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Protein sequencing

Protein sequencing is the process of determining the precise order of amino acids in a protein or peptide chain, which is fundamental to elucidating its three-dimensional structure, biological function, interactions with other molecules, and role in cellular processes.^[1] This technique underpins the field of proteomics, enabling the identification of proteins in complex biological samples, the study of post-translational modifications, and applications in diagnostics, drug discovery, and personalized medicine.^[2] Unlike DNA sequencing, which benefits from the genetic code's redundancy, protein sequencing directly reads the primary sequence without inferring it from nucleic acids, making it indispensable for validating gene predictions and analyzing non-genomic variations.^[3] The history of protein sequencing began in the early 20th century with initial efforts to analyze amino acid composition through hydrolysis, but the first complete sequence of a protein—insulin—was achieved by Frederick Sanger in the early 1950s using a combination of enzymatic and acid hydrolysis followed by chromatographic separation and identification of peptide fragments.^[2] This breakthrough, which demonstrated that proteins have defined amino acid sequences rather than random structures, earned Sanger the Nobel Prize in Chemistry in 1958.^[2] In 1949, Pehr Edman introduced the Edman degradation method, a chemical process that selectively cleaves and identifies the N-terminal amino acid of a peptide using phenylisothiocyanate, allowing up to 50-60 residues to be sequenced iteratively with high accuracy.^[2] In the 1980s, tandem mass spectrometry (MS/MS) emerged as a complementary tool, initially coupled with Edman sequencing, but it soon surpassed it due to its ability to handle smaller samples and generate sequence information from fragmentation patterns.^[3] Traditional protein sequencing methods, such as Edman degradation, require purified proteins and are limited to linear N-terminal reading, making them labor-intensive and unsuitable for high-throughput analysis of complex mixtures.^[1] Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has dominated since the 1990s, serving as the gold standard for proteomics by ionizing peptides, fragmenting them via collision-induced dissociation, and inferring sequences from mass-to-charge ratios, with sensitivities reaching femtomolar levels.^[3] However, LC-MS/MS faces challenges including limited dynamic range (typically 10^4 to 10^5), reliance on database matching for identification, and the need for extensive sample preparation, which can introduce biases.^[1] In recent decades, the field has shifted toward next-generation approaches emphasizing single-molecule resolution to overcome these limitations and enable de novo sequencing without prior genomic knowledge.^[2] Emerging technologies include fluorosequencing, which adapts Edman degradation with fluorescent labeling for optical detection of amino acids at the single-molecule level, and nanopore-based methods, where proteins are unfolded and translocated through a nanopore to generate electrical signals distinguishing the 20 amino acids based on current blockades or dwell times. As of 2025, advances in AI-driven analysis and nanopore technologies have further improved de novo sequencing accuracy and throughput.^[4]^[5] These innovations, pioneered in the 2010s, promise portability, lower costs, and the ability to sequence intact proteins or low-abundance species directly from single cells, though they still grapple with issues like uniform translocation control and amino acid discrimination accuracy.^[1] Ongoing developments in these areas are poised to transform proteomics into a routine tool comparable to genomics.^[2]

History and Fundamentals

Historical Development

The early development of protein sequencing began with foundational work on peptide chemistry in the early 20th century. In 1901, Emil Fischer synthesized the first dipeptide, glycylglycine, through partial hydrolysis of glycine diketopiperazine, and extended these experiments to analyze protein composition by hydrolyzing polypeptides into constituent amino acids, establishing the basis for understanding amino acid linkages in proteins.^[6]^[7] Advances accelerated in the mid-20th century with the development of end-group analysis techniques in the 1940s and 1950s, which allowed identification of terminal amino acids in polypeptide chains. British biochemist Frederick Sanger pioneered this approach using 2,4-dinitrofluorobenzene (DNFB) to label N-terminal residues, enabling their separation and quantification. Building on this, Sanger applied paper chromatography and partial acid hydrolysis to determine the complete amino acid sequence of insulin in the early 1950s, revealing its two-chain structure linked by disulfide bonds—a breakthrough that demonstrated proteins possess defined, genetically encoded sequences. For this work, Sanger received the 1958 Nobel Prize in Chemistry.^[8]^[9] A pivotal milestone came in 1950 with the introduction of Edman degradation by Pehr Edman, a cyclic chemical method that sequentially removes and identifies N-terminal amino acids from peptides without disrupting the remaining chain, greatly improving sequencing efficiency over partial hydrolysis. In the 1960s, mass spectrometry emerged as a complementary tool for protein sequencing, with early applications by Klaus Biemann in 1966 enabling the analysis of oligopeptides through fragmentation patterns, marking the shift toward instrumental methods.^[10]^[11] The 1980s and 1990s saw the transition to automated and high-throughput protein sequencing, driven by refinements in Edman degradation such as gas-phase sequencers introduced in the early 1980s, which minimized sample loss and enabled routine analysis of longer polypeptides. By the 1990s, integration with mass spectrometry further boosted throughput, supporting large-scale proteomic studies and paving the way for genome-protein correlations.^[12]^[13]

Basic Principles and Importance

Protein primary structure refers to the linear sequence of amino acids in a polypeptide chain, where individual amino acids are covalently linked by peptide bonds between the carboxyl group of one amino acid and the amino group of the next.^[14] This sequence determines the protein's unique identity and serves as the foundation for higher levels of structure, including secondary, tertiary, and quaternary folds that enable biological function.^[15] The genetic code, which translates nucleotide sequences in messenger RNA into protein sequences, specifies 20 standard amino acids using 64 possible codons, with most amino acids encoded by multiple codons to provide redundancy.^[16] These amino acids vary in their side chains, conferring diverse chemical properties that influence protein folding, stability, and interactions.^[17] Determining protein sequences is essential for elucidating protein function, as the primary structure dictates enzymatic activity, binding specificity, and cellular roles.^[18] In evolutionary biology, sequence comparisons reveal conservation patterns and divergence, illuminating phylogenetic relationships and adaptive changes.^[19] For disease research, sequencing identifies mutations that disrupt function; for instance, a single amino acid substitution (glutamic acid to valine at position 6) in the beta-globin chain causes sickle cell anemia by altering hemoglobin's solubility and leading to red blood cell deformation.^[20] In drug design, precise sequence knowledge enables targeted therapies, such as monoclonal antibodies or small molecules that bind specific epitopes.^[21] Protein sequencing also underpins proteomics, the large-scale study of proteomes, facilitating biomarker discovery and systems-level insights into cellular processes.^[18] Despite its value, protein sequencing faces challenges due to proteins' inherent heterogeneity, where isoforms arise from alternative splicing or genetic variants, complicating uniform analysis.^[22] Post-translational modifications (PTMs), such as phosphorylation or glycosylation, add chemical diversity that can obscure sequences and affect function without altering the genetic code.^[23] Additionally, proteins range from tens to thousands of amino acids in length—human titin, for example, comprises over 34,000 residues—posing technical hurdles for complete coverage in long chains.^[24] Protein sequencing approaches are broadly classified as direct (de novo) methods, which experimentally determine the amino acid order without prior genomic data, or indirect methods, which predict sequences from DNA/RNA templates or computational models.^[25] Direct methods provide empirical validation, especially for novel or modified proteins, while indirect approaches leverage genomic data for efficiency in well-annotated systems.^[26]

Amino Acid Composition Analysis

Hydrolysis Techniques

Hydrolysis techniques are essential for determining the amino acid composition of proteins, as they cleave peptide bonds to release free amino acids for subsequent analysis. These methods must balance complete hydrolysis with minimal degradation or modification of labile residues, though no single approach achieves perfect recovery for all 20 standard amino acids. Acid hydrolysis remains the most widely used due to its efficiency, while alternatives address specific limitations such as tryptophan destruction. Acid hydrolysis typically employs 6 M hydrochloric acid (HCl) at 110°C for 24 hours in sealed, evacuated tubes to prevent oxidation. This condition achieves near-complete cleavage of peptide bonds for most residues, with recoveries of 86–103% for standard proteins like ubiquitin and bovine serum albumin (BSA). However, it fully destroys tryptophan and partially degrades serine, threonine, tyrosine, and methionine, while converting asparagine and glutamine to aspartic and glutamic acids, respectively. To mitigate oxidation of sulfur-containing amino acids, additives like 0.4% β-mercaptoethanol or hydrogen peroxide are included.^[27] Base hydrolysis, using 4–6 M sodium hydroxide (NaOH) or lithium hydroxide (LiOH) at 110–112°C for 16–22 hours, is primarily employed to preserve tryptophan, which yields recoveries typically 80-100% under optimized conditions compared to none in acid conditions. It is performed in inert atmospheres or with antioxidants like partially hydrolyzed starch to minimize losses, and results in similar tryptophan values between NaOH and LiOH. This method, however, risks racemization and deamidation of other residues and is less suitable for comprehensive composition analysis due to incomplete hydrolysis of certain bonds.^[28]^[29] Enzymatic hydrolysis offers milder conditions using proteases such as trypsin, which cleaves at lysine and arginine residues, or broader enzymes like pronase for near-total breakdown. Conducted at 37–50°C and neutral pH for 24–72 hours, it preserves labile amino acids like tryptophan and avoids harsh chemical artifacts, but achieves only partial completeness (e.g., underestimating aspartic and glutamic acids) and is more costly for routine total composition work. It is better suited for generating peptides rather than free amino acids.^[30] Microwave-assisted hydrolysis accelerates traditional acid methods by applying focused microwave energy to 6 M HCl solutions, reducing processing time to 5–30 minutes at 100–150°C while maintaining high reproducibility and coverage of protein sequences. For instance, it generates up to 1,292 peptides from 2 μg of BSA, enabling faster sample preparation for mass spectrometry-based composition analysis without significant loss in yield compared to conventional 24-hour incubations.^[31] Common artifacts in these techniques include deamidation, where asparagine and glutamine convert to aspartic and glutamic acids during acid or base hydrolysis, leading to overestimation of the latter by up to 100% of the former's content. Racemization, producing D-isomers from L-amino acids (e.g., 1–4% D-Asp formation), occurs via cyclic intermediates under alkaline or prolonged acidic conditions, particularly affecting asparagine and isoleucine. These modifications necessitate corrections or alternative methods for accurate quantification, often followed by chromatographic separation for residue identification.^[27]^[32]

