Proteome Discovery Workflow: Techniques for Protein Identification and Characterization

Proteomics, the large-scale study of proteins, provides a fundamental basis for investigating cellular function and disease mechanisms, and for identifying biomarkers and therapeutic targets. The proteomic discovery workflow encompasses the complete systematized procedure for identifying and quantifying proteins and determining their properties within a biological system. The complete workflow uses both experimental methods as well as computational and analytical techniques to gain an understanding of the dynamic processes within cells.

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Introduction to Proteomics and Its Importance

Proteins are the functional workhorses of the cell, carrying out numerous biological tasks such as enzymatic reactions, providing structural support, transporting molecules, defending against pathogens, and transmitting signals. Understanding cellular mechanisms at the molecular level depends on proteomics, which focuses on the study of proteins and their functions. The genome is stable, but the proteome is highly dynamic as it adapts to environmental changes and cellular and physiological variations.

The field of proteomics serves as a fundamental component of biomedical research, advancing drug discovery and disease diagnostics, and driving innovation in biotechnology. The analysis of proteins, along with their modifications and interactions, provides researchers with valuable information about cellular pathways, biomarker identification, and understanding of disease at the molecular level. The combination of mass spectrometry, chromatography and bioinformatics technologies enables the detailed study of protein expression patterns along with their modifications and interactions, leading to the development of new therapeutic approaches.

Proteome Complexity

The proteome, the complete set of proteins expressed by a genome in a given cell or organism at a given time, is immensely complex. Unlike the relatively stable genome, the proteome is dynamic, responding to physiological conditions, environmental cues, and developmental stages. The complexity of the proteome results from several factors, each of which contributes to the diversity and functional variability of proteins.

• Alternative Splicing: A single gene can produce multiple protein isoforms through alternative splicing of pre-mRNA. This expands the functional repertoire of proteins beyond the coding capacity of the genome.
• Post-Translational Modifications (PTMs): Proteins undergo modifications such as phosphorylation, glycosylation, ubiquitination, and acetylation after translation. These modifications influence protein activity, localization, stability, and interactions.
• Protein-Protein Interactions: Proteins rarely act alone. Complex networks of interactions form multiprotein assemblies, significantly broadening functional possibilities.
• Proteolytic Processing: Some proteins are synthesized as precursors (zymogens or proproteins) and activated by cleavage, adding another layer to functional regulation.
• Tissue- and Cell-Specific Expression: Proteome composition varies across cell types and tissues, reflecting their unique functions and requirements.
• Temporal Regulation: Proteins are expressed and degraded in a tightly regulated manner, enabling precise responses to temporal cues like circadian rhythms or developmental stages.

Figure 1. The increase in complexity from the genome to the proteome. (Virág et al., 2020)

Four Main Aspects of Proteomics

Proteomics is generally divided into four main areas of study: sequence proteomics, structural proteomics, functional and interaction proteomics, and expression proteomics, each of which focuses on different protein properties such as structure, function, and abundance.

• Sequence Proteomics: This field focuses on the determination of amino acid sequences in proteins. Traditionally, Edman sequencing has been used, which uses chemical tagging and chromatographic methods to sequentially identify amino acids. While highly sensitive, it is labor intensive and unsuitable for complex protein mixtures, prompting the adoption of high-throughput systems such as mass spectrometry (MS).
• Structural Proteomics: This area studies protein structures to infer function using techniques such as protein crystallization, X-ray diffraction, nuclear magnetic resonance (NMR), and electron microscopy. Computational modeling also complements experimental methods to reveal three-dimensional protein configurations.
• Functional and Interaction Proteomics: This area studies protein functions, activities, and interactions with other proteins, DNA, ligands, or substrates. Techniques such as yeast one/two-hybrid systems and protein microarrays are used to characterize interactions and post-translational modifications. However, these approaches are often targeted and require prior knowledge of the interacting molecules.
• Expression Proteomics: Also known as discovery-based proteomics, this approach assesses global protein expression in a sample. High-throughput MS enables comprehensive identification and quantification of proteins, facilitating the study of protein composition and biomarker discovery. Recent advancements have produced a draft map of the human proteome, highlighting its utility in large-scale, system-level analyses.

