Today’s highly accurate spectra provided by modern tandem mass spectrometers offer considerable advantages for the analysis of proteomic samples of increased complexity. Among other factors, the quantity of reliably identified peptides is considerably influenced by the peptide identification algorithm. While most widely used search engines were developed when high-resolution mass spectrometry data were not readily available for fragment ion masses, we have designed a scoring algorithm particularly suitable for high mass accuracy. Our algorithm, MS Amanda, is generally applicable to HCD, ETD, and CID fragmentation type data. The algorithm confidently explains more spectra at the same false discovery rate than Mascot or SEQUEST on examined high mass accuracy data sets, with excellent overlap and identical peptide sequence identification for most spectra also explained by Mascot or SEQUEST. MS Amanda, available at , is provided free of charge both as standalone version for integration into custom workflows and as a plugin for the Proteome Discoverer platform.

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/.Electronic supplementary materialThe online version of this article (doi:10.1007/s00251-010-0441-4) contains supplementary material, which is available to authorized users.

The detailed molecular mechanism of action of secondgeneration BCR-ABL tyrosine kinase inhibitors, including perturbed targets and pathways, should contribute to rationalized therapy in chronic myeloid leukemia (CML) or in other affected diseases. Here, we characterized the target profile of the dual SRC/ABL inhibitor bosutinib employing a two-tiered approach using chemical proteomics to identify natural binders in whole cell lysates of primary CML and K562 cells in parallel to in vitro kinase assays against a large recombinant kinase panel. The combined strategy resulted in a global survey of bosutinib targets comprised of over 45 novel tyrosine and serine/ threonine kinases. We have found clear differences in the target patterns of bosutinib in primary CML cells versus the K562 cell line. A comparison of bosutinib with dasatinib across the whole kinase panel revealed overlapping, but distinct, inhibition profiles. Common among those were the SRC, ABL and TEC family kinases. Bosutinib did not inhibit KIT or platelet-derived growth factor receptor, but prominently targeted the apoptosis-linked STE20 kinases. Although in vivo bosutinib is inactive against ABL T315I, we found this clinically important mutant to be enzymatically inhibited in the midnanomolar range. Finally, bosutinib is the first kinase inhibitor shown to target CAMK2G, recently implicated in myeloid leukemia cell proliferation.

This protocol describes a workflow for creating structural models of proteins or protein complexes using distance restraints derived from cross-linking mass spectrometry experiments. The distance restraints are used (i) to adjust preliminary models that are calculated based on a homologous template and primary sequence and (ii) to select the model that is in best agreement with the experimental data. In the case of protein complexes, the cross-linking data is further used to dock the subunits to one another to generate models of the interacting proteins. Predicting models in such a manner has the potential to indicate multiple conformations and dynamic changes that occur in solution. This modeling protocol is compatible with many cross-linking workflows and uses open-source programs or programs that are free for academic users and do not require expertise in computational modeling. This protocol is an excellent additional application with which to use cross-linking results for building structural models of proteins. The established protocol is expected to take 6-12 days to complete, depending on the size of the proteins and the complexity of the cross-linking data.