Separation and Quantification Methods

Following hydrolysis of proteins into constituent amino acids, separation and quantification methods are essential to determine the molar composition, which serves as a foundational step for inferring sequence information.^[33] The classical approach employs ion-exchange chromatography, where amino acids are separated based on their differing affinities for a cation-exchange resin, typically using a gradient of buffers with increasing pH and ionic strength.^[34] This method, pioneered by Moore, Stein, and Spackman in 1958, utilizes a single-column, automated system that resolves up to 20 standard amino acids in sequence.^[34] Detection occurs post-column via reaction with ninhydrin, producing colored derivatives (purple for most amino acids, yellow for proline) that are quantified spectrophotometrically at 570 nm and 440 nm, respectively.^[34] This technique remains a gold standard for its reliability in physiological and protein hydrolysate samples.^[35] An alternative, widely adopted method is reverse-phase high-performance liquid chromatography (RP-HPLC), which offers faster separation and higher throughput compared to ion-exchange. Amino acids are derivatized pre-column to enhance detectability: phenylisothiocyanate (PITC) forms stable phenylthiocarbamyl (PTC) derivatives detected at 254 nm, as described by Heinrikson and Meredith in 1984. Alternatively, o-phthalaldehyde (OPA) reacts with primary amino acids to yield fluorescent isoindoles, enabling sensitive detection via fluorescence at excitation/emission wavelengths of 340/450 nm, per Jones and Gilligan's 1983 protocol. Separation occurs on a C18 reversed-phase column using an acetonitrile-water gradient, resolving amino acids in under 30 minutes. Quantification in both methods relies on peak area integration from chromatograms, calibrated against external standards of known amino acid concentrations to generate response factors.^[36] This approach achieves accuracy of 1-5% relative standard deviation for most amino acids, with internal standards like norleucine correcting for losses or variations.^[37] Modern enhancements include ultra-performance liquid chromatography (UPLC), which employs sub-2 μm particles for superior resolution and reduced analysis time to 10-15 minutes.^[38] Coupling with mass spectrometry (LC-MS/MS) provides confirmatory identification via mass-to-charge ratios, improving specificity for isobaric amino acids like leucine and isoleucine.^[39] Results are typically reported as molar ratios of each amino acid relative to a reference residue, such as alanine set to 1, facilitating comparison across protein samples and aiding in molecular weight estimation.^[40]

Terminal Residue Identification

N-Terminal Analysis

N-terminal analysis focuses on identifying the amino acid residue at the free α-amino group of a protein or peptide chain, providing key insights into protein identity, purity, and processing events such as post-translational modifications. This technique is particularly valuable in early stages of protein characterization, as the N-terminus often reflects the protein's maturation, including cleavage of signal peptides or leader sequences. Unlike total amino acid composition analysis, which yields overall residue frequencies, N-terminal methods target the specific endpoint residue, enabling confirmation of sequence starts in heterogeneous samples.^[41] The pioneering chemical approach for N-terminal determination was developed by Frederick Sanger in 1945 using 2,4-dinitrofluorobenzene (DNFB), also known as Sanger's reagent. The method involves reacting the intact protein with DNFB under mildly alkaline conditions, where the reagent selectively couples with the unprotonated α-amino group of the N-terminal residue to form a yellow-colored dinitrophenyl (DNP) derivative. Subsequent complete hydrolysis of the labeled protein with acid (e.g., 6 M HCl) breaks all peptide bonds, liberating the DNP-N-terminal amino acid, which remains intact due to its stability under these conditions, while other amino acids are released in free form. The mixture is then separated by two-dimensional paper chromatography, where the DNP-amino acid is identified by its characteristic Rf value and spot color upon comparison with standards. This technique was instrumental in Sanger's elucidation of insulin's structure, identifying phenylalanine as the N-terminal residue of the B-chain and glycine for the A-chain, marking a milestone in proving proteins have defined sequences. Limitations include its destructive nature, as it consumes the entire protein sample, and challenges with lysine residues, which also react to form ε-DNP-lysine, complicating identification.^[9] Enzymatic methods offer a milder alternative, utilizing exopeptidases like aminopeptidase M or leucine aminopeptidase to sequentially or selectively release the N-terminal amino acid. These enzymes catalyze the hydrolysis of the peptide bond adjacent to the N-terminus, liberating the free amino acid into solution, which is then quantified and identified via techniques such as reversed-phase high-performance liquid chromatography (HPLC) or post-column derivatization with ninhydrin followed by absorbance detection. For instance, controlled incubation with aminopeptidase can release one or a few residues, allowing stepwise analysis, though specificity varies—some enzymes prefer hydrophobic residues like leucine or phenylalanine. This approach is advantageous for native proteins, preserving structure during initial steps, and is often used in combination with inhibitors to limit digestion depth. However, it requires active, unblocked N-termini and can be hindered by secondary structure or modifications that sterically impede enzyme access.^[42]^[43] Mass spectrometry-based N-terminal analysis has become a cornerstone of modern proteomics due to its sensitivity and ability to handle complex samples. Proteins are typically digested with endoproteases like trypsin to generate peptides, followed by tandem mass spectrometry (MS/MS), where collision-induced dissociation produces fragment ions. The N-terminal sequence is inferred from b-ions, which retain the charge on the N-terminal fragment and exhibit mass-to-charge ratios differing by the residue masses of successive amino acids (e.g., a 14 Da difference for alanine vs. glycine). Techniques such as electron transfer dissociation (ETD) enhance coverage by generating c-ions, complementary to b-ions, for more robust identification. This method detects as little as femtomoles of material and can reveal modifications like acetylation by mass shifts (e.g., +42 Da for acetyl). Enrichment strategies, such as using negative selection for internal peptides, further isolate N-terminal peptides for targeted analysis.^[44]^[45] A related chemical strategy, previewed in Pehr Edman's 1950 method, employs phenylisothiocyanate (PITC) to derivatize the N-terminal amino group into a phenylthiohydantoin (PTH) adduct, which is mildly cleaved and identified by chromatography, setting the stage for iterative sequencing without full protein destruction. While N-terminal identification alone confirms endpoints, Edman degradation extends this principle to sequential residue determination.^[46] Applications of N-terminal analysis span quality control and structural biology, particularly in verifying recombinant proteins where the expressed N-terminus must match the predicted sequence post-cleavage of affinity tags or signal peptides, ensuring functionality and batch consistency. It is also critical for detecting blocked N-termini, such as N-acetylated residues (common in approximately 80-90% of eukaryotic proteins, particularly in humans) or pyroglutamyl formations, which obscure standard sequencing and signal regulatory roles like stability or localization; mass spectrometry often resolves these by precise mass mapping. In proteomics workflows, it aids de novo sequencing starts and impurity detection in therapeutic proteins.^[41]^[47]^[48]^[49]

