Chemical Denaturation and Protein Precipitation ... - ACS Publications

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Chemical Denaturation and Protein Precipitation Approach for Discovery and Quantitation of Protein-Drug Interactions He Meng, Renze Ma, and Michael C Fitzgerald Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b01772 • Publication Date (Web): 11 Jul 2018 Downloaded from http://pubs.acs.org on July 12, 2018

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Analytical Chemistry

Chemical Denaturation and Protein Precipitation Approach for Discovery and Quantitation of Protein-Drug Interactions

He Meng,1 Renze Ma,1 and Michael C. Fitzgerald1,*

1

Department of Chemistry, Duke University, Durham, North Carolina 27708

*Address reprint requests to: Professor Michael C. Fitzgerald Department of Chemistry, Box 90346 Duke University Durham, North Carolina 27708-0346 Tel: 919-660-1547 Fax: 919-660-1605E-mail: [email protected]

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Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

ABSTRACT Described here is a mass spectrometry-based proteomics approach for the large-scale analysis of protein-drug interactions. The approach involves the evaluation of ligand-induced protein folding free energy changes (∆∆ ) using chemical denaturant and protein precipitation (CPP) to identify the protein targets of drugs and to quantify protein-drug binding affinities. This is accomplished in a chemical denaturant-induced unfolding experiment were the folded and unfolded protein fractions in each denaturant containing buffer are quantified by the amount of soluble or precipitated protein (respectively) that forms upon abrupt dilution of the chemical denaturant and subsequent centrifugation of the sample. In the proof-of-principle studies performed here, the CPP technique was able to identify the well-known protein targets of cyclosporin A and geldanamycin in a yeast. The technique was also used to identify protein targets of sinefungin, a broad-based methyltransferase inhibitor, in a human MCF-7 cell lysate. The CPP technique also yielded dissociation constant (Kd) measurements for these well-studied drugs that were in general agreement with previously reported Kd or IC50 values. In comparison to a similar energetics-based technique, termed Stability of Proteins from Rates of Oxidation (SPROX), the CPP technique yielded significantly better (~50% higher) proteomic coverage and a largely reduced false discovery rate.


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INTRODUCTION Modern drug discovery efforts typically rely on one of two approaches: either a target-based approach in which drug molecules are designed and tested for binding to a disease-related protein or a phenotypic screening approach in which candidate drugs are screened for biological activity against the disease. In both approaches, the ability to screen large numbers of proteins for drug interactions is critical. In target-based approaches such an ability is critical for assessing the specificity of the designed drug. In phenotypic screening approaches such an ability is critical for elucidating the drug's mode-of-action.1-3 For many years protein target discovery efforts have relied on affinity capture techniques

involving

immobilization

of

the

drug

and

subsequent

pull-down

experiments.4-6 Affinity capture strategies have also taken advantage of suicide substrates that irreversibly react with protein targets and enable the subsequent capture of target proteins using pull-down approaches.7 While affinity capture strategies coupled with mass spectrometry can be useful for identifying the protein targets of drugs, such strategies are often only successful for high affinity protein-drug interactions that involve relatively abundant proteins. Frequently, it is also challenging to covalently modify the structure of the drug molecule with the necessary affinity tags and retain the integrity of the drug's interactions with its target protein(s).



More recently, several protein-ligand binding detection strategies based on the proteome-wide analysis of protein folding and stability changes have been developed.8-13 Such strategies are attractive because they are amenable to the detection of both direct and indirect protein targets and they do not require derivatization of the drug. Approaches have been developed that rely on the detection of drug-induced changes in protease cleavage patterns (e.g. drug affinity responsive target stability8), in chemical denaturation (e.g. stability of proteins from rates of oxidation9, pulse proteolysis10-12), and in thermal denaturation (e.g. thermal protein profiling13). The use of chemical denaturant in the stability of proteins from rates of oxidation (SPROX) and pulse proteolysis techniques is attractive because it enables the evaluation of thermodynamic stability measurements (e.g., folding and binding free energies as well as the evaluation of protein-ligand dissociation constants ). However, one drawback to the SPROX and pulse proteolysis techniques is that the proteomic coverage obtained using these strategies can be limited due to demands imposed by the proteomic workflows (e.g., the demand to detect and quantify methionine-containing peptides in SPROX and the demand to separate intact protein from the proteolytic fragments in pulse proteolysis prior to the proteomics analysis). The thermal protein profiling (TPP) approach has the advantage that the proteomics readout does not require the detection of special peptides. Therefore, the proteomic coverage is more similar to that observed in conventional bottom-up proteomics experiments. However, a drawback to the TPP approach is that it is a


