Nanopore-based massively parallel sensing for peptide profiling and protein identification

Domain: chemistry
Published: 2026-02-23
Editorial outcome: accept

Abstract
Nanopore-based single-molecule sensing holds immense promise for revolutionizing proteomics, however, its practical application remains constrained by low throughput, suboptimal library preparation, and limited analytical power for complex, stochastic signals. To overcome these challenges, we develop a high-throughput nanopore sensing platform that couples a streamlined peptide library preparation strategy with an AI-driven analytical workflow, enabling accurate peptide differentiation and protein identification. Our analytical framework captures the distinct statistical signatures of peptides within massive single-molecule event streams, transforming them into reliable, information-rich fingerprints to achieve remarkable classification accuracy. We also apply this platform to establish a rapid and cost-effective workflow for antibody validation, facilitating precise epitope screening and semi-quantitative affinity determination. Critically, in a blinded study, this high-throughput sensing system demonstrates its robustness by unambiguously identifying multiple proteins from their complex enzymatic digests. By establishing an end-to-end pipeline from native proteins/peptides modification, parallel sensing to their identification, this work develops a scalable and powerful method for proteomics research.

Introduction
Nanopore-based technologies, initially transformative for single-molecule nucleic acid sequencing by enabling direct, real-time analysis of single molecules, are now poised to revolutionize protein analysis1,2. Extending this paradigm to proteins promises a groundbreaking leap in proteomics, offering high-throughput, sensitive, and accurate detection within complex biological mixtures3–7. Initial breakthroughs have adapted strategies from DNA sequencing, such as employing motor proteins to guide oligo-conjugated peptides through pores, thereby improving translocation control and signal-to-noise ratios8–11. More recently, the successful fingerprinting of long peptide fragments on nanopore arrays using molecular motors like ClpX underscored the accelerating progress in this field, bringing routine nanopore-based protein analysis closer to reality12–14. Despite promising proof-of-concept demonstrations, translating nanopore-based peptide and protein analysis into a robust, widely applicable technology faces several fundamental limitations8–10,14–16. A primary limitation lies in the reliance on model systems, which frequently use chemically modified peptides or engineered proteins with specific sequences. While valuable for initial validation, these approaches have left a gap in effective strategies for analyzing unmodified native samples. Secondly, most methods are analyte-specific, often demanding bespoke functionalized pores or narrow biochemical conditions, which severely restricts inter-study comparability and cross-validation. Thirdly, the low data throughput from prevalent custom, single-channel platforms necessitates data curation, a potential source of inconsistency that can affect the robustness of subsequent statistical conclusions. Collectively, the absence of an integrated workflow for native sample preparation, high-throughput data acquisition, and automated analysis means that nanopore-based peptide/protein analysis still lacks practical applications.

Building on the principles and instrumentation of the nanopore DNA sequencing platform, our goal is to develop a parallel, high-throughput sensing platform for the analysis of natural proteins/peptides, providing a comprehensive end-to-end solution. This work introduces a versatile peptide library preparation protocol coupled with an optimized analysis workflow, which significantly enhances the accurate profiling of native peptides and their post-translational modifications (PTMs). Capitalizing on the high sensitivity of the nanopore sensing platform, we also demonstrate its application in the characterization of antibodies and native proteins. The reliability of immunoassays in proteomics is particularly challenged by the inconsistent quality of commercial antibodies, which rarely disclose detailed epitope information, provide vague affinity ranges, and lack third-party verification17–19. By integrating antibody-mediated enrichment, our platform enables rapid epitope screening and semi-quantitative affinity determination. Finally, using a peptide fingerprinting strategy, we demonstrate direct unambiguous differentiation of proteins.

Results
Library preparation and peptide sensing
Motor protein-driven nanopore sensing of peptides has previously employed peptidyl-oligonucleotide conjugates, achieving controlled translocation but often relying on libraries built from pre-modified peptides8–10,20. Here, we introduce a streamlined method to construct Oligo-Peptide-Oligo (OPO) conjugates directly from native proteins/peptides, ensuring compatibility with DNA motor-driven nanopore sensing systems. It is also applicable to synthetic peptides that still retain at least one primary amine group at their N- or C-termini. The library preparation workflow encompasses three key steps: (1) protein fragmentation and peptide terminal modification, (2) DNA-templated OPO conjugation, and (3) sequencing adaptor ligation and library purification (Fig. 1a). To circumvent harsh carboxyl modification chemistries that may compromise native peptide stability, we utilize LysC endopeptidase digestion, yielding peptides with C-terminal lysine residues. This allows for efficient dual-terminal functionalization using fluorosulfuryl azide (FSO2N3), a mild and effective diazo transfer reagent21. Treatment under ambient conditions (room temperature, 30 min) converts both the N-terminal α-amine and C-terminal ε-amine of lysine residues to azides (Fig. 1b; Supplementary Fig. 1), ready for click chemistry. This approach exhibited high efficiency across more than 50 peptides tested, although reduced yields were observed for peptides with multiple N-terminal tyrosine (likely steric hindrance) and no product for N-terminal proline (lacking a primary α-amine) (Supplementary Fig. 2 and Fig. 3). For sequential OPO assembly, we adapted the DNA-templated conjugation (DTC) strategy22,23. A rationally designed DNA template comprising two DBCO-modified oligonucleotides (DNA1, DNA2) was annealed to a complementary strand (DNA3) featuring a central abasic (AP) site (Supplementary Table 1). This flexible design accommodates peptides of various lengths (Fig. 1b). The template spatially pre-organizes the reactants, enhancing the probability that conjugation of one peptide terminus to DNA1 promotes subsequent conjugation of the other terminus to DNA2 via proximity effects (Supplementary Fig. 4). Efficient dual-terminal conjugation was demonstrated using a series of model peptides of varying lengths, with shorter peptides exhibiting higher conjugation efficiency (Supplementary Fig. 5 and Supplementary Table 2). Notably, additional AP sites incorporated within DNA1 and DNA2 are designed to generate distinct sensing signals, serving as markers flanking the peptide-specific signal during sequencing (Fig. 1b, c). After ligation with sequencing adaptors that have attached T4 Dda helicase motors, the final OPO library is enriched through beads purification to remove byproducts and excess adaptors. (see “Methods”, Supplementary Figs. 6 and 7). This dual-terminal conjugation for obtaining OPO reads facilitates library purification, improves capture efficiency, optimizes signal resolution, and enables objective peptide blockade extraction.

To enable high-throughput sequencing of the OPO libraries, we employed a sensing platform based on the CsgG nanopore, capitalizing on its proven utility of DNA sequencing in arrayed formats14,24,25 over previously explored pores like MspA (Mycobacterium smegmatis porin A), α-HL (α-Hemolysin), and aerolysin26–29. Our prototype device features a microwell array of 256 independent microwells (~200 µm pitch, ~28.9 pores/mm² density), where CsgG were inserted into lipid bilayers formed over integrated microelectrodes (Supplementary Fig. 8). A schematic of the nanopore sensing platform for OPO libraries is shown in Fig. 1d. Using a helicase motor under optimized ionic conditions (0.5 M KCl, 10 mM HEPES at pH 8.0, 0.5 mM ATP, 1 mM MgCl2, 180 mV bias, 5 kHz sampling), translocation events were acquired over 100,000 per each library in 2 h. To further validate this integrated workflow, we employed a defined set of synthetic peptides with specific sequences, along with peptides derived from LysC digests of three model human proteins: hNEDD8 (hp1), hCOMMD6 (hp2), and hGABARAPL2 (hp3)30–32, for subsequent model training and testing (Supplementary Table 2). A representative signal trace from a peptide (hp1_5: VERIK) (Fig. 1c) illustrates the characteristic current levels: the open pore baseline (I0), distinct signals corresponding to the adaptor and DNA portions, the central peptide blockade, and transient high-current peaks (~110 pA) associated with AP-sites within the DNA carrier. The known flanking DNA/adaptor sequences provide conserved signal anchors, enabling automated algorithmic extraction of the intervening peptide-specific electrical signature.

Following rigorous quality filtering of the raw data, a process that removed reads exhibiting anomalous durations, signal spikes, or incomplete DNA flanking sequences (Supplementary Fig. 9 and Supplementary Table 3), the central peptide blockade was extracted based on AP site-specific signals. Characterization of OPO reads focused on I/I0 (mean peptide blockade current to open pore current), STD (standard deviation of I/I0), and the dwell time τoff (redefined as the ratio of the peptide blockade duration to the total read duration to minimize variability). The I/I0 and τoff distributions closely approximated log-normal forms, while STD followed a Gaussian profile (Supplementary Fig. 10). Intriguingly, we found that τoff exhibited a strong positive linear correlation with peptide length, but this trend was specific to uncharged peptides (Fig. 1d). In contrast, no significant correlation between τoff and length was observed for charged peptides. Notably, the current blockade effect induced by peptide is more pronounced than that of DNA (Fig. 1c). Furthermore, we observed a significant positive linear correlation between the average molar volume of uncharged peptide and the (1−I/I0) × τoff (Fig. 1d). This suggests that, the observed sensing signal more likely corresponds to a current blockage feature generated as the entire peptide translocates through the pore in a ‘blob’ conformation, reflecting its overall volume and electrical properties33.

CNN-DM analysis
These collective variabilities mentioned above consequently manifest as substantial overlap when plotting conventional single-point metrics (I/I0, STD, and τoff), especially when coupled with the inherent randomness in peptide orientation during OPO library preparation, limiting the discriminatory power for closely related peptides. While initial quality control removes obvious artifacts like unstable baselines or misidentified AP sites, even stringent statistical filtering—selecting reads within one standard deviation of the mean for all three metrics (‘3D filtering’, Fig. 2a) to isolate the core signal population—does not fully resolve this ambiguity. Intriguingly, a detailed examination of these filtered, high-quality reads revealed a more subtle yet powerful discriminant: the temporal profile of the current blockade. Overlaid signal traces (I/I0 vs. normalized time) for a given peptide, despite individual variations, collectively form distinctive, characteristic temporal morphologies (Fig. 2b). To quantitatively capture this distinct spatio-temporal signature, we developed a ‘Density Matrix’ (DM) representation. DM generation involved initial low-pass filtering and uniform downsampling (to 100 points) of each trace. A Soft-DTW barycenter was then computed from these processed traces to establish a representative temporal profile, robust for non-linear time warping. Subsequently, each downsampled trace was aligned to this barycenter using dynamic time warping (DTW), thereby compensating for variations in individual translocation rates. The DM itself was constructed by binning the I/I0 values of these aligned traces at each of the 100 corresponding time points. This process yielded a 2D histogram (density map) that encapsulates the statistical distribution of signal amplitude across the normalized translocation event (Fig. 2b). Strikingly, DMs generated in this manner exhibit distinct features for different peptides, effectively transforming the seemingly noisy temporal signal into a stable, information-rich fingerprint.

While direct application of a Convolutional Neural Network (CNN) to the resulting high-quality dataset, partitioned into training (70%), validation (10%), and testing (20%) subsets, proved effective for initial peptide identification, we achieved enhanced performance by incorporating DMs as a secondary validation filter (Fig. 2c, “Methods”). In this two-step process, putative peptide identities assigned by the CNN were cross-validated against the corresponding reference DM. Reads were retained for downstream analysis only if their temporal signal patterns exhibited strong concordance with the probabilistic distribution of the reference DM, exceeding a predefined similarity score threshold. This integrated CNN-DM approach improved classification accuracy for Peptides 1, 2, and 3 from 97.5% (CNN alone) to 99.2% (Fig. 2d). This DM-augmented validation, therefore, not only improves identification accuracy but also provides a robust foundation for developing comprehensive reference libraries and advanced algorithms capable of filtering unknown analytes from complex biological signals.

Peptide profiling
Building upon this established workflow, our initial investigations focused on a set of model peptides featuring single amino acid variants (SAAVs) within a GSGSXSGSGK palindromic sequence (X being the variable residue) (Supplementary Table 2). OPO libraries incorporating all 20 canonical SAAVs at this position were sequenced, yielding substantial read depths. Although initial attempts to classify all 20 SAAVs directly showed limited success (Supplementary Fig. 11), the grouping by physicochemical properties revealed a striking sensitivity to amino acid charge. Classification accuracy exceeded 95% for distinguishing: positively charged (K and R), negatively charged (D and E), and neutral amino acids (A, C, F, G, H, I, L, M, N, P, Q, S, T, V, W and Y) (Fig. 3a). Furthermore, analysis of volumetric effects revealed a compelling trend for neutral amino acids. The system distinguished amino acids with large (I, L, M, F, Y and W) from those with small (A, C, G, N, P, S and T) side chains with over 92% accuracy34. Conversely, residues of intermediate volume (V, H and Q) proved challenging to resolve, often being miscategorized (Fig. 3a). This indicates that the sheer volume of data enables CNN-DM to discern subtle, yet consistent, physicochemical signatures related to peptide charge and volume, thereby achieving remarkable discriminatory power.

