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. 2025 Feb 24;97(9):5102–5108. doi: 10.1021/acs.analchem.4c06339

Fab-Induced Stabilization of an Ion Channel Receptor Enables Mechanistic Characterization of Small-Molecule Therapeutics

Soo Y Ro 1, Christine Jao 2, Angela Oh 2, Marc Kschonsak 2, Tianbo Li 1, Daniel Austin 1, Demi Maria Z Greiner 1, Lijuan Zhou 3, Yingnan Zhang 3, Jun Chen 1, John G Quinn 1,*
PMCID: PMC11913017  PMID: 39993138

Abstract

graphic file with name ac4c06339_0006.jpg

Developing small-molecule (SM) therapeutics that target membrane proteins (MPs) is often challenging, because few biophysical methods can handle the detergents required to maintain target stability. Here, we report a surface plasmon resonance (SPR)-based methodology that enables the characterization of interactions between SMs and an ion channel receptor (MP1) in complex with a stabilizing antibody fragment (Fab) and surfactant. Briefly, a stable MP1-Fab complex was formed by coimmobilizing MP1 with an anti-MP1-Fab within the hydrogel film to study the interactions of MP1 with SMs. The critical micelle concentration (CMC) is the concentration at which 50% of the surfactant monomers are assembled into micelles. Micelles readily absorb compounds resulting in compound-loaded micelles that generate high nonspecific binding and hinder resolution of SM binding responses. This micelle-induced interference was avoided by utilizing a weak detergent at a concentration below its CMC, allowing for the resolution of compound binding to a solvent-exposed pocket. Additional Fab stabilization was required to rescue binding at a second pocket buried within the transmembrane region of MP1. The resulting SPR-based assay proved invaluable during hit-to-lead optimization by progressing structure–activity relationship (SAR) studies and resolving the mechanism of action (MoA).

Introduction

MPs comprise an estimated 60% of druggable proteins and are targeted by 54% of the currently approved drugs.1 The development of therapeutic agents targeting MPs remains challenging especially for ion channels and some classes of GPCRs, which are implicated in many CNS disorders.2,3 18% of SM drugs bind to ion channels,4 and 35% of current drugs target GPCRs with more than 300 drug candidates in clinical trials.2,5,6 However, the development of SM drugs that target MPs is more challenging during early hit-to-lead and lead progression phases. MPs are more prone to instability and inactivation outside of their membrane environment, and identifying the lipids or detergents compatible with both the MP and assays requires extensive optimization, often without success.7 These same considerations have limited the availability of biophysical methods that enable the routine characterization of interactions between SM and MPs. Relatively new approaches such as native mass spectrometry have been utilized to determine SM occupancy and localization but can be expensive and laborious to develop.8−11 More traditional probe displacement assays12 require a known pocket binding compound and is generally unavailable for unprecedented targets.

SPR is the gold standard biophysical method for the characterization of SM binding affinity and kinetics. SPR is a label-free optical technique that detects changes in mass accumulating at a sensing surface in real time by tracking associated changes in the average refractive index at the surface. However, applying SPR to study SM–MP interactions is challenging due to the need to maintain the target in a conformation near its native state in the absence of the membrane environment. There are limited examples of using SPR to investigate SM–MP interactions, and most of these studies center around tractable, well-characterized GPCRs, such as the β2 adrenergic receptor (β2AR). Extensive mutagenesis studies of β2AR identified thermostable variants (StaRs) structurally stable after immobilization, allowing for SM binding characterization and SPR fragment library screening.13−15 However, creating thermostable variants of MPs is time-consuming and costly and may not reflect the native structure relevant for pharmacological studies. There are a few examples utilizing SM SPR analysis of nonengineered, wild-type MPs, such as chemokine receptors,16,17 β2AR,18 and NST1.19 Efforts to maintain the conformation of wild-type MPs using detergents are generally useful in many applications, but compounds can partition into both solution-phase and solid-phase micelles, leading to high interference and poor results when interrogated by SPR.

