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Each sample was sonicated (Syclon Ultrasonic Homogenizer) for 2 x 9 seconds at a power setting of 15%to disrupt the cell pellet. The protein samples were normalised to 300µg. Each sample was reduced by adding 8mM dithiothreitol (dtt) and mixing (thermomixer 1200rpm, 30°C) for 60 min and carboxylated by adding 20 mM iodoacetamide and mixing (thermomixer 1200rpm, 30°C) for 30 min the dark. The solution was diluted with 50mM Tris HCL to bring the urea concentration down to below 2M. NB: (Urea must be below 2M to prevent inhibition of trypsin). lyophilized trypsin (sequencing grade trypsin from Promega) was resuspended with 50Mm Tris HCL at a concentration of 0.5µg/µl and added to each solution. The samples were digested overnight with trypsin (1:100 enzyme to protein ratio) with gentle shaking (thermomixer 850rpm, 37°C). The digestion was terminated by adding formic acid to 1% final concentration and cleaned up using c18 (HyperSep SpinTip P-20, BioBasic C18, Thermo Scientific). Phosphopeptide enrichment was carried out with TiO2 (Titansphere Phos-TiO Bulk 10 µm, (GL Sciences Inc, Tokyo, Japan) using an adapted method previously described (1). In summary, each sample was incubated with TiO2 beads for 30 minutes by rotation in 80% acetonitrile, 6% trifluoroacetic acid, 5mM monopotassium phosphate, 20mg/ml 2,5- dihydroxybenzoic acid, this step was carried out twice. The beads were washed 5 times in 80% acetonitrile/1% trifluoroacetic acid, before elution of the phosphopeptides with 50% acetonitrile, 7% ammonium hydroxide. The two eluents from each sample were then pooled and dried down. Mass Spectrometry Samples were run on a Bruker timsTof Pro mass spectrometer connected to an Evosep One (5) or a Bruker nanoElute chromatography system. The mass spectrometer was operated in positive ion mode with a capillary voltage of 1600 V, dry gas flow of 3 l/min and a dry temperature of 180 °C. All data was acquired with the instrument operating in trapped ion mobility spectrometry data dependent acquisition mode (TIMS DDA) mode. Trapped ions were selected for ms/ms using parallel accumulation serial fragmentation (PASEF). A scan range of (100-1700 m/z) was performed at a rate of 5 or 10 PASEF MS/MS frames to 1 MS scan with a cycle time of 1.03s or 1.89s.(6)PrideMass Spectrometry Data Analysis For analysis of MS output data, peptide mapping was initially performed with MaxQuant (release 2.0.1.0) using the Homo sapiens subset of the Uniprot Swissprot database with specific parameters for TIMS data dependent acquisition (TIMS-DDA). The MaxQuant output file was imported into the Perseus (version 1.6.15.0) environment for protein quantification. LFQ (Label Free quantitation) intensities were loaded as main columns. Reverse proteins and proteins only identified by site were filtered out from further analysis. LFQ values were then log2 transformed. The data frame was then split into smaller datasets by group of samples (e.g., condition v control) allowing filtering of proteins that were not identified in more than one replicate. Missing values were imputed based on the normal distribution with a width of 0.3 and a down shift of 1.8. These condition-specific (such as control condition and treated condition) data frames were then exported and used for further analysis. For phosphoproteome analysis, label-free data was loaded into Perseus using ‘Intensity X_1’, ‘Intensity X_2’, ‘Intensity X_3’ (i.e., data for the same sample and site for peptides that are mono-, di-, or tri-phosphorylated) as main columns. In addition to reverse proteins and proteins only identified by site, potential contaminants, and proteins with localization probability < 0.75 were excluded from further analysis. The built-in function ‘Expand site table’ was then used to rearrange the columns to permit analysis of differing levels of phosphorylation (i.e., single, double, triple-phosphorylated). Following log2 transformation, the position within the protein, known sites of phosphorylation, and linear motifs were all added to the table based on PhosphoSitePlus data (https://www.phosphosite.org). Similar to the whole proteome data analysis, the data frame was divided based on the condition, proteins filtered based on the number of replicates in which they appeared before imputing the missing values and exporting the data.(7)ProteomicsProfessor Jonathan BondPARTIALUniversity College Dublin, School of Medicine, Systems Biology Ireland Belfield Dublin 4. Children’s Health Ireland at Crumlin, Dublin, Ireland.Homo Sapiens (human)Not availablekieran.wynne1@ucd.ieUniversity College DublinIrelandfalseA structure-based modelling approach identifies effective drug combinations for RAS-mutant acute myeloid leukemiaAbstract Mutations activating RAS/RAF/MEK/ERK signaling are associated with poor outcome in acute myeloid leukemia (AML), but therapeutic targeting of this pathway is challenging. Here, we employ a structure-based, dynamic RAS pathway model to successfully predict RAF inhibitor (RAFi) combinations which synergistically suppress ERK signaling in RAS-mutant AML. Our in silico models predicted therapeutic synergy of two iterations of conformation-specific RAF inhibitors: Type I½ + Type II and Type I + Type II. Predictions were validated in vitro in AML cell lines and patient samples, with synergy verified by the Loewe Additivity model. Lifirafenib (Type II) + encorafenib (Type I½) was highly synergistic against both NRAS- and KRAS-mutant lines, while synergy of lifirafenib + SB590885 (Type I) was specific to NRAS-mutants. Immunoblotting confirmed that combination efficacy correlated strongly with decreased RAS pathway activation. Leveraging the pharmacokinetic predictions of our in silico model, both combinations were then assessed in a pre-clinical NRAS-mutant AML patient-derived xenograft (PDX) model, showing significantly improved leukemia growth delay and event-free survival compared with single agent approaches. Assessment of leukemia burden in bone marrow and spleen during treatment further showed site-specific efficacy against circulating and spleen-resident blasts for both combinations. In summary, we report that our structure based-modelling approach can effectively identify novel, non-obvious, and well-tolerated RAFi combinations that are highly effective against in vitro and in vivo models, thereby suggesting alternative potential therapeutic strategies for high-risk RAS-mutant AML.  2026-06-242025-05-05PXD063606NEWT:10090NEWT:9606