apQuant: Accurate Label-Free Quantification by Quality Filtering.

Label-free quantification of shotgun proteomics data is a frequently used strategy, offering high dynamic range, sensitivity, and the ability to compare a high number of samples without additional labeling effort. Here, we present a bioinformatics approach that significantly improves label-free quantification results. We employ Percolator to assess the quality of quantified peptides. This allows to extract accurate and reliable quantitative results based on false discovery rate. Benchmarking our approach on previously published public data shows that it considerably outperforms currently available algorithms. apQuant is available free of charge as a node for Proteome Discoverer.

Author Correction: Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data.

In the version of this article initially published online, the authors used incorrectly defined restraints for specifying the distance between residues when using the HADDOCK portal. Following the publication of a Correspondence by the developers of the HADDOCK portal (Nat. Protoc. https://dx.doi.org/10.1038/s41596-018-0017-6, 2018) and a Reply by the authors of the Protocol (Nat. Protoc. https://dx.doi.org/10.1038/s41596-018-0018-5, 2018), the syntax in step 21 has been corrected. In addition, the input files (available as Supplementary Data 5-7) have been replaced.

Structural prediction of protein models using distance restraints derived from cross-linking mass spectrometry data.

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 on the basis of 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 are 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 d to complete, depending on the size of the proteins and the complexity of the cross-linking data.