# Identification of Conformational Variants for Bradykinin Biomarker Peptides from a Biofluid Using a Nanopore and Machine Learning

Sandra Greive, Laurent Bacri, Benjamin Cressiot, Juan Pelta

► To cite this version:

Sandra Greive, Laurent Bacri, Benjamin Cressiot, Juan Pelta. Identification of Conformational Variants for Bradykinin Biomarker Peptides from a Biofluid Using a Nanopore and Machine Learning. ACS Nano, 2023, 18 (1), pp.539-550. 10.1021/acsnano.3c08433. hal-04367044

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# Identification of conformational variants for bradykinin biomarker peptides from a biofluid using a nanopore and machine learning.

Sandra J. Greive<fnref n="1" />, Laurent Bacri<fnref n="2" />*, Benjamin Cressiot<fnref n="3" />*, Juan Pelta<fnref n="2" />,<fnref n="3" />*

**Keywords:** Nanopore, kinins, single molecule detection, aerolysin, machine learning, biomarker, conformation

## Abstract

There is a current need to develop methods for the sensitive detection of peptide biomarkers in biofluids, which are complex mixtures of molecules, to enable early disease detection. Moreover, to our knowledge, there is currently no detection method capable of identifying the different conformations of a peptide biomarker differing by a single amino acid. Single molecule nanopore sensing promises to provide this level of resolution. In order to be able to identify these differences in a biofluid such as serum, it is necessary to carefully characterize electrical parameters to obtain specific signatures of each biomarker population observed. We are interested here in a family of peptide biomarkers, kinins such as Bradykinin and Des-Arg9-Bradykinin, involved in many disabling pathologies (allergy, asthma, angioedema, sepsis, or cancer). We show the proof of concept for direct identification of these biomarkers in serum at the single molecule level using a protein nanopore. Each peptide exhibits two unique electrical signatures attributed to specific conformations in bulk. The same signatures are found in serum, allowing their discrimination and identification in a complex mixture such as biofluid. To extend the utility of our experimental results, we developed a principal component analysis approach to define the most relevant electrical parameters for their identification. Finally, we used semi-supervised classification to assign each event type to a specific biomarker at physiological serum concentration. In the future, single-molecule scale analysis of peptide biomarkers using a powerful nanopore coupled with machine learning will pave the way for identifying and quantifying other clinically relevant biomarkers from biofluids.

## Introduction:

<footnote n="1">¹ DreamPore S.A.S., 33 Boulevard du Port 95000, Cergy, France</footnote>

<footnote n="2">²Université Paris-Saclay, Univ Evry, CNRS, LAMBE, 91025, Evry-Courcouronnes, France</footnote>

<footnote n="3">³ CY Cergy Paris Université, CNRS, LAMBE, 95000, Cergy, France</footnote>

<footnote>* Corresponding authors: benjamin.cressiot@cyu.fr; laurent.bacri@univ-evry.fr; juan.pelta@univ-evry.fr</footnote>
Single molecule electrical sensing using a nanopore is a promising method for biomarker peptide detection and discrimination. This has proven to be a potent tool in detecting and characterizing biological molecules, including nucleic acid sequencing, protein conformational analysis, as well as defining peptide size, sequence variation, amino acid chirality, aggregation and post-translational modifications (PTMs), (1-8) and reviewed in (9,10). Recently, this technology has been extended to the direct detection and characterization of biomarker proteins and peptides, such as epidermal growth factor (EGF), angiotensin, fibrinopeptide A and tau peptides (1,11-13). In this work we extend this method to detect the conformational variation adopted by two kinin biomarker peptides and use this information to discriminate between these peptides in a biofluid containing a complex mixture of molecules.

Kinins, a family of related bioeffector peptides that differ in sequence by only one or two amino acids, are produced in both blood and tissue matrix to mediate the physiological processes of inflammation and innate immunity (14,15). Bradykinin (BK) and Des-Arg⁹-Bradykinin (DABK) are formed in the blood by plasma kallikrein, while Kallidin (also known as Lys-BK) and Des-Arg⁹-Kallidin are cleaved in the tissue matrix by tissue kallikreins. This Kinin-Kallikrein system, is tightly regulated by feedback loops and interconnection with other core homeostasis signaling pathways, such as the Renin-Angiotensin, contact, and coagulation systems (15,16). Consequently, the kinin peptides play an important role in many normal physiological processes; regulating blood pressure, cardiac function, kidney function, and innate immunity(Figure 1a) (17-19). When these systems are dysregulated, kinins also play a role in disease processes: allergy, asthma, arthritis, angioedema, cancer, and infections such as sepsis and COVID-19 (17,20,21). Indeed, the BK and DABK peptides have been found in numerous biofluids, including plasma, serum, saliva, urine, nasal secretions, as well as synovial- and tumor ascites- fluids (20,22-25).
<figure><img src="image_2.png" /><figcaption>**Figure 1: Production, Physiological Effect and Detection of Bradykinin Biomarker Peptides.** a) Schematic representation of the physiological effects of bradykinin and disease states related to dysregulation of bradykinin. b) Schematic of Bradykinin production. High Molecular Weight Kininogen (HMK, orange, AlphaFold prediction for KNG1) in plasma is cleaved by activated plasma kallikrein (cyan surface of PDB 6O1G) to release the Bradykinin peptide (white/grey in HMK, green cleaved peptide) which binds to receptors B¹R (upregulated by cellular damage and innate immunity, aqua rods) and B²R (constitutively expressed, purple rods). Bradykinin is further cleaved by carboxypeptidases to produce des-Arg⁹ Bradykinin (blue) which binds to the same receptors. Arrow width and solidity indicate relative binding affinity. c) Schematic representation of a microfluidic coupled nanopore based assay for biomarkers in blood.</figcaption></figure>

