AID	Name	Type	Comment
1772	Project utilizing multiplex HTS to identify specific small molecule inhibitors of Ras and Ras-related GTPases	summary	Summary report for GTPase target project
761	HTS to identify specific small molecule inhibitors of Ras and Ras-related GTPases specifically Cdc42 wildtype	screening	HTS to identify specific small molecule inhibitors of Ras and Ras-related GTPases specifically Cdc42 wildtype

University of New Mexico Assay Overview:
Assay Support: NIH I RO3 MH081231-01
HTS to identify specific small molecule inhibitors of Ras and Ras-related GTPases
PI: Angela Wandinger-Ness, Ph.D.
Co-PI: Larry Sklar, Ph.D.
Assay Development: Zurab Surviladze, Ph.D.
Assay Implementation: Lin Hong, Ph.D., J. Jacob Strouse, Ph.D., Mark Carter, M.S., Anna Waller, Ph.D.
Target Team Leader for the Center: Larry Sklar (lsklar@salud.unm.edu)
UNM Cheminformatics: Cristian Bologa, Ph.D., Oleg Ursu, Ph.D.

Chemistry: University of Kansas Specialized Chemistry Center
KU Specialized Chemistry Center PI: Jeff Aube, Ph.D.
KU SCC Project Manager: Jennifer E. Golden. Ph.D.
KU SCC Chemists on this project: Chad Schroeder, M.S., Denise Simpson, Ph.D., Julica Noeth, B.S.

Dose Response Assay Background and Significance:

Ras and related small molecular weight GTPases function in the regulation of signaling and cell growth, and collectively serve to control cell proliferation, differentiation and apoptosis [Tekai et al. 2001; Wennerberg et al. 2005]. The Ras-related GTPases are divided into four subfamilies with the Rab proteins regulating membrane transport, Rho proteins (including Rac and Cdc 42) regulating cytoskeletal rearrangements and responses to signaling, Arf/Sar proteins regulating membrane and microtubule dynamics as well as protein transport, and Ran proteins controlling nucleocytoplasmic transport. This project focuses on representative Ras, Rho, and Rab family members to validate the approach for the identification of new chemical compounds with novel therapeutic potential in cell signaling and growth control.

Ras and Ras-related GTPase functions are tightly regulated, and dysregulation is causal in a wide variety of human diseases. Ras mutations resulting in impaired GTP hydrolysis and plasma membrane hyperactivation are linked to many human cancers [Farnsworth et al. 1991; Sukumar et al. 1983; Taparowsky et al. 1982; Boylan et al. 1990; Hruban et al. 2004; Abrams et al. 1996]. Point mutations in the Rab and Rho GTPases are also causal in diverse human diseases affecting pigmentation, immune, and neurologic functions [Houlden et al. 2004; Verhoeven et al 2003; Williams et al. 2000; Bahaderan et al. 2003; and preliminary findings]. Rab and Rho mutants identified in human disease act as dominant negatives either due to a failure to bind GTP or due to inappropriate coupling of the active proteins with downstream effectors. To date, inhibition of Ras and Ras-related proteins has relied largely on altering membrane recruitment with various drugs affecting prenylation [Morgillo F and Lee HY, 2006; Russell RG, 2006; Park, et al. 2002]. Generally, Ras proteins must be farnesylated for proper membrane localization, while Rab and Rho proteins are geranylated. Such strategies lack specificity and are problematic because each of these prenylation machineries is required for the proper function of many Ras superfamily members. Rational drug design has only recently been applied to identify the first two small molecule inhibitors of Rho GTPase family members [Gao, et al. 2004; Nassar et al. 2006]. Therefore, broadly testing the Ras-related GTPases as targets for small molecule inhibitors and activators is expected to identify new classes of compounds that may be useful in the treatment of human disease, as well as in unraveling the molecular details of how Ras-related GTPases function.

The primary HTS assay was a no-wash fluorescent GTP-binding assay adapted to multiplexed, high-throughput measurements whereby multiple GTPases were simultaneously screened against the MLSCN library. The specificity is based on the observation that individual GTPases including wt and activated forms exhibit measurably distinct affinities for Bodipy-FI-GTP vs GTP. The assay involves the binding of fluorescent GTP to G protein-GST fusion proteins on GSH beads. A set of eight G proteins (Rab7 wt, Rac wt, Rac act, Ras wt, Ras act, Rho wt, Cdc42 wt, Cdc42 act) are arrayed under conditions of non-chelator buffer. Here we report the activity in Mg buffer conditions of various compounds that were synthesised by KU SCC under the Cdc42 Probe Extension Characterization project.

