A theoretical justification for single molecule peptide sequencing

Photolysis of the peptide bond at 193 and 222 nm

Ultraviolet (UV) light is a well-established tool for fragmenting peptides in vacuum. This study investigates the fragmentation of peptides using 193 and 222 nm light in aqueous solution. Changes in the absorption spectra of solutions of the model dipeptide glycylglycine are monitored using a combination of real-time in situ transmission measurements and UV-Vis spectroscopy to report peptide bond scission following UV irradiation. Irradiation by a broadband ultraviolet light source flattens the absorbance peak centered near 193 nm, indicating cleavage of peptide bonds. Irradiation with low-intensity, monochromatic 193 and 222 nm light enabled measurements of the single-photon quantum yield of peptide bond scission, found to be (1.50 ± 0.12)% at 193 nm and (0.16 ± 0.03)% at 222 nm. These findings indicate that peptides may be fragmented in solution prior to emission into a mass spectrometer for new types of single-molecule analyses. The susceptibility of peptide bonds to ultraviolet radiation also suggests limited lifetimes for peptides on the early Earth's surface, which are relevant to theories of the origins-of-life, and suggests a role for protein damage in explanations of the germicidal effect of 222 nm light exposure.

Towards Next Generation Protein Sequencing

Understanding the structure and function of proteins is a critical objective in the life sciences. Protein sequencing, a central aspect of this endeavor, was first accomplished through Edman degradation in the 1950s. Since the late 20th century, mass spectrometry has emerged as a prominent method for protein sequencing. In recent years, single-molecule technologies have increasingly been applied to this field, yielding numerous innovative results. Among these, nanopore sensing has proven to be a reliable single-molecule technology, enabling advancements in amino acid recognition, short peptide differentiation, and peptide sequence reading. These developments are set to elevate protein sequencing technology to new heights. The next generation of protein sequencing technologies is anticipated to revolutionize our understanding of molecular mechanisms in biological processes and significantly enhance clinical diagnostics and treatments.

Emerging protein sequencing technologies: proteomics without mass spectrometry?

INTRODUCTION

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry.

AREAS COVERED

We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture.

EXPERT OPINION

Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Nanofluidic Lab-On-A-Chip Systems for Biosensing in Healthcare

Biosensing plays a vital role in healthcare monitoring, disease detection, and treatment planning. In recent years, nanofluidic technology has been increasingly explored to be developed into lab-on-a-chip biosensing systems. Given now the possibility of fabricating geometrically defined nanometric channels that are commensurate with the size of many biomolecules, nanofluidic-based devices are likely to become a key technology for the analysis of various clinical biomarkers, including DNA (deoxyribonucleic acid) and proteins in liquid biopsies. This review summarizes the fundamentals and technological advances of nanofluidics from the purview of single-molecule analysis, detection of low-abundance molecules, and single-cell analysis at the subcellular level. The extreme confinement and dominant surface charge effects in nanochannels provide unique advantages to nanofluidic devices for the manipulation and transport of target biomarkers. When coupled to a microfluidic network to facilitate sample introduction, integrated micro-nanofluidic biosensing devices are proving to be more sensitive and specific in molecular analysis compared to conventional assays in many cases. Based on recent progress in nanofluidics and current clinical trends, the review concludes with a discussion of near-term challenges and future directions for the development of nanofluidic-based biosensing systems toward enabling a new wave of lab-on-a-chip technology for personalized and preventive medicine.

How Have Massively Parallel Sequencing Technologies Furthered Our Understanding of Oncogenesis and Cancer Progression?

Massively parallel sequencing technologies have been a boon to many fields of biological science, including oncology. Cancer is an umbrella term for many diseases featuring abnormal cellular growth due to genetic and epigenetic aberrations. Advances in sequencing technology allow for interrogation of the DNA and RNA of cancer cells and other cells in the tumor microenvironment down to a single-base resolution. However, these strides come after a rich history of ground-breaking biological assays, like the discovery of the Philadelphia chromosome in the context of leukemia. Many specific genetic and epigenetic modifications have been implicated in oncogenesis, cancer progression, and response to treatment. Sequencing technologies have also helped to associate populations of bacteria in the microbiome to cancer development and prognosis. However, all this new information, especially when procured via high-throughput methods, comes at the cost of being more computationally and staff-resource intensive. There is also more risk to the privacy of the individuals with sequenced genomes. Notwithstanding, the overall benefit of sequencing technologies can greatly outweigh the risks with careful advancements and continued focus on the goal: helping those affected by cancer via precision medicine. Cancer biology has been and will continue to be elucidated by sequencing innovations in ways unimaginable without it.

A generalized protein identification method for novel and diverse sequencing technologies

Protein sequencing is a rapidly evolving field with much progress towards the realization of a new generation of protein sequencers. The early devices, however, may not be able to reliably discriminate all 20 amino acids, resulting in a partial, noisy and possibly error-prone signature of a protein. Rather than achieving de novo sequencing, these devices may aim to identify target proteins by comparing such signatures to databases of known proteins. However, there are no broadly applicable methods for this identification problem. Here, we devise a hidden Markov model method to study the generalized problem of protein identification from noisy signature data. Based on a hypothetical sequencing device that can simulate several novel technologies, we show that on the human protein database (N = 20 181) our method has a good performance under many different operating conditions such as various levels of signal resolvability, different numbers of discriminated amino acids, sequence fragments, and insertion and deletion error rates. Our results demonstrate the possibility of protein identification with high accuracy on many early experimental devices. We anticipate our method to be applicable for a wide range of protein sequencing devices in the future.