Figure 2. Proteomics can be distinguished into four main aspects which are sequence, structural, functional and interaction as well as expression studies. Each of this aspect is given examples as detailed in text. Surface-enhanced laser desorption/ionization (SELDI), enzyme-linked immuno- sorbent assay (ELISA), surface plasmon resonance (SPR), nano-liquid chromatography-mass spectrometry (nano-LC-MS). (Aizat and Hassan, 2018)

Proteomics—Bridge Between Genotype and Phenotype

Proteomics plays a pivotal role in bridging the gap between genotype and phenotype, offering profound insights into the complex biological processes that link genetic information to observable traits. By systematically studying proteins, which are the primary effectors of cellular functions, proteomics provides a comprehensive understanding of various aspects of biology and medicine. Specifically, proteomics contributes to the following areas:

• Cellular Mechanisms in Health and Disease: Proteomics enables the detailed study of cellular processes by analyzing protein expression, localization, and post-translational modifications. This is critical for understanding normal physiological mechanisms and uncovering molecular changes underlying diseases such as cancer, neurodegenerative disorders, and metabolic syndromes.
• Biomarker Identification for Diagnostics and Prognostics: The proteomic approach is invaluable for identifying proteins that serve as biomarkers, which can indicate the presence, stage, or prognosis of a disease. These biomarkers are essential for developing diagnostic tools and predicting disease outcomes, leading to earlier and more accurate clinical interventions.
• Drug Target Discovery and Therapeutic Design: Proteomics facilitates the identification of potential drug targets by revealing proteins that are crucial to disease pathways. This knowledge is critical for designing drugs that specifically modulate these targets, enhancing therapeutic efficacy while minimizing side effects.
• Mechanisms of Action and Resistance in Pharmacology: Proteomics helps to elucidate how drugs interact with their targets and exert therapeutic effects. It also sheds light on mechanisms of drug resistance, such as protein alterations or compensatory pathways, enabling the development of strategies to overcome resistance.

Top-Down Proteomics vs. Bottom-Up Proteomics

Proteomics research uses different strategies to analyze proteins, with top-down and bottom-up proteomics being the two primary approaches. Each method has distinct advantages and applications, depending on the research goals and sample complexity.

Choosing Between Top-Down and Bottom-Up Proteomics

The choice between these approaches depends on the research objectives. Top-down proteomics is ideal for studying proteoforms and PTMs, while bottom-up proteomics is preferred for high-throughput protein identification and quantification in complex biological samples. The combination of both approaches, known as middle-down proteomics, can further enhance protein characterization in specific applications.

Figure 3. Top-down vs bottom-up proteomics.

Step 1: Sample Preparation

Sample preparation is a crucial initial step that determines the quality and reliability of proteomics data. It involves the extraction, solubilization, digestion, and enrichment of proteins from biological samples. The goal is to ensure minimal sample loss while maximizing protein recovery and maintaining protein integrity.

Sample Types

The type of biological sample used in proteomics studies affects the complexity and variability of the proteome. Common sample types include:

• Cell Lysates: Cultured cells (e.g., cancer cell lines, primary cells) are widely used due to their controlled growth conditions. Cell lysates provide a defined proteome and allow for targeted studies of signaling pathways and drug responses.
• Tissue Samples: Solid tissues (e.g., liver, muscle, brain) require homogenization before protein extraction. Tissue samples enable spatial proteomics studies and provide insights into tissue-specific protein expression and disease pathophysiology.
• Biofluids: Complex fluids such as blood plasma, serum, urine, cerebrospinal fluid (CSF), and saliva serve as rich sources for biomarker discovery. These samples require depletion of high-abundance proteins (e.g., albumin, immunoglobulins) to enhance the detection of low-abundance proteins of interest.

Protein Extraction and Solubilization

Efficient protein extraction is essential for obtaining a representative snapshot of the proteome. The method used depends on the sample type and the experimental goal.