Subsequently, we extended our investigation to the more challenging task of discriminating peptide isomers: positional and isobaric. Focusing first on isomers, notably leucine (L) and isoleucine (I), remains a formidable challenge in proteomics. As anticipated, the classification accuracy for distinguishing single L from I residues hovered around 70%. Interestingly, when challenged with distinguishing between LLL vs. III (GSGXXXGSGK), the classification accuracy slightly improved to 81%, even though their density matrices remained largely undifferentiated (Fig. 3b and Supplementary Table 2). This suggests that minute, cumulative volumetric and/or conformational differences between the consecutive propyl (L) and sec-butyl (I) side chains become sufficiently amplified to generate subtly divergent, yet learnable, nanopore signatures. The differentiation of positional isomers, where we located the variable residue X to the C-terminus (GSGSGSGSXK), thereby introducing asymmetry in the peptide’s charge or volume (Supplementary Table 2). Reflecting the stochastic N- or C-terminal conjugation inherent to our OPO library, and given that the “blob-like” conformation of peptides during translocation, one would presume they would exhibit minimal significant signal resolution. Remarkably, however, the CNN successfully achieved high classification accuracies for all investigated positional isomers. The accuracies for distinguishing 1D/1D’, 1Y/1Y’, 1R/1R’ and 1W/1W’ are 97.8%, 97.9%, 98.8% and 98.1%, respectively (Fig. 3c and Supplementary Fig. 12). These subtle differences are also reflected in variations in their DMs, this phenomenon that is particularly pronounced for charged or large amino acids. Notably, for peptides with asymmetric charge distribution, discernible differences in signal traces (reflected in their DMs) were observed among the N-terminal entry, C-terminal entry, and mixed-entry libraries (Supplementary Fig. 13). This observation underscores the potential of, and perhaps the necessity for, developing more efficient, site-directed conjugation strategies to further enhance peptide sensing resolution by ensuring uniform peptide entry.

Furthermore, the detection of PTMs represents a key frontier in nanopore sensing. While strategies employing modified nanopore or Phos-tag-mediated retardation effects for phosphorylation sensing have been reported20,35,36, the pronounced sensitivity of our system to charge and volume alterations suggested a direct avenue for PTMs discrimination. Indeed, investigations into phosphorylation and caprylation, inducing substantial negative charge or large side chain shifts (modification occurs at the underlined serine (S) residue in the peptide sequence GSGSSSGSGK), yielded high classification accuracies, further corroborated by distinct differences in their respective DMs. Conversely, methylation and acetylation, which impart more subtle changes, proved challenging to resolve; even when assaying peptides with three consecutive modifications of these types (GSGSSSGSGK), classification accuracy plateaued around 87% (Fig. 3d and Supplementary Table 2). Extending this to naturally occurring modifications in hp1 and hp3, we readily detected the deamidation of Q40 to E in hp1_7, a modification induced by bacterial effector proteins known to disrupt cellular function, owing to its significant charge alteration37. Similarly, both single and dual phosphorylation at S87 and S88 in hp3_11, modifications that critically destabilize its complex with the processing enzyme ATG438, were accurately detected and distinguished, with their DMs also reflecting these clear distinctions (Fig. 3e and Supplementary Table 2).

Our work shows that even with the CsgG nanopore, typically employed for DNA sequencing, strategic analysis of large datasets allows for the differentiation of critical peptide features. It is evident that the resulting sensing signals are significantly influenced by a combination of peptide properties, such as their entry orientation relative to the pore, and the intramolecular distribution of charge and volume. Furthermore, the implementation of a two-step analytical framework, integrating CNN-based classification with DM-based verification, proves highly effective. This approach not only captures nuanced distinctions from complex signal profiles but also systematically filters out low-confidence signals, thereby enhancing the robustness and reliability of the classification data for downstream biological insights.

Peptide identification
The peptide profiling results demonstrate that integrating efficient peptide library preparation, high-throughput sensing, and a CNN-DM analysis pipeline enables precise discrimination of peptides with minimal variations. This technological advancement enables precise peptide identification within diverse yet closely related peptide mixtures, thereby facilitating the development of customized solutions tailored for anti-peptide antibody validation, ensuring both specificity and selectivity.

Choosing C-peptide—a 31-amino acid (aa) segment released during proinsulin processing—as a model, we designed six overlapping peptides, tiled every four residues across the entire C-peptide sequence (Fig. 4b and Supplementary Fig. 14). Individual OPO conjugates were prepared for these six peptides, and subsequent CNN-DM analysis achieved high classification accuracy of 97.2% (Fig. 4b). These six OPO libraries were firstly pooled equally and incubated with magnetic beads coated with different commercially sourced antibodies (37°C for 1 h) (Fig. 4a). Subsequently, the bound OPO conjugates were dissociated for nanopore sensing and analysis, revealing distinct epitope preferences for different antibodies. For instance, Fig. 4b illustrates the distribution of the relative fold-change of OPO reads percentage for typical antibodies, showing that antibodies Ab1 and Ab2 (Sino Biological, Inc.) are both significantly enriched for CP-6 peptide (LQPLALEGSLQ). While the supplier’s information indicated that these two antibodies recognized distinct epitopes and are suitable for use as an antibody pair in sandwich ELISA assays, our validation clearly suggested that this antibody pair is evidently not optimal for such use. In contrast, antibody Ab3 (HeavyBio, Inc.) preferentially bound to the CP-5 peptide (GAGSLQPLAL), while antibody Ab4 (HeavyBio, Inc.) showed a primary enrichment for the CP-3 peptide (QVELGGGPGA). This suggests that this antibody pair may be an ideal candidate for the sandwich immunoassay. Antibody Ab5 (Medix Biochemica, Inc.) enriched both CP-5 and CP-6, suggesting a primary binding site towards LQPLAL (residues 21–26). This finding demonstrates a more refined epitope information compared to that provided by the manufacturer (the binding site spans residues 12–30). An independent antibody enrichment assay and LC-MS analysis corroborate the consistency of epitope information with our nanopore detection.

An intriguing observation from the C-peptide experiments was the apparent correlation between the enrichment efficiency for different peptides by the same antibody and the corresponding read output (e.g., Ab5 antibody enriched CP-5 and CP-6, yielding 8178 and 4170 reads, respectively). Likely, the affinity of the antibody-peptide interaction might quantitatively correlate with the final OPO library read output. To quantitatively test this hypothesis, we employed the well-characterized FLAG-tag system. Equimolar mixtures of the FLAG peptide and its four mutants (F1–F4) were incubated with the anti-FLAG M2 and FLAG-Tag Mouse mAb antibodies separately. After ultrafiltration, the retained peptides were used to prepare OPO libraries for parallel sensing (Fig. 4c). The analysis was underpinned by a CNN-DM model that identified these analogous peptides with an average accuracy of 96.4% (Fig. 4d). For the anti-FLAG M2, the relative nanopore reads abundance for each peptide showed a strong positive correlation with their reported binding affinities. This finding was also consistent with Surface Plasmon Resonance (SPR) validation, except for the F4 mutant, which exhibited an affinity comparable to that of the wild-type FLAG—rather than the superior affinity previously reported (Fig. 4d and Supplementary Fig. 15)39,40. And FLAG-Tag Mouse mAb displayed a selectivity profile with a preference for the FLAG, F4 and F1 peptides, a result that was also validated by LC-MS (Fig. 4d and Supplementary Fig. 15). To conclude, based on the integrated nanopore platform as well as the CNN-DM analysis workflow, our methodology enables the accurate identification of a series of closely related peptide mixtures. This approach establishes a rapid, cost-effective, and highly effective approach for comprehensive antibody characterization, offering precise epitope screening and semi-quantitative affinity assessment. This significantly streamlines the validation of antibody specificity, selectivity, and relative affinity, thereby accelerating immunoassay development and expanding the practical applications of nanopore sensing.

Protein identification
The established capacity of our nanopore sensing system for accurate peptide identification within complex mixtures suggested its potential application to protein identification. Adopting a peptide fingerprinting strategy, the workflow proceeds as follows: First, synthetic peptides derived from LysC-digested targets are prepared as OPO libraries and analyzed to train a CNN-DM model, establishing reference fingerprint profiles. Next, purified protein samples undergo LysC digestion and analogous library assembly to generate experimental OPO reads. Finally, the CNN-DM model classifies these reads against the reference profiles, compiling full peptide signatures to enable protein identification. To demonstrate the platform’s efficacy in distinguishing native proteins, twenty-four synthetic peptides derived from LysC digests of three human proteins (hp1, hp2, and hp3) were individually used to prepare OPO libraries, and their respective DMs were characterized (Supplementary Figs. 16–19 and Supplementary Table 2). Our analytical strategy, leveraging a CNN-DM framework, achieved a classification accuracy of 96.3%—a significant improvement over the 93.5% accuracy from CNN alone (Fig. 5a; Supplementary Figs. 20 and 21).

To challenge our platform with a blind identification task, we prepared three anonymized samples (Protein 1, 2, and 3) from Lys-C digests of proteins hp1, hp2 (both expressed in Escherichia coli BL21(DE3)) and hp3 (Sino Biological, Inc.), with digestion confirmed by SDS-PAGE (Supplementary Note). After azide modification and OPO library construction, nanopore sequencing generated >300,000 reads for each sample. When analyzed with our pre-trained CNN-DM model, the reads from each sample correctly clustered into three distinct and predictable classifications (Fig. 5b, c). These assignments were independently validated by LC-ESI-MS (Supplementary Note), demonstrating the robustness of our approach for protein identification.

Despite the high overall accuracy, we observed a lower relative abundance for peptides with high net negative charges (e.g., -5 net charge at pH 8.0 for hp1_4; -3 net charge at pH 8.0 for hp3_3). This suggests that the efficiency of DTC can be attenuated for highly anionic peptides, likely due to electrostatic repulsion to the DNA backbone, particularly in complex mixtures where species compete. Furthermore, distinguishing between dipeptides differing by a single amino acid (e.g., hp1_2 and hp2_6), represents a current resolution limit (Fig. 5c). Although an unexpected observation was the consistent detection of reads classified as hp3_10 within our recombinantly expressed hp1 and hp2 samples, it is crucial to note that such isolated artifacts are statistically unlikely to mimic the full peptide signature of an entire protein and therefore do not impede robust protein-level identification (Fig. 5c). Collectively, this work validates our methodology’s capacity for the successful blind identification and classification of proteins from their complex enzymatic digests. These results demonstrate the practical viability of our end-to-end analytical pipeline for proteomic applications, confirming that multi-peptide signatures acquired via nanopore sensing are sufficient for accurate protein-level assignments in biologically relevant mixtures.

Methods
General materials
All oligonucleotides and primers used for nanopore sensing and plasmid construction were synthesized by GCATbio Co., Ltd (Changzhou, Jiangsu, China) and Sangon Biotech (Shanghai, China). All peptides were synthesized by GCATbio Co., Ltd (Changzhou, Jiangsu, China), GenScript (Nanjing, Jiangsu, China) and Wuxi AppTec (Shanghai, China). All kits, reagents, solvents, buffers, and dyes were purchased from GCATbio Co., Ltd (Changzhou, Jiangsu, China), BBI Life Sciences (Shanghai, China), Beyotime (Shanghai, China) and Sangon Biotech (Shanghai, China). Other material sources have been indicated in the main text.

