# Nanostructural characterization of the tumor biomarker Alpha-Fetoprotein interacting with model cellular membranes

Beatrice Barletti, Melanie König, Nicoló Paracini, Giovanna Fragneto, Jean-Pierre Alcaraz, Andrew Nelson, Isabelle Vilgrain, Donald K Martin, Fabio Lolicato, Marco Maccarini

<figure><img src="image_1.png" /></figure>

To cite this version:

Beatrice Barletti, Melanie König, Nicoló Paracini, Giovanna Fragneto, Jean-Pierre Alcaraz, et al. Nanostructural characterization of the tumor biomarker Alpha-Fetoprotein interacting with model cellular membranes. 2025. hal-05392509v1

HAL Id: hal-05392509

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Preprint submitted on 9 Dec 2025 (v1), last revised 12 Jan 2026 (v2)

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Graphical Abstract

Nanostructural characterization of the tumor biomarker Alpha-Fetoprotein interacting with model cellular membranes

Beatrice Barletti, Melanie König, Nicoló Paracini, Giovanna Fragneto, Jean-Pierre Alcaraz, Andrew Nelson, Isabelle Vilgrain, Donald K. Martin, Fabio Lolicato, Marco Maccarini

<figure><img src="image_2.png" /></figure>
Nanostructural characterization of the tumor
biomarker Alpha-Fetoprotein interacting with model
cellular membranes

Beatrice Barletti<sup>a,b</sup>, Melanie König<sup>c</sup>, Nicoló Paracini<sup>d</sup>, Giovanna Fragneto<sup>d</sup>,
Jean-Pierre Alcaraz<sup>e</sup>, Andrew Nelson<sup>f</sup>, Isabelle Vilgrain<sup>g</sup>, Donald K.
Martin<sup>h</sup>, Fabio Lolicato<sup>c,i</sup>, Marco Maccarini<sup>a,h</sup>

<sup>a</sup>Université Grenoble-Alpes, Grenoble INP, TIMC-IMAG / SyNaBi (UMR 5525), 38000 Grenoble, Grenoble, France

<sup>b</sup>Institut Laue-Langevin, 38042 Grenoble, Grenoble, France

<sup>c</sup>Heidelberg University Biochemistry Center, Heidelberg, Germany

<sup>d</sup>European Spallation Source ERIC, P.O. Box 176, SE-221 00, Lund, Sweden

<sup>e</sup>Université Grenoble-Alpes, CNRS, Grenoble INP, TIMC/PRETA (UMR 5525), 38000 Grenoble, Grenoble, France

<sup>f</sup>Australian Nuclear Science and Technology Organisation (ANSTO), Sydney, New Illawarra Rd, Lucas Heights NSW 2234, Sydney, Australia

<sup>g</sup>Université Grenoble-Alpes, INSERM U13, CEA, Institute of Interdisciplinary Research of Grenoble (IRIG), Laboratory of Biosciences and Bioengineering for Health (BGE)-BIOMICS, 38054 Grenoble, Grenoble, France

<sup>h</sup>Université Grenoble-Alpes, CNRS, Grenoble INP, LMGP-UMR 5628, 38000 Grenoble, Grenoble, France

<sup>i</sup>Department of Physics, University of Helsinki, Helsinki, Finland

Abstract

This study systematically investigated the interactions of the tumor biomarker
alpha-fetoprotein (AFP) and bovine serum albumin (BSA) with planar lipid
membranes using neutron reflectometry, quartz crystal microbalance with
dissipation monitoring, and atomistic molecular dynamics simulations. By
examining a range of lipid compositions—including charged, neutral, and
nanodomain-containing bilayers—we dissected the molecular mechanisms gov-
erning protein–membrane interactions. AFP consistently exhibited stronger
interactions than BSA. While both proteins showed negligible binding to

Email addresses: fabio.lolicato@bzh.uni-heidelberg.de (Fabio Lolicato),
marco.maccarini@univ-grenoble-alpes.fr (Marco Maccarini)
zwitterionic POPC bilayers, BSA only interacted significantly with negatively charged POPS membranes. In contrast, AFP strongly engaged with both SM/CHOL nanodomains and POPS-containing bilayers, leading to protein incorporation and lipid extraction, respectively. On positively charged DOTAP membranes, AFP formed a stable adsorbed layer. These findings reveal how lipid composition modulates protein adsorption, insertion, and membrane integrity, offering new mechanistic insights into the differential binding affinities of AFP and BSA. The results underscore the importance of protein-lipid interactions in clinical assays for blood tumor biomarkers, as these interactions may influence the formation of protein-lipid complexes and impact detection accuracy. This work may guide the optimization of biosensing strategies, particularly for glycosylated biomarkers, to enhance the sensitivity and reliability of clinical diagnostics.

# 1. Introduction

The detection of blood biomarkers through traditional techniques presents many challenges, primarily due to their low abundance, the complexity of biological fluids, and the extent of their glycosylation or glycation. Biomarkers are typically present in blood at very low concentrations, often in the range of pico- or nanograms per milliliter.[1] Their detection from blood or serum is hampered by their formation of complexes with lipids and/or lipoproteins, which are structurally intricate particles composed of a core of cholesterol esters and triglycerides, surrounded by free cholesterol, phospholipids, and apolipoproteins.[2] The glycosylated state of biomarkers may play a role in recognition and in enhancing protein-lipid interactions, promoting the formation of these complexes.[3] This is particularly relevant in routine clinical practice for the detection of cancer biomarkers, since these tests do not typically require patient fasting. The amount of lipids and lipoproteins vary with food consumption, and it has been demonstrated that biomarkers may become embedded within lipid particles (e.g., HDL and LDL particles),[3] rendering them undetectable through traditional techniques like Enzyme-Linked Immunosorbent Assay (ELISA) due to competing interactions between the assay and lipidic moieties present in the blood.

Previous studies have shown that glycosylated proteins such as CLA-1,[4] CD36,[5, 6] and Hepatitis C virus glycoprotein E2,[7, 8] interact with lipoproteins. A recent study demonstrated for the first time the direct interaction between the glycosylated biomarker, soluble Vascular-Endothelial
cadherin (sVE), and HDL particles, suggesting that protein-lipid interactions contribute to the variability in biomarker detection.[3] However, the nanostructural mechanisms underlying these interactions remain poorly understood, particularly for specific cancer biomarkers. Understanding protein biomarkers-lipid interactions is crucial not only for advancing fundamental knowledge, but also for optimising the sensitivity and reliability of biosensing techniques used to identify protein biomarkers in blood samples, particularly when these proteins are glycosylated and challenging to detect with current methods. Additionally, lipids have the potential to serve as an effective platform for the development of innovative analytical and biosensing approaches.[9, 10, 11]

Here we focus on alpha-fetoprotein (AFP), a glycosylated biomarker associated with the development of liver cancer. AFP is a 69 kDa N-linked glycoprotein found in fetal serum, but its content is rapidly reduced in adults reaching values that fall typically into the range of 5-10 ng/mL.[12, 13, 14] On the other hand, AFP concentration in serum increase in hepatocellular carcinoma (HCC) and others liver diseases and its detection can anticipate the reappearance of the tumor. Studies have shown that an AFP concentration above 400 ng/mL can generally be considered as diagnostic tool for HCC high risk patients,[14] or consistently associated with poor prognosis in several treatments of HCC.[15] However, AFP has poor sensitivity and specificity,[16] and levels below 100 ng/mL are less specific since similar values can also be found in patients with chronic hepatitis.[17] Despite this, the protein is still considered and used as tumor-associated biomarker for HCC.

In this study, we characterise at the nanostructural level the interaction between the glycosylated biomarker AFP and cell membrane models, with a focus on understanding how different lipids influence this interaction. To provide a reference for the interactions of AFP, we compared its behaviour with that of bovine serum albumin (BSA), a widely used model protein due to its well-characterised interactions with lipid bilayers. Although BSA is known to bind lipophilic compounds, its interactions with zwitterionic supported lipid bilayers are generally weak, though they may vary depending on the specific lipid composition. While albumins, including BSA, have traditionally been considered non-glycosylated, recent evidence suggests that low levels of glycosylation may occur at non-canonical sites.[18] To support the interpretation of the experimental results, we performed fully atomistic molecular dynamics (MD) simulations of AFP and BSA interacting with in silico-generated lipid bilayers that matched the experimental lipid compositions, for a cumulative
simulation time of 40 microseconds. These simulations provided atomic-level information into protein-lipid interactions and how they are influenced by lipid composition, thereby enhancing our understanding of the molecular details governing these interactions and their potential structural implications. This strategy allowed us to systematically explore how distinct biophysical properties influence protein-lipid interactions. Moreover, our experimental results and MD simulations enhance our understanding of the interaction mechanisms between biomarkers and lipidic moieties, highlighting any specific or non-specific interactions that could be leveraged also for potential biosensor applications.

