FTIR spectra. ANFIS is employed to perform the classification task to demonstrate the potential of this architecture with moderately complex problems. Based on studies using a reference set of 17 proteins and an evaluation set of 4 proteins, improved predictions were achieved compared to a conventional neural network approach, where structure specialized neural networks are trained based on protein spectra of both "all alpha" and "all beta" proteins. The standard errors of prediction (SEPs) in % structure were improved by 4.05% for helix structure, by 5.91% for sheet structure, by 2.68% for turn structure, and by 2.15% for bend structure. For other structure, an increase of SEP by 2.43% was observed. Those results were confirmed by a "leave-one-out" run with the combined set of 21 FTIR spectra of proteins.

Fuzzy Logic↗
PubMed1995

FOLD: integrated analysis and display of protein secondary structure.

FOLD, a computer program for the definition and analysis of protein secondary structure, is described. Algorithms implemented in the software are reviewed. These include methods for the identification of simple features such as hydrogen bonds, alpha helices, beta strands, beta bulges, and beta and psi turns. Techniques are also described for the definition and analysis of higher-order structures, such as beta hairpins, beta sheets and their topology, and beta barrels. In addition to considerable textual output the program supports visualization of protein secondary structure in either an atom-based display style or one reproducing the characteristics of a so-called ribbon drawing.

Computer Graphics↗
PubMed2006

Improving the accuracy of protein secondary structure prediction using structural alignment.

BACKGROUND: The accuracy of protein secondary structure prediction has steadily improved over the past 30 years. Now many secondary structure prediction methods routinely achieve an accuracy (Q3) of about 75%. We believe this accuracy could be further improved by including structure (as opposed to sequence) database comparisons as part of the prediction process. Indeed, given the large size of the Protein Data Bank (>35,000 sequences), the probability of a newly identified sequence having a structural homologue is actually quite high. RESULTS: We have developed a method that performs structure-based sequence alignments as part of the secondary structure prediction process. By mapping the structure of a known homologue (sequence ID >25%) onto the query protein's sequence, it is possible to predict at least a portion of that query protein's secondary structure. By integrating this structural alignment approach with conventional (sequence-based) secondary structure methods and then combining it with a "jury-of-experts" system to generate a consensus result, it is possible to attain very high prediction accuracy. Using a sequence-unique test set of 1644 proteins from EVA, this new method achieves an average Q3 score of 81.3%. Extensive testing indicates this is approximately 4-5% better than any other method currently available. Assessments using non sequence-unique test sets (typical of those used in proteome annotation or structural genomics) indicate that this new method can achieve a Q3 score approaching 88%. CONCLUSION: By using both sequence and structure databases and by exploiting the latest techniques in machine learning it is possible to routinely predict protein secondary structure with an accuracy well above 80%. A program and web server, called PROTEUS, that performs these secondary structure predictions is accessible at http://wishart.biology.ualberta.ca/proteus. For high throughput or batch sequence analyses, the PROTEUS programs, databases (and server) can be downloaded and run locally.

Algorithms↗
PubMed2005

A hybrid genetic-neural system for predicting protein secondary structure.

BACKGROUND: Due to the strict relation between protein function and structure, the prediction of protein 3D-structure has become one of the most important tasks in bioinformatics and proteomics. In fact, notwithstanding the increase of experimental data on protein structures available in public databases, the gap between known sequences and known tertiary structures is constantly increasing. The need for automatic methods has brought the development of several prediction and modelling tools, but a general methodology able to solve the problem has not yet been devised, and most methodologies concentrate on the simplified task of predicting secondary structure. RESULTS: In this paper we concentrate on the problem of predicting secondary structures by adopting a technology based on multiple experts. The system performs an overall processing based on two main steps: first, a "sequence-to-structure" prediction is enforced by resorting to a population of hybrid (genetic-neural) experts, and then a "structure-to-structure" prediction is performed by resorting to an artificial neural network. Experiments, performed on sequences taken from well-known protein databases, allowed to reach an accuracy of about 76%, which is comparable to those obtained by state-of-the-art predictors. CONCLUSION: The adoption of a hybrid technique, which encompasses genetic and neural technologies, has demonstrated to be a promising approach in the task of protein secondary structure prediction.

