Identification Of Intact Proteins Research Articles

Direct Identification of Intact Proteins Using a Low-Resolution Mass Spectrometer with CIDn/ETnoD.

Over the past decades, proteomics has become increasingly important and a heavily discussed topic. The identification of intact proteins remains a major focus in this field. While most intact proteins are analyzed using high-resolution mass spectrometry, identifying them through low-resolution mass spectrometry continues to pose challenges. In our study, we investigated the capability of identifying various intact proteins using collision-induced dissociation (CID) and electron transfer without dissociation (ETnoD). Using myoglobin as our test protein, stable product ions were generated with CID, and the identities of the product ions were identified with ETnoD. ETnoD uses a short activation time (AcT, 5 ms) to create sequential charge-reduced precursor ion (CRI). The charges of the fragments and their sequences were determined with corresponding CRI. The product ions can be selected for subsequent CID (termed CIDn) combined with ETnoD for further sequence identification and validation. We refer to this method as CIDn/ETnoD. The use of a multistage CID activation (CIDn) and ETnoD protocol has been applied to several intact proteins to obtain multiple sequence identifications.

User-friendly platform for analysis of high mass intact proteins and glycopeptides by laser desorption/ionization-mass spectrometry based on copper oxide particles

Matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) based on micro/nanostructured materials with different natures has received increasing attention for the analysis of a wide variety of analytes. However, up to now, only a few studies have shown the application of simple platforms in MALDI-MS for the identification of intact proteins. The present work reports on the application of copper oxide particles (Cu2O PS), obtained by a greener route, in combination with low amounts of 2,5-dihydroxybenzoic acid (DHB) as a novel hybrid platform. The combined Cu2O PS@DHB matrix, containing only 2.5 mg mL−1 of particles and 10 mg mL−1 of DHB, was easily applicable in MALDI-MS without surface modification of target plates. Under optimal conditions, the analysis of intact proteins up to 150,000 Da was possible, including immunoglobulin G, bovine serum albumin, and cytochrome C with adequate spot-to-spot signal reproducibility (RSD < 10%). In addition, the analysis of glycopeptides from IgG digests was carried out to prove the multipurpose application of the Cu2O PS@DHB platform in the low m/z range (2500–3000 Da). From the obtained results, it can be concluded that the optical and surface properties of as-synthesized Cu2O PS are likely to be responsible for the superior performance of Cu2O PS@DHB in comparison with conventional matrices. In this sense, the proposed user-friendly methodology opens up the prospect for possible implementation in bioanalysis and diagnostic research.Graphical

Mass Spectrometry-Based Charge Heterogeneity Characterization of Therapeutic mAbs with Imaged Capillary Isoelectric Focusing and Ion-Exchange Chromatography as Separation Techniques.

Imaged capillary isoelectric focusing (icIEF) and ion-exchange chromatography (IEX) are two essential techniques that are routinely used for charge variant analysis of therapeutic monoclonal antibodies (mAbs) during their development and in quality control. These two techniques that separate mAb charge variants based on different mechanisms and IEX have been developed as front-end separation techniques for online mass spectrometry (MS) detection, which is robust for intact protein identification. Recently, an innovative, coupled icIEF-MS technology has been constructed for protein charge variant analysis in our laboratory. In this study, icIEF-MS developed and strong cation exchange (SCX)-MS were optimized for charge heterogeneity characterization of a diverse of mAbs and their results were compared based on methodological validation. It was found that icIEF-MS outperformed SCX-MS in this study by demonstrating outstanding sensitivity, low carryover effect, accurate protein identification, and higher separation resolution although SCX-MS contributed to higher analysis throughput. Ultimately, integrating our novel icIEF-HRMS analysis with the more common SCX-MS can provide a promising and comprehensive strategy for accelerating the development of complex protein therapeutics.

Collision Cross Sections for Native Proteomics: Challenges and Opportunities.

Recent advancements place a comprehensive catalog of protein structure, oligomeric state, sequence, and modification status tentatively within reach, thus providing an unprecedented roadmap to therapies for many human diseases. To achieve this goal, revolutionary technologies capable of bridging key gaps in our ability to simultaneously measure protein composition and structure must be developed. Much of the current progress in this area has been catalyzed by mass spectrometry (MS) tools, which have become an indispensable resource for interrogating the structural proteome. For example, methods associated with native proteomics seek to comprehensively capture and quantify the endogenous assembly states for all proteins within an organism. Such technologies have often been partnered with ion mobility (IM) separation, from which collision cross section (CCS) information can be rapidly extracted to provide protein size information. IM technologies are also being developed that utilize CCS values to enhance the confidence of protein identification workflows derived from liquid chromatography-IM-MS analyses of enzymatically produced peptide mixtures. Such parallel advancements in technology beg the question: can CCS values prove similarly useful for the identification of intact proteins and their complexes in native proteomics? In this perspective, I examine current evidence and technology trends to explore the promise and limitations of such CCS information for the comprehensive analysis of multiprotein complexes from cellular mixtures.

