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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↗

PubMed1993

Protein secondary structure prediction using nearest-neighbor methods.

We have studied the use of nearest-neighbor classifiers to predict the secondary structure of proteins. The nearest-neighbor rule states that a test instance is classified according to the classifications of "nearby" training examples from a database of known structures. In the context of secondary structure prediction, the test instances are windows of n consecutive residues, and the label is the secondary structure type (alpha-helix, beta-strand, or coil) of the center position of the window. To define the neighborhood of a test instance, we employed a novel similarity metric based on the local structural environment scoring scheme of Bowie et al. In this manner, we have attempted to exploit the underlying structural similarity between segments of different proteins to aid in the prediction of secondary structure. Furthermore, in addition to using neighborhoods of fixed radius, we explored a modification of the standard nearest-neighbor algorithm that involved defining an "effective radius" for each exemplar by measuring its performance on a training set. Using these ideas, we achieved a peak prediction accuracy of 68%. Finally, we sought to improve the biological utility of secondary structure prediction by identifying the subset of the predictions that are most likely to be correct. Toward this end, we developed a nearest-neighbor estimator that produced not the traditional "one-state" prediction (alpha-helix, beta-strand, or coil) but rather a probability distribution over the three states. It should be emphasized that this scheme estimates true probability values and that the resulting numbers are not pseudo-probability scores generated by simple normalization of the raw output of the predictor. Applying the mutual information statistic, we found that these probability triplets possess 58% more information than the one-state predictions. Furthermore, the probability estimates allow one to assign an a priori confidence level to the prediction at each residue. Using this approach, we found that the top 28% of the predictions were 86% accurate and the top 43% of the predictions were 81% accurate. These results indicate that, notwithstanding the limitations on overall accuracy of secondary structure prediction, a substantial proportion of a protein can be predicted with considerable accuracy.

Algorithms↗

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↗

PubMed1995

Comparison of and limits of accuracy for statistical analyses of vibrational and electronic circular dichroism spectra in terms of correlations to and predictions of protein secondary structure.

This work provides a systematic comparison of vibrational CD (VCD) and electronic CD (ECD) methods for spectral prediction of secondary structure. The VCD and ECD data are simplified to a small set of spectral parameters using the principal component method of factor analysis (PC/FA). Regression fits of these parameters are made to the X-ray-determined fractional components (FC) of secondary structure. Predictive capability is determined by computing structures for proteins sequentially left out of the regression. All possible combinations of PC/FA spectral parameters (coefficients) were used to form a full set of restricted multiple regressions with the FC values, both independently for each spectral data set as well as for the two VCD sets and all the data grouped together. The complete search over all possible combinations of spectral parameters for different types of spectral data is a new feature of this study, and the focus on prediction is the strength of this approach. The PC/FA method was found to be stable in detail to expansion of the training set. Coupling amide II to amide I' parameters reduced the standard deviations of the VCD regression relationships, and combining VCD and ECD data led to the best fits. Prediction results had a minimum error when dependent on relatively few spectral coefficients. Such a limited dependence on spectral variation is the key finding of this work, which has ramifications for previous studies as well as suggests future directions for spectral analysis of structure. The best ECD prediction for helix and sheet uses only one parameter, the coefficient of the first subspectrum. With VCD, the best predictions sample coefficients of both the amide I' and II bands, but error is optimized using only a few coefficients. In this respect, ECD is more accurate than VCD for alpha-helix, and the combined VCD (amide I' + II) predicts the beta-sheet component better than does ECD. Combining VCD and ECD data sets yields exceptionally good predictions by utilizing the strengths of each. However, the residual error, its distribution, and, most importantly, the lack of dependence of the method on many of the significant components derived from the spectra leads to the conclusion that the heterogeneity of protein structure is a fundamental limitation to the use of such spectral analysis methods. The underutilization of these data for prediction of secondary structure suggests spectral data could predict a more detailed descriptor.

Circular Dichroism↗

PubMed1992

Circular dichroism user facility at the National Synchrotron Light Source: estimation of protein secondary structure.

The ultraviolet circular dichroism of a protein can be used to estimate the net fraction of its amino acids in different classes of secondary structure. Recent advances in the accuracy of such calculations have resulted from improved computational techniques, as well as extension of the spectral region analyzed to wavelengths less than 180 nm, a wavelength range beyond the limit of most laboratory-based circular dichroism spectrometers. We describe a spectrometer that uses UV radiation from the National Synchrotron Light Source at the Brookhaven National Laboratory to record circular dichroism spectra of proteins (and other biologically important molecules) in aqueous solution over the optimum wavelength range required for calculation of secondary structures. This instrument is available for use by scientists from academic, commercial and research institutions.

Animals↗

PubMed1993

A self-consistent method for the analysis of protein secondary structure from circular dichroism.