## 2. Materials and methods

### 2.1. Materials

Alpha-fetoprotein (AFP) solution (catalogue number 105-11, purity > 99% (SDS-PAGE)) was purchased from Lee Biosolutions (Maryland Heights, MO USA); Bovine Serum Albumin (BSA) was purchased from Sigma-Aldrich (Darmstadt, Germany) in the form of lyophilized powder product number A8806-1G (CAS 9048-46-8, purity ≥ 96% (agarose gel electrophoresis)); Tris buffered saline pH 7.6 buffer was prepared using the BioUltra tablets from Sigma-Aldrich (Darmstadt, Germany). 1-palmitoyl-2-oleoyl-glycero-3-phosphocholine ($C_{42}H_{82}NO_8P$, 16:0-18:1 PC, POPC, 760.076 g/mol, CAS: 26853-31-6, purity >99% (TLC)), 1-palmitoyl-2-oleoyl-sn-glycero-3-phospho-L-serine sodium salt ($C_{40}H_{75}NO_{10}PNa$, 16:0-18:1 PS, POPS, 783.988 g/mol, CAS: 321863-21-2, purity >99% (TLC)), 1,2-dioleoyl-3-trimethylammonium-propane chloride salt ($C_{42}H_{80}NO_4Cl$, 18:1 TAP, DOTAP, 698.542 g/mol, CAS: 132172-61-3, purity >99% (TLC)), brain porcine sphingomyelin ($C_{41}H_{83}N_2O_6P$, SM, 760.223 g/mol, CAS: 383907-91-3, purity >99% (TLC)) and ovine wool cholesterol ($C_{27}H_{46}O$, CHOL, 386.654 g/mol, CAS: 57-88-5, purity >98% (TLC)) for SLBs preparation were purchased from Avanti Polar Lipids (Alabaster, AL, USA). The TBS buffer for protein preparation and formation of supported lipid bilayer (SLB) was previously degassed in bath sonicator and filtered to 0.2 µm.

### 2.2. Neutron reflectometry - Experimental

Time of flight Neutron reflectometry (NR) experiments were conducted on the FIGARO instrument[19] at the Institut Laue-Langevin (Grenoble, France) employing a vertical scattering geometry (horizontal sample plane)
with a $\Delta q/q$ resolution of 7%. Two incident angles, 0.7° and 3.0°, were employed in the measurements, which were conducted over a wavelength range of 2–20 Å. This setup yields a usable Q-range from 0.009 Å⁻¹ to approximately 0.25 Å⁻¹, beyond which the signal is dominated by the sample background.

The experiments employed solid/liquid sample cells, developed at the Institut Laue-Langevin, comprising a poly(ether ether ketone) (PEEK) trough that held the sample surface against a liquid-filled compartment. The sample cells, with a volume of approximately 2 mL, were connected via inlet teflon tubing to an HPLC pump for automated solvent exchange. The temperature of the cell was regulated by means of a water circuit constructed into the cell holder connected to a temperature-controlled water bath. The substrates for SLB formation were silicon blocks of surface area of 8 × 5 cm² with thickness of 15 mm, polished to a roughness of less than 5 Å. Silicon blocks were cleaned by sonication in a series of solvents from apolar to polar for 20 minutes. The solvents were, in order chloroform, acetone, ethanol and milliQ water. Crystals were then dried and placed in a UV/ozone cleaner for 30 minutes and then thoroughly rinsed with milliQ water. SLBs were formed on top of silicon blocks using the protocol described in section 2.5 *Supported lipid bilayer preparation*. After the measurements of the pristine lipid bilayers, aliquots of proteins of 1.5 µM (around 0.1 mg/mL) were injected in the subphase and left incubating for a period of 5-7 hours before rinsing with buffer solution.

Experiments were conducted on the same system using different isotopic compositions for the bulk subphase. Mixtures of H₂O and D₂O were used as contrast variation to measure as-formed SLBs and SLBs in presence of proteins: 100% D₂O, SLD = 6.36 × 10⁻⁶ Å⁻², 66% D₂O/34% H₂O, denoted 4-matched water (4MW) SLD = 4 × 10⁻⁶ Å⁻², 38% D₂O/62% H₂O, denoted silicon-matched water (SMW) SLD = 2.07 × 10⁻⁶ Å⁻² and 100% H₂O, SLD = -0.56 × 10⁻⁶ Å⁻², using three and four different isotopic contrasts to study the SLBs alone and SLBs with proteins respectively. The contrast variation method relies on the assumption that the principal nanostructural features of the sample remain unaltered with the change of the isotopic contrast. Conducting the measurements on the same system at different isotopic compositions provides additional data sets that highlight the scattering of chemically different structural components at the interface. Simultaneous co-refining of these data sets enhances the accuracy and reliability of the results during the fitting process.[20] After exposure of the SLBs to proteins, the water contrast (100% H₂O) was measured both before and after rinsing, to check
for changes in the system due to the first rinsing and ensure that the different water contrasts represent the same experimental system.

In neutron reflectometry experiment, an incident neutron beam hits a planar surface elastically and the intensity of the specular reflected beam is recorded as a function of wavelength (λ) and angle (θ). The reflectivity, defined as the ratio between the intensity of the reflected and incident beam, is measured as a function of the perpendicular component of the momentum transfer, $Q_z$[21]. The momentum transfer is expressed as:

$$Q_z = k_{i\perp} - k_{f\perp} = 2k_{i\perp} = \frac{4\pi}{\lambda} \sin\theta \quad (1)$$

where λ is the wavelength of the incoming neutron beam and θ is the incident angle formed with the interface

The reflectivity depends on the thickness, roughness and SLD of the layers present at the interface, with the SLD defined as:

$$\rho = \sum_j b_j n_j \quad (2)$$

where $n_j$ is the number density of the atomic nucleus j and $b_j$ represents its coherent scattering length, an empirical quantity that has been established for most nuclei and quantifies the strength of the interaction between a neutron and an atom.

## 2.3. Neutron reflectometry - Data analysis and Modeling

The analysis of neutron reflectometry data is often model-dependent and requires the selection of an appropriate model of the interface to accurately extract structural information and capture changes caused by the interaction with proteins. Here the interface is modeled as a series of parallel layers, named *slabs*, each defined by its average SLD, thickness, volume fraction of intercalated liquid phase, and roughness. These model parameters are used to compute the reflectivity profile, which is then matched against the experimental reflectivities through a fitting procedure that minimizes the difference between model and data.

We tested different models against the reflectivity data obtained in the measurements. These models represent a series of possible structural configurations describing different scenarios from a complete absence of protein, to protein adsorption onto the SLB, to protein penetration into the SLB at different depths. In order to build these models the experimental system
was divided into a set of slabs sandwiched between two infinitely thick slabs representing the fronting (silicon substrate) and the backing (liquid phase) media. The number of slabs varies depending on the given model chosen to represent the data. The pristine SLB was represented by a set of five slabs, the native SiO₂ layer, the inner head region, the inner tail region, the outer tail region and the outer head region of the lipids sandwiched between the fronting and backing medium (Figure 1A). The SLDs of the head and tail regions were calculated as linear combinations of the SLDs of the individual components, using values from the literature[22, 23] and the RefNX database, and were then fixed to these calculated values. The SLB with an adsorbed layer of proteins was represented by adding an extra slab on top the SLB, having the SLD of the protein (Figure 1B). In the models representing protein penetration in one or more slabs of the SLB, the layers containing both the lipid and the protein were considered a mixed slab with a SLD of the non-solvent components equal to a linear combination of the SLDs of the lipid and protein components, multiplied by their respective relative volume fraction (Figure 1C-D). The total protein volume fraction was constrained to remain consistent across successive slabs, with the contribution of water within each slab included in the calculation but not directly constrained. This constraint was applied to ensure consistency in the protein volume fractions across successive protein-containing layers and assumes that the protein behaves like a rigid body. The SLD of the proteins were calculated based on the atomic composition of the protein and of the estimated volume of the amino acid using the Biomolecular Scattering Length Density Calculator[24]. The effect of D-H exchange of labile hydrogen atoms was included in the calculations.

The RefNX software[25] was employed for the analysis of the data. The data for each lipid bilayer was tested against the different models, which are depicted in Figure S1 of the Supporting Information. Reflectivity calculations employed the Abeles matrix formalism[26], incorporating interface roughness via the Nevot-Croce approach[27]. Uncertainties for the thickness, solvent volume fraction, and roughness of the lipid bilayers were determined from the posterior distribution of each parameter, obtained through Markov Chain Monte Carlo (MCMC) analysis. The corner plots generated from this analysis provide a visual representation of the parameter correlations and uncertainties. An example of a corner plot for a pristine lipid bilayer is shown in Figure S2 in the SI, illustrating the posterior distributions and confidence intervals for the relevant parameters.

The quality of the fitting and the most appropriate model was chosen
<figure><img src="image_3.png" /><figcaption>Figure 1: Slab models used to build the model for the analysis of the reflectivity data. Reproduced with permission from reference [28]</figcaption></figure>

based on the smaller normalised $\chi^2$. In some cases the choice of the best model was further guided by a critical analysis of the posterior distribution obtained by the Bayesian error analysis of some key parameters as described previously [28].

## 2.4. Quartz Crystal Microbalance with Dissipation monitoring (QCM-D)

A Q-Sense E4 instrument (Q-sense, Biolin Scientific AB, Gothenburg, Sweden) was used to monitor in real time lipid and protein adsorption by measuring changes in resonance frequency and energy dissipation of silicon oxide-coated quartz crystals (QSX 303). QCM-D data were collected at the 3rd, 5th, 7th, 9th, and 11th odd overtones. The temperature was kept at 22°C during the whole measurement. Frequency shifts ($\Delta f$) indicate changes in the mass coupled to the sensor surface (analyte mass and hydrodynamically coupled water), whereas variations in energy dissipation shifts ($\Delta D$) are associated with the viscoelastic properties of the system, providing quantitative information on the rigidity/softness of the adsorbed film [29].

