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Abstract

Focal adhesion kinase (FAK) is a promising cancer drug target due to its massive overexpression in multiple solid tumors and its critical role in the integration of signals that control proliferation, invasion, apoptosis, and metastasis. Previous FAK drug discovery and high-throughput screening have exclusively focused on the identification of inhibitors that target the kinase domain of FAK. Because FAK is both a kinase and scaffolding protein, the development of novel screening assays that detect inhibitors of FAK protein–protein interactions remains a critical need. In this report, we describe the development of a high-throughput fluorescence polarization (FP) screening assay that measures the interactions between FAK and paxillin, a focal adhesion–associated protein. We designed a tetramethylrhodamine (TAMRA)-tagged paxillin peptide based on the paxillin LD2 motif that binds to the focal adhesion targeting (FAT) domain with significant dynamic range, specificity, variability, stability, and a Z’-factor suitable for high-throughput screening. In addition, we performed a pilot screen of 1593 compounds using this FP assay, showing its feasibility for high-throughput drug screening. Finally, we identified three compounds that show dose-dependent competition of FAT–paxillin binding. This assay represents the first described high-throughput screening assay for FAK scaffold inhibitors and can accelerate drug discovery efforts for this promising drug target.

Keywords

focal adhesion kinase FAT domain drug discovery

Introduction

Focal adhesion kinase (FAK) is a 125 kDa nonreceptor tyrosine kinase that is overexpressed in a variety of human malignancies, including melanoma, glioblastoma, and breast, colon, ovarian, and pancreatic cancers.¹ We were the first to show overexpression of FAK in human tumor samples,² and years of experimental validation have led to the conclusion that FAK is a critical component for human cancer progression. From a biological standpoint, FAK is involved in cell motility, invasion, angiocrine signaling, lymphangiogenesis, metastasis, and the epithelial-mesenchymal transition (EMT).^3,4 FAK also sequesters and inactivates proapoptotic proteins, such as p53 and receptor-interacting protein (RIP), to enhance the survival signals necessary for a cancer to invade and metastasize.^5,6 FAK’s role in cancer has been confirmed by multiple methodologies, including clinical prognostic studies, genetically engineered mouse models, gene knockout studies, and site-directed mutagenesis.^7–9

FAK functions as both a kinase and scaffolding protein, and it has three major domains: the N-terminal 4.1, ezrin, radixin, moesin (FERM) domain; the central kinase domain; and the C-terminal focal adhesion targeting (FAT) domain. FAK knockdown results in robust activation of apoptosis and growth arrest in cancer cells, with minimal effects in normal cells.^10,11 Conversely, FAK-kinase inhibitors have a partial effect on apoptosis and tumor growth,^12,13 and it is hypothesized that the FAK scaffold is the major modulator of FAK-dependent antiapoptosis.¹⁴ FAK directly binds p53 to suppress p53-mediated apoptosis,⁵ and disruption of the FAT domain by adenoviral FAK–carboxyl (C)-terminal domain of FAK (FAK-CD) was shown to induce apoptosis in cancer cells.¹⁵ We showed that FAK-kinase inhibitors do not drastically inhibit FAK phosphorylation at autophosphorylation residue Y397, and receptor tyrosine kinases (RTKs) transphosphorylate FAK as a drug resistance mechanism.¹⁶ Furthermore, FAK-kinase inhibitors have shown limited efficacy in Phase I and II clinical trials.^17,18

Recent attention has focused on the FAT domain of FAK as an alternative approach to target FAK in cancer.¹⁹ The FAT domain is a four-helical bundle at the C-terminus of FAK that contains key residue Y925 and is involved in multiple protein–protein interactions at the focal adhesion site. Numerous data have emerged showing the importance of the FAT domain as the initiator of FAK activation through its multiple interactions with paxillin, leupaxin, CD4, and DCC (Deleted in Colorectal Cancer).^20–22 The integrity of the FAT domain is essential for localization of FAK to focal adhesions, association with integrins and RTKs, and downstream FAK signaling.²³ More specifically, FAK localization to the focal adhesion is mediated by the FAT–paxillin interaction, and mutation of the binding site was shown to have drastic effects on FAK phosphorylation, paxillin phosphorylation, focal adhesion turnover, cell adhesion, migration, and invasion.^24,25 Paxillin contains alpha helical binding motifs termed LD motifs, named after the first two amino acids of the consensus sequence (LDXLLXXL). Two such LD motifs (LD2 and LD4) are required for binding to the FAT domain of FAK.²⁶ The FAT–paxillin LD2/LD4 interaction has been well characterized ( Fig. 1A ), has an equilibrium dissociation constant (K_D) of 10–50 µM, and has been validated by multiple orthogonal assays [X-ray, surface plasmon resonance (SPR), isothermal titration calorimetry (ITC), nuclear magnetic resonance (NMR), and mutagenesis].^20,27,28 LD2 and LD4 interact at two separate hydrophobic patches on the FAT domain (Helix 1-4 and Helix 2-3), and disruption of both sites is required for maximal biological effect.^20,27,29 In all, these data validate the FAT–paxillin binding site as an alternative cancer drug target.