Biomarker peptides (dark green, dark blue) are detected with an aerolysin protein nanopore (white surface representation of PDB 5JZT) inserted in a lipid bilayer (orange/light green), separating two compartments filled with an electrolyte solution (cyan), containing 4 M KCl (light blue and pink circles). Application of a ~50 mV voltage differential provides the electrophoretic (EP) driving force for movement of the positively charged peptides and K⁺ ions (light blue) from the cis compartment into the nanopore towards the negative electrode (red circle) in the trans compartment. Negatively charged Cl⁻ ions (pink) move from the trans compartment toward the positive electrode (blue circle) in the cis compartment. Image generated using Biorender.com and Servier Medical Art, provided by Servier, licensed under a Creative Commons Attribution 3.0 unported license.

In blood, BK, a nonapeptide, is released from high molecular weight kininogen by cleavage with activated plasma kallikrein (Figure 1b). BK binds with high affinity to the constitutively- and widely- expressed B²R receptor, a G protein-coupled receptor (GPCR) that induces vasodilation and vascular permeability, among others (Figure 1a). BK can also bind extremely
weakly to the B¹R GPCR, induced by tissue damage and cytokines to produce similar
inflammation responses (26). Carboxypeptidase activity removes the C-terminal arginine
residue from BK to form the octopeptide, DABK, which binds to the GPCR B¹R weakly, but
with higher affinity than BK. In crosstalk between the Renin-Angiotensin system, angiotensin
converting enzyme (ACE) also cleaves BK and DABK to form the inactive peptide BK1-5
(27).

Assays for BK or DABK are extremely difficult due to antibody cross-reactivity between the different kinin peptides and kininogen, their short half-life in blood (27,28), and ongoing plasma kallikrein activity during sample collection and processing (20,29). This has resulted in a large range (low pM – high nM) in the reported concentration levels for BK in serum and plasma (29). While use of mass spectrometry has increased the specificity of the assays, and the inclusion of protease inhibitors during sample collection reduces the reported plasma concentration to the pM range, slight differences in processing can greatly affect the levels detected by the assays (29). Despite this, bradykinin levels in blood show promise as a clinical biomarker for angioedema (30) and are reported to be increased in breast cancer (31), suggesting its potential as a biomarker for this condition. Given their central role in health and disease, there is an urgent need to develop new rapid and sensitive assays that can differentiate between the kinin peptide forms to better understand their function and exploit them as biomarkers.

Nanopore-based single molecule electrical sensing to detect specific target biomarkers in
biofluids, complex mixtures of molecules, can be achieved using different approaches. Indirect
methods, with a purification step, use bead-bound antibodies, aptamers or binding proteins
(32), to sequester the target analyte in serum and separate it from the other components, prior
to nanopore detection. This method has been used to detect thyroid-stimulating hormone,
prostate antigen (PSA), carcinogenic embryonic antigen and HIV p24 in serum (33-36).
Alternative assays have used binding partners to indirectly sense SARS-CoV-2 spike protein,
PSA, EGF, C-reactive protein, thrombin, vascular endothelial growth factor, glucose,
asparagine or thiamine, without purification, directly in biofluids, such as plasma, serum,
saliva, sweat or urine (37-46). Additional studies have indirectly detected biomarker enzyme
activity such as alkaline phosphatase or botulinum toxin in biofluids (47,48). Far fewer studies
have attempted to directly detect analytes in biofluids, reflecting the challenging nature of this
type of assay. Examples include detection of DNA sequences containing methylcytosine,
natriuretic peptides and mutant hemoglobin proteins, in biofluids such as serum or blood (37,49,50).