The QCM-D sensor crystals were cleaned by bath-sonication in different solvents from apolar to polar (chloroform, acetone, ethanol and ultrapure Milli Q water) for 10 minutes each, followed by drying with nitrogen stream and treatment with UV/ozone cleaner (BioForce Nanosciences, Inc., Ames, IA) for 30 minutes and thorough rinsing in Milli Q to remove organic material and render the surface hydrophilic.
SLBs were prepared through vesicles fusion of lipid vesicles formed from different lipid compositions. The fusion process was followed monitoring $\Delta f$ and $\Delta D$ overtones, until constant values were reached that indicated formation of a complete bilayer. The formation of a SLB has a characteristic signature in the time-evolution of the QCM frequency and dissipation as displayed in Figure S3. First, adsorption of intact vesicles on the quartz surface causes a decrease of the frequency accompanied by an increase in the dissipation. When the frequency shift reaches approximatively -60 Hz and the dissipation 5 ppm, vesicles reach the maximum crowding on the surface and coalesce into a lipid bilayer. This causes the release of water trapped in the vesicles which translates into a stabilisation of the frequency signal around -25 Hz. The difference between the initial and the final values of frequency and dissipation are related to the characteristics of the sample formed. For example, values of frequency and dissipation shifts indicating formation of a complete supported lipid bilayer of POPC are $\Delta f \sim -26$ Hz and $\Delta D \sim 0.2 \times 10^{-6}$ [30, 31]. Aliquots of proteins solutions at 1.5 µM were sequentially added to the SLB-coated QCM-D sensor while monitoring $\Delta f$ and $\Delta D$ overtones. All QCM-D experiments were performed in triplicate.

Relative frequency values relevant for the interpretation of the data are reported for the pristine bilayer ($f_1$), the bilayer in the presence of the protein ($f_2$), the bilayer in the presence of the protein after the incubation period ($f_3$) and the bilayer after rinsing away the protein with buffer ($f_4$). To assess whether the addition of the protein causes an increase in mass (adsorption) or a decrease in the mass (desorption) of the system, $\Delta F_a$ and $\Delta F_b$ are defined.

$$ \Delta F_a = f_2 - f_1 \qquad (3) $$

$$ \Delta F_b = f_4 - f_1 \qquad (4) $$

Positive values of these parameters correspond to desorption processes, i.e. loss of material, while negative values correspond to adsorption processes in the system.

To estimate the changes in adsorbed surface mass ($\Delta m$) based on the observed frequency shift ($\Delta f$) the Sauerbrey equation can be used. This relation provides a good estimation of the mass of adlayers when the dissipation-to-frequency ratio is low ($\Delta D/\Delta f < 0.1 \times 10^{-6} \text{ Hz}^{-1}$)[29]. The equation applies when the formed film is rigid, laterally homogeneous, and adsorbed rigidly onto the sensor surface. In QCM-D experiments, these conditions are
typically indicated by the overlap of all the frequency overtones and a ΔD value close to 0 for all the harmonics. The Sauerbrey equation is expressed as[32]:

$$ \Delta m = -\frac{C \cdot \Delta f}{n} \qquad (5) $$

where Δm is the mass change, Δf is the frequency shift expressed in absolute values, C is the mass sensitivity constant of the quartz crystal, which is dependent on the fundamental frequency and material properties (for standard quartz crystal with fundamental frequency of 5 MHz, C=17.7 ng·cm⁻²·Hz⁻¹), n is the overtone number (n = 1, 3, 5, ...).

Mass changes at the sensor surface for the different SLBs were calculated both after the addition of proteins (defined as Δm_a) and after long exposure to proteins and rinsing with buffer (defined as Δm_b). Δm_a and Δm_b are calculated from absolute values of ΔF_a and ΔF_b respectively using the Sauerbrey equation. In our experiments, the conditions for applying Sauerbrey equation were fulfilled for the pristine SLBs and the SLBs exposed to proteins after the rinsing with the buffer. While, as it will be discussed in the results section, for the calculation of Δm_a the use of the Sauerbrey equation is an approximation since the overtones are not overlapping and the dissipation is not negligible. The software Qsoft 401 v2.5.10 was used for data acquisition, while data were analysed using Igor64 Pro 9 and Qtool.

## 2.5. Supported lipid bilayer preparation

SLBs were formed through vesicle fusion using lipid vesicles of different lipid compositions. The vesicles were produced from films composed of pure, binary and tertiary lipid mixture of POPC, POPC/POPS (8:2), POPC/DOTAP (7:3) and POPC/SM/CHOL (6:3:1). The chemical structures of the lipids and the reference ternary phase diagram are shown in Figure S4 of the SI. These films were prepared by combining stock chloroform solutions at 1 mg/mL of each component in the specified molar ratios. The solvent was evaporated using a gentle nitrogen stream, followed by overnight drying under vacuum. After complete evaporation of the solvent, the dried mixed films were resuspended in 50 mM TBS pH 7.6 for QCM-D experiments or milliQ H₂O for NR experiments achieving a final lipid concentration of 0.1 and 0.2 mg/mL respectively. To facilitate vesicle formation, the suspensions underwent bath sonication for 20 minutes, followed by tip sonication on ice for 15 minutes at 50% amplitude, with 5-second on/off pulses, resulting in vesicles of approximately 100 nm in diameter. For NR experiments, SLB
formation was achieved via spontaneous vesicle fusion. Immediately before
injection, the sonicated lipid suspension was mixed 1:1 with a 4 mM solution
of CaCl₂, yielding a final concentration of 0.1 mg/mL lipids and 2 mM CaCl₂.
In the case of QCM-D experiments, vesicle fusion was typically induced by
osmotic shock using MilliQ H₂O. For these experiments, the tip-sonication
time was reduced to 5–10 minutes, and no ice bath was used.

## 2.6. *Molecular Dynamics simulations*

All-atom molecular dynamics simulations were performed using GRO-MACS 2024[33] with the CHARMM36m force field for proteins and lipids[34, 35]. All systems were solvated with CHARMM-compatible TIP3P water and neutralised with potassium chloride using standard CHARMM ion parameters. The simulations were based on the crystallographic structure of bovine serum albumin (BSA; PDB 3v03[36]) and the cryo-EM structure of human alpha-fetoprotein (AFP; PDB 7yim[37]). All termini were modeled as charged; AFP included a glycosylation at N251 with a core trisaccharide N-glycan (GlcNAcβ1-4GlcNAcβ1-4Manβ) linked via an N-glycosidic bond. The protein and membrane systems were built separately via the CHARMM-GUI web server[34], with membranes of 964 lipids in four compositions: POPC; POPC/POPS 8:2; POPC/DOTAP 7:3; and POPC/NSM/CHOL 6:3:1, where NSM mimics porcine brain sphingomyelin. Final systems were assembled by positioning the protein at a distance ≥ 2 nm from the membrane in 10 distinct orientations using an in-house script. Subsequently, each system was solvated, neutralised, and 150 mM KCl ions were added to mimic the experimental conditions. All systems were then energy-minimized, and equilibrated using a six-step restraint-release protocol, followed by a 500 ns simulation. Temperature was maintained at 310 K using the V-rescale thermostat[38] with a coupling time of 1.0 ps. Pressure was kept at 1 atm using a semi-isotropic C-rescale barostat[39] with a time constant of 5.0 ps and an isothermal compressibility of 4.5 × 10⁻⁵ bar⁻¹. The Verlet cutoff scheme was used with a neighbor list update every 20 steps. Electrostatic interactions were calculated using the Particle Mesh Ewald (PME) method[40] with a real-space cutoff of 1.2 nm. Van der Waals interactions were treated with a force-switch modifier between 1.0 and 1.2 nm. Simulations were performed using a 2 fs integration time step and run for 500 ns per system, resulting in a cumulative simulation time of 40 µs. If not stated otherwise, the last 200 ns of each trajectory were used for analysis. All analyses were conducted using an in-house tool in combination with MDAnalysis[41] and lipophilic[42].
### 2.6.1. Lipid depletion/enrichment around BSA and AFP

To quantify the local enrichment or depletion of lipid species around a protein, we compute the depletion/enrichment index (DEI) as:

$$DEI_i = \frac{\rho_i^{\text{contact}}}{\rho_i^{\text{bulk}}} - 1 \qquad (6)$$

where $\rho_i^{\text{contact}}$ is the relative abundance (or normalized number density) of lipid species i in contact with the protein $\rho_i^{\text{bulk}}$ is the corresponding abundance of lipid i in the bulk membrane.

### 2.6.2. Local lipid environment in POPC/NSM/CHOL membrane

A clear picture of the aggregation of certain lipid species can be gained by considering the enrichment/depletion index of each lipid species as described by Ingólfsson et al. [43]. In short, the lipid enrichment index of species B around species A, $E_{AB}$, may be defined as:

$$E_{AB} = \frac{N_{AB}}{\langle N_B \rangle} \qquad (7)$$

where $N_{AB}$ is the number of molecules of species B around species A, and $\langle N_B \rangle$ is the mean number of species B around any species.

## 3. Results

## 4. Mass adsorption/desorption analysis and viscoelastic properties using QCM-D

QCM-D allows the detection of real-time changes of surface-adsorbed materials in terms of mass and viscoelastic properties by monitoring the change in resonant frequency and energy dissipation occurring on a silicon oxide coated quartz crystal on top of which the material is adsorbed. Hence, QCM-D can give insights into the formation of a SLB and into the balance of adsorption-desorption processes occurring on the SLB.

Reproducible SLBs for each of the four lipid compositions selected were obtained with this method. In the case of negatively charged and zwitterionic lipid compositions, SLBs formed through adsorbed vesicles rupture and fusion at a critical surface coverage level or induced by osmotic shock (Figure S5)[44, 45]. The formation of SLB from the composition POPC/SM/CHOL 6:3:1 consistently showed higher values of frequency (~ -27 Hz) compared
to the other compositions. This is in agreement with the presence of more
densely packed lipids in the SLB due to the formation of SM- and CHOL-
enriched domains. Due to the cationic character, POPC/DOTAP 7:3 vesicles
did not accumulate on the sensor surface to the same extent of the other lipid
compositions but initiated the fusion process earlier as shown by the reduced
vesicle adsorption peak compared to the other SLBs (see Figure S5D of the
SI). In summary, all SLBs yielded $\Delta f$ values between -24.3 Hz and -27.6 Hz.

Once the SLBs were formed, a protein solution of concentration around
0.1 mg/mL (1.5 µM) was introduced in the liquid sub-phase and left incu-
bating for several hours (between 5 and 7 hours). Examples of the effects of
BSA and AFP additions for the pure POPC SLB are reported in Figure S6.
After the incubation, the system was rinsed with buffer solution to remove
the excess protein.