OPO library preparation
Azide modification reaction was carried out at room temperature for 0.5 h with a peptide (10 mM in 1× PBS) and FSO2N3 in a 1:3 molar ratio. The final product could be stored at −20 °C for weeks for subsequent processing. Annealed DNA template (25 μM) and azide-modified peptide were mixed in a 1:3 molar ratio in 1× PBS and incubated at room temperature for 3 h. Sequencing adapters that have attached T4 Dda helicase motors (M1G/E94C/C109A/C136A/K194L/A360C, Supplementary Table 1) were ligated to the OPO conjugates using T4 DNA ligase (GCATbio Co., Cat: LS-EZ-E-00008P) at 25 °C for 0.5 h. Subsequently, the OPO library was enriched through bead purification to remove reaction byproducts and excess adapters. Capture beads were obtained from an incubation of Streptavidin-coated (SA) magnetic beads (10 μL, Nanjing Vazyme Biotech Co., Ltd, Cat: N512-02) and capture DNA (20 pmol) in a binding buffer (50 mM Tris, 150 mM NaCl, 0.1 mM EDTA) at room temperature for 0.5 h and washed with wash buffer (50 mM Tris, 150 mM NaCl, 0.05% Tween-20) twice. Subsequently, the ligation products were incubated with 10 μL capture beads in a binding buffer at 30 °C for 0.5 h and washed twice with 1× PBS. The bead pellet was incubated in a 10 μL 1× RNase H buffer containing 0.2 μL RNase H (Yeasen Biotechnology (Shanghai) Co., Ltd., Cat: 12906ES76) at 37 °C for 20 min and washed with 1× PBS. The OPO library was finally liberated from the beads by incubation with 0.5 μL RNase A (Yeasen Biotechnology (Shanghai) Co., Ltd., Cat: 10405ES03) in 10 μL 1× PBS at 30 °C for 40 min. The enriched OPO library could be stored at 4 °C for weeks.

Peptide sensing data acquisition
The prototype device incorporates 256 independent channels compatible with a microwell chip array containing 256 discrete microwells (~200 µm pitch, ~28.9 pores/mm² density). Before each experiment, the CsgG transmembrane protein mutant (Y51A/F56Q/R97W/R192D, Supplementary Table 1) was inserted into lipid bilayers. Sequencing buffer (0.5 M KCl, 10 mM HEPES at pH 8.0, 0.5 mM ATP, 1 mM MgCl2) was perfused through the system to remove excess transmembrane proteins. To collect peptide sensing data, 300 μL sequencing buffer containing 5 μL OPO library and 3 μL anchor (1 μM) was slowly pumped into the chip from the sample loading port. After incubation at 25 °C for 10 min, a voltage of 180 mV was applied, and sequencing data were recorded at a sampling rate of 5 kHz. The sequencing data of each 2 h were saved as separate files for subsequent data analysis. A reversed membrane potential was used to help remove blockages from the pores (Supplementary Fig. 8).

Signal normalization
Given a time series R = r1, r2, ..., rL with length L, we first apply uniform downsampling to obtain a reduced signal S = s1, s2, ..., sN with length N, where
sj = R round L(j−1) + 1 , j = 1, 2, ..., N (1)
Here, we define R[j] = rj. Subsequently, the downsampled signal S is normalized by dividing each element by its open-pore current I0, yielding the normalized current signal N = n1, n2, ..., nN, where
nj = I0 sj, j = 1, 2, ..., N (2)
We denote this normalization process as Norm(N) that scales and normalizes a time series R into N with a length of N. Thus, the normalized signals have the same length of N and fall within the range of 0 to 1, as signals exhibiting any negative currents or values exceeding I0 have been filtered out during preprocessing. For computational efficiency, we set N = 100 to prepare input for DM construction and N = 1000 for CNN classification.

CNN model training
A CNN containing convolutional layers, batch normalization, activations, squeeze-and-excitation blocks, spatial attention blocks and fully-connected layers was developed to perform peptide reads classification. Its architecture follows a hierarchical pattern across six primary blocks. Each block consists of a convolutional layer (typically with a kernel size of 3, a stride of 1, and padding of 1), batch normalization, ReLU activation, a Squeeze-and-Excitation (SE) module, a spatial attention module and a max-pooling layer with a kernel size of 2 and a stride of 2. Following the convolutional backbone, the feature maps are flattened into a vector. A dropout layer with a rate of 0.5 is applied for regularization to mitigate overfitting. The classification head consists of two fully connected layers: the first projects the high-dimensional features onto a 256-unit representation, followed by a ReLU activation, and the final layer maps this to the output logits corresponding to the number of target classes. Initially, all current signals from the windows of clean peptide reads (Supplementary Fig. 9) were normalized to 1000 time points and normalized against I0, generating a dataset in which each read had 1000 features ranging from 0 to 1 and a known label indicating what peptide it came from. The dataset was randomly split into training (70%), validation (10%) and test (20%) sets (Fig. 2c). Adam was used as the optimizer with an initial learning rate of 5 × 10−3 and cross-entropy loss was applied as our optimized target for training. The performance of CNN and traditional machine learning classifiers was compared using nine peptides from the hp1 dataset, with CNN demonstrating superior performance over other classifiers (Supplementary Fig. 22).

DM reconstruction
Let the set of original d traces from a specific peptide be denoted as fR1, R2, ..., Rdg, where Ri = [ri1, ri2, ..., riL] represents the i-th trace (typically current signals) with length L. At this time, traces are not the same length. Each trace undergoes initial low-pass filtering through median filter followed by normalization to a length of 100:
Ni = Norm LPF Ri (3) 100
where LPF() denotes the median filter operation from the scipy Python package with a parameter size of 100 for high-frequency noise removal. Then a representative temporal profile is established by computing the Soft-DTW barycenter from the normalized trace set fN1, N2, ..., Ndg:
B = argmin SoftDTWγ Ni, B0 (4) B0 i=1
where SoftDTWγ() denotes the soft dynamic time warping distance with regularization parameter γ and B ∈ [0,1]100 represents the barycenter sequence with a length of 100, providing robustness to non-linear time warping. We set the γ as 0.001 here. After obtaining barycenter sequence, all normalized signals are aligned to barycenter and generate new aligned sequences as fA1, A2, ..., Adg, where
Ai j = π 1 i j k 2πi j B k , i = 1, 2, ..., d; j = 1, 2, ..., 100 (5)
Here, πi denotes the set of time points in B that are aligned to the time point j of Ni using DTW. Lastly, we bin the values of the aligned traces fA1, A2, ..., Adg into 100 equal-width bins at each of the 100 time points and calculate occurrence frequencies of all bins as DM matrix D:
1 Xd n o D(j, t) = d 1 b j−1 ≥ Ai[t] > bj , j = 1, 2, :::, 100; t = 1, 2, :::, 100 (6) i=1
where b0, b1, ..., b100 are the boundaries of 100 bins with b0 = 1 and b100 = 0, and is the indicator function. This process yields a two-dimensional density map that encapsulates the statistical distribution of signal amplitude across normalized translocation events.

DM reconfirming
As mentioned previously, we filtered out CNN results by the distance to DMs (Fig. 2c). Specifically, we discarded a read if CNN classified it as a peptide and its distance to the DM of the predicted peptide is smaller than the cutoff of this peptide (Supplementary Fig. 18). To calculate the distance between a read and a DM, we first uniformly downsampled the read to 100 time points, then normalized it based on its I0 to scale its values between 0 and 1. Second, the normalized current was superimposed onto the DM (Fig. 2b). Lastly, we took the logarithm of each DM element value traversed by the read, accumulated these logarithmic values, and finally took the negative of this sum as the distance. We kept reads with distances smaller than the cutoff of this peptide, which was defined as the distance value corresponding to the peak of the distribution of distances between all filtered reads from this peptide and its DM. Thus, the final results are supported by the CNN model and further reconfirmed by similarity to DMs.

Peptide identification
The C-peptide Antibody Validation: Synthetic C-peptide fragments (CP-1 to CP-6) were used to generate their respective OPO conjugates, according to the protocol described above (Supplementary Table 2). The data from the parallel sensing of the six OPO libraries were then used for CNN-DM modeling. Five commercial antibodies (Ab1: Sino Biological, Inc., Cat: 110121-MM12; Ab2: Sino Biological, Inc., Cat: 110121-MM15; Ab3: HeavyBio, Inc., Cat: HA127-1M; Ab4: HeavyBio, Inc., Cat: HA127-2M; Ab5: Medix Biochemica, Inc., Cat: 100113) were biotinylated using NHS-PEG4-Biotin. The labeling ratios (biotin:protein) ranged from 1.5 to 2.5, as verified by a Biotin Quantitation Assay Kit (Beyotime, Cat: P0371M). 50 μg biotinylated antibodies were incubated with SA magnetic beads (1 mg, Nanjing Vazyme Biotech Co., Ltd, Cat: N512-02) at RT for 1 h in 1× binding buffer. The antibody-coated beads were then washed twice with wash buffer and stored in binding buffer for subsequent use. The six OPO conjugates were quantified and quality-controlled using dsDNA HS Assay Kit (Yeasen Biotechnology (Shanghai) Co., Ltd., Cat: 12640ES76) and PAGE gel analysis. The six OPO conjugate samples were then pooled in equimolar amounts to a final concentration of 2.0 μM. The OPO conjugate pool was divided into equal aliquots. And they were individually incubated with the individual antibody-coated SA magnetic beads in 50 μL of 1× binding buffer at 4 °C with 1000 rpm shaking for 3 h, while keeping one aliquot as a control. Subsequently, the beads were washed twice with 1× wash buffer. The bound OPO conjugates were dissociated in 10 μL of elution buffer (2 M NaCl) at 37 °C for 1 h. Finally, the control sample and the dissociated products were diluted to 60 μL and ligated with sequencing adaptors, ready for high-throughput sensing and data acquisition. Each sample underwent six replicate tests.

Antibody Affinity Profiling: OPO libraries were generated from a synthetic FLAG peptide and its four variants (F1-F4) using the procedure described above (Supplementary Table 2). The resulting high-throughput sensing data from each library were subsequently used for CNN-DM modeling. For the affinity selection, the five synthetic peptides (FLAG and F1-F4) were premixed in equimolar amounts to a final concentration of 500 μM. The peptide pool was then divided into three equal aliquots. One aliquot was kept as a control, while the other two were incubated with anti-FLAG M2 antibody (0.5 mg/mL, Sigma, Cat: F1804) and FLAG-Tag Mouse mAb (0.5 mg/mL, Yeasen Biotechnology (Shanghai) Co., Ltd., Cat: 30505ES90) in 1× binding buffer at 4 °C with 1000 rpm shaking for 3 h. After incubation, the mixture was diluted with 1× PBS by centrifuging in a 30 kDa MWCO ultrafiltration column three times (14,000 × g for 10 min at 4 °C) to remove unbound peptides. Following, target peptides were dissociated from the antibodies in 25 μL 1× PBS via competitive elution with an excess of native 2× FLAG peptide. The control sample and the two dissociated products were then used for OPO library preparation, nanopore sensing and data analysis. Each sample underwent three replicate tests.

Model protein preparation and LysC digestion
GABARAPL2, referred to as hp3, was commercially obtained from Sino Biological, Inc. (Cat: 14563-H07E). For hp1 and hp2, the codon-optimized gene fragments for NEDD8 and COMMD6 (BGI Write: Beijing, China) were synthesized and cloned into pET vectors using the In-Fusion Cloning technique. Both expression vectors (pET-NEDD8-6×His and pET-6×His-SUMO-COMMD6) were chemically transformed into E. coli BL21(DE3) competent cells to afford expression seeding stocks. Expression starters were prepared with a small amount of each seeding stock in 50 mL LB medium containing 50 μg/mL kanamycin. Overnight starter of each protein was inoculated into LB medium containing 50 μg/mL kanamycin using 1:100 (v:v) ratio and allowed to grow at 37 °C until OD600 reached 0.6–0.9. Afterwards, 0.5 mM IPTG was added to induce the expression at 37 °C for 4-5 h. Upon saturation, cells were collected by centrifugation (8000 × g, 30 min, 4 °C) and stored at −80 °C if not lysed directly. NEDD8-6×His and 6×His-SUMO-COMMD6 were further purified by AKTA Protein Purifier. COMMD6 was obtained by further 6×His-SUMO removal using ULP protease. LysC digestion reaction was carried out in 10 mM ammonium bicarbonate (pH 8) solution by protein (500 ng/μL): LysC = 200:1 (m:m) ratio and incubated at 37 °C for 0.5~3 h. The digested products were then purified via a 5000 MWCO spin filter column to remove LysC. The flow-through was dispensed into several aliquots for either subsequent reactions or lyophilization, and stored at −80 °C for future use (Supplementary Note).

