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Protamine Characterization by Top-Down Proteomics: Boosting Proteoform Identification with DBSCAN.

ABSTRACT: Protamines replace histones as the main nuclear protein in the sperm cells of many species and play a crucial role in compacting the paternal genome. Human spermatozoa contain protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or the P1/P2 ratio may be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteoforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down proteomics permits a more in-depth analysis of protamine PTMs and proteoforms, thereby opening up new perspectives to unravel their impact on male fertility. We report on the analysis of two normozoospermic semen samples by top-down proteomics. We discuss the difficulties encountered with the data analysis and propose solutions as this step is one of the current bottlenecks in top-down proteomics with the bioinformatics tools currently available. Our strategy for the data analysis combines two software packages, ProSight PD (PS) and TopPIC suite (TP), with a clustering algorithm to decipher protamine proteoforms. We identified up to 32 protamine proteoforms at different levels of characterization. This in-depth analysis of the protamine proteoform landscape of normozoospermic individuals represents the first step towards the future study of sperm pathological conditions opening up the potential personalized diagnosis of male infertility.

SUBMITTER: Arauz-Garofalo G

PROVIDER: S-EPMC8162566 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Protamine characterization by top-down proteomics: Boosting proteoform identification with DBSCAN

Project description:Protamines replace histones as the main nuclear protein in sperm cells having a crucial role in compacting the paternal genome. Human spermatozoa contain the protamine 1 (P1) and the family of protamine 2 (P2) proteins. Alterations in protamine PTMs or on the P1/P2 ratio could be associated with male infertility. Top-down proteomics enables large-scale analysis of intact proteforms derived from alternative splicing, missense or nonsense genetic variants or PTMs. In contrast to current gold standard techniques, top-down allows a more in-depth analysis of protamine PTMs and proteoforms, opening up new perspectives to unravel their impact on male fertility. We analyzed two normozoospermic semen samples by top-down and discussed in detail the difficulties we found in the data analysis and the suggested solutions, as this step is one of the current bottlenecks in top-down proteomics with currently available bioinformatic tools. Our strategy for the data analysis combines two different software, ProSight PD (PS) and TopPIC suite (TP), with a clustering algorithm to decipher protamine proteoforms. We identified up to 32 protamine proteoforms at different levels of characterization. This in-depth analysis of the protamine proteoform landscape of an individual boosts personalized diagnosis of male infertility.

2021-05-13 | PXD024405 | Pride

SPECTRUM - A MATLAB Toolbox for Proteoform Identification from Top-Down Proteomics Data.

Project description:Top-Down Proteomics (TDP) is an emerging proteomics protocol that involves identification, characterization, and quantitation of intact proteins using high-resolution mass spectrometry. TDP has an edge over other proteomics protocols in that it allows for: (i) accurate measurement of intact protein mass, (ii) high sequence coverage, and (iii) enhanced identification of post-translational modifications (PTMs). However, the complexity of TDP spectra poses a significant impediment to protein search and PTM characterization. Furthermore, limited software support is currently available in the form of search algorithms and pipelines. To address this need, we propose 'SPECTRUM', an open-architecture and open-source toolbox for TDP data analysis. Its salient features include: (i) MS2-based intact protein mass tuning, (ii) de novo peptide sequence tag analysis, (iii) propensity-driven PTM characterization, (iv) blind PTM search, (v) spectral comparison, (vi) identification of truncated proteins, (vii) multifactorial coefficient-weighted scoring, and (viii) intuitive graphical user interfaces to access the aforementioned functionalities and visualization of results. We have validated SPECTRUM using published datasets and benchmarked it against salient TDP tools. SPECTRUM provides significantly enhanced protein identification rates (91% to 177%) over its contemporaries. SPECTRUM has been implemented in MATLAB, and is freely available along with its source code and documentation at https://github.com/BIRL/SPECTRUM/.

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PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics.

Project description:PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron).

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FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts.

Project description:The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.