Proteomic Database Search Engine for Two-Dimensional Partial Covariance Mass Spectrometry.

We present a protein database search engine for the automatic identification of peptide and protein sequences using the recently introduced method of two-dimensional partial covariance mass spectrometry (2D-PC-MS). Because the 2D-PC-MS measurement reveals correlations between fragments stemming from the same or consecutive decomposition processes, the first-of-its-kind 2D-PC-MS search engine is based entirely on the direct matching of the pairs of theoretical and the experimentally detected correlating fragments, rather than of individual fragment signals or their series. We demonstrate that the high structural specificity afforded by 2D-PC-MS fragment correlations enables our search engine to reliably identify the correct peptide sequence, even from a spectrum with a large proportion of contaminant signals. While for peptides, the 2D-PC-MS correlation-matching procedure is based on complementary and internal ion correlations, the identification of intact proteins is entirely based on the ability of 2D-PC-MS to spatially separate and resolve the experimental correlations between complementary fragment ions.

Capillary electrophoresis-mass spectrometry and its application to proteomic analysis

Proteomic analysis plays an important role in basic biological studies and precision medicine. However, real samples contain numerous proteins with a wide dynamic distribution range. Such high complexity of the samples has a drastic effect on the identification coverage of proteins. Consequently, with advancements in mass spectrometry (MS) technology, concomitant improvements in separation technologies for simplifying the sample should be critical. With the advantages of small sample loading volume, high separation efficiency, and high speed, capillary electrophoresis (CE) coupled to MS has been gained much attention in the field of proteomics research. A nanoflow sheath liquid interface and a sheathless interface have been developed and commercialized, boosting the development of the CE-MS technology. Capillary zone electrophoresis (CZE), capillary isoelectric focusing (CIEF), and capillary electrochromatography (CEC) have been successfully combined with MS, and CZE-MS has widespread application. In proteomic research, the "bottom-up" strategy, which is based on the separation and identification of enzymatic peptides, is widely applied. With the limit of detection as low as 1 zmol for peptides, CE-MS has been successfully applied to single-cell proteomic analysis. Besides, CE is complementary to reversed-phase liquid chromatography (RPLC), providing a new approach for the separation and identification of peptides with similar hydrophobic properties (especially, post-translational peptides). The "top-down" strategy, which is based on the separation and identification of intact proteins, can directly provide more accurate and complete information about proteins. For protein separation, CE is advantageous in terms of the high separation resolution and high protein recovery, thereby improving the sensitivity and coverage of protein identification. Native MS enables successful identification and characterization of protein complexes under nondenaturing conditions. Because of the good compatibility of CE with MS, attempts have been made to use CE coupled with native MS for the separation and identification of protein complexes. In this review, the development of the CE-MS technology is first reported, including a robust and sensitive CE-MS interface, and a separation mode coupled to MS. Then, the application of the CE-MS technology to "bottom-up", "top-down" and native MS analysis is discussed. The superiority of CE-MS in proteomic analysis is also emphasized. Finally, the promising future prospects of CE-MS are discussed.

Native Mass Spectrometry Imaging and In Situ Top-Down Identification of Intact Proteins Directly from Tissue.

Mass spectrometry imaging (MSI) provides information on the spatial distribution of molecules within a biological substrate without the requirement for labeling. Its broad specificity, i.e., the capability to spatially profile any analyte ion detected, constitutes a major advantage over other imaging techniques. A separate branch of mass spectrometry, native mass spectrometry, provides information relating to protein structure through retention of solution-phase interactions in the gas phase. Integration of MSI and native mass spectrometry ("native MSI") affords opportunities for simultaneous acquisition of spatial and structural information on proteins directly from their physiological environment. Here, we demonstrate significant improvements in native MSI and associated protein identification of intact proteins and protein assemblies in thin sections of rat kidney by use of liquid extraction surface analysis on a state-of-the-art Orbitrap mass spectrometer optimized for intact protein analysis. Proteins of up to 47 kDa, including a trimeric protein complex, were imaged and identified.