C-Terminal Analysis

C-terminal analysis in protein sequencing focuses on identifying the amino acid residue at the carboxyl terminus, providing essential information for verifying the directionality of the polypeptide chain and confirming overall sequence integrity. Unlike N-terminal methods, which target the amino group, C-terminal approaches exploit the reactivity of the carboxyl group to release or label the terminal residue sequentially. Early techniques emphasized enzymatic and chemical degradation, while contemporary methods integrate mass spectrometry for enhanced precision and throughput. The primary enzymatic approach involves carboxypeptidases, which are exopeptidases that sequentially hydrolyze peptide bonds from the C-terminus, releasing free amino acids that can be quantified over time to deduce the sequence. Carboxypeptidase A (CPA), derived from bovine pancreas, preferentially cleaves non-basic, non-proline residues such as aromatic and aliphatic amino acids, making it suitable for initial C-terminal identification in many proteins.^[50] Carboxypeptidase B (CPB) complements CPA by specifically targeting basic residues like arginine and lysine at the C-terminus, allowing for a combined enzymatic strategy to handle diverse terminal sequences.^[51] For broader applicability, carboxypeptidase Y (CPY) from yeast is widely used due to its broad substrate specificity, cleaving nearly all C-terminal residues including proline, though it is often employed for limited sequencing of 5-10 residues to avoid incomplete reactions.^[52] A classical chemical method for C-terminal determination is hydrazinolysis, developed by Shiro Akabori in the early 1950s. In this procedure, the protein is treated with anhydrous hydrazine at elevated temperatures (around 100°C for several hours), which selectively converts the C-terminal carboxyl group to a hydrazide while internal peptide bonds undergo partial cleavage, yielding free amino acids from non-terminal positions that can be separated. The C-terminal hydrazide is then isolated and identified via chromatography or derivatization, such as with dinitrophenyl (DNP) reagents, enabling unambiguous assignment.^[53] This method, first applied to peptides and proteins like insulin, marked a significant advance in the 1940s-1950s for confirming C-terminal residues without enzymatic biases. In modern workflows, mass spectrometry enhances C-terminal analysis, particularly through ladder sequencing coupled with carboxypeptidase digestion. Time- or concentration-dependent digestion with CPY generates a series of truncated peptides, which are analyzed by matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS); the mass differences between peaks correspond to specific amino acid residues, revealing the C-terminal sequence.^[54] In tandem MS (MS/MS), fragmentation of peptides produces y-ions—characteristic fragments retaining the C-terminus—whose masses allow direct inference of the terminal sequence from the low-mass end of the spectrum.^[55] Despite these advances, C-terminal sequencing faces challenges, including slow or incomplete digestion by carboxypeptidases for hydrophobic residues like isoleucine, valine, and leucine, which can hinder sequential release and lead to ambiguous results.^[56] Hydrazinolysis, while specific, risks partial degradation of sensitive residues such as serine and threonine, and requires anhydrous conditions to minimize side reactions. These limitations often necessitate orthogonal methods for verification, particularly in complex proteomes.^[57]

Edman Degradation

Peptide Fragmentation

Peptide fragmentation is a critical step in protein sequencing, where intact proteins are cleaved into smaller peptides to facilitate subsequent analysis by methods such as Edman degradation or mass spectrometry. This process generates manageable fragments typically 5–50 amino acids long, allowing for the determination of partial sequences that can be assembled into the full protein sequence. Cleavage is achieved through either enzymatic or chemical means, each offering specific advantages in terms of site selectivity and conditions.^[58] Enzymatic digestion employs proteases with defined specificity to hydrolyze peptide bonds under mild aqueous conditions, preserving the integrity of amino acid side chains. Trypsin, a serine protease, cleaves exclusively at the C-terminal side of lysine (Lys) and arginine (Arg) residues, except when followed by proline, producing peptides with basic C-termini that are amenable to further purification.^[59] Chymotrypsin preferentially cleaves after large hydrophobic residues such as phenylalanine (Phe), tyrosine (Tyr), and tryptophan (Trp), though it can also act on leucine (Leu) and methionine (Met) at lower rates, generating aromatic-containing peptides useful for mapping hydrophobic regions.^[60] Endoproteinase Glu-C (also known as V8 protease) targets glutamic acid (Glu) residues at the C-terminus, with activity extending to aspartic acid (Asp) under certain pH conditions (e.g., pH 4.0 in phosphate buffer), enabling the production of acidic peptides for complementary coverage.^[61] Chemical cleavage methods provide alternatives when enzymatic approaches are insufficient, often targeting less frequent residues for broader fragment spacing. Cyanogen bromide (CNBr) reacts with the sulfur of methionine (Met) residues to cleave at the C-terminal side, converting Met to homoserine lactone and yielding peptides suitable for N-terminal sequencing; this method is particularly effective for proteins with few Met residues, as demonstrated in early structural studies of cytochromes.^[62] Endoproteinase Asp-N, a metalloprotease, cleaves on the N-terminal side of aspartic acid (Asp) residues, and to a lesser extent glutamic acid (Glu), producing peptides with Asp at the N-terminus that aid in resolving regions resistant to other cleavages.^[63] To reconstruct the complete protein sequence from fragmented peptides, an overlap strategy is employed, involving multiple parallel digests with different enzymes or chemicals to generate sets of peptides that share overlapping sequences. These overlaps allow alignment and assembly, as pioneered in the sequencing of insulin where tryptic and chymotryptic fragments were compared to order the chain.^[64] Following digestion, peptides are often separated by gel-based electrophoresis to isolate individual components prior to sequencing; sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) resolves peptides by molecular weight under denaturing conditions, while two-dimensional (2D) electrophoresis combines isoelectric focusing with SDS-PAGE for enhanced resolution of complex mixtures.^[65] Optimization of fragmentation yield is essential, particularly for proteins with disulfide bonds that can hinder protease access. Reduction of cystine (Cys-Cys) bridges using agents like dithiothreitol (DTT), followed by alkylation of free cysteine thiols with iodoacetamide (IAA), unfolds the protein and prevents re-formation of disulfides, ensuring complete digestion and higher sequence coverage in downstream Edman or mass spectrometry workflows.^[66]

Chemical Reaction Mechanism

The Edman degradation proceeds through a cyclic series of chemical reactions that selectively label, cleave, and identify the N-terminal amino acid of a peptide, enabling sequential sequencing without disrupting the remaining chain. In the initial coupling step, phenylisothiocyanate (PITC) is reacted with the free α-amino group of the N-terminal residue under mildly basic conditions (pH 8–9), typically in a buffered aqueous solution. The nucleophilic nitrogen of the amine attacks the electrophilic central carbon of the isothiocyanate group, forming a stable phenylthiocarbamoyl (PTC) derivative via addition-elimination, with the release of aniline as a byproduct. This step is highly selective for the unprotonated primary amine, minimizing side reactions with other nucleophilic groups in the peptide.^[67] Following coupling, the PTC-peptide undergoes cleavage in the presence of anhydrous trifluoroacetic acid (TFA) at room temperature for approximately 10–30 minutes. The acidic conditions protonate the sulfur atom in the PTC group, facilitating an intramolecular nucleophilic attack by the peptide carbonyl oxygen on the PTC carbon, which leads to cyclization and formation of a five-membered thiazolinone ring. This cyclization cleaves the scissile peptide bond adjacent to the N-terminal residue, releasing the thiazolinone derivative while leaving the shortened peptide intact and ready for the next cycle. The reaction is quantitative under anhydrous conditions, ensuring minimal hydrolysis of internal peptide bonds.^[68] The unstable thiazolinone is then converted to the stable phenylthiohydantoin (PTH) derivative through acid-catalyzed rearrangement, often by brief treatment with aqueous TFA or heating in an acidic medium. This involves ring opening and recyclization, incorporating the side chain of the original amino acid into a thiohydantoin heterocycle that is soluble in organic solvents and amenable to chromatographic identification. The PTH-amino acid is extracted into an organic phase (e.g., ethyl acetate) and analyzed, typically by reverse-phase HPLC, by comparison of retention times with PTH standards derived from known amino acids. The overall process per cycle can be represented as:

\text{Peptide-NH}_2 + \text{PITC} \rightarrow \text{PTC-Peptide} \xrightarrow{\text{TFA}} \text{Thiazolinone} + \text{Peptide(-1)-NH}_2 \xrightarrow{\text{aq. acid}} \text{PTH-AA}

This mechanism ensures specificity, with one residue released per cycle and yields of 95–99% efficiency, allowing reliable sequencing of up to 50–60 residues before cumulative losses become prohibitive.^[69]

Automated Sequencing Instrumentation

Automated protein sequencers for Edman degradation revolutionized the field by enabling high-throughput, reproducible sequencing of peptides and proteins with minimal manual intervention. Early designs, such as the spinning cup sequenator developed by Edman and Begg in 1967, featured a rotating cup where the peptide sample was applied to the inner wall, often coated with polybrene—a quaternary ammonium polymer—to immobilize the peptide and prevent losses during sequential solvent extractions and washes.^[70] This liquid-phase system automated the delivery of reagents and collection of fractions, allowing for the processing of up to 50 cycles with initial yields from 10-100 nmol samples, though it suffered from cumulative losses due to the solubility of peptides in organic solvents.^[70] Advancements in the 1980s addressed these limitations through gas-phase instrumentation, notably the Applied Biosystems model 470A sequencer introduced in 1982, which delivered coupling and cleavage reagents (phenylisothiocyanate and trifluoroacetic acid) as vapors to a reaction chamber containing the immobilized peptide.^[71] This design minimized peptide solubility issues and extraction losses, enabling sequencing from as little as 1-10 pmol of sample while maintaining high efficiency.^[71] Subsequent models, such as the 477A released in the mid-1980s, further improved performance by incorporating polybrene-coated glass fiber filters or discs for sample application, which enhanced peptide adsorption and stability during the gas-phase reactions, and allowed for multiple reaction cartridges to support continuous, unattended operation. These supports, often treated with polybrene to promote electrostatic binding, were particularly effective for handling sub-picomole quantities electroblotted from gels. Detection in these automated systems relies on high-performance liquid chromatography (HPLC) to separate and quantify the phenylthiohydantoin (PTH) amino acid derivatives released each cycle, with UV absorbance at 269 nm as the standard detection method for identification against known standards; fluorescence detection has also been integrated in later variants for increased sensitivity down to femtomole levels.^[72] Throughput typically supports 30-60 cycles per run, often completed overnight with cycle times of 45-60 minutes, yielding sequences of 20-50 residues depending on sample purity and size.^[71] Proprietary software in models like the 477A automates data analysis by matching HPLC peak retention times and areas to a library of PTH-amino acid standards for residue assignment, while calculating key metrics such as initial yield (from the first cycle) and repetitive yield—typically 95-99% per cycle, reflecting the efficiency of successive degradations. For example, a repetitive yield of ~98% allows reliable sequencing over multiple cycles before signal lag becomes prohibitive.^[73] These tools also flag lag or carryover artifacts, ensuring accurate interpretation, and can integrate briefly with upstream peptide fragmentation workflows to extend sequence coverage.