Estimating error rates for single molecule protein sequencing experiments

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

Amplifiable protein identification via residue-resolved barcoding and composition code counting

Ultrasensitive protein identification is of paramount importance in basic research and clinical diagnostics but remains extremely challenging. A key bottleneck in preventing single-molecule protein sequencing is that, unlike the revolutionary nucleic acid sequencing methods that rely on the polymerase chain reaction (PCR) to amplify DNA and RNA molecules, protein molecules cannot be directly amplified. Decoding the proteins via amplification of certain fingerprints rather than the intact protein sequence thus represents an appealing alternative choice to address this formidable challenge. Herein, we report a proof-of-concept method that relies on residue-resolved DNA barcoding and composition code counting for amplifiable protein fingerprinting (AmproCode). In AmproCode, selective types of residues on peptides or proteins are chemically labeled with a DNA barcode, which can be amplified and quantified via quantitative PCR. The operation generates a relative ratio as the residue-resolved 'composition code' for each target protein that can be utilized as the fingerprint to determine its identity from the proteome database. We developed a database searching algorithm and applied it to assess the coverage of the whole proteome and secretome via computational simulations, proving the theoretical feasibility of AmproCode. We then designed the residue-specific DNA barcoding and amplification workflow, and identified different synthetic model peptides found in the secretome at as low as the fmol/L level for demonstration. These results build the foundation for an unprecedented amplifiable protein fingerprinting method. We believe that, in the future, AmproCode could ultimately realize single-molecule amplifiable identification of trace complex samples without further purification, and it may open a new avenue in the development of next-generation protein sequencing techniques.

Full-length single-molecule protein fingerprinting

Proteins are the primary functional actors of the cell. While proteoform diversity is known to be highly biologically relevant, current protein analysis methods are of limited use for distinguishing proteoforms. Mass spectrometric methods, in particular, often provide only ambiguous information on post-translational modification sites, and sequences of co-existing modifications may not be resolved. Here we demonstrate fluorescence resonance energy transfer (FRET)-based single-molecule protein fingerprinting to map the location of individual amino acids and post-translational modifications within single full-length protein molecules. Our data show that both intrinsically disordered proteins and folded globular proteins can be fingerprinted with a subnanometer resolution, achieved by probing the amino acids one by one using single-molecule FRET via DNA exchange. This capability was demonstrated through the analysis of alpha-synuclein, an intrinsically disordered protein, by accurately quantifying isoforms in mixtures using a machine learning classifier, and by determining the locations of two O-GlcNAc moieties. Furthermore, we demonstrate fingerprinting of the globular proteins Bcl-2-like protein 1, procalcitonin and S100A9. We anticipate that our ability to perform proteoform identification with the ultimate sensitivity may unlock exciting new venues in proteomics research and biomarker-based diagnosis.

Molecular sensitised probe for amino acid recognition within peptide sequences

The combination of low-temperature scanning tunnelling microscopy with a mass-selective electro-spray ion-beam deposition established the investigation of large biomolecules at nanometer and sub-nanometer scale. Due to complex architecture and conformational freedom, however, the chemical identification of building blocks of these biopolymers often relies on the presence of markers, extensive simulations, or is not possible at all. Here, we present a molecular probe-sensitisation approach addressing the identification of a specific amino acid within different peptides. A selective intermolecular interaction between the sensitiser attached at the tip-apex and the target amino acid on the surface induces an enhanced tunnelling conductance of one specific spectral feature, which can be mapped in spectroscopic imaging. Density functional theory calculations suggest a mechanism that relies on conformational changes of the sensitiser that are accompanied by local charge redistributions in the tunnelling junction, which, in turn, lower the tunnelling barrier at that specific part of the peptide.

Beam search decoder for enhancing sequence decoding speed in single-molecule peptide sequencing data

Next-generation single-molecule protein sequencing technologies have the potential to significantly accelerate biomedical research. These technologies offer sensitivity and scalability for proteomic analysis. One auspicious method is fluorosequencing, which involves: cutting naturalized proteins into peptides, attaching fluorophores to specific amino acids, and observing variations in light intensity as one amino acid is removed at a time. The original peptide is classified from the sequence of light-intensity reads, and proteins can subsequently be recognized with this information. The amino acid step removal is achieved by attaching the peptides to a wall on the C-terminal and using a process called Edman Degradation to remove an amino acid from the N-Terminal. Even though a framework (Whatprot) has been proposed for the peptide classification task, processing times remain restrictive due to the massively parallel data acquisicion system. In this paper, we propose a new beam search decoder with a novel state formulation that obtains considerably lower processing times at the expense of only a slight accuracy drop compared to Whatprot. Furthermore, we explore how our novel state formulation may lead to even faster decoders in the future.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Robust and scalable single-molecule protein sequencing with fluorosequencing

The need to accurately survey proteins and their modifications with ever higher sensitivities, particularly in clinical settings with limited samples, is spurring development of new single molecule proteomics technologies. Fluorosequencing is one such highly parallelized single molecule peptide sequencing platform, based on determining the sequence positions of select amino acid types within peptides to enable their identification and quantification from a reference database. Here, we describe substantial improvements to fluorosequencing, including identifying fluorophores compatible with the sequencing chemistry, mitigating dye-dye interactions through the use of extended polyproline linkers, and developing an end-to-end workflow for sample preparation and sequencing. We demonstrate by fluorosequencing peptides in mixtures and identifying a target neoantigen from a database of decoy MHC peptides, highlighting the potential of the technology for high sensitivity clinical applications.

Purification and characterization of novel isoforms of the polyphenol oxidase from Malus domestica fruit pulp

Polyphenol oxidases (PPOs), belong to the group of oxidoreductases that are copper containing enzymes and are responsible for plant browning. PPOs are extensively distributed in plant kingdom and can oxidize wide range of aromatic compounds of industrial importance. The aim of this study was purification and characterization of PPO isoforms from the fruit pulp of Golden delicious apple. High performance liquid chromatography was used to purify the two novel isoforms of PPO and further their molecular weights (45 and 28 kDa) were determined using sodium dodecyl sulfate polyacrylamide gel electrophoresis. The purified isoforms have optimum pH (6.5), optimum temperature (40°C), the Vmax (4.45 μM/min) and Km (74.21 mM) with catechol substrate. The N-terminal microsequences of both PPO isoforms were determined using a pulse liquid protein sequencer and found to be AKITFHG (28 kDa) and APGGG (45 kDa). Polyphenol oxidases are efficiently used in the pharmaceutical, paper and pulp, textiles and food industries. Recently, the PPOs have been used for bioremediation and in the development of biosensors.

