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

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.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information

Background The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. Results In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. Conclusion A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

CSSP (Consensus Secondary Structure Prediction): a web-based server for structural biologists

Sequence-structure correlation studies are important in deciphering the relationships between various structural aspects, which may shed light on the protein-folding problem. The first step of this process is the prediction of secondary structure for a protein sequence of unknown three-dimensional structure. To this end, a web server has been created to predict the consensus secondary structure using well known algorithms from the literature. Furthermore, the server allows users to see the occurrence of predicted secondary structural elements in other structure and sequence databases and to visualize predicted helices as a helical wheel plot. The web server is accessible at http://bioserver1.physics.iisc.ernet.in/cssp/.

CSSI-PRO: a method for secondary structure type editing, assignment and estimation in proteins using linear combination of backbone chemical shifts

Estimation of secondary structure in polypeptides is important for studying their structure, folding and dynamics. In NMR spectroscopy, such information is generally obtained after sequence specific resonance assignments are completed. We present here a new methodology for assignment of secondary structure type to spin systems in proteins directly from NMR spectra, without prior knowledge of resonance assignments. The methodology, named Combination of Shifts for Secondary Structure Identification in Proteins (CSSI-PRO), involves detection of specific linear combination of backbone H-1(alpha) and C-13' chemical shifts in a two-dimensional (2D) NMR experiment based on G-matrix Fourier transform (GFT) NMR spectroscopy. Such linear combinations of shifts facilitate editing of residues belonging to alpha-helical/beta-strand regions into distinct spectral regions nearly independent of the amino acid type, thereby allowing the estimation of overall secondary structure content of the protein. Comparison of the predicted secondary structure content with those estimated based on their respective 3D structures and/or the method of Chemical Shift Index for 237 proteins gives a correlation of more than 90% and an overall rmsd of 7.0%, which is comparable to other biophysical techniques used for structural characterization of proteins. Taken together, this methodology has a wide range of applications in NMR spectroscopy such as rapid protein structure determination, monitoring conformational changes in protein-folding/ligand-binding studies and automated resonance assignment.

Evaluating viewpoint entropy for ribbon representation of protein structure

While many measures of viewpoint goodness have been proposed in computer graphics, none have been evaluated for ribbon representations of protein secondary structure. To fill this gap, we conducted a user study on Amazon’s Mechanical Turk platform, collecting human viewpoint preferences from 65 participants for 4 representative su- perfamilies of protein domains. In particular, we evaluated viewpoint entropy, which was previously shown to be a good predictor for human viewpoint preference of other, mostly non-abstract objects. In a second study, we asked 7 molecular biology experts to find the best viewpoint of the same protein domains and compared their choices with viewpoint entropy. Our results show that viewpoint entropy overall is a significant predictor of human viewpoint preference for ribbon representations of protein secondary structure. However, the accuracy is highly dependent on the complexity of the structure: while most participants agree on good viewpoints for small, non-globular structures with few secondary structure elements, viewpoint preference varies considerably for complex structures. Finally, experts tend to choose viewpoints of both low and high viewpoint entropy to emphasize different aspects of the respective structure.

Analysis of Hydrogen Bonding and Stability of Protein Secondary Structures in Molecular Dynamics Simulation

Molecular Dynamics (MD) simulations provide an atomic level account of the molecular motions and have proven to be immensely useful in the investigation of the dynamical structure of proteins. Once an MD trajectory is obtained, specific interactions at the molecular level can be directly studied by setting up appropriate combinations of distance and angle monitors. However, if a study of the dynamical behavior of secondary structures in proteins becomes important, this approach can become unwieldy. We present herein a method to study the dynamical stability of secondary structures in proteins, based on a relatively simple analysis of backbone hydrogen bonds. The method was developed for studying the thermal unfolding of beta-lactamases, but can be extended to other systems and adapted to study relevant properties.

Determination of the secondary structure of the king cobra neurotoxin CM-11

The king cobra neuotoxin CM-11 is a small protein with 72 amino acid residues. After its complete assignments of H-1-NMR resonance's were obtained using various 2D-NMR technologies, including of DQF-COSY, clean-TOCSY AND NOESY, the secondary structure was analysed by studying the various NOEs extracted from the NOESY spectra and the distribution of chemical shifts. The secondary structure was finally determined by MCD as follows: a triple-strand antiparallel beta sheet with I20-W36, R37-A43 and V53--S59 as its beta strands, a short alpha helix formed by W30-G35 and four turns formed by P7-K10, C14-G17, K50-V53 and D61-N64.