Figure 1.

Characterization of the focal adhesion targeting (FAT)–paxillin interaction through modeling, surface plasmon resonance (SPR), and fluorescence polarization (FP). (A) 3D model of the LD2 motif of paxillin binding to both binding sites on FAT. The model was edited from a previously determined crystal structure to contain and demonstrate both LD2 binding sites on the same FAT molecule. (B) Binding curve between tetramethylrhodamine (TAMRA)–LD2 and FAT as determined through FP. TAMRA–LD2 was kept constant at 0.1 µM, while FAT was titrated from 200 µM down to 0.2 µM. The calculated K_D of the interaction was 48.6 ± 11.4 µM. (C) Dose–response curve of 0.1 µM TAMRA–LD2 and 20 µM FAT inhibited by unlabeled LD2 titrated from 250 µM down to 0.5 µM. The calculated K_i was 45.9 ± 4.2 µM. (D) Surface plasmon resonance (SPR) binding curve of the FAT–paxillin interaction. The determined K_D was 51.5 ± 0.2 µM, a similar value to that determined by FP. FP experiments were performed in triplicate, and SPR in duplicate.

Here, we report the development of a high-throughput FP assay to measure FAK–paxillin interactions and use in small-molecule drug-screening efforts. We show the structure-based optimization of a tetramethylrhodamine (TAMRA)–paxillin LD2 probe that binds to both the Helix 1-4 and Helix 2-3 binding sites on the FAK FAT domain. We use SPR as well as site-directed mutagenesis to validate the specificity of the signal. We evaluate the high-throughput characteristics of the assay and perform a pilot screen using the National Cancer Institute (NCI) Diversity Set V (Div5) 1593-compound library and automated liquid handling systems. Finally, we identify three compounds that display dose-dependent inhibition of FAT–paxillin binding. These results demonstrate a novel drug-screening approach for the identification of potential FAK scaffold inhibitors.

Methods

Reagents, Chemicals, and Peptides

TAMRA-tagged and unlabeled peptides were acquired as lyophilized powder through Biomatik (Cambridge, ON, Canada). High-performance liquid chromatography (HPLC) and mass spectrometry data confirmed proper identity and purity >95% for each peptide. Peptides were dissolved at 1–10 mM in 40 mM Tris (pH 8.1). For assay optimization studies, 384-well plates were acquired from Thermo Fisher (Nunc cat. nos. 264576, 242764, 262260, and 267461; Waltham, MA), Greiner (cat. nos. 781209 and 784900; Monroe, NC), and Corning (cat. nos. 3575 and 4514; Corning, NY). The NCI Diversity Set V library of 1593 compounds was graciously provided by Dr. David Azorsa (University of Arizona).

Expression and Purification of His-Tagged FAT and Avitag-FAT

The FAT domain of FAK (residues 892–1052) was cloned into the pET15b vector to produce an N-terminally His(6)-tagged FAT domain. The peT15b–FAT vector was transformed into Escherichia coli BL21(DE3) chemically competent cells (Thermo Fisher). Bacteria were grown in LB broth at 37 °C to an absorbance of 0.8 at wavelength 600 nm. To induce protein expression, 0.2 mM of IPTG (isopropyl β-D-1-thiogalactopyranoside) was added. Bacteria were further incubated for 4 h at 37 °C, after which cells were collected through centrifugation at 5000 ×g for 10 min. Cell pellets were resuspended using lysis buffer [20 mM Tris (pH 8.0), 200 mM NaCl, 5 mM β-mercaptoethanol (β-me), 10 mM imidazole, and 1× Halt protease inhibitor cocktail; Thermo Fisher] and frozen overnight at −80 °C.

On thawing, cells were further lysed through sonication (mild sonication for 2 min, 50% cycle, 5 s per cycle), and insoluble components were then removed through centrifugation at 12,000 rpm for 15 min. The cell lysate was incubated with HisPur Ni-NTA resin (Thermo Fisher) overnight while gently shaking at 4 °C. The resin was previously equilibrated with lysis buffer. For purification, the nickel resin with bound His-tagged FAT was transferred to a spin column and washed with 20 mM Tris (pH 8.0), 200 mM NaCl, 5 mM β-me, and 25 mM imidazole. The protein was eluted using 20 mM Tris (pH 8.0), 200 mM NaCl, 5 mM β-me, and 250 mM imidazole. The eluted protein was buffer exchanged using Zeba spin desalting columns (Thermo Fisher) into 20 mM Tris pH 8.0, 200 mM NaCl, and 5 mM β-me. Coomassie-stained SDS-PAGE (sodium dodecyl sulfate–polyacrylamide gel electrophoresis) analysis of the purified protein demonstrated around 90% purity.

