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PubMed1996

Secondary structure determination of proteins in aqueous solution by infrared spectroscopy: a comparison of multivariate data analysis methods.

The accuracy of the secondary structure prediction from an infrared spectra data base of 39 proteins with known X-ray structure was investigated by different methods of multivariate data analysis. The best agreements with the secondary structure determined by X-ray crystallography are obtained if both the amide I and amide II bands are used for calibration. With optimized parameters the methods singular value decomposition, partial least squares, and ridge regression yield similar results. As judged by the standard error of prediction, the secondary structure elements helix and beta-sheet can be predicted with the highest accuracy. Small data sets of less than 20 protein spectra, which exhibit the variance in secondary structure content of the whole set, can pretend an increased prediction accuracy only if column cross-validation is used as reference; however, with these calibration sets the average secondary structure prediction of all 39 proteins is debased. The hydrogen-bonded turns or bridges are predicted with higher accuracy than the assigned secondary structure types helix and beta-sheet.

Multivariate Analysis↗
PubMed2005

Spectral magnitude effects on the analyses of secondary structure from circular dichroism spectroscopic data.

The effects of spectral magnitude on the calculated secondary structures derived from circular dichroism (CD) spectra were examined for a number of the most commonly used algorithms and reference databases. Proteins with different secondary structures, ranging from mostly helical to mostly beta-sheet, but which were not components of existing reference databases, were used as test systems. These proteins had known crystal structures, so it was possible to ascertain the effects of magnitude on both the accuracy of determining the secondary structure and the goodness-of-fit of the calculated structures to the experimental data. It was found that most algorithms are highly sensitive to spectral magnitude, and that the goodness-of-fit parameter may be a useful tool in assessing the correct scaling of the data. This means that parameters that affect magnitude, including calibration of the instrument, the spectral cell pathlength, and the protein concentration, must be accurately determined to obtain correct secondary structural analyses of proteins from CD data using empirical methods.

Albumins↗
PubMed1996

Improving prediction of protein secondary structure using structured neural networks and multiple sequence alignments.

The prediction of protein secondary structure by use of carefully structured neural networks and multiple sequence alignments has been investigated. Separate networks are used for predicting the three secondary structures alpha-helix, beta-strand, and coil. The networks are designed using a priori knowledge of amino acid properties with respect to the secondary structure and the characteristic periodicity in alpha-helices. Since these single-structure networks all have less than 600 adjustable weights, overfitting is avoided. To obtain a three-state prediction of alpha-helix, beta-strand, or coil, ensembles of single-structure networks are combined with another neural network. This method gives an overall prediction accuracy of 66.3% when using 7-fold cross-validation on a database of 126 nonhomologous globular proteins. Applying the method to multiple sequence alignments of homologous proteins increases the prediction accuracy significantly to 71.3% with corresponding Matthew's correlation coefficients C alpha = 0.59, C beta = 0.52, and Cc = 0.50. More than 72% of the residues in the database are predicted with an accuracy of 80%. It is shown that the network outputs can be interpreted as estimated probabilities of correct prediction, and, therefore, these numbers indicate which residues are predicted with high confidence.

Amino Acid Sequence↗
PubMed1993

Quantification of secondary structure prediction improvement using multiple alignments.

The use of multiple sequence alignments for secondary structure predictions is analysed. Seven different protein families, containing only sequences of known structure, were considered to provide a range of alignment and prediction conditions. Using alignments obtained by spatial superposition of main chain atoms in known tertiary protein structures allowed a mean of 8% in secondary structure prediction accuracy, when compared to those obtained from the individual sequences. Substitution of these alignments by those determined directly from an automated sequence alignment algorithm showed variations in the prediction accuracy which correlated with the quality of the multiple alignments and distance of the primary sequence. Secondary structure predictions can be reliably improved using alignments from an automatic alignment procedure with a mean increase of 6.8%, giving an overall prediction accuracy of 68.5%, if there is a minimum of 25% sequence identity between all sequences in a family.

Amino Acid Sequence↗
PubMed1984

Polypeptide secondary structure determination by nuclear magnetic resonance observation of short proton-proton distances.

The use of proton-proton nuclear Overhauser enhancement (NOE) distance information for identification of polypeptide secondary structures in non-crystalline proteins was investigated by stereochemical studies of standard secondary structures and by statistical analyses of the secondary structures in the crystal conformations of a group of globular proteins. Both regular helix and beta-sheet secondary structures were found to contain a dense network of short 1H-1H distances. The results obtained imply that the combined information on all these distances obtained from visual inspection of the two-dimensional NOE (NOESY) spectra is sufficient for determination of the helical and beta-sheet secondary structures in small globular proteins. Furthermore, cis peptide bonds can be identified from unique, short sequential proton-proton distances. Limitations of this empirical approach are that the exact start or end of a helix may be difficult to define when the adjoining residues form a tight turn, and that unambiguous identification of tight turns can usually be obtained only in the hairpins of antiparallel beta-structures. The short distances between protons in pentapeptide segments of the different secondary structures have been tabulated to provide a generally applicable guide for the analysis of NOESY spectra of proteins.

