Name: development of inhibitors of protein-protein interactions through replace: application to the design and development non-atp competitive cdk inhibitors
Uploaded: 2015-10-26T14:00:00.000+00:00
Duration: 10 min 33 s
Description: Watch this Scientific Journal Video about Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors at JoV

We thank Dr&#8217;s. Douglas Pittman and Michael Wyatt for their assistance with cell culture and Dr Wyatt and Ms. Erin Anderson for help in development of the binding assays. We acknowledge Mike Walla and Bill Cotham in the Department of Chemistry and Biochemistry at the University of South Carolina for assistance with Mass Spectrometry, Helga Cohen and Dr. Perry Pellechia for NMR spectrometry. This work was funded by the National Institutes of Health through the research project grant, 5R01CA131368.

1. Computational Identification of Potential Small Molecule Capping Groups
Note: In principle, a variety of docking or pharmacophore search methods can be used to predict potential capping groups. The main purpose of computational studies in REPLACE is to identify small molecules that retain the features and interactions of the amino acids that are substituted.
<ol>
	<li>Validation of the LigandFit docking protocol14</li>
</ol>
Note: In previous studies, the docking method (LigandFit15, a module in the molecular modeling program suite, Discovery Studio 3.0) was validated to ensure that this algorithm is sufficient to reproduce binding modes of known Ncaps and to show that the results obtained for unknown compounds are predictive14.
<ol style="margin-left: 40px;">
	<li>Using the following steps (1.2 &#8211; 1.5), identify optimal parameters for LigandFit as follows: PLP1 energy grid for pose generation, minimization of generated poses and the PLP1 scoring function for the analysis of poses.</li>
	<li>In order to limit the conformations obtained and to position ligands appropriately for covalent bond formation in the capped peptide, set the indole nitrogen and amide nitrogen atoms of Trp217 and Gln254 respectively as interaction filters for hydrogen bonding.</li>
	<li>Design and dock a library of fragment alternatives into the CDK2/cyclin A receptor (2UUE). Prioritize potential capping groups for synthesis and commercial sourcing based on PLP1 scoring, interactions with interaction filters and complementarity with the CBG. The various steps required for LigandFit docking are as follows.</li>
</ol>
<ol start="2">
	<li>Preparation of receptor binding site14
	<ol>
		<li>Import the CDK2/cyclin A crystal structure (pdb id: 2UUE) into a visualization window in the software. This structure contains the previously identified FLIP 5-methyl-1-phenyl-1H-1,2,4-triazole-3-carboxamido(3,5-DCPT)RLIF bound to the cyclin groove (Figure 3).</li>
		<li>Delete or keep in place the residues RLIF (C-terminus). When the peptide sequence is retained, use the N-terminal amino group of the peptide as an interaction filter for the ligand. From the &#8220;Receptor-ligand interactions&#8221; tools, create a sphere around the N-cap 3,5-DCPT from Show/Hide site sphere. The sphere is created to define the active region in the binding site where the ligand-receptor interactions are allowed to occur during binding.</li>
		<li>Define the binding site further from the &#8220;find site as volume of selected ligand&#8221; and delete the ligand 3,5-DCPT from the protein. Carry out this step to define the volume of binding within the sphere created.</li>
		<li>Set the indole nitrogen and amide nitrogen atoms of the residues Trp217 and Gln254 as interaction filters for hydrogen bonding. Set the interaction filters so that only those poses that interact with the atoms of set residues will be filtered during the docking process therefore preserving important interactions in the docked poses.</li>
	</ol>
	</li>
	<li>Preparation of ligands14
	<ol>
		<li>Design a library of 100 potential capping groups based on criteria including molecular weight less than 500, presence of a carboxylate group for ligation with the truncated peptide and inclusion of appropriate hydrophobic groups (substituted phenyl group) for van der Waals interaction in the secondary pocket as shown in Table 1.</li>
		<li>Select heterocyclic rings for mimicking peptide hydrogen bonding interactions with Trp217 and substituents included for replacing ion-pairing interactions of basic side chains of the HAKRRLIF. Dock the ligands as respective aldehyde molecules so that the carbonyl oxygen can interact with the amide nitrogen atom of Gln254 similar to peptide backbone in the parent peptide (Table 1).</li>
		<li>Prior to docking, minimize designed ligands to a low energy conformation and prepare in an appropriate ionization state using the &#8220;Prepare ligands&#8221; protocol. Draw and export these potential N caps in the .sdf file format in ChemDraw for a spreadsheet prior to being imported into the software. 
		Note: Prepare ligands protocol is used to minimize all the ligands to be docked to their lowest energy states and the ionization charges were applied to the applicable atoms. Default parameters are used for this step.</li>
	</ol>
	</li>
	<li>Docking of the ligands into the Cyclin A Groove14
	<ol>
		<li>Select the LigandFit routine from receptor-ligand interactions protocol set of the software. Use the receptor and the prepared ligands as input for this protocol.</li>
		<li>For these runs select PLP1 as energy grid, specify that poses be energy minimized and that the number of generated poses as 10. Leave all the other parameters at the default values. 
		Note: LigandFit is a docking method which can identify and generate poses of high complementarity and potential affinity with the active site of a protein using a shape based comparison filter which is combined with a Monte Carlo conformational search algorithm. The energy grid used by the docking program is the force field for generating ligand-receptor interactions and potential poses. PLP1 is piecewise linear potential which specifically prioritizes hydrogen bonding interactions and where the PLP atom type is assigned for each non-hydrogen ligand atom or non-hydrogen receptor atom.</li>
	</ol>
	</li>
	<li>Analysis of results
	<ol>
		<li>In the first instance, display poses in the visualizer program and then sort by descending values of the PLP1 score. Use scoring functions such as PLP1 to estimate the binding affinity of a docked ligand based on a candidate ligand pose geometry and non-covalent interaction with the target receptor structure. 
		Note: Here, the PLP1 scoring function was found to give the best results as it includes an estimation of hydrogen bonding.</li>
		<li>Analyze visually the top 25% of the scored poses for 1) superimposability with the known capping group (having a relative mean square deviation (RMSD) value less than 2 &#197;), 2) fulfilled interaction filters and 3) visual complementarity with the cyclin groove. It is accepted in that a correctly docked pose (compared to an experimental structure) has an RMSD value of &#60; 2 &#197;.</li>
		<li>Examine poses for visual complementarity, which is defined as having efficient filling of the binding pocket in a manner that is consistent with known structure-activity relationships. Interaction filters are set to include atom restraints that require intermolecular contacts known to be critical for binding and/or are required to position the potential capping group in the correct geometry for amide bond formation. Three examples of docked poses of potential N-capping groups making hydrogen bonds with interaction filters are shown in Figure 4.</li>
	</ol>
	</li>
</ol>
2. Synthesis and Characterization of Potential N-capping Groups
<ol start="1">
	<li>Synthesis of N-capping groups.</li>
	<li>For synthesis, use all commercial starting materials, solvents and reagents as obtained. Synthesize potential N-capping groups by conventional synthetic organic chemistry (Figure 512,13 for an example).</li>