The protein nanopore, aerolysin (Figure 1c), has long been used to sense and characterize a variety of biomolecules, including biomarkers {Stierlen, 2023 #1; Jiang, 2023 #33}, and reviewed in (9). More recently, this includes peptide sizing, the detection of amino acid sequence variation and PTMs, real-time analysis of peptide cleavage, and the discrimination of peptide biomarkers from a mixture (1-3,12,13,51,52). Indeed, this nanopore has also been used to discriminate between biomolecules from biofluids (37,47,48,53), although only one of these use direct detection (37). To the best of our knowledge, it has not yet been possible to directly identify and quantify a peptide biomarker in a biofluid using aerolysin.

The aerolysin nanopore sensor creates a tiny hole (<2 nm) in an impermeable lipid membrane, separating two compartments that are filled with an electrolyte solution. Application of an electric field causes the ions to move towards the oppositely charged electrode creating a characteristic open pore current ($I_o$), reduced to the blockade current ($I_b$) level when the analyte enters the pore (Figure 2a). The features of these current blockade events, depth ($I_b$), current blockade fluctuation, dwell time, and frequency, (Figure 2a) are characteristic parameters related to the size of the analyte relative to the pore diameter, analyte-pore interactions, the applied electrical forces, as well as analyte charge and concentration (54). The combination of parameters provides a unique signature which can be used to discriminate between analytes in a mixture (1,51,55-63). Recently, when coupled with clustering analysis and machine learning (5,57-59,62-66), these unique signatures provide a powerful method for discriminating between analytes.

In this study, we show the proof of concept for direct identification and discrimination of a peptide biomarker family in serum at the single molecule level using a nanopore. We used an electrical detection method to define the features of the current blockades for the kinin peptides, Bradykinin and Des-Arg⁹-Bradykinin, including the observation of two discrete populations for each peptide, attributed to two different conformations. The unique combination of parameters (blockade fraction, current blockade fluctuation, dwell time and population) was used to discriminate between these peptides in a mixture, as well as from a complex mixture, such as a biofluid. In order to better discriminate each peptide in serum, we used a principal
component analysis (PCA), to reduce the number of dimensions in the data characterizing each blockade and to combine the most relevant parameters for biomarker identification. These were then used in semi-supervised classification to assign types of events to specific bradykinin peptides in biofluid. This method provides a robust approach for detecting and quantifying members of a peptide biomarker family from a complex mixture in a biofluid.

<figure><img src="image_3.png" /><figcaption>Figure 2. Electrical Detection and Discrimination of des-Arg⁹ Bradykinin and Bradykinin.</figcaption></figure>

Biomarker peptides des-Arg⁹ Bradykinin (blue, DABK) and Bradykinin (green, BK) were detected with an Aerolysin nanopore (white) inserted into a lipid bilayer (green/yellow), with an applied voltage of -50 mV in an electrolyte solution of 25 mM Tris pH 7.4, 4 M KCl. Peptides were analysed individually at 2 µM each of DABK (blue, a-c) or BK (green, d-f). a) Current trace against time (filtered at 5 kHz) of an aerolysin nanopore, showing the open pore current (I₀) and the blockade currents (I<sub>b1</sub>, I<sub>b2</sub>) that occur when a bradykinin biomarker peptide enters the pore. Current blockade events have characteristic features: blockade depth (ΔI=I₀-I<sub>b</sub>; vertical arrow), blockade length or dwell time (small horizontal arrows), and interevent time (horizontal arrow). Data shown are comprised of: a typical current trace (a,d) showing event blockades over a 50 ms timescale; representative bidimensional distribution of average blockade fraction (ΔI<sub>b</sub>/I₀) against dwell time (b,e); and representative histograms of the average blockade fraction against number of events (c,g). Open pore currents, I₀ = 107.2 ± 2.1pA for DABK (a), 105 ± 1.8 pA for BK (c). Blockade fraction (ΔI<sub>b</sub>/I₀) histograms were fit to gaussian functions (black lines) to determine the average blockade fraction for each population (P1 and P2) as 0.69 ± 0.01 and 0.33 ± 0.01 for DABK (c) and 0.76 ± 0.01 and 0.46 ± 0.01 for BK (f), respectively. Mean and standard errors calculated from 3 fits to distributions binned at 0.01 Blockade Fraction. N<sub>DABK</sub>=1939 (c); N<sub>BK</sub>= 978 (f).

**Results and Discussion:**

Discrimination of des-Arg⁹ Bradykinin and Bradykinin

We first characterized the electrical signals for each peptide individually. After the addition of
2 µM DABK or BK into the set-up (Figure 1), we observed discrete current blockades (Figure
2a, d and S1). Surprisingly, two kinds (or populations) of events (named P1 and P2) were observed for each peptide, differing in their average blockade fraction (Figure 2b, c, e, f, and S2). The average blockade fraction for each population (P1 and P2) is 0.69 ± 0.01 and 0.33 ± 0.01 for DABK and 0.76 ± 0.01 and 0.46 ± 0.01 for BK, respectively. For the P1 populations, the average blockade fractions found for each peptide are similar to those obtained using unstructured poly-arginine peptides of 9 and 8 amino acids(3) under the same experimental conditions.

