Quirks of Error Estimation in Cross-Linking/Mass Spectrometry

Abstract

Cross-linking/mass spectrometry is an increasingly popular approach to obtain structural information on proteins and their complexes in solution. However, methods for error assessment are under current development. We note that false-discovery rates can be estimated at different points during data analysis, and are most relevant for residue or protein pairs. Missing this point led in our example analysis to an actual 8.4% error when 5% error was targeted. In addition, prefiltering of peptide-spectrum matches and of identified peptide pairs substantially improved results. In our example, this prefiltering increased the number of residue pairs (5% FDR) by 33% (n = 108 to n = 144). This number improvement did not come at the expense of reduced accuracy as the added data agreed with an available crystal structure. We provide an open-source tool, xiFDR ( https://github.com/rappsilberlab/xiFDR ), that implements our observations for routine application. Data are available via ProteomeXchange with identifier PXD004749.

Figure 1

Validation of FDR on different levels by crystal structure. (A) Schematic distance-histograms showing the expected overlap of false positive and decoys and resulting overlap of overlength cross-links with decoy cross-links. (B) Residue-pair distance-histogram based on identified PSMs for a PSM FDR of 50%. (C) Residue-pair distance-histogram based on identified peptide pairs for a peptide-pair FDR cutoff of 50%, calculated at the level of peptide pairs. (D) Residue pair distance-histogram for a residue-pair FDR of 50%. All distances are Cα–Cα distances of the identified residue pairs in a crystal structure of Pol II (PDB|1WCM).

Figure 2

FDR propagation from PSMs to peptide pairs and residue pairs. (A) Actual peptide-pair FDR (solid gray) and residue-pair FDR (solid black) in dependence of PSM FDR (dashed gray line) for a cross-link data set of RNA Pol II. The protein-pair FDR is plotted as a trend only, due to data sparseness. (B) Exemplification of the error propagation, in form of wrong identifications, from PSMs to peptide pairs and residue pairs. Correctly identified PSMs (true positives = green) tend to cluster, for example, several correctly identified PSMs support the same unique peptide pair and correctly identified peptide pairs in turn support one residue pair. Incorrectly identified PSMs (false positives = red) are random and do not cluster to the same extend.

Figure 3

Increased search sensitivity by prefiltering. (A) The number of identified residue pairs (at 5% FDR, z-axis) depends on the FDR-threshold applied to PSMs (x-axis) and peptide pairs (y-axis). (B) Optimal FDR thresholds on PSMs and peptide pairs (left) return more cross-links (at 5% FDR) than not applying prefilters (right). (C) Distance distribution of the residue pairs (5% residue-pair FDR). The prefiltering does increase the number of cross-links but does not lead to a notable increase in long distance links (see text for a more detailed discussion).