Abstract 21: LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes

Abstract Background Target identification is a critical step in elucidating the mechanism of action (MoA) for bioactive compounds. For target-based and phenotypic drug discovery pipelines, extensive potential target knowledge for a lead compound provides essential insights that enable potency and selectivity optimization. The tedious process of target deconvolution for a compound often necessitates a plethora of biochemical and biophysical techniques. To expand the toolbox of unbiased target ID approaches, we developed a novel workflow combining limited proteolysis with quantitative mass spectrometry (LiP-MS) that exploits the drug binding phenomena of protein structural alterations and steric hindrance. Advantageously, LiP-MS's unique peptide-centric focus exploits signature peptide detection to discern ligand binding. Additionally, the LiP-MS workflow enables binding affinity estimation (EC50) and binding site prediction. Here we demonstrate the performance of LiP-MS using two well-characterized kinase inhibitors (KIs), an AstraZeneca CDK9 inhibitor (AZ) and Selumetinib (SE). Methods Mechanically sheared HeLa or U2OS cell lysate was incubated with compound at multiple concentrations. Next, a limited proteolytic digest was performed using proteinase K. After quenching this digestion, lysate was trypsin digested to peptides for mass spectrometry analysis. The resulting peptides were analyzed quantitatively using data-independent acquisition (DIA)-MS. Results Herein, we use LiP-MS to unbiasedly identify unique peptides generated by the binding of two distinct classes of kinase inhibitors in human cell lysate. For the ATP-competitive inhibitor AZ, LiP-Quant shows a strong enrichment for CDKs in the target space defined by LiP score, including CDK1, 2, 4, 6, 9 and 11A. In addition, our data indicates that this KI targets members of the CDKs with different selectivity, with CDK9 displaying the highest compound affinity (nM range). For Selumetinib, a non-competitive allosteric inhibitor, LiP-MS clearly identified the direct targets MEK1/2 as the main hits by LiP score. Both cases represent a highly specific enrichment given that we quantified > 120,000 peptides in each of the experiments. These findings confirm our approach's ability to identify genuine drug targets regardless of drug MoA in a complex biological matrix. Importantly, in both KI target ID studies, identified LiP peptides could be successfully deployed to map compound binding site, demonstrating the potential of LiP-MS to pinpoint regions involved in drug-protein interactions. Conclusions Collectively, this data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties in complex proteomes independent of the compound's MoA and without compound modification or labeling. These capabilities make LiP-MS a powerful addition to the target ID toolbox. Citation Format: Nigel Beaton, Yuehan Feng, Roland Bruderer, Adam Hendricks, Ghaith Hamza, Eric Miele, Rick Davies, Kristina Beeler, Ilaria Piazza, Paola Picotti, Paola Castaldi, Lukas Reiter. LiP-MS, a machine learning-based chemoproteomic approach to identify drug targets in complex proteomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 21.