Mass Spectrometry-Based Sequencing

Proteolytic Digestion Strategies

Proteolytic digestion is a cornerstone of the bottom-up approach in mass spectrometry-based protein sequencing, where complex protein mixtures are enzymatically or chemically cleaved into smaller peptides to enhance ionization efficiency and facilitate liquid chromatography-mass spectrometry (LC-MS) analysis.^[74] This strategy generates peptides typically 5-50 amino acids long, which are more amenable to tandem mass spectrometry (MS/MS) fragmentation than intact proteins.^[75] Trypsin is the most commonly used enzyme for digestion due to its high specificity, cleaving peptide bonds C-terminal to lysine and arginine residues under neutral pH conditions, producing peptides with basic C-termini that ionize well in positive-ion mode LC-MS.^[76] In-gel digestion involves excising protein bands from sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), destaining, reducing disulfide bonds, and incubating with trypsin overnight at 37°C, which is particularly useful for separating complex samples prior to analysis.^[77] In-solution digestion, performed directly on solubilized proteins, offers higher throughput and is often conducted in urea or guanidine hydrochloride buffers with trypsin-to-protein ratios of 1:20 to 1:50 (w/w) for 4-18 hours at 37°C, improving compatibility with LC-MS workflows.^[78] To achieve greater sequence coverage and reduce missed cleavages, multi-enzyme combinations such as Lys-C followed by trypsin are employed; Lys-C specifically cleaves at lysine residues, generating longer peptides that are subsequently refined by trypsin's dual specificity, often increasing protein identifications by 10-20% in complex samples.^[79] This sequential digestion minimizes incomplete cleavages at arginine-proline bonds, which trypsin alone may overlook.^[80] Chemical adjuncts like cyanogen bromide (CNBr) provide orthogonal cleavage at methionine residues, complementing enzymatic methods for proteins with low lysine/arginine content or to target specific regions; CNBr reacts in acidic conditions (e.g., 70% formic acid) to form homoserine lactone, yielding peptides suitable for MS when enzymatic coverage is insufficient.^[81] Sample preparation prior to digestion includes denaturation with chaotropes like 8 M urea to unfold proteins, reduction of disulfide bonds using dithiothreitol (DTT) or tris(2-carboxyethyl)phosphine (TCEP), and alkylation of free cysteines with iodoacetamide (IAA) at 15-50 mM in the dark to prevent reformation of disulfide bridges, ensuring complete accessibility for proteases.^[82] Post-digestion cleanup employs C18 solid-phase extraction tips or spin columns to desalt peptides and remove detergents or salts, concentrating samples in 0.1% trifluoroacetic acid for optimal LC-MS loading.^[83] These strategies aim for >70% sequence coverage through overlapping peptides from multiple cleavages, enabling robust assembly of full protein sequences via MS/MS data.^[75] The resulting peptide mixtures are then separated by LC and sequenced by MS for comprehensive protein identification.^[74]

De Novo Peptide Sequencing

De novo peptide sequencing determines the amino acid sequence of peptides directly from tandem mass spectrometry (MS/MS) data, independent of reference databases, making it essential for discovering novel proteins or sequences in non-model organisms. This approach typically follows proteolytic digestion of proteins into peptides, which are then separated, ionized, and selected for fragmentation in the second stage of MS/MS to produce diagnostic fragment ions whose mass-to-charge ratios reveal the sequence through pattern matching.^[84] In tandem MS, fragmentation is achieved via methods such as collision-induced dissociation (CID), higher-energy collisional dissociation (HCD), or electron transfer dissociation (ETD), each generating distinct ion series for sequence inference. CID and HCD primarily cleave the peptide backbone at amide bonds to yield b-ions (N-terminal fragments retaining the charge) and y-ions (C-terminal fragments), with b-ions often showing further neutral losses like water or ammonia. ETD, in contrast, transfers electrons to multiply charged peptides, producing c-ions (N-terminal) and z-ions (C-terminal) while preserving labile post-translational modifications. The complementary nature of these ions—where the sum of a b-ion and corresponding y-ion masses equals the protonated peptide mass plus 1 Da—enables bidirectional sequencing validation.^[85] Sequence reconstruction relies on identifying "ladders" of consecutive fragment ions, where mass differences between adjacent peaks match known amino acid residue masses, such as +57.0215 Da for glycine or +71.0371 Da for alanine. For example, a b-ion series with mass increments of 71 Da and 113 Da would indicate alanine followed by leucine or isoleucine. These ladders are assembled by aligning observed peaks to theoretical ion positions, accounting for common neutral losses (e.g., -18 Da for H₂O from serine or threonine). High mass accuracy (e.g., <5 ppm) is crucial for resolving near-isobaric differences, though challenges arise with truly isobaric residues like leucine and isoleucine (both 113.0841 Da), which require orthogonal methods like MS³ or NMR for distinction.^[85] Computational tools automate this process using de novo algorithms that model spectra as graphs, with nodes as possible prefix masses and edges weighted by amino acid probabilities based on ion intensities and cleavage preferences. PEAKS employs a scoring scheme to evaluate fragment ion matches and generate the optimal sequence with confidence tags for variable regions, outperforming earlier tools like Lutefisk on Q-TOF data from tryptic digests. Novor, a real-time alternative, uses dynamic programming for initial ladder building followed by machine learning refinement with decision trees trained on spectral features, achieving 7-37% more correct residues than PEAKS across diverse datasets.^[86]^[87] A more recent advancement is Casanovo (2024), which reframes de novo sequencing as a sequence-to-sequence translation problem using a transformer model on raw spectral data, achieving an average precision of 0.95 on benchmark datasets, outperforming Novor and other tools by up to 25-37% in peptide identification.^[88] Accuracy at the amino acid level reaches 80-95% for short peptides (5-15 residues), where spectra exhibit clearer ion series, but drops for longer sequences due to incomplete fragmentation or spectral noise; HCD often boosts this to ~95% by enhancing higher-energy fragments. Isobaric ambiguities, particularly Leu/Ile, limit full-sequence fidelity to 30-55% without additional resolution.^[89] As an illustrative example, consider the MS/MS spectrum of the tryptic peptide SGNFSFQTVK ([M+2H]^{2+} at m/z 557.8). The y-ion ladder includes peaks at m/z 147.1 (y₁: K, 128.095 Da residue), 304.2 (y₂: VK, +99.068 Da for V), 405.2 (y₃: TVK, +101.071 Da for T), and higher ions up to y₁₀ at m/z 1027.5, with differences matching Gln/Lys (128.095 Da), Phe (147.069 Da), Ser (87.032 Da), etc., to read the C-terminus as ...QTVK; complementary b-ions (e.g., m/z 145.1 for b₂: SG, +57.0215 Da for G after S) confirm the N-terminus SGNFSF, yielding the full sequence. Neutral losses like -17 Da (NH₃ from K) or -18 Da (H₂O) annotate side peaks, aiding ladder extension.^[85]

Terminal Residue Determination

In mass spectrometry (MS)-based protein sequencing, terminal residue determination focuses on identifying the N- and C-terminal amino acids of peptides or intact proteins through characteristic fragmentation patterns observed in tandem MS (MS/MS) spectra.^[90] These modern MS approaches complement classical chemical methods by enabling high-throughput analysis in complex mixtures.^[91] For N-terminal identification, immonium ions—low-mass fragments derived from single amino acid residues—serve as diagnostic markers in collision-induced dissociation (CID) MS/MS, providing residue-specific signals that confirm the N-terminal sequence.^[90] Additionally, the a-ion series, which are N-terminal fragments resulting from cleavage of the peptide backbone and loss of carbon monoxide from b-ions, further supports N-terminal assignment by forming a ladder of ions that map the sequence from the amino end.^[90] C-terminal residues are primarily identified via the y-ion series in MS/MS, where these C-terminal fragments arise from amide bond cleavages and retain the carboxyl terminus, allowing sequential readout of the peptide's end.^[90] Exocyclic fragmentation patterns, observed in certain peptide structures, can enhance C-terminal detection by producing side-chain-involved ions that highlight the terminal residue without internal backbone disruption.^[92] Isobaric labeling techniques, such as isobaric tags for relative and absolute quantitation (iTRAQ) and tandem mass tags (TMT), improve terminal signal detection by covalently modifying primary amines at the N-terminus (and lysine side chains), which boosts ionization efficiency and identification rates of N-terminal peptides in bottom-up workflows.^[93] These labels yield higher peptide-spectrum matches compared to unlabeled samples, facilitating reliable terminal residue confirmation in quantitative proteomics.^[94] In top-down MS, electron transfer dissociation (ETD) preserves terminal sequences by generating c- and z-ion series with minimal loss of labile modifications, enabling intact protein analysis up to 80 kDa and providing extensive N- and C-terminal coverage.^[95] ETD's non-ergodic fragmentation mechanism ensures that terminal product ions remain intact, supporting precise endpoint sequencing in proteoform characterization.^[91] This MS-centric terminal determination is particularly useful for confirming splice variants, where variant-specific termini arise from alternative exon usage, and for verifying post-translational processing events like proteolytic cleavage that alter protein ends.^[96] For instance, top-down MS with ETD has been applied to distinguish periostin splice isoforms at the protein level through terminal mass differences.^[96] Similarly, it aids in detecting N- or C-terminal proteoforms involved in diverse protein complexes or processing pathways.^[97]