Aprotinin↗
PubMed2004

DFold: PCR design that minimizes secondary structure and optimizes downstream genotyping applications.

Secondary structures in polymerase chain reaction (PCR) target sequences have a negative impact on amplification success rates and on downstream uses of PCR products. For example, signal strength and allele discrimination in single nucleotide polymorphism (SNP) genotyping methods can be compromised by allele-biased amplification and/or by PCR product folding that limits access of interrogating probes. To increase the fidelity and robustness of PCR, and to aid follow-on applications, we have developed DFold (http://dfold.cgb.ki.se)-a generalized software solution that creates PCR oligonucleotide primer designs devoid of stable secondary structures. We demonstrate the effectiveness of the tool by applying it to a range of dynamic allele-specific hybridization (DASH) assay designs, many of which we evaluate in the laboratory. We further consider how the system throughput may be made sufficiently high for use upon millions of target sequences in order to support whole-genome analyses.

Algorithms↗
PubMed1991

Secondary structure perturbations in salt-induced protein precipitates.

The secondary structure implications of precipitation induced by a chaotropic salt, KSCN, and a structure stabilizing salt, Na2SO4, were studied for twelve different proteins. alpha-helix and beta-sheet content of precipitate and native structures were estimated from the analysis of amide I band Raman spectra. A statistical analysis of the estimated perturbations in the secondary structure contents indicated that the most significant event is the formation of beta-sheet structures with a concomitant loss of alpha-helix on precipitation with KSCN. The conformational changes for each protein were also analyzed with respect to elements of primary, secondary and tertiary structure existing in the native protein; primary structure was quantified by the fractions of hydrophobic and charged amino acids, secondary structure by x-ray estimates of alpha-helix and beta-sheet contents of native proteins and tertiary structure by the dipole moment and solvent-accessible surface area. For the KSCN precipitates, factors affecting beta-sheet content included the fraction of charged amino acids in the primary sequence and the surface area. Changes in alpha-helix content were influenced by the initial helical content and the dipole moment. The enhanced beta-sheet contents of precipitates observed in this work parallel protein structural changes occurring in other aggregative phenomena.

Chemical Precipitation↗
PubMed2003

Widespread selection for local RNA secondary structure in coding regions of bacterial genes.

Redundancy of the genetic code dictates that a given protein can be encoded by a large collection of distinct mRNA species, potentially allowing mRNAs to simultaneously optimize desirable RNA structural features in addition to their protein-coding function. To determine whether natural mRNAs exhibit biases related to local RNA secondary structure, a new randomization procedure was developed, DicodonShuffle, which randomizes mRNA sequences while preserving the same encoded protein sequence, the same codon usage, and the same dinucleotide composition as the native message. Genes from 10 of 14 eubacterial species studied and one eukaryote, the yeast Saccharomyces cerevisiae, exhibited statistically significant biases in favor of local RNA structure as measured by folding free energy. Several significant associations suggest functional roles for mRNA structure, including stronger secondary structure bias in the coding regions of intron-containing yeast genes than in intronless genes, and significantly higher folding potential in polycistronic messages than in monocistronic messages in Escherichia coli. Potential secondary structure generally increased in genes from the 5' to the 3' end of E. coli operons, and secondary structure potential was conserved in homologous Salmonella typhi operons. These results are interpreted in terms of possible roles of RNA structures in RNA processing, regulation of mRNA stability, and translational control.

Computational Biology↗
PubMed1997

Secondary structure prediction using segment similarity.

We present a secondary structure prediction method based on finding similarities between sequence segments from the target sequence and segments contained in the database of proteins with known structures. The similarity definition is optimized using a genetic algorithm and is based on a 21 x 40 similarity matrix, comparing a target sequence with the sequence and burial status of the proteins from the database. The three-state secondary structure prediction accuracy reaches 72.4% on a non homologous (maximum sequence identity <25%) data set derived from PDB and is reproduced on two independent testing sets, including the set of CASP2 prediction targets and a group of newly solved PDB structures. The prediction method was developed with simplicity and open architecture in mind, allowing for an easy extension to other types of predictions and to the analysis of the contributions to the local structure formation. For instance, the design of the prediction procedure allows us to trace back segments of the database that contributed to the prediction. It can be shown that those segments came from various structural classes and that even complete exclusion of related folds from the database does not result in a significant decrease in prediction accuracy.

Databases, Factual↗
PubMed2005

Secondary structure and dynamics of an intrinsically unstructured linker domain.

