Towards Universal Structure-Based Prediction of Class II MHC Epitopes for Diverse Allotypes

The binding of peptide fragments of antigens to class II MHC proteins is a crucial step in initiating a helper T cell immune response. The discovery of these peptide epitopes is important for understanding the normal immune response and its misregulation in autoimmunity and allergies and also for vaccine design. In spite of their biomedical importance, the high diversity of class II MHC proteins combined with the large number of possible peptide sequences make comprehensive experimental determination of epitopes for all MHC allotypes infeasible. Computational methods can address this need by predicting epitopes for a particular MHC allotype. We present a structure-based method for predicting class II epitopes that combines molecular mechanics docking of a fully flexible peptide into the MHC binding cleft followed by binding affinity prediction using a machine learning classifier trained on interaction energy components calculated from the docking solution. Although the primary advantage of structure-based prediction methods over the commonly employed sequence-based methods is their applicability to essentially any MHC allotype, this has not yet been convincingly demonstrated. In order to test the transferability of the prediction method to different MHC proteins, we trained the scoring method on binding data for DRB1*0101 and used it to make predictions for multiple MHC allotypes with distinct peptide binding specificities including representatives from the other human class II MHC loci, HLA-DP and HLA-DQ, as well as for two murine allotypes. The results showed that the prediction method was able to achieve significant discrimination between epitope and non-epitope peptides for all MHC allotypes examined, based on AUC values in the range 0.632–0.821. We also discuss how accounting for peptide binding in multiple registers to class II MHC largely explains the systematically worse performance of prediction methods for class II MHC compared with those for class I MHC based on quantitative prediction performance estimates for peptide binding to class II MHC in a fixed register

Strong coulomb drag and broken symmetry in double-layer graphene

Contains fulltext : 103786.pdf (author's version ) (Open Access

Eco-evolutionary dynamics in Pacific salmon

Increasing acceptance of the idea that evolution can proceed rapidly has generated considerable interest in understanding the consequences of ongoing evolutionary change for populations, communities and ecosystems. The nascent field of ‘eco-evolutionary dynamics' considers these interactions, including reciprocal feedbacks between evolution and ecology. Empirical support for eco-evolutionary dynamics has emerged from several model systems, and we here present some possibilities for diverse and strong effects in Pacific salmon (Oncorhynchus spp.). We specifically focus on the consequences that natural selection on body size can have for salmon population dynamics, community (bear-salmon) interactions and ecosystem process (fluxes of salmon biomass between habitats). For example, we find that shifts in body size because of selection can alter fluxes across habitats by up to 11% compared with ecological (that is, numerical) effects. More generally, we show that selection within a generation can have large effects on ecological dynamics and so should be included within a complete eco-evolutionary framework