Although the existence of P2 was unexpected, it is reminiscent of the second population of events observed for phosphorylated fibrinopeptide A, attributed to conformational variation (1). As members of proline-rich immune system effector peptides(67), the kinins contain both an N-terminal β-turn and a C-terminal γ-turn(68-70). Indeed, both turn structures play a role in receptor binding (71). Approximately 20% of BK or DABK have a conformation with the C-terminal turn in aqueous solutions at room temperature(72), with the propensity for being in this conformation increasing with temperature and in apolar solvents or the gas phase(70,72,73). While the effect of high salt concentrations on the conformational landscape of turn-containing peptides has not been well studied, helical peptides retain significant helicity in 4M KCl (74), hence it is not unlikely that BK and DABK would also maintain conformation variety in 4M KCl. Interestingly, we find a ratio of ~30% for the DABK P2 population, similar to previous observations for the propensity for the C-terminal turn(72). In contrast, we see an increase in this ratio for BK (~40%). It is possible that this increase could be due to the stabilization of the C-terminal turn by hydrogen bonding between Arg9 and Pro7 of BK with increasing salt concentration, compared to DABK, which does not have Arg9 (75,76). The results suggest that this nanopore technique has the sensing resolution to detect two different conformational varieties, and their relative proportions, for individual peptides that differ by a single amino-acid.

To demonstrate that the average blockade fractions observed for each population of each peptide individually can be used to discriminate between these peptides, we performed these experiments using a 1.3 µM equimolar mixture of BK and DABK (Figure 3). We observed different current blockades attributed to both populations of each peptide producing 4 different average blockade fractions (Figure 3 b, c), similar to the ones found in independent experiments. We obtained average blockade fractions of 0.33 ± 0.01 and 0.46 ± 0.01, attributed
to P2 of DABK and BK, respectively, and 0.69 ± 0.01 and 0.74 ± 0.01 for P1 of DABK and BK, respectively.

Since dwell time can also be used to discriminate between peptides, we determined the most probable dwell times for each population of each peptide (Figure S3) and plotted these against the average blockade fraction (Figures 3d and S4a). As found previously for unstructured polyarginine peptides (3), while the P1 population exhibits slightly different values for average blockade fraction, the dwell time distributions overlap significantly (Figure 3b and S3). Thus, it is impossible to discriminate between DABK and BK in a mixture according to the characteristic dwell time of P1. However, the dwell time distributions for the P2 population are well separated, allowing discrimination of DABK and BK using both parameters for P2: most probable dwell time; and average blockade fraction (Figure 3d and S4). These additional parameters introduced by the observed conformational variation contribute to the discrimination of these peptides from a mixture.

The purpose of our study is to not only identify and discriminate between the different peptides but also to quantify them. We varied the concentration of the equimolar mixture of BK and DABK to determine the change in frequency for the P1 and P2 populations over the reported physiological concentration range for serum, nM to µM (Figure 3e and S5). Since, we cannot easily deconvolute all the events in the P1 populations for each peptide in a mixture, the frequency was determined for P1 events from both peptides together (BOTH P1). We observe a linear dependency for the frequency as a function of peptide concentration for the P1 and P2 populations. Interestingly, the frequency for the BK P2 population is higher than for DABK, as is expected for the loss of a charged residue (Arg9) for DABK. The dwell times for each population are independent of the peptide concentration (Figure S4b) showing that peptides are stable under our experimental conditions.
<figure><img src="image_4.png" /><figcaption>**Figure 3: Discrimination of des-Arg⁹ Bradykinin and Bradykinin biomarker peptides in a mixture.** A 1.3 µM equimolar mixture of biomarker peptides des-Arg⁹ Bradykinin (blue, DABK) and Bradykinin (green, BK) was detected with an Aerolysin nanopore (white) inserted into a lipid bilayer (green/yellow), with an applied voltage of -50 mV in an electrolyte solution of 25 mM Tris pH 7.4, 4 M KCl. Data shown are comprised of: a typical current trace (a) low-pass Bessel filtered at 5 kHz, showing event blockades over a 50 ms timescale; representative bidimensional distribution of average blockade fraction against dwell time (b); and a representative histogram of the average blockade fraction against number of events (c). Open pore current I₀ = 107 ± 1.8 pA (a). Average blockade fraction (ΔI_b/I₀) histograms were fit to gaussian functions (black lines) to determine the average blockade fractions for population 2 (P2) in the mixture as 0.33 ± 0.01 and 0.46 ± 0.01 for DABK and BK respectively (c). The distribution for Population 1 (P1) in the mixture was fit to a bi-gaussian function (black curve, (c) to determine average blockade fractions of 0.69 ± 0.01 and 0.74 ± 0.01 for DABK and BK respectively. Mean and standard errors calculated from 3 fits to distributions binned at 0.01 Blockade Fraction. N_mix = 2938 (c). Plot of average blockade fraction against most probable dwell time (Figure S3) for both populations (P1, magenta; and P2, cyan and light green) of each peptide (DABK, triangles; and BK, circles) in the mixture (d). Points and error bars represent the mean and standard deviation calculated from parameter values determined from 3 independent experiments. Plot showing the effect of concentration of an equimolar mix on frequency (e) for P1 of BOTH peptides (diamonds, magenta) and P2 for DABK (cyan, triangles) and BK (light green, circles). Linear fits are shown as colored lines, with slopes of (2.87 ± 0.04) x 10⁻³, (6.59 ± 0.09) x 10⁻⁴, and (3.93 ± 0.09) x 10⁻⁴ nM⁻¹ s⁻¹ for P1 BOTH (magenta), BK P2 (light green) and DABK P2 (cyan) respectively. Populations of events (selected as in Figure S3) from different concentrations (10-1000nM) of equimolar mixtures of des-Arg⁹ Bradykinin (DABK) and Bradykinin (BK) were used to determine the frequency as in Figure S5 (e). Events from population 1 (P1; magenta diamonds) of both peptides were analyzed together (MIX, magenta) while events for population (P2) were analyzed separately, (DABK, cyan; BK, light green). Points and error bars are the mean and standard deviation of the values calculated from three global fits to data from three independent experiments, or from individual fits to 3 independent experiments.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 4: Discrimination of des-Arg⁹ Bradykinin and Bradykinin biomarker peptides in serum.**</figcaption></figure>