Prior studies have reconstituted MPs into lipid bilayers or nanodiscs to mimic the native cell environment and reduce micelle interference during SPR studies.15,20−22 While nanodisc reconstitution has enabled significant advances in structural and biophysical characterization of MPs, the process is complex, and reconstitution does not guarantee maintenance of native conformation. In addition, the high mass of the nanodisc relative to the MP reduces the amount of MPs captured on the SPR surface, thereby lowering the signal-to-noise ratio to unacceptable levels. It is possible that the binding of SMs to nanodiscs may lead to false positives. Amphipols are a useful alternative to nanodiscs in many cases but are often ineffective.23 In summary, a general SPR methodology suitable for the interrogation of SM–MP interactions would be highly impactful, and here, we present a proof-of-principle study describing a simple but effective approach. In this study, we utilized the Fab clamping of an ion channel target (MP1) to stabilize MP1 into a desired conformational state on an SPR sensing surface, enabling affinity and kinetic analysis of MP1–SM interactions.24

Results

SPR Analysis of SM–MP1 Interactions in Detergent Micelles

MP1 is an ion channel receptor target in our SM drug discovery portfolio. MP1 is a > 100 kDa complex composed mainly of at least 15 integral transmembrane helices. An internal screening campaign identified SM binders in two distinct binding sites S1 and S2 (Figure 1). Site 1 is the more accessible pocket located adjacent to the protein–lipid interface. SM binding to S1 stabilizes a flexible peripheral helix ∼5 Å away. Site 2 is buried within five transmembrane (TM) helices in which SM binders engage and stabilize two TM helices. Stabilization of these S1 or S2 helices leads to internal helical rearrangement near the ion channel pore, impacting function. A set of 10 SM analogs, internally validated via cellular electrophysiology (Ephys) activity assay and structurally binned by binding site location, was selected as negative (cmpd 1) and positive (cmpd 2–10) controls for investigation via SPR (Table 1 and Figure 1). SPR monitors changes in mass accumulation as response units (RUs) with respect to time, reporting all components that contribute to the overall binding process.

Figure 1.

Figure 1

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Graphical representation of ion channel MP1 and its SM binding sites. Binding sites site 1 (S1) and site 2 (S2) are depicted. TM helices are depicted as rods and detergents colored yellow.

Table 1. SM Compound Set Used for SPR Assay Developmenta.

compound binding site MW (g/mol) cLogP EPhys EC50 (μM) [±SE]
1-NC NA 417 0.56 >50
2-S1 S1 502 4.3 3.2 [±1.5]
3-S1 S1 572 5.9 6.9 [±4.5]
4-S2 S2 482 5 4.3 [±1.7]
5-S2 S2 403 4.1 1.6 [±1.0]
6-S2 S2 376 2.5 1.7 [±0.6]
7-S2 S2 358 2.3 1.0 [±0.3]
8-S2 S2 394 2.3 0.2 [±0.07]
9-S2 S2 397 2.2 0.09 [±0.03]
10-S2 S2 470 3.2 0.04 [±0.02]
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a

Electrophysiology EC50 average value from replicates of n ≥ 3. Abbreviations: NC (negative control), S1 (site 1), S2 (site 2), EC50 (half maximal effective concentration), M (Molar), SE (standard error).

Biotinylated MP1, which is stable while solubilized in lauryl maltose neopentyl glycol (LMNG) detergent, was immobilized on the sensor chip in LMNG above its CMC (0.05 mM). SM titrations in constant 0.05 mM LMNG were injected over the MP1 surface. SM binding responses for both negative (cmpd 1-NC) and positive controls (representative cmpd 7-S2) exhibited high systematic interference during association and dissociation (Figure 2A-1,2 and Suppl. Figure 1A), producing drifting response curves that do not report changes in target occupancy. The artifactual response for the negative control (cmpd 1-NC) resembles the response for compound 7-S2 and is likely caused by nonspecific binding of detergent micelles containing adsorbed SMs. The use of LMNG at concentrations 10-fold below CMC (0.001 mM) significantly reduced the artifactual response, but interference from an upward drifting nonspecific binding (NSB) response remained (Figure 2A-3). Other commonly used detergents, n-dodecyl-B-D-maltoside (DDM), glyco-diosgenin (GDN), and polysorbate 20 (Tween 20), possessing low CMC thresholds were screened well below CMC but exhibited noisy artifactual responses (Figure 2B-1,2,3 and Suppl. Figure 1D,E,F).