Panel dose response of eight GTPase proteins in Mg Buffer for Cdc42 probe extension project

Data Table(Active)    Data Table(All)

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PID§		Name	Substance		Panel Targets	Description	Additional Information
			Active	Inactive
1		Cdc42ACT	3	2	cell division cycle 42 (GTP binding protein, 25kDa) [Homo sapiens] [gi:56202836]	cell division cycle 42 activated mutant (GTP binding protein, 25kDa) [Homo sapiens]	Taxonomy id: 9606
2		Cdc42WT	4	1	cell division cycle 42 (GTP binding protein, 25kDa) [Homo sapiens] [gi:56202836]	cell division cycle 42 (GTP binding protein, 25kDa) [Homo sapiens]	Taxonomy id: 9606
3		Rab7	2	3	GTP-binding protein (rab7) [Canis lupus familiaris] [gi:164058]	GTP-binding protein (rab7) [Canis lupus familiaris]	Taxonomy id: 9615
4		RacACT	1	4	Rac1 protein [Homo sapiens] [gi:8574038]	rac GTPase-activating protein 1 activated mutant [Homo sapiens]	Taxonomy id: 9606
5		RacWT	1	4	Rac1 protein [Homo sapiens] [gi:8574038]	rac GTPase-activating protein 1 [Homo sapiens]	Taxonomy id: 9606
6		RasACT		5	ras protein [Homo sapiens] [gi:190938]	ras protein activated mutant [Homo sapiens]	Taxonomy id: 9606
7		RasWT		5	ras protein [Homo sapiens] [gi:190938]	ras protein [Homo sapiens]	Taxonomy id: 9606
8		RhoWT	1	4	GTPase [Homo sapiens] [gi:407699]	transforming protein RhoA precursor [Homo sapiens]	Taxonomy id: 9606

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§ Panel component ID.

Bead sets are coated with individual GST-small G proteins, blocked with Buffer (0.01% NP-40; 30mM HEPES pH 7.5; 100mM KCl; 20mM NaCl; 1mM MgCl2; 0.1% BSA and 1mM DTT), incubated overnight at 4 degrees C, and finally washed in buffer. The different bead sets, acquired from Duke Scientific, have similar size (~ 4 microm diameter) however they are distinguished by varied magnitude of emission at 665 +/-10 nm with excitation at 635 nm.

Test compounds were serially diluted 1:3 eight times for a total of nine different test compound concentrations in DMSO. Final compound dilutions in DMSO ranged from 1 microM to 10mM. These dilutions were then diluted 1 to 100 to give an assay concentration range of 10 nanoM to 100 microM.

The assay is conducted in 384-well microplates in a total well volume of 10.1 microliters (5 microliters of bead mixture, 0.1 microliters of test compound, and 5 microliters of 200 nanoM Bodipy-FL-GTP in buffer containing BSA and DTT for a final concentration of GTP of 100 nanoM). Negative Controls, which contain bead mixture and fluorescent GTP but no test compound, are located in columns 1 and 13 on plate. Positive Controls containing bead mixture with fluorescent GTP and 0.5 mM unlabeled GTP, are collected separately (either on teh same plate or different plate). Plates are placed on rotators and incubated for 40-45 minutes at 4 degrees C. Dose response experiments reported here include one multiplex format for: Rab7 wt, H-Ras wt, H-Ras constitutively active, Rac wt, Rac constitutively active, Rho wt, Cdc42 wt, and Cdc42 constitutively active.