Fluorescence Polarization Assay

The FP assay buffer used was 20 mM Tris, 200 mM NaCl, 0.05% β-me, 0.1% Triton X-100, 5% glycerol, and 1× Halt protease inhibitor cocktail. All final FP reactions were placed into a 384-well plate (Nunc cat no. 267461; Thermo Fisher) at 30 µL and shaken for 3 h at room temperature to reach equilibrium. The plates were read on a PerkinElmer EnVision plate reader with software EnVision Manager version 1.13 (PerkinElmer, Waltham, MA). A boron-dipyrromethene (BODIPY) tetramethylrhodamine (TMR) FP optical module (2100-4100) was used as the mirror. The excitation filter (2100-5830) used a wavelength of 531 nm, and both emission filters (2100-5800 and 2100-5810) were at wavelengths of 579 nm. The baseline mP of TAMRA–LD2–L10D (leucine 10 to aspartic acid conversion) only was set to 15 mP through the assay optimization wizard on the EnVision Manager software. The assay optimization set the measurement height to 6.5 mm, excitation light to 100%, G-factor to 1.01, detector gain to 300, and number of flashes per well at 25.

Saturation Binding Assays

For K_D determination for TAMRA–LD2 and TAMRA–LD2–L10D, FAT was titrated from 0.2 µM to 200 µM into FP buffer containing 0.1 µM TAMRA–LD2 or TAMRA–LD2–L10D for a final volume of 30 µL. Wells with no FAT were used as a baseline value that was subtracted from the raw values to produce ΔmP values. The saturation binding data were processed through GraphPad Prism (GraphPad, San Diego, CA) using the “One site − Total” model to produce a saturation curve and a calculated K_D with standard error (SE).

Competition Assay

For IC₅₀ determination of unlabeled LD2 as an inhibitor, LD2 was titrated from 1 µM to 250 µM into FP buffer containing 20 µM FAT and 0.1 µM TAMRA–LD2 or TAMRA–LD2–L10D. Wells with no FAT were used as a baseline value that was subtracted from the raw values to produce ΔmP values. The plate was read at every hour for 4 h to test for time differences in IC₅₀. The titration data were processed through GraphPad Prism to produce a dose–response curve and a calculated IC₅₀ with SE. A four-parameter dose–response inhibition model was used, and the bottom fit was constrained to the lower plateau of the curve. K_i was determined from the following calculation:³⁰

K_{i} = \frac{I_{50}}{\frac{L_{50}}{K_{D}} + \frac{P_{0}}{K_{D}} + 1}

(1)

where I₅₀ is the concentration of free inhibitor at 50% inhibition, L₅₀ is the concentration of free ligand at 50% inhibition, P₀ is the concentration of free protein, and K_D is calculated from the saturation curve in Figure 1 . I₅₀ is calculated by the following equation:

I_{50} = I C_{50} - P_{T} + P L_{50} (\frac{K_{D}}{L_{50}} + 1)

(2)

where P_T is the total protein concentration, L_T is the total labeled-ligand concentration, P₀ is the positive root of P₀² + (K_D + L_T) × P₀ − P_T, PL₅₀ = PL₀ / 2 (where PL₀ = P − P₀), and L₅₀ = L_T − PL₅₀ (where L₀ = L_T − PL₀). The compounds for the dose–response curves were serially diluted one to one from a concentration of 5 mM to 5 µM in DMSO. Then, 2 µL of compound at each respective concentration was added to 30 µL of a master mix containing 20 µM FAT and 0.1 µM TAMRA–LD2–L10D for a final DMSO concentration of 6.25% in each well, and for a final compound concentration range of 312.5 µM to 0.31 µM. The dose–response curves were fitted on GraphPad Prism, and the K_i was calculated using Equations (1) and (2). The SE of the K_i was calculated similarly by inputting IC₅₀ SE into Equations (1) and (2). Commercially available compounds were purchased for follow-up studies at higher concentrations.

