Nesvizhskii, A. I. | Search bioRxiv

bioRxiv2018

LiF-MS: Mapping unstructured peptide-protein interactions using Ligand-Footprinting Mass Spectrometry

Unstructured peptides, or linear motifs, present a poorly understood molecular language within the context of cellular signaling. These modular regions are often short, unstructured and interact weakly and transiently with folded target proteins. Thus, they are difficult to study with conventional structural biology methods. We present Ligand-Footprinting Mass Spectrometry, or LiF-MS, as a method of mapping the binding sites and dynamic disorder of these peptides on folded protein domains. LiF-MS uses a cleavable crosslinker to mark regions of a protein contacted by a bound linear motif. We demonstrate this method can detect both conformation ensembles and binding orientations of a linear motif in its binding pocket to amino-acid-level detail. Furthermore, marked amino acids can be used as constraints in peptide-protein docking simulations to improve model quality. In conclusion, LiF-MS proves a simple and novel method of elucidating peptide docking structural data not accessible by other methods in the context of a purified system.

biochemistry↗

bioRxiv2017

PIQED: Automated Identification And Quantification Of Protein Modifications From DIA-MS Data

Label-free quantification using data-independent acquisition (DIA) is a robust method for deep and accurate proteome quantification1,2. However, when lacking a pre-existing spectral library, as is often the case with studies of novel post-translational modifications (PTMs), samples are typically analyzed several times: one or more data dependent acquisitions (DDA) are used to generate a spectral library followed by DIA for quantification. This type of multi-injection analysis results in significant cost with regard to sample consumption and instrument time for each new PTM study, and may not be possible when sample amount is limiting and/or studies require a large number of biological replicates. Recently developed software (e.g. DIA-Umpire) has enabled combined peptide identification and quantification from a data-independent acquisition without any pre-existing spectral library3,4. Still, these tools are designed for protein level quantification. Here we demonstrate a software tool and workflow that extends DIA-Umpire to allow automated identification and quantification of PTM peptides from DIA. We accomplish this using a custom, open-source graphical user interface DIA-Pipe (https://github.com/jgmeyerucsd/PIQEDia/releases/tag/v0.1.2) (figure 1a).\n\nO_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC=\"FIGDIR/small/141382_fig1.gif\" ALT=\"Figure 1\">\nView larger version (46K):\norg.highwire.dtl.DTLVardef@41b8f3org.highwire.dtl.DTLVardef@d59733org.highwire.dtl.DTLVardef@b9a722org.highwire.dtl.DTLVardef@8be50c_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Automated Qualitative and Quantitative Analysis of Post-Translational Modifications using DIA-Pipe. (a) Workflow of the all-DIA strategy for identification and quantification of PTMs. Modified peptides enriched from biological samples are analyzed by data-independent acquisition. All data analysis steps starting from instrument. wiff files, acquired on a TripleTOF 5600, can be completed using the DIA-Pipe GUI, including: (1) file conversion and pseudo-MS/MS spectra generation using DIA-Umpire, (2) database searching by MS-GF+, X! Tandem, and Comet followed by results refinement and combination using PeptideProphet/iProphet/PTMProphet, (3) automated spectral library generation and fragment area extraction using SkylineRunner, and finally, (4) Skyline report filtering and formatting for significance testing with mapDIA. (b) mProphet composite score distributions of target and second-best peaks picked by Skylineshowing essentially error-free peak picking by Skyline of peptides identified using pseudo-MS/MS spectra. (c) Distribution of coefficient of variations observed from three technical replicates for 1,182 acetylation sites. (d) Observed distributions of log2(fold change) computed by mapDIA using three technical replicates of 1X injection volume compared to three technical replicates of 0.5X injection volume; expected log2(fold change) value =1. (e) Number of identified peptides from each single replicate injection and from the combination of two or three replicates. An average of 1,655 acetylated peptides were identified per replicate. The combination of two or three replicates increased the number of identifications by 19% or 29%, respectively.\n\nC_FIG

systems biology↗