A 50 nM equimolar mixture of biomarker peptides des-Arg⁹ Bradykinin (blue, DABK) and Bradykinin (green, BK) was detected with an Aerolysin nanopore (white) inserted into a lipid bilayer (green/yellow), with an applied voltage of -50 mV in an electrolyte solution of 25 mM Tris pH 7.4, 4 M KCl containing 2% human serum (burgundy drop). a) Current trace over 30 s, low-pass Bessel filtered at 5 kHz. Open pore current I₀ = 105 ± 1.6 pA. b) Histogram of the average blockade fraction (ΔI<sub>b</sub>/I<sub>0</sub>) were fit to gaussian functions (black lines) to determine the average value for population 2 (P2) in the mixture as 0.33 ± 0.01 and 0.48 ± 0.01 for DABK and BK respectively. The distribution for Population 1 (P1) in the mixture was fit to a bi-gaussian function (black curve), to determine average values of 0.71 ± 0.01 and 0.76 ± 0.01 for DABK and BK respectively. Mean and standard errors calculated from 3 fits to histograms of data for events longer 200 µs, pooled from 4 independent experiments, binned at 0.008 Average Blockade Fraction. N<sub>DABKP2</sub> = 122, N<sub>BKP2</sub> = 131, N<sub>P1BOTH</sub> = 317, from 42.5 minutes of recorded data.

**Discrimination of des-Arg⁹ Bradykinin and Bradykinin in serum and semi-supervised classification**

The key challenge in developing nanopore biomarker assays is to directly identify and quantify
the target biomarker from biological fluids that contain a mixture of molecules. We next
demonstrated that each peptide can be detected in a biological media such as serum. In the
presence of 2% human serum and after the addition of 50 nM equimolar peptide mixture, we
observed various current blockades (Figure 4a), with average blockade fractions for 9 different
populations of events. We note that these generally do not overlap with the events observed in
2% serum alone (Figure S6). Four of these correspond to the average blockade fractions found
for P1 and P2 of DABK and BK, both from independent experiments and equimolar mixtures
(Figure 4b and Figure S7). We found values of 0.33 ± 0.01 and 0.48 ± 0.01 for DABK and BK
respectively for the P2 population and 0.71 ± 0.01 and 0.76 ± 0.01 for DABK and BK
respectively for the P1 population in the serum background. Therefore, each peptide can be
well discriminated in serum because none of the other molecules present in serum have current
signatures that overlap with those of BK and DABK. This approach allows us to discriminate
the bradykinin peptides in a biological medium.
However, in order to quantify each peptide, we need to confidently attribute specific events to individual peptides and determine the frequency with which these occur. Additional unique parameters, such as conformational variety of the bradykinin peptides, increase the confidence of event identification. More detailed analysis of current blockade fluctuation within these events (Figure 5a) revealed additional features that can be described by minimum and maximum event blockade currents, I<sub>bmin</sub> and I<sub>bmax</sub> respectively (Figure 5), and the standard deviation of the blockade current, I<sub>bσ</sub> (Figure S8). These parameters allow discrimination of each blockade type according to their shape (Figure 5b). Plotting the average blockade fraction as a function of the minimum blockade fraction, (ΔI<sub>bmin</sub>/I<sub>0</sub>; Figure 5c), reveals several populations, one of which is a linear cluster representing events with minimal fluctuation in the blockade current (I<sub>bσ</sub> < 1pA). As we show below, these are very short events (<100 µs) that contain no discriminating information for peptide identification. Removing this population (Figure 5d) allows us to focus on events that are discriminating. Using four parameters (defined in Figure 5b; ΔI<sub>bmin</sub>, ΔI<sub>bmax</sub>, ΔI<sub>b</sub>, and I<sub>bσ</sub>) to discriminate the events is possible using a principal component analysis (PCA)(77) (Figure 5e) to determine PC1 and PC2, characterized by 68 and 29% explained variance, respectively. The distribution of PC1 vs PC2 revealed 3 main clusters.