Table 1 shows frequency values of the pristine bilayer ($f_1$), the bilayer after the addition of the protein ($f_2$), the bilayer exposed to the protein after the long incubation period ($f_3$) and the bilayer exposed to the protein after rinsing with buffer ($f_4$) for all the lipid compositions. In addition, to facilitate the interpretation of the data, values of $\Delta F_a$ and $\Delta F_b$ are also shown (see Methods section).

In the case of BSA, the frequency/dissipation curves showed only minor
changes after protein injection, as illustrated in Figure S7 of the SI. During
the prolonged incubation period, the values of frequency and dissipation re-
mained nearly stable and the rinsing with buffer caused negligible changes for
most of the lipid compositions (close to the standard errors), with exception
of POPC/DOTAP SLB, where a continuous increase in the frequency shift
was observed throughout the incubation time, suggesting degradation of the
bilayer due to removal of lipids (Figure S7D). An interesting behavior was
also observed for the composition containing POPS (negative charged lipid),
in which the addition of BSA resulted in a slight increase in frequency ($f_2$
in Table 1), indicating the removal of lipids. However, this was followed by
a subsequent decrease in frequency during the long incubation ($f_3$ in Table
1), suggesting the possible adsorption of BSA molecules onto the bilayer.

In contrast to BSA, the injection of AFP resulted in a significant decrease
in frequency and an increase in dissipation for all lipid compositions (see Fig-
ure 2A-D). The differences in frequency shift between pristine bilayers and
bilayers exposed to AFP (defined as $\Delta F_a$) are -9.9 Hz, -5.8 Hz, -8.3 Hz, and
-8.4 Hz for POPC, POPC/SM/CHOL, POPC/POPS, and POPC/DOTAP,
respectively (Table 1). These negative values of $\Delta F_a$ indicated a notable in-
<table><caption>Table 1: Frequency shifts (7th overtone) of SLBs before and after the exposure to AFP and BSA proteins. The reported values are calculated as mean values between three repeated measurements and errors represent standard errors. Examples of the corresponding QCM-D experiments are showed in Figure S7 and 2A-D. ΔF<sub>a</sub> and ΔF<sub>b</sub> are calculated according to equations reported in the method section.</caption><thead><tr><th>Frequency shift (Hz)</th><th>AFP</th><th>BSA</th></tr></thead><tbody><tr><td colspan="3"><strong>POPC</strong></td></tr><tr><td>Pristine bilayer (f<sub>1</sub>)</td><td>-24.3 ± 0.7</td><td>-24.9 ± 0.7</td></tr><tr><td>SLB + addition of protein (f<sub>2</sub>)</td><td>-34.2 ± 1.0</td><td>-24.3 ± 0.5</td></tr><tr><td>SLB + protein after long incubation (f<sub>3</sub>)</td><td>-32.7 ± 1.7</td><td>-23.9 ± 0.6</td></tr><tr><td>SLB + protein after rinsing with buffer (f<sub>4</sub>)</td><td>-22.6 ± 1.2</td><td>-24.5 ± 0.6</td></tr><tr><td>ΔF<sub>a</sub> = f<sub>2</sub> - f<sub>1</sub></td><td>-9.9 ± 1.7</td><td>+0.6 ± 0.2</td></tr><tr><td>ΔF<sub>b</sub> = f<sub>4</sub> - f<sub>1</sub></td><td>+1.7 ± 1.0</td><td>+0.4 ± 0.4</td></tr><tr><td colspan="3"><strong>POPC/SM/CHOL 6:3:1</strong></td></tr><tr><td>Pristine bilayer</td><td>-27.0 ± 0.7</td><td>-27.6 ± 0.2</td></tr><tr><td>SLB + addition of protein</td><td>-32.8 ± 0.4</td><td>-27.7 ± 0.3</td></tr><tr><td>SLB + protein after long incubation</td><td>-32.3 ± 0.5</td><td>-28.9 ± 0.9</td></tr><tr><td>SLB + protein after rinsing with buffer</td><td>-26.1 ± 0.6</td><td>-28.4 ± 0.6</td></tr><tr><td>ΔF<sub>a</sub></td><td>-5.8 ± 0.4</td><td>-0.1 ± 0.1</td></tr><tr><td>ΔF<sub>b</sub></td><td>+0.9 ± 0.1</td><td>-0.8 ± 0.4</td></tr><tr><td colspan="3"><strong>POPC/POPS 8:2</strong></td></tr><tr><td>Pristine bilayer</td><td>-25.4 ± 1.0</td><td>-24.4 ± 1.3</td></tr><tr><td>SLB + addition of protein</td><td>-33.7 ± 1.4</td><td>-22.9 ± 1.0</td></tr><tr><td>SLB + protein after long incubation</td><td>-33.2 ± 1.4</td><td>-23.4 ± 2.0</td></tr><tr><td>SLB + protein after rinsing with buffer</td><td>-24.8 ± 0.9</td><td>-23.2 ± 2.0</td></tr><tr><td>ΔF<sub>a</sub></td><td>-8.3 ± 1.7</td><td>+1.5 ± 0.1</td></tr><tr><td>ΔF<sub>b</sub></td><td>+0.6 ± 0.1</td><td>+1.2 ± 0.7</td></tr><tr><td colspan="3"><strong>POPC/DOTAP 7:3</strong></td></tr><tr><td>Pristine bilayer</td><td>-25.25 ± 0.04</td><td>-24.4 ± 0.6</td></tr><tr><td>SLB + addition of protein</td><td>-33.7 ± 1.5</td><td>-23.0 ± 0.5</td></tr><tr><td>SLB + protein after long incubation</td><td>-33.7 ± 0.6</td><td>-21.0 ± 0.5</td></tr><tr><td>SLB + protein after rinsing with buffer</td><td>-28.0 ± 0.4</td><td>-20.7 ± 0.5</td></tr><tr><td>ΔF<sub>a</sub></td><td>-8.4 ± 1.5</td><td>+0.6 ± 0.1</td></tr><tr><td>ΔF<sub>b</sub></td><td>-2.8 ± 0.4</td><td>+3.6 ± 0.1</td></tr></tbody></table>

crease in mass at the sensor surface following injection of AFP. This suggests that, in the presence of excess protein, the interaction behavior was similar across all lipid compositions with a small but detectable interaction of the
protein with lipids. Moreover, $\Delta f$ and $\Delta D$ corresponding to the different overtones were no longer superimposed, indicating the formation of a viscoelastic system, indicative of adsorption of a hydrated protein layer on the bilayer[46].

Conversely, the behaviour of the SLBs differed during the long incubation period and especially after the final rinsing with buffer, in which small but significant changes were specific to different lipid composition of the SLBs. SLBs composed of POPC/SM/CHOL and POPC/POPS remained stable in terms of frequency and dissipation shifts during the long incubation (Figure 2B and C), whereas the POPC SLB showed a minor increase in frequency of +1.7 ± 1.0 Hz, suggesting a possible removal of some of the lipids (Figure 2A). By contrast, the SLB composed of POPC/DOTAP exhibited a decrease in frequency of -2.8 ± 0.4 Hz, suggesting the presence of stably adsorbed proteins (Figure 2D).

At the end of the incubation period, the SLBs were rinsed with buffer solution to remove excess protein. In the case of AFP, the values of frequency and dissipation shifts increased and decreased, respectively, with rinsing for all the different compositions, reaching values similar to those of the pristine bilayers, indicating the washing away of the majority of proteins. Nevertheless, small but significant changes in frequency compared to the pristine bilayers (represented by $\Delta F_b$) indicated that the interaction was not entirely reversible and affected the structure of bilayers. In addition, the rinsing with buffer restored the superimposition of $\Delta f$ and $\Delta D$ overtones, indicating the return to a more rigid system, most likely removing excess proteins adsorbed onto the SLBs which formed more viscoelastic systems.

These changes were composition-dependent. For zwitterionic (POPC and POPC/SM/CHOL) and negatively charged (POPC/POPS) lipid compositions, frequencies reached a higher value compared to that of the pristine bilayer (positive $\Delta F_b$ values, Table 1) indicating mass loss on the SLBs due to a predominant removal of lipids. In contrast, for the cationic SLB with DOTAP the frequency value after rinsing was lower than that of the pristine bilayer (negative $\Delta F_b$ values, Table 1) indicating a positive mass gain (+47.1 ± 7.3 ng/cm²) that could be attributed to the presence of residual adsorbed proteins on the bilayer. Interestingly, the same lipid composition exposed to BSA produced the opposite effect, with a mass loss of the SLB equals to -61.3 ± 1.7 ng/cm². These values were calculated using the Sauerbrey equation with $\Delta F_b$ in absolute value as explained in the Methods section 2.4.

Mass changes at the sensor surface for the different SLBs following protein
addition ($\Delta m_a$) and after prolonged exposure plus buffer rinsing ($\Delta m_b$) are represented in the histograms in Figure 2E and F, respectively. $\Delta m_a$ and $\Delta m_b$ were calculated from the absolute values of $\Delta F_a$ and $\Delta F_b$ using the Sauerbrey equation. It should be noted that for the calculation of $\Delta m_a$, the use of the Sauerbrey equation is an approximation since the overtones were not overlapping and the dissipation was non-negligible. In this case, a viscoelastic model would be more appropriate for an accurate interpretation. However, given that the dissipation remained small, the Sauerbrey equation was applied to provide an approximate estimate of the mass.