Algorithms↗
PubMed2000

Spectroscopic methods for analysis of protein secondary structure.

Several methods for determination of the secondary structure of proteins by spectroscopic measurements are reviewed. Circular dichroism (CD) spectroscopy provides rapid determinations of protein secondary structure with dilute solutions and a way to rapidly assess conformational changes resulting from addition of ligands. Both CD and Raman spectroscopies are particularly useful for measurements over a range of temperatures. Infrared (IR) and Raman spectroscopy require only small volumes of protein solution. The frequencies of amide bands are analyzed to determine the distribution of secondary structures in proteins. NMR chemical shifts may also be used to determine the positions of secondary structure within the primary sequence of a protein. However, the chemical shifts must first be assigned to particular residues, making the technique considerably slower than the optical methods. These data, together with sophisticated molecular modeling techniques, allow for refinement of protein structural models as well as rapid assessment of conformational changes resulting from ligand binding or macromolecular interactions. A selected number of examples are given to illustrate the power of the techniques in applications of biological interest.

Animals↗
PubMed2004

Improved protein secondary structure prediction using support vector machine with a new encoding scheme and an advanced tertiary classifier.

Prediction of protein secondary structures is an important problem in bioinformatics and has many applications. The recent trend of secondary structure prediction studies is mostly based on the neural network or the support vector machine (SVM). The SVM method is a comparatively new learning system which has mostly been used in pattern recognition problems. In this study, SVM is used as a machine learning tool for the prediction of secondary structure and several encoding schemes, including orthogonal matrix, hydrophobicity matrix, BLOSUM62 substitution matrix, and combined matrix of these, are applied and optimized to improve the prediction accuracy. Also, the optimal window length for six SVM binary classifiers is established by testing different window sizes and our new encoding scheme is tested based on this optimal window size via sevenfold cross validation tests. The results show 2% increase in the accuracy of the binary classifiers when compared with the instances in which the classical orthogonal matrix is used. Finally, to combine the results of the six SVM binary classifiers, a new tertiary classifier which combines the results of one-versus-one binary classifiers is introduced and the performance is compared with those of existing tertiary classifiers. According to the results, the Q3 prediction accuracy of new tertiary classifier reaches 78.8% and this is better than the best result reported in the literature.

Algorithms↗
PubMed2006

Sequence representation and prediction of protein secondary structure for structural motifs in twilight zone proteins.

Characterizing and classifying regularities in protein structure is an important element in uncovering the mechanisms that regulate protein structure, function and evolution. Recent research concentrates on analysis of structural motifs that can be used to describe larger, fold-sized structures based on homologous primary sequences. At the same time, accuracy of secondary protein structure prediction based on multiple sequence alignment drops significantly when low homology (twilight zone) sequences are considered. To this end, this paper addresses a problem of providing an alternative sequences representation that would improve ability to distinguish secondary structures for the twilight zone sequences without using alignment. We consider a novel classification problem, in which, structural motifs, referred to as structural fragments (SFs) are defined as uniform strand, helix and coil fragments. Classification of SFs allows to design novel sequence representations, and to investigate which other factors and prediction algorithms may result in the improved discrimination. Comprehensive experimental results show that statistically significant improvement in classification accuracy can be achieved by: (1) improving sequence representations, and (2) removing possible noise on the terminal residues in the SFs. Combining these two approaches reduces the error rate on average by 15% when compared to classification using standard representation and noisy information on the terminal residues, bringing the classification accuracy to over 70%. Finally, we show that certain prediction algorithms, such as neural networks and boosted decision trees, are superior to other algorithms.

Algorithms↗
PubMed2000

Estimation of protein secondary structure from circular dichroism spectra: inclusion of denatured proteins with native proteins in the analysis.