In order to classify each event of the three main clusters observed, we first performed unsupervised classification using K-Means, Birch, DBSCAN and Bayesian Gaussian mixture methods (Figure S9 a-e)(78-80), which were unsuccessful. We then performed a semi-supervised classification using Logistic Regression (81,82) (Figure 5f and Figure S9 f-h), which confirmed the three main clusters.
<figure><img src="image_6.png" /><figcaption>**Figure 5:** *Principal Component Analysis of des-Arg⁹ Bradykinin and Bradykinin biomarker peptides in a mixture.* a) Current trace of a 2 µM equimolar mixture of biomarker peptides des-Arg⁹ Bradykinin and Bradykinin. Each event identified with the threshold I₀-7σ is indicated by a green circle. ΔV = -50 mV, 25 mM Tris pH 7.4, 4 M KCl. b) Representative blockade characterized by its duration ΔT, open pore current I₀, the min blockade depth ΔIₐmin=I₀-Iₐmax, the average blockade depth ΔIₛ=I₀-Iₛ, and the max blockade depth ΔIₐmax=I₀-Iₐmin. c) Scatter plot of the average blockade fraction ΔIₛ/I₀ as a function of the min blockade fraction ΔIₐmin/I₀. d) Same scatter plot as c) after removing blockades characterized by a standard deviation Iₐσ <1 pA. e) Scatter plot of the two highest Principal Components (PC2vsPC 1) calculated from ΔIₛ, ΔIₐmin, ΔIₐmax and Iₐσ. f) Classification by Logistic Regression of PC1 and PC2 showing 3 types (red, green, blue).</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>**Figure 6:** *Semi-supervised Event Classification by Logistic Regression of Principal Components:* a) Current trace of a 2 µM equimolar mixture of biomarker peptides des-Arg⁹ Bradykinin and Bradykinin. Each event identified and classified is denoted by a circle colored by type defined in Figure 5 (Type 0, black; Type 1, red; Type 2, green; Type 3, blue), where Type 0 or black events correspond to those where I<sub>bσ</sub> <1 pA. ΔV = -50 mV,</figcaption></figure>
25 mM Tris pH 7.4, 4 M KCl. b) Representative current vs time trace for each type of event observed in the current trace. c) Scatter plot of the average blockade fraction (ΔI<sub>b</sub>/I<sub>0</sub>) against dwell time for each event colored by the classification type. d) Plot of average blockade level against location time for each identified and classified event. e) Scatter plot of the second Principal Component against the first, after classification for blockades observed in a 100 nM equimolar peptide mixture under serum-free conditions (diamond) and for a 50 nM equimolar mixture of biomarker peptides in 2% serum (cross). f) Scatter plot of the second Principal Component according to the first, after classification of blockades observed in either 50 nM (circle) or 2 µM (cross) equimolar peptide mixtures in 2% serum. In all panels, events are colored according to classification type (Type 0, black; Type 1, red; Type 2, green; Type 3, blue).

Each blockade was labeled according to the classification type 0, 1, 2, and 3 (Figure 6 a) where the type 0 events are characterized by a standard deviation Ibσ <1pA (see above). Each type of event has a different characteristic shape in its current vs time trace (Figure 6b) producing different populations in the average blockade fraction vs dwell time distribution (Figure 6c). Indeed, these overall shape patterns are generally consistent across ten representative current traces for each type of blockade (Figures S10-13) taken from points equally distributed across the time frame of the current trace (Figure 6d), reproducing the range observed for dwell time for each type (Figure 6c). Type 0 events are characterized by a very short blockade duration with a sharp shape. Type 1 are deeper and longer events with a square shape compared to type 0, and have dwell times ranging between 100 to 500 µs. Type 0 and 1 events have distributions for average blockade fraction vs dwell time similar to those observed for both peptides independently (Figure 2b, e; Figure S2a, b), and therefore cannot be attributed to an individual peptide. On the other hand, type 2 and type 3 events are characterized by the same shaped plateau as type 1 events, but both contain one or two deeper short sub-blockades. The main difference between these two types of events is their duration. Type 2 events have a duration ranging from 500 µs to 5 ms, while type 3 have durations of 100 µs to 2 ms (Figure 6c, Figure S12-13). These dwell time distributions are similar to those observed for population 2 of BK and DABK respectively from individual experiments (Figure 3f, g), suggesting that type 2 events correlate with BK P2 while type 3 events correlate with DABK P2. Plotting the average blockade fraction against dwell time for the classified data from a 2 µM equimolar mixture of both biomarker peptides (Figure 6c) produces two main populations for each peptide as observed previously (Figure 3b). P1 from both peptides correlates with type 1 events, P2 for BK is mostly associated with type 2, and P2 for DABK with type 3 events. As described above, we attribute these lower blockade level events (type 2 and 3) with their specific electrical signature to peptides that are in a given conformation, while the higher blockade level events (type 1), with their sharp electrical signature, could be unstructured peptides in our experimental conditions, as described above.
Plotting the average blockade fraction against blockade location with each event colored to their classification type (Figure 6d) determines that the average blockade fraction for type 0 events is distributed across the entire signal range, while type 1 events occurred at the same level between 0.7 and 0.8, type 2 between 0.5 and 0.7, and type 3 between 0.3 and 0.7.