Post-Translational Modification Analysis

Post-translational modifications (PTMs) significantly impact protein function and are integral to mass spectrometry (MS)-based protein sequencing, where detection and precise localization within peptide sequences are essential for accurate structural elucidation.^[98] In bottom-up proteomics workflows, PTMs are identified by mass shifts in peptide spectra following enzymatic digestion, enabling site-specific mapping when combined with appropriate fragmentation techniques.^[98] Common PTMs analyzed in MS include phosphorylation, which introduces a mass shift of approximately +80 Da due to the addition of a phosphate group (HPO₃), glycosylation, which exhibits variable mass increases depending on glycan composition (e.g., +203 Da for an N-acetylhexosamine residue in N-linked forms), and ubiquitination, detected via a +114 Da Gly-Gly remnant on lysine residues after tryptic cleavage of the ubiquitin chain.^[99]^[100]30248-6/pdf) These modifications are enriched prior to MS analysis to enhance detection sensitivity, as they often occur at low stoichiometry.^[98] Localization of PTMs relies on fragmentation methods that preserve modification integrity, such as electron transfer dissociation (ETD) and electron capture dissociation (ECD), which generate intact PTM-peptide fragments (c- and z-type ions) for unambiguous site assignment, particularly for labile modifications like phosphorylation and O-GlcNAc glycosylation.^[98] In contrast, collision-induced dissociation (CID) is less suitable for these, as it frequently results in neutral losses (e.g., 98 Da H₃PO₄ from phosphopeptides), complicating localization.^[98] Software tools like MaxQuant and Proteome Discoverer facilitate site-specific PTM assignment by calculating localization probabilities based on fragment ion matching and mass accuracy, integrating ETD/ECD data to score potential modification sites with high confidence (e.g., >0.75 probability threshold). These platforms process raw MS spectra to output PTM-localized peptides, supporting integration with de novo sequencing for ambiguous cases. Challenges in PTM analysis include the loss of labile modifications during CID fragmentation and neutral losses that reduce signal intensity, often requiring hybrid fragmentation approaches or enrichment strategies to achieve reliable detection.^[98] For quantitative assessment of PTM dynamics, methods such as stable isotope labeling by amino acids in cell culture (SILAC) or label-free quantification compare modification abundances across conditions, revealing regulatory changes (e.g., phosphorylation stoichiometry shifts in signaling pathways).^[98]

Whole-Protein Mass Measurement

Whole-protein mass measurement, a cornerstone of top-down mass spectrometry (MS) in protein sequencing, involves the precise determination of the molecular weight of intact proteins to infer sequence-related features without prior enzymatic digestion. This approach enables the characterization of proteoforms—variants arising from alternative splicing, genetic mutations, or post-translational modifications (PTMs)—by directly ionizing and analyzing the full protein structure. Unlike peptide-centric methods, top-down MS preserves the connectivity of modifications across the entire sequence, providing insights into protein heterogeneity that are critical for understanding biological function and disease mechanisms.^[101]^[102] Ionization of intact proteins is typically achieved using electrospray ionization (ESI) or matrix-assisted laser desorption/ionization (MALDI), which generate multiply charged ions suitable for mass analysis. ESI is the preferred method for most top-down experiments due to its ability to produce soft ionization of proteins up to approximately 100 kDa, facilitating the transfer of noncovalent complexes into the gas phase while minimizing fragmentation during ionization. MALDI, while effective for larger proteins exceeding 100 kDa, often yields singly charged ions that require high-resolution analyzers to resolve isotopic patterns accurately. These techniques allow for the initial intact mass measurement, serving as the foundation for subsequent sequencing efforts by establishing the baseline molecular weight against which modifications can be mapped.^[102]^[103]^[101] High mass accuracy is essential for distinguishing subtle mass shifts from PTMs or isoforms, with Fourier transform ion cyclotron resonance (FT-ICR) and Orbitrap mass analyzers achieving resolutions below 1 ppm. FT-ICR provides parts-per-billion accuracy and ultra-high resolving power, enabling unambiguous assignment of elemental compositions for proteins up to 200 kDa, while Orbitrap systems deliver sub-1 ppm precision in a more compact format suitable for routine laboratory use. In top-down MS/MS, these analyzers facilitate fragmentation of intact ions using methods like electron capture dissociation (ECD) or collision-activated dissociation (CAD), generating sequence tags—short stretches of contiguous fragment ions—that confirm the protein identity and localization of modifications without full de novo sequencing. This fragmentation yields complementary c- and z-type ions that retain labile PTMs, enhancing the reliability of structural annotations.^[104]^[105]^[106]^[107] Applications of whole-protein mass measurement include distinguishing protein isoforms with near-identical sequences but differing masses, such as those from splice variants, and quantifying PTM stoichiometry to assess functional regulation. For instance, top-down MS has resolved histone isoforms differing by single amino acid substitutions, revealing their roles in epigenetic control, and determined the occupancy of phosphorylations in signaling proteins to quantify activation states. These capabilities are particularly valuable in clinical proteomics, where proteoform profiling aids in biomarker discovery for diseases like cancer. However, limitations persist, including inefficient fragmentation for proteins larger than 50-70 kDa, where charge state distributions become complex and yield fewer informative sequence tags, often necessitating hybrid approaches or advanced instrumentation to overcome signal suppression and resolution challenges.^[108]^[109]^[102]^[110]^[111]

Method Limitations and Challenges

Mass spectrometry-based protein sequencing, while powerful, faces several inherent limitations that can impact its reliability and applicability, particularly in complex biological samples. One primary challenge is sensitivity, as typical mass spectrometers require at least femtomolar (10^{-15} mol) quantities of protein for reliable detection, corresponding to roughly 10^9 molecules for a 50-kDa protein.^[112] In complex mixtures, ion suppression further exacerbates this issue, where co-eluting compounds compete for ionization, reducing signal intensity for low-abundance peptides and leading to under-detection of rare proteoforms.^[113] Sequence coverage often remains incomplete, with gaps particularly pronounced in hydrophobic regions such as transmembrane domains of integral membrane proteins. These regions resist proteolytic digestion and exhibit poor solubility, resulting in low recovery during extraction and analysis, sometimes achieving less than 50% coverage in bottom-up workflows.^[114] This incomplete coverage hinders full proteoform characterization, especially for multi-spanning membrane proteins critical in cellular signaling. Ambiguities in spectrum interpretation pose another significant hurdle, notably with isobaric residues like leucine and isoleucine, which share identical masses (113.084 Da) and cannot be distinguished solely by mass-to-charge ratios, leading to potential misassignments in de novo sequencing.^[115] Post-translational modifications (PTMs) introduce additional interferences by altering peptide masses and fragmentation patterns, complicating site localization in up to 50% of modified peptides without targeted enrichment.^[116] De novo sequencing is computationally demanding and time-intensive compared to database-matching approaches, often requiring extensive processing resources and achieving lower throughput, with costs escalating for large-scale analyses due to the need for high-resolution instruments and software.^[117] Database-dependent methods, while faster and more cost-effective, rely on comprehensive reference databases, limiting utility for novel or non-model organisms. To mitigate these challenges, hybrid strategies integrating mass spectrometry with Edman degradation have been developed, where Edman provides precise N-terminal sequence validation for short peptides, complementing MS coverage in low-sensitivity scenarios.^[118] Such combinations enhance overall accuracy, particularly for resolving ambiguities in therapeutic proteins like monoclonal antibodies.