These results indicate that, in the dilute concentration regime, AFP adsorbs on the membrane when the protein is in excess in the buffer, whereas BSA only induces minor changes. However, upon rinsing, the interpretation of QCM-D data becomes more complex. This is because the final frequency value ($f_4$) is the result of different contributions, namely the adsorption of proteins upon injection and incubation, the removal of lipids and also the washing away of proteins after the rinsing. QCM-D experiments are valuable since they quantify the net mass balance (adsorption/desorption) occurring at the lipid bilayer upon interaction with the proteins. However, they are limited by the fact that they cannot distinguish between the contribution to the mass balance of the specific components (lipids and proteins). To overcome this limitation, we complemented the QCM-D experiments with neutron reflectometry, which can disentangle the structural contributions of proteins, lipids, and solvent.

## 5. Nanostructural characterisation with neutron reflectometry

Neutron reflectometry experiments were performed on the four supported lipid bilayers adsorbed on silicon blocks. The different SLBs were structurally characterised prior and after the exposure to the two proteins as described in the Methods section 2.2.

After the exposure of the SLBs to AFP and BSA for a period of time between 5 and 7 hours, the liquid phase was exchanged by flowing buffer solution for around 10 minutes. The reflectivity profiles showed different evolutions according to the specific lipid mixtures and proteins, as shown in Figure 3 where we report a summary of the reflectivity profiles in D₂O of the different SLB before and after the interaction with AFP and BSA. In the SLBs exposed to BSA the effect on the reflectivity profile were minor with the exception of the SLB made of POPC/POPS 8:2, where we observed
<figure><img src="image_4.png" /><figcaption>Figure 2: Figures (A)-(D) represent different interaction behaviors for different SLBs in presence of AFP after long incubation and rinsing with buffer. The graphs are an example of one measurement of each sample. (E) Histogram representing mass changes ($\Delta m_a$) at the sensor surface for different SLBs after the addition of AFP and BSA. (F) Histogram representing mass changes ($\Delta m_b$) at the sensor surface for different SLBs after the addition of AFP and BSA, long incubation and rinsing with buffer. Error bars indicate the standard error, calculated from tripli-cate independent measurements.</figcaption></figure>
significant differences between the reflectivity profiles before and after the exposure to the protein.

On the other hand, when the SLBs were exposed to AFP we saw sig-
nificant changes with all the SLBs with the exception of the SLB made of
POPC. The most drastic changes appeared in the case of POPC/POPS 8:2.

Among the tested models described in the Methods section 2.3, only the best-fitting model for each sample is presented. The selection was based on the lowest normalised χ² value, complemented by the analysis of the Bayesian posterior distributions.

5.1. Interaction of SLBs with BSA

The analysis of BSA data, including model selection and Bayesian poste-
rior evaluation, was performed in our previous work [28]. To summarize, BSA
had negligible effects on the structure of POPC, POPC/SM/CHOL (6:3:1),
and POPC/DOTAP (7:3) bilayers, as the reflectivity profiles remained un-
changed after protein exposure. The only significant interaction was observed
for POPC/POPS (8:2), where BSA penetration into the outer leaflet was de-
tected, with a protein volume fraction of approximately 11% accompanied by
increased hydration and interfacial roughness, consistent with partial lipid re-
moval. A comparable effect was also reported previously for HSA interacting
with PC/PS (8:2) bilayers[47]. These findings support the view that BSA
interacts weakly with zwitterionic membranes but shows enhanced affinity
for negatively charged bilayers, most likely mediated by localized cationic
patches on BSA surface[48, 49, 50].

5.2. Interaction of SLBs with AFP

5.3. POPC

Figure S8 (SI) shows the reflectivity profiles of the POPC SLB before and after exposure to AFP, along with the corresponding best-fit curves, scattering length density (SLD), and volume fraction profiles. After testing the different models (Table S1 in the supporting information), the model without the presence of the protein was chosen as the one better representing the data. Accordingly, the fitting parameters (Table 2) confirmed no relevant changes in the structural characteristics of the POPC bilayer upon AFP exposure. These results demonstrate that AFP does not significantly interact with or incorporate into POPC membranes under the conditions tested.
<figure><img src="image_5.png" /><figcaption>Figure 3: Reflectivity profiles in the D₂O contrast for the four types of SLB before and after the exposure to BSA and AFP. BSA data from reference</figcaption></figure>
5.3.1. POPC/SM/Chol 6:3:1

Figure 4 displays the reflectivity profiles at different contrasts of the SLB with composition POPC/SM/CHOL 6:3:1 before and after the exposure to AFP, together with the best-fit curves, SLD and volume fraction profiles. After testing different models and evaluating both χ² values and posterior distributions of key parameters, the best fit was obtained with the partial incorporation of AFP into the outer leaflet without an additional adsorbed protein layer onto the SLB (model 3b, see Figure S1 in the SI). The χ² value obtained for Model 3, which includes a putative extra layer representing the portion of protein protruding into the aqueous phase above the bilayer, is very close to that of Model 3b. However, analysis of the posterior distributions for this model (corner plot in Figure S9 in the Supporting Information) indicated that the supplemental layer lacks physical relevance, as its thickness distribution peaks around 0 Å. Including such a layer would therefore count as overparameterization rather than providing a meaningful description of the system.

The selected model (fitting parameters shown in Table 2) indicates that AFP was incorporated in the outer leaflet of the SLB with a volume fraction of 38%. The incorporation of the protein is accompanied by a decrease of the thickness of the inner head region and an increase of the hydration of the SLB especially in the proximal leaflet. The penetration of AFP into the outer leaflet might induce defects and disorder in the overall structure of the SLB exposing the bilayer to penetration of water. In the distal leaflet the water is replaced by the protein mitigating the amount of water present in this region. In the proximal leaflet the increase of the solvent penetration is likely linked to the removal of lipids after the interaction with the protein and successive rinsing of the liquid phase, which are not replaced by the proteins that remain in the distal region.

While the radius of gyration (Rg) of AFP has not been experimentally determined in literature, the protein shares a comparable molecular weight and overall fold with serum albumins. Accordingly, an Rg in the same range as that reported for albumins can be reasonably assumed as a first approximation. The radius of gyration for proteins such as human and bovine serum albumin is approximately 27–29 Å[51, 52]. In low-resolution experiments like neutron reflectometry, this value represents the characteristic length scale associated with the protein's spatial distribution. Given that the thickness of the outer lipid leaflet is 23 Å and that this layer is typically smoothed by a
<figure><img src="image_6.png" /><figcaption>Figure 4: Reflectivity curves and fits at different contrasts (A), the SLDs obtained from the analysis (B) and the corresponding volume fraction distribution (C) of the SLB POPC/SM/Chol 6:3:1 before and after the exposure to the AFP. Symbols represent the raw data; continuous lines represent the fit. The model that describes better the experimental behavior is the model representing a partial penetration of the protein in the outer leaflet of the lipid bilayer with no adsorbed layer of proteins onto the SLB (model 3b).</figcaption></figure>

5 Å roughness on both interfaces, the effective thickness of the outer leaflet remains compatible with the incorporation of the protein within this region.

