RT as primary therapy resulted in good outcome in early-stage disease, and the addition of CT to RT was not accompanied by an improvement in survival.

Identifying protein surface regions preferentially recognizable by antibodies (antigenic epitopes) is at the heart of new immuno-diagnostic reagent discovery and vaccine design, and computational methods for antigenic epitope prediction provide crucial means to serve this purpose. Many linear B-cell epitope prediction methods were developed, such as BepiPred, ABCPred, AAP, BCPred, BayesB, BEOracle/BROracle, and BEST, towards this goal. However, effective immunological research demands more robust performance of the prediction method than what the current algorithms could provide. In this work, a new method to predict linear antigenic epitopes is developed; Support Vector Machine has been utilized by combining the Tri-peptide similarity and Propensity scores (SVMTriP). Applied to non-redundant B-cell linear epitopes extracted from IEDB, SVMTriP achieves a sensitivity of 80.1% and a precision of 55.2% with a five-fold cross-validation. The AUC value is 0.702. The combination of similarity and propensity of tri-peptide subsequences can improve the prediction performance for linear B-cell epitopes. Moreover, SVMTriP is capable of recognizing viral peptides from a human protein sequence background. A web server based on our method is constructed for public use. The server and all datasets used in the current study are available at http://sysbio.unl.edu/SVMTriP.

BackgroundAccurate prediction of antigenic epitopes is important for immunologic research and medical applications, but it is still an open problem in bioinformatics. The case for discontinuous epitopes is even worse - currently there are only a few discontinuous epitope prediction servers available, though discontinuous peptides constitute the majority of all B-cell antigenic epitopes. The small number of structures for antigen-antibody complexes limits the development of reliable discontinuous epitope prediction methods and an unbiased benchmark to evaluate developed methods.ResultsIn this work, we present two novel server applications for discontinuous epitope prediction: EPSVR and EPMeta, where EPMeta is a meta server. EPSVR, EPMeta, and datasets are available at http://sysbio.unl.edu/services.ConclusionThe server application for discontinuous epitope prediction, EPSVR, uses a Support Vector Regression (SVR) method to integrate six scoring terms. Furthermore, we combined EPSVR with five existing epitope prediction servers to construct EPMeta. All methods were benchmarked by our curated independent test set, in which all antigens had no complex structures with the antibody, and their epitopes were identified by various biochemical experiments. The area under the receiver operating characteristic curve (AUC) of EPSVR was 0.597, higher than that of any other existing single server, and EPMeta had a better performance than any single server - with an AUC of 0.638, significantly higher than PEPITO and Disctope (p-value < 0.05).

MiRNAs (microRNAs) are a group of endogenous, small noncoding RNA with the length of 18-25 nucleotides, which have recently been demonstrated to play important roles in a wide range of biological processes. In this work, we developed a simple, sensitive, specific, and inexpensive assay through the combination of enzymatic probe ligation and real-time PCR amplification for the measurement of mature miRNAs. A couple of novel DNA probes with a stem-loop structure were implemented to reduce nonspecific ligation by at least 100-fold. The assay has several remarkable features including wide dynamic range, low total RNA input (0.02-0.2 ng), distinct anti-interference from precursor miRNAs (signal-to-noise ratio > 500), and single-base mismatch discrimination among miRNA sequences. In addition, a one-tube assay could be accomplished by designing a couple of universal probes, which makes it feasible to examine the expression of a whole family of miRNA (such as let-7) at one time. Finally, we validated the method for quantifying the expression of four mature miRNAs including miR-122, miR-1, miR-34a, and let-7a across 10 mouse tissues, where U6 snRNA could be simultaneously examined as an endogenous control. Thus, this method revealed a great potential for miRNA quantitation in ordinary laboratory studies and clinical diagnoses.