Surface Plasmon Resonance

SPR binding studies were performed on a ForteBio Pioneer FE SPR system (ForteBio, Fremont, CA). In brief, a SADH (streptavidin in dextran hydrogel) biosensor (ForteBio) was docked onto the flow cell and preconditioned with two injections of 10 mM NaOH, 1 M NaCl for 1 min at 50 µl/min. Subsequently, biotinylated Avitag-FAT protein was diluted in running buffer (20 mM Tris-HCl, 200 mM NaCl, and 0.05% Tween-20) and injected at 10 µl/min to achieve approximately 1000 RU of immobilized protein on channel 1. Empty channel 2 served as the reference control. After achieving a stable baseline, a concentration series (200 µM–0.01 µM) of paxillin LD2 wild-type (WT) and L10D peptide was prepared in final running buffer (20 mM Tris-HCl, 200 mM NaCl, 0.05% Tween-20, and 5% DMSO) and injected using the OneStep gradient injection method at a flow rate of 75 µl/min. 3% sucrose was used as a bulk standard control for OneStep injection, and a DMSO calibration curve was performed using a concentration range of 3.5% to 6.5% DMSO. Raw SPR data were appropriately processed in Qdat software (ForteBio) by normalizing the baseline prior to injection, aligning the channels, subtracting the reference channel, and blank subtraction. Kinetic data were fitted to a pseudo-first-order 1:1 interaction binding model used to calculate K_D. In addition, a steady-state model and R_eq data points were used to validate the binding affinity. Visual inspection of the SPR sensograms was performed to verify appropriate model fitting, lack of mass transport effects, return to baseline, and lack of irregular kinetics.

High-Throughput Screening

A small-molecule diversity set library, Div5, was obtained through the NCI. Two microliters of each small molecule in DMSO were aliquoted into 384-well Nunc plates using the Acoustic Transfer System Gen 4 from Biosero (San Diego, CA). A master mix of 20 µM His-tagged FAT and 0.1 µM TAMRA–LD2–L10D in FP buffer was added into each well by a Tecan Liquid Handling machine using the MCA96 pipetting head (Tecan, Zurich, Switzerland), for a final drug concentration of 312.5 µM. A negative control of 20 µM FAT, 0.1 µM TAMRA–LD2–L10D, and 6.25% DMSO; a positive control of 0.1 µM TAMRA–LD2–L10D and 6.25% DMSO; and a positive control of 20 µM FAT, 0.1 µM TAMRA–LD2–L10D, 250 µM unlabeled LD2, and 6.25% DMSO were added to each plate for baseline referencing and Z’-factor calculation. Data were processed through GraphPad Prism to produce a hit plot. Z’-factor calculations for high-throughput screening (HTS) efficiency were calculated by Equation (3):³¹

Z^{'} f a c t o r = 1 - \frac{3 (σ_{p} + σ_{n})}{| μ_{p} - μ_{n} |}

(3)

where $σ_{p}$ and $σ_{n}$ are the standard deviations of the positive and negative controls, respectively; and µp and µn are the average values for the positive and negative controls, respectively. A Z’-factor of 0.5–1.0 represents an excellent assay, with 1.0 being an ideal assay. For each repetition of the assay, 16–32 control wells per control were ran for the purpose of Z’-factor calculations.

Molecular Modeling

The PyMOL molecular graphics program (Schrödinger, New York, NY) was used for general structural representation and examination of the FAK FAT–paxillin LD2 complex [Protein Data Bank (PDB) 1OW8]. Residues that were proximal to the interaction interface (<5Å) and showed key intermolecular contacts were selected for mutagenesis. The PyMOL mutagenesis wizard was used to predict the effects of mutations on FAT–LD2 interaction, protein folding, and steric clashes. Publication-quality images were generated using the ray-trace command.

Site-Directed Mutagenesis of the FAT Domain

Mutation primers were designed through the Agilent QuickChange Primer Design website (Agilent, Santa Clara, CA) and ordered through Invitrogen (Waltham, MA). Primer sequences are listed in Supplemental Table S1 . Site-directed mutagenesis was performed using the QuickChange Lightning mutagenesis kit (Agilent). Mutations were confirmed through sequencing at the University of Arizona Genetics Core.

Results

Development of the Initial TAMRA–LD2 Probe and Establishment of the Fluorescence Polarization Assay

To develop a high-throughput FP assay capable of detecting inhibitors of the FAK–paxillin interaction, we first started with the design of a fluorescently labeled paxillin probe to be used in the assay. We used the X-ray crystal structure of the FAK FAT–paxillin LD2 complex (PDB 1OW8) to guide design efforts. As shown in Figure 1A , the paxillin LD2 peptide is a 13-amino-acid (NLSELDRLLLELN) amphipathic alpha helix that binds to two hydrophobic patches on the FAT domain (the Helix 1-4 and Helix 2-3 binding sites). Using this structure, we determined that the N-terminus of paxillin LD2 is orientated toward solvent and is a logical site for peptide modification. To fluorescently modify LD2 peptide without perturbing binding, we added a 6-carbon linker on the N-terminus (6-aminohexanoic acid) for linking to 5-TAMRA. Next, we performed FP saturation binding studies using a constant concentration of TAMRA–LD2 probe (0.1 µM) and increasing concentrations of purified FAT domain protein (0.1–200 µM). We identified a clean binding isotherm with a calculated K_D of 48.6 µM ( Fig. 1B ). To validate the specificity of the binding interaction, we performed competition binding studies with constant concentrations of TAMRA–LD2 probe (0.1 µM) and FAT protein (20 µM), while titrating increasing concentrations of unlabeled LD2 peptide (0.1–500 µM). We observed dose-dependent competition with a calculated K_i of 45.9 µM ( Fig. 1C ), which was comparable to the K_D obtained in saturation binding experiments. A time course demonstrated no change in IC₅₀ throughout time. Finally, to validate that the FP-derived K_D was comparable to independent methodology, we performed SPR studies with biotinylated FAT domain protein and unlabeled paxillin LD2 peptide. We found a K_D of 51.5 µM in SPR binding studies ( Fig. 1D ), which was similar to the FP-derived K_D of 48.6 µM, thus validating the accuracy of the FP assay.