In situ mass spectrometry analysis of intact proteins and protein complexes from biological substrates.

Advances in sample preparation, ion sources and mass spectrometer technology have enabled the detection and characterisation of intact proteins. The challenges associated include an appropriately soft ionisation event, efficient transmission and detection of the often delicate macromolecules. Ambient ion sources, in particular, offer a wealth of strategies for analysis of proteins from solution environments, and directly from biological substrates. The last two decades have seen rapid development in this area. Innovations include liquid extraction surface analysis, desorption electrospray ionisation and nanospray desorption electrospray ionisation. Similarly, developments in native mass spectrometry allow protein–protein and protein–ligand complexes to be ionised and analysed. Identification and characterisation of these large ions involves a suite of hyphenated mass spectrometry techniques, often including the coupling of ion mobility spectrometry and fragmentation techniques. The latter include collision, electron and photon-induced methods, each with their own characteristics and benefits for intact protein identification. In this review, recent developments for in situ protein analysis are explored, with a focus on ion sources and tandem mass spectrometry techniques used for identification.

Ion-Ion Proton Transfer and Parallel Ion Parking for the Analysis of Mixtures of Intact Proteins on a Modified Orbitrap Mass Analyzer

We have enabled parallel ion parking on a modified Orbitrap Elite™ as a way to control ion-ion proton transfer reactions via selective activation of a range of ions. The result is the concentration of the majority of ion current from multiple charge states of each precursor proteoform into a single charge state, maximizing signal intensity and increasing effective sensitivity compared to conventional MS1 spectra. These techniques were applied in an on-line HPLC, data-dependent MS/MS analysis of intact E. coli ribosomal proteins with HCD fragmentation. With one injection, all but two ribosomal proteins were selected for fragmentation and subsequently identified. The techniques described facilitate rapid identification of intact proteins in complex mixtures and an enhanced ability to observe proteins of low abundance.

Bridged Hybrid Monolithic Column Coupled to High-Resolution Mass Spectrometry for Top-Down Proteomics.

Top-down mass spectrometry (MS)-based proteomics has become a powerful tool for comprehensive characterization of intact proteins. However, because of the high complexity of the proteome, highly effective separation of intact proteins from complex mixtures prior to MS analysis remains challenging. Monolithic columns have shown great promise for intact protein separation due to their high permeability, low backpressure, and fast mass transfer. Herein, for the first time, we developed bridged hybrid bis(triethoxysilyl)ethylene (BTSEY) monolith with C8 functional groups (C8@BTSEY) for highly effective protein separation and coupled it to high-resolution MS for identification of intact proteins from complex protein mixtures. We have optimized mobile phase conditions of our monolith-based reverse-phase chromatography (RPC) for online liquid chromatography (LC)-MS analysis and evaluated separation reproducibility of the C8@BTSEY column. We further assessed the chromatographic performance of this column by separating a complex protein mixture extracted from swine heart tissue. Using our monolithic column (i.d. 100 μm × 35 cm), we separated over 300 proteoforms (up to 104 kDa) from 360 ng of protein mixture in an 80 min one-dimensional (1D) LC run. The highly effective separation and recovery of intact proteins from this monolithic column allowed unambiguous identification of ∼100 proteoforms including a large protein, αactinin2 (103.77 kDa), by online 1D LC-MS/MS analysis for the first time. As demonstrated, this C8@BTSEY column is reproducible and effective in separation of intact proteins, which shows high promise for top-down proteomics.

Origin and Prediction of Highly Specific Bond Cleavage Sites in the Thermal Activation of Intact Protein Ions.

Predicting the fragmentation patterns of proteins would be beneficial for the reliable identification of intact proteins by mass spectrometry. However, the ability to accurately make such predictions remains elusive. An approach to predict the specific cleavage sites in whole proteins resulting from collision-induced dissociation by use of an improved electrostatic model for calculating the proton configurations of highly-charged protein ions is reported. Using ubiquitin, cytochrome c, lysozyme and β-lactoglobulin as prototypical proteins, this approach can be used to predict the fragmentation patterns of intact proteins. For sufficiently highly charged proteins, specific cleavages occur near the first low-basicity amino acid residues that are protonated with increasing charge state. Hybrid QM/QM' (QM=quantum mechanics) and molecular dynamics (MD) simulations and energy-resolved collision-induced dissociation measurements indicated that the barrier to the specific dissociation of the protonated amide backbone bond is significantly lower than competitive charge remote fragmentation. Unlike highly charged peptides, the protons at low-basicity sites in highly charged protein ions can be confined to a limited sequence of low-basicity amino acid residues by electrostatic repulsion, which results in highly specific fragmentation near the site of protonation. This research suggests that the optimal charge states to form specific sequence ions of intact proteins in higher abundances than the use of less specific ion dissociation methods can be predicted a priori.

