155 research outputs found

    MSBooster: improving peptide identification rates using deep learning-based features

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    Abstract Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform

    Autoinhibited kinesin-1 adopts a hierarchical folding pattern

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    Conventional kinesin-1 is the primary anterograde motor in cells for transporting cellular cargo. While there is a consensus that the C-terminal tail of kinesin-1 inhibits motility, the molecular architecture of a full-length autoinhibited kinesin-1 remains unknown. Here, we combine crosslinking mass spectrometry (XL-MS), electron microscopy (EM), and AlphaFold structure prediction to determine the architecture of the full-length autoinhibited kinesin-1 homodimer (kinesin-1 heavy chain [KHC]) and kinesin-1 heterotetramer (KHC bound to kinesin light chain 1 [KLC1]). Our integrative analysis shows that kinesin-1 forms a compact, bent conformation through a break in coiled-coil 3. Moreover, our XL-MS analysis demonstrates that kinesin light chains stabilize the folded inhibited state rather than inducing a new structural state. Using our structural model, we show that disruption of multiple interactions between the motor, stalk, and tail domains is required to activate the full-length kinesin-1. Our work offers a conceptual framework for understanding how cargo adaptors and microtubule-associated proteins relieve autoinhibition to promote activation

    dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts.

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    The dia-PASEF technology uses ion mobility separation to reduce signal interferences and increase sensitivity in proteomic experiments. Here we present a two-dimensional peak-picking algorithm and generation of optimized spectral libraries, as well as take advantage of neural network-based processing of dia-PASEF data. Our computational platform boosts proteomic depth by up to 83% compared to previous work, and is specifically beneficial for fast proteomic experiments and those with low sample amounts. It quantifies over 5300 proteins in single injections recorded at 200 samples per day throughput using Evosep One chromatography system on a timsTOF Pro mass spectrometer and almost 9000 proteins in single injections recorded with a 93-min nanoflow gradient on timsTOF Pro 2, from 200ÔÇëng of HeLa peptides. A user-friendly implementation is provided through the incorporation of the algorithms in the DIA-NN software and by the FragPipe workflow for spectral library generation

    SP3-Enabled Rapid and High Coverage Chemoproteomic Identification of Cell-State-Dependent Redox-Sensitive Cysteines.

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    Proteinaceous cysteine residues act as privileged sensors of oxidative stress. As reactive oxygen and nitrogen species have been implicated in numerous pathophysiological processes, deciphering which cysteines are sensitive to oxidative modification and the specific nature of these modifications is essential to understanding protein and cellular function in health and disease. While established mass spectrometry-based proteomic platforms have improved our understanding of the redox proteome, the widespread adoption of these methods is often hindered by complex sample preparation workflows, prohibitive cost of isotopic labeling reagents, and requirements for custom data analysis workflows. Here, we present the SP3-Rox redox proteomics method that combines tailored low cost isotopically labeled capture reagents with SP3 sample cleanup to achieve high throughput and high coverage proteome-wide identification of redox-sensitive cysteines. By implementing a customized workflow in the free FragPipe computational pipeline, we achieve accurate MS1-based quantitation, including for peptides containing multiple cysteine residues. Application of the SP3-Rox method to cellular proteomes identified cysteines sensitive to the oxidative stressor GSNO and cysteine oxidation state changes that occur during T cell activation

    Implementing the MSFragger Search Engine as a Node in Proteome Discoverer

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    Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/

    Implementing the MSFragger Search Engine as a Node in Proteome Discoverer

    No full text
    Here, we describe the implementation of the fast proteomics search engine MSFragger as a processing node in the widely used Proteome Discoverer (PD) software platform. PeptideProphet (via the Philosopher tool kit) is also implemented as an additional PD node to allow validation of MSFragger open (mass-tolerant) search results. These two nodes, along with the existing Percolator validation module, allow users to employ different search strategies and conveniently inspect search results through PD. Our results have demonstrated the improved numbers of PSMs, peptides, and proteins identified by MSFragger coupled with Percolator and significantly faster search speed compared to the conventional SEQUEST/Percolator PD workflows. The MSFragger-PD node is available at https://github.com/nesvilab/PD-Nodes/releases/
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