28 research outputs found
A combined prediction strategy increases identification of peptides bound with high affinity and stability to porcine MHC class I molecules SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01
Affinity and stability of peptides bound by major histocompatibility complex (MHC) class I molecules are important factors in presentation of peptides to cytotoxic T lymphocytes (CTLs). In silico prediction methods of peptide-MHC binding followed by experimental analysis of peptide-MHC interactions constitute an attractive protocol to select target peptides from the vast pool of viral proteome peptides. We have earlier reported the peptide binding motif of the porcine MHC-I molecules SLA-1*04:01 and SLA-2*04:01, identified by an ELISA affinity-based positional scanning combinatorial peptide library (PSCPL) approach. Here, we report the peptide binding motif of SLA-3*04:01 and combine two prediction methods and analysis of both peptide binding affinity and stability of peptide-MHC complexes to improve rational peptide selection. Using a peptide prediction strategy combining PSCPL binding matrices and in silico prediction algorithms (NetMHCpan), peptide ligands from a repository of 8900 peptides were predicted for binding to SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01 and validated by affinity and stability assays. From the pool of predicted peptides for SLA-1*04:01, SLA-2*04:01, and SLA-3*04:01, a total of 71, 28, and 38 % were binders with affinities below 500 nM, respectively. Comparison of peptide-SLA binding affinity and complex stability showed that peptides of high affinity generally, but not always, produce complexes of high stability. In conclusion, we demonstrate how state-of-the-art prediction and in vitro immunology tools in combination can be used for accurate selection of peptides for MHC class I binding, hence providing an expansion of the field of peptide-MHC analysis also to include pigs as a livestock experimental model.Fil: Pedersen, Lasse Eggers. Technical University of Denmark; DinamarcaFil: Rasmussen, Michael. Universidad de Copenhagen; DinamarcaFil: Harndahl, Mikkel. Universidad de Copenhagen; DinamarcaFil: Nielsen, Morten. Technical University of Denmark; Dinamarca. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - La Plata. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas (subsede ChascomĂşs) | Universidad Nacional de San MartĂn. Instituto de Investigaciones BiotecnolĂłgicas. Instituto de Investigaciones BiotecnolĂłgicas (subsede ChascomĂşs); ArgentinaFil: Buus, Søren. Universidad de Copenhagen; DinamarcaFil: Jungersen, Gregers. Technical University of Denmark; Dinamarc
A Community Dataset for Comparing Automated Coronal Hole Detection Schemes
This is the author accepted manuscript.Automated detection schemes are nowadays the standard approach for locating coronal holes in
EUV images from the Solar Dynamics Observatory (SDO). But factors such as the noisy nature of
solar imagery, instrumental effects, and others make it challenging to identify coronal holes using these
automated schemes. While discrepancies between detection schemes have been noted in the literature,
a comprehensive assessment of these discrepancies is still lacking. The contribution of the Coronal
Hole Boundary Working Team in the COSPAR ISWAT initiative is threefold to close this gap. First,
we present the first community dataset for comparing automated coronal hole detection schemes. This
dataset consists of 29 SDO images, all of which were selected by experienced observers to challenge
automated schemes. Second, we use this community dataset as input to 14 widely-applied automated
schemes to study coronal holes and collect their detection results. Third, we study three SDO images
from the dataset that exemplify the most important lessons learned from this effort. Our findings
show that the choice of the automated detection scheme can have a significant effect on the physical properties of coronal holes, and we discuss the implications of these findings for open questions in solar
and heliospheric physics. We envision that this community dataset will serve the scientific community
as a benchmark dataset for future developments in the field.Austrian Science Fund (FWF)European Research Council (ERC)NAS