	<li>Perform thin layer chromatography (TLC) on silica gel for monitoring reactions.
	<ol>
		<li>Dissolve the starting material and reaction mixture (1 mg) in the mobile phase and spot them on the TLC plate using capillary tubes.</li>
		<li>Place the TLC plate in to the chamber containing mobile phase (ethyl acetate and hexane at a 35:65 ratio). Once the mobile phase reaches 90% of the TLC plate, remove and air dry the plate.</li>
		<li>Use UV light to detect the starting material and reaction mixtures as visible spots. Calculate the Rf of all the spots (ratio of distance travelled by the spot and the mobile phase). 
		Note: The reaction is complete when there is no spot seen at the Rf of the starting material.</li>
	</ol>
	</li>
	<li>After the completion of reaction work up the reaction mixture by placing in a separatory funnel and washing with aqueous acid or base solution as appropriate. Collect the organic solvent, evaporate it in rotary evaporator and dry the crude product obtained under vacuum.</li>
	<li>Dissolve 500 mg of crude product in 3-5 ml of suitable solvent and add to a 1 g of silica or RP18 samplet and dry under air. Place the samplet containing the crude product in the silica/ RP SNAP flash cartridges and purify the crude material using automated high performance flash chromatography (according to manufacturer&#8217;s protocol) employing a SNAP 100 g column with a gradient run starting from 6% ethyl acetate: 94% hexanes to 50% ethyl acetate and 50% hexanes over 15 column volumes.</li>
	<li>Dry the purified product collected in solvent from flash chromatography using a rotary evaporator by evaporating all the solvent to dryness and further dry the product under vacuum to remove all the residual solvents. Perform characterization of the purified product by NMR, MS and analytical HPLC.
	<ol>
		<li>For 1H NMR and 13C NMR, weigh approximately 10 mg of the purified sample, dissolve it in an appropriate deuterated solvent, and transfer the contents to a dry NMR tube for recording the NMR spectra2,14.</li>
		<li>Record the 1H NMR and 13C NMR spectra with a high field NMR Spectrometer (minimum 300 MHz). Acquire mass spectra using a QTOF (Tandem quadruple-1 time of flight mass spectrometer), electrospray ionization (ESI) or a VG 70S (Double-focusing magnetic sector mass spectrometer, EI)2,14.</li>
		<li>Analyze the purity of products by HPLC with a diode-array detector and equipped with a C18 (2) 100 A, 250 x 4.6 mm, 5 &#181;m column for analysis. Use a gradient run starting from 100% water (0.1% trifluoroacetic acid) to 60% acetonitrile (0.1% trifluoroacetic acid) over 30 min and hold the gradient for 4 min. Extract the chromatograms at 226 and 254 nm2,14. 
		Note: Analytical purities of evaluated compounds were &#62; 95%.</li>
	</ol>
	</li>
</ol>
3. Solid Phase Synthesis for the Generation of FLIPs 2
<ol start="1">
	<li>Synthesis of N-capped FLIPs
	<ol>
		<li>Assemble N-capped peptidic compounds through standard solid-phase synthesis methods using the following steps.
		<ol>
			<li>Activate 5 equivalents of the C-terminal amino acid (Fmoc-Phe) in 4.4 equivalents of O-Benzotriazole-N,N,N&#8217;,N&#8217;-tetramethyl-uronium-hexafluoro-phosphate (HBTU, 221.93 mg) in 2 ml of DMF for 5 min. Load the Fmoc-Phe HBTU mixture onto the Rink resin using 6 equivalents of diisopropylethyl amine (DIPEA, 103.13 mg) for 1 hr at&#160;RT.</li>
			<li>Remove the Fmoc protecting group from the C-terminal residue using 20% piperidine in 3 ml of DMF for 10 min. Couple subsequent amino acids (e.g., Fmoc-Ile, Fmoc-Leu, Fmoc-Arg-pmc) step by step. At each step, couple 5 equiv of the next amino acid using 6 equivalents of DIPEA (103.13 mg) and 4.4 equivalents of HBTU (221.93 mg) in 2 ml of DMF for 1 hr at RT.</li>
			<li>Apply wash cycles (5 x 10 ml of DMF + 5 x 10 ml of DCM) after amino acid coupling and Fmoc deprotection steps described above. After peptide assembly, couple N-capping groups using 6 equivalents of DIPEA (103.13 mg) and 4.4 equivalents of HBTU (221.93 mg) in 2 ml of DMF for 1 hr at RT.</li>
			<li>Upon completion of peptide assembly, treat the reaction mixture with 2 ml of the cleavage mixture (90:5:5 TFA/H2O/TIPS) O/N to remove side chain protecting groups, and cleave FLIPs from the resin. Triturate the resulting product with cold diethyl ether to precipitate and if necessary concentrate in a rotary evaporator.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Synthesis of N-capped-C-capped FLIPs
	<ol>
		<li>Weigh and dissolve the N-terminally capped and protected Arg-Leu peptide (16.1 mg, 0.02 mmol) in dichloromethane, and add [4-(3-chlorophenoxy)pyridin-2-yl]methanamine (C-cap) along with O-benzotriazole-N,N,N&#39;,N&#39;-tetramethyluronium hexafluorophosphate (HBTU; 16.7 mg, 0.02 mmol) and N,N-diisopropylethylamine (DIPEA; 6.5 mg, 0.02 mmol).</li>
		<li>Stir and monitor the reaction at RT until TLC/HPLC indicates the complete consumption of starting material. After the completion of the reaction, concentrate the crude reaction mixture using a rotary evaporator and then partition between ethyl acetate and water.</li>
		<li>Separate the aqueous and organic layer; wash the organic layer with 1 N NaOH, 1 N HCl, and brine. Dry the organic layer with about 1 g of sodium sulfate and concentrate the product in rotary evaporator. Finally treat the product O/N with the cleavage mixture (95:2.5:2.5 trifluoroacetic acid/H2O/TIPS) for deprotection.</li>
		<li>Triturate the resulting product with cold diethyl ether and if necessary concentrate in rotary evaporator to complete dryness in order to remove all the solvent. Further dry the product under vacuum to remove all the residual solvents.</li>
	</ol>
	</li>
	<li>Purification and characterization of FLIPs
	<ol>
		<li>Purify crude FLIPs using semi preparative reverse-phase HPLC methods. Lyophilize the purified FLIPs and characterize their molecular mass using mass spectrometry2,14.</li>
		<li>Determine the analytical purity of the FLIPs by HPLC with a diode-array detector and equipped with a C18 (2) 100 A, 250 x 4.6 mm, 5 &#181;m column. Use a gradient method starting from 95% H2O (0.1% trifluoroacetic acid)/5% acetonitrile (0.1% trifluoroacetic acid) to 35% H2O (0.1% trifluoroacetic acid)/ 65% acetonitrile (0.1% trifluoroacetic acid) over 30 min and hold the gradient for 4 min. Extract chromatograms at 226 and 254 nm.</li>
	</ol>
	</li>
</ol>
4. Fluorescence Polarization Binding Assay for the Determination of Competitive Binding 2,14
<ol start="1">
	<li>Perform the assay in black 384-well (138 &#181;l) plates using the following procedure.</li>
	<li>Dilute the samples, tracer peptides (4 nM), and kinase complexes (CDK2/cyclin A &#8212; 18 &#181;g/ml, CDK4/cyclin D &#8212; 37 &#181;g/ml) to required concentrations in assay buffer (25 mM HEPES pH 7, 10 mM NaCl, 0.01% Nonidet P-40, 1 mM dithiothreitol (DTT).</li>
	<li>To each well of the 384-well plate, add: 5 &#181;l of CDK4D1 or CDK2CA (0.3 &#181;g/well purified recombinant human kinase complex), 5 &#181;l compound solution, 5 &#181;l of 30 nM tracer peptide (fluoresceinyl-Ahx-Pro-Val-Lys-Arg-Arg-Leu-(3ClPhe)-Gly or fluoresceinyl-Ahx-Pro-Val-Lys-Arg-Arg-Leu-Phe-Gly tracer peptide). Use 5 &#181;l of each DMSO (6%), buffer, tracer and 5 &#181;l DMSO (6%), kinase complex, tracer as control wells</li>
	<li>After the addition of the all the components, centrifuge the plate at 500 rpm for 1 min (41.16 x g) at RT and then incubate the plate with agitation for 45 min at RT. Using a multimode plate reader and detector fitted with 485 nm/535 nm excitation/emission filters and a dichroic mirror read the fluorescein intensity of each well in the plate.</li>
	<li>Calculate the relative mean polarization (mp) for all the test samples using the equation showing below. Determine IC50 values by logarithmic regression by correlating relative mps and testing concentrations.</li>
</ol>
<img alt="Equation 1" src="/files/ftp_upload/52441/52441eq1.jpg" />