Statistical analysis
All analyses and visualizations were conducted using GraphPad Prism Version 8.0.2 and Python (v3.8), with the four most relevant packages: numpy (v1.26.4), pandas (v2.2.3), matplotlib (v3.9.2) and pytorch (v2.7.0). For nanopore sensing, all raw reads underwent rigorous quality filtering, which removed reads with anomalous dwell times, signal spikes, or incomplete flanking DNA sequences. No statistical method was employed to predetermine sample size. Randomization was not applicable to this study. The protein identification assay was performed in a single-blind manner; blinding was not used for other experiments.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have responded to all the questions I have raised. The manuscript offers an excellent and balanced overview of the capability of the technology.", "reviewer_2": "The authors have answered most of my questions. However, they did not give details to some of the questions regarding the experimental procedure involving the nanopore CsgG and the motor protein DDA. Without this information, reproducing the experiments might be quite difficult.", "reviewer_3": "Most of my comments have been addressed, however, I am not satisfied with the response to the questions regarding the ML part.\nFirstly, the authors talk about ‘handcrafted features’ and ‘manual feature engineering’ the other cited studies use which is misleading and incorrect (as well as ill defined). The other studies have employed and studied different strategies of using features of the signal to feed into the ML algorithm, including the full signal similar to what the authors have done here. This was shown to yield no improvement over downsampling the signals. The notion of ‘handcrafted’ features which may introduce a ‘bias’ implies subjectivity. The features used are statistical descriptors of the signal which are objective. The authors narrative implies inferiority of the other studies which has not been demonstrated. Such language should have no place in a scientific paper and misrepresents existing literature. Furthermore, the graph of the accuracy of different models (in response to Q3, referee 3) needs a thorough description with a detailed account of how all values were obtained (and it could go into the supplementary material). Currently there is no way of knowing if this is a fair comparison."}, "author_response": "Response to Reviewers\nReviewer #1:\nQ1: This methodology will most likely not work with human proteins, which are N terminally\nacetylated (~80%). The authors should mention this limitation.\nREPLY:\nWe thank the reviewer for this insight question, which highlights a key strategic aspect of our\nmethod. We agree that factors such as N-terminal acetylation and other PTMs (e.g., on lysine\nresidues) would not adapt this method and thus prevent full sequence coverage for specific proteins.\nSimilarly, C-terminal peptides lacking lysine would also be undetectable. However, the pursuit of\nfull-sequence coverage is not only computationally prohibitive but is also often unnecessary for\nanswering focused biological questions. Instead, the integration with targeted enrichment strategies\nmakes this assay a more practical tool for investigating interested peptide regions. For example,\nanalyzing disease-relevant phosphorylation sites on the Tau protein, mapping antigenic linear\nepitopes, or defining protein-protein interaction interfaces are far more strategic applications of\nnanopore technology.\nACTION:\nWe have added a comprehensive discussion to the manuscript that candidly addresses the method's\nlimitations. We then pivot to highlight how these very characteristics make it an ideal platform for\ntargeted proteomics, outlining this as a significant direction for future research and application.\nLine 339-346: Additionally, we introduce a streamlined methodology for the differentiation of\nnative proteins through classification against reference libraries. This approach successfully\nidentifies three blind-labeled proteins (hp1, hp2, and hp3) as their respective targets. However, OPO\nconstruction can be compromised by lysine-deficient C-terminal peptides or specific PTMs (e.g.,\nN-acetylation/lysine modifications), which limits achievable coverage. While the pursuit of full-\nsequence coverage is not only computationally prohibitive but is also often unnecessary for\nanswering focused biological questions. Integration with targeted enrichment thus renders this\nmethodology practical for investigating specific peptide regions of interest.\nabout peptides with different lengths? Did the authors see any preference for a given length?\nREPLY:\nWe are very grateful to the reviewer for this highly relevant question. This touches upon the complex,\nmultifactorial nature of peptide-DNA conjugation, and we appreciate the opportunity to elaborate\non our findings and present new data.\nThe reviewer is correct to identify peptide length as a crucial variable. The overall success of the\nconjugation is governed by several interdependent properties of the peptide: 1) Physicochemical\nProperties: Peptides with poor solubility or stability exhibit significantly reduced reactivity in the\nclick chemistry reaction. 2) Net Charge: As we noted, electrostatic repulsion between negatively\ncharged peptides and the DNA backbone reduces conjugation efficiency. However, this effect can\nbe effectively mitigated by increasing the peptide-to-DNA molar ratio (e.g., >3:1, as described in\nMethods), which still yields a sufficient amount of library for nanopore analysis. 3) Peptide Length:\nThe influence of peptide length is a bit complex. Normally, longer peptides have less conjugation\nefficiency. However, beyond a simple linear relationship, length can dictate the formation of\nsecondary structures. These structures may either sterically block the terminal reactive groups or,\nconversely, present them in a more favorable orientation for conjugation.\nA foundational design parameter of our study was the physical limit of the CsgG nanopore. Based\non recent work (Motone et al., 2024), the pore’s sensing region can effectively analyze peptides up\nto ~20 amino acids. Accordingly, our work was intentionally focused on peptides within this\nfunctional length window, and we did not initially perform a systematic analysis of conjugation\nefficiency for much longer peptides that would be incompatible with the sensor. Nevertheless, the\nreviewer's point is exceptionally well-taken: a systematic investigation of how length affects\nconjugation efficiency within this operational range is a valuable addition.\nRef: Motone, K., Kontogiorgos-Heintz, D., Wee, J. et al. Multi-pass, single-molecule\nnanopore reading of long protein strands. Nature 633, 662–669 (2024).\nACTION:\nTo provide a definitive and quantitative answer, we have executed the following: we designed a\nnew series of model peptides of varying lengths. Subsequently, we performed a systematic analysis\n2\nusing gel electrophoresis to precisely quantify the conjugation efficiency as a function of peptide\nlength. The results of this investigation have been compiled and added to the revised manuscript as\nSupplementary Fig. 5. This new dataset not only directly addresses the reviewer’s important\nquestion but also substantially enhances the manuscript by providing a practical framework that will\nbe valuable for other researchers seeking to utilize our method.\nSupplementary Fig. 5 Dual-terminal conjugation of variable-length peptides\n(left) Denaturing urea PAGE gel (12%) analysis of OPO conjugation. DNA template and azide-\nmodified variable-length peptides were mixed at a 1:3 molar ratio. The desired product, DNA1-\nPeptide-DNA2, is indicated by the red star. (right) Relative grey value of DNA1-Peptide-DNA2\nrelative to the 100 nt ladder.\nLine 87-89: Efficient dual-terminal conjugation was demonstrated using a series of model peptides\nof varying lengths, with shorter peptides exhibiting significantly higher conjugation efficiency\n(Supplementary Fig. 5, Table S2).\nQ3: The authors largely ignore the steps that might be observed when the peptides are read. Is this\nbecause a relationship could not be found? From figure 1c, five steps from the 5-amino acid peptide\nmight be observed.\nREPLY:\nastute; the step-like features in the current traces are indeed a phenomenon we also observed and\nare actively investigating. We did attempt to develop a step-detection algorithm to analyze the\nfeature differences among the 20 SAAVs. Unfortunately, we were unable to find a robust correlation\nthat links a specific step to a specific peptide or amino acid. Despite these challenges, we strongly\nagree with the reviewer that decoding these sub-signals is a critical and exciting frontier. To this\nend, our group is concurrently pursuing to further enhance sensing stability and provides a potential\nwindow for a more detailed analysis of the relationship between step signals and peptide\ncomposition. Given that this line of inquiry represents a significant research direction in its own\nright, we plan to report further progress in a future publication. We are grateful to the reviewer for\nhighlighting this important and forward-looking aspect of our work.\nQ4: Line 112. Please specify that the STD, I/I , and τ are for the whole blockade. It becomes clear\n0 off\nlater on in the text, but it is confusing at first.\nREPLY:\nWe sincerely thank the reviewer for highlighting this lack of clarity. We apologize that our initial\ndescription was confusing and appreciate the opportunity to improve it. The features I/I , STD, and\n0\nτ are calculated specifically for the peptide blockade segment of the signal, not the entire read. In\noff\nour analysis pipeline, we first identify and extract the central peptide blockade from the full event\ntrace. This extraction is guided by the characteristic high-current signals (~110 pA) produced by the\nAP-sites flanking the peptide. Only after this segmentation are the features quantified from the\nisolated peptide signal. We have now clarified this sequence of operations in the manuscript.\nACTION:\nWe have revised lines 112-116 to make the feature extraction process explicit from the outset.\n\"Following rigorous quality filtering of the raw data, a process that removed reads exhibiting\nanomalous durations, signal spikes, or incomplete DNA flanking sequences (Supplementary Fig.\n9, Table S3), the central peptide blockade was extracted based on AP site-specific signals.\nCharacterization of OPO reads focused on I/I (mean peptide blockade current to open pore current),\n0\n0 off\nduration to the total reads duration to minimize variability).\"\nQ5: In the main text it is said that the τ is only for non-charged peptides, and figure 1c is referenced.\noff\nHowever, Figure 1d shows a good correlation between the τ and the peptide size. This is confusing.\noff\nREPLY:\nWe sincerely thank the reviewer for pointing out this confusing statement. We apologize for the\nambiguity in our writing and appreciate the opportunity to clarify our findings.\nThe reviewer is correct to question this phrasing. Our intention was not to state that the τ metric\noff\nitself is only for non-charged peptides; rather, we meant to communicate that the strong linear\ncorrelation between τ and peptide length is a phenomenon observed exclusively for the non-\noff\ncharged peptide set. For charged peptides, we also calculated τ , but we did not observe a similar\noff\nsignificant correlation with length. We have now revised the sentence to state this relationship\nprecisely, making the distinction clear.\nACTION:\nWe have revised lines 118-120 to remove the ambiguity. \"Intriguingly, we found that τ exhibited\noff\na strong positive linear correlation with peptide length, but this trend was specific to uncharged\npeptides (Fig. 1d). In contrast, no significant correlation between τ and length was observed for\noff\ncharged peptides (data not shown).\"\nQ6: The authors say: This likely indicates that geometric constraints within the pore-helicase\ncomplex govern translocation kinetics for shorter peptides, whereas electric forces exert a greater\ninfluence on longer or charged peptides, resulting in uncontrolled translocation. I cannot follow\ntheir reasoning.\nREPLY:\nWe sincerely thank the reviewer for this critical feedback and for questioning the basis of our\nreasoning. Upon re-evaluation, we agree that this statement was overly speculative and not\n5\nsufficiently supported by the data presented in this study. While we considered it a potential\nhypothesis, we recognize that presenting it as a conclusion is not appropriate. To improve the\nscientific rigor of the manuscript, we have removed the speculative sentence.\nACTION:\nIn response to the reviewer's valid point, we have deleted the original statement from the manuscript.\nQ7: Line 182. Please specify the classification accuracy.\nREPLY:\nWe have updated the manuscript to include the specific classification accuracies as requested, which\nimproves the clarity of our results.\nACTION:\nThe text on Line 186-187 has been amended as follows:\nRemarkably, however, the CNN successfully achieved high classification accuracies for all\ninvestigated positional isomers. The accuracies for distinguishing 1D/1D’, 1Y/1Y’, 1R/1R’ and\n1W/1W’ are 97.8%, 97.9%, 98.8% and 98.1%, respectively (Fig. 3c, Supplementary Fig. 12).\nQ8: Line 185. It is not clear what the site-directed conjugation strategies the authors are referring\nto are, and how these could enhance the resolution.\nREPLY:\nWe sincerely apologize for the lack of clarity in the manuscript regarding this point. We thank the\nreviewer for highlighting this and providing us with the opportunity to elaborate.