Identification Of Proteoforms Research Articles

Identification of Splice Variants and Isoforms in Transcriptomics and Proteomics.

Alternative splicing is pivotal to the regulation of gene expression and protein diversity in eukaryotic cells. The detection of alternative splicing events requires specific omics technologies. Although short-read RNA sequencing has successfully supported a plethora of investigations on alternative splicing, the emerging technologies of long-read RNA sequencing and top-down mass spectrometry open new opportunities to identify alternative splicing and protein isoforms with less ambiguity. Here, we summarize improvements in short-read RNA sequencing for alternative splicing analysis, including percent splicing index estimation and differential analysis. We also review the computational methods used in top-down proteomics analysis regarding proteoform identification, including the construction of databases of protein isoforms and statistical analyses of search results. While many improvements in sequencing and computational methods will result from emerging technologies, there should be future endeavors to increase the effectiveness, integration, and proteome coverage of alternative splicing events.

Characterization of charge variants, including post-translational modifications and proteoforms, of bispecific antigen-binding protein by cation-exchange chromatography coupled to native mass spectrometry

Charge variant characterization of biologics is critical to ensure that product meets the required quality and regulatory requirements to ensure safety and efficacy of the biotherapeutic. Charge variants arise from post-translation modifications (PTMs) during upstream processing and due to enzymatic and non-enzymatic chemical reactions that occur during downstream processing and storage. Some of these modifications may impact therapeutic potency, efficacy, or immunogenicity of a biotherapeutic. The traditional workflow for characterizing charge variants that involves fraction enrichment is time-consuming and labor-intensive. This approach can be especially challenging if the product is manufactured at low concentrations (e.g., ≤2 mg/mL). Recent advances in pH-based elution for ion-exchange chromatography utilizing volatile buffers have enabled rapid native mass-spectrometry-based identification of PTMs and proteoforms associated with protein therapeutics. In this study, we develop a novel workflow to rapidly and unambiguously characterize modifications associated with a new class of biotherapeutics known as bispecific antigen-binding protein (BsABP), including low-level modifications. A cation-exchange separation was optimized using volatile buffers to provide online hyphenation for native mass spectrometry to profile modifications and proteoforms present at the native level of a biotherapeutic, such as deamidation, O-glycosylation, amino acid substitution, N-linked glycosylation and oxidation. Furthermore, a limited proteolysis method was developed to specifically inform about modifications in the different domains of the bispecific antibody. Using this approach, we could efficiently identify PTMs in unstressed, thermally and photo-stressed samples, and provide information about the impact of downstream purification in clearing out modified BsABP species. Furthermore, peptide mapping was performed to identify and confirm modifications at the amino acid residue level. The developed workflow is less time-consumable and reduces sample processing- and analysis-related artifacts compared to traditional approaches.

Enzyme-less nanopore detection of post-translational modifications within long polypeptides

Means to analyse cellular proteins and their millions of variants at the single-molecule level would uncover substantial information previously unknown to biology. Nanopore technology, which underpins long-read DNA and RNA sequencing, holds potential for full-length proteoform identification. We use electro-osmosis in an engineered charge-selective nanopore for the non-enzymatic capture, unfolding and translocation of individual polypeptides of more than 1,200 residues. Unlabelled thioredoxin polyproteins undergo transport through the nanopore, with directional co-translocational unfolding occurring unit by unit from either the C or N terminus. Chaotropic reagents at non-denaturing concentrations accelerate the analysis. By monitoring the ionic current flowing through the nanopore, we locate post-translational modifications deep within the polypeptide chains, laying the groundwork for compiling inventories of the proteoforms in cells and tissues.

Sodium chloride impacts glycosylation and N- and O-glycan site occupancy of an Fc-fusion protein.

Fc-fusion proteins are highly complex molecules, difficult to manufacture at scale. In this work, undesired proteoforms were detected during the manufacture of a therapeutic fusion protein produced in CHO cells. These species were characterized using gel electrophoresis, size exclusion chromatography and liquid chromatography-mass spectrometry leading to the identification of low molecular weight proteoforms presenting low N- and O-glycan site occupancy, as well as a low sialylation content. Upstream process parameters were investigated, and fusion protein quality was shown to be linked to the sodium chloride content of the medium. A mitigation strategy was developed to avoid formation of unwanted glyco-variants, resulting in an increased yield of highly glycosylated Fc-fusion protein. The effect of sodium chloride was shown to be independent of the osmolality increase and was hypothesized to be linked to a modulation of Golgi acidity, which is required for the correct localization and function of glycosyltransferases. Altogether, this study highlights the importance of the salt balance in cell culture media used to produce highly sialylated and occupied glycoproteins, helping to maximize the yield and increase robustness of processes aiming at producing biopharmaceutical complex therapeutic molecules.

