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Abstract
Proteolysis targeting chimeras, or PROTACs, are an emerging class of drugs that offer the potential to develop therapeutics targeting “undruggable” proteins by stabilizing protein-protein interactions (PPI). This involves leveraging the physiological protein degradation mechanism based on ubiquitination through stabilization of target protein-E3 ubiquitin ligase PPI mediated by the PROTAC. Existing computational methods for ligand design are not typically designed for the ternary complex problem and may have limited accuracy or efficiency, due to the use of either rigid docking or full molecular dynamics (MD) simulations. Here we present a method which uses SILCS (site identification by ligand competitive saturation) to address the challenge of designing ligands which stabilize PPI by using of precomputed ensembles of 1) functional group affinity patterns, termed FragMaps, for efficient and accurate ligand docking and of 2) a collection of putative PPI dimer 3D structures as docking targets. SILCS simulations involving aqueous, multi-solute grand canonical Monte Carlo (GCMC)/MD calculations generate the FragMaps for both the target and ligase proteins. An ensemble of PPI dimer conformations are generated using the FragMaps and then dimer FragMaps are generated by merging the two sets of FragMaps. PROTAC molecules are docked into the ensemble of dimer FragMaps, and final scoring metrics are extracted from the most favorable ternary complex. The scoring metrics, including energetics, binding site geometry and physicochemical terms, are weighted together to construct an activity score. The method is benchmarked on a diverse set of ternary crystal structures of different proteins and PROTACs, and the derived activity score shows modest correlation with DC50 values in cells for a wide variety of systems. The SILCS-xTAC method is a powerful tool to facilitate PROTAC optimization by predicting binding geometries and energetics of ternary complexes.
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Supporting Tables and Figures
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Table S1: PROTAC activity (DC50) data sets.
Table S2: Proteins used in the SILCS simulations.
Table S3: Overlap coefficients quantifying convergence of SILCS μex-GCMC/MD simulations.
Table S4: Experimental complex inter-site distances, Cα-Cα cutoffs, contacts, and interaction energies.
Table S5: Confounding variables complicating the prediction of PROTAC DC50 activity.
Table S6: Overlap coefficients of merged FragMaps using experimental alignment (M) against FragMaps from SILCS simulations of full experimental dimer structures (Expt).
Table S7: Regression weights determined by LOO-CV for each system.
Figure S1: Experimental complex and SILCS-PPI of minimum iRMSD among those clustered.
Figure S2: Experimental complexes overlaid with structures used to generate the merged FragMaps.
Figure S3: PROTACs and highlighted warheads from experimental structures.
Figure S4: Correlation of RMSD and Standard Error of RMSD of the full PROTACs.
Figure S5: 2-step PROTAC docking wide-miss cases.
Figure S6: PROTACs and highlighted warheads.
Figure S7: Performance of LOO-CV.
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Additional supplementary data
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Information about the compounds described in the article, including SDF and SMILES strings for all PROTACs and warheads, MD simulation input and parameter files, as well as the SILCS-xTAC data derived in this article and their experimental DC50 values from the literature and corresponding DOIs, are available on GitHub at https://github.com/mackerell-lab/silcs-xtac-si.
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