Based on these findings, AFP interacts with zwitterionic membranes containing SM and CHOL through partial incorporation into specific regions of the bilayer. This behavior might be likely influenced by the presence of ordered nanodomains, as the inclusion of SM and CHOL markedly alters AFP binding compared to pure POPC SLBs. These observations align with previous studies demonstrating the role of ordered nanodomains in promoting protein-lipid interactions [53, 54, 55].
<table><caption>Table 2: Structural parameters of the SLBs before and after the exposure to the AFP. The corresponding fits are in Figures 4, 28, S10, and S11. The values without error were fixed or kept at their nominal value.</caption><thead><tr><th>Layer</th><th>Thickness [Å]</th><th>SLD [×10<sup>-6</sup> Å<sup>-2</sup>]</th><th>Solvent [v/v]</th><th>Roughness [Å]</th></tr></thead><tbody><tr><td colspan="5" style="text-align: center; font-weight: bold;">POPC</td></tr><tr><td>SiO<sub>2</sub></td><td>16 ± 1</td><td>3.47</td><td>0.116 ± 0.003</td><td>2 ± 1</td></tr><tr><td>Inner headgroup</td><td>9 ± 1</td><td>1.88</td><td>0.58 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Inner tail region</td><td>15 ± 1</td><td>-0.28</td><td>0.00 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Outer tail region</td><td>15 ± 1</td><td>-0.28</td><td>0.00 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Outer headgroup</td><td>5 ± 1</td><td>1.88</td><td>0.148 ± 0.009</td><td>2 ± 1</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">After exposure to AFP</td></tr><tr><td>Inner headgroup</td><td>7 ± 1</td><td>1.88</td><td>0.751 ± 0.008</td><td>3 ± 1</td></tr><tr><td>Inner tail region</td><td>15 ± 1</td><td>-0.28</td><td>0.00 ± 0.01</td><td>3 ± 1</td></tr><tr><td>Outer tail region</td><td>15 ± 1</td><td>-0.28</td><td>0.00 ± 0.01</td><td>3 ± 1</td></tr><tr><td>Outer headgroup</td><td>5 ± 1</td><td>1.88</td><td>0.12 ± 0.01</td><td>3 ± 1</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">POPC/SM/CHOL 6:3:1</td></tr><tr><td>SiO<sub>2</sub></td><td>10 ± 1</td><td>3.47</td><td>0.28 ± 0.01</td><td>6 ± 1</td></tr><tr><td>Inner headgroup</td><td>10 ± 1</td><td>1.725</td><td>0.27 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Inner tail region</td><td>16 ± 1</td><td>-0.238</td><td>0.0011 ± 0.0002</td><td>2 ± 1</td></tr><tr><td>Outer tail region</td><td>16 ± 1</td><td>-0.238</td><td>0.0011 ± 0.0002</td><td>2 ± 1</td></tr><tr><td>Outer headgroup</td><td>5 ± 1</td><td>1.725</td><td>0.13 ± 0.04</td><td>2 ± 1</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">After exposure to AFP</td></tr><tr><td>Inner headgroup</td><td>6 ± 1</td><td>1.725</td><td>0.87 ± 0.03</td><td>5 ± 1</td></tr><tr><td>Inner tail region</td><td>18 ± 1</td><td>-0.238</td><td>0.15 ± 0.02</td><td>5 ± 1</td></tr><tr><td>Outer tail region (*)</td><td>18 ± 1</td><td>-0.238</td><td>0.03 ± 0.02</td><td>5 ± 1</td></tr><tr><td>Outer headgroup (*)</td><td>5 ± 1</td><td>1.725</td><td>0.56 ± 0.4</td><td>5 ± 1</td></tr><tr><td>Volume fraction protein</td><td>0.38 ± 0.07</td><td>in the slabs labelled with *</td><td></td><td></td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">POPC/POPS 8:2</td></tr><tr><td>SiO<sub>2</sub></td><td>11 ± 1</td><td>3.47</td><td>0.33 ± 0.003</td><td>6 ± 1</td></tr><tr><td>Inner headgroup</td><td>9 ± 1</td><td>2.285</td><td>0.20 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Inner tail region</td><td>15 ± 1</td><td>-0.282</td><td>0.00 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Outer tail region</td><td>15 ± 1</td><td>-0.282</td><td>0.00 ± 0.01</td><td>2 ± 1</td></tr><tr><td>Outer headgroup</td><td>5 ± 1</td><td>2.285</td><td>0.148 ± 0.009</td><td>2 ± 1</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">After exposure to AFP</td></tr><tr><td>Unstructured layer</td><td>36 ± 1</td><td>2.49 ± 0.2</td><td>0.0003 ± 0.0001</td><td>11 ± 1</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">POPC/DOTAP 70:30</td></tr><tr><td>SiO<sub>2</sub></td><td>10 ± 1</td><td>3.47</td><td>0.21 ± 0.01</td><td>8 ± 1</td></tr><tr><td>Inner headgroup</td><td>12 ± 1</td><td>1.796</td><td>0.31 ± 0.01</td><td>4 ± 1</td></tr><tr><td>Inner tail region</td><td>14 ± 1</td><td>-0.262</td><td>0.002 ± 0.003</td><td>4 ± 1</td></tr><tr><td>Outer tail region</td><td>14 ± 1</td><td>-0.262</td><td>0.002 ± 0.003</td><td>4 ± 1</td></tr><tr><td>Outer headgroup</td><td>6 ± 1</td><td>1.796</td><td>0.17 ± 0.01</td><td>4 ± 1</td></tr><tr><td colspan="5" style="text-align: center; font-weight: bold;">After exposure to AFP</td></tr><tr><td>Inner headgroup</td><td>11 ± 1</td><td>1.796</td><td>0.68 ± 0.07</td><td>3 ± 1</td></tr><tr><td>Inner tail region</td><td>16 ± 1</td><td>-0.262</td><td>0.109 ± 0.004</td><td>3 ± 1</td></tr><tr><td>Outer tail region</td><td>16 ± 1</td><td>-0.262</td><td>0.109 ± 0.004</td><td>3 ± 1</td></tr><tr><td>Outer headgroup</td><td>11 ± 1</td><td>1.796</td><td>0.67 ± 0.05</td><td>3 ± 1</td></tr><tr><td>Protein layer (*)</td><td>39 ± 1</td><td></td><td>0.948 ± 0.005</td><td>8 ± 1</td></tr><tr><td>Volume fraction protein</td><td>0.052 ± 0.005</td><td>in the slabs labelled with *</td><td></td><td></td></tr></tbody></table>
### 5.3.2. POPC/POPS 8:2

As shown in Figure 3, the interaction of AFP with the POPC/POPS (8:2) SLB induces drastic changes in the reflectivity profile. The reflectivity profiles of the SLB before and after the exposure to the AFP together with the fitting curves of the best model, and the resulting SLD and volume fraction profiles are reported in Figure S10. While the pristine SLB was well described using the standard bilayer model, none of the bilayer-based models could adequately fit the data after AFP exposure. Instead, the best fit was obtained with a single unstructured slab of intermediate SLD between lipid and protein (model 6), indicating that the bilayer structure was heavily disrupted. These findings suggest that AFP strongly interacts with negatively charged membranes, where electrostatic interactions drive significant bilayer destabilization and mixing of lipids with proteins on the substrate.

### 5.3.3. POPC/DOTAP 70:30

The reflectivity profiles of the SLB with composition POPC/DOTAP 7:3 before and after the exposure to AFP together with the fitting curves of the best model and the resulting SLD and volume fraction profiles are reported in Figure S11 in the Supporting Information. In this case, the interaction with AFP was best represented by the formation of an adsorbed protein layer of approximately 39 Å thickness and a volume fraction of around 5%, without any incorporation of the protein into the SLB (model 1, see Figure S1). The fitting parameters are listed in Table 2. The resulting thickness of the adsorbed protein layer is consistent with physical dimensions of AFP. Moreover, the interaction with the proteins induces a thickening of the tail region and an increasing of hydration of the head and tail region. This is consistent with removal of a fraction of the lipids by AFP. Based on these results, electrostatic interactions between AFP and the positively charged lipid might be the main driving force for the observed behaviour, given that at physiological pH AFP is negatively charged.

Figure 5 provides a summary of the NR results, highlighting the interactions of BSA and AFP with the four different lipid compositions.

## 6. Molecular Dynamics simulations of protein-membrane interactions

To investigate protein-lipid interactions at atomic resolution, we performed all-atom molecular dynamics (MD) simulations of bovine serum al-
<figure><img src="image_7.png" /><figcaption>Figure 5: *Volume fraction profiles corresponding to the best-fit of the reflectivity data, illustrating how BSA and AFP interact with each of the four SLB lipid compositions. BSA data from reference [28].*</figcaption></figure>

bumin (BSA; PDB ID: 3v03 [36]) and human alpha-fetoprotein (AFP: PDB ID: 7yim [37]) with four different lipid bilayers: POPC, POPC/NSM/CHOL 6:3:1, POPC/POPS 8:2 and POPC/DOTAP 7:3. Nervonoyl-sphingomyelin (NSM) was chosen to approximate porcine brain sphingomyelin used experimentally. AFP was modeled with a core N-glycan (GlcNAcβ1-4GlcNAcβ1-4Manβ) attached at residue N251. For each protein-membrane combination, the protein was initially placed at least 2 nm from the membrane surface in 10 different, randomly chosen orientations, resulting in 40 µs of cumulative simulation time. Unless stated otherwise, analyses were performed on the final 200 ns of each trajectory.

## 6.1. Protein-Membrane Binding and Contact Frequency

To assess membrane association, we first computed the fraction of bound versus unbound states, defining a protein as “bound” when any of its non-hydrogen atoms were within 1 nm of a lipid (Figure 6A). While both proteins display similar binding propensities, the extent and nature of their interactions depend strongly on the membrane composition. In particular, BSA and AFP remained closely associated with DOTAP-containing membranes throughout most of the simulation time, suggesting that electrostatic interactions are a key driver of membrane binding captured by the MD simulations.
This trend was confirmed by a detailed contact analysis (Figure S12). Both proteins formed the highest number of contacts in DOTAP-containing membranes: BSA made ~ 74 lipid contacts, and AFP made ~ 100 contacts during the final 200 ns (Figure 6B). For the other membrane compositions, contact numbers were lower and similar across both proteins. As all systems primarily contain POPC, it accounts for the majority of contacts; however, the presence of DOTAP enhances the interaction frequency.

## 6.2. Lipid Occupancy and Interaction Surfaces

To further localize protein-lipid interaction sites, we analyzed lipid occupancies per residue over the final 200 ns of the simulation (Figure 6C-E for AFP and S13A-C for BSA). Structural domains and subdomains of BSA and AFP were visualized based on previously established classifications (ref.[56] and ref.[57], respectively). Both proteins adopt heart-shaped, asymmetric folds dominated by α-helices and loop regions, and organized into three domains (I–III), each with two subdomains (A and B).

Figure 6D (AFP) and Supplementary Figure S13B (BSA) show lipid occupancies per residue for the four different membrane compositions: Lipid occupancies for both proteins are broadly distributed across all three domains, with domains I and II showing the highest values. In DOTAP-containing membranes, POPC exhibits the highest occupancy, consistent with the contact frequency analysis. For clarity, values above 40% were capped in the visualizations, although some residues reached up to 51% in BSA and 77% in AFP.

Finally, surface representations of lipid-interacting residues (Figure 6E for AFP and S13C for BSA) revealed distinct interaction patterns. BSA showed a relatively uniform binding interface across domains I and II, whereas AFP's interactions were more localized, particularly around a flexible loop region that is absent in BSA. These findings suggest that specific structural motifs contribute to differences in membrane interaction between the two proteins.

## 6.3. Lipid Depletion and Enrichment Near Proteins

To investigate the local lipid composition around the proteins, we calculated the depletion/enrichment index (Figure 7A), which compares the local abundance of lipids near the protein with its abundance in the bulk membrane. Surprisingly, POPS was the most enriched lipid near both proteins despite a relatively low overall contact number. In contrast, DOTAP was
<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>Figure 6: MD simulations of BSA and AFP interacting with four different membranes (pure POPC; POPC/POPS 8:2; POPC/DOTAP 7:3; and POPC/NSM/CHOL 6:3:1. (A) Bound vs. unbound states and (B) protein-lipid contacts per lipid type are averaged over 10 replicas; error bars indicate standard deviation. (C) Protein structure of AFP with color coded domains (I-III) and subdomains (A and B) and N-glycosylation at N251. (D) Occupancy per AFP residue for each lipid type. Protein domains and subdomains are indicated using the same color code in (C). (E) Surface representation of AFP highlights the regions of highest lipid occupancies with DOTAP-containing membranes.</figcaption></figure>
depleted in the vicinity of both BSA and AFP. This may seem counterintuitive given the large net negative charge of both proteins (BSA: -16e; AFP: -17e), but it reflects the presence of positively charged surface patches on the proteins that drive localized interactions (Figures S12 and S15 in the supporting information). This interpretation aligns with experimental findings that both proteins insert into POPS-containing supported bilayers. Cholesterol showed the most substantial depletion, likely due to its deep position within the membrane; almost no cholesterol molecules were detected within the 1 nm interaction cutoff in any simulation snapshot.