Full Separation and Sequencing of Isomeric Proteoforms in the Middle-Down Mass Range Using Cyclic Ion Mobility and Electron Capture Dissociation.

Unambiguous identification of distinct proteoforms and their biological functions is a significant analytical challenge due to the many combinations of post-translational modifications (PTM) that generate isomeric proteoforms. Resulting chimeric tandem mass spectra hinder detailed structural characterization of individual proteoforms for mixtures with more than two isomers. Large isomeric peptides and intact isomeric proteins are extremely difficult to distinguish with traditional chromatographic separation methods. Gas-phase ion separation techniques such as ion mobility spectrometry (IMS) methods now offer high resolving power that may enable separation of isomeric biomolecules, such as peptides and proteins. We explored novel high-resolution cyclic ion mobility spectrometry (cIM) combined with an electro-magnetostatic cell for "on-the-fly" electron capture dissociation (ECD) for separation and sequencing of large isomeric peptides. We demonstrate the effectiveness of this approach on ternary mixtures of mono- and trimethylated isomers of histone H3 N-tails (∼5.4 kDa), achieving a complete separation of these isomers with an average resolving power of ∼400 and a resolution of 1.5 and with nearly 100% amino acid sequence coverage. Our results demonstrate the potential of the cIM-MS/MS(ECD) technology to enhance middle-down and top-down proteomics workflows, thereby facilitating the identification of near-identical proteoforms with essential biological functions in complex mixtures.

Network-Based Prediction of Side Effects of Repurposed Antihypertensive Sartans against COVID-19 via Proteome and Drug-Target Interactomes.

The potential of targeting the Renin-Angiotensin-Aldosterone System (RAAS) as a treatment for the coronavirus disease 2019 (COVID-19) is currently under investigation. One way to combat this disease involves the repurposing of angiotensin receptor blockers (ARBs), which are antihypertensive drugs, because they bind to angiotensin-converting enzyme 2 (ACE2), which in turn interacts with the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein. However, there has been no in silico analysis of the potential toxicity risks associated with the use of these drugs for the treatment of COVID-19. To address this, a network-based bioinformatics methodology was used to investigate the potential side effects of known Food and Drug Administration (FDA)-approved antihypertensive drugs, Sartans. This involved identifying the human proteins targeted by these drugs, their first neighbors, and any drugs that bind to them using publicly available experimentally supported data, and subsequently constructing proteomes and protein-drug interactomes. This methodology was also applied to Pfizer's Paxlovid, an antiviral drug approved by the FDA for emergency use in mild-to-moderate COVID-19 treatment. The study compares the results for both drug categories and examines the potential for off-target effects, undesirable involvement in various biological processes and diseases, possible drug interactions, and the potential reduction in drug efficiency resulting from proteoform identification.

MASH Native: a unified solution for native top-down proteomics data processing.

Native top-down proteomics (nTDP) integrates native mass spectrometry (nMS) with top-down proteomics (TDP) to provide comprehensive analysis of protein complexes together with proteoform identification and characterization. Despite significant advances in nMS and TDP software developments, a unified and user-friendly software package for analysis of nTDP data remains lacking. We have developed MASH Native to provide a unified solution for nTDP to process complex datasets with database searching capabilities in a user-friendly interface. MASH Native supports various data formats and incorporates multiple options for deconvolution, database searching, and spectral summing to provide a "one-stop shop" for characterizing both native protein complexes and proteoforms. The MASH Native app, video tutorials, written tutorials, and additional documentation are freely available for download at https://labs.wisc.edu/gelab/MASH_Explorer/MASHSoftware.php. All data files shown in user tutorials are included with the MASH Native software in the download .zip file.