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The authors declare that they have no competing financial interests.

Targeting protein-protein interactions (PPI) in drug discovery is highly challenging as these typically involve a large shallow contact interface comprised of numerous and diffuse contacts19. Furthermore, peptidic compounds which inhibit PPI&#8217;s that are amenable to drug discovery are problematic due to their higher molecular mass, metabolic instability and poor bioavailability20. Current strategies that have been applied for the development of PPI inhibitors include design of proteomimetics and fragment based design. Examples of proteomimetics include stapled peptides, porphyrin scaffolds and cyclic &#945;-helical mimetics although these are not without disadvantages21,22. Porphyrins (heterocyclic macrocycles comprised of pyrrole rings) are high molecular weight structures and are very hydrophobic23. Stapled peptides can be complex to synthesize and their mechanism of cell permeability is currently not fully known6. Conventional fragment based approaches require a highly sensitive detection method for testing weakly binding compounds and which also must be highly soluble. REPLACE is an iterative strategy comprised of computational prediction, synthetic organic chemistry and biological evaluation in order to convert peptidic inhibitors into FLIPs and ultimately non-peptidic drug-like compounds. Using REPLACE, fragment alternatives are designed and identified to mimic the interactions of binding determinants of a peptidic inhibitor. The combination of structural analysis along with synthetic methodologies and biological evaluation results in the efficient design of molecules that interact with hot spots (sites of critical interaction) and complement the large contact surface area of the PPI binding site1. REPLACE overcomes the disadvantages of conventional fragment based design since the peptide acts as an affinity and solubility scaffold. Also the iterative nature of REPLACE means that the inhibitor is gradually converted in several steps therefore minimizing the possibility of complete potency loss through conformation change. PLAs are optimized in the context of the peptide prior to further truncation. In the case study we describe, REPLACE has been exploited to generate more drug-like inhibitors that selectively inhibit cell cycle CDK/cyclin complexes and possess significant anti-proliferative activity. Capped peptides have been identified as more drug-like mimics of the lead octapeptide. Key fragment alternatives discovered include N-caps based on (i) phenyl heterocyclic carboxylic acids2, (ii) furan and thiazole carboxylic acids substituted with alkylamino groups and (iii) pyridine carboxylic acids combined with alkoxy or basic groups14. 
In this case study, the first stage of the REPLACE strategy was identification of fragment alternatives for key peptide determinants using high-throughput docking. LigandFit, a shape based docking method was employed for evaluating binding of fragments in the cyclin groove. LigandFit15 generates poses that have high complementarity with a binding site through the use of Monte Carlo conformational search methods in conjunction with a shape based comparison filter. Use of a LigandFit protocol with the PLP1 energy grid and a minimization step to refine generated poses, was shown to reproduce known binding modes of previously determined N-capping groups. An accurate prediction of the binding affinity of the generated poses was provided by using the PLP1 scoring function which was shown to obtain the best results since it takes into account hydrogen bonding between the ligand and receptor. Specific H-bonds of CGI ligands with the cyclin groove have been shown to be important determinants of high affinity binding. Potential fragment alternatives for the N-terminus were synthesized or obtained from commercial sources and then used to generate FLIP molecules through coupling to an assembled peptide on a solid support. The ability of FLIPs to recapitulate the affinity of the native peptide is determined in a competitive binding assay using a known cyclin groove inhibitory peptide which is fluorescently labeled. After confirmation of the ability of FLIPs to bind with sufficient avidity to the CBG, the potency of the peptide-fragment hybrids can then be further refined through modifications to the Ncapping group. In addition, REPLACEments for C-terminus can explored for eventual combination with the optimized Ncaps. Further to this N terminal and C-terminal capping groups can be combined into a single more drug-like molecule2. Using this approach, compounds were obtained that have anti-proliferative activity in cancer cells lines that have deregulated CDK/cyclin/Rb/E2F1 pathways. Although REPLACE has been successfully applied, this strategy is not without limitations. These include the inaccuracy of any computational technique in predicting binding of docked molecules and synthetic issues in generating capping groups and FLIPs. Despite these, we have utilized REPLACE to generate more drug-like compounds that are cell permeable and have anti-proliferative activity. These compounds therefore have significant potential for further development as anti-tumor therapeutics.

