Single-molecule Protein Sequencing Research Articles

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.

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.

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.

Abstract 5301: Detection of arginine posttranslational modifications by single-molecule protein sequencing on the Quantum-Si platform

Abstract Aberrant post-translational modifications (PTMs) on arginine residues, such as methylation and citrullination, are closely linked to oncogenic processes. Detection of these PTMs is challenging with current technology and can be hampered by low abundance and mass differences that are difficult to resolve by mass spectrometry. To overcome these challenges, we developed methods for PTM detection using Quantum-Si's Platinum single-molecule protein sequencing platform. Proteins are sequenced on Quantum-Si's Platinum instrument using our library prep protocol. Briefly, proteins are digested into peptide fragments, conjugated to linkers, then immobilized at the bottom of nanoscale reaction chambers on a semiconductor chip, resulting in exposed N-termini for sequencing. During sequencing, the immobilized peptides are exposed to a solution of dye-labeled N-terminal amino acid (NAA) recognizers that reversibly bind to their cognate NAAs with distinct kinetic properties. Aminopeptidases sequentially remove NAAs to expose subsequent amino acids for recognition. Fluorescence lifetime, intensity, and kinetic data are collected in real time and analyzed to determine primary sequence and PTM content. The data output consists of distinct pulsing regions called recognition segments (RSs). Each RS corresponds to a period of time between aminopeptidase cleavage events, during which an NAA recognizer binds on/off to its exposed target NAA. Chemical modification to a NAA or to a nearby downstream amino acid can modulate recognizer affinity, resulting in a characteristic change in the average pulse duration (PD) during an RS. To demonstrate the detection and differentiation of arginine PTMs, we applied our platform to distinguish between asymmetric dimethylarginine (ADMA) and symmetric dimethylarginine (SDMA) and between citrulline and native arginine residues. Sequencing traces revealed that arginine and ADMA bind the arginine recognizer (PS621) with similar PD, whereas SDMA exhibited no binding. These results indicate that SDMA, in contrast to ADMA, has reduced binding affinity with PS621, providing a clear kinetic difference between these isomeric arginine PTMs. Moreover, citrulline and arginine side chains also exhibited distinguishable kinetic signatures. Citrullination eliminated the N-terminal arginine recognized by PS621 and resulted in a large increase in the median PD of preceding amino acid residues. Taken together, single-molecule protein sequencing offers an alternative approach to detection of arginine PTMs that is not based on m/z, but rather on the kinetic signature of binding between recognizers and N-terminal amino acids. The ability to directly detect arginine PTMs offers potential for biomedical research. We envisage applications using Quantum-Si's platform to key areas of cancer research including biomarker development and drug discovery. Citation Format: Kenneth Skinner, David Kamber, Brian Reed. Detection of arginine posttranslational modifications by single-molecule protein sequencing on the Quantum-Si platform. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5301.

Single-molecule identification of folded proteins from nanopore ionic current signatures.

Single-molecule protein sequencing would provide unprecedented insights into cellular processes and the diseases that arise from protein misfunction. Recently, nanopores have emerged as a platform for fast label-free analyte detection guided by the ionic current blockades generated from single molecules translocating under an applied electric field. The major bottleneck to nanopore protein sequencing is the need to distinguish twenty proteinogenic amino acids accurately at every single residue. Here, we demonstrate a proof-of-principle proteome characterization system that relies on ionic current signatures produced by complete translocation of natively folded proteins through an array of nanopores. Using the steric exclusion model, we computed the ionic current spectra for all structurally known proteins, initially excluding all membrane proteins, proteins containing partially-disordered domains, and multimeric protein assemblies - about 4000 proteins in total. For each protein, we computationally simulated a fluid driven tumbling through a nanopore by calculating current for 5000 randomly chosen orientations that changes the effective cross-section and thus modulates current. Ionic current signatures of proteins with varying molecular weight clearly differed by the magnitude of blockades confirming the steric exclusion origin of the current. Interestingly, proteins with similar molecular weight also showed a varied range of mean blockade currents due to changing current signatures originating from the protein shape. Using a support vector machine learning algorithm trained on our data set, we were able to theoretically distinguish the weight and shape of the 4000 chosen proteins to near perfect accuracy, in the absence of instrumentation noise. We then theoretically introduced a thermal noise source into our simulated ionic current recordings to demonstrate the minimum requirements of an experimental system that would permit accurate protein identification from a single molecule translocation.