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TopPIC: a software tool for top-down mass spectrometry-based proteoform identification and characterization.

Project description:Top-down mass spectrometry enables the observation of whole complex proteoforms in biological samples and provides crucial information complementary to bottom-up mass spectrometry. Because of the complexity of top-down mass spectra and proteoforms, it is a challenging problem to efficiently interpret top-down tandem mass spectra in high-throughput proteome-level proteomics studies. We present TopPIC, a tool that efficiently identifies and characterizes complex proteoforms with unknown primary structure alterations, such as amino acid mutations and post-translational modifications, by searching top-down tandem mass spectra against a protein database.Availability and implementationhttp://proteomics.informatics.iupui.edu/software/toppic/ CONTACT: xwliu@iupui.eduSupplementary information: Supplementary data are available at Bioinformatics online.

| S-EPMC5181555 | biostudies-literature

Defining the Sarcomeric Proteoform Landscape in Ischemic Cardiomyopathy by Top-Down Proteomics.

Project description:Ischemic cardiomyopathy (ICM) is a prominent form of heart failure, but the molecular mechanisms underlying ICM remain relatively understudied due to marked phenotypic heterogeneity. Alterations in post-translational modifications (PTMs) and isoform switches in sarcomeric proteins play important roles in cardiac pathophysiology. Thus, it is essential to define sarcomeric proteoform landscape to better understand ICM. Herein, we have implemented a top-down liquid chromatography (LC)-mass spectrometry (MS)-based proteomics method for the identification and quantification of sarcomeric proteoforms in the myocardia of donors without heart diseases (n = 16) compared to end-stage ICM patients (n = 16). Importantly, quantification of post-translational modifications (PTMs) and expression reveal significant changes in various sarcomeric proteins extracted from ICM tissues. Changes include altered phosphorylation and expression of cardiac troponin I (cTnI) and enigma homologue 2 (ENH2) as well as an increase in muscle LIM protein (MLP) and calsarcin-1 (Cal-1) phosphorylation in ICM hearts. Our results imply that the contractile apparatus of the sarcomere is severely dysregulated during ICM. Thus, this is the first study to uncover significant molecular changes to multiple sarcomeric proteins in the LV myocardia of the end-stage ICM patients using liquid chromatography-mass spectrometry (LC-MS)-based top-down proteomics. Raw data are available via the PRIDE repository with identifier PXD038066.

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Top-Down Proteomics Reveals Myofilament Proteoform Heterogeneity among Various Rat Skeletal Muscle Tissues.

Project description:Heterogeneity in skeletal muscle contraction time, peak power output, and resistance to fatigue, among others, is necessary to accommodate the wide range of functional demands imposed on the body. Underlying this functional heterogeneity are a myriad of differences in the myofilament protein isoform expression and post-translational modifications; yet, characterizing this heterogeneity remains challenging. Herein, we have utilized top-down liquid chromatography (LC)-mass spectrometry (MS)-based proteomics to characterize myofilament proteoform heterogeneity in seven rat skeletal muscle tissues including vastus lateralis, vastus medialis, vastus intermedius, rectus femoris, soleus, gastrocnemius, and plantaris. Top-down proteomics revealed that myofilament proteoforms varied greatly across the seven different rat skeletal muscle tissues. Subsequently, we quantified and characterized myofilament proteoforms using online LC-MS. We have comprehensively characterized the fast and slow skeletal troponin I isoforms, which demonstrates the ability of top-down MS to decipher isoforms with high sequence homology. Taken together, we have shown that top-down proteomics can be used as a robust and high-throughput method to characterize the molecular heterogeneity of myofilament proteoforms from various skeletal muscle tissues.

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Comparing Top-Down Proteoform Identification: Deconvolution, PrSM Overlap, and PTM Detection.

