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Accurate and Automated High-Coverage Identification of Chemically Cross-Linked Peptides with MaxLynx.

ABSTRACT: Cross-linking combined with mass spectrometry (XL-MS) provides a wealth of information about the three-dimensional (3D) structure of proteins and their interactions. We introduce MaxLynx, a novel computational proteomics workflow for XL-MS integrated into the MaxQuant environment. It is applicable to noncleavable and MS-cleavable cross-linkers. For both, we have generalized the Andromeda peptide database search engine to efficiently identify cross-linked peptides. For noncleavable peptides, we implemented a novel dipeptide Andromeda score, which is the basis for a computationally efficient N-squared search engine. Additionally, partial scores summarize the evidence for the two constituents of the dipeptide individually. A posterior error probability (PEP) based on total and partial scores is used to control false discovery rates (FDRs). For MS-cleavable cross-linkers, a score of signature peaks is combined with the conventional Andromeda score on the cleavage products. The MaxQuant 3D peak detection was improved to ensure more accurate determination of the monoisotopic peak of isotope patterns for heavy molecules, which cross-linked peptides typically are. A wide selection of filtering parameters can replace the manual filtering of identifications, which is often necessary when using other pipelines. On benchmark data sets of synthetic peptides, MaxLynx outperforms all other tested software on data for both types of cross-linkers and on a proteome-wide data set of cross-linked Drosophila melanogaster cell lysate. The workflow also supports ion mobility-enhanced MS data. MaxLynx runs on Windows and Linux, contains an interactive viewer for displaying annotated cross-linked spectra, and is freely available at https://www.maxquant.org/.

SUBMITTER: Yılmaz S

PROVIDER: S-EPMC8792900 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Chemical cross-linking and high resolution MS have been integrated successfully to capture protein interactions and provide low resolution structural data for proteins that are refractive to analyses by NMR or crystallography. Despite the versatility of these combined techniques, the array of products that is generated from the cross-linking and proteolytic digestion of proteins is immense and generally requires the use of labeling strategies and/or data base search algorithms to distinguish actual cross-linked peptides from the many side products of cross-linking. Most strategies reported to date have focused on the analysis of small cross-linked protein complexes (<60 kDa) because the number of potential forms of covalently modified peptides increases dramatically with the number of peptides generated from the digestion of such complexes. We report herein the development of a user-friendly search engine, CrossSearch, that provides the foundation for an overarching strategy to detect cross-linked peptides from the digests of large (>or=170-kDa) cross-linked proteins, i.e. conjugates. Our strategy combines the use of a low excess of cross-linker, data base searching, and Fourier transform ion cyclotron resonance MS to experimentally minimize and theoretically cull the side products of cross-linking. Using this strategy, the (alpha beta gamma delta)(4) phosphorylase kinase model complex was cross-linked to form with high specificity a 170-kDa betagamma conjugate in which we identified residues involved in the intramolecular cross-linking of the 125-kDa beta subunit between its regulatory N terminus and its C terminus. This finding provides an explanation for previously published homodimeric two-hybrid interactions of the beta subunit and suggests a dynamic structural role for the regulatory N terminus of that subunit. The results offer proof of concept for the CrossSearch strategy for analyzing conjugates and are the first to reveal a tertiary structural element of either homologous alpha or beta regulatory subunit of phosphorylase kinase.

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Accurate and Automated High-Coverage Identification of Chemically Cross-Linked Peptides with MaxLynx.

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