Optimization of the TAMRA–LD2 Probe and FP Assay

On further inspection of FP saturation and competition binding curves, we noticed that the maximum binding (B_max) level was relatively lower compared to other published similar FP assays.^32,33 Given the high hydrophobicity of our TAMRA–LD2 probe, we hypothesized that the peptide could be self-associating or nonspecifically binding to the plate surface, therefore preventing maximum binding to FAK FAT protein. To test this, we examined the X-ray crystal structure of the FAT–paxillin LD2 complex (PDB 1OW8) and identified leucine 10 as a potential residue for hydrophobic-to-hydrophilic substitution ( Fig. 2A ). Leucine 10 is orientated toward the solvent in the complex and is not part of the hydrophobic interaction surface. We synthesized a TAMRA–LD2–L10D (leucine-to–aspartic acid) probe and performed FP saturation studies in comparison with TAMRA–LD2 WT ( Fig. 2B ). TAMRA–LD2–L10D gave a slightly higher B_max compared to WT (81 vs. 75) while showing a similar K_D. Also, at any given concentration of FAT, the measured assay window is larger for TAMRA–LD2–L10D than TAMRA–LD2. Furthermore, the intrinsic polarization (measured in mP) of TAMRA–LD2–L10D was lower compared to WT (19.5 vs. 35.8 mP) despite its similar molecular weight, thus suggesting a reduced amount of self-association and nonspecific binding by the L10D peptide ( Suppl. Fig. S1 ). Next, we compared the ability of unlabeled LD2 to competitively inhibit TAMRA–LD2 WT/FAT binding versus TAMRA–LD2–L10D/FAT binding ( Fig. 1C ). Unlabeled LD2 effectively inhibited both interactions with similar inhibition constants (K_i of WT = 45.9 µM; K_i of L10D = 52.3 µM). These results led us to use TAMRA–LD2–L10D over TAMRA–LD2 for all future studies. We also tested whether the 6× His-tag from recombinant FAT purification would interfere with binding compared to the cleaved version and found minimal difference in binding affinities (K_D of His-tagged = 49.7 µM; K_D of untagged = 47.3 µM; Fig. 1D ).

Figure 2.

Peptide and protein optimization for the fluorescence polarization (FP) assay. (A) 3D model of the mutated L10D–LD2 motif of paxillin binding to both binding sites on focal adhesion targeting (FAT) with a close-up view of the mutation. The mutation is on the nonbinding side of the peptide to prevent any loss in binding while reducing hydrophobic aggregation of the peptide. (B) Binding curve between 0.1 µM tetramethylrhodamine (TAMRA)–LD2–L10D and FAT titrated from 200 µM down to 0.2 µM, determined through FP. The calculated K_D of the interaction was 49.7 ± 12.0 µM. (C) Dose–response curve of 0.1 µM TAMRA–LD2–L10D and 20 µM FAT inhibited by unlabeled LD2 titrated from 250 µM down to 0.3 µM. The calculated K_i is 52.3 µM ± 9.3 µM. (D) Binding curve comparison of His-tagged FAT and untagged FAT. The curves and calculated K_D indicate marginal differences between cleaved and tagged proteins. Three independent experiments were performed.