In this article, a case study of applying the REPLACE (Replacement with partial ligand alternatives using computational enrichment) strategy to convert peptidic inhibitors of protein-protein interactions into more pharmaceutically relevant molecules is described1-3. While PPIs represent a rich but underexploited source of potential drug targets, existing methodologies are largely insufficient to make these widely accessible. Current strategies including fragment based design4, high-throughput screening5 and stapled peptides6 have provided advances, however these are in many cases ineffective. As a result, more progress and more efficient approaches are required. REPLACE has been fully validated in the development of kinase inhibitors that have improved drug-like properties and have potential for further development as anti-tumor therapeutics. This strategy is exemplified in the development of non-ATP inhibitors of cell cycle CDKs and involves as follows: 1) obtaining 3D structural information on the interactions of HAKRRLIF/RRLIF with the cyclin binding groove; 2) determining the important binding determinants for peptide interaction; 3) truncation of the peptide N-terminus containing one or more binding determinants; 4) computational identification of potential small molecule alternatives (partial ligand alternatives, PLAs) for the truncated portion of the peptide and which retain key interactions of the parent peptide; 5) synthesis or commercial sourcing of PLAs predicted to bind avidly with the sub site previously occupied by the deleted peptide residue(s); 6) synthesis of FLIPs through ligation of the best PLAs to the truncated peptide using solid phase synthesis; 7) testing of FLIPs in an in vitro binding or functional assay (fluorescence polarization in the CDK/cyclin context) followed by further characterization in a cell viability assay. A schematic representation of REPLACE strategy is shown in Figure 1. In this article, iterations of the REPLACE strategy are discussed and the application to CDK2/cyclin A described in detail. CDKs are believed to be directly or indirectly deregulated in the majority of tumors and are therefore considered appropriate cancer drug targets7. CDKs require association with cyclins for full activation and subsequently phosphorylate key proteins involved in cell cycle regulation8. The two major groups of CDKs are the isotypes that control cell cycle checkpoints [G1/S (CDK4/Cyclin D, CDK6/cyclin D and CDK4/cyclin E), S phase (CDK2/cyclin A) and G2/M (CDK1/cyclin B)] and the regulators of RNA polymerase through phosphorylation (CDK7/cyclin H, CDK8/cyclin C, CDK9/cyclin T). A key step in S phase progression occurs when the E2F1 transcription factor forms a complex with the DP protein which then binds to DNA and initiates gene transcription. CDK2/cyclin A is required to neutralize E2F1 transcriptional activity through phosphorylation thereby leading to release of the E2F1-DP complex and its subsequent degradation. Inhibition of CDK2/cyclin A is believed to maintain E2F1 in its DNA bound state leading to persistent activation. The resultant level of E2F-1 activity will surpass the threshold required to induce p53 independent apoptosis therefore suggesting a therapeutic strategy. Due to deregulated p53 and pRb pathways, high levels of E2F-1 frequently occur in cancer cells and inhibition of CDK2/cyclin A should lead to selective apoptosis in tumors and can be considered as a validated cancer target7.
Clinically investigated CDK inhibitors target the highly conserved ATP binding site leading to cross reactivity among the greater than 500 protein kinases in the human kinome and potentially giving rise to side effects and toxicity9. An alternate approach is non-ATP competitive inhibition by targeting substrate recruitment through the CBG present on cyclin positive regulatory subunit and which is therefore distinct and distant from ATP binding site10,11. The CBG is primarily a hydrophobic groove present in cyclin A, cyclin D and cyclin E and has been shown to recognize a consensus sequence found in substrates and tumor suppressors. As an isolated peptide, the cyclin binding motif (CBM) binds to the CBG and has been shown to inhibit kinase activity of the cell cycle CDKs. The CBM has been optimized to an octapeptide (HAKRRLIF, CDK2/cyclin A IC50 0.07&#177;0.02 &#181;M , CDK4/cyclin D, IC50 0.88&#177;0.34 &#181;M) and furthermore truncated to a pentapeptide representing a good compromise between molecular weight for drug-likeness and potency (RRLIF, CDK2/cyclin A IC50 1.01&#177;0.17 &#181;M, CDK4/cyclin D, IC50 25.12&#177;2.97 &#181;M)12,13. The CBGs consist of a large primary and smaller secondary hydrophobic pocket which are bridged by an acidic region (includes Glu220, Glu224 and Asp283). The key binding determinants of HAKRRLIF include the interaction of Ala2 with the secondary hydrophobic pocket, ion pairing and hydrogen bonds of Lys3, Arg 4 and Arg5 with the acidic region and a high degree of complementarity of Leu6 and Phe8 with the primary lipophilic site. In addition, numerous hydrogen bonds are contributed from the peptide backbone while Ile7 acts as a spacer residue allowing optimal contact with the primary pocket. The binding mode and interactions of HAKRRLIF with CBG is shown in Figure 2.
Targeting the CBM/CBG protein-protein interaction will inhibit kinase activity of CDK2/cyclin A, CDK2/cyclin E &#38; CDK4/cyclin D and this should trigger E2F1 mediated apoptosis of cancer cells while not affecting normal cells7. Although CBM derived peptides are effective inhibitors of cell cycle CDKs, it is unlikely that they will be useful as drugs due to their metabolic instability and general lack of cell permeability. To this end, we have applied the REPLACE strategy in order to convert these potent peptidic inhibitors into more drug-like compounds for further development of anti-tumor therapeutics exploiting deregulated E2F1 through CDK2/cyclin A inhibition. The following protocol summarizes work that has been completed in the application of REPLACE to the cyclin groove. In the first instance, drug-like capping group replacements for the N-terminal tetrapeptide of HAKRRLIF were identified. Furthermore improvements in these groups were investigated in an additional validation study for REPLACE. Representative results from these studies are also presented.

<table><tbody><tr><td colspan="4">&lt;strong&gt;Computational Chemistry&lt;/strong&gt;</td></tr><tr><td>Accelyrs Discovery studio 3.0</td><td></td><td></td><td></td></tr><tr><td>Dell Optiplex Workstations</td><td></td><td></td><td></td></tr><tr><td colspan="4">&lt;strong&gt;Synthetic Organic Chemistry&lt;/strong&gt;</td></tr><tr><td colspan="4">Silica gel (GF-254 plates) for TLC, Biotage (Uppsala, Sweden) for flash chromatography, Waters Alliance 2695 HPLC with a 2996 diode-array detector and equipped with a C18 (2) 100 A, 250 x 4.6&amp;nbsp;mm, 5&amp;nbsp;&amp;mu;m column (Phenomenox Luna) for purity determination, &lt;sup&gt;1&lt;/sup&gt;H NMR and &lt;sup&gt;13&lt;/sup&gt;C NMR spectra were recorded with a Varian Mercury 300 and 400 Spectrometer, respectively. Mass spectra were measured with a Micromass QTOF (Tandem quadruple-1 time of flight mass spectrometer), electrospray ionization (ESI) and VG 70S (Double-focusing magnetic sector mass spectrometer, EI).</td></tr><tr><td colspan="4">&lt;strong&gt;Flourescence Polarization Assay&lt;/strong&gt;</td></tr><tr><td>384 micro well plates, Micro pipets</td><td>Grenier Bio-one</td><td>110256602</td><td></td></tr><tr><td>CDK4D1 and CDK2CA (well purified recombinant human kinase complex)</td><td>BPS Bio Sciences</td><td>40094(CDK4/Cyclin D), 41101(CDK2/Cyclin A)</td><td></td></tr><tr><td colspan="4">assay buffer (25 nM HEPES pH 7, 10 mM NaCl, 0.01% Nonidet P-40, 1&amp;nbsp;mM dithiothretiol (DTT))</td></tr><tr><td>25 nM HEPES</td><td>CALBIOCHEM</td><td>375368</td><td></td></tr><tr><td>NaCl</td><td>Fisher</td><td>127838</td><td></td></tr><tr><td>Nonidet P-40</td><td>US Biological</td><td>N3500</td><td></td></tr><tr><td>DTT</td><td>Aldrich</td><td></td><td></td></tr><tr><td>-70 &amp;#176;C freezer</td><td>Revco (Ultima II)</td><td></td><td></td></tr><tr><td>DTX880 multimode detector fitted with 485&amp;nbsp;nm/535&amp;nbsp;nm excitation/emission filters and a dichroic mirror suitable for fluorescein</td><td>Beckman Coulter, Brea, CA</td><td></td><td></td></tr><tr><td colspan="4">&lt;strong&gt;Cell Culture&lt;/strong&gt;</td></tr><tr><td>96 well plates</td><td>Fisher</td><td></td><td></td></tr><tr><td>Frozen stocks of U2OS (osteosarcoma) and DU145 (prostate cancer) cell lines</td><td>ATCC</td><td></td><td></td></tr><tr><td>NU serum, DMEM media, trypsin, PEN/STRIP, MTT reagent</td><td>Fisher, Life technology, Alfa Aesar</td><td></td><td></td></tr><tr><td>Heamocytometer</td><td>VWR</td><td></td><td></td></tr><tr><td>-70 &amp;#176;C freezer</td><td>Revco (Ultima II)</td><td></td><td></td></tr><tr><td>Incubator</td><td>Thermo electron corporation</td><td></td><td></td></tr><tr><td>Centrifuge</td><td>Eppendorf</td><td>5804 R</td><td></td></tr><tr><td>Refrigerator 4-8 &amp;#176;C</td><td>Isotemp Fisher</td><td></td><td></td></tr><tr><td>DTX880 multimode detector fitted with 595&amp;nbsp;nm filter</td><td>Beckman Coulter, Brea, CA</td><td></td><td></td></tr></tbody></table>