We have expanded our reference set of proteins used in the estimation of protein secondary structure by CD spectroscopy from 29 to 37 proteins by including 3 additional globular proteins with known X-ray structure and 5 denatured proteins. We have also modified the self-consistent method for analyzing protein CD spectra, SELCON3, by including a new selection criterion developed by W. C. Johnson, Jr. (Proteins Struct. Funct. Genet. 35, 307-312, 1999). The secondary structure corresponding to the denatured proteins was approximated to be 90% unordered, owing to the spectral similarity of the denatured proteins and unordered structures. We examined the thermal denaturation of ribonuclease T1 by CD using both the original and expanded sets of reference proteins and obtained more consistent results with the expanded set. The expanded set of reference proteins will be helpful for the determination of protein secondary structure from protein CD spectra with higher reliability, especially of proteins with significant unordered structure content and/or in the course of denaturation.

Circular Dichroism↗
PubMed2002

Automatic amide I frequency selection for rapid quantification of protein secondary structure from Fourier transform infrared spectra of proteins.

Here we report the development of a new neural network based approach for rapid quantification of protein secondary structure from Fourier transform infrared (FTIR) spectra of proteins. A technique for efficiently reducing the amount of spectral data by almost 90% is suggested to facilitate faster neural network analysis. Additionally, an automatic procedure is introduced for selecting only those regions within the amide I band of protein FTIR spectra, which can be best related to secondary structure contents by subsequent neural network analysis. Based on a given reference set of FTIR spectra from proteins with known secondary structure, a subset of merely 29 out of 101 amide I absorbance values could be identified, which lead to an improved prediction accuracy. The average prediction accuracy achieved for helix, sheet, turn, bend, and other is 4.96% which is better than that achieved by alternative methods that have been previously reported indicating the significant potential of this approach. Our suggested automatic amide I frequency selection procedure may be easily extended to identify promising regions from spectral data recorded by other spectroscopic techniques, like for example circular dichroism spectroscopy.

Algorithms↗
PubMed2000

Computational methods for protein secondary structure prediction using multiple sequence alignments.

Efforts to use computers in predicting the secondary structure of proteins based only on primary structure information started over a quarter century ago [1-3]. Although the results were encouraging initially, the accuracy of the pioneering methods generally did not attain the level required for using predictions of secondary structures reliably in modelling the three-dimensional topology of proteins. During the last decade, however, the introduction of new computational techniques as well as the use of multiple sequence information has lead to a dramatic increase in the success rate of prediction methods, such that successful 3D modelling based on predicted secondary structure has become feasible [e.g., Ref 4]. This review is aimed at presenting an overview of the scale of the secondary structure prediction problem and associated pitfalls, as well as the history of the development of computational prediction methods. As recent successful strategies for secondary structure prediction all rely on multiple sequence information, some methods for accurate protein multiple sequence alignments will also be described. While the main focus is on prediction methods for globular proteins, also the prediction of trans-membrane segments within membrane proteins will be briefly summarised. Finally, an integrated iterative approach tying secondary structure prediction and multiple alignment will be introduced [5].

Algorithms↗
PubMed2007

MUPRED: a tool for bridging the gap between template based methods and sequence profile based methods for protein secondary structure prediction.

Predicting secondary structures from a protein sequence is an important step for characterizing the structural properties of a protein. Existing methods for protein secondary structure prediction can be broadly classified into template based or sequence profile based methods. We propose a novel framework that bridges the gap between the two fundamentally different approaches. Our framework integrates the information from the fuzzy k-nearest neighbor algorithm and position-specific scoring matrices using a neural network. It combines the strengths of the two methods and has a better potential to use the information in both the sequence and structure databases than existing methods. We implemented the framework into a software system MUPRED. MUPRED has achieved three-state prediction accuracy (Q3) ranging from 79.2 to 80.14%, depending on which benchmark dataset is used. A higher Q3 can be achieved if a query protein has a significant sequence identity (>25%) to a template in PDB. MUPRED also estimates the prediction accuracy at the individual residue level more quantitatively than existing methods. The MUPRED web server and executables are freely available at http://digbio.missouri.edu/mupred.

Algorithms↗