Maximizing Selective Cleavages at Aspartic Acid and Proline Residues for the Identification of Intact Proteins.

A new approach for the identification of intact proteins has been developed that relies on the generation of relatively few abundant products from specific cleavage sites. This strategy is intended to complement standard approaches that seek to generate many fragments relatively non-selectively. Specifically, this strategy seeks to maximize selective cleavage at aspartic acid and proline residues via collisional activation of precursor ions formed via electrospray ionization (ESI) under denaturing conditions. A statistical analysis of the SWISS-PROT database was used to predict the number of arginine residues for a given intact protein mass and predict a m/z range where the protein carries a similar charge to the number of arginine residues thereby enhancing cleavage at aspartic acid residues by limiting proton mobility. Cleavage at aspartic acid residues is predicted to be most favorable in the m/z range of 1500-2500, a range higher than that normally generated by ESI at low pH. Gas-phase proton transfer ion/ion reactions are therefore used for precursor ion concentration from relatively high charge states followed by ion isolation and subsequent generation of precursor ions within the optimal m/z range via a second proton transfer reaction step. It is shown that the majority of product ion abundance is concentrated into cleavages C-terminal to aspartic acid residues and N-terminal to proline residues for ions generated by this process. Implementation of a scoring system that weights both ion fragment type and ion fragment area demonstrated identification of standard proteins, ranging in mass from 8.5 to 29.0kDa. Graphical Abstract ᅟ.

Combined Mass Spectrometry Imaging and Top-down Microproteomics Reveals Evidence of a Hidden Proteome in Ovarian Cancer

BackgroundRecently, it was demonstrated that proteins can be translated from alternative open reading frames (altORFs), increasing the size of the actual proteome. Top-down mass spectrometry-based proteomics allows the identification of intact proteins containing post-translational modifications (PTMs) as well as truncated forms translated from reference ORFs or altORFs. MethodsTop-down tissue microproteomics was applied on benign, tumor and necrotic-fibrotic regions of serous ovarian cancer biopsies, identifying proteins exhibiting region-specific cellular localization and PTMs. The regions of interest (ROIs) were determined by MALDI mass spectrometry imaging and spatial segmentation. FindingsAnalysis with a customized protein sequence database containing reference and alternative proteins (altprots) identified 15 altprots, including alternative G protein nucleolar 1 (AltGNL1) found in the tumor, and translated from an altORF nested within the GNL1 canonical coding sequence. Co-expression of GNL1 and altGNL1 was validated by transfection in HEK293 and HeLa cells with an expression plasmid containing a GNL1-FLAG(V5) construct. Western blot and immunofluorescence experiments confirmed constitutive co-expression of altGNL1-V5 with GNL1-FLAG. ConclusionsTaken together, our approach provides means to evaluate protein changes in the case of serous ovarian cancer, allowing the detection of potential markers that have never been considered.

Single-molecule protein identification by sub-nanopore sensors.

Recent advances in top-down mass spectrometry enabled identification of intact proteins, but this technology still faces challenges. For example, top-down mass spectrometry suffers from a lack of sensitivity since the ion counts for a single fragmentation event are often low. In contrast, nanopore technology is exquisitely sensitive to single intact molecules, but it has only been successfully applied to DNA sequencing, so far. Here, we explore the potential of sub-nanopores for single-molecule protein identification (SMPI) and describe an algorithm for identification of the electrical current blockade signal (nanospectrum) resulting from the translocation of a denaturated, linearly charged protein through a sub-nanopore. The analysis of identification p-values suggests that the current technology is already sufficient for matching nanospectra against small protein databases, e.g., protein identification in bacterial proteomes.