Estimating error rates for single molecule protein sequencing experiments

The practical application of new single molecule protein sequencing (SMPS) technologies requires accurate estimates of their associated sequencing error rates. Here, we describe the development and application of two distinct parameter estimation methods for analyzing SMPS reads produced by fluorosequencing. A Hidden Markov Model (HMM) based approach, extends whatprot, where we previously used HMMs for SMPS peptide-read matching. This extension offers a principled approach for estimating key parameters for fluorosequencing experiments, including missed amino acid cleavages, dye loss, and peptide detachment. Specifically, we adapted the Baum-Welch algorithm, a standard technique to estimate transition probabilities for an HMM using expectation maximization, but modified here to estimate a small number of parameter values directly rather than estimating every transition probability independently, which should help prevent overfitting. We demonstrate a high degree of accuracy on simulated data, but on experimental datasets, we observed that the model needed to be augmented with an additional error type, N-terminal blocking. This, in combination with data pre-processing, results in reasonable parameterizations of experimental datasets that agree with controlled experimental perturbations. A second independent implementation using a hybrid of DIRECT and Powell's method to reduce the root mean squared error (RMSE) between simulations and the real dataset was also developed. We compare these methods on both simulated and real data, finding that our Baum-Welch based approach outperforms DIRECT and Powell's method by most, but not all, criteria. Although some discrepancies between the results exist, we also find that both approaches provide similar error rate estimates from experimental single molecule fluorosequencing datasets.

Amino acid sequence assignment from single molecule peptide sequencing data using a two-stage classifier

We present a machine learning-based interpretive framework (whatprot) for analyzing single molecule protein sequencing data produced by fluorosequencing, a recently developed proteomics technology that determines sparse amino acid sequences for many individual peptide molecules in a highly parallelized fashion. Whatprot uses Hidden Markov Models (HMMs) to represent the states of each peptide undergoing the various chemical processes during fluorosequencing, and applies these in a Bayesian classifier, in combination with pre-filtering by a k-Nearest Neighbors (kNN) classifier trained on large volumes of simulated fluorosequencing data. We have found that by combining the HMM based Bayesian classifier with the kNN pre-filter, we are able to retain the benefits of both, achieving both tractable runtimes and acceptable precision and recall for identifying peptides and their parent proteins from complex mixtures, outperforming the capabilities of either classifier on its own. Whatprot's hybrid kNN-HMM approach enables the efficient interpretation of fluorosequencing data using a full proteome reference database and should now also enable improved sequencing error rate estimates.

Sampling the proteome by emerging single-molecule and mass spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.

Single-molecule fluorescence methods for protein biomarker analysis

Proteins have been considered key building blocks of life. In particular, the protein content of an organism and a cell offers significant information for the in-depth understanding of the disease and biological processes. Single-molecule protein detection/sequencing tools will revolutionize clinical (proteomics) research, offering ultrasensitivity for low-abundance biomarker (protein) detection, which is important for the realization of early-stage disease diagnosis and single-cell proteomics. This improved detection/measurement capability delivers new sets of techniques to explore new frontiers and address important challenges in various interdisciplinary areas including nanostructured materials, molecular medicine, molecular biology, and chemistry. Importantly, fluorescence-based methods have emerged as indispensable tools for single protein detection/sequencing studies, providing a higher signal-to-noise ratio (SNR). Improvements in fluorescent dyes/probes and detector capabilities coupled with advanced (image) analysis strategies have fueled current developments for single protein biomarker detections. For example, in comparison to conventional ELISA (i.e., based on ensembled measurements), single-molecule fluorescence detection is more sensitive, faster, and more accurate with reduced background, high-throughput, and so on. In comparison to MS sequencing, fluorescence-based single-molecule protein sequencing can achieve the sequencing of peptides themselves with higher sensitivity. This review summarizes various typical single-molecule detection technologies including their methodology (modes of operation), detection limits, advantages and drawbacks, and current challenges with recent examples. We describe the fluorescence-based single-molecule protein sequencing/detection based on five kinds of technologies such as fluorosequencing, N-terminal amino acid binder, nanopore light sensing, and DNA nanotechnology. Finally, we present our perspective for developing high-performance fluorescence-based sequencing/detection techniques.

A binary/digital approach to amino acid identification and its application to peptide sequencing and protein identification

A binary/digital method is proposed in theory for the identification of single amino acids (AAs) in the bulk or with a few molecules from a single binary measurement. Combined with Edman degradation (or other cleaving method), it can be used to sequence a peptide or identify the parent protein from a partial sequence. The approach is centered on the superspecificity property of transfer RNAs (tRNAs). Markedly different from conventional and recent single molecule (SM) sequencing methods based on analog measurements, it changes the analytical question 'Which AA is it?' to the much simpler one 'Is there an AA in the detection space?'. Each of 20 terminal residues cleaved from 20 copies of a peptide enters a different cavity with a unique tRNA; tRNA charging (or binding with AA) occurs only in the cavity with the cognate AA. The bound AA or the AA separated from the tRNA is detected with a single binary measurement; its identity is known from the position of the single high bit in the resulting 20-bit output. Alternatively, a 20-stage pipeline can be used with sparse samples. Detection of the bound AA can be done optically by tagging the AAs with a fluorescent dye, or of the freed AA electrically with a nanopore. Necessary conditions for accurate AA identification are satisfied in principle; related computations and simulation results are presented. A modified version that can be used for de novo sequencing in parallel of large numbers of peptides immobilized on a glass slide with the tRNAs carrying a fluorescent tag is also proposed. Both methods can be used for protein identification from partial sequences containing 2 or 3 AA types by using only the corresponding tRNAs. Experiments may be performed to validate them, followed by translation into practice with existing technology; potential implementation issues are discussed. Binary/digital amino acid identification for peptide sequencing.

Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device

Studies of the proteome would benefit greatly from methods to directly sequence and digitally quantify proteins and detect posttranslational modifications with single-molecule sensitivity. Here, we demonstrate single-molecule protein sequencing using a dynamic approach in which single peptides are probed in real time by a mixture of dye-labeled N-terminal amino acid recognizers and simultaneously cleaved by aminopeptidases. We annotate amino acids and identify the peptide sequence by measuring fluorescence intensity, lifetime, and binding kinetics on an integrated semiconductor chip. Our results demonstrate the kinetic principles that allow recognizers to identify multiple amino acids in an information-rich manner that enables discrimination of single amino acid substitutions and posttranslational modifications. With further development, we anticipate that this approach will offer a sensitive, scalable, and accessible platform for single-molecule proteomic studies and applications.

Evaluating the Effect of Dye-Dye Interactions of Xanthene-Based Fluorophores in the Fluorosequencing of Peptides

A peptide sequencing scheme utilizing fluorescence microscopy and Edman degradation to determine the amino acid position in fluorophore-labeled peptides was recently reported, referred to as fluorosequencing. It was observed that multiple fluorophores covalently linked to a peptide scaffold resulted in a decrease in the anticipated fluorescence output and worsened the single-molecule fluorescence analysis. In this study, we report an improvement in the photophysical properties of fluorophore-labeled peptides by incorporating long and flexible (PEG)₁₀ linkers at the peptide attachment points. Long linkers to the fluorophores were installed using copper-catalyzed azide-alkyne cycloaddition conditions. The photophysical properties of these peptides were analyzed in solution and immobilized on a microscope slide at the single-molecule level under peptide fluorosequencing conditions. Solution-phase fluorescence analysis showed improvements in both quantum yield and fluorescence lifetime with the long linkers. While on the solid support, photometry measurements showed significant increases in fluorescence brightness and 20 to 60% improvements in the ability to determine the amino acid position with fluorosequencing. This spatial distancing strategy demonstrates improvements in the peptide sequencing platform and provides a general approach for improving the photophysical properties in fluorophore-labeled macromolecules.

Protein Sequencing, One Molecule at a Time

Despite tremendous gains over the past decade, methods for characterizing proteins have generally lagged behind those for nucleic acids, which are characterized by extremely high sensitivity, dynamic range, and throughput. However, the ability to directly characterize proteins at nucleic acid levels would address critical biological challenges such as more sensitive medical diagnostics, deeper protein quantification, large-scale measurement, and discovery of alternate protein isoforms and modifications and would open new paths to single-cell proteomics. In response to this need, there has been a push to radically improve protein sequencing technologies by taking inspiration from high-throughput nucleic acid sequencing, with a particular focus on developing practical methods for single-molecule protein sequencing (SMPS). SMPS technologies fall generally into three categories: sequencing by degradation (e.g., mass spectrometry or fluorosequencing), sequencing by transit (e.g., nanopores or quantum tunneling), and sequencing by affinity (as in DNA hybridization-based approaches). We describe these diverse approaches, which range from those that are already experimentally well-supported to the merely speculative, in this nascent field striving to reformulate proteomics.

Navigated Delivery of Peptide to the Nanopore Using In-Plane Heterostructures of MoS2 and SnS2 for Protein Sequencing

The impressive success of DNA sequencing using nanopores makes it possible to realize nanopore based protein sequencing. Well-controlled capture and linear movement of the protein are essential for accurate nanopore protein sequencing. Here, by taking advantage of different binding affinities of protein to two isomorphic materials, we theoretically designed a heterostructual platform for delivering the unfolded peptide to the nanopore sensing region. Due to the stronger binding between the peptide and SnS₂ compared to MoS₂, the peptide would adsorb to the SnS₂ nanostripe and keep its threadlike conformation in the MoS₂/SnS₂/MoS₂ heterostructure. Through switching the direction of the applied electric field in real time, the peptide was strategically driven to move along the designed path to the target nanopore. The ionic current blockades were also found to be different as the compositions of the peptide were changed, indicating the possibility for differentiating different peptides using this platform.

Proteomics for Early Detection of Non-Muscle-Invasive Bladder Cancer: Clinically Useful Urine Protein Biomarkers

Bladder cancer is the fourth most common cancer in men, and most cases are non-muscle-invasive. A high recurrence rate is a critical problem in non-muscle-invasive bladder cancer. The availability of few urine tests hinders the effective detection of superficial and small bladder tumors. Cystoscopy is the gold standard for diagnosis; however, it is associated with urinary tract infections, hematuria, and pain. Early detection is imperative, as intervention influences recurrence. Therefore, urinary biomarkers need to be developed to detect these bladder cancers. Recently, several protein candidates in the urine have been identified as biomarkers. In the present narrative review, the current status of the development of urinary protein biomarkers, including FDA-approved biomarkers, is summarized. Additionally, contemporary proteomic technologies, such as antibody-based methods, mass-spectrometry-based methods, and machine-learning-based diagnosis, are reported. Furthermore, new strategies for the rapid and correct profiling of potential biomarkers of bladder cancer in urine are introduced, along with their limitations. The advantages of urinary protein biomarkers and the development of several related technologies are highlighted in this review. Moreover, an in-depth understanding of the scientific background and available protocols in research and clinical applications of the surveillance of non-muscle bladder cancer is provided.