Multiplex in‐vivo quantification of tau by mass spectrometry: Method optimization for comprehensive detection of tau proteoforms and biomarker identification

AbstractBackgroundNeurodegenerative diseases diagnosis is still challenging, especially at the early stages. The diagnosis of Alzheimer’s disease (AD) relies on the identification of tau proteoforms (i.e., phosphorylation) in the cerebrospinal fluid (CSF) and plasma. However, pathophysiological fluid biomarkers are still lacking for non‐AD tauopathies. Multiplex liquid chromatography (LC)‐mass spectrometry (MS/MS) offers the opportunity to quantify the vast diversity of tau proteoforms (3R and 4R isoforms, truncations, phosphorylations, acetylations). The current gold standard for tau extraction before LC‐MS/MS analysis of very low concentrations in biofluids relies on immunoprecipitation (IP) approaches. Nonetheless, IP may miss some proteoforms due to its high selectivity. Here, we tested anti‐body free and IP approaches and compared their ability to detect tau proteoforms with LC‐MS/MS.MethodWe analyzed samples of artificial CSF (recombinant tau protein in diluted serum), and human CSF (from AD and non‐AD patients). Fully 15N‐labeled tau was added as an internal standard to the samples. Regarding IP, we first assessed a panel of commercial tau antibodies (in line with the literature) coated on magnetic beads. For the antibody‐free approach, we performed perchloric acid precipitation, followed by micro‐Solid Phase Extraction (Pc‐µSPE) using Oasis hydrophilic‐lipophilic balance sorbent. Purified proteins were digested into peptides using trypsin before multiplex quantitative analysis on reversed‐phase capillary LC, coupled with a qExactive high‐resolution mass spectrometer operated in the Parallel Reaction Monitoring (PRM) acquisition mode.ResultIP optimization led to a combination of antibodies targeting the central and N‐ter domains of tau. IP and Pc‐µSPE comparison in artificial CSF showed a higher sensitivity of IP: limits of detection decreased up to a factor 5. In the human CSF, Pc‐µSPE allowed for better detection of peptides from the N‐ter to the C‐ter than IP, and a higher signal for tau proteoforms (e.g., 3R and 4R isoforms and phosphorylated tau species).ConclusionDespite better performances when dealing with canonical tau, IP was overcome by the antibody‐free sample preparation when identifying tau proteoforms with LC‐MS/MS in human CSF. The protocol is readily applicable to samples of clinical cohorts. This result opens the possibility of better detecting tau proteoforms and identifying pathophysiological biomarkers for non‐AD tauopathies.

Digital Microfluidics and Magnetic Bead-Based Intact Proteoform Elution for Quantitative Top-down Nanoproteomics of Single C. elegans Nematodes.

While most nanoproteomics approaches for the analysis of low-input samples are based on bottom-up proteomics workflows, top-down approaches enabling proteoform characterization are still underrepresented. Using mammalian cell proteomes, we established a facile one-pot sample preparation protocol based on protein aggregation on magnetic beads and intact proteoform elution using 40 % formic acid. Performed on a digital microfluidics device, the workflow enabled sensitive analyses of single Caenorhabditis elegans nematodes, thereby increasing the number of proteoform identifications compared to in-tube sample preparation by 46 %. Label-free quantification of single nematodes grown under different conditions allowed to identify changes in the abundance of proteoforms not distinguishable by bottom-up proteomics. The presented workflow will facilitate proteoform-directed analysis on samples of limited availability.

Coupling High-Field Asymmetric Waveform Ion Mobility Spectrometry with Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Top-Down Proteomics.

Capillary zone electrophoresis-tandem mass spectrometry (CZE-MS/MS) has emerged as an essential technique for top-down proteomics (TDP), providing superior separation efficiency and high detection sensitivity for proteoform analysis. Here, we aimed to further enhance the performance of CZE-MS/MS for TDP via coupling online gas-phase proteoform fractionation using high-field asymmetric waveform ion mobility spectrometry (FAIMS). When the compensation voltage (CV) of FAIMS was changed from -50 to 30 V, the median mass of identified proteoforms increased from less than 10 kDa to about 30 kDa, suggesting that FAIMS can efficiently fractionate proteoforms by their size. CZE-FAIMS-MS/MS boosted the number of proteoform identifications from a yeast sample by nearly 3-fold relative to CZE-MS/MS alone. It particularly benefited the identification of relatively large proteoforms, improving the number of proteoforms in a mass range of 20-45 kDa by 6-fold compared to CZE-MS/MS alone. FAIMS fractionation gained nearly 20-fold better signal-to-noise ratios of randomly selected proteoforms than no FAIMS. We expect that CZE-FAIMS-MS/MS will be a useful tool for further advancing the sensitivity and coverage of TDP. This work shows the first example of coupling CE with ion mobility spectrometry (IMS) for TDP.