This method of classification was applied to data collected in 2% serum, where we performed the PCA for the serum data using a covariance matrix previously calculated with serum-free data. Figure 6e shows a comparison between serum and serum-free experiments using this PCA analysis. Classification is performed similarly, revealing similar sets of classifications in both data. Finally, we tested the same classification by using serum data spiked with two different concentrations of equimolar mixtures. We observe the same populations as for serum-free data confirming the analysis is not concentration dependent. This classification is robust even at low concentration (50 nM).

## Conclusion

We showed that we can directly detect, identify, and classify each blockade event from either BK or DABK, under different experimental conditions: independently; in a mixture; and in biofluids, which are complex mixtures of molecules, at the physiological concentrations reported for serum. We demonstrated sensing and discrimination between peptide conformations, even at 4 M KCl, allowing better identification of both biomarker peptides directly in serum by using additional parameters that define blockade level fluctuations. PCA and semi-supervised classification of these parameters allow confident identification and quantitation of these events in biofluids, which can be attributed to particular peptides.

The challenge remains to develop a system that can be applied to other clinically relevant biomarkers from different types of biofluids, where each target peptide has a different specific conformation or net charge and exists in a complex mixture of molecules. Part of this challenge is to develop robust methods that are resistant to contaminating proteases (serum, plasma, urine etc), pH variations (urine) or components that destabilize the supporting membrane. It could also be necessary to decrease the level of detection below the nM range. We used here a wild-type aerolysin that can be engineered as shown in recent publications (9,83) to tune the driving force for more rapid sensing of other peptide biomarkers at their physiological concentrations.
## Methods:

### Peptides:

Kininin peptides, Bradykinin (BK) RPPGFSPFR and Des-Arg⁹-Bradykinin (DABK) RPPGFSPF were synthesized and purified by HPLC (Proteogenics, Schiltigheim, France) and dissolved in 25 mM Tris pH 7.5 to either 1 mM or 10 mM, aliquoted and stored at -20 °C until use.

### Protein Production:

Proaerolysin was produced by Dreampore SAS (Cergy, France) as described previously (1). Briefly, C-terminally histagged protein was expressed into the periplasm of BL21 Ros 2 cells (MilliporeSigma), harvested by osmotic shock before further purification by nickel affinity and buffer exchange chromatography (Cytiva, Malborough MA, USA) in standard buffers containing 350 mM NaCl. Purified protein was stored at 4 °C until further use, when it was activated using trypsin bound beads (Thermo Scientific, Waltham MA, USA) and used in nanopore experiments at 0.5-1 nM final concentration of aerolysin monomers.

### Nanopore experiments:

Nanopore experiments were performed using a vertical planar lipid bilayer setup (Warner Instruments, Hamden CT, USA) with a 150 µM aperture, as described previously (84). A 10 mg/ml stock of diphytanoylphosphatidylcholine (DphPC, Avanti Polar Lipids, Alabaster AL USA), dissolved in decane was used to create a planar lipid bilayer separating compartments containing 1 ml of electrolyte solution 4 M KCl, 25 mM Tris pH 7.4. Upon insertion of a single aerolysin pore, peptide analytes (either individually or in a mix) were introduced at different concentrations before a -50 mV voltage differential was applied through Ag/AgCl electrodes.

For serum experiments, 980 ml of electrolyte solution was added to both chambers before lipid bilayer formation and insertion of a single aerolysin pore. An additional 20 µl (2% final concentration) of pooled Human AB Serum (MP Biomedicals, Irvine CA USA) subsequently added to both compartments prior to addition of the analyte.