Sequence Prediction from Nucleic Acids

Translation from DNA Sequences

Translating protein sequences from DNA involves interpreting the genomic sequence through the genetic code, which maps nucleotide triplets (codons) to amino acids. In prokaryotes, this process is relatively straightforward, as genes are often continuous coding sequences without interruptions. However, in eukaryotes, the primary DNA transcript (pre-mRNA) undergoes processing, including the removal of non-coding introns to form mature mRNA, which is then translated into protein. RNA serves as the intermediate for this translation, carrying the genetic information from DNA to the ribosome where amino acids are assembled. This DNA-to-protein inference is fundamental in genomics for predicting proteomes from sequenced genomes.^[119] A key step in deducing protein sequences is identifying open reading frames (ORFs), which are stretches of DNA beginning with a start codon (typically ATG, encoding methionine) and ending with a stop codon (TAA, TAG, or TGA), uninterrupted by other stop codons in the reading frame. ORFs are scanned computationally to locate potential protein-coding regions, with tools like NCBI's ORFfinder searching user-input DNA sequences and providing the range and translated protein for each identified ORF. Accurate ORF annotation is crucial for understanding how genetic information translates to functional proteins, as evidenced by studies revealing thousands of novel translated ORFs in human genomes.^[120]^[121] In eukaryotic genomes, splicing complicates direct translation from DNA, as introns—non-coding sequences interspersed within genes—are precisely excised by the spliceosome, joining exons to form the coding mRNA. This process enables alternative splicing, where different exon combinations produce multiple protein isoforms from a single gene, vastly increasing proteomic diversity; for instance, over 95% of human multi-exon genes undergo alternative splicing. Seminal work has shown that splicing regulation involves cis-acting elements and trans-factors, allowing tissue-specific isoform expression. Failure to account for splicing variants can lead to incomplete or erroneous protein sequence predictions from genomic DNA.^[119]^[122] The genetic code exhibits degeneracy, meaning most amino acids are encoded by multiple codons (up to six for some, like leucine), primarily differing in the third position due to the wobble hypothesis. Proposed by Francis Crick in 1966, this hypothesis explains that the third base in a codon-anticodon pairing allows non-standard base pairing (wobble), enabling a single tRNA to recognize multiple synonymous codons and reducing the need for 61 unique tRNAs. This redundancy minimizes the impact of certain mutations but requires careful consideration in sequence prediction to resolve ambiguities.^[123] Computational tools facilitate genome-to-protein translation by generating predictions across all possible reading frames. The six-frame translation method translates DNA in three forward frames (starting at positions 1, 2, or 3) and three reverse frames (from the complementary strand), helping identify ORFs without prior knowledge of gene orientation; this is implemented in tools like EMBOSS Transeq, which outputs peptide sequences for all six frames. Such approaches are integral to genome annotation pipelines, as seen in comparative analyses where six-frame translations aid in detecting coding regions across species assemblies.^[124]^[125] Despite these advances, translating from DNA has limitations, as it cannot capture post-translational modifications (PTMs) like phosphorylation or glycosylation, which alter protein function but occur after translation and are not encoded in the DNA sequence. Additionally, non-coding regions, including regulatory elements and alternative ORFs, influence protein expression and diversity but are not directly reflected in primary sequence predictions. Alternative splicing further generates isoforms whose sequences deviate from the genomic template, necessitating RNA-level data for full accuracy. These gaps highlight why DNA-based predictions often require experimental validation through proteomics.^[126]^[127]

Inference from RNA Sequencing

RNA sequencing (RNA-Seq) enables the inference of protein sequences by first capturing the transcriptome through reverse transcription of RNA into complementary DNA (cDNA), fragmentation, and high-throughput next-generation sequencing (NGS) to generate short reads that represent expressed transcripts.^[128] These reads are typically mapped to a reference genome or transcriptome assembly to reconstruct mRNA sequences, with poly-A tail selection during library preparation enriching for mature mRNAs and facilitating identification of 3' untranslated regions (UTRs) during alignment.^[129] Once assembled, open reading frames (ORFs) within the mRNA sequences are identified in silico by scanning for start (AUG) and stop codons, followed by translation into amino acid sequences using the standard genetic code, which accounts for codon degeneracy and ensures accurate prediction of polypeptide chains from expressed genes.^[130] To detect sequence variants that alter protein sequences, RNA-Seq reads are analyzed for single nucleotide polymorphisms (SNPs) in coding regions, where nonsynonymous SNPs can lead to amino acid substitutions; for example, tools like GATK or SAMtools variant callers process aligned reads to identify heterozygous or homozygous variants with high confidence when coverage exceeds 20x depth.^[131] Gene fusions, which produce chimeric proteins, are inferred by detecting discordant read pairs or split reads spanning fusion junctions in the transcriptome, often using algorithms such as STAR-Fusion or Arriba that filter for biologically plausible events based on genomic proximity and expression levels.^[132] Ribosome profiling (Ribo-Seq), an extension of RNA-Seq, enhances variant detection by sequencing ribosome-protected mRNA fragments, revealing translationally active ORFs including those with SNPs or fusions that affect protein synthesis; this method isolates ~30-nucleotide footprints from translating ribosomes, allowing precise mapping of variant impacts on the proteome.^[133] A key advantage of inferring proteins from RNA-Seq is its ability to capture alternatively spliced transcripts and low-abundance isoforms that may not be evident in genomic data, as the method directly profiles mature mRNAs and quantifies isoform-specific expression through differential splicing analysis tools like rMATS.^[134] This approach reveals dynamic proteomes under specific conditions, such as tissue-specific splicing events that generate protein diversity. However, challenges include RNA instability, which necessitates rapid extraction and RNase-free protocols to prevent degradation and ensure representative sampling of fragile transcripts like those with short half-lives.^[129] Alignment errors arise from splicing junctions and sequence polymorphisms, potentially leading to misassembled transcripts if short reads fail to span complex exons; mitigation involves splice-aware aligners like HISAT2, though error rates can still exceed 5% in repetitive regions.^[129]

Emerging Sequencing Technologies

Nanopore-Based Methods

Nanopore-based methods for protein sequencing rely on the translocation of unfolded proteins or peptides through a nanoscale pore embedded in a lipid or synthetic membrane that separates two electrolyte-filled compartments. An applied electric field drives the negatively charged polypeptide chain through the pore, where it partially blocks the flow of ions, producing characteristic changes in electrical current that are unique to each amino acid based on its size, charge, and hydrophobicity. This single-molecule approach allows for label-free detection without the need for chemical labeling or amplification, enabling real-time analysis of native protein sequences.^[135] Oxford Nanopore Technologies has adapted its nucleic acid sequencing platform for protein analysis by engineering biological nanopores, such as modified protein channels, to accommodate peptide bonds and generate amino acid-specific signals. In their peptide-based detection workflow, proteins are first digested into shorter peptides, which are then attached to DNA handles and translocated using a motor protein, producing current blockades that reflect the peptide's amino acid composition for identification and barcoding. Engineered pores like the glycine-to-phenylalanine substituted Fragaceatoxin C (G13F-FraC-T1) nanopore enhance peptide capture and residence time, allowing differentiation of peptides with approximately 40 Da resolution and detection of small chemical modifications at low pH to minimize electroosmotic flow. These adaptations enable protein identification through protease-generated peptide spectra, akin to fingerprinting but at the single-molecule level.^[136]^[137] As of 2025, significant advances include the integration of molecular motor proteins, such as helicase-like enzymes, to control the rate of protein unfolding and feeding into the nanopore, improving signal resolution for longer chains. Oxford Nanopore's roadmap outlines progress toward full-length protein sequencing without digestion, with prototypes demonstrating biomarker detection in complex samples and proteoform analysis. Accuracy for individual amino acid identification reaches up to 98.8% in optimized setups, while short peptide chains achieve around 90% sequencing fidelity, though challenges persist in distinguishing similar amino acids like leucine and isoleucine. These methods complement mass spectrometry by offering direct, single-molecule insights into protein dynamics.^[138]^[139]^[140] The primary advantages of nanopore-based protein sequencing are its label-free nature, real-time readout, and potential for portable, high-throughput applications in biomarker discovery and variant screening. However, key challenges include efficient protein unfolding, especially for folded domains, and achieving sufficient current blockade distinction for all 20 amino acids in longer sequences, limiting current use to short peptides or digested samples.^[136]^[135]

Single-Molecule Recognition Techniques

Single-molecule recognition techniques represent an emerging class of methods for protein sequencing that rely on detecting unique electronic or optical signatures of individual amino acids (AAs) without enzymatic fragmentation, enabling potential de novo sequencing of intact polypeptides.^[141] These approaches leverage nanoscale devices to probe AAs at the single-molecule level, offering high sensitivity for distinguishing subtle structural differences, including post-translational modifications (PTMs).^[142]

Fluorosequencing

Fluorosequencing is an optical single-molecule method that combines Edman degradation chemistry with fluorescent labeling and high-throughput microscopy to determine peptide sequences. Specific amino acids, such as lysine, cysteine, and others, are labeled with distinct fluorophores that are stable and brightly emissive. Cyclic rounds of Edman degradation selectively remove and cleave the N-terminal residue, releasing a fluorescent tag whose color and position are imaged using total internal reflection fluorescence (TIRF) microscopy on a chip with millions of immobilized peptides. This enables parallel sequencing of thousands to millions of peptides per run, with read lengths up to 50 residues.^[143]^[144] Developed initially at the University of Texas and advanced by companies like Erisyon, fluorosequencing supports de novo sequencing without genomic references and detects PTMs through altered fluorescence or cleavage patterns. As of 2025, improvements include brighter, more photostable dyes and automated sample preparation, achieving single-molecule sensitivity down to attomolar concentrations and integration with probabilistic models for abundance inference in complex proteomes. Advances also encompass expanded labeling of up to 10 amino acid types, enhancing coverage for proteins with sparse target residues.^[145]^[146] Key advantages include high parallelism comparable to next-generation DNA sequencing, direct PTM localization, and compatibility with low-input samples like single cells. Challenges involve incomplete labeling of all 20 amino acids (typically 4-6 types per run), potential residue-specific biases in cleavage efficiency, and the need for sophisticated image analysis to handle stochastic tag release. These limitations restrict full coverage but position fluorosequencing as a complementary tool for targeted proteomics.^[143]

Recognition Tunneling

Recognition tunneling (RT) employs a pair of nanoelectrodes separated by a narrow gap (approximately 1-2 nm) functionalized with recognition molecules, such as imidazole-carboxamide, that selectively bind to specific AAs. As a polypeptide translocates through the gap—often via controlled pulling or diffusion—the transverse quantum tunneling current modulates in a characteristic manner for each AA due to its unique electronic orbital overlap with the recognition layer. This signature is measured and decoded using machine learning algorithms to identify the AA with over 95% accuracy for many residues.^[141]^[147] Early demonstrations distinguished all 20 proteinogenic AAs, including isobaric isomers like leucine and isoleucine, and detected PTMs such as phosphorylation on serine. The technique avoids mass spectrometry's need for ionization and fragmentation, preserving sequence context in native-like conditions. However, practical implementation requires stable nanojunction fabrication and mitigation of noise from aqueous environments or contaminants.^[142] Recent prototypes have explored graphene-based electrodes to enhance gap uniformity and signal-to-noise ratios, achieving discrimination of structurally similar AAs like serine and tyrosine.

