High sensitivity proteome-scale search for crosslinked peptides using CRIMP 2.0

Abstract

Crosslinking mass spectrometry (XL-MS) is a valuable technique for the generation of point-to-point distance measurements in protein space. Applications involving in situ chemical crosslinking have created the possibility of mapping whole protein interactomes with high spatial resolution. However, an XL-MS experiment carried out directly on cells requires highly efficient software that can detect crosslinked peptides with sensitivity and controlled error rates. Many algorithmic approaches invoke a filtering strategy designed to reduce the size of the database prior to mounting a search for crosslinks, but concern has been expressed over the possibility of reduced sensitivity with such strategies. Here we present a full upgrade to CRIMP, the crosslinking app in the Mass Spec Studio, which implements a new strategy for the detection of both component peptides in the MS2 spectrum. Using several published datasets, we demonstrate that this pre-searching method is sensitive and fast, permitting whole proteome searches on a conventional desktop computer for both cleavable and noncleavable crosslinkers. We introduce a new strategy for scoring crosslinks, adapted from computer vision algorithms, that properly resolves conflicting XL hits from other crosslinking reaction products, and we present a method for enhancing the detection of protein-protein interactions that relies upon compositional data.

Rescuing error control in crosslinking mass spectrometry

“…Generally, using information about individual proteins for the assessment of protein pairs, both derived from within the search and derived from external sources, has to be done with great care. Search engines or post-processing tools such as ECL-PF 20 and CRIMP 2.0 21 utilise protein self-crosslinks to assign scores or confidence values to protein heteromeric matches. XLinkProphet 22 uses information about individual proteins being present to rescore matches.…”

Innovation in Cross-Linking Mass Spectrometry Workflows: Toward a Comprehensive, Flexible, and Customizable Data Analysis Platform

“…20 An obvious advantage of Mass spec studio is that it supports multiple custom additions in its user interface, allowing visualization of MS1 and MS2 data in the results interface and allowing users to map cross-linking results directly to protein structures with PyMol (Figure 3D). Besides, the recently updated CRIMP 2.0 integrates an improved two-pass library reduction engine and a new scoring algorithm, 43 allows for a search of both noncleavable and MS-cleavable cross-linkers. However, the overall performance of the engine and its capabilities to identify various cross-linkers remain to be tested.…”

Cell fixation improves performance ofin situcrosslinking mass spectrometry while preserving cellular ultrastructure

“…After crosslink installation, cells are lysed and digested in a standard “bottom-up” workflow common to conventional proteomics routines, generating linked peptides for detection by MS. Crosslinked peptides are minor reaction products and cannot be identified with the conventional database searching strategies used in the proteomics community. New software makes the detection of crosslinks more efficient than ever before 14–18 , but very little progress has been made in improving the crosslinking reaction itself. To faithfully sample both the spatial and temporal properties of the proteome, crosslinkers must cross the cell membrane, perfuse freely throughout the cell, and react quickly.…”