development of inhibitors of protein-protein interactions through replace: application to the design and development non-atp competitive cdk inhibitors

REPLACE is a unique strategy developed to more effectively target protein-protein interactions (PPIs). It aims to expand available drug target space by providing improved methodology for the identification of inhibitors for such binding sites and which represent the majority of potential drug targets. The main goal of this paper is to provide a methodological overview of the use and application of the REPLACE strategy which involves computational and synthetic chemistry approaches. REPLACE is exemplified through its application to the development of non-ATP competitive cyclin dependent kinases (CDK) inhibitors as anti-tumor therapeutics. CDKs are frequently deregulated in cancer and hence are considered as important targets for drug development. Inhibition of CDK2/cyclin A in S phase has been reported to promote selective apoptosis of cancer cells in a p53 independent manner through the E2F1 pathway. Targeting the protein-protein interaction at the cyclin binding groove (CBG) is an approach which will allow the specific inhibition of cell cycle over transcriptional CDKs. The CBG is recognized by a consensus sequence derived from CDK substrates and tumor suppressor proteins termed the cyclin binding motif (CBM). The CBM has previously been optimized to an octapeptide from p21Waf (HAKRRIF) and then further truncated to a pentapeptide retaining sufficient activity (RRLIF). Peptides in general are not cell permeable, are metabolically unstable and therefore the REPLACE (REplacement with Partial Ligand Alternatives through Computational Enrichment) strategy has been applied in order to generate more drug-like inhibitors. The strategy begins with the design of Fragment ligated inhibitory peptides (FLIPs) that selectively inhibit cell cycle CDK/cyclin complexes. FLIPs were generated by iteratively replacing residues of HAKRRLIF/RRLIF with fragment like small molecules (capping groups), starting from the N-terminus (Ncaps), followed by replacement on the C-terminus. These compounds are starting points for the generation of non-ATP competitive CDK inhibitors as anti-tumor therapeutics.