A self-consistent procedure for estimating the secondary structure content from circular dichroism spectra of proteins is presented. In this method the spectrum of the protein to be analyzed is included in the basis set and an initial guess is made for the unknown structure as a first approximation. The resulting matrix equation is solved using the singular value decomposition algorithm and the initial guess is replaced by the solution. The process is repeated until self-consistency is attained. The best features of the variable selection and the locally linearized methods are incorporated in this procedure. We have applied this method to examine the inconsistencies in the CD data, to compare the predictions with different ranges and resolutions of the CD data, and to compare different assignments of secondary structures from X-ray structure analyses in the context of secondary structure predictions. The results are compared using the root mean square differences and correlation coefficients. The results obtained are as good as or better than the previous analyses. For most of the proteins considered the self-consistent solutions obtained with different initial guesses were similar. We find the Kabsch and Sander protein crystal structure analysis to be most suitable for our prediction method.

Circular Dichroism↗

PubMed2003

Distances and classification of amino acids for different protein secondary structures.

Window profiles of amino acids in protein sequences are used to describe the amino acid environment. The relative entropy or Kullback-Leibler distance derived from these profiles is used as a measure of dissimilarity for comparison of amino acids and secondary structure conformations. Distance matrices of amino acid pairs at different conformations are obtained, which display a non-negligible dependence of amino acid similarity on conformations. Based on the conformation specific distances, a clustering analysis for amino acids is conducted.

Algorithms↗

PubMed2001

Multiple linear regression for protein secondary structure prediction.

In the present work, a novel method was proposed for prediction of secondary structure. Over a database of 396 proteins (CB396) with a three-state-defining secondary structure, this method with jackknife procedure achieved an accuracy of 68.8% and SOV score of 71.4% using single sequence and an accuracy of 73.7% and SOV score of 77.3% using multiple sequence alignments. Combination of this method with DSC, PHD, PREDATOR, and NNSSP gives Q3 = 76.2% and SOV = 79.8%.

Databases, Factual↗

PubMed1999

Using pair-coupled amino acid composition to predict protein secondary structure content.

The pair-coupled amino acid composition is introduced to predict the secondary structure contents of a protein. Compared with the existing methods all based on singlewise amino acid composition as defined in a 20D (dimensional) space, this represents a step forward to the consideration of the sequence coupling effect. The test results indicate that the introduction of the pair-coupled amino acid composition can significantly improve the prediction quality. It is anticipated that the concept of the pair-coupled amino acid composition can be used to simplify the formulation of sequence coupling (or sequence order) effects and to study many other features of proteins as well.

Algorithms↗

PubMedDate not supplied

Heat-induced changes in the photochemical centres and the protein secondary structures of photosystem II studied by variable fluorescence and difference FT-IR spectroscopy.

Variable fluorescence (Fv), i.e., Fv = Fm-Fo where Fo is the minimal fluorescence and Fm the maximum fluorescence, and difference Fourier transform infrared (FT-IR) spectroscopy were used to study the effect of heat stress in the 25-55 degrees C range on photosystem II (PSII) structure and function. First, the Fv intensity reflects accurately the changes in the number of open photochemical centers in PSII. Secondly, the use of Fv in combination with FT-IR spectroscopy can disclose structure-function correlations in the heat inactivation of the PSII complex. Analysis of the midpoint temperatures of thermal denaturation, i.e., 50% inactivation, reported so far in investigations of the thylakoid membrane components has revealed that most of the thermal transitions attributed to PSII are in the 39-46 degrees C range. In this work, it is shown specifically that the midpoint temperature of PSII inactivation is at about 40 degrees C. Moreover, it was clearly demonstrated that the heat-induced changes above 40 degrees C are the result of a marked decrease in the number of open photochemical centers in PSII. It was also seen that above this same temperature the loss of photochemical centers has its structural counterpart in overall modifications of the secondary structures of the PSII proteins resulting from the decrease in the alpha-helix content concomitant with the increase in extended chain (beta-strand) conformations. In brief, a novel finding reported here is that the number of open photochemical centers in PSII is dependent on a dynamic equilibrium between the contents of the PSII proteins in alpha-helix and extended chains (beta-strands), but not in beta-sheets and beta-turn structures except for the antiparallel-beta-sheet conformations. This therefore associates the thermal inactivation of the photochemical centers in photosystem II with distinct conformational changes in the proteins of the PSII supramolecular complex. In the particular context of the present study, these findings constitute a significant contribution to the investigation of structure-function correlations in the photosynthetic membrane. In a broader context, this information might be essential for the comprehension of the molecular arrangements or local structure order that are involved directly or indirectly in biological catalysis.

Pisum sativum↗

PubMed2005

Cooperative effects in hydrogen-bonding of protein secondary structure elements: a systematic analysis of crystal data using Secbase.