In-depth analysis of protein inference algorithms using multiple search engines and well-defined metrics

In mass spectrometry-based shotgun proteomics, protein identifications are usually the desired result. However, most of the analytical methods are based on the identification of reliable peptides and not the direct identification of intact proteins. Thus, assembling peptides identified from tandem mass spectra into a list of proteins, referred to as protein inference, is a critical step in proteomics research. Currently, different protein inference algorithms and tools are available for the proteomics community. Here, we evaluated five software tools for protein inference (PIA, ProteinProphet, Fido, ProteinLP, MSBayesPro) using three popular database search engines: Mascot, X!Tandem, and MS-GF+. All the algorithms were evaluated using a highly customizable KNIME workflow using four different public datasets with varying complexities (different sample preparation, species and analytical instruments). We defined a set of quality control metrics to evaluate the performance of each combination of search engines, protein inference algorithm, and parameters on each dataset. We show that the results for complex samples vary not only regarding the actual numbers of reported protein groups but also concerning the actual composition of groups. Furthermore, the robustness of reported proteins when using databases of differing complexities is strongly dependant on the applied inference algorithm. Finally, merging the identifications of multiple search engines does not necessarily increase the number of reported proteins, but does increase the number of peptides per protein and thus can generally be recommended. SignificanceProtein inference is one of the major challenges in MS-based proteomics nowadays. Currently, there are a vast number of protein inference algorithms and implementations available for the proteomics community. Protein assembly impacts in the final results of the research, the quantitation values and the final claims in the research manuscript. Even though protein inference is a crucial step in proteomics data analysis, a comprehensive evaluation of the many different inference methods has never been performed. Previously Journal of proteomics has published multiple studies about other benchmark of bioinformatics algorithms (PMID: 26585461; PMID: 22728601) in proteomics studies making clear the importance of those studies for the proteomics community and the journal audience.This manuscript presents a new bioinformatics solution based on the KNIME/OpenMS platform that aims at providing a fair comparison of protein inference algorithms (https://github.com/KNIME-OMICS). Six different algorithms - ProteinProphet, MSBayesPro, ProteinLP, Fido and PIA- were evaluated using the highly customizable workflow on four public datasets with varying complexities. Five popular database search engines Mascot, X!Tandem, MS-GF+ and combinations thereof were evaluated for every protein inference tool. In total >186 proteins lists were analyzed and carefully compare using three metrics for quality assessments of the protein inference results: 1) the numbers of reported proteins, 2) peptides per protein, and the 3) number of uniquely reported proteins per inference method, to address the quality of each inference method. We also examined how many proteins were reported by choosing each combination of search engines, protein inference algorithms and parameters on each dataset.The results show that using 1) PIA or Fido seems to be a good choice when studying the results of the analyzed workflow, regarding not only the reported proteins and the high-quality identifications, but also the required runtime. 2) Merging the identifications of multiple search engines gives almost always more confident results and increases the number of peptides per protein group. 3) The usage of databases containing not only the canonical, but also known isoforms of proteins has a small impact on the number of reported proteins. The detection of specific isoforms could, concerning the question behind the study, compensate for slightly shorter reports using the parsimonious reports. 4) The current workflow can be easily extended to support new algorithms and search engine combinations.

PTop 1.0: A High-Accuracy and High-Efficiency Search Engine for Intact Protein Identification.

There has been tremendous progress in top-down proteomics (TDP) in the past 5 years, particularly in intact protein separation and high-resolution mass spectrometry. However, bioinformatics to deal with large-scale mass spectra has lagged behind, in both algorithmic research and software development. In this study, we developed pTop 1.0, a novel software tool to significantly improve the accuracy and efficiency of mass spectral data analysis in TDP. The precursor mass offers crucial clues to infer the potential post-translational modifications co-occurring on the protein, the reliability of which relies heavily on its mass accuracy. Concentrating on detecting the precursors more accurately, a machine-learning model incorporating a variety of spectral features was trained online in pTop via a support vector machine (SVM). pTop employs the sequence tags extracted from the MS/MS spectra and a dynamic programming algorithm to accelerate the search speed, especially for those spectra with multiple post-translational modifications. We tested pTop on three publicly available data sets and compared it with ProSight and MS-Align+ in terms of its recall, precision, running time, and so on. The results showed that pTop can, in general, outperform ProSight and MS-Align+. pTop recalled 22% more correct precursors, although it exported 30% fewer precursors than Xtract (in ProSight) from a human histone data set. The running speed of pTop was about 1 to 2 orders of magnitude faster than that of MS-Align+. This algorithmic advancement in pTop, including both accuracy and speed, will inspire the development of other similar software to analyze the mass spectra from the entire proteins.

A new scoring function for top-down spectral deconvolution.