The interactions of HAKRRLIF with the cyclin groove are shown in Figure 2. The peptide residues that represent the key binding determinants include Ala2, Arg4, Leu6 and Phe8 with other residues providing smaller contributions12,13,18. In this case study the REPLACE strategy has been utilized in order to find fragment alternatives for residues in the N-terminal tetrapeptide of HAKRRLIF, primarily mimicking the interactions of Ala2 and Arg4. A library of potential Ncap fragments (Table 1) was constructed based on the criteria. Each molecule was docked into the vacated binding site created by truncation of the N-capping group from its crystal structure with cyclin A (PDB ID: 2UUE). Potential ligand alternatives were selected based on pose analysis and PLP1 scoring as a prediction of affinity (examples of docked poses are shown in Figure 4). The synthesis of a confirmed Ncap (1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid) is shown in Figure 5 where this molecule was generated in three steps and purified by automated flash chromatography (Supplementary Figure 1). The 1HNMR, 13CNMR, MS and HPLC characterization data of a representative compound 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid are shown in Supplementary Figures 1-5 respectively. The synthesis of N-capped FLIPs and N-capped-C-capped FLIPs are represented in Figures 7 and 8 and the characterization data are shown in Table 2. The binding affinity for both CDK2/cyclin A and CDK4/cyclin D was determined using the described FP assay and the results are shown in Table 3. Of the compounds tested, FLIPs containing triazole based N-caps were determined to have the greatest affinity for CDK2/cyclin A and CDK4/cyclin D and representative compounds were tested in cell based assays. As a whole, FLIP molecules identified were found to be more drug like, and to retain most of the interactions of the HAKRRLIF octapeptide. Capped tetrapeptides possessed lower potency compared to HAKRRLIF however were of comparable activity to the RRLIF pentapeptide (Tables 3 and 4). These results show that the compounds obtained had improved drug likeness but this was obtained at the cost of somewhat compromised binding affinity.
The anti-proliferative activity these CGI ligands was evaluated and cellular IC50&#8217;s below 30 &#181;M were observed in U2OS and DU145 cancer cell lines.
<img alt="Figure 1" src="/files/ftp_upload/52441/52441fig1.jpg" /> 
Figure 1. Overview of the REPLACE strategy. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52441/52441fig2highres.jpg" width="700" /> 
Figure 2. Binding and interactions of HAKRRLIF in CBG. Left panel: Modeled structure of HAKRRLIF bound to the CBG. The cyclin groove consists of two hydrophobic pockets &#8212; a larger primary pocket (right, occupied by Leu6 and Phe8) and a smaller secondary pocket (left, occupied by Ala2) and which are bridged by acidic residues depicted in red. Right panel: Other important interactions include hydrogen bonding contacts of the peptide backbone (with Trp217, Gln254, Ile281), and ion-pairing interactions of the three basic residues of the peptide (with Glu220, Glu224, Asp283). <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52441/52441fig3.jpg" /> 
Figure 3. Binding mode of 3,5-DCPTRLIF in CBG. Binding of 3,5-DCPT-RLIF is shown with hydrogen bonding interactions (depicted by green dotted lines) with sidechain atoms of Trp217 and Gln254. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/52441/52441fig4.jpg" /> 
Figure 4. Selected docking results. Docked poses of 3 representative N-capping groups are shown. Each of these compounds make H-bonds with the interaction filter atoms of Trp217 and Gln254 and as depicted by green dotted lines. The phenyl substituent makes van der Waals interactions with the secondary hydrophobic pocket. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" src="/files/ftp_upload/52441/52441fig5highres.jpg" width="700" /> 
Figure 5. Synthesis of 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" src="/files/ftp_upload/52441/52441fig6highres.jpg" width="700" /> 
Figure 6. Synthesis of N-capped FLIPs by solid phase synthesis. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig6highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" src="/files/ftp_upload/52441/52441fig7highres.jpg" width="700" /> 
Figure 7. Synthesis of N-capped-C-capped FLIPs. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Table 1" src="/files/ftp_upload/52441/52441table1ahighres.jpg" width="700" />
<img alt="Table 1" src="/files/ftp_upload/52441/52441table1bhighres.jpg" width="700" />
Table 1. Library of small molecules docked and the resulting predictions of binding energy using the PLP1 scoring function.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Peptide</td>
			<td>Purity</td>
			<td>Column Dimensions</td>
			<td>Method</td>
			<td>Flow Rate</td>
			<td>HPLC Retention Time</td>
			<td>Theoretical MW</td>
			<td>Observed MW</td>
		</tr>
		<tr>
			<td>5843</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>23.9</td>
			<td>732</td>
			<td>732.6</td>
		</tr>
		<tr>
			<td>5773</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>22.4</td>
			<td>801.77</td>
			<td>800.55</td>
		</tr>
		<tr>
			<td>5774</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>24.2</td>
			<td>767.33</td>
			<td>766.5</td>
		</tr>
		<tr>
			<td>5762</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>9.0</td>
			<td>731.91</td>
			<td>731.65</td>
		</tr>
		<tr>
			<td>5763</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>11.2</td>
			<td>800.8</td>
			<td>799.5</td>
		</tr>
		<tr>
			<td>5764</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>10.1</td>
			<td>766.34</td>
			<td>765.55</td>
		</tr>
		<tr>
			<td>5771</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>9.4</td>
			<td>749.9</td>
			<td>749.6</td>
		</tr>
		<tr>
			<td>5765</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>10.1</td>
			<td>766.35</td>
			<td>755.65</td>
		</tr>
		<tr>
			<td>5766</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>9.4</td>
			<td>761.9</td>
			<td>761.55</td>
		</tr>
		<tr>
			<td>5767</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>20.4</td>
			<td>761.9</td>
			<td>761.55</td>
		</tr>
		<tr>
			<td>5776</td>
			<td>&#62;75%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>23.3</td>
			<td>785.7</td>
			<td>784.55</td>
		</tr>
		<tr>
			<td>5775</td>
			<td>&#62;90%</td>
			<td>4.6 &#215; 250 mm</td>
			<td>5-65% acetonitrile/water/0.1% TFA</td>
			<td>1 ml/min</td>
			<td>9.9</td>
			<td>751.34</td>
			<td>750.45</td>
		</tr>
	</tbody>
</table>
Table 2. Analytical Data for CGI Binding Peptides and FLIPS.
<img alt="Table 3" src="/files/ftp_upload/52441/52441table3.jpg" /> 
Table 3. In vitro binding of selected N-capped FLIPs to CDK2CA and CDK4CD.
<img alt="Table 4" src="/files/ftp_upload/52441/52441table4.jpg" /> 
Table 4. In vitro binding of selected N &#38; C capped FLIPs to CDK2CA and CDK4CD.
Supplementary Figure 1-5. Purification and characterization of 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid.
<img alt="Sup Figure 1" src="/files/ftp_upload/52441/52441supfig1.jpg" /> 
Supplementary Figure 1. Flash chromatogram of product obtained from step 1. There are four peaks eluting, the major peak eluting at 55% ethyl acetate / 45% hexanes was found to be the desired product. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441supfig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Sup Figure 2" src="/files/ftp_upload/52441/52441supfig2.jpg" /> 
Supplementary Figure 2. 1HNMR of 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid. The NMR spectrum shows 3 aliphatic methyl protons and 3 aromatic protons. The integration for all the three peaks is shown below the NMR spectrum. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441supfig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Sup Figure 3" src="/files/ftp_upload/52441/52441supfig3.jpg" /> 
Supplementary Figure 3. 13CNMR 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid. There are ten carbon atoms in the structure, 13CNMR with 10 signals for each carbon atom. The integration for each peak is shown below the NMR spectrum. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441supfig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Sup Figure 4" src="/files/ftp_upload/52441/52441supfig4.jpg" /> 
Supplementary Figure 4. MS of 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid. The molecular weight of the compound is shown as parent peak at 237. The actual molecular weight of the compound is 237.64. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441supfig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Sup Figure 5" src="/files/ftp_upload/52441/52441supfig5.jpg" /> 
Supplementary Figure 5. HPLC of 1-(3-chlorophenyl)-5-methyl-1H-1,2,4-triazole-3-carboxylic acid. The purity of the product is determined as 100% as shown in the HPLC chromatogram. <a href="https://www-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/52441/52441supfig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors at JoV

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

We describe implementation of the REPLACE strategy for targeting protein-protein interactions. REPLACE is an iterative strategy involving synthetic and computational approaches for the conversion of optimized peptidic inhibitors into drug like molecules.

REPLACE is a unique strategy developed to more effectively target protein-protein interactions (PPIs). It aims to expand available drug target space by ...

development-inhibitors-protein-protein-interactions-through-replace

Universidad San Sebastian

Research

JoVE Journal

Biology

11.2K Views.  University of South Carolina. We describe implementation of the REPLACE strategy for targeting protein-protein interactions. REPLACE is an iterative strategy involving synthetic and computational approaches for the conversion of optimized peptidic inhibitors into drug like molecules.

Video: Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Cyclin-dependent kinase 1 (Cdk1) is a master controller for the cell cycle in all eukaryotes and phosphorylates an estimated 8 - 13% of the proteome; however, the number of identified targets for Cdk1, particularly in human cells is still low. The identification of Cdk1-specific phosphorylation sites is important, as they provide mechanistic insights into how Cdk1 controls the cell cycle. Cell cycle regulation is critical for faithful chromosome segregation, and defects in this complicated process lead to chromosomal aberrations and cancer.
Here, we describe an in vitro kinase assay that is used to identify Cdk1-specific phosphorylation sites. In this assay, a purified protein is phosphorylated in vitro by commercially available human Cdk1/cyclin B. Successful phosphorylation is confirmed by SDS-PAGE, and phosphorylation sites are subsequently identified by mass spectrometry. We also describe purification protocols that yield highly pure and homogeneous protein preparations suitable for the kinase assay, and a binding assay for the functional verification of the identified phosphorylation sites, which probes the interaction between a classical nuclear localization signal (cNLS) and its nuclear transport receptor karyopherin &#945;. To aid with experimental design, we review approaches for the prediction of Cdk1-specific phosphorylation sites from protein sequences. Together these protocols present a very powerful approach that yields Cdk1-specific phosphorylation sites and enables mechanistic studies into how Cdk1 controls the cell cycle. Since this method relies on purified proteins, it can be applied to any model organism and yields reliable results, especially when combined with cell functional studies.