Figure 2.

Figure 2

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Screening of detergents at high and low CMC to support binding of SM to immobilized MP1. (A) Sensograms for analysis of the negative control (cmpd 1) and representative site 2 binder (cmpd 7-S2) at LMNG above CMC (labeled “High LMNG”, 0.05 mM) and compound 7-S2 at LMNG below CMC (labeled “Low LMNG”, 0.001 mM). Compounds are injected at 10 μM top concentration, twofold dilutions, while detergent concentrations were held constant in the analyte. (B) Representative sensograms of site 2 binders (cmpd 7-S2) injected over immobilized MP1 in 10-fold below CMC (labeled “Low”) for the following surfactants: 0.01 mM DDM, 0.001 mM Tween 20, 1 μM GDN, 2 mM OG, nanodiscs, and amphipol. Compounds are injected at 10 μM top concentration, twofold dilutions. (C) Binding profiles of site 1 compounds (2-S1 and 3-S1) in 2 mM OG. Sensograms of site 1 and site 2 compounds are highlighted in green and purple, respectively. As for all sensograms in this paper, the X-axis is time (s) and the Y-axis is the % target occupancy.

In contrast to low CMC surfactants, n-octyl-β-d-glucopyranoside (OG), which has a higher CMC of 25 mM (in H2O), significantly reduced artifactual responses related to site 2 compounds (Figure 2B-4 and Suppl. Figure 1C) when used well below its CMC (2 mM). Site 1 selective compounds showed dose-dependent responses with stoichiometrically bounded specific binding (Figure 2C), as also observed when using 0.001 mM LMNG (Suppl. Figure 1B). Despite the lower levels of NSB, binding profiles of site 2 compounds exhibited low occupancy and an absence of kinetics, indicating weak affinity and/or NSB component (Figure 2B-4 and Suppl. Figure 1C). These observations suggest that for MP1 in detergents below CMC, the solvent-accessible site 1 pocket is intact while the TM regions containing site 2 are not structurally stable. MP1 was also reconstituted in nanodiscs and amphipols in efforts to maintain structural stability, but these efforts yielded low target occupancy for all compounds (Figure 2B-5,6 and Suppl. Figure 1G,H), resembling the low SM binding responses reported for MP1 without detergent (Suppl. Figure 2). Site 2 exhibited promising SM SAR and became the priority target for drug discovery, so detecting SM interactions at site 2 was a priority, prompting efforts to stabilize site 2. A previous study demonstrated that a bimolecular complex formed between an antibody-derived Fab and KRAS G12C can “clamp” a target’s conformational ensemble into a particular desired state on an SPR sensing surface, enabling robust affinity and kinetic analyses for SAR progression.24 Hence, the Fab-induced stabilization of MP1 was investigated.

Characterization of Fab-MP1 Interactions

We determined the affinity and stability of each anti-MP1-Fab binding to MP1 (Figure 3), and the resulting kinetic and affinity constants are shown (Table 2). The anti-MP1 Fabs (Fab1 and Fab2) exhibited low association rate constants (ka), low dissociation rate constants (kd), and relatively high affinity returning low affinity constants (KD < 40 nM). We next sought to characterize the interactions of our SM panel with Fab-stabilized MP1. We implemented cocapture of both the Fab and MP1 to avoid interference from baseline drift due to dissociation of Fab during this SM assay.

Figure 3.

Figure 3

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Single-cycle kinetics (SCK) analysis of the binding of Fab to active-MP1. Raw sensograms and fits for Fab1 and Fab2 are depicted, and the associated interaction constants are shown in Table 2.