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.

Single-molecule protein sequencing on a semiconductor device.

Single-molecule protein sequencing on a semiconductor device.

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.

Identification of Single Amino Acid Chiral and Positional Isomers Using an Electrostatically Asymmetric Nanopore.

Chirality is essential in nearly all biological organizations and chemical reactions but is rarely considered due to technical limitations in identifying L/D isomerization. Using OmpF, a membrane channel from Escherichia coli with an electrostatically asymmetric constriction zone, allows discriminating chiral amino acids in a single peptide. The heterogeneous distribution of charged residues in OmpF causes a strong lateral electrostatic field at the constriction. This laterally asymmetric constriction zone forces the sidechains of the peptides to specific orientations within OmpF, causing distinct ionic current fluctuations. Using statistical analysis of the respective ionic current variations allows distinguishing the presence and position of a single amino acid with different chiralities. To explore potential applications, the disease-related peptide β-Amyloid and its d-Asp1 isoform and a mixture of the icatibant peptide drug (HOE 140) and its d-Ser7 mutant have been discriminated. Both chiral isomers were not applicable to be distinguished by mass spectroscopy approaches. These findings highlight a novel sensing mechanism for identifying single amino acids in single peptides and even for achieving single-molecule protein sequencing.

Label-Free Optical Analysis of Biomolecules in Solid-State Nanopores: Toward Single-Molecule Protein Sequencing.

Sequence identification of peptides and proteins is central to proteomics. Protein sequencing is mainly conducted by insensitive mass spectroscopy because proteins cannot be amplified, which hampers applications such as single-cell proteomics and precision medicine. The commercial success of portable nanopore sequencers for single DNA molecules has inspired extensive research and development of single-molecule techniques for protein sequencing. Among them, three challenges remain: (1) discrimination of the 20 amino acids as building blocks of proteins; (2) unfolding proteins; and (3) controlling the motion of proteins with nonuniformly charged sequences. In this context, the emergence of label-free optical analysis techniques for single amino acids and peptides by solid-state nanopores shows promise for addressing the first challenge. In this Perspective, we first discuss the current challenges of single-molecule fluorescence detection and nanopore resistive pulse sensing in a protein sequencing. Then, label-free optical methods are described to show how they address the single-amino-acid identification within single peptides. They include localized surface plasmon resonance detection and surface-enhanced Raman spectroscopy on plasmonic nanopores. Notably, we report new data to show the ability of plasmon-enhanced Raman scattering to record and discriminate the 20 amino acids at a single-molecule level. In addition, we discuss briefly the manipulation of molecule translocation and liquid flow in plasmonic nanopores for controlling molecule movement to allow high-resolution reading of protein sequences. We envision that a combination of Raman spectroscopy with plasmonic nanopores can succeed in single-molecule protein sequencing in a label-free way.

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.

Using new techniques to detect and sequence individual proteins

Developing technologies for single molecule protein sequencing to improve results in clinical and biological research

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.

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

SummaryWe 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.

Photoredox-Catalyzed Decarboxylative C-Terminal Differentiation for Bulk- and Single-Molecule Proteomics.