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Cyclin-dependent kinase 1 (Cdk1) is activated in the G2 phase of the cell cycle and regulates many cellular pathways. Here, we present a protocol for an in vitro kinase assay with Cdk1, which allows the identification of Cdk1-specific phosphorylation sites for establishing cellular targets of this important kinase.

Cyclin-dependent kinase 1 (Cdk1) is a master controller for the cell cycle in all eukaryotes and phosphorylates an estimated 8 - 13% of the proteome; ...

Biochemistry

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

The study of kinase-substrate relationships is essential to gain a complete understanding of the functions of these enzymes and their downstream targets in both physiological and pathological states. CK2 is an evolutionarily conserved serine/threonine kinase with a growing list of hundreds of substrates involved in multiple cellular processes. Due to its pleiotropic properties, identifying and characterizing a comprehensive set of CK2 substrates has been particularly challenging and remains a hurdle in the study of this important enzyme. To address this challenge, we have devised a versatile experimental strategy that enables the targeted enrichment and identification of putative CK2 substrates. This protocol takes advantage of the unique dual co-substrate specificity of CK2 allowing for specific thiophosphorylation of its substrates in a cell or tissue lysate. These substrate proteins are subsequently alkylated, immunoprecipitated, and identified by liquid chromatography/tandem mass spectrometry (LC-MS/MS). We have previously used this approach to successfully identify CK2 substrates from Drosophila ovaries and here we extend the application of this protocol to human glioblastoma cells, illustrating the adaptability of this method to investigate the biological roles of this kinase in various model organisms and experimental systems.

The objective of this protocol is to label, enrich, and identify substrates of protein kinase CK2 from a complex biological sample such as a cell lysate or tissue homogenate. This method leverages unique aspects of CK2 biology for this purpose.

The study of kinase-substrate relationships is essential to gain a complete understanding of the functions of these enzymes and their downstream targets ...

High-Throughput Cellular Profiling of Targeted Protein Degradation Compounds Using HiBiT CRISPR Cell Lines

Targeted protein degradation compounds, including molecular glues or proteolysis targeting chimeras, are an exciting new therapeutic modality in small molecule drug discovery. This class of compounds induces protein degradation by bringing into proximity the target protein and the E3 ligase machinery proteins required to ubiquitinate and ultimately degrade the target protein through the ubiquitin-proteasomal pathway (UPP). Profiling of target protein degradation in a high-throughput fashion, however, remains highly challenging given the complexity of cellular pathways required to achieve degradation. Here we present a protocol and screening strategy based on the use of CRISPR/Cas9 endogenous tagging of target proteins with the 11 amino acid HiBiT tag which complements with high affinity to the LgBiT protein, to produce a luminescent protein. These CRISPR targeted cell lines with endogenous tags can be used to measure compound induced degradation in either real-time, kinetic live cell or endpoint lytic modes by monitoring luminescent signal using a luminescent plate-based reader. Here we outline the recommended screening protocols for the different formats, and&#160;also describe the calculation of key degradation parameters of rate, Dmax, DC50, Dmax50, as well as multiplexing with cell viability assays. These approaches enable rapid discovery and triaging of early stage compounds while maintaining endogenous expression and regulation of target proteins in relevant cellular backgrounds, allowing for efficient optimization of lead therapeutic compounds.

This protocol describes the quantitative luminescent detection of protein degradation kinetics in living cells that have been engineered using CRISPR/Cas9 to express antibody free endogenous protein detection tag fused to a target protein. Detailed instructions for calculating and obtaining quantitative degradation parameters, rate, Dmax, DC50, and Dmax50 are included.

Targeted protein degradation compounds, including molecular glues or proteolysis targeting chimeras, are an exciting new therapeutic modality in small ...

Cancer Research

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

The screening of kinase inhibitors is crucial for better understanding properties of a drug and for the identification of potentially new targets with clinical implications. Several methodologies have been reported to accomplish such screening. However, each has its own limitations (e.g., the screening of only ATP analogues, restriction to using purified kinase domains, significant costs associated with testing more than a few kinases at a time, and lack of flexibility in screening protein kinases with novel mutations). Here, a new protocol that overcomes some of these limitations and can be used for the unbiased screening of kinase inhibitors is presented. A strength of this method is its ability to compare the activity of kinase inhibitors across multiple proteins, either between different kinases or different variants of the same kinase. Self-assembled protein microarrays generated through the expression of protein kinases by a human-based in vitro transcription and translation system (IVTT) are employed. The proteins displayed on the microarray are active, allowing for measurement of the effects of kinase inhibitors. The following procedure describes the protocol steps in detail, from the microarray generation and screening to the data analysis.

A detailed protocol for the generation of self-assembled human protein microarrays for the screening of kinase inhibitors is presented.

The screening of kinase inhibitors is crucial for better understanding properties of a drug and for the identification of potentially new targets with ...

Identification of Mediators of T-cell Receptor Signaling via the Screening of Chemical Inhibitor Libraries

The T-cell receptor (TCR) signaling pathway comprises a multitude of mediators that transmit signals upon the activation of the TCR. Different strategies have been proposed and implemented for the identification of new mediators of TCR signaling, which would improve the understanding of T-cell processes, including activation and thymic selection. We describe a screening assay that enables the identification of molecules that influence TCR signaling based on the activation of developing thymocytes. Strong TCR signals cause developing thymocytes to activate apoptotic machinery in a process known as negative selection. Through the application of kinase inhibitors, those with targets that affect TCR signaling are able to override the process of negative selection. The method detailed in this paper can be used to identify inhibitors of canonical kinases with established roles in the TCR signaling pathways and also inhibitors of new kinases yet to be established in the TCR signaling pathways. The screening strategy here can be applied to screens of higher throughput for the identification of novel druggable targets in TCR signaling.

Identification of Mediators of T-cell Receptor Signaling via the Screening of Chemical Inhibitor Libraries

This paper uses a flow-cytometry-based assay to screen libraries of chemical inhibitors for the identification of inhibitors and their targets that influence T-cell receptor signaling. The methods described here can also be expanded for high-throughput screenings.

The T-cell receptor (TCR) signaling pathway comprises a multitude of mediators that transmit signals upon the activation of the TCR. Different strategies ...

