Secondary structure prediction with support vector machines

Motivation: A new method that uses support vector machines (SVMs) to predict protein secondary structure is described and evaluated. The study is designed to develop a reliable prediction method using an alternative technique and to investigate the applicability of SVMs to this type of bioinformatics problem. Methods: Binary SVMs are trained to discriminate between two structural classes. The binary classifiers are combined in several ways to predict multi-class secondary structure. Results: The average three-state prediction accuracy per protein (Q3) is estimated by cross-validation to be 77.07 ± 0.26% with a segment overlap (Sov) score of 73.32 ± 0.39%. The SVM performs similarly to the 'state-of-the-art' PSIPRED prediction method on a non-homologous test set of 121 proteins despite being trained on substantially fewer examples. A simple consensus of the SVM, PSIPRED and PROFsec achieves significantly higher prediction accuracy than the individual methods. Availability: The SVM classifier is available from the authors. Work is in progress to make the method available on-line and to integrate the SVM predictions into the PSIPRED server.

Benchmarking secondary structure prediction for fold recognition

If secondary structure predictions are to be incorporated into fold recognition methods, an assessment of the effect of specific types of errors in predicted secondary structures on the sensitivity of fold recognition should be carried out. Here, we present a systematic comparison of different secondary structure prediction methods by measuring frequencies of specific types of error. We carry out an evaluation of the effect of specific types of error on secondary structure element alignment (SSEA), a baseline fold recognition method. The results of this evaluation indicate that missing out whole helix or strand elements, or predicting the wrong type of element, is more detrimental than predicting the wrong lengths of elements or overpredicting helix or strand. We also suggest that SSEA scoring is an effective method for assessing accuracy of secondary structure prediction and perhaps may also provide a more appropriate assessment of the “usefulness” and quality of predicted secondary structure, if secondary structure alignments are to be used in fold recognition.

Rapid protein domain assignment from amino acid sequence using predicted secondary structure

The elucidation of the domain content of a given protein sequence in the absence of determined structure or significant sequence homology to known domains is an important problem in structural biology. Here we address how successfully the delineation of continuous domains can be accomplished in the absence of sequence homology using simple baseline methods, an existing prediction algorithm (Domain Guess by Size), and a newly developed method (DomSSEA). The study was undertaken with a view to measuring the usefulness of these prediction methods in terms of their application to fully automatic domain assignment. Thus, the sensitivity of each domain assignment method was measured by calculating the number of correctly assigned top scoring predictions. We have implemented a new continuous domain identification method using the alignment of predicted secondary structures of target sequences against observed secondary structures of chains with known domain boundaries as assigned by Class Architecture Topology Homology (CATH). Taking top predictions only, the success rate of the method in correctly assigning domain number to the representative chain set is 73.3%. The top prediction for domain number and location of domain boundaries was correct for 24% of the multidomain set (±20 residues). These results have been put into context in relation to the results obtained from the other prediction methods assessed

Secondary structure analysis of the dissimilatory sulphite reductase in Desulfovibrio desulfuricans

The complete sequences of the dsrA and dsrB genes coding for the α− and β−subunits, respectively, of the sulphite reductase enzyme in Desulfovibrio desulfuricans were determined. Analyses of the amino acid sequences indicated a number of serohaem/Fe4S4 binding consensus sequences whilst predictive secondary structure analysis revealed a similar pattern of α−helix and β−strand structures between the two subunits which was indicative of gene duplication.

Towards an assessment of character interdependence in avian RNA phylogenetics: A general secondary structure model for the avian mitochondrial 16S rRNA

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

gamma-zein secondary structure in solution by circular dichroism

The proline-rich N-terminal domain of gamma-zein has been reported in relevant process, which include its ability to cross the cell membranes. Evidences indicate that synthetic hexapeptide (PPPVHL), naturally found in N-terminal portion of gamma-zein, can adopt the polyproline II (PPII) conformation in aqueous solution. The secondary structure of gamma-zein in maize protein bodies had been analyzed by solid state Fourier transform infrared and nuclear magnetic resonance spectroscopies. However, it was not possible to measure PPII content in physiological environment since the beta-sheet and PPII signals overlap in both solid state techniques. Here, the secondary structure of gamma-zein has been analyzed by circular dichroism in SDS aqueous solution with and without ditiothreitol (DTT), and in 60% of 2-propanol and water with DTT The results show that gamma-zein has high helical content in all solutions. The PPII conformation was present at about 7% only in water/DTT solution. (c) 2007 Wiley Periodicals, Inc.