Cataloguing the proteome: Current developments in single-molecule protein sequencing

The cellular proteome is complex and dynamic, with proteins playing a critical role in cell-level biological processes that contribute to homeostasis, stimuli response, and disease pathology, among others. As such, protein analysis and characterization are of extreme importance in both research and clinical settings. In the last few decades, most proteomics analysis has relied on mass spectrometry, affinity reagents, or some combination thereof. However, these techniques are limited by their requirements for large sample amounts, low resolution, and insufficient dynamic range, making them largely insufficient for the characterization of proteins in low-abundance or single-cell proteomic analysis. Despite unique technical challenges, several single-molecule protein sequencing (SMPS) technologies have been proposed in recent years to address these issues. In this review, we outline several approaches to SMPS technologies and discuss their advantages, limitations, and potential contributions toward an accurate, sensitive, and high-throughput platform.

Nanopore-Based Protein Identification

The implementation of a reliable, rapid, inexpensive, and simple method for whole-proteome identification would greatly benefit cell biology research and clinical medicine. Proteins are currently identified by cleaving them with proteases, detecting the polypeptide fragments with mass spectrometry, and mapping the latter to sequences in genomic/proteomic databases. Here, we demonstrate that the polypeptide fragments can instead be detected and classified at the single-molecule limit using a nanometer-scale pore formed by the protein aerolysin. Specifically, three different water-soluble proteins treated with the same protease, trypsin, produce different polypeptide fragments defined by the degree by which the latter reduce the nanopore's ionic current. The fragments identified with the aerolysin nanopore are consistent with the predicted fragments that trypsin could produce.

ProtSeq: Toward high-throughput, single-molecule protein sequencing via amino acid conversion into DNA barcodes

We demonstrate early progress toward constructing a high-throughput, single-molecule protein sequencing technology utilizing barcoded DNA aptamers (binders) to recognize terminal amino acids of peptides (targets) tethered on a next-generation sequencing chip. DNA binders deposit unique, amino acid-identifying barcodes on the chip. The end goal is that, over multiple binding cycles, a sequential chain of DNA barcodes will identify the amino acid sequence of a peptide. Toward this, we demonstrate successful target identification with two sets of target-binder pairs: DNA-DNA and Peptide-Protein. For DNA-DNA binding, we show assembly and sequencing of DNA barcodes over six consecutive binding cycles. Intriguingly, our computational simulation predicts that a small set of semi-selective DNA binders offers significant coverage of the human proteome. Toward this end, we introduce a binder discovery pipeline that ultimately could merge with the chip assay into a technology called ProtSeq, for future high-throughput, single-molecule protein sequencing.

•

Designed ProtSeq protein sequencing method compatible with widely used NGS technology

•

Built Target-Switch SELEX to isolate aptamers specific to N-terminal amino acids (AAs)

•

Showed binding, ligation, cleavage, and NGS of six DNA binders in ordered barcode chain

•

Developed pipeline to deconvolve AAs from DNA barcodes to identify putative proteins

What Is the Role of HLA-I on Cancer Derived Extracellular Vesicles? Defining the Challenges in Characterisation and Potential Uses of This Ligandome

The Human Leukocyte Antigen class I (HLA-I) system is an essential part of the immune system that is fundamental to the successful activation of cytotoxic lymphocytes, and an effective subsequent immune attack against both pathogen-infected and cancer cells. The importance of cytotoxic T cell activity and ability to detect foreign cancer-related antigenic peptides has recently been highlighted by the successful application of monoclonal antibody-based checkpoint inhibitors as novel immune therapies. Thus, there is an increased interest in fully characterising the repertoire of peptides that are being presented to cytotoxic CD8+ T cells by cancer cells. However, HLA-I is also known to be present on the surface of extracellular vesicles, which are released by most if not all cancer cells. Whilst the peptide ligandome presented by cell surface HLA class I molecules on cancer cells has been studied extensively, the ligandome of extracellular vesicles remains relatively poorly defined. Here, we will describe the current understanding of the HLA-I peptide ligandome and its role on cancer-derived extracellular vesicles, and evaluate the aspects of the system that have the potential to advance immune-based therapeutic approaches for the effective treatment of cancer.

Single-molecule mechanical fingerprinting with DNA nanoswitch calipers

Decoding the identity of biomolecules from trace samples is a longstanding goal in the field of biotechnology. Advances in DNA analysis have substantially affected clinical practice and basic research, but corresponding developments for proteins face challenges due to their relative complexity and our inability to amplify them. Despite progress in methods such as mass spectrometry and mass cytometry, single-molecule protein identification remains a highly challenging objective. Towards this end, we combine DNA nanotechnology with single-molecule force spectroscopy to create a mechanically reconfigurable DNA nanoswitch caliper capable of measuring multiple coordinates on single biomolecules with atomic resolution. Using optical tweezers, we demonstrate absolute distance measurements with ångström-level precision for both DNA and peptides, and using multiplexed magnetic tweezers, we demonstrate quantification of relative abundance in mixed samples. Measuring distances between DNA-labelled residues, we perform single-molecule fingerprinting of synthetic and natural peptides, and show discrimination, within a heterogeneous population, between different posttranslational modifications. DNA nanoswitch calipers are a powerful and accessible tool for characterizing distances within nanoscale complexes that will enable new applications in fields such as single-molecule proteomics.

Evaluation of FRET X for single-molecule protein fingerprinting

Single-molecule protein identification is an unrealized concept with potentially ground-breaking applications in biological research. We propose a method called FRET X (Förster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands and accumulated FRET efficiencies constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines, and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

•

We propose a FRET-based single-molecule protein identification method

•

Peptides are experimentally distinguishable by their fingerprints

•

Our approach can classify the constituents of complex samples with 95% accuracy

Herding cats: Label-based approaches in protein translocation through nanopore sensors for single-molecule protein sequence analysis

Proteins carry out life's essential functions. Comprehensive proteome analysis technologies are thus required for a full understanding of the operating principles of biological systems. While current proteomics techniques suffer from limitations in sensitivity and/or throughput, nanopore technology has the potential to enable de novo protein identification through single-molecule sequencing. However, a significant barrier to achieving this goal is controlling protein/peptide translocation through the nanopore sensor for processive strand analysis. Here, we review recent approaches that use a range of techniques, from oligonucleotide conjugation to molecular motors, aimed at driving protein strands and peptides through protein nanopores. We further discuss site-specific protein conjugation chemistry that could be combined with these translocation approaches as future directions to achieve single-molecule protein detection and sequencing of native proteins.

