StavroX—A Software for Analyzing Crosslinked Products in Protein Interaction Studies

Abstract

Chemical crosslinking in combination with mass spectrometry has matured into an alternative approach to derive low-resolution structural information of proteins and protein complexes. Yet, one of the major drawbacks of this strategy remains the lack of software that is able to handle the large MS datasets that are created after chemical crosslinking and enzymatic digestion of the crosslinking reaction mixtures. Here, we describe a software, termed StavroX, which has been specifically designed for analyzing highly complex crosslinking datasets. The StavroX software was evaluated for three diverse biological systems: (1) the complex between calmodulin and a peptide derived from Munc13, (2) an N-terminal ß-laminin fragment, and (3) the complex between guanylyl cyclase activating protein-2 and a peptide derived from retinal guanylyl cyclase. We show that the StavroX software is advantageous for analyzing crosslinked products due to its easy-to-use graphical user interface and the highly automated analysis of mass spectrometry (MS) and tandem mass spectrometry (MS/MS) data resulting in short times for analysis. StavroX is expected to give a further push to the chemical crosslinking approach as a routine technique for protein interaction studies.

Crosslinking and Mass Spectrometry: An Integrated Technology to Understand the Structure and Function of Molecular Machines

“…Over the past few years the field has seen significant progress and several methods to enrich crosslinks [13][14][15], various crosslinking chemistries [9,10,[15][16][17][18][19][20][21][22], and the introduction of multiple detection and identification strategies [9][10][11][23][24][25][26][27][28]. Statistical models that differentiate true from false identifications have also been developed [10].…”

“…However, due to multiple major technical obstacles it had been impossible until recently to directly and reliably identify crosslinked peptides from protein complexes by MS (see [1][2][3][4] for recent reviews). After early work on individual proteins [5][6][7] and protein complexes [8,9], notably by the Sinz and Rappsilber groups, Aebersold and coworkers introduced the first robust general workflow by optimizing wet-lab protocols and the development of the publicly available xQuest/xProphet open-source software suite for the analysis and validation of crosslinks from large protein complexes by MS [10][11][12].Over the past few years the field has seen significant progress and several methods to enrich crosslinks [13][14][15], various crosslinking chemistries [9,10,[15][16][17][18][19][20][21][22], and the introduction of multiple detection and identification strategies [9][10][11][23][24][25][26][27][28]. Statistical models that differentiate true from false identifications have also been developed [10].…”

Architecture of the Human Mitochondrial Iron-Sulfur Cluster Assembly Machinery

“…Based on the amino acid sequences of FXN, ISCU, NFS1, and ISD11, the molecular weight and amino acid specificity of BS 3 , and the cleavage specificity of GluC and AspN, the software calculated all theoretical possible combinations of crosslinked peptides and compared them with the precursor peptide masses in the MS file (73). Each identified cross-linked peptide was assigned a score based on a comparison between the theoretical fragmentation and the actual MS/MS spectrum of the cross-linked peptide (73). The software also calculated the FDR by comparing the number of candidates in each experimental dataset with the number of false-positive candidates from a decoy dataset, created from the inverted amino acid sequences of FXN, ISCU, NFS1, and ISD11.…”

Architecture of the Yeast Mitochondrial Iron-Sulfur Cluster Assembly Machinery

“…Based on the amino acid sequences of Yfh1 and Isu1, the molecular weight and amino acid specificity of BS 3 , and the cleavage specificity of GluC, the software calculated all theoretical possible combinations of cross-linked peptides and compared them with the precursor peptide masses in the MS file (52). Each identified cross-linked peptide was then assigned a score based on a comparison between the theoretical fragmentation and the actual MS/MS spectrum of the crosslinked peptide (52). The software also calculated the false discovery rate (FDR) by comparing the number of candidates in each of the two experimental datasets to the number of falsepositive candidates from a decoy dataset, created from the inverted amino acid sequences of Yfh1 and Isu1.…”

“…Size-exclusion chromatography revealed one major peak (Fig. 1, A and B, fractions [52][53][54][55][56] with an apparent molecular mass of ϳ330 kDa (measured at the center of the peak; i.e. fraction 53), and a minor peak with apparent molecular mass of ϳ44 kDa (Fig.…”