Validation of Binding Specificity by Site-Directed Mutagenesis

To validate that the FP binding signal was in fact due to specific binding at the FAT–paxillin interface, we designed a series of mutant FAT proteins at both the Helix 1-4 and Helix 2-3 binding sites. In addition, mutant proteins could be used to characterize Helix 1-4 or Helix 2-3 selective binding of future small molecules. We used the X-ray crystal structure of the FAT–paxillin LD2 complex (PDB 1OW8) as well as a previously described report²⁷ to select a series of surface residues at each binding site suitable for mutation ( Fig. 3A ). Using site-directed mutagenesis and mutant-containing primers, we made 18 total mutant constructs with mutations engineered at both paxillin binding sites. Subsequently, we performed small-scale 6× His-tag protein purification and examined elutions for protein yield and purity ( Fig. 3B ). Single-site mutants L1035D, L994E, I936E, I936A, and KER (K955A, E948A, and R962A) had high levels of initial yield and purity; however, mutants I936D, I998A, I998D, V932A, and V932D had limited yield and purity. Two-site mutants KER+I936D, KER+V932A, and I936E+L994E had high levels of yield and purity, while I936E+I998A, I936E+I998D, KER+V932D, KER+H1025A, KER+L1035D, and I936E+L965D had limited yield and purity. We moved forward with protein constructs that gave high yield and purity, and tested them in FP saturation studies in comparison to FAT WT ( Fig. 3C ). Single-site mutants L994E (K_D = 82.3 µM) and I936A (K_D = 70.8 µM) had slightly lower binding affinities compared to FAT WT (K_D = 48.6 µM), suggesting reduction to a single-site binding mode. Single-site mutant L1035D displayed a linear-shaped binding curve and a K_D that could not be determined, suggesting substantial disruption of protein folding by mutation and therefore TAMRA–LD2–L10D binding. We also performed similar FP saturation studies with two-site mutants and demonstrated that KER+V932A and I936E+L994E mutants had no binding (K_D could not be calculated), while the KER+I936D mutant had a weak binding affinity (K_D > concentrations tested). In all, these data showed the specificity of FAT–paxillin interactions measured by FP and identified single-site mutants that could be used for site-specific binding.

Figure 3.

Assay validation through mutagenesis of focal adhesion targeting (FAT). (A) 3D model highlighting the key amino acid interactions in both paxillin binding sites that were mutated as an assay control. (B) Sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) of the purification of various FAT single- and double-site mutations. Certain mutations were unable to either express or purify. (C) On the left, binding curve comparisons of wild-type (WT) FAT and various single-site mutations. L994E and L1035D are mutations of the 2-3 helix binding site. I936A is a mutation of the 1-4 helix binding site. The mutations demonstrate a decrease in binding affinity from WT FAT but not complete binding loss, indicating the dual binding nature of the FAT–paxillin interaction. On the right, binding curve comparisons of WT FAT and various double-site mutations. All double-site mutations demonstrate a severe loss in binding affinity compared to WT FAT. The K_D indicated as ND (nondeterminable) could not be calculated since the estimated affinities are outside the concentration range tested. Curves represent data from three independent experiments, each performed in technical triplicates.

Optimization and Validation of High-Throughput Characteristics

Next, we sought to optimize the 384-well plate material and vendor to achieve the best possible assay window and signal variability. We tested eight different 384-well plates that varied in plate material (polypropylene, polystyrene, or nonbinding surface), well volume, well shape (round, square, flat bottom, or round bottom), and vendor ( Fig. 4A ). We analyzed positive controls (0.1 µM TAMRA–LD2–L10D only) and negative controls (50 µM FAT + 0.1 µM TAMRA–LD2–L10D) in all plates, and calculated assay window (ΔmP) and Z’-factor (Eq. 3). Plate Nunc 267461, which is a shallow-well, square-shaped, rounded-bottom, black polypropylene 384-well plate, had the overall best combination of Z’-factor (0.72) and assay window (52.2 mP). This plate was selected for all subsequent assays. Because this assay would be used in screening of compounds dissolved in DMSO, we next tested the tolerability of the FP assay to increasing concentrations of DMSO. We performed assay window studies using 20 µM of FAT protein, because this lower concentration would have lower percentage occupancy of probe and therefore would be more suitable for competition studies. As shown in Figure 4B , the FP binding signal was tolerated up to 13.33% DMSO, indicating suitability for compound screening applications. Next, we performed time-course studies on an entire 384-well plate with both positive and negative controls to monitor signal stability and variation throughout time ( Fig. 4C ). After 2 h of assay incubation time, the Z’-factor was >0.60, indicating good signal separation and an excellent assay. This signal variability was stable up to 4 h. Three hours was selected as an appropriate incubation time for producing a good Z’-factor. Finally, to test for potential edge and drift effects over a complete 384-well plate, we plotted well number versus mP for both positive and negative controls. As shown in Figure 4D , positive and negative control signals were uniformly distributed over all areas of the plate, and no trends were observed at the plate edge. The positive control has a standard deviation of 2.07, and the negative control a standard deviation of 1.9, with the overall Z’-factor for the plate being 0.63. In all, these data validated the use of our FP assay for HTS.

Figure 4.

High-throughput screening metric optimization. (A) Plate optimization using various 384-well plates. Assay window [the difference between signal at 20 µM focal adhesion targeting (FAT) and 0.1 µM tetramethylrhodamine (TAMRA)–LD2–L10D (top) and 0.1 µM TAMRA–LD2–L10D only (bottom)] was plotted for each plate as well as calculated Z’-factor. Nunc 267461 was selected as the plate to move forward with, because it has the best combination of a high assay window and low Z’-factor. (B) DMSO effects on assay window at various concentrations of DMSO. Assay window variability was not statistically significant up to 13.33% DMSO. (C) Time optimization by incubating a plate of controls while shaking at room temperature. Assay window and Z’-factor were calculated at each time, and while assay window remains consistent, Z’-factor increases throughout time. (D) Drift effect analysis by plotting two entire 384-well plates, one with FAT + TAMRA–LD2–L10D and the other with only TAMRA–LD2–L10D. The plot demonstrates no drift effect with the current assay conditions in the Nunc 267461 plates.