The transient secondary structure and dynamics of an intrinsically unstructured linker domain from the 70 kDa subunit of human replication protein A was investigated using solution state NMR. Stable secondary structure, inferred from large secondary chemical shifts, was observed for a segment of the intrinsically unstructured linker domain when it is attached to an N-terminal protein interaction domain. Results from NMR relaxation experiments showed the rotational diffusion for this segment of the intrinsically unstructured linker domain to be correlated with the N-terminal protein interaction domain. When the N-terminal domain is removed, the stable secondary structure is lost and faster rotational diffusion is observed. The large secondary chemical shifts were used to calculate phi and psi dihedral angles and these dihedral angles were used to build a backbone structural model. Restrained molecular dynamics were performed on this new structure using the chemical shift based dihedral angles and a single NOE distance as restraints. In the resulting family of structures a large, solvent exposed loop was observed for the segment of the intrinsically unstructured linker domain that had large secondary chemical shifts.

DNA Replication↗
PubMed1993

MacMatch: a tool for pattern-based protein secondary structure prediction.

A program is described for predicting the secondary structure of globular proteins on an Apple Macintosh computer. MacMatch produces turn predictions using augmented regular expression pattern matching. Helix and strand predictions are based on a neural network. Protein structural class (alpha/alpha), beta/beta, alpha/beta can be exploited to improve secondary structure prediction. The program is simple to use, and the package includes sets of tested patterns and trained neural networks.

Microcomputers↗
PubMed1992

Hybrid system for protein secondary structure prediction.

We have developed a hybrid system to predict the secondary structures (alpha-helix, beta-sheet and coil) of proteins and achieved 66.4% accuracy, with correlation coefficients of C(coil) = 0.429, C alpha = 0.470 and C beta = 0.387. This system contains three subsystems ("experts"): a neural network module, a statistical module and a memory-based reasoning module. First, the three experts independently learn the mapping between amino acid sequences and secondary structures from the known protein structures, then a Combiner learns to combine automatically the outputs of the experts to make final predictions. The hybrid system was tested with 107 protein structures through k-way cross-validation. Its performance was better than each expert and all previously reported methods with greater than 0.99 statistical significance. It was observed that for 20% of the residues, all three experts produced the same but wrong predictions. This may suggest an upper bound on the accuracy of secondary structure predictions based on local information from the currently available protein structures, and indicate places where non-local interactions may play a dominant role in conformation. For 64% of the residues, at least two experts were the same and correct, which shows that the Combiner performed better than majority vote. For 77% of the residues, at least one expert was correct, thus there may still be room for improvement in this hybrid approach. Rigorous evaluation procedures were used in testing the hybrid system, and statistical significance measures were developed in analyzing the differences among different methods. When measured in terms of the number of secondary structures (rather than the number of residues) that were predicted correctly, the prediction produced by the hybrid system was also better than those of individual experts.

Amino Acid Sequence↗
PubMed2006

Evaluation of the information content in infrared spectra for protein secondary structure determination.

Fourier-transform infrared spectroscopy is a method of choice for the experimental determination of protein secondary structure. Numerous approaches have been developed during the past 15 years. A critical parameter that has not been taken into account systematically is the selection of the wavenumbers used for building the mathematical models used for structure prediction. The high quality of the current Fourier-transform infrared spectrometers makes the absorbance at every single wavenumber a valid and almost noiseless type of information. We address here the question of the amount of independent information present in the infrared spectra of proteins for the prediction of the different secondary structure contents. It appears that, at most, the absorbance at three distinct frequencies of the spectra contain all the nonredundant information that can be related to one secondary structure content. The ascending stepwise method proposed here identifies the relevance of each wavenumber of the infrared spectrum for the prediction of a given secondary structure and yields a particularly simple method for computing the secondary structure content. Using the 50-protein database built beforehand to contain as little fold redundancy as possible, the standard error of prediction in cross-validation is 5.5% for the alpha-helix, 6.6% for the beta-sheet, and 3.4% for the beta-turn.

Algorithms↗
PubMed2006

A reference database for circular dichroism spectroscopy covering fold and secondary structure space.

MOTIVATION: Circular Dichroism (CD) spectroscopy is a long-established technique for studying protein secondary structures in solution. Empirical analyses of CD data rely on the availability of reference datasets comprised of far-UV CD spectra of proteins whose crystal structures have been determined. This article reports on the creation of a new reference dataset which effectively covers both secondary structure and fold space, and uses the higher information content available in synchrotron radiation circular dichroism (SRCD) spectra to more accurately predict secondary structure than has been possible with existing reference datasets. It also examines the effects of wavelength range, structural redundancy and different means of categorizing secondary structures on the accuracy of the analyses. In addition, it describes a novel use of hierarchical cluster analyses to identify protein relatedness based on spectral properties alone. The databases are shown to be applicable in both conventional CD and SRCD spectroscopic analyses of proteins. Hence, by combining new bioinformatics and biophysical methods, a database has been produced that should have wide applicability as a tool for structural molecular biology.