### 6.4. Membrane Thickness and Domain Formation

Next, we examined how lipid composition affects the membrane structure by calculating the average membrane thickness, defined as the distance between phosphate groups in opposing leaflets (Figure 7B). The pure POPC bilayer showed a thickness of 3.90 ± 0.03 nm. Adding charged lipids (POPS, DOTAP) caused a slight thickening, whereas the inclusion of NSM and cholesterol had a more pronounced effect, increasing the thickness by ~ 0.45 nm. The spatially resolved thickness maps (Figure 7D) revealed that most membranes were laterally homogeneous, except the NSM/CHOL-containing system. In this case, local deviations exceeding 0.3 nm from the mean (4.35 ± 0.03 nm) suggest the formation of nanodomains. To explore these potential nanodomains further, we quantified the local lipid environment using neighborhood enrichment analysis (Figure 7D); see Methods). This analysis characterizes the composition of lipid patches surrounding each reference lipid. Heat maps of depletion/enrichment values, averaged over 10 independent replicas from 300 ns onward, show that local environment around POPC is consistently depleted. At the same time, the area surrounding NSM lipids is strongly enriched, indicating localized lipid clustering. In particular, cholesterol (CHOL) is markedly enriched near NSM, supporting the presence of NSM/CHOL-rich domains and POPC-rich regions with a lower packing density.

These lateral patterns were further confirmed by normalizing lipid densities to a uniform distribution, yielding the 2D depletion/enrichment maps shown in Figure S14 A-B for CHOL and NSM in the upper leaflet. Strong enrichment of CHOL near NSM-rich patches was again observed and is visually confirmed in the simulation snapshot shown in Figure S14C.
<figure><img src="image_13.png" /><figcaption>Figure 7: (A) Depletion/enrichment of lipids within 1 nm cutoff of BSA and AFP in membrane-bound states averaged over 10 replicas; error bars indicate standard deviation. (B) Box plot/violin plot of the membrane thickness for the four different membrane compositions in the presence of BSA and AFP. The data combines each simulation frame from all the 10 replicas starting at 300 ns (n=20.000 per membrane composition). (C) Heat maps of lipid neighborhood enrichment averaged over 10 replicas; error bars indicate standard deviation. (D) 2D deflection of membrane thickness with respect to the average thickness of single replica for the four different membrane compositions in the presence of BSA and AFP averaged over the last 200 ns.</figcaption></figure>
7. Discussions and conclusions

To investigate the interaction mechanisms of BSA and AFP with sup-
ported lipid bilayers (SLBs) of varying compositions, we employed two com-
plementary experimental techniques, QCM-D and NR, in combination with
MD simulations. This integrated approach allowed us to capture both nanoscopic
binding behavior and molecular-level structural details.

BSA has been extensively studied due to its structural similarity to human serum albumin (HSA) and its commercial availability, making it a widely used model protein in biophysical and biochemical research, including studies of protein-lipid interactions. BSA has a molecular weight of approximately 66.5 kDa and is comprised of 583 amino acids arranged into three homologous domains, giving it an overall ellipsoidal shape. Its theoretical isoelectric point (pI) of 5.6 results in a net negative charge at physiological pH. The solution properties of BSA have been well characterized, with a reported monomeric gyration radius of 27.6Å [52]. BSA contains residues susceptible to glycation, including lysine, arginine, and cysteine[58] and is well known for its multiple fatty acid (FA)-binding sites with high affinity for lipophilic moieties. Previous studies have shown that BSA binds more strongly to charged lipids compared to zwitterionic lipids[49, 48, 59].

AFP is structurally related to albumin, containing approximately 585
amino acid residues organised into in three homologous domains. Domain III
at the C-terminus is the most conserved, while Domain I at the N-terminus
shows more structural variation [60, 57]. AFP adopts asymmetric, heart-
shaped conformation with dimensions of 93.2 Å x 83.3 Å x 59.6 Å, contains
four natural FA-binding sites and is N-glycosylated at residue N251 [57].
Structural studies combining electron density map and LC-MS/MS have
provided insight into its glycan composition including the mutant N251S.[57]
The external surface of AFP is predominantly hydrophilic, favoring solubility
in the bloodstream, whereas the FA-binding sites are lined by hydrophobic
residues and flanked by positively charged side chains, particularly arginine
and lysine[61, 62]. Similar to BSA, AFP has a low theoretical pI (5.48),
conferring a net negative charge at physiological pH. Given its comparable
size and shape to BSA, we assume a similar gyration radius for AFP in this
study.

The integration of QCM-D data with NR results provided valuable in-
sights into how the interaction of the biomarker AFP with SLBs is influenced
by the lipid composition of the bilayer. Specifically, the frequency and dis-
sipation changes measured by QCM-D, reflecting adsorption and desorption processes at the quartz surface, were complemented by the nanostructural insights from NR, including changes in overall bilayer thickness, in the thickness of individual sub-layers, and in their hydration, roughness, and leaflet composition in response to protein binding.

MD simulations were employed to provide an atomic-scale perspective on the interactions between in silico-generated lipid bilayers and proteins. These simulations offered complementary insight into how lipid composition, bilayer organization, and protein surface characteristics govern protein-lipid interactions.

While direct comparison with in vitro experiments cannot be expected due to intrinsic methodological differences, MD nonetheless yields mechanistic details that help interpret experimental findings. The main differences between the approaches arise from the distinct timescales and system configurations they probe. Reflectometry and QCM-D capture near-equilibrium structures over minutes to hours, whereas MD explores dynamics on nanosecond-microsecond timescales. Moreover, the simulated bilayer is free-standing, laterally finite, and idealized—lacking the substrate effects or imperfections present in the SLBs used experimentally. In vitro systems extend over much larger surface area, increasing the likelihood of structural defects such as domain boundaries, pinholes, or discontinuities, which may influence protein-bilayer interactions. Finally, the simulations model a single protein interacting with the membrane and therefore do not account for potential cooperative effects such as clustering, crowding, or cooperative insertion, which could significantly influence the nature and extent of protein-bilayer interactions[63]. These effects, however, were beyond the scope of the present MD simulations but remain an important avenue for future investigation.

As shown in Figure 6 A–D, which provides a visual summary of the interactions between the two proteins and the various lipid bilayers, the bound/unbound fractions (panel A), as well as the average number of contacts and contact occupancy (panels B and D), are highest for the POPC/DOTAP bilayer. However, this elevated level of interaction does not correspond to the strongest effect observed in the neutron scattering experiments, which instead identified POPC/POPS as the bilayer composition with the most pronounced structural changes. Interestingly, the MD simulations showed that POPC/POPS exhibited the lowest average number of contacts, emphasizing that MD simulations alone may not fully capture the complexity
of experimentally observed behaviors, as previously noted. Nevertheless, when comparing the relative interaction profiles of AFP and BSA across all lipid compositions, the experimentally observed trend of generally stronger AFP-bilayer interactions is consistently reflected in the simulation results. These contrasting interaction patterns provide valuable insights into the distinct ways each protein engages with the lipid bilayers.

To integrate the experimental and computational findings, we discuss the interactions of BSA and AFP with each lipid composition sequentially, combining QCM-D, NR, and MD results for a comprehensive picture.

### 7.1. Membrane composed by POPC

For the pure zwitterionic POPC SLB, both experimental techniques indicated negligible interaction with AFP or BSA. QCM-D showed either a slight net negative mass balance upon AFP exposure or only minor variations within experimental error for BSA, while NR confirmed the absence of structural perturbations, with the best-fitting model corresponding to no interaction. These results are consistent with MD simulations, which showed that although both proteins contacted the bilayer in approximately 60% of the frames (Figure 6A), the average number of contacts and contact occupancy remained very low (Figure 6B,D), indicative of non-specific binding, with no particular region of the proteins engaging strongly with the membrane. The overall contact levels were comparable between AFP and BSA.

### 7.2. Membrane composed of POPC/CHOL/SM

With the mixed zwitterionic lipid composition, POPC/SM/CHOL SLB, AFP exhibited a significant interaction, in contrast to its negligible effect on pure POPC. QCM-D measurements indicated a net decrease in the total adsorbed mass after exposure to AFP and subsequent buffer rinsing. In contrast, NR data were best described by a model incorporating AFP within the outer leaflet of the SLB, with a volume fraction of 38%, accompanied by a 15% increase in lipid hydration, suggesting partial lipid removal. This apparent discrepancy can be explained by considering the simultaneous interplay of lipid desorption and protein incorporation, leading to a complex structural rearrangement at the interface. The net negative mass detected by QCM-D points toward a dominant lipid loss, whereas NR emphasizes the concurrent protein insertion and increased bilayer hydration. While the NR model supports the presence of incorporated protein, it does not allow definitive quantification of lipid loss, leaving some uncertainty in the net
mass balance. Overall, given that the interaction of AFP with pure POPC bilayer was undetectable by both NR and QCM-D, these findings suggest that ordered lipid domains, stabilized by SM and CHOL, may play a critical role in modulating AFP interaction. In contrast, BSA showed no measurable interaction in either technique.