A systematic analysis of the hydrogen-bonding geometry in helices and beta sheets has been performed. The distances and angles between the backbone carbonyl O and amide N atoms were correlated considering more than 1500 protein chains in crystal structures determined to a resolution better than 1.5 A. They reveal statistically significant trends in the H-bond geometry across the different secondary structural elements. The analysis has been performed using Secbase, a modular extension of Relibase (Receptor Ligand Database) which integrates information about secondary structural elements assigned to individual protein structures with the various search facilities implemented into Relibase. A comparison of the mean hydrogen-bond distances in alpha helices and 3(10) helices of increasing length shows opposing trends. Whereas in alpha helices the mean H-bond distance shrinks with increasing helix length and turn number, the corresponding mean dimension in 3(10) helices expands in a comparable series. Comparing similarly the hydrogen-bond lengths in beta sheets there is no difference to be found between the mean H-bond length in antiparallel and parallel beta sheets along the strand direction. In contrast, an interesting systematic trend appears to be given for the hydrogen bonds perpendicular to the strands bridging across an extended sheet. With increasing number of accumulated strands, which results in a growing number of back-to-back piling hydrogen bonds across the strands, a slight decrease of the mean H-bond distance is apparent in parallel beta sheets whereas such trends are obviously not given in antiparallel beta sheets. This observation suggests that cooperative effects mutually polarizing spatially well-aligned hydrogen bonds are present either in alpha helices and parallel beta sheets whereas such influences seem to be lacking in 3(10) helices and antiparallel beta sheets.

Crystallography, X-Ray↗

PubMed2002

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles.

Secondary structure predictions are increasingly becoming the workhorse for several methods aiming at predicting protein structure and function. Here we use ensembles of bidirectional recurrent neural network architectures, PSI-BLAST-derived profiles, and a large nonredundant training set to derive two new predictors: (a) the second version of the SSpro program for secondary structure classification into three categories and (b) the first version of the SSpro8 program for secondary structure classification into the eight classes produced by the DSSP program. We describe the results of three different test sets on which SSpro achieved a sustained performance of about 78% correct prediction. We report confusion matrices, compare PSI-BLAST to BLAST-derived profiles, and assess the corresponding performance improvements. SSpro and SSpro8 are implemented as web servers, available together with other structural feature predictors at: http://promoter.ics.uci.edu/BRNN-PRED/.

Algorithms↗

PubMed2006

Binding of glucose to the D-galactose/D-glucose-binding protein from Escherichia coli restores the native protein secondary structure and thermostability that are lost upon calcium depletion.

The effect of the depletion of calcium on the structure and thermal stability of the D-galactose/D-glucose-binding protein (GGBP) from Escherichia coli was studied by fluorescence spectroscopy and Fourier-transform infrared spectroscopy. The calcium-depleted protein (GGBP-Ca) was also studied in the presence of glucose (GGBP-Ca/Glc). The results show that calcium depletion has a small effect on the secondary structure of GGBP, and, in particular it affects a population of alpha-helices with a low exposure to solvent. Alternatively, glucose-binding to GGBP-Ca eliminates the effect induced by calcium depletion by restoring a secondary structure similar to that of the native protein. In addition, the infrared and fluorescence data obtained reveal that calcium depletion markedly reduces the thermal stability of GGBP. In particular, the spectroscopic experiments show that the depletion of calcium mainly affects the stability of the C-terminal domain of the protein. However, the binding of glucose restores the thermal stability of GGBP-Ca. The thermostability of GGBP and GGBP-Ca was also studied by molecular dynamics simulations. The simulation data support the spectroscopic results. New insights into the role of calcium in the thermal stability of GGBP contribute to a better understanding of the protein function and constitute important information for the development of biotechnological applications of this protein. Mutations and/or labelling of amino acid residues located in the protein C-terminal domain may affect the stability of the whole protein structure.

Binding Sites↗

PubMed2006

Measures for the assessment of fuzzy predictions of protein secondary structure.

Many of the recent secondary structure prediction methods incorporate the idea of fuzzy set theory, where instead of assigning a definite secondary structure to a query residue, probability for the residue being in each of the conformational states is estimated. Moreover, continuous assignment of conformational states to the experimentally observed protein structures can be performed in order to reflect inherent flexibility. Although various measures have been developed for evaluating performances of secondary structure prediction methods, they depend only on the most probable secondary structures. They do not assess the accuracy of the probabilities produced by fuzzy prediction methods, and they cannot incorporate information contained in continuous assignments of conformational states to observed structures. Three important measures for evaluating performance of a secondary structure prediction algorithm, Q score, Segment OVerlap (SOV) measure, and the k-state correlation coefficient (Corr), are deformed into fuzzy measures F score, Fuzzy OVerlap (FOV) measure, and the fuzzy correlation coefficient (Forr), so that the new measures not only assess probabilistic outputs of fuzzy prediction methods, but also incorporate information from continuous assignments of secondary structure. As an example of application, prediction results of four fuzzy secondary structure prediction methods, PSIPRED, PROFking, SABLE, and PREDICT, are assessed using the new fuzzy measures.

Computer Simulation↗