Paulo C. Carvalho

PYR/PYL/RCAR family members are major in-vivo ABI1 protein phosphatase 2C-interacting proteins in Arabidopsis

Abscisic acid (ABA) mediates resistance to abiotic stress and controls developmental processes in plants. The group-A PP2Cs, of which ABI1 is the prototypical member, are protein phosphatases that play critical roles as negative regulators very early in ABA signal transduction. Because redundancy is thought to limit the genetic dissection of early ABA signalling, to identify redundant and early ABA signalling proteins, we pursued a proteomics approach. We generated YFP-tagged ABI1 Arabidopsis expression lines and identified in vivo ABI1-interacting proteins by mass-spectrometric analyses of ABI1 complexes. Known ABA signalling components were isolated including SnRK2 protein kinases. We confirm previous studies in yeast and now show that ABI1 interacts with the ABA-signalling kinases OST1, SnRK2.2 and SnRK2.3 in plants. Interestingly, the most robust in planta ABI1-interacting proteins in all LC-MS/MS experiments were nine of the 14 PYR/PYL/RCAR proteins, which were recently reported as ABA-binding signal transduction proteins, providing evidence for in vivo PYR/PYL/RCAR interactions with ABI1 in Arabidopsis. ABI1–PYR1 interaction was stimulated within 5 min of ABA treatment in Arabidopsis. Interestingly, in contrast, PYR1 and SnRK2.3 co-immunoprecipitated equally well in the presence and absence of ABA. To investigate the biological relevance of the PYR/PYLs, we analysed pyr1/pyl1/pyl2/pyl4 quadruple mutant plants and found strong insensitivities in ABA-induced stomatal closure and ABA-inhibition of stomatal opening. These findings demonstrate that ABI1 can interact with several PYR/PYL/RCAR family members in Arabidopsis, that PYR1–ABI1 interaction is rapidly stimulated by ABA in Arabidopsis and indicate new SnRK2 kinase-PYR/PYL/RCAR interactions in an emerging model for PYR/PYL/RCAR-mediated ABA signalling.

Search engine processor: Filtering and organizing peptide spectrum matches

The search engine processor (SEPro) is a tool for filtering, organizing, sharing, and displaying peptide spectrum matches. It employs a novel three‐tier Bayesian approach that uses layers of spectrum, peptide, and protein logic to lead the data to converge to a single list of reliable protein identifications. SEPro is integrated into the PatternLab for proteomics environment, where an arsenal of tools for analyzing shotgun proteomic data is provided. By using the semi‐labeled decoy approach for benchmarking, we show that SEPro significantly outperforms a commercially available competitor.

XDIA: improving on the label-free data-independent analysis

SUMMARY XDIA is a computational strategy for analyzing multiplexed spectra acquired using electron transfer dissociation and collision-activated dissociation; it significantly increases identified spectra (approximately 250%) and unique peptides (approximately 30%) when compared with the data-dependent ETCaD analysis on middle-down, single-phase shotgun proteomic analysis. Increasing identified spectra and peptides improves quantitation statistics confidence and protein coverage, respectively. AVAILABILITY The software and data produced in this work are freely available for academic use at http://fields.scripps.edu/XDIA CONTACT: paulo@pcarvalho.com SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.

Integrated analysis of shotgun proteomic data with PatternLab for proteomics 4.0

PatternLab for proteomics is an integrated computational environment that unifies several previously published modules for the analysis of shotgun proteomic data. The contained modules allow for formatting of sequence databases, peptide spectrum matching, statistical filtering and data organization, extracting quantitative information from label-free and chemically labeled data, and analyzing statistics for differential proteomics. PatternLab also has modules to perform similarity-driven studies with de novo sequencing data, to evaluate time-course experiments and to highlight the biological significance of data with regard to the Gene Ontology database. The PatternLab for proteomics 4.0 package brings together all of these modules in a self-contained software environment, which allows for complete proteomic data analysis and the display of results in a variety of graphical formats. All updates to PatternLab, including new features, have been previously tested on millions of mass spectra. PatternLab is easy to install, and it is freely available from http://patternlabforproteomics.org.