Velocity control of protein translocation through a nanopore by tuning the fraction of benzenoid residues

Protein sequencing is essential to unveil the mechanism of cellular processes that govern the function of living organisms, and which play a crucial role in the field of drug design and molecular diagnostics. Nanopores have been proved to be effective tools in single molecule sensing, but the fast translocation speed of a peptide through a nanopore is one of the major obstacles that hinders the development of nanopore-based protein sequencing. In this work, by using molecular dynamics simulations (MDS) it is found that the peptide containing more hydrophobic residues permeates slower through a molybdenum disulfide nanopore, which originates from the strong interaction between the membrane surface and the hydrophobic residues. The binding affinity is remarkable especially for benzenoid residues as they contain a hydrophobic aromatic ring that is composed of relatively non-polar C-C and C-H bonds. By tuning the fraction of benzenoid residues of the peptide, the velocity of the protein translocation through the nanopore is well controlled. The peptide with all the hydrophobic residues being benzenoid residues is found to translocate through the nanopore almost ten times slower than the one without any benzenoid residues, which is beneficial for gathering adequate information for precise amino acid identification.

Proteomics in the pharmaceutical and biotechnology industry: a look to the next decade

INTRODUCTION

Pioneering technologies such as proteomics have helped fuel the biotechnology and pharmaceutical industry with the discovery of novel targets and an intricate understanding of the activity of therapeutics and their various activities in vitro and in vivo. The field of proteomics is undergoing an inflection point, where new sensitive technologies are allowing intricate biological pathways to be better understood, and novel biochemical tools are pivoting us into a new era of chemical proteomics and biomarker discovery. In this review, we describe these areas of innovation, and discuss where the fields are headed in terms of fueling biotechnological and pharmacological research and discuss current gaps in the proteomic technology landscape.

AREAS COVERED

Single cell sequencing and single molecule sequencing. Chemoproteomics. Biological matrices and clinical samples including biomarkers. Computational tools including instrument control software, data analysis.

EXPERT OPINION

Proteomics will likely remain a key technology in the coming decade, but will have to evolve with respect to type and granularity of data, cost and throughput of data generation as well as integration with other technologies to fulfill its promise in drug discovery.

Single Molecule Ratcheting Motion of Peptides in a Mycobacterium smegmatis Porin A (MspA) Nanopore

Diverse functions of proteins, including synthesis, catalysis, and signaling, result from their highly variable amino acid sequences. The technology allowing for direct analysis of protein sequences, however, is still unsatisfactory. Recent developments of nanopore sequencing of DNA or RNA have motivated attempts to realize nanopore sequencing of peptides in a similar manner. The core challenge has been to achieve a controlled ratcheting motion of the target peptide, which is currently restricted to a limited choice of compatible enzymes. By constructing peptide-oligonucleotide conjugates (POCs) and measurements with nanopore-induced phase-shift sequencing (NIPSS), direct observation of the ratcheting motion of peptides has been successfully achieved. The generated events show a clear sequence dependence on the peptide that is being tested. The method is compatible with peptides with either a conjugated N- or C-terminus. The demonstrated results suggest a proof of concept of nanopore sequencing of peptide and can be useful for peptide fingerprinting.

The emerging landscape of single-molecule protein sequencing technologies

Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.

Theoretical Considerations for Next-Generation Proteomics

While mass spectrometry still dominates proteomics research, alternative and potentially disruptive, next-generation technologies are receiving increased investment and attention. Most of these technologies aim at the sequencing of single peptide or protein molecules, typically labeling or otherwise distinguishing a subset of the proteinogenic amino acids. This note considers some theoretical aspects of these future technologies from a bottom-up proteomics viewpoint, including the ability to uniquely identify human proteins as a function of which and how many amino acids can be read, enzymatic efficiency, and the maximum read length. This is done through simulations under ideal and non-ideal conditions to set benchmarks for what may be achievable with future single-molecule sequencing technology. The simulations reveal, among other observations, that the best choice of reading N amino acids performs similarly to the average choice of N+1 amino acids, and that the discrimination power of the amino acids scales with their frequency in the proteome. The simulations are agnostic with respect to the next-generation proteomics platform, and the results and conclusions should therefore be applicable to any single-molecule partial peptide sequencing technology.

Computational assessment of the feasibility of protonation-based protein sequencing

Recent advances in DNA sequencing methods revolutionized biology by providing highly accurate reads, with high throughput or high read length. These read data are being used in many biological and medical applications. Modern DNA sequencing methods have no equivalent in protein sequencing, severely limiting the widespread application of protein data. Recently, several optical protein sequencing methods have been proposed that rely on the fluorescent labeling of amino acids. Here, we introduce the reprotonation-deprotonation protein sequencing method. Unlike other methods, this proposed technique relies on the measurement of an electrical signal and requires no fluorescent labeling. In reprotonation-deprotonation protein sequencing, the terminal amino acid is identified through its unique protonation signal, and by repeatedly cleaving the terminal amino acids one-by-one, each amino acid in the peptide is measured. By means of simulations, we show that, given a reference database of known proteins, reprotonation-deprotonation sequencing has the potential to correctly identify proteins in a sample. Our simulations provide target values for the signal-to-noise ratios that sensor devices need to attain in order to detect reprotonation-deprotonation events, as well as suitable pH values and required measurement times per amino acid. For instance, an SNR of 10 is required for a 61.71% proteome recovery rate with 100 ms measurement time per amino acid.