• Cell Lysis: Cells are disrupted using mechanical (e.g., sonication, bead milling, homogenization), chemical (e.g., detergent-based buffers), or enzymatic (e.g., trypsin digestion) methods to release proteins.
• Solubilization: Proteins must be solubilized for downstream analysis, requiring chaotropic agents (e.g., urea, guanidine hydrochloride) and detergents (e.g., SDS, Triton X-100) to maintain protein stability and prevent aggregation.
• Protein Stabilization: Protease and phosphatase inhibitors (e.g., PMSF, aprotinin) are added to prevent protein degradation and preserve post-translational modifications (PTMs).

Protein Digestion

Most proteomics workflows utilize bottom-up proteomics, where proteins are enzymatically digested into peptides before analysis.

• Trypsin Digestion: The most commonly used protease, trypsin, cleaves proteins specifically at the C-terminal side of lysine (K) and arginine (R) residues, generating peptides suitable for mass spectrometry.
• Alternative Proteases: Other proteases such as Lys-C, Glu-C, and Asp-N provide complementary cleavage patterns, increasing sequence coverage and improving protein identification.

Enrichment and Fractionation

Proteome complexity requires enrichment techniques to isolate specific protein subsets or enhance the detection of low-abundance proteins.

• Phosphoproteomics: Phosphorylated peptides are enriched using metal oxide affinity chromatography (MOAC) or immobilized metal affinity chromatography (IMAC), improving the detection of signaling molecules.
• Glycoproteomics: Glycosylated proteins are enriched using lectin affinity chromatography, which binds to specific carbohydrate moieties.
• Subcellular Fractionation: Organelle-specific fractionation (e.g., nuclear, mitochondrial, cytoplasmic) enhances the analysis of compartmentalized proteins.

Step 2: Protein Separation

Before mass spectrometry analysis, proteins or peptides must be separated to reduce sample complexity and increase detection sensitivity.

Gel-Based Separation

Electrophoresis techniques separate proteins based on their physicochemical properties:

• SDS-PAGE (Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis): Denatures proteins and separates them by molecular weight, providing a quick assessment of protein integrity and yield.
• Two-Dimensional Gel Electrophoresis (2D-GE): Separates proteins based on isoelectric point (pI) using isoelectric focusing (IEF), followed by SDS-PAGE for molecular weight separation, enabling high-resolution protein profiling.

Liquid Chromatography (LC)

High-performance liquid chromatography (HPLC) is a powerful tool for separating peptides before MS analysis.

• Reverse-Phase Liquid Chromatography (RP-LC): The most commonly used LC method, RP-LC separates peptides based on hydrophobicity using a gradient of organic solvents (e.g., acetonitrile) in water.
• Ion Exchange Chromatography (IEX): Separates proteins/peptides based on charge differences, with cation exchange chromatography (CEX) and anion exchange chromatography (AEX) being the two primary modes.
• Size-Exclusion Chromatography (SEC): Separates proteins by molecular size, useful for analyzing protein complexes.

Step 3: Mass Spectrometry Analysis

Mass spectrometry (MS) is the gold standard for protein identification and quantification. The workflow includes ionization, mass analysis, and tandem MS for peptide sequencing.

Ionization Techniques

Ionization converts peptides into charged species for MS detection:

• Electrospray Ionization (ESI): Generates charged droplets from liquid samples, ideal for coupling with LC-MS.
• Matrix-Assisted Laser Desorption/Ionization (MALDI): Uses laser energy to ionize peptides embedded in a matrix, suitable for high-throughput and imaging applications.

Mass Analyzers

Mass analyzers separate ions based on their mass-to-charge (m/z) ratio:

• Time-of-Flight (TOF): Measures ion travel time to determine m/z values with high resolution.
• Quadrupole: Uses oscillating electric fields to filter ions by m/z.
• Orbitrap: Achieves ultra-high-resolution mass analysis through harmonic ion oscillation.