Project description:Generating top-down tandem mass spectra (MS/MS) from complex mixtures of proteoforms benefits from improvements in fractionation, separation, fragmentation, and mass analysis. The algorithms to match MS/MS to sequences have undergone a parallel evolution, with both spectral alignment and match-counting approaches producing high-quality proteoform-spectrum matches (PrSMs). This study assesses state-of-the-art algorithms for top-down identification (ProSight PD, TopPIC, MSPathFinderT, and pTop) in their yield of PrSMs while controlling false discovery rate. We evaluated deconvolution engines (ThermoFisher Xtract, Bruker AutoMSn, Matrix Science Mascot Distiller, TopFD, and FLASHDeconv) in both ThermoFisher Orbitrap-class and Bruker maXis Q-TOF data (PXD033208) to produce consistent precursor charges and mass determinations. Finally, we sought post-translational modifications (PTMs) in proteoforms from bovine milk (PXD031744) and human ovarian tissue. Contemporary identification workflows produce excellent PrSM yields, although approximately half of all identified proteoforms from these four pipelines were specific to only one workflow. Deconvolution algorithms disagree on precursor masses and charges, contributing to identification variability. Detection of PTMs is inconsistent among algorithms. In bovine milk, 18% of PrSMs produced by pTop and TopMG were singly phosphorylated, but this percentage fell to 1% for one algorithm. Applying multiple search engines produces more comprehensive assessments of experiments. Top-down algorithms would benefit from greater interoperability.

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Proteoform Identification by Combining RNA-Seq and Top-Down Mass Spectrometry.

Project description:In proteogenomic studies, genomic and transcriptomic variants are incorporated into customized protein databases for the identification of proteoforms, especially proteoforms with sample-specific variants. Most proteogenomic research has been focused on combining genomic or transcriptomic data with bottom-up mass spectrometry data. In the last decade, top-down mass spectrometry has attracted increasing attention because of its capacity to identify various proteoforms with alterations. However, top-down proteogenomics, in which genomic or transcriptomic data are combined with top-down mass spectrometry data, has not been widely adopted, and there is still a lack of software tools for top-down proteogenomic data analysis. In this paper, we introduce TopPG, a proteogenomic tool for generating proteoform sequence databases with genetic alterations and alternative splicing events. Experiments on top-down proteogenomic data of DLD-1 colorectal cancer cells showed that TopPG coupled with database search confidently identified proteoforms with sample-specific alterations.

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Distinct hypertrophic cardiomyopathy genotypes result in convergent sarcomeric proteoform profiles revealed by top-down proteomics.

Project description:Hypertrophic cardiomyopathy (HCM) is the most common heritable heart disease. Although the genetic cause of HCM has been linked to mutations in genes encoding sarcomeric proteins, the ability to predict clinical outcomes based on specific mutations in HCM patients is limited. Moreover, how mutations in different sarcomeric proteins can result in highly similar clinical phenotypes remains unknown. Posttranslational modifications (PTMs) and alternative splicing regulate the function of sarcomeric proteins; hence, it is critical to study HCM at the level of proteoforms to gain insights into the mechanisms underlying HCM. Herein, we employed high-resolution mass spectrometry-based top-down proteomics to comprehensively characterize sarcomeric proteoforms in septal myectomy tissues from HCM patients exhibiting severe outflow track obstruction (n = 16) compared to nonfailing donor hearts (n = 16). We observed a complex landscape of sarcomeric proteoforms arising from combinatorial PTMs, alternative splicing, and genetic variation in HCM. A coordinated decrease of phosphorylation in important myofilament and Z-disk proteins with a linear correlation suggests PTM cross-talk in the sarcomere and dysregulation of protein kinase A pathways in HCM. Strikingly, we discovered that the sarcomeric proteoform alterations in the myocardium of HCM patients undergoing septal myectomy were remarkably consistent, regardless of the underlying HCM-causing mutations. This study suggests that the manifestation of severe HCM coalesces at the proteoform level despite distinct genotype, which underscores the importance of molecular characterization of HCM phenotype and presents an opportunity to identify broad-spectrum treatments to mitigate the most severe manifestations of this genetically heterogenous disease.

| S-EPMC7547245 | biostudies-literature

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