Solid-Phase Peptide Capture and Release for Bulk and Single-Molecule Proteomics

The field of proteomics has expanded recently with more sensitive techniques for the bulk measurement of peptides as well as single-molecule techniques. One limiting factor for some of these methods is the need for multiple chemical derivatizations and highly pure proteins free of contaminants. We demonstrate a solid-phase capture-release strategy suitable for the proteolysis, purification, and subsequent chemical modification of peptides. We use this resin on an HEK293T cell lysate and perform one-pot proteolysis, capture, and derivatization to survey peptide capture biases from over 40 000 unique peptides from a cellular proteome. We also show that this capture can be reversed in a traceless manner, such that it is amenable for single-molecule proteomics techniques. With this technique, we perform a fluorescent labeling and C-terminal derivatization on a peptide and subject it to fluorosequencing, demonstrating that washing the resin is sufficient to remove excess dyes and other reagents prior to single-molecule protein sequencing.

Peptide science: A "rule model" for new generations of peptidomimetics

Peptides have been heavily investigated for their biocompatible and bioactive properties. Though a wide array of functionalities can be introduced by varying the amino acid sequence or by structural constraints, properties such as proteolytic stability, catalytic activity, and phase behavior in solution are difficult or impossible to impart upon naturally occurring α-L-peptides. To this end, sequence-controlled peptidomimetics exhibit new folds, morphologies, and chemical modifications that create new structures and functions. The study of these new classes of polymers, especially α-peptoids, has been highly influenced by the analysis, computational, and design techniques developed for peptides. This review examines techniques to determine primary, secondary, and tertiary structure of peptides, and how they have been adapted to investigate peptoid structure. Computational models developed for peptides have been modified to predict the morphologies of peptoids and have increased in accuracy in recent years. The combination of in vitro and in silico techniques have led to secondary and tertiary structure design principles that mirror those for peptides. We then examine several important developments in peptoid applications inspired by peptides such as pharmaceuticals, catalysis, and protein-binding. A brief survey of alternative backbone structures and research investigating these peptidomimetics shows how the advancement of peptide and peptoid science has influenced the growth of numerous fields of study. As peptide, peptoid, and other peptidomimetic studies continue to advance, we will expect to see higher throughput structural analyses, greater computational accuracy and functionality, and wider application space that can improve human health, solve environmental challenges, and meet industrial needs. STATEMENT OF SIGNIFICANCE: Many historical, chemical, and functional relations draw a thread connecting peptides to their recent cognates, the "peptidomimetics". This review presents a comprehensive survey of this field by highlighting the width and relevance of these familial connections. In the first section, we examine the experimental and computational techniques originally developed for peptides and their morphing into a broader analytical and predictive toolbox. The second section presents an excursus of the structures and properties of prominent peptidomimetics, and how the expansion of the chemical and structural diversity has returned new exciting properties. The third section presents an overview of technological applications and new families of peptidomimetics. As the field grows, new compounds emerge with clear potential in medicine and advanced manufacturing.

The Human Immunopeptidome Project: A Roadmap to Predict and Treat Immune Diseases

The science that investigates the ensembles of all peptides associated to human leukocyte antigen (HLA) molecules is termed "immunopeptidomics" and is typically driven by mass spectrometry (MS) technologies. Recent advances in MS technologies, neoantigen discovery and cancer immunotherapy have catalyzed the launch of the Human Immunopeptidome Project (HIPP) with the goal of providing a complete map of the human immunopeptidome and making the technology so robust that it will be available in every clinic. Here, we provide a long-term perspective of the field and we use this framework to explore how we think the completion of the HIPP will truly impact the society in the future. In this context, we introduce the concept of immunopeptidome-wide association studies (IWAS). We highlight the importance of large cohort studies for the future and how applying quantitative immunopeptidomics at population scale may provide a new look at individual predisposition to common immune diseases as well as responsiveness to vaccines and immunotherapies. Through this vision, we aim to provide a fresh view of the field to stimulate new discussions within the community, and present what we see as the key challenges for the future for unlocking the full potential of immunopeptidomics in this era of precision medicine.

Resolving Chemical Modifications to a Single Amino Acid within a Peptide Using a Biological Nanopore

While DNA sequencing is now amply available, fast, and inexpensive, protein sequencing remains a tremendous challenge. Nanopores may allow for developing a protein sequencer with single-molecule capabilities. As identification of 20 different amino acids currently presents an unsurmountable challenge, fingerprinting schemes are pursued, in which only a subset of amino acids is labeled and detected. This requires modification of amino acids with chemical structures that generate a distinct nanopore ionic current signal. Here, we use a model peptide and the fragaceatoxin C nanopore to characterize six potential tags for a fingerprinting approach using nanopores. We find that labeled and unlabeled proteins can be clearly distinguished and that sensitive detection is obtained for labels with a spectrum of different physicochemical properties such as mass (427-1275 Da), geometry, charge, and hydrophobicity. Additionally, information about the position of the label along the peptide chain can be obtained from individual current-blockade event features. The results represent an important advance toward the development of a single-molecule protein-fingerprinting device with nanopores.

Strategies for Development of a Next-Generation Protein Sequencing Platform

Proteomic analysis can be a critical bottleneck in cellular characterization. The current paradigm relies primarily on mass spectrometry of peptides and affinity reagents (i.e., antibodies), both of which require a priori knowledge of the sample. An unbiased protein sequencing method, with a dynamic range that covers the full range of protein concentrations in proteomes, would revolutionize the field of proteomics, allowing a more facile characterization of novel gene products and subcellular complexes. To this end, several new platforms based on single-molecule protein-sequencing approaches have been proposed. This review summarizes four of these approaches, highlighting advantages, limitations, and challenges for each method towards advancing as a core technology for next-generation protein sequencing.