Tandem MS (MS/MS)

Tandem mass spectrometry enhances peptide identification:

• Collision-Induced Dissociation (CID): Peptides collide with gas molecules, leading to fragmentation.
• Electron Transfer Dissociation (ETD): Preserves post-translational modifications during fragmentation, useful for PTM analysis.

Quantitative Proteomics

Quantitative proteomics enables protein abundance measurement:

• Label-Free Quantification (LFQ): Compares peptide peak intensities across samples.
• Isobaric Labeling (TMT/iTRAQ): Uses chemical tags to multiplex samples, allowing comparative quantification.

Step 4: Bioinformatics and Data Analysis

Proteomics generates vast datasets that require sophisticated computational tools for interpretation. Key steps include:

Database Searching

Protein identification relies on matching MS/MS spectra against protein databases (e.g., UniProt, NCBI).

De Novo Sequencing

When database information is unavailable, de novo sequencing reconstructs peptide sequences directly from MS data.

Functional Annotation

Bioinformatics tools annotate proteins based on their functions, pathways, and interactions. Examples include:

• Gene Ontology (GO): Categorizes proteins based on biological processes, cellular components, and molecular functions.
• KEGG Pathways: Maps proteins to metabolic and signaling pathways.

Step 5: Quantitative Analysis

Software like MaxQuant and Skyline processes quantitative data to determine differential protein expression.

Figure 4. Workflow of proteome discovery.

Applications of Proteomics

Proteome discovery workflows have broad applications across diverse fields, each leveraging proteomics for transformative outcomes:

Biomarker Discovery

Proteomics is playing a key role in identifying protein biomarkers for early disease diagnosis, monitoring disease progression and assessing therapeutic response. For example, proteomic analyses have revealed specific protein panels used in cancer diagnostics, including PSA for prostate cancer and HER2 for breast cancer. Proteomics is also expanding biomarker discovery in neurodegenerative, cardiovascular and infectious diseases.

Drug Development

Proteomics accelerates drug development through:

• Target Discovery: Identifying novel protein targets involved in disease pathways, such as kinases in cancer or enzymes in metabolic disorders.
• Mechanistic Insights: Assessing drug interactions, their molecular effects, and downstream signaling pathways.
• Resistance Prediction: Uncovering mechanisms of drug resistance to develop next-generation therapeutics. Applications extend to personalized medicine, where proteomics tailors drug interventions based on individual proteome profiles.

Systems Biology

In systems biology, proteomics is integrated with genomics, transcriptomics and metabolomics to construct comprehensive biological networks. This approach elucidates cellular processes, signaling cascades, and adaptive responses under different conditions. Examples include mapping protein-protein interactions and constructing dynamic models of metabolic flow in health and disease.

Agricultural Biotechnology

Proteomics is revolutionizing agriculture by increasing crop resilience and productivity. Key applications include:

• Stress Response Analysis: Identifying proteins involved in drought, salinity, and pathogen resistance.
• Nutritional Improvement: Unveiling pathways to enhance the protein content and quality of staple crops.
• Breeding Programs: Guiding genetic engineering and marker-assisted selection for improved crop traits.

In summary, proteome discovery workflows are a cornerstone of modern biological research, providing unparalleled insight into protein function, interactions, and dynamics. Through systematic sample preparation, advanced separation techniques, high-resolution mass spectrometry, and bioinformatics, researchers can unravel the complexity of the proteome.

Creative Biostructure specializes in protein and structural analysis services, including cryo-electron microscopy (cryo-EM) services, X-ray crystallography services, NMR spectroscopy services, and custom membrane protein production. We also offer advanced proteomic solutions, such as exosome proteomics analysis. Committed to delivering first-class service, we are dedicated to supporting your research. Contact us to learn more about how we can assist you.

References

1. Aizat WM, Hassan M. Proteomics in systems biology. In: Aizat WM, Goh HH, Baharum SN, eds. Omics Applications for Systems Biology. Vol 1102. Springer International Publishing; 2018:31-49.
2. Virág D, Dalmadi-Kiss B, Vékey K, et al. Current trends in the analysis of post-translational modifications. Chromatographia. 2020;83(1):1-10.