\nThe \"site-directed conjugation strategies\" we referred to were conceived to address a key limitation\nof our current OPO library preparation method. In the present study, the conjugation between the\npeptide and the two DNA strands is random, leading to a mixed population of two distinct molecular\norientations: DNA1- Peptide -DNA2 and DNA1- Peptide -DNA2. In an effort to resolve this\nC N N C\nambiguity, we attempted to implement a site-directed conjugation strategy. This strategy consisted\n6\nof sequential, terminus-specific modifications: first, we performed a specific azidation of the\npeptide's N-terminus to couple it with DNA1 (Rosen et al., 2017); subsequently, we azidated the C-\nterminus to conjugate it with DNA2. The objective was to produce a homogeneous population of\nthe DNA1- Peptide -DNA2 construct. Unfortunately, this multi-step conjugation proved to have a\nN C\nvery low yield. It also necessitated complex gel purification, which ultimately led us not to proceed\nwith this protocol for the work presented in the manuscript.\nRef: Rosen, C. B. & Francis, M. B. Targeting the N terminus for site-selective protein\nmodification. Nat Chem Biol 13, 697–705 (2017).\nWhile we critically assessed the potential impact of mixed-entry populations, such heterogeneity—\nthough generating subtle signal differences—did not impair classification accuracy in our current\nstudy (a panel of ~20 peptides, Fig 5a). Nevertheless, for peptides exhibiting high structural or\ncharge asymmetry, we could discern differences in the signal traces corresponding to N-terminal\nentry, C-terminal entry, and the mixed-entry population (Supplementary Fig. 13). This contributes\nto signal trace heterogeneity and may introduce a degree of uncertainty in more complex mixed\nsamples. We believe that eliminating this variability through controlled, site-directed conjugation\nwould enhance signal trace uniformity and, as a result, holds the promise of further improving the\nresolution and accuracy of our classifier. Therefore, we remain optimistic and fully committed to\nthis strategy. It represents a clear path toward refining our methodology, and we are actively\npursuing the development of more robust and efficient site-specific modification reagents. We\nbelieve this is a promising direction for future research.\nACTION:\nThese new results have been compiled and are now included in the revised manuscript’s\nSupplementary Fig. 13.\npeptide libraries. (left) The N-terminal-entry library (DNA1- Peptide -DNA2); (middle) The C-\nN C\nterminal-entry library (DNA1- Peptide -DNA2); (right) The mixed-entry library. The peptide\nC N\nemployed in this experiment was GSGSGSGSDK (N→C), which exhibits an asymmetric charge\ndistribution.\nLine 189-191: Notably, for peptides with asymmetric charge distribution, discernible differences in\nsignal traces (reflected in their DMs) were observed among the N-terminal entry, C-terminal entry,\nand mixed-entry libraries (Supplementary Fig. 13).\nQ9: Line 193. Acetylation is removing a positive charge from the peptide and is expected to provide\na strong signal.\nREPLY:\nWe absolutely agree with the reviewer's premise. Typically, protein acetylation involves the\nmodification of a lysine side chain, a process catalyzed by lysine acetyltransferases, which results\nin the neutralization of its positive charge. We concur that such a change in charge would\nundoubtedly induce a strong and discernible signal in a nanopore system.\nThe objective of this particular experiment in the manuscript, however, was to probe the\nfundamental resolution limits of our platform. Specifically, we aimed to determine if it could\ndifferentiate between modifications that impart only very minor steric (volume) differences, such\nas methylation and acetylation. To construct a controlled and valid comparison for this question, it\n8\nwas essential to keep the peptide backbone and net charge constant. Therefore, we designed our\nSAAVs based on a uniform template, GSGSXSGSGK. For the acetylated variant in this series, the\nmodification was intentionally placed on the hydroxyl group of a serine residue (at position X), as\ndocumented for peptide 1SAc (GSGS{Ser(Ac)}SGSG{Lys(N3)}) in Supplementary Table S2. As\nserine is a neutral amino acid, its acetylation does not cause a loss of charge.\nThis design ensures that any observed signal difference (or lack thereof) can be attributed solely to\nthe physical volume of the side-chain modification rather than to an electrostatic effect. Our results\nultimately indicated that this minute difference between acetylation and methylation could not be\neffectively resolved.\nQ10: Lines 233-236. Ab6 shows no enrichment in the peptides analyzed, and the authors conclude\nthat the results are inconsistent with the manufacturer’s claimed epitope region (residues 4-19).\nHowever, the authors did not test the peptide 4-19. In addition, it is possible (even likely) that the\n4-19 region in the folded structure of the full-length peptide is recognized by the Ab6, rather than\nthe structure adopted by the peptide when chopped. Hence, this does not appear to be a good test\nto disprove the manufacturer’s claim.\nREPLY:\nWe sincerely thank the reviewer for this insightful and critical point. We agree completely. When\nwe first obtained the initial results for Ab6, we were also puzzled and shared the reviewer's initial\nhypothesis that Ab6 might recognize a conformational epitope rather than a linear one.\nTo investigate this, we proactively contacted the FAS from Medix, Inc.. Unfortunately, they were\nunable to provide more detailed technical specifications. They clarified that the position \"4-19\"\ndesignation serves only as a general reference area where the epitope might reside, and the true\nepitope has not been precisely confirmed. They also could not provide experimental evidence to\nsupport the existence of a conformational epitope in this region. To more directly address this issue,\nwe also tested Ab6's ability to enrich the full-length C-peptide. This experiment also showed no\nsignificant enrichment effect. In a subsequent discussion with the FAS from Medix, Inc., they\nconcurred that antibody inactivation was a plausible explanation.\nvaluable example that underscores the necessity of antibody validation in routine biological\nexperiments. However, the reviewer's comment has made us realize that including results from a\nnon-functional sample in the main text is inappropriate, can cause confusion, and could also have\nan unfair negative impact on the supplier. Accordingly, we have removed this data in the revised\nmanuscript. We thank the reviewer again for the suggestion, which has helped make our conclusions\nclearer and more focused.\nACTION:\nTo avoid any ambiguity for the reader, we have revised the manuscript to remove the data related\nto Ab6. We are grateful for the opportunity to clarify this point, which has improved the accuracy\nof our manuscript.\nQ11: Line 239. What does 8178 and 4170 refer to?\nREPLY:\nWe sincerely apologize for this lack of clarity and thank the reviewer for pointing it out. The\nnumbers 8178 and 4170 refer to the number of reads obtained for the peptides CP-5 and CP-6,\nrespectively, in a nanopore detection experiment that followed enrichment with the Ab5 antibody.\nWe included these values as they suggest a potential correlation between the enrichment efficiency\nof an antibody for different peptides and their corresponding read counts in the nanopore assay.\nACTION:\nWe have revised the manuscript to explicitly define these values to prevent any confusion for the\nreader.\nLine 245: e.g., Ab5 antibody enriched CP-5 and CP-6, yielding 8178 and 4170 reads, respectively.\nQ12 and Q13: Why is hp3_10 detected in protein 1 and 2? This is not really explained. Any ideas?\nLine 253. The authors claim: This innovative approach establishes a rapid, cost-effective, and\nhighly effective approach for comprehensive antibody characterization. I am not sure this is an\n10\n‘innovative’ approach. What is the advantage of using this approach versus MS? Simplicity? Cost?\nSpeed? Why?\nREPLY:\nOn the Anomalous Detection of Peptide hp3_10 in hp1 and hp2.\nThis was indeed a puzzling result that we also observed and investigated thoroughly.\nObservation and Hypothesis: we have performed technical replicates. Unfortunately, the signal for\nhp3_10 was consistently detected in the datasets for both hp1 and hp2. This result leads us to\nhypothesize that the detection of hp3_10 is likely an artifact stemming from our protein production\nprocess. It is possible that a contamination from the host protein expression system, which shares\nsignal similarity with hp3_10, is responsible for this misidentification during the read-matching\nprocess. While it is technically difficult to isolate and confirm this contaminating peptide directly\nfrom the complex mixture, we believe this is a logical explanation for these observations.\nImpact on Overall Conclusion: Despite the presence of this potential artifact, a single anomalous\npeptide signal detection is insufficient to reconstruct the major peptide fingerprint of hp3; therefore,\nit does not compromise the overall reliability and validity of the hp1 and hp2 protein identifications\npresented in the study (Fig. 5c).\nOn the Novelty and Advantages of Our Approach vs. Mass Spectrometry.\nWe sincerely apologize for the imprecise description in our manuscript and are very grateful to the\nreviewer for this important correction. We completely agree that referring to our work as an\n\"innovative method\" was an overstatement. Our intention is not to propose a competition for the\ngold-standard technologies of MS or SPR. Instead, we present a novel technical approach and an\nend-to-end solution that serves as a valuable complementary tool, offering a new option for antibody\nanalysis.\n1) Throughput Potential & Speed: While our current in-house prototype has limitations (e.g.,\nlower channel count), it already demonstrates the ability to analyze a large peptide library in\na single run. More importantly, this work establishes a clear path forward: by integrating our\nstrategy with commercial nanopore sequencers, mature basecalling models can be used for\nsample barcoding. This unlocks the potential for massive multiplexing, which would\n11\ndramatically accelerate analysis time and expand experimental scale far beyond what is\nshown in current work.\n2) Cost-Effectiveness & Flexibility: MS instrumentation requires significant capital\ninvestment and expertise, while nanopore sequencers are compact, affordable, and easily\ndeployable in most labs. The reusability of commercial flow cells will further reduce the\ncost per experiment. This makes our method exceptionally well-suited for dynamic, in-house\nresearch needs, such as iterative screening and personalized assay development, where the\ncost and turnaround time of outsourced MS can be prohibitive. Indeed, we have already\nsuccessfully used this method to provide rapid and flexible screening of linear epitope\nantibodies for our institute's antibody development team, demonstrating its real-world value.\nWe appreciate this opportunity to elaborate on our perspective. As explained above, we see our\nwork not as a competitor to MS, but as a customizable, user-friendly, and low-cost solution for\npreliminary and targeted analyses. We are optimistic about its role in expanding the analytical\ntoolkit available to the scientific community. We will therefore revise the manuscript to present a\nmore nuanced and balanced discussion on the complementary relationship and synergistic potential\nof our approach with established methods like MS, clearly outlining its advantages in specific\ncontexts.\nACTION:\nLine 259, we have removed the claim of \"innovative\".\nLine 337-339, we have rephrased our contribution. “The nanopore-based platform provides a rapid,\nlow-cost, scalable, and end-to-end solution for antibody analysis, serving as a valuable\ncomplementary tool to conventional methods such as MS or SPR.”\nQ14: The authors should add point mutations to the proteins and see if the classifier would have\npicked them.\nREPLY:\nWe are very grateful to the reviewer for this highly constructive suggestion. Assessing the\nclassifier's ability to detect point mutations is crucial for its practical application, and we agree that\n12\nthis point warrants further investigation. To address this, we have performed a new experiment\ndesigned specifically to test this capability.\nFirst, we would like to briefly note that our original manuscript already contained strong preliminary\nevidence. Our classifier successfully distinguished peptides with SAAVs, such as the naturally\noccurring Q40E mutation in peptide hp1_7 (95.3% accuracy, Fig. 3e) and several single-residue\nvariants of the FLAG peptide (96.4% accuracy, Fig. 4d).\nSimulated Detection of a Point Mutation in a Peptide Mixture\n1) Design: A mixture containing peptides derived from the protein hp1 served as the wild-type\ngroup. The point-mutation variant peptide hp1_7(Q40E) was added to this mixture to create\nthe mutant group. Both groups underwent nanopore sequencing, and the resulting reads were\nanalyzed using a CNN-DM model for peptide identification.\n2) Results:While trace reads mapping to hp1_7(Q40E) were observed in the wild-type group,\nthe mutant group displayed a substantially higher and specific enrichment of the variant.\nThis clear distinction allows for the unambiguous identification of the mutant sample,\nconfirming that the method possesses the necessary sensitivity to detect point mutations,\nwith further refinement in specificity likely to minimize background signals.