Algorithms↗
PubMed1999

RNA secondary structure and compensatory evolution.

The classic concept of epistatic fitness interactions between genes has been extended to study interactions within gene regions, especially between nucleotides that are important in maintaining pre-mRNA/mRNA secondary structures. It is shown that the majority of linkage disequilibria found within the Drosophila Adh gene are likely to be caused by epistatic selection operating on RNA secondary structures. A recently proposed method of RNA secondary structure prediction based on DNA sequence comparisons is reviewed and applied to several types of RNAs, including tRNA, rRNA, and mRNA. The patterns of covariation in these RNAs are analyzed based on Kimura's compensatory evolution model. The results suggest that this model describes the substitution process in the pairing regions (helices) of RNA secondary structures well when the helices are evolutionarily conserved and thermodynamically stable, but fails in some other cases. Epistatic selection maintaining pre-mRNA/mRNA secondary structures is compared to weak selective forces that determine features such as base composition and synonymous codon usage. The relationships among these forces and their relative strengths are addressed. Finally, our mutagenesis experiments using the Drosophila Adh locus are reviewed. These experiments analyze long-range compensatory interactions between the 5' and 3' ends of Adh mRNA, the different constraints on secondary structures in introns and exons, and the possible role of secondary structures in RNA splicing.

Alcohol Dehydrogenase↗
PubMed1995

A simple and fast approach to prediction of protein secondary structure from multiply aligned sequences with accuracy above 70%.

To improve secondary structure predictions in protein sequences, the information residing in multiple sequence alignments of substituted but structurally related proteins is exploited. A database comprised of 70 protein families and a total of 2,500 sequences, some of which were aligned by tertiary structural superpositions, was used to calculate residue exchange weight matrices within alpha-helical, beta-strand, and coil substructures, respectively. Secondary structure predictions were made based on the observed residue substitutions in local regions of the multiple alignments and the largest possible associated exchange weights in each of the three matrix types. Comparison of the observed and predicted secondary structure on a per-residue basis yielded a mean accuracy of 72.2%. Individual alpha-helix, beta-strand, and coil states were respectively predicted at 66.7, and 75.8% correctness, representing a well-balanced three-state prediction. The accuracy level, verified by cross-validation through jack-knife tests on all protein families, dropped, on average, to only 70.9%, indicating the rigor of the prediction procedure. On the basis of robustness, conceptual clarity, accuracy, and executable efficiency, the method has considerable advantage, especially with its sole reliance on amino acid substitutions within structurally related proteins.

Algorithms↗
PubMed1993

A secondary structure prediction of the hemorrhagic metalloprotease family.

A secondary structure has been predicted for the hemorrhagic metalloproteases using a method developed in Zurich that extracts structural information from patterns of conservation and variation in homologous protein sequences. This prediction tests the limits of the method when applied to a small number of homologous sequences that have undergone only modest evolutionary divergence. Predictions were also obtained using a neural network developed by Sander and coworkers, to date the best fully automated method for predicting secondary structure, and using the classical Chou-Fasman and GOR heuristics. The predictions are different. No crystal structure is known within this protein family, but one is expected shortly. Therefore, this prediction should contribute significantly to the evaluation of the relative merits of these prediction methods.

Amino Acid Sequence↗
PubMed2001

Statistical prediction of single-stranded regions in RNA secondary structure and application to predicting effective antisense target sites and beyond.

Single-stranded regions in RNA secondary structure are important for RNA-RNA and RNA-protein interactions. We present a probability profile approach for the prediction of these regions based on a statistical algorithm for sampling RNA secondary structures. For the prediction of phylogenetically-determined single-stranded regions in secondary structures of representative RNA sequences, the probability profile offers substantial improvement over the minimum free energy structure. In designing antisense oligonucleotides, a practical problem is how to select a secondary structure for the target mRNA from the optimal structure(s) and many suboptimal structures with similar free energies. By summarizing the information from a statistical sample of probable secondary structures in a single plot, the probability profile not only presents a solution to this dilemma, but also reveals 'well-determined' single-stranded regions through the assignment of probabilities as measures of confidence in predictions. In antisense application to the rabbit beta-globin mRNA, a significant correlation between hybridization potential predicted by the probability profile and the degree of inhibition of in vitro translation suggests that the probability profile approach is valuable for the identification of effective antisense target sites. Coupling computational design with DNA-RNA array technique provides a rational, efficient framework for antisense oligonucleotide screening. This framework has the potential for high-throughput applications to functional genomics and drug target validation.

Algorithms↗