Sample acquisition and preliminary analysis is conducted with the HyperCyt(R) high throughput flow cytometry platform. The HyperCyt system interfaces a flow cytometer and autosampler for high-throughput microliter volume sampling from 384-well microtiter plates [Kuckuck, et al., 2001; Ramirez, et al., 2003]. The stream of particles is excited at 488 nm and 635 nm, and flow cytometric data of light scatter and fluorescence emission at 530 +/- 20 nm (FL1) and emission at 665 +/- 10 nm (FL8) are collected on a Cyan Flow Cytometer (Dako). Analysis of the time-resolved acquisition data file uses HyperView software to merge the flow cytometry data files with compound worklist files generated by HyperSip software. Gating based on forward scatter (FS) and side scatter (SS) parameters is used to identify singlet bead populations. Gating based on FL8 emission distinguishes the beads coated with different proteins, and the green median fluorescence intensity (MFI) per bead population (well) is calculated. The raw data are parsed in HyperView to produce annotated fluorescence summary data for each well. The parsed data are then processed through an Excel template file constructed specifically for the assay to fit the data via GraphPad Prism.

Calculations:

In dose response experiments, the percent response was calculated for each GTPase target by the following equation:
%Response = (SampleMFI - PCntrl)/(NCntrl - PCntrl)
where SampleMFI is the median fluorescence intensity of the compound sample, PCntrl is the median fluorescence intensity measured in the presence of access blocking non-fluorescent GTP, and NCntrl is the median fluorescence intensity measured in the presence of DMSO. The %Response was 100% for compounds not affecting the binding of Bodipy-FL-GTP, and greater than 100% if the compound increased the binding of Bodipy-FL-GTP and less than 100% if the compound decreased the binding of Bodipy-FL-GTP.

The % response values for the entire concentration range of a test compound were fitted by Prism(R) software (GraphPad Software, Inc., San Diego, CA) using nonlinear least-squares regression in a sigmoidal dose response model with variable slope, also known as the four parameter logistic equation. Curve fit statistics were used to determine the following parameters of the model: EC50, microM - concentration of added test compound competitor that inhibited fluorescent ligand binding by 50 percent; LOGEC50 - the logarithm of EC50; TOP - the response value at the top plateau; BOTTOM - the response value at the bottom plateau; HILLSLOPE - the slope factor, or the Hill coefficient; STD_LOGEC50, STD_TOP, STD_BOTTOM, STD_HILLSLOPE - standard errors of LOGEC50, TOP, BOTTOM, and HILLSLOPE ; EC50_95CI_LOW, EC50_95CI_HIGH - the low and high boundaries of the 95% confidence interval of the EC50 estimate, RSQR - the correlation coefficient (r squared) indicative of goodness-of-fit.

Assessment of the validity of the estimates from the Prism fits was filtered via the following criteria:
- -8 < LOGEC50 < -4 (the computed EC50 value should be in the interval of tested concentrations)
- STD_LOGEC50/|LOGEC50| <0.15 (the standard error of LOGEC50 should be no greater than 15% of the absolute value of LOGEC50)
- 0.5 < |HILLSLOPE| < 2 (the absolute value of HILLSLOPE should be higher than 0.5 and lower than 2)

PUBCHEM_ACTIVITY_SCORE were calculated based on an EC50 cutoff of 10 microM, by using the following equations:
SCORE = 100*(1-EC50/10 )
And compounds were demeaned active if the EC50 is equal to or less than 10 microM and the span of the %Response is greater than or equal to 30%. Span is the magnitude of %Response change over the tested concentration range based on either fitted values of TOP minus BOTTOM or from %Response values at lowest to highest concentration.

An active compound have a PUBCHEM_ACTIVITY_SCORE greater than 0.

Keywords: NIH Roadmap, NMMLSC, high throughput flow cytometry, Rab7 wt, Ras wt, Ras activated, Rac wt, Rac activated, Rho wt, Cdc42 wt, Cdc42 activated, multiplex, bead-based, screening, dose response, SAR, UNMCMD

BAO: version: 1.4b1080

BAO: bioassay specification: assay stage: lead optimization

BAO: bioassay specification: assay biosafety level: bsl1

BAO: bioassay specification: assay measurement type: endpoint assay

BAO: bioassay specification: assay readout content: assay readout method: regular screening

BAO: bioassay specification: assay readout content: content readout type: multiplexed readout

BAO: meta target: molecular target: protein target: enzyme: protease

BAO: meta target detail: binding reporter specification: interaction: protein-nucleotide

BAO: detection technology: fluorescence: flow cytometry

Grant Number: NIH I RO3 MH081231-01