\nFig. Specific detection of point mutation hp1_7(Q40E) amidst wild-type background\n3) Conclusion: Despite the minor background signal observed in the wild-type control, this\nstudy represents a pioneering achievement in detecting single amino acid mutations directly\nat the native protein level. This establishes a viable technical framework for real-world\n13\napplications. Moving forward, we are confident that further refinements—specifically\noptimizing site-specific conjugation and peptide translocation—will significantly enhance\nthe method's sensitivity and resolution.\nQ1 and Q2: What concrete benefits does dual-terminal DNA conjugation provide for sensing? Is it\nprimarily for better alignment within the pore, improved data extraction via AP site markers, or\nenhanced detection reproducibility? The manuscript should more clearly state what problem dual-\nterminal coupling solves, and ideally provide direct comparative data with single-terminal\nconjugates.\nREPLY:\nWe are grateful to the reviewer for this insightful query, which touches upon the core rationale of\nour design. Dual-terminal DNA conjugation is indeed pivotal to our sensor's performance, offering\nspecific advantages in operational workflow, platform universality, and signal fidelity. In response\nto the reviewer’s request, we have clarified these benefits in the manuscript and provided\ncomparative data (single vs. dual-terminal).\nThe specific benefits are threefold:\nStreamlined Library Preparation (Efficiency): A primary innovation is the use of heterologous\nDNA1 and DNA2 sequences. This design facilitates a highly efficient, single-tube library\npurification workflow via magnetic bead capture (see Supplementary Figs. 4-7). Unlike single-\nterminal approaches, this strategy minimizes sample loss, enhances the yield of usable reads, and\nsignificantly improves the scalability of the preparation process.\nPlatform Compatibility (Practicality): By integrating peptides into a continuous single-stranded\nDNA backbone, our method ensures compatibility with standard commercial nucleic acid\nsequencing platforms. This universal structure is a prerequisite for broader accessibility and\nadoption beyond specialized nanopore laboratories.\nEnhanced Signal Quality and Automation (Reproducibility): The negatively charged DNA1\nleader actively draws the peptide-DNA conjugate into the nanopore, overcoming the stochastic\ncapture limitations of free peptides or simple conjugates, thereby boosting data throughput. DNA2\nis used to regulate translocation speed. This controlled velocity is essential for the effective\nresolution of abasic site (AP) markers. These markers generate distinct electronic signatures that\nenable a fully automated, bias-free data extraction pipeline, significantly enhancing detection\nreproducibility.\nterminal (OP) and dual-terminal (OPO) constructs in the revised Supplementary Fig. 7. This data\nquantitatively demonstrates the superior capture efficiency and signal clarity provided by the dual-\nterminal design.\nACTION:\nWe have prepared a revised Supplementary Fig. 7 and provided the corresponding explanations in\nthe manuscript.\na, Non-enriched libraries: Raw current traces showing abundant adaptors, a portion of OP reads,\nand a minority of target OPO reads. b, Libraries enriched using Streptavidin-coated (SA) magnetic\nbeads with RNaseA treatment: Raw current traces following adaptor removal, increased numbers\nof both OP reads and OPO reads. c, Libraries enriched using SA magnetic beads with RNaseH/A\ntreatment: Raw current traces exhibiting predominance of target OPO reads. d, Expanded\ncomparison of OP and OPO read signals.\nLine 93-95. This dual-terminal conjugation for obtaining OPO reads facilitates library purification,\nimproves capture efficiency, optimizes signal resolution, and enables objective peptide blockade\nextraction.\nQ3: A schematic diagram explaining the overall sequencing principle is essential. The current\ndescription is complex, and a clear figure showing OPO structure, CsgG pore, helicase, adaptors,\nand translocation events would help readers.\nREPLY:\nWe are grateful to the reviewer for this constructive feedback. We agree that the complexity of our\nmethodology was not adequately conveyed by the text alone and that a comprehensive schematic is\ncrucial for reader comprehension.\nACTION:\nTo remedy this, we have designed a new figure (Fig. 1d) that provides a visual guide to the entire\nsequencing process.\ncontaining an embedded CsgG nanopore separates cis and trans chambers equipped with electrodes.\nA sequencing adapter of the OPO library molecule, attached with a T4 Dda helicase motor, is\ncaptured by the nanopore. Under optimized ionic conditions (including ATP and MgCl ), the ATP-\n2\ndependent helicase motor controls the translocation of the OPO structure through the nanopore.\nQ4 and Q5: Methods for CsgG and motor protein expression and purification are not described.\nAre wild-type or mutant forms used? Given that engineered CsgG variants exist, the exact construct\nis important for reproducibility. The adapter sequences should be explicitly provided. Are motor\nproteins pre-loaded onto adaptors prior to sequencing, or do they assemble in situ? Is there any\npossibility that the helicase binds to unintended single-stranded DNA regions (such as DNA3 in the\ntemplate)? These methodological uncertainties should be clarified.\nREPLY:\nThank you for this suggestion. We have added the adapter sequences to Supplementary Information\n(Supplementary Table S1). Since the T4 Dda helicase was pre-loaded onto the adaptors, binding\nto unintended single-stranded DNA regions is implausible.\nACTION:\nLine 456-458. Sequencing adapters that have attached T4 Dda helicase motors\n(M1G/E94C/C109A/C136A/K194L/A360C, Supplementary Table S1) were ligated to the OPO\nconjugates using T4 DNA ligase (GCATbio Co., Cat: LS-EZ-E-00008P) at 25 °C for 0.5h.\nLine 471-474. Before each experiment, CsgG transmembrane protein mutant\n(Y51A/F56Q/R97W/R192D) was inserted into lipid bilayers. Sequencing buffer (0.5 M KCl, 10\nmM HEPES at pH 8.0, 0.5 mM ATP, 1 mM MgCl ) was perfused through the system to remove\n2\nexcess transmembrane proteins.\nQ6: For peptides derived from protein digests, what is the efficiency of dual-terminal conjugation\nto DNA1 and DNA2? Are there quantitative yields available?\nREPLY:\nconjugation yields. Regarding the efficiency of dual-terminal conjugation, it certainly varies\ndepending on the specific characteristics of the peptide. Generally, the reaction efficiency is\ninfluenced by the following factors: 1) Physicochemical Properties: Peptides exhibiting low\nsolubility or instability tend to show reduced reactivity during the click chemistry reaction. 2) Net\nCharge: As discussed, electrostatic repulsion between anionic peptides and the negatively charged\nDNA backbone can hinder conjugation. Nevertheless, this issue can be effectively counteracted by\nraising the peptide-to-DNA molar ratio (e.g., >3:1), which ensures sufficient library generation for\nnanopore sequencing. 3) Peptide Length: The impact of length on conjugation efficiency has been\ndetailed in our response to Question 2 of Reviewer #1.\nFor protein digests specifically, it is more complicated. We previously attempted to quantify the\nOPO constructs derived from protein digests using LC-MS; however, the high complexity of the\nreaction products precluded reliable quantitative analysis of the MS data.\nTo address this concern and provide a quantitative perspective, we chose seven representative\npeptide fragments identified in hp2 as a model and individually evaluated their specific conjugation\nefficiencies, and performed a comparative gel electrophoresis analysis between these individual\nOPO constructs and the OPO constructs derived from the bulk hp2 digest mixture. Notably, the\nbands corresponding to the seven individual peptides were generally identifiable within the complex\nconstructs mix.\nFigure. Dual-terminal conjugation of peptides derived from hp2\nin downstream analysis?\nREPLY:\nWe thank the reviewer for raising this critical point regarding coupling efficiency and byproduct\nmanagement. We acknowledge that single-terminal coupling species are inherent byproducts of this\nreaction scheme. Specifically, the “peptide-DNA2” byproduct lacks the DNA1 handle required for\nadaptor ligation, making its entry into the nanopore statistically unlikely. However, the “DNA1-\npeptide” species can indeed consume sequencing resources and generate truncated reads. To address\nthis, we implemented a comprehensive three-tiered strategy to minimize these byproducts and\nensure data quality:\n1. DNA-Templated Conjugation (DTC): We introduced a template strand (DNA3) to spatially\nalign DNA1 and DNA2. This significantly enhances the probability of dual-terminal coupling\ncompared to non-templated conjugation (NTC). As shown in Supplementary Fig. 4, the full\nassembly (DNA1-peptide-DNA2, marked in red) becomes the dominant product under DTC\nconditions.\n2. Library Enrichment: To further reduce single-terminal species (which may still exist as \"OP\nReads\"), we employed a magnetic bead purification strategy leveraging an RNA cleavage site\ndesigned on the DNA3 template. This process facilitates the dissociation of single-terminal\nbyproducts and then selective release of the target library. Following this optimization, the\nproportion of valid OPO reads increased to over 90% (Supplementary Fig. 6).\n3. Bioinformatic Filtering: Finally, in the downstream analysis, any residual single-terminal reads\nare rigorously filtered out due to the absence of the two characteristic peaks generated by the AP\nsites (Supplementary Fig. 7).\nQ8: How does orientation heterogeneity (peptide conjugated with N- vs. C-terminal entry) affect\nsignal quality and classification? The manuscript acknowledges this as a limitation, but no\nsequencing data are provided to show the practical impact. Including representative traces or\nclassification comparisons between different coupling modes would strengthen the technical\nfoundation of the work.\nWe appreciate the reviewer’s sharp observation regarding orientation heterogeneity. We apologize\nfor any ambiguity in our original text and value the chance to clarify the practical implications of\nour current coupling strategy with specific experimental evidence.\nUnder our current synthesis scheme, the ligation of peptides to the flanking DNA strands is non-\ndirectional. Consequently, the sequencing library inevitably consists of a heterogeneous mixture of\ntwo molecular configurations: DNA1- Peptide -DNA2 and DNA1- Peptide -DNA2. This results\nN C C N\nin peptides entering the nanopore in either N- or C-terminal orientation. While these orientations\ngenerate subtle signal differences, such heterogeneity did not impair classification accuracy in our\ncurrent study (a panel of ~20 peptides, Fig 5a). Notably, for peptides possessing significant\nstructural or charge asymmetry, distinct signal variations can emerge depending on the entry\norientation. To demonstrate this, we performed a comparative analysis using the peptide\nGSGSGSGSDK (N→C) We prepared three distinct libraries: N-terminal entry, C-terminal entry,\n.\nand the mixed-entry. As shown in the Supplementary Fig. 13, the DM for the N- and C-entry\nlibraries exhibit distinct topological features. Critically, the DM for the mixed-entry library\neffectively resembles a superposition of the features observed in the pure N- and C-entry datasets.\nWe are concerned that as we scale up to larger, more complex libraries, orientation-dependent signal\nsplitting could introduce unwanted data variance, potentially confounding the classifier. Therefore,\nwe are fully committed to implementing a directional conjugation strategy moving forward. We are\nactively developing robust site-specific modification chemistries to ensure uniform peptide\norientation. We consider this an essential step to reduce data complexity and enhance the scalability\nof our nanopore sequencing platform.\nACTION:\nThese results shown above have been compiled and are now included in the revised manuscript’s\nSupplementary Fig. 13.\npeptide libraries.\nQ9: Provide raw sequencing data and CNN-DM code to allow independent benchmarking.\nREPLY:\nWe fully agree that open access to data and code is essential for reproducibility and independent\nbenchmarking. Accordingly, we have ensured that both the raw dataset and the source code are\npublicly accessible to support the community.\nACTION:\nLine 585 and Line 589:\nData availability\nThe data that support the findings of this study have been deposited into CNGB Sequence Archive\n(CNSA) with accession number CNP0006016.\nCode availability\nThe source code is released on GitHub under BSD-2-Clause License in this link:\nhttps://github.com/BGINPS/npspy.\nQ10: Clarify whether the method is primarily intended for profiling against known libraries\n(classification) or for true de novo identification.\nWe thank the reviewer for seeking this clarification. We wish to clarify that our current method is\nprimarily intended for profiling against known libraries, rather than for de novo identification.\nSimilar to reference-based sequencing strategies, our approach requires the prior sensing and\nmodeling of specific peptide targets. Therefore, it is ideally suited for the targeted detection of\nspecific peptides or proteins of interest, rather than for large-scale, exploratory proteomics. We have\nadded a dedicated paragraph in the Discussion section to explicitly outline this scope and limitation.\nACTION:\nLine 339-341: Additionally, we introduce a streamlined methodology for the differentiation of\nnative proteins through classification against reference libraries.\nQ11: Condense the Introduction slightly to sharpen the focus on how this work overcomes specific\nprior limitations.\nREPLY: We appreciate this constructive suggestion. We have streamlined the Introduction to\nimprove conciseness and more sharply focus on the specific limitations of previous studies that our\nwork overcomes.