Pilot Screen

To further validate our FP assay for high-throughput drug screening, we proceeded with a pilot drug screen using our Tecan Freedom Evo 100 liquid handling robot. Tecan scripts were programmed using aqueous buffer and DMSO as test solutions. We selected Div5, the Diversity Set V library of 1593 compounds available from the NCI, to use for our pilot screen. Compounds were screened at a final concentration of 312.5 µM (6.25% DMSO) in 384-well format ( Fig. 5A ). Both fluorescence polarization and fluorescent intensity values were measured for each plate. Because naturally fluorescent or quenching compounds can perturb FP measurements and thus can serve as false positives,^34,35 fluorescence intensity was used as an internal counterscreen. Compounds that caused a >25% increase or decrease in fluorescence intensity were excluded as hits. Manual inspection of these wells confirmed that the majority of fluorescent or quenching compounds had a red-orange tint. In addition, we noticed that all compounds that precipitated on dilution in the assay mixture had an associated decrease in mP, sometimes lower than that of the TAMRA–LD2–L10D control. As such, precipitating compounds were also excluded as hits. In total, after removal of 13 interfering compounds, we identified 16 single-point hits that had a decreased mP value with >40% inhibition compared to the negative control average (FAT + TAMRA–LD2–L10D + DMSO). This corresponded to a hit rate of 1.0%. To validate that identified single-point hits had dose-dependent pharmacological activity, we tested all 16 hits in FP dose–response curve studies from 312.5 µM to 0.3 µM (Fig. 5B and Table 1). We found three validated hits that showed dose-dependent inhibition of FAT–paxillin binding. After purchasing commercially available compounds and repeating the dose response at higher concentrations, the most potent compound (compound no. 1223) showed a calculated K_i of 111.2 µM. Thirteen of the 16 original hits did not show sufficient dose-dependent inhibition to be considered validated hits. In all, this pilot screen confirmed the use of our FP assay for HTS and identified three potential FAT–paxillin inhibitors for follow-up studies.

Figure 5.

Pilot screen using National Cancer Institute (NCI) Diversity Set V. (A) Plot of the results from a single-point (312.5 µM) screen of all compounds in the set at standard fluorescence polarization (FP) conditions [20 µM focal adhesion targeting (FAT) + 0.1 µM tetramethylrhodamine (TAMRA)–LD2–L10D]. Hit threshold was set at >40% inhibition. Compounds lower than the hit threshold that exhibit fluorescence 25% higher or lower than the average of the controls were labeled as fluorescent or quenchers, and excluded in the final list of hits. Other compounds lower than the threshold were observed to have formed a precipitate at the highest concentration and were also removed from the final list. Labeled on the plot are also potential aggregators that increased FP signal relative to the positive control. The final list contained 16 hits to be validated through dose response. (B) Six representative plots of the dose–response curves for the hits are presented. The three on the left represent good dose–response inhibition, while the plots on the right show false positives. The false positives demonstrate minimal decreases and potential increases in FP signal up to the final concentration, which experiences a sharp drop. All dose–response curves represent data from three independent experiments, each performed in technical triplicates.

Table 1.

Dose–Response Inhibition Data for Validated Hits.

Compound	I_max	IC₅₀ ± SE (µM)	K_i ± SE (µM)
168	100	474.6 ± 33.8	329.8 ± 24.1
1223	65.0	167.7 ± 20.4	111.2 ± 14.5
1429	100	474.9 ± 43.7	330.1 ± 31.1

I_max indicates the maximum inhibitory effect relative to the tetramethylrhodamine (TAMRA)–LD2–L10D and TAMRA–LD2–L10D + focal adhesion targeting (FAT) controls. Calculated IC₅₀ and K_i ± standard error (SE) values are shown. Results show only 3 out of 16 compounds are true validated hits; the rest had ND (nondeterminable) K_i. Values represent data from three independent experiments, each performed in technical triplicates. FP, Fluorescence polarization; SE, standard error.

Discussion

FAK is widely expressed in multiple different tumor types and is a central node to multiple oncogenic signaling pathways.^1,36 Despite FAK’s biological importance in cancer, drug discovery efforts have solely focused on targeting the kinase domain and have not addressed the critical scaffolding functions of the protein.^37,38 This report represents a significant advancement to the FAK drug discovery field because we disclose the first described HTS assay for noncatalytic FAK inhibitor discovery. The FAK FAT–paxillin interaction is gaining traction as an alternative site for FAK inhibitor discovery due to its importance in regulating invasion, metastasis, and apoptosis, as well as the breadth of X-ray crystallographic information available.^19,20 Furthermore, traditional adenosine triphosphate (ATP)-competitive FAK–kinase inhibitors have had limited success in clinical trials, and resistance mechanisms have emerged.^17,18 These studies highlight the need for FAK–paxillin inhibitors to alternatively target FAK in cancer.

