Prediction of proteasomal cleavage sites using PCPS The proteasome complex is the main responsible of proteolytic degradation of cytosolic proteins, generating the Cterminus of MHC Irestricted peptide ligands and CD8 T cell epitopes. Prediction of proteasomal cleavage sites is thus relevant for predicting CD8 T cell epitopes. We previously reported an  n gram based method to predict proteasomal cleavage sites named PCPS, which is implemented for free public use online at http:imed.med.ucm.espcps. Here, we used a set of 59 Hepatitis C Virus (HCV) CD8 Tcell epitopes to analyze cleavage predictions by PCPS and compared them with those provided by a related method implemented in NetChop web server. PCPS clearly outperformed NetChop in sensitivity (0.88 and 0.78, respectively); it identified much better than NetChop that the Cterminus of tested CD8  T  cell epitopes likely resulted from proteasomal cleavage. However, PCPS identified additional preferential cleavage sites within the tested epitopes more often than NetChop, which resulted in lower specificity (0.57 vs 0.66, respectively). Proteasomal cleavage site predictions are used to enhance CD8 T cell epitopes by discarding peptides resulting from peptideMHC I binding models that do not have a Cterminus compatible with proteasome production. Thereby, we have now implemented proteasomal cleavage site predictions provided by PCPS  n grams in RANKPEP, a tool to identify peptides presented by MHC molecules, allowing to filter out peptides predicted to bind to MHC I molecules that are unlikely to result from proteasomal cleavage. RANKPEP is available for free public use online at http:imed.med.ucm.esToolsrankpep_new.html.

Compositional framework for multitask learning in the identification of cleavage sites of HIV1 protease Abstract   Inadequate patient samples and costly annotated data generations result into the smaller dataset in the biomedical domain. Due to which the predictions with a trained model that usually reveal a single small dataset association are fail to derive robust insights. To cope with the data sparsity, a promising strategy of combining data from the different related tasks is exercised in various application. Motivated by, successful work in the various bioinformatics application, we propose a multitask learning model based on multikernel that exploits the dependencies among various related tasks. This work aims to combine the knowledge from experimental studies of the different dataset to build stronger predictive models for HIV1 protease cleavage sites prediction. In this study, a set of peptide data from one source is referred as xe2x80x98taskxe2x80x99 and to integrate interactions from multiple tasks; our method exploits the common features and parameters sharing across the data source. The proposed framework uses feature integration, feature selection, multikernel and multifactorial evolutionary algorithm to model multitask learning. The framework considered seven different feature descriptors and four different kernel variants of support vector machines to form the optimal multikernel learning model. To validate the effectiveness of the model, the performance parameters such as average accuracy, and area under curve have been evaluated on the suggested model. We also carried out Friedman and post hoc statistical test to substantiate the significant improvement achieved by the proposed framework. The result obtained following the extensive experiment confirms the belief that multitask learning in cleavage site identification can improve the performance.