DNA-PAINT Variants

Variants of DNA point accumulation in nanoscale topology (DNA-PAINT) adapt super-resolution fluorescence microscopy for protein sequencing by using fluorophore-labeled DNA probes that transiently bind to specific AA tags or epitopes on immobilized polypeptides. In quantitative PAINT (qPAINT), the binding kinetics of probes to lysine or cysteine residues provide a readout proportional to AA abundance, enabling compositional "fingerprinting" for protein identification.^[148] Discrete molecular imaging (DMI), a DNA-PAINT extension, unfolds proteins on a surface and images probe binding sites with sub-5 nm resolution, allowing partial sequence inference from spatially resolved tag patterns.^[142] These methods excel in multiplexing, with orthogonal DNA sequences enabling simultaneous detection of multiple AA types, and have identified over 50% of the human proteome via lysine labeling alone.^[148] Sample preparation involves chemical tagging and surface tethering, which can introduce biases but supports high-throughput analysis on DNA origami substrates.^[142] As of 2025, advances in RT include gold nanojunctions that distinguish all 20 AAs plus phosphorylated serine, threonine, and tyrosine variants through enhanced tunneling signals, with nanojunction gaps of 5 Å providing sixfold higher sensitivity than larger capacitor designs.^[149] Integration efforts with atomic force microscopy (AFM) for precise translocation control have improved resolution, while optimized setups report sequencing speeds approaching 10 AAs per second in controlled prototypes.^[149] DNA-PAINT variants have seen speed enhancements via left-handed DNA sequences, enabling 10-plex imaging for faster tag readout.^[150] Key advantages of these techniques include label-free (for RT) or minimally invasive optical detection, enabling intact protein analysis without digestion, and potential for PTM localization.^[142] Limitations encompass the need for surface immobilization, which risks conformational artifacts, and scalability challenges in achieving long-read sequences beyond 50-100 AAs due to junction instability or probe off-rates.^[151] Compared to parallel approaches like nanopore methods, single-molecule recognition emphasizes static binding signatures over dynamic translocation currents.^[142]

Bioinformatics Tools

Sequence Alignment and Assembly

In protein sequencing, fragmented data from techniques such as tandem mass spectrometry (MS/MS) often requires computational assembly to reconstruct full-length sequences, particularly when dealing with unknown or novel proteins. Sequence alignment and assembly processes integrate overlapping peptide fragments—derived from enzymatic digestion or partial degradation—into contiguous sequences, while alignment tools compare these to known homologs for validation and extension. These methods are essential for de novo sequencing scenarios where no reference database is available, enabling the elucidation of protein primary structures from raw spectral data. De novo assembly of protein sequences typically employs an overlap-layout-consensus (OLC) paradigm, where short peptide ladders or tags are first aligned based on sequence overlaps, then laid out into a scaffold, and finally refined via consensus to resolve ambiguities. This approach is particularly suited to peptide fragments generated from MS/MS, as it handles variable-length overlaps from enzymatic cleavages like trypsin digestion. For instance, the SPIDER software facilitates reconstructive homology searches by incorporating de novo sequenced tags with tolerated errors, allowing assembly of partial peptides into longer contigs through iterative overlap matching. Similarly, tools like PASS apply OLC to short peptide sequences (6-100 amino acids), using hash tables and prefix trees to efficiently identify and merge overlaps, achieving contigs with high identity to reference proteins in proteomics datasets.^[152]^[153] Once assembled, sequences undergo alignment to detect homologies and refine boundaries. BLASTP (Basic Local Alignment Search Tool for proteins) performs local alignments by identifying high-scoring segment pairs between query peptides and database entries, using a substitution matrix like BLOSUM62 to score matches while penalizing gaps and mismatches. For multiple sequence alignment, Clustal Omega employs progressive alignment strategies with guide trees derived from pairwise distances, enabling robust homology matching across related protein fragments and revealing conserved regions that inform assembly decisions. These tools enhance accuracy by cross-validating de novo contigs against evolutionary relatives. Error correction is integrated throughout assembly and alignment to mitigate inaccuracies from noisy MS data or degradation inefficiencies. In MS-based workflows, peptide-spectrum match (PSM) scores and de novo sequencing confidence metrics—such as those from spectral intensity correlations—are weighted to prioritize reliable overlaps, with algorithms discarding low-scoring fragments or applying voting schemes across redundant peptides. For Edman degradation-derived sequences, repetitive yield measurements (typically 95-99% per cycle) provide quantitative error estimates, allowing correction by adjusting for cumulative signal loss in ladder alignments. Hybrid methods combine these, using MS scores to validate Edman-derived residues and vice versa, reducing false positives in contig formation.^[154]^[155] For short-read data, graph-based models like de Bruijn graphs offer an alternative to OLC by representing peptides as k-mers (overlapping subsequences of length k) and resolving Eulerian paths through the graph to reconstruct sequences. Weighted variants incorporate edge scores from MS intensities or alignment probabilities, as seen in antibody sequencing pipelines where de Bruijn graphs assemble variable regions from fragmented de novo peptides with improved contiguity over traditional methods. This is computationally efficient for high-throughput data but requires careful k-mer selection to balance coverage and error propagation.^[156] The output of these processes is typically formatted as FASTA files, where assembled sequences are presented alongside headers containing metadata like confidence scores (e.g., average PSM or yield-adjusted probabilities) and assembly statistics such as contig length and overlap coverage. These scores, often derived from log-likelihood ratios or posterior probabilities, enable downstream assessment of reliability, with thresholds above 95% commonly used for biomedical applications.

Database Resources and Prediction Software

UniProt serves as a central repository for curated protein sequences and functional annotations, compiling data from various sources including nucleotide sequence translations and direct protein sequencing efforts. It provides access to over 199 million protein sequences in UniProtKB, with detailed annotations on function, interactions, and modifications, enabling researchers to retrieve sequences in formats like FASTA for downstream analysis.^[157]^[158] The Protein Data Bank (PDB) maintains a collection of three-dimensional protein structures determined experimentally, with associated primary amino acid sequences that link structural data to sequence information for over 200,000 entries as of 2025. These structure-linked sequences facilitate studies on protein folding and interactions, often integrated with tools for sequence visualization and alignment.^[159]^[160] RefSeq, curated by the National Center for Biotechnology Information (NCBI), offers a non-redundant set of reference protein sequences derived from genomic annotations, transcripts, and proteins, totaling millions of entries that provide stable identifiers for genome annotation and comparative genomics. These sequences are generated through automated and manual curation to ensure consistency and biological relevance.^[161]^[162] For identifying proteins from mass spectrometry (MS) data, search tools like Mascot and SEQUEST perform peptide-spectrum matching against these databases. Mascot, a widely used engine, compares experimental MS/MS spectra to theoretical peptide fragments from sequence databases, scoring matches based on mass accuracy and fragmentation patterns to achieve high-confidence identifications.^[163] Similarly, SEQUEST employs cross-correlation algorithms to align observed tandem MS spectra with predicted peptides, enabling the identification of proteins in complex mixtures with sensitivity for low-abundance species.^[164] Prediction software complements experimental sequencing by inferring structural and modification features from primary sequences. AlphaFold, developed by DeepMind, uses deep learning to predict three-dimensional protein structures directly from amino acid sequences, achieving near-experimental accuracy for many targets and revolutionizing structural biology since its 2021 release.^[165] SignalP predicts signal peptides—N-terminal sequences directing protein localization and often subject to cleavage as a post-translational processing event—using machine learning models trained on diverse eukaryotic and prokaryotic proteins, with versions up to 6.0 handling metagenomic data.^[166]^[167] Proteomics pipelines such as MaxQuant integrate these databases and tools for end-to-end analysis, processing raw MS data through peptide identification via embedded search engines like Andromeda, followed by quantification and annotation by querying UniProt or RefSeq for sequence validation. This workflow supports label-free and labeled experiments, linking spectral matches to database entries for comprehensive protein profiling.^[168]^[169] As of 2025, database resources have expanded to incorporate data from next-generation protein sequencing (NGPS) technologies, enhancing coverage of proteoforms and post-translational modifications in repositories like UniProt and dbPTM. These updates, including over 2.79 million PTM sites in dbPTM, reflect growing integration of high-throughput sequencing outputs for improved sequence diversity and functional insights.^[170]^[171]

