Advancements in LiP-MS: short-gradient LC-MS and automated sample preparation for improved drug target identification

Drug development faces a significant bottleneck due to the high failure rate of candidate drugs during clinical translation. Efficiently identifying drug targets and potential off-targets is crucial to address this challenge. Technologies such as limited proteolysis coupled with mass spectrometry (LiP-MS) aim to overcome this hurdle. LiP-MS offers a unique approach for unbiased drug target deconvolution and binding site identification in complex proteomes. However, current LiP-MS protocols remain challenging, low in throughput, and manual labor-intensive, which restricts their use in larger phenotypic screening for drug discovery. Here, we present the successful transfer of our current manual LiP-MS pipeline (based on LiP-Quant, Piazza et al. 2020) to a fully automated sample preparation workflow in combination with short gradient LC-MS methods.

Initially, we adapted our in-house manual LiP-MS sample preparation protocol to enhance its automation compatibility. Subsequently, we started automating the LiP-MS workflow by integrating the drug treatment and limited proteolysis step. Notably, the semi-automated method exhibited no discernible row- or column-wise trends, but it did yield a significant (~20%) reduction in the precursor coefficient of variation (CV) compared to the manual protocol. Furthermore, number of identified drug targets could be maintained in a benchmark experiment with the broad-spectrum kinase inhibitor staurosporine. Encouraged by these findings, we started to fully integrate the protocol, encompassing both up- and downstream processes of limited proteolysis, into our robotic system. The automated sample processing was integrated on a liquid handler which is robotically linked to various third-party devices such as an ultrasonicator, plate reader or positive pressure module. The integrated workflow allows the full sample preparation for a LiP-MS experiment including main processing steps of native cell lysis, drug dilution and treatment, limited proteolysis, proteolytic digestion, peptide clean-up, concentration determination and normalization. As a result, hands-on time decreased by over 70%, while data quality improved. Looking ahead, we plan to extend this assay to simultaneously screen several compounds in parallel.

To further improve the LiP-MS pipeline, the developed sample preparation was combined with a short-gradient dia-PASEF® LC-MS/MS measurement on the timsTOF HT mass spectrometer. We optimized the method for identification of both, semi-tryptic and tryptic peptides, using adjusted dia-PASEF window architecture and a non-linear gradient ramp. The final method with a gradient length of 45 minutes achieves a throughput of 24 samples per day, which reduces the acquisition time by 75% compared to our current method.

Taken together, by integrating automated sample preparation with short-gradient LC-MS, we achieved substantial improvements in our LiP-MS pipeline to make it suitable for larger drug screenings.