YADA: a tool for taking the most out of high-resolution spectra

Summary: YADA can deisotope and decharge high-resolution mass spectra from large peptide molecules, link the precursor monoisotopic peak information to the corresponding tandem mass spectrum, and account for different co-fragmenting ion species (multiplexed spectra). We describe how YADA enables a pipeline consisting of ProLuCID and DTASelect for analyzing large-scale middle-down proteomics data. Availability: http://fields.scripps.edu/yada Contact: paulo@pcarvalho.com Supplementary information: Supplementary data are available at Bioinformatics online.

Improving the TFold test for differential shotgun proteomics

UNLABELLED We present an updated version of the TFold software for pinpointing differentially expressed proteins in shotgun proteomics experiments. Given an FDR bound, the updated approach uses a theoretical FDR estimator to maximize the number of identifications that satisfy both a fold-change cutoff that varies with the t-test P-value as a power law and a stringency criterion that aims to detect lowly abundant proteins. The new version has yielded significant improvements in sensitivity over the previous one. AVAILABILITY Freely available for academic use at http://pcarvalho.com/patternlab.

Identifying components of protein complexes in C. elegans using co-immunoprecipitation and mass spectrometry.

Mass spectrometry-based proteomics is rapidly becoming an essential tool for biologists. One of the most common applications is identifying the components of protein complexes isolated by co-immunoprecipitation. In this review, we discuss the co-immunoprecipitation, mass spectrometry and data analysis techniques that have been used successfully to define protein complexes in C. elegans research. In this discussion, two strategies emerged. One approach is to use stringent biochemical purification methods and attempt to identify a small number of complex components with a high degree of certainty based on MS data. A second approach is to use less stringent purification and identification parameters, and ultimately test a longer list of potential binding partners in biological validation assays. This should provide a useful guide for biologists planning proteomic experiments.

PatternLab: From Mass Spectra to Label‐Free Differential Shotgun Proteomics

PatternLab for proteomics is a self‐contained computational environment for analyzing shotgun proteomic data. Recent improvements incorporate modules to facilitate the computational analysis, such as FastaDBXtractor for sequence database preparation and ProLuCID runner for simplifying and managing the protein identification search engine; modules for pushing the limits on proteomics standards, such as SEPro, which relies on a semi‐labeled decoy approach for increasing confidence in filtering and organizing peptide spectrum matches; and modules with novel features, such as SEProQ for enabling label‐free quantitation by extracted ion chromatograms according to a distributed normalized ion abundance factor approach (dNIAF). Existing modules were also improved, such as the TFold module for pinpointing differentially expressed proteins. These new modules are integrated into the previously described arsenal of tools for further data analysis. Here we provide detailed instructions for operating and understanding them. Curr. Protoc. Bioinform. 40:13.19.1‐13.19.18.

Single-step inline hydroxyapatite enrichment facilitates identification and quantitation of phosphopeptides from mass-limited proteomes with MudPIT

Herein we report the characterization and optimization of single-step inline enrichment of phosphopeptides directly from small amounts of whole cell and tissue lysates (100-500 μg) using a hydroxyapatite (HAP) microcolumn and Multidimensional Protein Identification Technology (MudPIT). In comparison to a triplicate HILIC-IMAC phosphopeptide enrichment study, ∼80% of the phosphopeptides identified using HAP-MudPIT were unique. Similarly, analysis of the consensus phosphorylation motifs between the two enrichment methods illustrates the complementarity of calcium- and iron-based enrichment methods and the higher sensitivity and selectivity of HAP-MudPIT for acidic motifs. We demonstrate how the identification of more multiply phosphorylated peptides from HAP-MudPIT can be used to quantify phosphorylation cooperativity. Through optimization of HAP-MudPIT on a whole cell lysate we routinely achieved identification and quantification of ca. 1000 phosphopeptides from a ∼1 h enrichment and 12 h MudPIT analysis on small quantities of material. Finally, we applied this optimized method to identify phosphorylation sites from a mass-limited mouse brain region, the amygdala (200-500 μg), identifying up to 4000 phosphopeptides per run.