BackgroundTop-down mass spectrometry plays an important role in intact protein identification and characterization. Top-down mass spectra are more complex than bottom-up mass spectra because they often contain many isotopomer envelopes from highly charged ions, which may overlap with one another. As a result, spectral deconvolution, which converts a complex top-down mass spectrum into a monoisotopic mass list, is a key step in top-down spectral interpretation.ResultsIn this paper, we propose a new scoring function, L-score, for evaluating isotopomer envelopes. By combining L-score with MS-Deconv, a new software tool, MS-Deconv+, was developed for top-down spectral deconvolution. Experimental results showed that MS-Deconv+ outperformed existing software tools in top-down spectral deconvolution.ConclusionsL-score shows high discriminative ability in identification of isotopomer envelopes. Using L-score, MS-Deconv+ reports many correct monoisotopic masses missed by other software tools, which are valuable for proteoform identification and characterization.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1140) contains supplementary material, which is available to authorized users.

Top‐down mass spectrometry imaging of intact proteins by laser ablation ESI FT‐ICR MS

Laser ablation ESI (LAESI) is a recent development in MS imaging. It has been shown that lipids and small metabolites can be imaged in various samples such as plant material, tissue sections or bacterial colonies without any sample pretreatment. Further, LAESI has been shown to produce multiply charged protein ions from liquids or solid surfaces. This presents a means to address one of the biggest challenges in MS imaging; the identification of proteins directly from biological tissue surfaces. Such identification is hindered by the lack of multiply charged proteins in common MALDI ion sources and the difficulty of performing tandem MS on such large, singly charged ions. We present here top-down identification of intact proteins from tissue with a LAESI ion source combined with a hybrid ion-trap FT-ICR mass spectrometer. The performance of the system was first tested with a standard protein with electron capture dissociation and infrared multiphoton dissociation fragmentation to prove the viability of LAESI FT-ICR for top-down proteomics. Finally, the imaging of a tissue section was performed, where a number of intact proteins were measured and the hemoglobin α chain was identified directly from tissue using CID and infrared multiphoton dissociation fragmentation.

Native SILAC: Metabolic Labeling of Proteins in Prototroph Microorganisms Based on Lysine Synthesis Regulation

Mass spectrometry (MS)-based quantitative proteomics has matured into a methodology able to detect and quantitate essentially all proteins of model microorganisms, allowing for unprecedented depth in systematic protein analyses. The most accurate quantitation approaches currently require lysine auxotrophic strains, which precludes analysis of most existing mutants, strain collections, or commercially important strains (e.g. those used for brewing or for the biotechnological production of metabolites). Here, we used MS-based proteomics to determine the global response of prototrophic yeast and bacteria to exogenous lysine. Unexpectedly, down-regulation of lysine synthesis in the presence of exogenous lysine is achieved via different mechanisms in different yeast strains. In each case, however, lysine in the medium down-regulates its biosynthesis, allowing for metabolic proteome labeling with heavy-isotope-containing lysine. This strategy of native stable isotope labeling by amino acids in cell culture (nSILAC) overcomes the limitations of previous approaches and can be used for the efficient production of protein standards for absolute SILAC quantitation in model microorganisms. As proof of principle, we have used nSILAC to globally analyze yeast proteome changes during salt stress.

On the utility of predictive chromatography to complement mass spectrometry based intact protein identification

The amino acid sequence determines the individual protein three-dimensional structure and its functioning in an organism. Therefore, "reading" a protein sequence and determining its changes due to mutations or post-translational modifications is one of the objectives of proteomic experiments. The commonly utilized approach is gradient high-performance liquid chromatography (HPLC) in combination with tandem mass spectrometry. While serving as a way to simplify the protein mixture, the liquid chromatography may be an additional analytical tool providing complementary information about the protein structure. Previous attempts to develop "predictive" HPLC for large biomacromolecules were limited by empirically derived equations based purely on the adsorption mechanisms of the retention and applicable to relatively small polypeptide molecules. A mechanism of the large biomacromolecule retention in reversed-phase gradient HPLC was described recently in thermodynamics terms by the analytical model of liquid chromatography at critical conditions (BioLCCC). In this work, we applied the BioLCCC model to predict retention of the intact proteins as well as their large proteolytic peptides separated under different HPLC conditions. The specific aim of these proof-of-principle studies was to demonstrate the feasibility of using "predictive" HPLC as a complementary tool to support the analysis of identified intact proteins in top-down, middle-down, and/or targeted selected reaction monitoring (SRM)-based proteomic experiments.