Local alignment refinement using structural assessment

In silico prediction of T- and B-cell epitopes in PmpD: First step towards to the design of a Chlamydia trachomatis vaccine

BACKGROUND

Chlamydia trachomatis is the most common sexually transmitted bacterial infection globally. Currently, there are no vaccines available despite the efforts made to develop a protective one. Polymorphic membrane protein D (PmpD) is an attractive immunogen candidate as it is conserved among strains and it is target of neutralizing antibodies. However, its high molecular weight and its complex structure make it difficult to handle by recombinant DNA techniques. Our aim is to predict B-cell and T-cell epitopes of PmpD.

METHOD

A sequence (Genbank AAK69391.2) having 99-100% identity with various serovars of C. trachomatis was used for predictions. NetMHC and NetMHCII were used for T-cell epitope linked to MHC I or MHC II alleles prediction, respectively. BepiPred predicted linear B-cell epitopes. For three dimensional epitopes, PmpD was homology-modeled by Raptor X. Surface epitopes were predicted on its globular structure using DiscoTope.

RESULTS

NetMHC predicted 271 T-cell epitopes of 9-12aa with weak affinity, and 70 with strong affinity to MHC I molecules. NetMHCII predicted 2903 T-cell epitopes of 15aa with weak affinity, and 742 with strong affinity to MHC II molecules. Twenty four linear B-cell epitopes were predicted. Raptor X was able to model 91% of the three-dimensional structure whereas 57 residues of discontinuous epitopes were suggested by DiscoTope. Six regions containing B-cell and T-cell epitopes were identified by at least two predictors.

CONCLUSIONS

PmpD has potential B-cell and T-cell epitopes distributed throughout the sequence. Thus, several fragments were identified as valuable candidates for subunit vaccines against C. trachomatis.

StralSV: assessment of sequence variability within similar 3D structures and application to polio RNA-dependent RNA polymerase

Background

Most of the currently used methods for protein function prediction rely on sequence-based comparisons between a query protein and those for which a functional annotation is provided. A serious limitation of sequence similarity-based approaches for identifying residue conservation among proteins is the low confidence in assigning residue-residue correspondences among proteins when the level of sequence identity between the compared proteins is poor. Multiple sequence alignment methods are more satisfactory--still, they cannot provide reliable results at low levels of sequence identity. Our goal in the current work was to develop an algorithm that could help overcome these difficulties by facilitating the identification of structurally (and possibly functionally) relevant residue-residue correspondences between compared protein structures.

Results

Here we present StralSV (structure-alignment sequence variability), a new algorithm for detecting closely related structure fragments and quantifying residue frequency from tight local structure alignments. We apply StralSV in a study of the RNA-dependent RNA polymerase of poliovirus, and we demonstrate that the algorithm can be used to determine regions of the protein that are relatively unique, or that share structural similarity with proteins that would be considered distantly related. By quantifying residue frequencies among many residue-residue pairs extracted from local structural alignments, one can infer potential structural or functional importance of specific residues that are determined to be highly conserved or that deviate from a consensus. We further demonstrate that considerable detailed structural and phylogenetic information can be derived from StralSV analyses.

Conclusions

StralSV is a new structure-based algorithm for identifying and aligning structure fragments that have similarity to a reference protein. StralSV analysis can be used to quantify residue-residue correspondences and identify residues that may be of particular structural or functional importance, as well as unusual or unexpected residues at a given sequence position. StralSV is provided as a web service at http://proteinmodel.org/AS2TS/STRALSV/.

Protein structure prediction and model quality assessment

Protein structures have proven to be a crucial piece of information for biomedical research. Of the millions of currently sequenced proteins only a small fraction is experimentally solved for structure and the only feasible way to bridge the gap between sequence and structure data is computational modeling. Half a century has passed since it was shown that the amino acid sequence of a protein determines its shape, but a method to translate the sequence code reliably into the 3D structure still remains to be developed. This review summarizes modern protein structure prediction techniques with the emphasis on comparative modeling, and describes the recent advances in methods for theoretical model quality assessment.