Table 2. Kinetic Constants and Affinity Values for Fab Binding to MP1a.

analyte model KD (nM) %Rmax
Fab1 2-site 5.0 [±1.9] 57 [±14]
Fab2 2-site 1.9 [±0.3] 79 [±1]
analyte ka (M–1 s–1) kd (s–1)
Fab1 3.5 [±1.0] × 103 1.9 [±1.3] × 10–4
Fab2 3.7 [±0.7] × 103 7.1 [±1.0] × 10–5
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a

n = 3 replicates

Bimolecular Complex Formation Between MP1 and Fab1 toward Stabilizing the S2 Pocket

Compound binding was enhanced by coimmobilizing a specific Fab with MP1 within the hydrogel on the sensing surface. Biotinylated MP1 and biotinylated Fab1, or Fab2, were captured on a sensing surface. Active SPR channels were immobilized with either active-MP1 or negative control MP1 (ref-MP1) with mutations destabilizing both binding sites, and compound binding was investigated in OG below the CMC (2 mM). Binding profiles for ref-MP1 (+) Fab1 exhibited fast-on/fast-off kinetics and linear dose–response at low response levels, likely driven by NSB (Figure 4A). A biphasic dissociation curve was observed for SM interactions with active-MP1 (+) Fab1 not present in active-MP1 alone (Figure 4C) and was fit using a two-state reaction model to obtain full kinetic parameters (Suppl. Table 2). Use of LMNG below CMC with Fab1 stabilization still produced upward drifting NSB responses and lacked the dissociation kinetics present in 2 mM OG for site 2 compounds (Suppl. Figure 3). In contrast, Fab2 did not show evidence of rescuing compound binding at the site 2 pocket (Suppl. Figure 4).

Figure 4.

Figure 4

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Sensograms of SM interactions with the Fab-MP1 complex (A) ref-MP1 (+) Fab1 and (B) active-MP1 (+) Fab1 in 2 mM OG. (C) Extracted dissociation curves of SM interactions with active-MP1 (+) Fab1 and (−) Fab1. Experimental data is displayed as dashed black lines and kinetic fits are displayed in colors shown in the legend. Sensograms of site 1 and site 2 compounds are highlighted in green and purple, respectively.

The detection of Fab1-stabilized site 2 specific binding with SMs prompted further SPR assay development. The complexity of binding curves observed in Figure 4 and the biphasic binding curves for S2 compounds induced by Fab1 suggested the need for additional controls in order to ensure confidence in the SPR data. The following additional control configurations were included: (1) ref-MP1 was immobilized onto the reference channel and (2) a MP1 minimal mutant with only the S2 site perturbed (ΔS2-MP1) was included as an active ligand. The SM tool compounds were tested against all three MP1 variants (ref, active, ΔS2 mutant) in complex with Fab1 on the sensing surface.

Using ref-MP1 on the reference channel allowed for reduction of NSB responses through double-referencing. The resulting binding response curves showed dose-dependent, stoichiometry-bound SM binding responses for active-MP1 and ΔS2-MP1. The negative control (cmpd 1-NC) did not display any significant responses, while site 1 compounds exhibited binding kinetics for both ΔS2-MP1 and active-MP1 ± Fab1, as expected (Figure 5A, cpmds 2-S1, 3-S1). In consideration of the conformational changes induced by stabilization of the peripheral S1 helices, a two-state reaction model was applied and produced fits with similar affinities and dissociation rate constants across all three MP1 variants (Suppl. Tables 3 and 4).

Figure 5.

Figure 5

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Binding profiles of site 1 and 2 compounds interacting with Fab1-stabilized MP1. (A) Multicycle kinetic (MCK) analysis of compounds binding to (A) active-MP1 (±) Fab1 and ΔS2-MP1 (+) Fab1. (B) SCK analysis of more potent site 2 compounds binding to active-MP1 (+) Fab. Experimental data is displayed in dashed black lines, and kinetic fits are displayed in colors corresponding to concentrations shown in the legend. Sensograms of site 1 and site 2 compounds are highlighted in green and purple, respectively. (C) A first linear regression analysis of affinity (Log(KD)) versus compound activity (EC50) and a second showing Log(KD)) versus Log(kd).

