Abstract C015: Limited proteolysis coupled to mass spectrometry (LiP-Quant), a novel drug target deconvolution strategy
Authors: Feng Yuehan , Beaton Nigel , Bruderer Roland , Piazza Ilaria , Picotti Paola , Reiter Lukas 
Published: March 2020

Background: High attrition rates in target-centric drug development approaches, as well as a limited number of targets, have shifted the focus of drug development back towards phenotypic screening. In parallel, novel proteomics-based target deconvolution approaches to drug target identification have gained popularity. Limited proteolysis coupled with mass spectrometry (LiP-MS) is a new target deconvolution technique that exploits protein structural alterations, as well as steric effects driven by drug binding. A major advantage of LiP-MS is its unique focus on detection of signature peptides that report on ligand binding, which are generated by a limited digestion and identified by proteomic analysis. Here we demonstrate the performance of LiP-MS using two well-characterized kinase inhibitors, Selumetinib (SE) and Staurosporine (ST), as well as two natural product-derived phosphatase inhibitors Calyculin A and Fostriecin in HeLa cell lysate. Additionally, we introduce a LiP workflow that can be used to dissect protein-drug interactions in a more nuanced manner. Results Herein, we demonstrate the ability of our LiP-MS approach to identify unique peptides generated by the binding of either a highly-specific (SE) or broad specificity (ST) kinase inhibitor. Taking the top 200 identified target candidate peptides ranked by LiP score from the broad inhibitor (staurosporine), GO enrichment analysis confirmed a highly significant 3-fold enrichment for kinase targets (p &lt; 2E-5). For the more challenging selumetinib experiment, the direct targets MEK1 and MEK2 were clearly identified as main hits with 5 peptides in the top 16 (sorted by LiP score). This represents a highly specific enrichment given that we quantified &gt; 100,000 peptide precursors in this experiment. These findings confirm our approach’s ability to identify genuine drug targets regardless of drug specificity. To characterize the specificity of LiP-MS, we treated lysate with two separate protein phosphatase inhibitors. According to literature calyculin A targets protein phosphatase 1 and 2 (PP1A and PP2A) and fostriecin also targets PP2A, in addition to protein phosphatase 4 (PP4C) but does not bind PP1A. We identified both targets of calyculin A, with 14 of the top 15 peptides by LiP score being from either PP2A or PP1A. Robust phosphatase identification (three main targets PP2A, PP4C and PP6C) was also observed with fostriecin treatment with a different profile from calyculin A. In line with literature, the hits of fostriecin did not include PP1A, despite the high homology of the protein phosphatase family. Finally, taking advantage of the peptide resolution of LiP-MS, we employed this workflow to investigate the Ca2+-induced conformational switch in the recombinant protein calmodulin by monitoring structure-specific peptides which cover its entire amino acid sequence. Conclusions Collectively, this data demonstrates that LiP-MS can be used to effectively identify protein drug targets and characterize the binding properties, regardless of the specificity of the compound. These capabilities make LiP-MS a powerful target deconvolution and identification strategy. In addition, we envision the development of LiP-MS for recombinant proteins as a high-throughput strategy for binding site characterization of protein-small molecule and protein-protein interactions.

Citation Format: Yuehan Feng, Nigel Beaton, Roland Bruderer, Ilaria Piazza, Paola Picotti, Lukas Reiter. Limited proteolysis coupled to mass spectrometry (LiP-Quant), a novel drug target deconvolution strategy [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference on Molecular Targets and Cancer Therapeutics; 2019 Oct 26-30; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2019;18(12 Suppl):Abstract nr C015. doi:10.1158/1535-7163.TARG-19-C015