In presence of SM and CHOL, MD simulations reveal an increase in bilayer thickness (~0.4 nm) compared to all other systems studied (Figure 7B), in agreement with NR and QCM-D results. Interestingly, despite the low number of protein-lipid contacts, simulations show a ~50% enrichment of SM near the proteins (Figure 7A), suggesting a preference for sphingomyelin-rich domains over pure POPC regions. CHOL, on the other hand, is strongly depleted near the adsorbed proteins because it resides deeper within the membrane, beyond the immediate protein-lipid interface. However, CHOL exhibits a strong tendency to colocalize with SM, highlighting their mutual affinity, as shown in the lipid neighborhood enrichment heatmaps (Figure 7C). This co-localization is further supported by the two-dimensional enrichment maps of CHOL and SM of the POPC/SM/CHOL membranes in the presence of BSA and AFP (Figure S14A-C). Together, these findings suggest the formation of cholesterol- and sphingomyelin-enriched nanodomains characterized by significant local thickness variations, as shown in Figure 7D.

Although the simulations do not fully align with experimental results, which show that only AFP significantly interacts with the bilayer, they may help explain the observed differences compared to the POPC pure system. Specifically, the formation of localized sphingomyelin-cholesterol-enriched patches appears to influence protein-membrane interactions and may be the basis for the distinct behavior of AFP and BSA in this ternary bilayer system, as the flexible loop region present in the AFP - and absent in the BSA - contributes to bindings by forming more contacts, suggesting that binding events might be more stable with AFP.

### 7.3. *Membrane composed by POPC/POPS*

For the anionic bilayer containing POPS, both QCM-D and NR consistently indicated strong protein-membrane interactions, though with different outcomes for AFP and BSA. AFP induced substantial lipid removal, leading to large-scale bilayer disruption. The effect was significantly more pronounced in the NR data, which allowed for a detailed assessment of the substantial structural impact of AFP on the bilayer. NR revealed that AFP transformed the bilayer into an unstructured layer of mixed proteins and
lipids, while QCM-D showed a net negative total adsorbed mass following protein exposure and rinsing with buffer, consistent with the predominant lipid loss. Although NR could not resolve the precise structural details related to the loss of material, the overall thinning of the bilayer is in agreement with the results from QCM-D. For BSA, the combined analysis was consistent with lipid removal accompanied by the adsorption and/or incorporation of BSA into the SLB. QCM-D detected a net decrease in total adsorbed mass, reflecting a greater loss of lipids relative to protein incorporation. NR provided further insight, with the best-fitting model indicating 11% protein incorporation and a significant increase in solvent content within the bilayer, corresponding to 22% hydration of the tail region. Together, these findings demonstrate that both proteins perturb the negatively charged bilayer, but AFP induces a more drastic structural rearrangement compared to BSA.

However, POPC/POPS SLB showed the greatest discrepancy between simulations and experimental results, particularly in terms of absolute interaction strength. Whereas experiments revealed the strongest perturbations for both proteins, simulations showed the lowest overall number of protein-lipid contacts (Figure 6A-B). However, when considering the relative interaction strength between the two proteins, the simulations still captured the experimental trend: AFP exhibited a stronger interaction with the bilayer than BSA, based on the number of contacts. Clearly, additional factors must be considered when evaluating how these proteins interact with anionic lipid bilayers. From the simulation perspective, the low number of interactions may stem from the high net negative charges of the proteins (BSA: -16e; AFP: -17e), which could generate electrostatic repulsion with the negatively charged bilayer and hinder membrane association within the simulation timescale. This suggests that protein adsorption may be kinetically limited due to energy barriers.

Surprisingly, as shown by the enrichment data in Figure 7A, both proteins exhibit the highest level of PS lipid enrichment in their vicinity (exceeding 100%). This is likely due to positively charged surface patches, sparsely distributed on the proteins, as illustrated in Figure S15. Despite the lack of direct agreement with experimental data, potentially due to kinetic barriers and concentration-dependent effects, simulations suggest that once proteins approach the membrane surface, they tend to cluster PS lipids.

A previous study by Lolicato et al. [63] showed that cationic nanoparticles can extract lipids by recruiting negatively charged lipids to their surface. This process is amplified by the tendency of anionic lipids to promote
nanoparticle aggregation, suggesting that cooperative effects may enhance
lipid extraction. Although AFP is not globally cationic, it contains posi-
tively charged surface patches which, if the interaction with the membrane is
sustained over sufficiently long time, could drive a similar lipid recruitment
mechanism. This may explain the experimentally observed lipid extraction
associated with AFP.

7.4. *Membrane composed by POPC/DOTAP*

For the cationic bilayer containing DOTAP, both experimental techniques
revealed the formation of a AFP protein layer adsorbed on top of the SLB,
with minimal impact on the bilayer’s structure. This conclusion was supported
by QCM-D data, which showed a net increase in total adsorbed mass after
rinsing with buffer, and corroborated by NR results, where the best-fitting
model indicated the presence of an adsorbed protein layer approximately 39
Å thick. This thickness value is approximately 25% greater than the radius
of gyration associated with albumins, such as those used in this study. Such a
discrepancy suggests a preferential orientation of the adsorbed AFP relative
to the lipid bilayer. If AFP adopts an orientation where one of its dimen-
sions is significantly larger than the others, this could directly impact the
measured thickness of the adsorbed protein layer. As illustrated in Figure
6E, DOTAP preferentially interacts with the flexible domain I of AFP, which
may further influence the protein’s orientation and the resulting layer thick-
ness. These observations indicated that electrostatic forces may play a pri-
mary role in modulating the interaction between the net negatively charged
AFP (at physiological pH) and the positively charged lipid, DOTAP. In the
case of BSA, QCM-D measurements revealed a significant lipid removal from
the bilayer, as indicated by a net decrease in total adsorbed mass following
protein exposure. Conversely, NR did not detect any appreciable adsorption
or incorporation of the protein within the SLB structure.

For this lipid composition, MD simulations revealed the strongest pro-
tein-lipid interactions, measured by the number of contacts, among all lipid
compositions examined (Figure 6). Notably, AFP establishes more contacts
with the membrane than BSA, suggesting a higher membrane affinity. For
AFP, lipid contacts are predominantly localized around domain 1A (Fig-
ures 6D and E), indicating a well-defined interaction interface. A close-up
view in Figure S12B reveals that a specific region of the protein (residues
69-100) is primarily responsible for engaging with the membrane. This
could explain the membrane adsorption observed experimentally. In con-
trast, BSA's lipid interactions, though comparable in contact number, are
more broadly distributed across domains I and II and involve distinct nega-
tively charged residues (Figures S13C and S12A). This suggests the presence
of two distinct binding motifs in BSA which, although capable of supporting
interactions in MD simulations (driven by electrostatic forces and limited
sampling) may not be sufficient to promote stable membrane association un-
der experimental conditions.

Interestingly, across all simulations, no significant interactions were ob-
served between the membrane and the glycosylated moiety near residue N251
of AFP. This suggests that the glycosylation site does not play a major role
in membrane binding under the tested conditions. Instead, structural fea-
tures such as domain flexibility seem to have a greater influence on pro-
tein-membrane interactions.

8. Conclusions

Our study elucidates the interactions of the clinical biomarker alpha-
fetoprotein, as well as bovine serum albumin, with model lipid membranes,
offering novel insights relevant to both fundamental biophysics and biomarker
detection applications. To our knowledge, this is the first report on the in-
teraction of AFP with supported lipid bilayers. Our findings demonstrate
that AFP engages significantly with lipid moieties, with the strength and
nature of this interaction being highly dependent on the lipid composition of
the membrane. These observations have important implications for conven-
tional tumor assays, such as ELISA, where such lipid-protein interactions
may influence assay performance and interpretation.

Notably, in our previous work on glycated BSA (gBSA)[28], we demon-
strated that glycation enhances the interaction of BSA with negatively charged
membranes. This finding suggests that carbohydrate-related modifications
can modulate protein-membrane interactions by altering both surface charge
and protein conformation. By analogy, investigating how the removal of N-
linked glycans influences AFP-membrane binding would be highly informa-
tive. Future studies comparing native and deglycosylated AFP on SLBs,
particularly those composed of POPC/POPS, could elucidate the specific
role of glycosylation in these interactions. Furthermore, extending this ap-
proach to biomarkers with higher glycosylation levels, such as soluble vascular
endothelial cadherin[64, 65], would provide valuable additional insights.
Overall, our study provides new insights into how clinically relevant proteins, such as AFP and BSA, interact with model lipid membranes. These findings could contribute to a better understanding of protein-membrane interactions and may offer considerations for future biomarker detection strategies.

## Acknowledgements

We acknowledge the InnovaXN (European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 847439) for funding the InnovaXN-25-2020 project, the Institut Laue-Langevin that provided allocation of neutron beam time (Experiment DOI: doi:10.5291/ILL-DATA.9-13-1048, doi:10.5291/ILL-DATA.9-13-1084, doi:10.5291/ILL-DATA.9-13-1125) and the use of Partnership for Soft Condensed Matter laboratories. This work is supported by grants of the Deutsche Forschungsgemeinschaft to FL (SFB-1638/1 - 511488495 - Z01, DFG LO 2821/1-1) and to MK (SFB-1638/1 - 511488495 - Z01). FL acknowledged the computing resources provided by the CSC - IT Center for Science Ltd. (Espoo, Finland) and supported by the state of Baden-Württemberg through bwHPC and the German Research Foundation (DFG) through grant INST 35/1597-1 FUGG. FL and MK gratefully acknowledge the data storage service SDS@hd supported by the Ministry of Science, Research and the Arts Baden-Württemberg (MWK) and the German Research Foundation (DFG) through grant INST 35/1503-1 FUGG.

## Data availability

All molecular dynamics input and configuration files used in this study (`.trp`, `.gro`, `.mdp`, `.itp`) have been deposited in ZENODO and are publicly available at: DOI:10.5281/zenodo.15628978

## Supporting Information

The following files are available free of charge: SI.pdf: supporting information
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