Site 2 binders displayed a more pronounced biphasic binding kinetics for active-MP1 (+) Fab1 (Figure 5A, cpmds 4-S2 to 8-S2) absent for ΔS2-MP1 (+) Fab1, suggesting that these interactions are specific to the site 2 pocket. Additionally, the lack of site 2 SM interactions with active-MP1 alone further suggests Fab1 is essential for the stabilization of the S2 pocket in SPR conditions. Biphasic kinetic profiles of site 2 compounds show an initial fast association rate (ka1) that transitions to a slower increasing component (ka2). This is followed by a rapid dissociation component (kd1) and a slower dissociation component (kd2) (Figure 5A and Supplemental Tables 3 and 4). Structural analysis of site 2 suggests conformational gating via the SM-induced global rearrangement of the S2 and pore-adjacent helices. In consideration of this complex binding MOA, a two-state reaction model was applied to produce an approximate kinetic fit for site 2 compounds (Suppl. Tables 3 and 4). The slow kd2 component may arise from MP1 conformational changes that induce a modified site 2 pocket presenting longer compound residence times and kd2 values >10-fold more potent than kd1 values (Suppl. Table 3). Furthermore, compounds with potent nanomolar regime affinities were characterized via SCK (Figure 5B). While ka1 values for site 2 compounds generally fall within 103–104 M–1 s–1, kd2 values decrease with improving EC50 potency and affinity suggesting kd2 as the predominant driver of compound potency (Figure 5C).

Discussion

The literature provides limited examples of biosensor-based characterization of SMs interacting with MPs.13−19 In this study, we demonstrate that Fab-induced clamping of an ion channel receptor on the sensing surface can provide binding kinetics of SM interactions, even for binding pockets buried within TM domains. We show that the binding profiles are distinct between the site 1 and site 2 pockets, which align with the location of the binding sites and structural understanding of the MOA. Additionally, we observe selective binding to the active protein conformation and correlations between binding affinity and cellular potency.

The addition of multiple protein controls further validates these site-specific binding interactions. Typical standard SPR controls include double-referencing of binding response curves to resolve specific binding to the target-coated surface from NSB, which is recoded on a reference surface.25 A control curve to subtract baseline drift is also performed by injecting a sample that does not contain a test compound. This double-referencing procedure is typically sufficient for SPR analysis of soluble target proteins, but here, we augment these controls for greater assurance in compound characterization given that weak interferences often persist when working with complex detergent solubilized MPs (Suppl. Figure 5). High-quality controls were therefore added to ensure confidence in the effective potencies being measured via SPR. This, in combination with approximate kinetic/affinity models, was found sufficient to allow critical decision-making. The following three controls were included: (1) use of an MP1 variant lacking site 1 and site 2 binding (ref-MP1). Ref-MP1 was bound to the working channel to estimate the magnitude of NSB. Capturing it onto the reference channel with active-MP1 on the working SPR channel improved the resolution of the specific binding component. (2) This format was repeated with a second mutant lacking site 2 (ΔS2-MP1) in the working channel, which allowed the site specificity of each compound to be resolved. (3) This format was further expanded by the addition of Fab clamping that enhanced S2-specific compound binding, in contrast to a nonstabilizing Fab that failed to produce this allosteric effect. Together, these three controls allowed a higher tolerance for interferences in the binding curves and provided confidence in compound SAR despite nonideal SPR binding curves.