Methods for the selective labeling of biogenic functional groups on peptides are being developed and used in the workflow of both current and emerging proteomics technologies, such as single-molecule fluorosequencing. To achieve successful labeling with any one method requires that the peptide fragments contain the functional group for which labeling chemistry is designed. In practice, only two functional groups are present in every peptide fragment regardless of the protein cleavage site, namely, an N-terminal amine and a C-terminal carboxylic acid. Developing a global-labeling technology, therefore, requires one to specifically target the N- and/or C-terminus of peptides. In this work, we showcase the first successful application of photocatalyzed C-terminal decarboxylative alkylation for peptide mass spectrometry and single-molecule protein sequencing that can be broadly applied to any proteome. We demonstrate that peptides in complex mixtures generated from enzymatic digests from bovine serum albumin, as well as protein mixtures from yeast and human cell extracts, can be site-specifically labeled at their C-terminal residue with a Michael acceptor. Using two distinct analytical approaches, we characterize C-terminal labeling efficiencies of greater than 50% across complete proteomes and document the proclivity of various C-terminal amino-acid residues for decarboxylative labeling, showing histidine and tryptophan to be the most disfavored. Finally, we combine C-terminal decarboxylative labeling with an orthogonal carboxylic acid-labeling technology in tandem to establish a new platform for fluorosequencing.

Is the Volume Exclusion Model Practicable for Nanopore Protein Sequencing?

The nanopore approach holds the possibility for achieving single-molecule protein sequencing. However, ongoing challenges still remain in the biological nanopore technology, which aims to identify 20 natural amino acids by reading the ionic current difference with the traditional current-sensing model. In this paper, taking aerolysin nanopores as an example, we calculate and compare the current blockage of each of 20 natural amino acids, which are all far from producing a detectable current blockage difference. Then, we propose a modified solution conductivity of σ' in the traditional volume exclusion model for nanopore sensing of a peptide. The σ' value describes the comprehensive result of ion mobility inside a nanopore, which is related to but not limited to nanopore-peptide interactions, and the positions, orientations, and conformations of peptides inside the nanopore. The nanopore experiments of a short peptide (VQIVYK) in wild type and mutant nanopores further demonstrate that the traditional volume exclusion model is not enough to fully explain the current blockage contribution and that many other factors such as enhanced nanopore-peptide interactions could contribute to a dominant part of the current change. This modified sensing model provides insights into the further development of nanopore protein sequencing methods.

Protseq: An Investigation of High-Throughput, Single-Molecule Protein Sequencing via Amino Acid Conversion into DNA Barcodes

Despite a need for high-throughput protein sequencing, existing methods are not capable of sequencing the entire human proteome. We demonstrate early progress toward constructing the components of a protein sequencing technology that requires sequential capture and recording of binding over many cycles. The Barcode Cycle Sequencing (BCS) assay uses DNA-barcoded binders to construct a chain of barcodes directly onto a DNA sequencing chip that are used to identify a target. We demonstrated successful barcode capture and DNA-barcode chain sequencing for DNA-DNA and nanobody-protein binding pairs. For DNA-DNA binding, we showed assembly and sequencing of barcodes over 6 consecutive binding cycles. We additionally introduce a binder discovery pipeline called Target-Switch SELEX to discover aptamer binders. Ultimately, we envision merging BCS and Target-Switch SELEX into an end-to-end technology called ProtSeq, a high-throughput, single-molecule protein sequencing method in which DNA-barcoded aptamers read a protein’s amino acid sequence over multiple cycles of terminal amino acid degradation.

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.

A single-molecule electrical approach for amino acid detection and chirality recognition.

One of the ultimate goals of analytic chemistry is to efficiently discriminate between amino acids. Here we demonstrate this ability using a single-molecule electrical methodology based on molecular nanocircuits formed from stable graphene-molecule-graphene single-molecule junctions. These molecular junctions are fabricated by covalently bonding a molecular machine featuring a permethylated-β-cyclodextrin between a pair of graphene point contacts. Using pH to vary the type and charge of the amino acids, we find distinct multimodal current fluctuations originating from the different host-guest interactions, consistent with theoretical calculations. These conductance data produce characteristic dwell times and shuttling rates for each amino acid, and allow accurate, statistical real-time, in situ measurements. Testing four amino acids and their enantiomers shows the ability to distinguish between them within a few microseconds, thus paving a facile and precise way to amino acid identification and even single-molecule protein sequencing.