Algorithmic computation of knot polynomials of secondary structure elements of proteins

The classification of protein structures is an important and still outstanding problem. The purpose of this paper is threefold. First, we utilize a relation between the Tutte and homfly polynomial to show that the Alexander-Conway polynomial can be algorithmically computed for a given planar graph. Second, as special cases of planar graphs, we use polymer graphs of protein structures. More precisely, we use three building blocks of the three-dimensional protein structure-alpha-helix, antiparallel beta-sheet, and parallel beta-sheet-and calculate, for their corresponding polymer graphs, the Tutte polynomials analytically by providing recurrence equations for all three secondary structure elements. Third, we present numerical results comparing the results from our analytical calculations with the numerical results of our algorithm-not only to test consistency, but also to demonstrate that all assigned polynomials are unique labels of the secondary structure elements. This paves the way for an automatic classification of protein structures.

Modeling protein evolution using secondary structures

L’évolution des protéines est un domaine important de la recherche en bioinformatique et catalyse l'intérêt de trouver des outils d'alignement qui peuvent être utilisés de manière fiable et modéliser avec précision l'évolution d'une famille de protéines. TM-Align (Zhang and Skolnick, 2005) est considéré comme l'outil idéal pour une telle tâche, en termes de rapidité et de précision. Par conséquent, dans cette étude, TM-Align a été utilisé comme point de référence pour faciliter la détection des autres outils d'alignement qui sont en mesure de préciser l'évolution des protéines. En parallèle, nous avons élargi l'actuel outil d'exploration de structures secondaires de protéines, Helix Explorer (Marrakchi, 2006), afin qu'il puisse également être utilisé comme un outil pour la modélisation de l'évolution des protéines.

Redesigning secondary structure to invert coenzyme specificity in isopropylmalate dehydrogenase.

Rational engineering of enzymes involves introducing key amino acids guided by a knowledge of protein structure to effect a desirable change in function. To date, all successful attempts to change specificity have been limited to substituting individual amino acids within a protein fold. However, the infant field of protein engineering will only reach maturity when changes in function can be generated by rationally engineering secondary structures. Guided by x-ray crystal structures and molecular modeling, site-directed mutagenesis has been used to systematically invert the coenzyme specificity of Thermus thermophilus isopropylmalate dehydrogenase from a 100-fold preference for NAD to a 1000-fold preference for NADP. The engineered mutant, which is twice as active as wild type, contains four amino acid substitutions and an alpha-helix and loop that replaces the original beta-turn. These results demonstrate that rational engineering of secondary structures to produce enzymes with novel properties is feasible.

Identifying sequence regions undergoing conformational change via predicted continuum secondary structure

Motivation: Conformational flexibility is essential to the function of many proteins, e.g. catalytic activity. To assist efforts in determining and exploring the functional properties of a protein, it is desirable to automatically identify regions that are prone to undergo conformational changes. It was recently shown that a probabilistic predictor of continuum secondary structure is more accurate than categorical predictors for structurally ambivalent sequence regions, suggesting that such models are suited to characterize protein flexibility. Results: We develop a computational method for identifying regions that are prone to conformational change directly from the amino acid sequence. The method uses the entropy of the probabilistic output of an 8-class continuum secondary structure predictor. Results for 171 unique amino acid sequences with well-characterized variable structure (identified in the 'Macromolecular movements database') indicate that the method is highly sensitive at identifying flexible protein regions, but false positives remain a problem. The method can be used to explore conformational flexibility of proteins (including hypothetical or synthetic ones) whose structure is yet to be determined experimentally.

Translation of the flavivirus Kunjin NS3 gene in cis but not its RNA sequence or secondary structure is essential for efficient RNA packaging

Our previous studies using trans-complementation analysis of Kunjin virus (KUN) full-length cDNA clones harboring in-frame deletions in the NS3 gene demonstrated the inability of these defective complemented RNAs to be packaged into virus particles (W. J. Liu, P. L. Sedlak, N. Kondratieva, and A. A. Khromykh, J. Virol. 76:10766-10775). In this study we aimed to establish whether this requirement for NS3 in RNA packaging is determined by the secondary RNA structure of the NS3 gene or by the essential role of the translated NS3 gene product. Multiple silent mutations of three computer-predicted stable RNA structures in the NS3 coding region of KUN replicon RNA aimed at disrupting RNA secondary structure without affecting amino acid sequence did not affect RNA replication and packaging into virus-like particles in the packaging cell line, thus demonstrating that the predicted conserved RNA structures in the NS3 gene do not play a role in RNA replication and/or packaging. In contrast, double frameshift mutations in the NS3 coding region of full-length KUN RNA, producing scrambled NS3 protein but retaining secondary RNA structure, resulted in the loss of ability of these defective RNAs to be packaged into virus particles in complementation experiments in KUN replicon-expressing cells. Furthermore, the more robust complementation-packaging system based on established stable cell lines producing large amounts of complemented replicating NS3-deficient replicon RNAs and infection with KUN virus to provide structural proteins also failed to detect any secreted virus-like particles containing packaged NS3-deficient replicon RNAs. These results have now firmly established the requirement of KUN NS3 protein translated in cis for genome packaging into virus particles.