This study suggests that target stabilization via Fab and weak detergent might be utilized for the biosensor-based characterization of other difficult MPs. This methodology bypasses the need to engineer thermostable variants and extensive detergent/lipid screens while enabling hit validation, binning of binding sites, and SAR for lead series optimization. In addition to providing binding affinity and kinetics, SPR can also identify nonspecific binders and false positives early in the hit finding stage. These contributions are especially valuable for MP targets by reducing the burden on structural determination and cellular assays, which can be more labor intensive than for soluble targets. Assessing how broadly applicable this biosensor methodology is for analysis of SM–MP interactions is beyond the scope of this initial study, but we would anticipate this would depend strongly on the generation and selection of optimal conformationally specific clamping antibodies. Clamping strategies to stabilize the specific conformational state of MPs on the sensing surface may not be limited to antibody-derived molecules and could include peptides, compounds, and protein partners. This approach provides new opportunities targeting MPs in SM drug discovery and may improve the odds of delivering high-quality candidate drug molecules against challenging targets that address unmet patient needs.

Experimental Methods

Materials

All SPR assays were conducted using the Biacore S200 instrument, and data were evaluated using the Biacore S200 Evaluation v1.0 software. SPR chips used in this study were from Xantec (SAD200M). All coupling kits and SPR reagents were purchased from Cytiva. Compounds were synthesized and characterized internally, with purity ≥97% confirmed via LC-MS. The following detergents were used: 0.05 mM or 0.001 mM LMNG, 2 mM OG, 0.01 mM DDM, 1 μM GDN, 0.001 mM polysorbate 20 (Tween 20). The gene for MP1 with both Flag and Avi tags was expressed in Expi293 cells. Cells were resuspended in buffer A (50 mM HEPES pH 7.5, 150 mM KCl, 1 mM TCEP, 1 μg/mL benzonase, 1 mM PMSF, and Roche protease inhibitor tablets) and lysed by dounce homogenization. Whole-cell solubilization was performed by the addition of 2% LMNG with 0.2% cholesteryl hemisuccinate and gently stirred at 4 °C for 2 h. Cell debris was removed by centrifugation at 185,000 × g for 1 h at 4 °C. To capture MP1, the sample was affinity purified with anti-Flag M2 resin (Sigma). Unbound proteins were washed off with at least 10 column volumes of buffer B (25 mM Hepes, pH 7.5, 150 mM KCl, 1 mM TCEP, 0.1% LMNG, 0.01% CHS). Proteins were eluted in buffer C (25 mM HEPES pH 7.5, 150 mM KCl, 1 mM TCEP, 0.01% LMNG) supplemented with 300 μg/mL Flag peptide. Proteins were polished by size exclusion chromatography in buffer C and concentrated to ∼4 mg/mL. The ΔS2-MP1 mutant contains a single-point mutation, in which an alanine residue was substituted with a bulky residue to clash with SM binding at site 2. The ref-MP1 is composed of four substitutions with bulky and charged residues. Fabs were selected via phage display screening against immobilized MP1.

SCK Analysis of Fab Interactions with Active-MP1

Biotinylated active-MP1 was immobilized onto the SA sensor chip to ∼5000 RU using a capture buffer (50 mM HEPES pH 7.0, 150 mM KCl, 0.05 mM LMNG) at a 5 μL/min flow rate at 20 °C. The reference flow cell was left empty. All flow cells were capped with biotin. The Biacore S200 system was primed using a running buffer (50 mM HEPES pH 7.0, 200 mM KCl, 0.05 mM LMNG) at 10 °C. Fabs were injected at a 30 μL/min flow rate for 100 s, starting at a top concentration of 1 μM, with twofold dilutions, 5-point dose response via SCK analysis. The dissociation time was set between 1000 and 2000 s. Sensograms were fit using the 1:1 binding simple 2-site model.

MCK Analysis of SM Interactions with Active-MP1

Biotinylated active-MP1 was immobilized onto the SA sensor chip to ∼5000 RU using a capture buffer (50 mM HEPES pH 7.0, 150 mM KCl, 0.05 mM LMNG) at a 5 μL/min flow rate at 20 °C. The reference flow cell was left empty. All channels were capped with biotin. The system was primed with a running buffer (50 mM HEPES pH 7.0, 200 mM KCl, detergent*) at 10 °C (*0.05 mM LMNG, or 0.001 mM LMNG, or 2 mM OG, or 0.01 mM DDM, or 1 μM GDN, or 0.001 mM Tween 20, or no detergent). Compounds at a top concentration of 50 μM, with twofold dilution, 6-point dose response, were injected at a flow rate of 50 μL/min for 45–100 s with dissociation times between 30 and 300 s.

