Extract2Chip-Bypassing Protein Purification in Drug Discovery Using Surface Plasmon Resonance.

Modern drug discovery relies on combinatorial screening campaigns to find drug molecules targeting specific disease-associated proteins. The success of such campaigns often relies on functional and structural information of the selected therapeutic target, only achievable once its purification is mastered. With the aim of bypassing the protein purification process to gain insights on the druggability, ligand binding, and/or characterization of protein-protein interactions, herein, we describe the Extract2Chip method. This approach builds on the immobilization of site-specific biotinylated proteins of interest, directly from cellular extracts, on avidin-coated sensor chips to allow for the characterization of molecular interactions via surface plasmon resonance (SPR). The developed method was initially validated using Cyclophilin D (CypD) and subsequently applied to other drug discovery projects in which the targets of interest were difficult to express, purify, and crystallize. Extract2Chip was successfully applied to the characterization of Yes-associated protein (YAP): Transcriptional enhancer factor TEF (TEAD1) protein-protein interaction inhibitors, in the validation of a ternary complex assembly composed of Dyskerin pseudouridine synthase 1 (DKC1) and RuvBL1/RuvBL2, and in the establishment of a fast-screening platform to select the most suitable NUAK family SNF1-like kinase 2 (NUAK2) surrogate for binding and structural studies. The described method paves the way for a potential revival of the many drug discovery campaigns that have failed to deliver due to the lack of suitable and sufficient protein supply.

High-Throughput Screening and Proteomic Characterization of Compounds Targeting Myeloid-Derived Suppressor Cells.

Myeloid-derived suppressor cells (MDSC) are a heterogeneous cell population of incompletely differentiated immune cells. They are known to suppress T cell activity and are implicated in multiple chronic diseases, which make them an attractive cell population for drug discovery. Here, we characterized the baseline proteomes and phospho-proteomes of mouse MDSC differentiated from a progenitor cell line to a depth of 7000 proteins and phosphorylation sites. We also validated the cellular system for drug discovery by recapitulating and identifying known and novel molecular responses to the well-studied MDSC drugs entinostat and mocetinostat. We established a high-throughput drug screening platform using a MDSC/T cell coculture system and assessed the effects of ∼21,000 small molecule compounds on T cell proliferation and IFN-γ secretion to identify novel MDSC modulator. The most promising candidates were validated in a human MDSC system, and subsequent proteomic experiments showed significant upregulation of several proteins associated with the reduction of reactive oxygen species (ROS). Proteome-wide solvent-induced protein stability assays identified Acyp1 and Cd74 as potential targets, and the ROS-reducing drug phenotype was validated by measuring ROS levels in cells in response to compound, suggesting a potential mode of action. We anticipate that the data and chemical tools developed in this study will be valuable for further research on MDSC and related drug discovery.

Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects.

Accurate ranking of compounds with regards to their binding affinity to a protein using computational methods is of great interest to pharmaceutical research. Physics-based free energy calculations are regarded as the most rigorous way to estimate binding affinity. In recent years, many retrospective studies carried out both in academia and industry have demonstrated its potential. Here, we present the results of large-scale prospective application of the FEP+ method in active drug discovery projects in an industry setting at Merck KGaA, Darmstadt, Germany. We compare these prospective data to results obtained on a new diverse, public benchmark of eight pharmaceutically relevant targets. Our results offer insights into the challenges faced when using free energy calculations in real-life drug discovery projects and identify limitations that could be tackled by future method development. The new public data set we provide to the community can support further method development and comparative benchmarking of free energy calculations.