Elution von intakten Proteoformen von magnetischen Partikeln kombiniert mit digitaler Mikrofluidik für die quantitative Top‐down‐Nanoproteomik von einzelnen C. elegans‐Nematoden

AbstractWährend die meisten Ansätze zur Analyse von geringen Probemengen durch Nanoproteomik auf der bottom‐up‐Strategie basieren, sind top‐down‐Ansätze zur Charakterisierung von Proteoformen nach wie vor unterrepräsentiert. An Proteomen von Säugerzellen haben wir eine einfache Probenvorbereitungsmethode etabliert, welche auf Proteinaggregation an magnetischen Partikeln und anschließender Elution intakter Proteoformen durch 40 %ige Ameisensäure basiert. Die Implementierung der Methode auf einer Plattform für digitale Mikrofluidik ermöglichte die sensitive Analyse von einzelnen Individuen des Nematoden Caenorhabditis elegans. Die Anzahl identifizierter Proteoformen konnte dabei im Vergleich zu herkömmlicher Probenvorbereitung in einem Reaktionsgefäß um 46 % erhöht werden. Ferner erlaubte die markierungsfreie (label‐free) Quantifizierung von Proteomen einzelner Nematoden, welche unter verschiedenen Bedingungen kultiviert wurden, Änderungen der Abundanz von Proteoformen zu erkennen, welche in bottom‐up‐Experimenten nicht festgestellt wurden. Die hier präsentierte Methode wird die Proteoform‐zentrische Analytik in Proben mit limitierter Verfügbarkeit vereinfachen.

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

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.

TopFD: A Proteoform Feature Detection Tool for Top-Down Proteomics.

Top-down liquid chromatography-mass spectrometry (LC-MS) analyzes intact proteoforms and generates mass spectra containing peaks of proteoforms with various isotopic compositions, charge states, and retention times. An essential step in top-down MS data analysis is proteoform feature detection, which aims to group these peaks into peak sets (features), each containing all peaks of a proteoform. Accurate protein feature detection enhances the accuracy in MS-based proteoform identification and quantification. Here, we present TopFD, a software tool for top-down MS feature detection that integrates algorithms for proteoform feature detection, feature boundary refinement, and machine learning models for proteoform feature evaluation. We performed extensive benchmarking of TopFD, ProMex, FlashDeconv, and Xtract using seven top-down MS data sets and demonstrated that TopFD outperforms other tools in feature accuracy, reproducibility, and feature abundance reproducibility.

Seeing the complete picture: proteins in top-down mass spectrometry.

Top-down protein mass spectrometry can provide unique insights into protein sequence and structure, including precise proteoform identification and study of protein-ligand and protein-protein interactions. In contrast with the commonly applied bottom-up approach, top-down approaches do not include digestion of the protein of interest into small peptides, but instead rely on the ionization and subsequent fragmentation of intact proteins. As such, it is fundamentally the only way to fully characterize the composition of a proteoform. Here, we provide an overview of how a top-down protein mass spectrometry experiment is performed and point out recent applications from the literature to the reader. While some parts of the top-down workflow are broadly applicable, different research questions are best addressed with specific experimental designs. The most important divide is between studies that prioritize sequence information (i.e., proteoform identification) versus structural information (e.g., conformational studies, or mapping protein-protein or protein-ligand interactions). Another important consideration is whether to work under native or denaturing solution conditions, and the overall complexity of the sample also needs to be taken into account, as it determines whether (chromatographic) separation is required prior to MS analysis. In this review, we aim to provide enough information to support both newcomers and more experienced readers in the decision process of how to answer a potential research question most efficiently and to provide an overview of the methods that exist to answer these questions.

Identification of proteoforms by top-down proteomics using two-dimensional low/low pH reversed-phase liquid chromatography-mass spectrometry.

In top-down (TD) proteomics, efficient proteoform separation is crucial to reduce the sample complexity and increase the depth of the analysis. Here, we developed a two-dimensional low pH/low pH reversed-phase liquid chromatography separation scheme for TD proteomics. The first dimension for offline fractionation was performed using a polymeric reversed-phase (PLRP-S) column with trifluoroacetic acid as ion-pairing reagent. The second dimension, a C4 nanocolumn with formic acid as ion-pairing reagent, was coupled online with a high-field asymmetric ion mobility spectrometry (FAIMS) Orbitrap Tribrid mass spectrometer. For both dimensions several parameters were optimized, such as the adaption of the LC gradients in the second dimension according to the elution time (i.e., fraction number) in the first dimension. Avoidance of elevated temperatures and prolonged exposure to acidic conditions minimized cleavage of acid labile aspartate-proline peptide bonds. Furthermore, a concatenation strategy was developed to reduce the total measurement time. We compared our low/low pH with a previously published high pH (C4, ammonium formate)/low pH strategy and found that both separation strategies led to complementary proteoform identifications, mainly below 20kDa, with a higher number of proteoforms identified by the low/low pH separation. With the optimized separation scheme, more than 4900 proteoforms from 1250 protein groups were identified in Caco-2 cells.