### Data Acquisition and Analysis:
Electrical measurements were performed using an Axopatch 200B and either a Digidata 1440 or 1550B digitizer. Data were filtered at 5 kHz and recorded at 250 kHz or 4 ms sampling time using Clampex software (Axon Instruments, Union City, CA, USA). Three-minute recordings taken at -50 mV, were inverted (multiplied by -1), cleaned and analyzed with Igor Pro (Wavemetrics, Portland OR USA), using in-house algorithms to detect events and extract their characteristic parameters (current baseline level I<sub>0</sub>, values for the average (I<sub>b</sub>), maximum (I<sub>bmax</sub>) and minimum (I<sub>bmin</sub>) current blockade level within the event, event noise or standard deviation of the current values within the event (I<sub>bσ</sub>), dwell time and blockade time location in the dataset). The average open pore current (I<sub>0</sub>) and standard deviation (s) for each recording were determined statistically (54) and a threshold of I<sub>0</sub>-5σ was used to define events for the target peptides in the characterization experiments (Figures 2 and 3b, c), all other experiments used a threshold of I<sub>0</sub>-7σ to screen out the shallow background events that occur in the low frequency experiments. For low frequency events, extracted parameters from multiple recordings were concatenated to provide sufficient events for robust statistics. To remove bias introduced by short bumping events with a high blockade fraction (Figure S3a), events with dwell-times equal to- or longer than- 200 µs were used for further analysis. This is a reasonable approach given the overall distribution of average blockade fraction vs dwell time data (Figure S2), relative length of event dwell times and the use of a 5 kHz filter. Histograms of the average blockade fraction for each event (ΔI<sub>b</sub>/I<sub>0</sub>, where ΔI<sub>b</sub>=I<sub>0</sub> - I<sub>b</sub>; binned at 0.01, or 0.008 where stated) were fit with gaussian functions to determine the average blockade fraction for each population of events. Events from individual populations, defined by ranges of average blockade fraction and dwell time parameters as described in the Figure S3, were further analyzed to define the most probable dwell time and event frequency of each population. Semi-log histograms of dwell-time, with 20 bins per decade, were fit with log-normal functions to determine the most probably dwell time. Semi-log distributions of interevent time, binned at 400 ms for population 1 and 1200 ms for population 2, for concentrations 100-1000 nM were fit with single exponential functions to determine the event frequency. Each population contained at least 400 events. For each individual fit, the average and standard deviation determined from 3 independent fits, were used to account for fit robustness. For low frequency events (low concentration) the data from at least 3 independent experiments was pooled and globally fit as described above. For high frequency events, fit values from at least 3 independent experiments (3 fits each, at least 9 values) were used to calculate the average and standard deviation of the parameter. The event frequency for each event population in the 10 nM equimolar mixture
was determined by dividing the population event count over the experiment time (s) with the mean and standard deviation calculated for the values from 4 independent experiments. For serum data displayed in Figure 4, each population contained at least 100 events.

## Semi-supervised Clustering using Principal Component Analysis (PCA) and Logistic Regression classification.

The four parameters ($\Delta I_{bmin}$, $\Delta I_{bmax}$, $\Delta I_b$, and $I_{b\sigma}$) were determined (Figure 5B) using $\Delta I_{bmin}=I_{0}-I_{bmax}$, $\Delta I_{bmax}=I_{0}-I_{bmin}$, and $\Delta I_b=I_{0}-I_b$ respectively. Principal Component Analysis (PCA) was performed by using the PCA module of the scikit-learn library in Python (77,85). Briefly, the covariance matrix between these previously normalized four parameters were calculated to determine the eigen values and vectors of this matrix. Each parameter is projected onto each eigen vector, with only the eigen vectors characterized by the largest eigen values (PC1 and PC2) being used for further analysis. The unsupervised classifications (K-Means, Birch, DBSCAN, Bayesian Gaussian mixture)(78-80) and semi-supervised Logistic Regression classification(82) were performed by using the corresponding module available in the scikit-learn Python library. Since the histogram of PC1 showed 3 gaussian peaks characterized by their position $x_p$ and standard deviation $\sigma$ (Figure S9 f), the train data was defined as $x_p-\sigma \le x \le x_p+\sigma$ according to the corresponding peak (1,2,3) (Figure S9 g). The remaining data defined the test data to classify using Logistic Regression (Figure S9 h).

### Notes

The authors declare the following competing financial interest(s): J.P. is co-founder of DreamPore S.A.S., and S.J.G. is the head of research development at DreamPore S.A.S.

### Supporting Information:

Additional figures showing details of the data analysis process, including representative plots of intermediate analyses (PDF).

### Acknowledgements:

This work was supported by the Agence Nationale de la Recherche (ANR-23-CE44-0013). We acknowledge financial support from CY Initiative of Excellence (“Investissements d’Avenir” ANR-16-IDEX-0008), from the Region Ile-de-France in the framework of DIM ResPore. We thank Jean Roman for his useful coding on Igor.
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