Immunology and Infection

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific properties, such as binding affinity, agonist or antagonist behavior, or stability, relative to the native sequence. Protein design lies at the center of current advances drug design and discovery. Not only does protein design provide predictions for potentially useful drug targets, but it also enhances our understanding of the protein folding process and protein-protein interactions. Experimental methods such as directed evolution have shown success in protein design. However, such methods are restricted by the limited sequence space that can be searched tractably. In contrast, computational design strategies allow for the screening of a much larger set of sequences covering a wide variety of properties and functionality. We have developed a range of computational de novo protein design methods capable of tackling several important areas of protein design. These include the design of monomeric proteins for increased stability and complexes for increased binding affinity.
To disseminate these methods for broader use we present Protein WISDOM (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>), a tool that provides automated methods for a variety of protein design problems. Structural templates are submitted to initialize the design process. The first stage of design is an optimization sequence selection stage that aims at improving stability through minimization of potential energy in the sequence space. Selected sequences are then run through a fold specificity stage and a binding affinity stage. A rank-ordered list of the sequences for each step of the process, along with relevant designed structures, provides the user with a comprehensive quantitative assessment of the design. Here we provide the details of each design method, as well as several notable experimental successes attained through the use of the methods.

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

We developed computational de novo protein design methods capable of tackling several important areas of protein design. To disseminate these methods we present Protein WISDOM, an online tool for protein design (<a href="http://www.proteinwisdom.org" target="_blank">http://www.proteinwisdom.org</a>). Starting from a structural template, design of monomeric proteins for increased stability and complexes for increased binding affinity can be performed.

The aim of de novo protein design is to find the amino acid sequences that will fold into a desired 3-dimensional structure with improvements in specific ...

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors

The discovery of BCR/ABL as a driver oncogene in chronic myeloid leukemia (CML) resulted in the development of Imatinib, which, in fact, demonstrated the potential of targeting the kinase in cancers by effectively treating the CML patients. This observation revolutionized drug development to target the oncogenic kinases implicated in various other malignancies, such as, EGFR, B-RAF, KIT and PDGFRs. However, one major drawback of anti-kinase therapies is the emergence of drug resistance mutations rendering the target to have reduced or lost affinity for the drug. Understanding the mechanisms employed by resistant variants not only helps in developing the next generation inhibitors but also gives impetus to clinical management using personalized medicine. We reported a retroviral vector based screening strategy to identify the spectrum of resistance conferring mutations in BCR/ABL, which has helped in developing the next generation BCR/ABL inhibitors. Using Ruxolitinib and JAK2 as a drug target pair, here we describe in vitro screening methods that utilizes the mouse BAF3 cells expressing the random mutation library of JAK2 kinase.

Emergence of genetic resistance against kinase inhibitor therapy poses significant challenge for effective cancer therapy. Identification and characterization of resistant mutations against a newly developed drug helps in better clinical management and next generation drug design. Here, we describe our protocol for in vitro screening and validation of resistant mutations.

The discovery of BCR/ABL as a driver oncogene in chronic myeloid leukemia (CML) resulted in the development of Imatinib, which, in fact, demonstrated the ...

Identification of Kinase-substrate Pairs Using High Throughput Screening

We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel signal transduction pathways. Our approach features the use of a library of purified GST-tagged human protein kinases and a recombinant protein substrate of interest. We have used this technology to identify MAP/microtubule affinity-regulating kinase 2 (MARK2) as the kinase for a glucose-regulated site on CREB-Regulated Transcriptional Coactivator 2 (CRTC2), a protein required for beta cell proliferation, as well as the Axl family of tyrosine kinases as regulators of cell metastasis by phosphorylation of the adaptor protein ELMO. We describe this technology and discuss how it can help to establish a comprehensive map of how cells respond to environmental stimuli.

Protein phosphorylation is a central feature of how cells interpret and respond to information in their extracellular milieu. Here, we present a high throughput screening protocol using kinases purified from mammalian cells to rapidly identify kinases that phosphorylate a substrate(s) of interest.


We have developed a screening platform to identify dedicated human protein kinases for phosphorylated substrates which can be used to elucidate novel ...

Developing and Testing Methylated Nano-Structured Dipeptides that Inhibit Src Kinase Activity In Vitro for Anti-Cancer Applications

Here, with the aim of developing a novel anti-cancer treatment, seven dipeptides were designed that contained methylated tryptophan and/or methylated arginine and were produced using Fmoc solid-phase peptide synthesis. Overexpression of the Src tyrosine kinase enzyme has been implicated in the development of different cancers. Dipeptides containing unnatural amino acids such as methylated arginine (RCH3), dimethylated arginine (R(CH3)2), and/or methylated tryptophan (WCH3) residues have earlier been shown to inhibit Src kinase. In this study, three such dipeptides, W-RCH3, WCH3-RCH3, and W-R(CH3)2, were tested using acellular assays and were found to have IC50 values (the concentration at which 50% inhibition occurs) of 510 nM, 916 nM, and 1 &#181;M, respectively. These values were comparable to those obtained for cyclic penta- to nano-W-R peptides ([W-R]5-[W-R]9) synthesized in previous studies. However, the unmethylated versions of the dipeptides did not show any inhibitory activity against Src kinase. All of these dipeptides (50 &#181;M) did not show any cytotoxicity after incubation up to 72 h with three different cancer cell lines, including leukemia (CCRF-CEM), breast adenocarcinoma (MDA-MB-231), and ovarian adenocarcinoma (SK-OV-3) cell lines, indicating the limited permeability of the peptides through the cell membrane. Therefore, further study is needed to improve the permeability of these peptides for anticancer applications, such as by using a peptide carrier or additional peptide functionalization. In summary, this study provides a protocol to synthesize and test peptides that inhibit Src kinase activity, and thus possess promising anticancer ability, as demonstrated using acellular and cellular assays.

Developing and Testing Methylated Nano-Structured Dipeptides that Inhibit Src Kinase Activity In Vitro for Anti-Cancer Applications

Presented here is a protocol for designing and testing dipeptides that can inhibit Src kinase enzyme activity using acellular and cellular assays for anti-cancer applications. The peptides formulated here (W-RCH3, WCH3-RCH3, and W-R(CH3)2)&#160;inhibited Src kinase with IC50 values of 510 nM, 916 nM, and 1 &#181;M, respectively.

Here, with the aim of developing a novel anti-cancer treatment, seven dipeptides were designed that contained methylated tryptophan and/or methylated ...

Development of Inhibitors of Protein-protein Interactions through REPLACE: Application to the Design and Development Non-ATP Competitive CDK Inhibitors

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Rozdziały w tym wideo

Identification of Cyclin-dependent Kinase 1 Specific Phosphorylation Sites by an In Vitro Kinase Assay

Identification of Novel CK2 Kinase Substrates Using a Versatile Biochemical Approach

High-Throughput Cellular Profiling of Targeted Protein Degradation Compounds Using HiBiT CRISPR Cell Lines

Kinase Inhibitor Screening In Self-assembled Human Protein Microarrays

Identification of Mediators of T-cell Receptor Signaling via the Screening of Chemical Inhibitor Libraries

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

A Method for Screening and Validation of Resistant Mutations Against Kinase Inhibitors

Identification of Kinase-substrate Pairs Using High Throughput Screening

Developing and Testing Methylated Nano-Structured Dipeptides that Inhibit Src Kinase Activity In Vitro for Anti-Cancer Applications

Hamowanie aktywności CDK