Influence of N-Terminus Modifications on the Biological Activity, Membrane Interaction, and Secondary Structure of the Antimicrobial Peptide Hylin-a1

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Electrochemical genosensors for the detection of Bonamia parasite. Selection of single strand-DNA (ssDNA) probes by simulation of the secondary structure folding

A post-PCR nucleic acid work by comparing experimental data, from electrochemical genosensors, and bioinformatics data, derived from the simulation of the secondary structure folding and prediction of hybridisation reaction, was carried out in order to rationalize the selection of ssDNA probes for the detection of two Bonamia species, B. exitiosa and B. ostreae, parasites of Ostrea edulis.Six ssDNA probes (from 11 to 25 bases in length, 2 thiolated and 4 biotinylated) were selected within different regions of B. ostreae and B. exitiosa PCR amplicons (300 and 304 bases, respectively) with the aim to discriminate between these parasite species. ssDNA amplicons and probes were analyzed separately using the "Mfold Web Server" simulating the secondary structure folding behaviour. The hybridisation of amplicon-probe was predicted by means of "Dinamelt Web Server". The results were evaluated considering the number of hydrogen bonds broken and formed in the simulated folding and hybridisation process, variance in gaps for each sequence and number of available bases. In the experimental part, thermally denatured PCR products were captured at the sensor interface via sandwich hybridisation with surface-tethered probes (thiolated probes) and biotinylated signalling probes. A convergence between analytical signals and simulated results was observed, indicating the possibility to use bioinformatic data for ssDNA probes selection to be incorporated in genosensors. (C) 2011 Elsevier B.V. All rights reserved.

Structure-activity relationship of mastoparan analogs: effects of the number and positioning of Lys residues on secondary structure, interaction with membrane-mimetic systems and biological activity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Water-in-Oil Micro-Emulsion Enhances the Secondary Structure of a Protein by Confinement

A scheme is presented in which an organic solvent environment in combination with surfactants is used to confine a natively unfolded protein inside an inverse microemulsion droplet. This type of confinement allows a study that provides unique insight into the dynamic structure of an unfolded, flexible protein which is still solvated and thus under near-physiological conditions. In a model system, the protein osteopontin (OPN) is used. It is a highly phosphorylated glycoprotein that is expressed in a wide range of cells and tissues for which limited structural analysis exists due to the high degree of flexibility and large number of post-translational modifications. OPN is implicated in tissue functions, such as inflammation and mineralisation. It also has a key function in tumour metastasis and progression. Circular dichroism measurements show that confinement enhances the secondary structural features of the protein. Small-angle X-ray scattering and dynamic light scattering show that OPN changes from being a flexible protein in aqueous solution to adopting a less flexible and more compact structure inside the microemulsion droplets. This novel approach for confining proteins while they are still hydrated may aid in studying the structure of a wide range of natively unfolded proteins.

Sequence-specific polypeptoids: A diverse family of heteropolymers with stable secondary structure

We have synthesized and characterized a family of structured oligo-N-substituted-glycines (peptoids) up to 36 residues in length by using an efficient solid-phase protocol to incorporate chemically diverse side chains in a sequence-specific fashion. We investigated polypeptoids containing side chains with a chiral center adjacent to the main chain nitrogen. Some of these sequences have stable secondary structure, despite the achirality of the polymer backbone and its lack of hydrogen bond donors. In both aqueous and organic solvents, peptoid oligomers as short as five residues give rise to CD spectra that strongly resemble those of peptide α-helices. Differential scanning calorimetry and CD measurements show that polypeptoid secondary structure is highly stable and that unfolding is reversible and cooperative. Thermodynamic parameters obtained for unfolding are similar to those obtained for the α-helix to coil transitions of peptides. This class of biomimetic polymers may enable the design of self-assembling macromolecules with novel structures and functions.

A physical basis for protein secondary structure

A physical theory of protein secondary structure is proposed and tested by performing exceedingly simple Monte Carlo simulations. In essence, secondary structure propensities are predominantly a consequence of two competing local effects, one favoring hydrogen bond formation in helices and turns, the other opposing the attendant reduction in sidechain conformational entropy on helix and turn formation. These sequence specific biases are densely dispersed throughout the unfolded polypeptide chain, where they serve to preorganize the folding process and largely, but imperfectly, anticipate the native secondary structure.

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 β-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.