To develop a FAK–paxillin screening assay, we performed significant assay validation and optimization, including validation by SPR, site-directed mutagenesis, competition studies, plate optimization, and high-throughput metrics. In addition, we performed structure-guided optimization of the TAMRA–LD2 probe to promote lower background mP signal and a greater assay window. Intriguingly, when we tested small-scale protein purification of 18 FAT mutants, only a small amount had sufficient yield and purity for FP studies. We speculate that specific FAT mutations may confer low bacterial expression, affect protein folding, decrease protein stability, or increase proteolytic cleavage. Our results further emphasize the need to empirically test multiple mutant constructs when attempting a site-directed mutagenesis approach. Another observation was that our FP assay was very stable at high concentrations of DMSO (>5%). This DMSO tolerability may be due to the high thermostability of four-helical bundle proteins like the FAK FAT domain that have a high degree of hydrophobic packing and electrostatic interactions to stabilize 3D structure.³⁹ Note that in plate optimization studies, polypropylene plates seemed to correlate best with assay performance, perhaps due to the amphipathic nature of the LD2 peptide. Future studies may include further optimization of the TAMRA–LD2 probe to reduce potential aggregation or modification of the FAK protein construct (e.g., length) to allow greater mP change on binding.

On processing the data from our FP pilot screen, we applied a computational PAINS (pan-assay interfering substances) filter on the entire NCI Div5 library to identify compounds that had potentially reactive or promiscuous moieties.⁴⁰ This process is standard in our laboratory as well as in many HTS labs. Unexpectedly, we found that 163 of 1593 total compounds (10.2%) were positive for PAINS moieties after substructure filtering. These included known reactive or promiscuous structures such as Michael acceptors, azo compounds, quinones, and others. These findings caution the use of publicly available compound libraries such as the NCI Div5 library for drug discovery applications and highlight the importance of flagging compounds that are positive for PAINS moieties. Furthermore, we emphasize the careful consideration of rigorous follow-up assays to rule out nonspecific inhibition, compound aggregation, and pan-assay interference.

The major purpose of the pilot screen with the NCI Div5 library was to validate signal variability from plate to plate, establish a method for counterscreening and hit triage, and establish a reasonable hit rate. The use of our FAT–paxillin FP assay in a pilot drug screen has established the feasibility of this assay for a potential larger drug screen using a larger library such as a 3D diversity set or a protein–protein interaction-focused library. We obtained three hits from the pilot screen that showed valid dose-dependent inhibition and intend to follow up on these compounds using secondary assays such as SPR, saturation transfer difference (STD) NMR, heteronuclear single quantum coherence (HSQC) NMR, X-ray crystallography, aggregation studies, and various cell-based assays. Future screening studies may include development of a SPR- or NMR-based FAT domain assay for fragment-based drug discovery.

In conclusion, we have presented a high-throughput fluorescence polarization assay that measures FAK–paxillin interactions in 384-well format for drug discovery applications. To the best of our knowledge, this represents the first described high-throughput assay focused on the detection of FAK scaffold inhibitors as opposed to FAK kinase inhibitors. Through structure-based design of the TAMRA–paxillin LD2 probe, we were able to design an assay that had a sufficient assay window, low variability, a Z’-factor of 0.63, a stable 384-well signal, and straightforward robotic automation. Future goals include the screening of additional small-molecule libraries to identify potent FAK–paxillin inhibitors that will be tested as an alternative approach to target FAK in cancer.

Supplemental Material

DS_DISC874313 – Supplemental material for Development of a High-Throughput Fluorescence Polarization Assay to Detect Inhibitors of the FAK–Paxillin Interaction

Supplemental material, DS_DISC874313 for Development of a High-Throughput Fluorescence Polarization Assay to Detect Inhibitors of the FAK–Paxillin Interaction by Timothy Marlowe, Carlos Alvarado, Andrew Rivera, Felicia Lenzo, Rohini Nott, Dena Bondugji, Justin Montoya, Alana Hurley, Matt Kaplan, Andrew Capaldi and William Cance in SLAS Discovery

Footnotes

Acknowledgements

We would like to thank Dr. David Azorsa for use of the NCI Diversity Set V for pilot screening studies.

Supplemental material is available online with this article.

Abbreviations

focal adhesion kinase (FAK)

focal adhesion targeting (FAT)

protein data bank (PDB)

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: National Cancer Institute (Grant No. R01 CA065910 to William Cance and Timothy Marlowe).

ORCID iD

Timothy Marlowe

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