\nQ1: The chemistry appears sound (although I am not a specialist in that), it is however unclear to\nme how the protein is identified based on the peptide fragments. This should be explained for a\ngeneral scientific readership.\nREPLY:\nWe sincerely thank the reviewer for this essential question. They have pinpointed a critical aspect\nthat we regrettably overlooked and agree is fundamental for a broad readership. The fundamental\nprinciple of our method is protein identification via 'peptide fingerprinting'. Our goal is to identify\na protein by matching its unique peptide pattern against a pre-defined set of known proteins. To\naddress the reviewer's valid point, we have now added a reader-friendly explanation in the\nmanuscript's.\nACTION:\nLine 267-272: Adopting a peptide fingerprinting strategy, the workflow proceeds as follows: First,\nsynthetic peptides derived from LysC-digested targets are prepared as OPO libraries and analyzed\nto train a CNN-DM model, establishing reference fingerprint profiles. Next, purified protein\nsamples undergo LysC digestion and analogous library assembly to generate experimental OPO\nreads. Finally, the CNN-DM model classifies these reads against the reference profiles, compiling\nfull peptide signatures to enable protein identification.\nQ2: The authors mention the data is rigorously filtered after acquisition and some of the relevant\nparameters are listed in the supplementary information. How many events are discarded by the\nfiltering? How were the parameters determined and how do they change when the measurement\nparameters change? It is important to answer these questions.\nREPLY:\nWe thank the reviewer for this insightful question regarding our data processing workflow. We have\napplied a rigorous quality control protocol to the OPO reads, consisting of four key steps\n(Supplementary Fig.9):\n1. Duration Filtering: Exclusion of peptide signals shorter than 1,000 data points, as\nextremely short durations typically indicate incomplete reads or fragments.\n24\n2. Negative Current Removal: Exclusion of peptide signals containing negative current\nvalues, which are indicative of potential signal crosstalk or amplifier artifacts.\n3. Overflow Filtering: Exclusion of peptide signals exceeding the open-pore current threshold,\nalso indicative of crosstalk or baseline instability.\n4. Carrier DNA Screening: Preliminary filtering based on the characteristic translocation\nlengths of adaptor+DNA1 and DNA2. Thresholds were established by analyzing the\nstatistical distribution of translocation events, with valid ranges defined as [8,900-12,900]\nfor adaptor+DNA1 and [7,800-12,200] for DNA2 (visualized in the revised Supplementary\nFig. 9).\nSupplementary Fig. 9. The thresholds of adaptor+DNA1 and DNA2\nSince we used the identical measurement parameters throughout all the experiment, the filtering\nparameters also remained unchanged and required no adjustment. If measurement parameters need\nto be changed in future studies, new data filtering parameters - specifically the translocation lengths\nfor adaptor+DNA1 and DNA2 - should then be determined based on updated statistical results.\nWhile our current statistical filtering approach remains effective, we recognize the need for more\nintelligent, unbiased, and automated QC algorithms. We are currently developing such methods for\nour next research phase.\nACTION:\nWe have updated the Supplementary Figures to include the specific pre-filtering threshold settings\n(Supplementary Fig. 9). Furthermore, we have shown the detailed statistical table listing the\nnumber of valid events retained after each filtering step for every peptide.\nSupplementary Table 3. Data retention after each QC step for the peptide present in this work.\n0\n1A 80937 80704 77231 76735 56014 56014\n1C 36526 36262 33670 33480 26326 26326\n1D 132911 132469 124038 122438 86235 86235\n1E 87955 87516 82761 81858 62962 62962\n1F 50961 50810 48197 47625 37600 37600\n1G 100387 99984 92048 89696 70493 70493\n1H 39147 39024 37601 37406 28939 28939\n1I 235242 234678 216660 212454 162163 162163\n1K 245245 244578 232811 230682 148928 148928\n1L 193647 193165 178826 175605 134361 134361\n1M 51154 50974 48442 48048 36298 36298\n1N 64158 63950 59680 58777 43400 43400\n1P 103897 103602 99288 98608 79662 79662\n1Q 114885 114348 104536 101698 80472 80472\n1R 110122 109835 106466 105733 68836 68836\n1S 44810 44602 41683 40945 32081 32081\n1T 56255 56072 51868 50827 37009 37009\n1V 85119 84742 79904 78143 55641 55641\n1W 44380 44121 40723 39326 29570 29570\n1Y 35828 35701 34080 33839 26504 26504\n1D' 46152 45688 43240 42831 35155 35155\n1Y' 95997 95567 86618 84941 67789 67789\n1W' 58479 58205 55552 55300 44148 44148\n1R' 81353 81099 75935 75554 56621 56621\n3I 220685 220022 209322 207422 167553 167553\n3L 171792 171035 154160 149760 111322 111322\n1pS 49677 49354 46386 45486 35783 35783\n1SAc 43875 43668 41258 40723 32116 32116\n3SAc 68390 68198 65746 65280 49483 49483\n1SMe 51146 50705 47372 46555 36342 36342\n3SMe 36914 36756 35428 35047 28365 28365\n1Soct 67826 67439 62662 61437 50027 50027\n3Soct 69304 68759 63935 63577 44180 44180\nQ40E 57325 57096 50190 48735 37343 37343\npS88 158522 158035 145132 142734 109882 109882\npS87S88 80957 80503 74593 73160 53323 53323\nCP5 56081 55806 53184 52675 40297 40297\nCP9 39473 39341 37826 37627 30062 30062\nCP13 34552 34490 33243 33054 22688 22688\nCP17 18363 18290 17462 17370 13925 13925\nCP21 35617 35506 33816 33623 26467 26467\nF1 46324 45909 42476 41691 30312 30312\nF2 57963 57747 52939 51727 33861 33861\nF3 34956 34890 32468 32106 23968 23968\nF4 69387 69059 63642 62894 43295 43295\nFLAG 64903 64591 57134 55900 38146 38146\nhp1_1 210238 206921 192098 191266 80051 80051\nhp1_2 507548 503708 442869 439140 199417 199417\nhp1_3 290134 288639 273746 271564 130725 130725\nhp1_4 191491 188311 179796 178814 93387 93387\nhp1_5 181807 180998 169635 168955 82675 82675\nhp1_6 200868 199185 192393 191388 93288 93288\nhp1_7 238711 238058 214433 212789 115504 115504\nhp1_8 108935 107731 104672 104348 45207 45207\nhp1_9 193211 191340 174206 173130 70589 70589\nhp2_1 470332 467999 436373 432486 201050 201050\nhp2_2 143995 143312 138821 138270 66543 66543\nhp2_3 263739 262878 238702 236944 127367 127367\nhp2_4 126024 125146 108511 108021 52159 52159\nhp2_5 171484 170504 162371 161593 81078 81078\nhp2_6 151522 150459 135094 134446 63321 63321\nhp3_2 133036 130903 116867 116315 50464 50464\nhp3_3 122620 121029 108523 107078 24613 24613\nhp3_4 189009 187207 160321 159336 63307 63307\nhp3_5 161457 159913 131862 130547 53392 53392\nhp3_6 119939 118806 107574 106896 45883 45883\nhp3_7 184768 182777 151569 150785 63487 63487\nhp3_9 180583 178384 164155 162096 70905 70905\nhp3_10 115866 114272 107615 106778 41400 41400\nhp3_11 131869 131273 118783 117804 50986 50986\nas compared to a simpler decision tree algorithm that has been mostly used in such studies. Details\non this would be very useful.\nREPLY:\nWe sincerely appreciate this insightful comment. In response, we have revised the Methods section\nto include a detailed specification of the CNN architecture used in this study.\nTo address your concern regarding the necessity of using a CNN over simpler algorithms (such as\nDecision Trees), we conducted a comprehensive comparative analysis using nine peptides from the\nhp1 dataset. We evaluated the performance of the CNN against traditional machine learning\nclassifiers using the following input strategies:\n1. CNN Input: Normalized current signals.\n2. type1: Basic statistical features, including relative current (I/I , STD, τ )\n0 off\n3. type2: type1 augmented with current amplitude distribution (projected onto the Y-axis using\n1,000 bins between normalized current levels 0 and 1).\nThe dataset comprised 20,000 training reads and 7,000 testing reads per peptide species.\nAs illustrated in Figure below, the experimental results demonstrate that the CNN significantly\noutperforms traditional classifiers, even when the latter are supplied with high-dimensional\ndistribution features (type 2). This superiority can be attributed to the CNN's ability to perform end-\nto-end learning, autonomously extracting discriminative deep temporal patterns and signal trends.\nUnlike manual feature engineering—which may oversimplify the complex, time-varying nature of\nnanopore signals—the CNN captures subtle kinetic variations essential for accurate distinction.\nTherefore, given the dynamic characteristics of the current signals generated by our system, the\nCNN represents the most robust methodological choice.\nACTION:\nLINE 494-501: Its architecture follows a hierarchical pattern across six primary blocks. Each block\nconsists of a convolutional layer (typically with a kernel size of 3, a stride of 1, and padding of 1),\nbatch normalization, ReLU activation, a Squeeze-and-Excitation (SE) module, a spatial attention\nmodule and a max-pooling layer with a kernel size of 2 and stride of 2. Following the convolutional\nbackbone, the feature maps are flattened into a vector. A dropout layer with a rate of 0.5 is applied\nfor regularization to mitigate overfitting. The classification head consists of two fully connected\nlayers: the first projects the high-dimensional features onto a 256-unit representation followed by a\nReLU activation, and the final layer maps this to the output logits corresponding to the number of\ntarget classes.\nQ4: How do they normalize time? One thing is very intriguing. On page 5 the authors note that they\nobserve a positive linear correlation between average molar volume and blockage signal, which is\ninterpreted by the peptide going through the pore as a ‘blob’. This opens up a few issues. How can\nthe distinguish isomers when the peptide moves through the pore as a blob? This would also mean\nlonger peptides should have a higher dI. Is that observed? And if yes, what is the impact of the\nfiltering on this and the time normalisation?\nREPLY:\nconformation (\"blob-like\"), and signal characteristics. We appreciate this opportunity to engage in\na deeper discussion regarding these intriguing experimental phenomena. (We have updated the\nMethods section with a detailed explanation of the signal time normalization process.)\n1. Is the peptide translocation strictly \"blob-like\"?\nWe observed that most peptides generate blockade signals significantly deeper than those of DNA\nsequencing, which is initially counter-intuitive given that amino acids generally have smaller\ndiameters than nucleotides. As discussed by Sauciuc et al., this suggests a \"blob-like\" conformation\nresulting from peptide-nanopore interactions, where the peptide forms a complex coiled structure\nthat hinders translocation.\nRef: Sauciuc,J. Whittaker,M. Tadema,K. Tych,A. Guskov, & G. Maglia, Blobs form during\nthe single-file transport of proteins across nanopores, Proc. Natl. Acad. Sci. U.S.A. 121 (38)\ne2405018121 (2024).\n2. How are isomers distinguished if they pass as a \"blob\"?\nThis is a critical question. A plausible explanation is that even within a coiled \"blob,\" specific amino\nacid side chains may retain transient interactions within the nanopore. However, we candidly\nacknowledge that the sensing signals for some peptide isomers—such as leucine versus isoleucine—\nexhibit highly similar signal traces (Fig. 3b). Distinguishing single leucine and isoleucine (L versus\nI, GSGSXSGSGK) using nanopore signals is challenging, achieving only ~70% classification\naccuracy. This accuracy only improves to 81% when distinguishing between LLL versus III\n(GSGXXXGSGK). Consequently, this method effectively discriminates against isomers exhibiting\ndistinct blockade signal traces, but demonstrates limited resolution for isomers with minimal trace\ndifferences.\n3. What is the relationship between peptide length and current?\nFirst, for neutral peptides, we observe a clear positive correlation between peptide length and τ ,\noff\nwhich aligns with general expectations. Second, regarding current blockade, we initially approached\nthis with the same hypothesis as the reviewer—expecting that longer peptides would inherently\ninduce higher blockade signals (dI). While a general trend is apparent, the correlation between dI\nand peptide length was not statistically significant (see Figure below). Consequently, we are unable\n30\nto conclude that “longer peptides should have a higher dI”, and neither our data filtering methods\n(described in response to Question 2) nor the time normalization procedure correlate with dI.\nFigure. Scatter plot of peptide length vs. dI (I/I ), peptide molar volume vs. dI (I/I )\n0 0\nACTION:\nWe have added the Signal normalization section (line 474) description in Methods.\nQ5: Another interesting point is that from Fig 2a one can see that translocation times for the\npeptides shown are all below 0.2 s and most below 0.1 s. With a sampling rate of 5kHz 0.1s delivers\n500 points but the authors mention they use 1000 points. Do they extrapolate? How do they fill the\nmissing points?\nREPLY:\nWe sincerely thank the reviewer for this insightful question and for their careful examination of our\ndata.\nClarification of Figure 2a's Axis (τ ): The x-axis in Figure 2a does not represent the absolute\noff\ntranslocation time in seconds. Instead, it represents a normalized metric, τ , which we defined as\noff\nthe ratio of the peptide's blocking duration to the total duration of the read. We adopted this\nnormalization approach to mitigate the inherent variability in the absolute translocation times of\nindividual events, allowing for a more stable and comparable feature across experiments.