Protein fingerprinting with digital sequences of linear protein subsequence volumes: a computational study

Proteins in a proteome can be identified from a sequence of K integers equal to the digitized volumes of subsequences with L residues from the primary sequence of a stretched protein. Exhaustive computations on the proteins of Helicobacter pylori (UniProt id UP000000210) with L and K in the range 4-8 show that approx. 90% of the proteins can be identified uniquely in this manner. This computational result can be translated into practice with a nanopore, an emerging technology that does not require analyte immobilization, proteolysis or labeling. Unlike other methods, most of which focus on a specific target protein, nanopore-based methods enable the identification of multiple proteins from a sample in a single run. Recent work by Kennedy, Kolmogorov and associates shows that the blockade current due to a protein molecule translocating through a nanopore is roughly proportional to one or more contiguous residues. The present study points to a modified version in which the volumes of subsequences (rather than of single residues) may be obtained by integrating the blockade current due to L contiguous residues. The advantages arising from this include lower detector bandwidth, elimination of the homopolymer problem and reduced noise. Because an identifier is based on near as well as distant (up to 2KL-L) residues, this approach uses more global information than an approach based on single residues and short-range correlations. The results of the study, which are available in a data supplement, are discussed in detail. Potential implementation issues are addressed.

A theoretical analysis of single molecule protein sequencing via weak binding spectra

We propose and theoretically study an approach to massively parallel single molecule peptide sequencing, based on single molecule measurement of the kinetics of probe binding (Havranek, et al., 2013) to the N-termini of immobilized peptides. Unlike previous proposals, this method is robust to both weak and non-specific probe-target affinities, which we demonstrate by applying the method to a range of randomized affinity matrices consisting of relatively low-quality binders. This suggests a novel principle for proteomic measurement whereby highly non-optimized sets of low-affinity binders could be applicable for protein sequencing, thus shifting the burden of amino acid identification from biomolecular design to readout. Measurement of probe occupancy times, or of time-averaged fluorescence, should allow high-accuracy determination of N-terminal amino acid identity for realistic probe sets. The time-averaged fluorescence method scales well to weakly-binding probes with dissociation constants of tens or hundreds of micromolar, and bypasses photobleaching limitations associated with other fluorescence-based approaches to protein sequencing. We argue that this method could lead to an approach with single amino acid resolution and the ability to distinguish many canonical and modified amino acids, even using highly non-optimized probe sets. This readout method should expand the design space for single molecule peptide sequencing by removing constraints on the properties of the fluorescent binding probes.

FraC nanopores with adjustable diameter identify the mass of opposite-charge peptides with 44 dalton resolution

A high throughput single-molecule method for identifying peptides and sequencing proteins based on nanopores could reduce costs and increase speeds of sequencing, allow the fabrication of portable home-diagnostic devices, and permit the characterization of low abundance proteins and heterogeneity in post-translational modifications. Here we engineer the size of Fragaceatoxin C (FraC) biological nanopore to allow the analysis of a wide range of peptide lengths. Ionic blockades through engineered nanopores distinguish a variety of peptides, including two peptides differing only by the substitution of alanine with glutamate. We also find that at pH 3.8 the depth of the peptide current blockades scales with the mass of the peptides irrespectively of the chemical composition of the analyte. Hence, this work shows that FraC nanopores allow direct readout of the mass of single peptide in solution, which is a crucial step towards the developing of a real-time and single-molecule protein sequencing device.

Differentiation of selectively labeled peptides using solid-state nanopores

Determination of the amino acid sequence of a protein is critical for understanding various biological processes. Mass spectrometry has mainly been used for protein identification; however, there are limitations to its sensitivity when detecting low abundance proteins. In this study, we attempted to distinguish between three similar peptide sequences (∼40 amino acids, ∼5 kDa) that differed only by the location or number of cysteine residues with solid-state nanopores. The cysteine residues are located at one end, one at the center, and at both ends for each of the three peptides. We found that differentiation of the three types of peptides by nanopore signals was difficult. However, when the cysteine residue was labeled with a negatively charged molecule, Flamma® 496, the labeled peptides showed distinct signals for each peptide. Comparing the relative current blockades of labeled peptides with applied voltages, we found that the label was able to change peptide conformations and the resulting ionic current signals from the three labeled peptides were distinguished based on the relative current blockade, full width at half-maximum of the current blockade distribution, and single-molecule level peak shape analysis. Our results suggest that solid-state nanopores combined with a targeted labeling strategy could be used to obtain characteristic peptide signatures that could ultimately be used for protein identification.

Engineering ClpS for selective and enhanced N-terminal amino acid binding

One of the central challenges in the development of single-molecule protein sequencing technologies is achieving high-fidelity sequential recognition and detection of specific amino acids that comprise the peptide sequence. An approach towards achieving this goal is to leverage naturally occurring proteins that function through recognition of amino (N)-terminal amino acids (NAAs). One such protein, the N-end rule pathway adaptor protein ClpS, natively recognizes NAAs on a peptide chain. The native ClpS protein has a high specificity albeit modest affinity for the amino acid Phe at the N-terminus but also recognizes the residues Trp, Tyr, and Leu at the N-terminal position. Here, we employed directed evolution methods to select for ClpS variants with enhanced affinity and selectivity for two NAAs (Phe and Trp). Using this approach, we identified two promising variants of the Agrobacterium tumefaciens ClpS protein with native residues 34-36 ProArgGlu mutated to ProMetSer and CysProSer. In vitro surface binding assays indicate that the ProMetSer variant has enhanced affinity for Phe at the N-terminus with sevenfold tighter binding relative to wild-type ClpS, and that the CysProSer variant binds selectively to Trp over Phe at the N-terminus while having a greater affinity for both Trp and Phe. Taken together, this work demonstrates the utility of engineering ClpS to make it more effective for potential use in peptide sequencing applications.