Applications

Biomedical and Proteomic Uses

Protein sequencing plays a pivotal role in drug discovery by enabling the precise characterization of therapeutic proteins such as monoclonal antibodies (mAbs) and enzymes. De novo sequencing via mass spectrometry (MS) allows for the direct determination of antibody sequences from purified products, facilitating the identification of sequence variants and optimization for therapeutic efficacy. For instance, this approach has been applied to sequence intact IgG antibodies, revealing subtle modifications that impact binding affinity and stability in drug development pipelines. Similarly, sequencing enzymes aids in engineering variants with enhanced catalytic properties for targeted therapies. In disease proteomics, protein sequencing through MS has revolutionized the identification of cancer biomarkers, particularly in phosphoproteomics, which maps phosphorylation sites to uncover dysregulated signaling pathways. High-resolution MS techniques enable the profiling of thousands of phosphosites in tumor samples, highlighting alterations in kinases and downstream effectors that serve as diagnostic or prognostic markers. For example, phosphoproteomic analyses of cancer cell lines and tissues have identified novel therapeutic targets by revealing hyperphosphorylated proteins associated with tumor progression and metastasis. Protein sequencing supports personalized medicine by characterizing human leukocyte antigen (HLA) peptidomes, which are critical for immunotherapy design. MS-based immunopeptidomics sequences HLA-presented peptides on tumor cells, identifying neoantigens that can be targeted by T-cell therapies or vaccines tailored to individual HLA alleles. This has advanced precision oncology, where sequencing the HLA ligandome helps predict immune responses and select patients for checkpoint inhibitors or adoptive cell therapies. Comparative protein sequencing contributes to evolutionary studies by enabling phylogenetics through alignment of amino acid sequences across species, reconstructing evolutionary relationships and functional divergences. MS-driven sequencing of ancient or low-abundance proteins, combined with bioinformatics, has resolved phylogenetic trees for protein families, shedding light on adaptation and conservation in biological systems. High-throughput next-generation protein sequencing (NGPS) technologies are emerging as key tools in single-cell proteomics, allowing the analysis of proteomes at cellular resolution to study heterogeneity in diseases like cancer. As of 2025, platforms using single-molecule fluorescence detection sequence thousands of peptides per cell, enabling the mapping of protein expression variations that inform tumor microenvironments and therapeutic resistance. These advances, supported by bioinformatics tools for data integration, promise deeper insights into cellular dynamics.

Cryptographic and Novel Applications

Protein sequences have been proposed as robust keys for encryption due to their high variability and complexity, leveraging the 20 standard amino acids to generate unique, hard-to-crack codes. In the 2000s, early proposals explored amino acid variability in the context of biological computing, extending DNA cryptography principles to protein-level encoding by mapping codons to amino acids for secure data transformation. More recent advancements, such as proteinoid assemblies, utilize the self-organizing electrical properties of amino acids to create bio-inspired encryption schemes resistant to traditional brute-force attacks.^[172] A 2025 introduction to protein cryptography formalizes encoding data directly into amino acid sequences, enabling storage and transmission within synthetic proteins that can be sequenced for decryption.^[173] In synthetic biology, protein sequencing provides essential feedback for iterative design of novel proteins with tailored functions, allowing researchers to verify structural outcomes and refine sequences through cycles of synthesis and analysis. De novo protein design tools now integrate sequencing data to create unprecedented folds and assemblies, such as modular scaffolds for enzymatic applications, enhancing the precision of engineering beyond natural evolution. This feedback loop has enabled the development of customizable proteins for diverse biotechnological uses, including biosensors and therapeutic agents. Protein sequencing supports forensic applications by identifying species through variant peptide markers and profiling individuals via proteomic signatures from degraded samples where DNA is unavailable. For instance, mass spectrometry-based sequencing detects single amino acid polymorphisms in body fluids or tissues, enabling ethnic group determination and post-mortem interval estimation. In species identification, metaproteomic analysis distinguishes microbial or animal origins in trace evidence, aiding wildlife crime investigations. Environmental applications of protein sequencing include metaproteomics, which profiles functional proteins in microbiomes to assess ecosystem health and microbial dynamics without relying on genomic inference. In soil and water samples, sequencing reveals active enzymatic pathways in microbial communities, informing bioremediation strategies and biodiversity monitoring. Recent ultra-sensitive metaproteomic methods have expanded this to low-biomass environments, quantifying protein functions in complex consortia for climate impact studies.

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Top-down Proteomics | Thermo Fisher Scientific - US
Top-down proteomics detects degradation products, sequence variants, and PTMs. HRAM MS is essential for resolving intact proteins and their charge states.
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Top–down Proteomics of Large Proteins up to 223 kDa Enabled by ...
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Mass spectrometry based proteomics: existing capabilities and ...
Mass spectrometry (MS)-based proteomics is emerging as a broadly effective means for identification, characterization, and quantification of proteins.
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High-Throughput Sequencing of Peptoids and Peptide−Peptoid ...
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Mechanism of alternative splicing and its regulation - PMC
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ORFfinder Home - NCBI - NIH
ORF finder searches for open reading frames (ORFs) in the DNA sequence you enter. The program returns the range of each ORF, along with its protein translation.
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Thousands of novel translated open reading frames in humans ... - NIH
Abstract. Accurate annotation of protein coding regions is essential for understanding how genetic information is translated into function.
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EMBOSS TRANSEQ < Job Dispatcher < EMBL-EBI
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Mixing genome annotation methods in a comparative analysis ...
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The Expanding Landscape of Alternative Splicing Variation in ...
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Alternative Splicing, RNA Editing, and the Current Limits of Next ...
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A high-throughput SNP discovery strategy for RNA-seq data
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Genome-Wide Analysis in Vivo of Translation with Nucleotide ...
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RNA-Seq also provides a far more precise measurement of levels of transcripts and their isoforms than other methods.
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[PDF] Single-molecule protein sequencing with nanopores
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Oxford Nanopore's roadmap to proteomics
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Protein identification by nanopore peptide profiling - Nature
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Nanopore protein sequencing achieves significant new milestones
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Multi-pass, single-molecule nanopore reading of long protein strands
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The emerging landscape of single-molecule protein sequencing ...
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software for protein identification from sequence tags with de novo ...
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[2208.05598] PASS: De novo assembler for short peptide sequences
Aug 11, 2022 · Here we present PASS, a de novo assembler for short peptide sequences that can be used to reconstruct large portions of protein targets.
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Highly Robust de Novo Full-Length Protein Sequencing
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Estimating error rates for single molecule protein sequencing ...
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Antibody sequences assembly method based on weighted de Bruijn ...
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UniProt
UniProt is the world's leading high-quality, comprehensive and freely accessible resource of protein sequence and functional information.Blast · Downloads · UniProtKB 199,579,900 results · About UniProt
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Where do the UniProtKB protein sequences come from? | UniProt help
Aug 21, 2025 · Most UniProtKB sequences come from EMBL-Bank/GenBank/DDBJ translations, with some from PDB, direct sequencing, literature scans, and gene ...
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RCSB PDB: Homepage
RCSB Protein Data Bank (RCSB PDB) enables breakthroughs in science and education by providing access and tools for exploration, visualization, and analysis.About RCSB PDB · PDB Statistics · Team Members · Protein Data Bank
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RefSeq: NCBI Reference Sequence Database - NIH
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NCBI Reference Sequence (RefSeq): a curated non-redundant ...
RefSeq is a public database of nucleotide and protein sequences with corresponding feature and bibliographic annotation.
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Mascot help: Overview of sequence database searching
Mascot search overview. Mascot is a powerful search engine which uses mass spectrometry data to identify proteins from primary sequence databases.
[154]
Faster SEQUEST Searching for Peptide Identification from Tandem ...
The goal of peptide identification by database search is to label each experimentally observed spectrum from an MS/MS run with the peptide most likely to have ...
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Highly accurate protein structure prediction with AlphaFold - Nature
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SignalP 6.0 - DTU Health Tech - Bioinformatic Services
The SignalP 6.0 server predicts the presence of signal peptides and the location of their cleavage sites in proteins from Archaea, Gram-positive Bacteria, Gram ...
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SignalP 6.0 predicts all five types of signal peptides using protein ...
We introduce SignalP 6.0, a machine learning model that detects all five SP types and is applicable to metagenomic data.
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MaxQuant
MaxQuant is a quantitative proteomics software package designed for analyzing large mass-spectrometric data sets. It is specifically aimed at ...MaxQuant.Live · Download MaxQuant v2.7.5.0 · Summer School · MQ Community
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Andromeda: A Peptide Search Engine Integrated into the MaxQuant ...
The computational proteomics pipeline starting from raw data files to reported protein groups and their quantitative ratios now appears unified to the user.
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Quantum-Si and Researchers to Showcase Next-Generation Protein ...
Feb 13, 2025 · Quantum-Si and Researchers to Showcase Next-Generation Protein Sequencing™ at US HUPO 2025 · Protein Sequencing with Single Amino Acid Resolution ...Missing: databases | Show results with:databases
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dbPTM 2025 update: comprehensive integration of PTMs and ...
Nov 11, 2024 · The dbPTM 2025 update significantly expands the database to include over 2.79 million PTM sites, of which 2.243 million are experimentally ...Missing: NGPS | Show results with:NGPS