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"non-equilibrium" approach, as the thermal denaturation of many proteins is irreversible. Also, beyond a Tm value, it is difficult to extract thermodynamic parameters (e.g., ∆ , ∆∆ , and values) from TPP data. Moreover, the magnitude of protein's Tm shift upon ligand binding does not always correlate with the ligand binding affinity.13 Described here is an experimental approach, termed chemical denaturation and protein precipitation (CPP), for the large-scale detection and quantitation of protein-drug interactions. The described approach is a cross between the SPROX and TPP techniques. It utilizes chemical denaturant like SPROX but employs a protein precipitation strategy (analogous to that utilized in TPP) to separate folded and unfolded proteins in the chemical denaturant containing buffers. Use of a chemical denaturant enables a more rigorous thermodynamic measurement than the use of temperature. The chemical denaturation of proteins is not only more generally reversible than the thermal denaturation of proteins, but the relationship between the chemical denaturant-induced unfolding properties of a protein and it's folding free energy are well established.14 Use of the protein precipitation step also enables every peptide from a given protein that is identified and quantified in the proteomics readout to contribute information about the chemical denaturation behavior of the protein. This maximizes both data quality and the number of proteins in a sample that can effectively be assayed. As part of this work, the CPP approach is evaluated using three model drugs including: cyclosporin A, geldanamycin, and sinefungin.



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EXPERIMENTAL SECTION Materials. The following materials were from Sigma-Aldrich (St. Louis, MO): guanidine

hydrochloride

(GdmCl),

S-methylmethanesulfonate

(MMTS),

dimethyl

sulfoxide (DMSO), urea, centrifugal filter units 10k (Amicon Ultra-0.5), sinefungin, tris(hydroxymethyl)aminomethane hydrochloride (Tris•HCl), and trypsin from porcine pancreas (used in the yeast geldanamycin binding experiment). The following materials were from Thermo Scientific (Waltham, MA): 4-(2-aminoethyl)benzenesulfonyl fluoride hydrochloride (AEBSF), Bestatin, E-64, Leupeptin, Pepstatin A, trypsin protease MS grade (Pierce, used in experiments other than yeast geldanamycin binding). Tris(2-carboxyethyl)phosphine

hydrochloride

(TCEP)

was

from

Santa

Cruz

Biotechnology (Dallas, TX). MacroSpin columns (Silica C18) were from Nest Group (Southborough, MA). Phosphate-buffered saline (PBS, pH 7.4, 1X) was from Gibco (Gaithersburg, MD). Geldanamycin (≥98 wt%) was from Chem-Impex International, Inc. (Wood Dale, IL, USA). Cyclosporin A (CsA) was purchased from LKT Laboratories, Inc. Cell Culture and Lysis. Yeast strain YDR155C was obtained from Open Biosystems, and cultured in YPD medium according to standard protocol, which is further described in the Supporting Information. Yeast cell pellets were lysed in PBS with protease inhibitor cocktail (1 mM AEBSF, 10 µM pepstatin A, 20 µM leupeptin, 15 µM E-64 and 500 µM bestatin). Cell lysis was accomplished using glass beads (0.5 mm) at 4 °C with 20 s of


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disruption 20 times with 1 min intervals on ice in between. Lysate was centrifuged 14,000 x g at 4 °C for 10 min and the supernatant was saved for subsequent analysis. MCF-7 cells from ATCC, were cultured in a humidified 37 oC incubator with 5% CO2 according to ATCC guideline. MCF-7 cell pellets were lysed in PBS with protease inhibitor cocktail (1 mM AEBSF, 10 µM pepstatin A, 20 µM leupeptin, 15 µM E-64 and 500 µM bestatin). Cell lysis was accomplished using zirconia/silica beads (1 mm) at 4 °C with 20 s of disruption 20 times with 1 min intervals on ice in between. The cell lysate was centrifuged 14,000 x g at 4 °C for 10 min and the supernatant was saved for subsequent analysis. The total protein concentration in all lysates was determined using a Bradford assay and ranged from 7 – 15 mg/ml. For each analysis, the test lysate was divided into two equal portions. One portion was spiked with a solution of the test ligand prepared in DMSO (CsA and geldanamycin) or PBS (sinefungin) and one was spiked with DMSO or PBS as vehicle. The solutions were equilibrated for either 1 (CsA and sinefungin) or 24 hours (geldanamycin). CPP Analysis. In all experiments, the (+) and (-) ligand-containing lysate samples were distributed into a series of GdmCl-containing buffers (PBS pH 7.4) with the final GdmCl concentrations ranging from 0 to 2.5 M. The final volume in each buffer was 20 µL. The final concentration of drug was 100 µM in the CsA-binding experiment, 20 µM in the geldanamycin binding experiment, and 0.2, 1.2, or 2.5 mM in the sinefungin binding experiments. The solutions were equilibrated at room temperature for 10 min before 480



