A framework for protein structure classification and identification of novel protein structures

BACKGROUND: Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important. RESULTS: In this paper we present a unified framework for protein structure classification and identification of novel protein structures. The framework consists of a set of components for comparing, classifying, and clustering protein structures. These components allow us to accurately classify proteins into known folds, to detect new protein folds, and to provide a way of clustering the new folds. In our evaluation with SCOP 1.69, our method correctly classifies 86.0%, 87.7%, and 90.5% of new domains at family, superfamily, and fold levels. Furthermore, for protein domains that belong to new domain families, our method is able to produce clusters that closely correspond to the new families in SCOP 1.69. As a result, our method can also be used to suggest new classification groups that contain novel folds. CONCLUSION: We have developed a method called proCC for automatically classifying and clustering domains. The method is effective in classifying new domains and suggesting new domain families, and it is also very efficient. A web site offering access to proCC is freely available a

Tableau-based protein substructure search using quadratic programming

<p>Abstract</p> <p>Background</p> <p>Searching for proteins that contain similar substructures is an important task in structural biology. The exact solution of most formulations of this problem, including a recently published method based on tableaux, is too slow for practical use in scanning a large database.</p> <p>Results</p> <p>We developed an improved method for detecting substructural similarities in proteins using tableaux. Tableaux are compared efficiently by solving the quadratic program (QP) corresponding to the quadratic integer program (QIP) formulation of the extraction of maximally-similar tableaux. We compare the accuracy of the method in classifying protein folds with some existing techniques.</p> <p>Conclusion</p> <p>We find that including constraints based on the separation of secondary structure elements increases the accuracy of protein structure search using maximally-similar subtableau extraction, to a level where it has comparable or superior accuracy to existing techniques. We demonstrate that our implementation is able to search a structural database in a matter of hours on a standard PC.</p

Relation between myocardial edema and myocardial mass during the acute and convalescent phase of myocarditis – a CMR study

<p>Abstract</p> <p>Background</p> <p>Myocardial edema is a substantial feature of the inflammatory response in human myocarditis. The relation between myocardial edema and myocardial mass in the course of healing myocarditis has not been systematically investigated. We hypothesised that the resolution of myocardial edema as visualised by T2-weighted cardiovascular magnetic resonance (CMR) is associated with a decrease of myocardial mass in steady state free precession (SSFP)-cine imaging.</p> <p>Methods</p> <p>21 patients with acute myocarditis underwent CMR shortly after onset of symptoms and 1 year later. For visualization of edema, a T2-weighted breath-hold black-blood triple-inversion fast spin echo technique was applied and the ratio of signal intensity of myocardium/skeletal muscle was assessed. Left ventricular (LV) mass, volumes and function were quantified from biplane cine steady state free precession images.</p> <p>11 healthy volunteers served as a control group for interstudy reproducibility of LV mass.</p> <p>Results</p> <p>In patients with myocarditis, a significant decrease in LV mass was observed during follow-up compared to the acute phase (156.7 ± 30.6 g vs. 140.3 ± 28.3 g, p < 0.0001). The reduction of LV mass paralleled the normalization of initially increased myocardial signal intensity on T2-weighted images (2.4 ± 0.4 vs. 1.68 ± 0.3, p < 0.0001).</p> <p>In controls, the interstudy difference of LV mass was lower than in patients (5.1 ± 2.9 g vs. 16.3 ± 14.2 g, p = 0.02) resulting in a lower coefficient of variability (2.1 vs 8.9%, p = 0.04).</p> <p>Conclusion</p> <p>Reversible abnormalities in T2-weighted CMR are paralleled by a transient increase in left ventricular mass during the course of myocarditis. Myocardial edema may be a common pathway explaining these findings.</p

Prediction of backbone dihedral angles and protein secondary structure using support vector machines

<p>Abstract</p> <p>Background</p> <p>The prediction of the secondary structure of a protein is a critical step in the prediction of its tertiary structure and, potentially, its function. Moreover, the backbone dihedral angles, highly correlated with secondary structures, provide crucial information about the local three-dimensional structure.</p> <p>Results</p> <p>We predict independently both the secondary structure and the backbone dihedral angles and combine the results in a loop to enhance each prediction reciprocally. Support vector machines, a state-of-the-art supervised classification technique, achieve secondary structure predictive accuracy of 80% on a non-redundant set of 513 proteins, significantly higher than other methods on the same dataset. The dihedral angle space is divided into a number of regions using two unsupervised clustering techniques in order to predict the region in which a new residue belongs. The performance of our method is comparable to, and in some cases more accurate than, other multi-class dihedral prediction methods.</p> <p>Conclusions</p> <p>We have created an accurate predictor of backbone dihedral angles and secondary structure. Our method, called DISSPred, is available online at <url>http://comp.chem.nottingham.ac.uk/disspred/</url>.</p

Fast and accurate protein substructure searching with simulated annealing and GPUs

<p>Abstract</p> <p>Background</p> <p>Searching a database of protein structures for matches to a query structure, or occurrences of a structural motif, is an important task in structural biology and bioinformatics. While there are many existing methods for structural similarity searching, faster and more accurate approaches are still required, and few current methods are capable of substructure (motif) searching.</p> <p>Results</p> <p>We developed an improved heuristic for tableau-based protein structure and substructure searching using simulated annealing, that is as fast or faster and comparable in accuracy, with some widely used existing methods. Furthermore, we created a parallel implementation on a modern graphics processing unit (GPU).</p> <p>Conclusions</p> <p>The GPU implementation achieves up to 34 times speedup over the CPU implementation of tableau-based structure search with simulated annealing, making it one of the fastest available methods. To the best of our knowledge, this is the first application of a GPU to the protein structural search problem.</p

A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches

Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch

Assignment of PolyProline II Conformation and Analysis of Sequence – Structure Relationship

International audienceBACKGROUND: Secondary structures are elements of great importance in structural biology, biochemistry and bioinformatics. They are broadly composed of two repetitive structures namely α-helices and β-sheets, apart from turns, and the rest is associated to coil. These repetitive secondary structures have specific and conserved biophysical and geometric properties. PolyProline II (PPII) helix is yet another interesting repetitive structure which is less frequent and not usually associated with stabilizing interactions. Recent studies have shown that PPII frequency is higher than expected, and they could have an important role in protein - protein interactions. METHODOLOGY/PRINCIPAL FINDINGS: A major factor that limits the study of PPII is that its assignment cannot be carried out with the most commonly used secondary structure assignment methods (SSAMs). The purpose of this work is to propose a PPII assignment methodology that can be defined in the frame of DSSP secondary structure assignment. Considering the ambiguity in PPII assignments by different methods, a consensus assignment strategy was utilized. To define the most consensual rule of PPII assignment, three SSAMs that can assign PPII, were compared and analyzed. The assignment rule was defined to have a maximum coverage of all assignments made by these SSAMs. Not many constraints were added to the assignment and only PPII helices of at least 2 residues length are defined. CONCLUSIONS/SIGNIFICANCE: The simple rules designed in this study for characterizing PPII conformation, lead to the assignment of 5% of all amino as PPII. Sequence - structure relationships associated with PPII, defined by the different SSAMs, underline few striking differences. A specific study of amino acid preferences in their N and C-cap regions was carried out as their solvent accessibility and contact patterns. Thus the assignment of PPII can be coupled with DSSP and thus opens a simple way for further analysis in this field