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

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.

The Integration of Data from Different Long-Read Sequencing Platforms Enhances Proteoform Characterization in Arabidopsis.

The increasing availability of massive omics data requires improving the quality of reference databases and their annotations. The combination of full-length isoform sequencing (Iso-Seq) with short-read transcriptomics and proteomics has been successfully used for increasing proteoform characterization, which is a main ongoing goal in biology. However, the potential of including Oxford Nanopore Technologies Direct RNA Sequencing (ONT-DRS) data has not been explored. In this paper, we analyzed the impact of combining Iso-Seq- and ONT-DRS-derived data on the identification of proteoforms in Arabidopsis MS proteomics data. To this end, we selected a proteomics dataset corresponding to senescent leaves and we performed protein searches using three different protein databases: AtRTD2 and AtRTD3, built from the homonymous transcriptomes, regarded as the most complete and up-to-date available for the species; and a custom hybrid database combining AtRTD3 with publicly available ONT-DRS transcriptomics data generated from Arabidopsis leaves. Our results show that the inclusion and combination of long-read sequencing data from Iso-Seq and ONT-DRS into a proteogenomic workflow enhances proteoform characterization and discovery in bottom-up proteomics studies. This represents a great opportunity to further investigate biological systems at an unprecedented scale, although it brings challenges to current protein searching algorithms.

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis.

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

Spatially Resolved Top-Down Proteomics of Tissue Sections Based on a Microfluidic Nanodroplet Sample Preparation Platform

Conventional proteomic approaches measure the averaged signal from mixed cell populations or bulk tissues, leading to the dilution of signals arising from subpopulations of cells that might serve as important biomarkers. Recent developments in bottom-up proteomics have enabled spatial mapping of cellular heterogeneity in tissue microenvironments. However, bottom-up proteomics cannot unambiguously define and quantify proteoforms, which are intact (i.e., functional) forms of proteins capturing genetic variations, alternatively spliced transcripts and posttranslational modifications. Herein, we described a spatially resolved top-down proteomics (TDP) platform for proteoform identification and quantitation directly from tissue sections. The spatial TDP platform consisted of a nanodroplet processing in one pot for trace samples–based sample preparation system and an laser capture microdissection–based cell isolation system. We improved the nanodroplet processing in one pot for trace samples sample preparation by adding benzonase in the extraction buffer to enhance the coverage of nucleus proteins. Using ∼200 cultured cells as test samples, this approach increased total proteoform identifications from 493 to 700; with newly identified proteoforms primarily corresponding to nuclear proteins. To demonstrate the spatial TDP platform in tissue samples, we analyzed laser capture microdissection–isolated tissue voxels from rat brain cortex and hypothalamus regions. We quantified 509 proteoforms within the union of top-down mass spectrometry–based proteoform identification and characterization and TDPortal identifications to match with features from protein mass extractor. Several proteoforms corresponding to the same gene exhibited mixed abundance profiles between two tissue regions, suggesting potential posttranslational modification–specific spatial distributions. The spatial TDP workflow has prospects for biomarker discovery at proteoform level from small tissue sections.

Proteoform Identification in 2D Electrophoresis Maps by Using Isoelectric Point Prediction

The possibility of identifying specific protein proteoforms with post-translational modifications (PTM) by analyzing two-dimensional (2D) gel electrophoresis maps and using the prediction of the isoelectric point of proteins (pI) has been investigated. The pI values were predicted using the pIPredict 3 program, supporting a wide range of chemical and post-translational modifications. Eleven 11 proteins (albumin, alpha-1-microglobulin, annexin A2, apolipoprotein E, gastric triacylglycerol lipase, mitochondrial isocitrate dehydrogenase, clusterin, plasmin, prothrombin, endoplasmic reticulum chaperone, S-adenosylmethionine synthase type 1) identified on six 2D electrophoresis maps were used as examples. Various options for selecting hypotheses are considered. These take into consideration the following available information about a particular protein: possible modification sites, processing features, variability of the amino acid composition. The obtained results indicate that the use of predicting the pI value for proteins with hypothetical PTMs can form a set of hypotheses about specific proteoform occurrence on 2D electrophoresis maps.