µL of deionized water was added into each solution to initiate protein precipitation. After 10 min, the samples were centrifuged. The (+) and (-) ligand samples in the 2.5 mM sinefungin binding experiment were centrifuged at 100,000 X g and 4 oC for 20 min. All the samples in the other experiments were centrifuged at 14,000 X g and 4 oC for 20 min. The same volume of supernatant in each (+) and (-) ligand sample (typically between 200 – 300 µL) from each centrifuged sample was transferred into a 10k centrifugal filter unit. The precipitated protein pellets in the 1.2 mM sinefungin binding experiment were rinsed with 200 µL ice-cold deionized water three times and re-dissolved in 200 µL of 0.1 M Tris•HCl buffer (pH 8.5) containing 8 M urea and 5 mM TCEP, before 120 µL of each solution was transferred into a 10k centrifugal filter unit. All the samples were subjected to filter aided sample preparation (FASP) protocol that involved reduction with TCEP, reaction with MMTS, digestion with trypsin, and labelling with a TMT-10plex reagent kit according to the manufacturer's protocol. The TMT-10plex labeling scheme involved labeling the protein samples derived from each of the denaturant concentrations in the (-) ligand samples with the reagents from one TMT-10plex, and labelling each of the denaturant concentrations in the (+) ligand samples with the reagents from another TMT-10plex. Quantitative LC-MS/MS Analyses. LC-MS/MS analyses were performed on a Q Exactive HF high-resolution mass spectrometer (Thermo) with a nano-Acquity UPLC system (Waters) and a nano-electrospray ionization source fitted with a SilicaTip emitter


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(New Objective). Samples were trapped on a 2D Symmetry C18 trapping column with dimensions 180 µm x 20 mm and particle diameter of 5 µm, pore size 100 Å. The trapping time was 5 minutes at 5 µL/minute (99.9:0.1 v/v water/acetonitrile 0.1% formic acid). The samples were separated on a 75 µm x 250 mm high strength silica (HSS) T3 column with 1.8 µm particle diameter (Waters) heated to 55oC. Peptides were separated using a gradient of 3-30% acetonitrile with 0.1% formic acid over 90 minutes at a flow rate of 0.3 µL/min. Data collection was performed in a data-dependent acquisition (DDA) mode with a resolution of 120,000 (at m/z 200) for full MS scan from m/z 375-1600. The target AGC value was 3 x 106 ions, a maximum ion trap fill time of 50 msec, and the normalized collision energy was 27V. This MS scan was followed by 20 product ion scans at a resolution of 30,000 (at m/z 200), using a minimum AGC target value of 2.25 x 106 ions, and an isolation window of 1.2 m/z and a dynamic exclusion time of 20.0 sec. Proteomic Data Analysis. Proteome Discoverer 2.2 was used to search the raw LC-MS/MS files against either the yeast or human proteins in the 2017-06-07 release of the UniProt Knowledgebase. The searches were performed with fixed MMTS modification on cysteine, TMT-10plex labeling of lysine side chains and peptide N-termini, variable oxidation of methionine, variable deamidation of asparagine and glutamine, and acetylation of the protein N-terminus. The precursor mass tolerance was set at 10 ppm. The fragment mass tolerance was set at 0.02 Da. Trypsin was set as the enzyme, and up to two missed cleavages were allowed. For peptide and protein quantification, reporter



abundance was set as intensity, co-isolation threshold was set as 60%, normalization mode and scaling mode were each set as none. Also, only proteins with protein FDR confidence labelled as "high" (i.e., FDR

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