Fab stabilized the SPR assay for characterization of SM binding to coimmobilized active-MP1 and Fab1. Biotinylated active-MP1 was immobilized onto the SAD200 M sensor chip to ∼5000 RU using capture buffer (50 mM HEPES pH 7.0, 150 mM KCl, 0.05 mM LMNG) at a 5 μL/min flow rate at 20 °C. Next, biotinylated Fab1 was injected into the same flow cell and captured to ∼7000 RU. For the reference channel, biotinylated ref-MP1 was captured to ∼5000 RU, followed by capture of Fab1 to ∼7000 RU. All channels were capped with biotin. The system was primed with running buffer (50 mM HEPES pH 7.0, 150 mM KCl, 2 mM OG) at 10 °C. Compounds were injected at 50 μL/min flow rate for 100 s with dissociation times between 150 and 500 s via MCK analysis of analytes at twofold dilution, 6-point dose response.

Data Analysis and Statistical Methods

The model fitting of binding sensograms was performed using the BiacoreS200 Evaluation software. Sensograms were fit using the two-state reaction model. Biophysical models are considered to be approximations of the “true” interaction mechanism as high binding complexity is expected for MP interactions. The following statistical parameters were evaluated to assess the quality of curve fitting: the standard error of the fit (SEF) should be <10% of the estimated parameter value. Chi squared (λ2) should generally be <5% of the calculated Rmax, but more importantly, residuals should not show a systematic bias over time. GraphPad Prism 10 was used to determine the correlation between kinetic constants and compound potency via linear regression analysis.

Cellular Electrophysiological Assay

In vitro pharmacological potency EC50 was measured by patch clamp testing the regulators’ dose-dependent effect to target overexpression cells’ whole-cell current. A similar protocol was applied that was described previously by using an automated patch clamp equipment SyncroPatch 768PE (Nanion Technologies, Germany).26

Glossary

Abbreviations

SM

Small molecule

SPR

Surface plasmon resonance

Fab

Antigen-binding fragments

RU

Response unit

SCK

Single-cycle kinetics

s

Seconds

ka

Association rate constant

kd

Dissociation rate constant

KD

Dissociation affinity constant

τ

Residence time

Rmax

Response at saturation

% Rmax

Fractional occupancy

nM

Nanomolar

μM

Micromolar

SEF

Standard error of fitted parameter

M–1 s–1

Inverse molar seconds

s–1

Inverse seconds

HEPES

4-(2-Hydroxyethyl)-1-piperazineethanesulfonic acid

SA

Streptavidin

CM

Carboxy methylated dextran

LMNG

Lauryl maltose neopentyl glycol

OG

Octyl-beta-glucoside

GDN

Glyco-diosgenin

TWEEN20

Polyoxyethylene(20)sorbitan monolaurate

DDM

n-Dodecyl-β-D-maltopyranoside

NaCl

Sodium chloride

KCl

Potassium chloride

ref

Reference

MP1

Membrane protein 1

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.4c06339.

  • Sensograms of SM interactions with immobilized active-MP1, sensograms of SM interactions with immobilized active-MP1 without any detergent, sensograms of interactions between Site 2 compounds and immobilized active-MP1 (+) Fab1 in 0.001 mM LMNG, sensograms of Site 2 compounds binding to active-MP1 co-immobilized with Fab2 in 2 mM OG, graphical schematic of SPR surface preparation for MP targets and tables of estimated binding interaction parameters (PDF)

Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript.

The authors declare no competing financial interest.

Supplementary Material

ac4c06339_si_001.pdf (1.1MB, pdf)

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