\nData Filtering and Processing: To directly answer the reviewer's question about the 1,000 data\npoints: our method does not involve extrapolation or filling missing data. Our data processing\n31\npipeline is as follows: 1) Filtering: We first apply a stringent filter to the raw data. Peptide signals\nwith a blocking duration of less than 0.2 seconds (which corresponds to 1,000 data points at our 5\nkHz sampling rate) are discarded from the analysis (as shown in figure below). This ensures that\nevery peptide signal used for subsequent analysis is sufficiently long. 2) Resampling: For the\nremaining valid peptide signals (≥ 1,000 points), we normalize their length to a uniform 1,000 points\nusing a signal normalization algorithm. This technique compresses or stretches the signal to a\nconsistent length, preserving the signal's characteristic shape without artificially inventing data\npoints.\nFigure. Blocking duration of peptide signals (pep1-3, as in Fig. 2a)\nQ6: There is not sufficient information on the actual nanopore measurements. How many channels\nwere used? How did they deal with blockages and why was ATP and MgCl used?\nWe thank the reviewer for this excellent and highly relevant question, which gives us the opportunity\nto clarify the experimental details of our nanopore measurements.\n1. Number of Active Channels:\nOur prototype device is designed with 256 independent channels (an array of 256 microwells, each\nwith its own membrane and bottom electrode). We sincerely apologize for this omission. This\ninformation was indeed present in an earlier version of our manuscript but was inadvertently\n32\nexcluded during the final revision process. We have now restored this information in the main text.\nThank you for pointing this out.\n2. Handling Pore Blockages:\nTo deal with transient blockages, we apply a reverse membrane potential. This \"unblocking\" voltage\npulse effectively dislodges occluding molecules from the pore and restores its conductance, enabling\ncontinuous parallel measurements across the array.\n3. Role of ATP and MgCl :\n2\nemploy an ATP-dependent T4 Dda helicase to ratchet the DNA construct through the nanopore in\na stepwise manner. ATP serves as the molecular fuel that powers the helicase. Mg2+ is a critical\ncofactor required for the helicase to hydrolyze ATP.\nACTION:\nLine 470-471: The prototype device incorporates 256 independent channels compatible with a\nmicrowell chip array containing 256 discrete microwells (~200 µm pitch, ~28.9 pores/mm² density).\nLine 478: A reversed membrane potential was used to help remove blockages from the pores\n(Supplementary Fig. 8).\nLine 631-632: Under optimized ionic conditions (including ATP and MgCl ), the ATP-dependent\n2\nhelicase motor controls the translocation of the OPO structure through the nanopore.\nQ7: It is worth noting that generating the DMs and using them for ML discrimination is not new.\nThis is very similar to what has been described by Dutt et al., Small Methods 2023, 2300676, and\nSoni et al. Nat. Nanotechnol. (2025). https://doi.org/10.1038/s41565-025-02016-w, which have\nboth not been cited (although the latter one is very recent). Overall, the ML part is not well described\nsuch that it can be reproduced and not really novel.\nREPLY:\nWe thank the reviewer for the insightful comments and for pointing us to the relevant work by Dutt\net al. and the very recent work by Soni et al. We have now cited both papers in our revised\nmanuscript. We agree that the ML portion of our manuscript required a clearer description, and we\nhave taken this opportunity to substantially improve it.\ndiffers fundamentally from the cited works in its methodology, novelty, and overall objective. We\nhave expanded the Methods section with a full description to ensure reproducibility and have\nsummarized the key distinctions below:\n1. End-to-End Learning vs. Handcrafted Feature Extraction:\nThe methods in Dutt et al. and Soni et al. rely on pre-defined, handcrafted features—such as current\nvalues in chunks of a signal, current drop at full width at half maximum, the peak drop in current or\nevent characteristics (pulse number, DTW distance)—which are then used for classification.\nOur approach, in contrast, feeds the entire normalized current trace directly into a CNN. It learns\nthe relevant discriminative features automatically from the raw data. This end-to-end learning\nstrategy avoids potential biases introduced by manual feature selection and is designed to capture\ncomplex patterns that might be missed by pre-defined metrics.\n2. The Role of the DM: Post-Filter vs. Classification Feature:\nThis is the most critical distinction. In previous work, metrics like DTW distance serve as the\nprimary features for classification.\nIn our pipeline, the DM is not a classification feature. Instead, it serves as a post-validation filter.\nCNN first makes a prediction based on the learned features. The DM is then used to confirm whether\nthe signal's global time-amplitude trend is consistent with that of the reference signal. This two-step\nprocess is designed specifically to increase confidence and reduce false positives when analyzing\nhighly complex and noisy data from mixed peptide populations.\n3. Comprehensive Signal Trend Characterization:\nOur DM construction uniquely considers signal fluctuations along both the time axis (X) and the\namplitude axis (Y). This provides a more holistic representation of a peptide's translocation\nsignature compared to methods that focus on a limited set of morphological or temporal features.\nWe concede that CNNs and DM-like analyses are not, in isolation, novel concepts. Our contribution,\nhowever, lies in the synergistic integration of these two components into a new analytical pipeline.\nThis CNN-DM framework is specifically tailored to address the significant challenge of high-\n34\nthroughput, high-confidence analysis of complex mixed peptide raw signals from a nanopore sensor.\nWe believe this combined approach is a powerful and novel strategy for this specific application.\nACTION:\nWe have added citations of Dutt et al. and Soni et al.\nWe have added a detailed, step-by-step description of the signal normalization, CNN architecture,\nand DM construction to the Methods section to ensure full reproducibility.\nI co-reviewed this manuscript with one of the reviewers who provided the listed reports. This is\npart of the Nature Communications initiative to facilitate training in peer review and to provide\nappropriate recognition for Early Career Researchers who co-review manuscripts.\nREPLY:\nThank you for participating in the review of our manuscript as a co-reviewer. Your contribution to\nthe peer-review process is much appreciated.\nReviewer #1:\nThe authors have responded to all the questions I have raised. The manuscript offers an excellent\nand balanced overview of the capability of the technology.\nREPLY:\nWe sincerely thank the reviewer for the positive assessment and final approval. Your insightful\ncomments throughout the review process have been instrumental in refining our manuscript.\nSpecifically, the suggestions to supplement experimental details have not only enhanced the\nreadability of the paper but also ensured that our core ideas are conveyed more accurately to the\nreaders. We believe this revision process has significantly strengthened the practical significance of\nour research. While we acknowledge that limitations inevitably exist and warrant further exploration,\nwe remain confident in the robustness of the current findings and their contribution to the field. We\nare grateful for your valuable time and constructive guidance.\nReviewer #2:\nQ1: The authors have answered most of my questions. However, they did not give details to some of\nthe questions regarding the experimental procedure involving the nanopore CsgG and the motor\nprotein DDA.\nREPLY:\nWe fully agree with the reviewer that detailed reporting of experimental procedures is crucial for\nreproducibility, and we wish to reaffirm our strong commitment to open data and transparency.\nRegarding the details of the CsgG nanopore and Dda motor protein, we would like to clarify that\ncomprehensive descriptions—including the specific information for every mutation site—were\nincorporated into the previous manuscript. Their full sequences have been indicated below. To\nensure the information is immediately accessible to readers, we have now summarized in the\nSupplementary Information Table S1. Furthermore, we respectfully note that the adaptor\nsequences and the preparation for the adaptor complex were also included in the Methods section\nof the previous revision. We have double-checked these descriptions to ensure they are accurate and\ncomplete. We hope this clearer presentation resolves the concern regarding reproducibility.\nCLTAPPKEAARPTLMPRAQSYKDLTHLPAPTGKIFVSVYNIQDETGQFKPAPASNQSTAV\nCsgG mutant PQSATAMLVTALKDSRWFIPLERQGLQNLLNERKIIWAAQENGTVAINNRIPLQSLTAANI\n(Y51A/F56Q/R MVEGSIIGYESNVKSGGVGARYFGIGADTQYQLDQIAVNLRVVNVSTGEILSSVNTSKTI\n97W/R192D) LSYEVQAGVFDFIDYQRLLEGEVGYTSNEPVMLCLMSAIETGVIFLINDGIDRGLWDLQ\nNKAERQNDILVKYRHMSVPPES\nGTFDDLTEGQKNAFNIVMKAIKEKKHHVTINGPAGTGKTTLTKFIIEALISTGETGIILAA\nPTHAAKKILSKLSGKEASTIHSILKINPVTYECNVLFEQKEVPDLAKARVLICDEVSMYD\nT4 Dda mutant RKLFKILLSTIPPWATIIGIGDNKQIRPVDPGENTAYISPFFTHKDFYQCELTEVKRSNAPII\n(M1G/E94C/C1 DVATDVRNGLWIYDKVVDGHGVRGFTGDTALRDFMVNYFSIVKSLDDLFENRVMAFT\n09A/C136A/K1 NKSVDKLNSIIRKKIFETDKDFIVGEIIVMQEPLFKTYKIDGKPVSEIIFNNGQLVRIIEAEY\n94L/A360C) TSTFVKARGVPGEYLIRHWDLTVETYGDDEYYREKIKIISSDEELYKFNLFLGKTCETYK\nNWNKGGKAPWSDFWDAKSQFSKVKALPASTFHKAQGMSVDRAFIYTPCIHYADVELA\nQQLLYVGVTRGRYDVFYV\nACTION:\nSupplementary Information Table S1:\nNanopore protein CsgG mutant (Y51A/F56Q/R97W/R192D)\nMotor protein T4 Dda helicase mutant (M1G/E94C/C109A/C136A/K194L/A360C)\nReviewer #3:\nQ1: Firstly, the authors talk about ‘handcrafted features’ and ‘manual feature engineering’ the\nother cited studies use which is misleading and incorrect (as well as ill defined). The other studies\nhave employed and studied different strategies of using features of the signal to feed into the ML\nalgorithm, including the full signal similar to what the authors have done here. This was shown to\nyield no improvement over downsampling the signals. The notion of ‘handcrafted’ features which\nmay introduce a ‘bias’ implies subjectivity. The features used are statistical descriptors of the signal\nwhich are objective. The authors narrative implies inferiority of the other studies which has not\nbeen demonstrated. Such language should have no place in a scientific paper and misrepresents\nexisting literature.\nREPLY:\nWe fully accept the reviewer’s criticism and sincerely apologize for the confusion caused by our\nearlier use of the terms \"handcrafted features\" and “manual feature engineering” in the previous\nresponse. We completely agree that statistical descriptors (such as I/I , STD, τ , etc.) are objective,\n0 off\nphysical parameters directly related to single-molecule sensing states, and they are foundational to\nthe field. We do not deny the objectivity or reliability of previous studies.\nour specific sensing data, standard pre-defined features failed to capture the subtle, high-\ndimensional patterns necessary for accurate classification. We also realize that the discussion\nregarding \"manual\" processes was ambiguous; we originally aimed to highlight that in high-\nthroughput scenarios (e.g., processing tens of thousands of reads), manually screening data entry-\nby-entry becomes impractical and may introduce challenges in operational consistency. This was\nnot meant to question the objectivity of feature extraction methods used in other studies.\nWe deeply regret that our previous writing gave the reviewer the impression that we were implying\nthe inferiority of other research. This was absolutely not our intention. Different experimental\ndesigns and objectives necessitate different analytical strategies, and we fully respect the validity of\nmethods employed in peer studies.\nACTION:\nTo ensure our tone is respectful and accurate, we have rigorously scanned the entire manuscript and\nremoved all language related to \"bias\" or \"subjectivity\" when referring to prior studies. We have\nrevised the text to focus strictly on the technical applicability of our method to high-throughput data\nprocessing, ensuring no misrepresentation of existing literature remains.\nQ2: Furthermore, the graph of the accuracy of different models (in response to Q3, referee 3) needs\na thorough description with a detailed account of how all values were obtained (and it could go into\nthe supplementary material). Currently there is no way of knowing if this is a fair comparison.\nREPLY:\nWe fully agree with the reviewer’s expert assessment. A mere visual comparison without\ntransparency regarding the training protocols and hyperparameters is indeed insufficient to judge\nthe fairness of the model selection.\nACTION:\nFollowing the reviewer's valuable suggestion, we have included a comparative analysis of different\nmodels (SVM, RF, KNN, DT and CNN) in the Supplementary Information (Supplementary Figure\n3\n22), which details the dataset preparation, specific hyperparameters, data partitioning, and\nevaluation metrics for each model.\nOUTCOME:\nOur empirical results on this specific dataset show that the CNN model achieved the most\nsatisfactory classification accuracy, likely due to its ability to capture the specific features of our\nsensing traces (as discussed regarding signal complexity). While other models did not perform as\nwell in this specific instance, we do not dismiss their utility in other contexts. While the primary\nfocus of this manuscript remains on the sensing platform and biological application, we agree that\nalgorithmic benchmarking is significant. We have provided the essential data in the SI to support\nour current model choice, and we look forward to conducting a more dedicated, in-depth theoretical\nstudy on algorithmic optimization in our future work.\n4"}]

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