Biology subjects

Barente, A. S.

Publications and source records attributed to Barente, A. S..

4 recordsLinked to original sources

bioRxiv2022

The regulatory landscape of the yeast phosphoproteome

The cellular ability to react to environmental fluctuations depends on signaling networks that are controlled by the dynamic activities of kinases and phosphatases. To gain insight into these stress-responsive phosphorylation networks, we generated a quantitative mass spectrometry-based atlas of early phosphoproteomic responses in Saccharomyces cerevisiae exposed to 101 environmental and chemical perturbations. We report phosphosites on 59% of the yeast proteome, with 18% of the proteome harboring a phosphosite that is regulated within 5 minutes of stress exposure. We identify shared and perturbation-specific stress response programs, uncover dephosphorylation as an integral early event, and dissect the interconnected regulatory landscape of kinase-substrate networks, as we exemplify with TOR signaling. We further reveal functional organization principles of the stress-responsive phosphoproteome based on phosphorylation site motifs, kinase activities, subcellular localizations, shared functions, and pathway intersections. This information-rich map of 25,000 regulated phosphosites advances our understanding of signaling networks. HighlightsO_LIUltra-deep reference yeast phosphoproteome covers 36,000 phosphorylation sites and reveals general principles of eukaryotic protein phosphorylation. C_LIO_LIHigh-dimensional quantitative atlas of early phosphoproteomic responses of yeast across 101 environmental and chemical perturbations identifies 25,000 regulated perturbation-phosphosite pairs. C_LIO_LIIdentification of shared and perturbation-specific stress response phosphorylation programs reveals the importance of dephosphorylation as an early stress response. C_LIO_LIDissection of the TOR signaling network uncovers subnetworks with differential stress responsiveness and points of pathway cross-talk C_LIO_LIIdentification of functional organization of the phosphoproteome by dimensionality reduction and co-regulation analysis. C_LI

systems biology↗

bioRxiv2022

Coisolation of peptide pairs for peptide identification and MS/MS-based quantification

SILAC-based metabolic labeling is a widely adopted proteomics approach that enables quantitative comparisons among a variety of experimental conditions. Despite its quantitative capacity, SILAC experiments analyzed with data dependent acquisition (DDA) do not fully leverage peptide pair information for identification and suffer from undersampling compared to label-free proteomic experiments. Herein, we developed a data dependent acquisition strategy that coisolates and fragments SILAC peptide pairs and uses y-ions for their relative quantification. To facilitate the analysis of this type of data, we adapted the Comet sequence database search engine to make use of SILAC peptide paired fragments and developed a tool to annotate and quantify MS/MS spectra of coisolated SILAC pairs. In an initial feasibility experiment, this peptide pair coisolation approach generally improved expectation scores compared to the traditional DDA approach. Fragment ion quantification performed similarly well to precursor quantification in the MS1 and achieved more quantifications. Lastly, our method enables reliable MS/MS quantification of SILAC proteome mixtures with overlapping isotopic distributions, which are difficult to deconvolute in MS1-based quantification. This study demonstrates the initial feasibility of the coisolation approach. Coupling this approach with intelligent acquisition strategies has the potential to improve SILAC peptide sampling and quantification.

systems biology↗

bioRxiv2022

Proteome-wide identification of amino acid substitutions deleterious for protein function

DNA sequencing has led to the discovery of millions of mutations that change the encoded protein sequences, but the impact of nearly all of these mutations on protein function is unknown. We addressed this scarcity of functional data by developing Miro, a proteomic technology that uses mistranslation to introduce amino acid substitutions and biochemical assays to quantify functional differences of thousands of protein variants by mass spectrometry. We apply this technology to the proteome of yeast to reveal amino acid substitutions that impact protein structure, ligand binding, protein-protein interactions, protein post-translational modifications, and protein thermal stability. Adapting Miro to human cells will provide a means to efficiently accelerate our mechanistic interpretation of genomic mutations to predict disease risk.

genomics↗

bioRxiv2022

A Python Package for the Localization of Protein Modifications in Mass Spectrometry Data

Determining the correct localization of post-translational modifications (PTMs) on peptides aids in interpreting their effect on protein function. While most algorithms for this task are available as standalone applications or incorporated into software suites, improving their versatility through access from popular scripting languages facilitates experimentation and incorporation into novel workflows. Here we describe pyAscore, an efficient and versatile implementation of the Ascore algorithm in Python for scoring the localization of user defined PTMs in data dependent mass spectrometry. pyAscore can be used from the command line or imported into Python scripts and accepts standard file formats from popular software tools used in bottom-up proteomics. Access to internal objects for scoring and working with modified peptides adds to the toolbox for working with PTMs in Python. pyAscore and is available as an open source package for Python 3.6+ on all major operating systems and can be found at pyascore.readthedocs.io.

bioinformatics↗