Challenge #6

We propose a detailed computational strategy aimed at discovering and optimizing novel ligands that target the histone binding groove of the SETDB1 triple Tudor domain (TTD). By focusing on the aromatic cages and the acetylated lysine (Kac) binding pocket, Our methodology involves a comprehensive exploration of the histone binding groove's sub-cavities to identify ligands with high affinity. The approach integrates structure-based and ligand-based pharmacophore modeling, followed by a virtual screening workflow (VSW), molecular docking, molecular dynamics (MD) simulations, and free energy perturbation (FEP) calculations to ensure precise and targeted ligand discovery. We will utilize the 3D structures of SETDB1 TTD obtained from the Protein Data Bank (PDB IDs: 7CJT, 8UWP, 6AU3). These protein structures will undergo preparation and minimization to produce accurate models of the SETDB1 TTD. Long-run MD simulations (~1 µs) will be conducted to capture the dynamic behavior and flexibility of the binding grooves and pockets. Clustering of MD trajectories will yield representative conformations for subsequent docking studies, ensuring comprehensive sampling of all relevant sub-cavities. Both “single ligand-based” and “protein-ligand structure-based” pharmacophore models will be developed using data from known SETDB1 ligands to generate six pharmacophore hypotheses. These models will focus on the interaction patterns observed in the PDB entries 7CJT, 8UWP, and 6AU3. Single ligand-based models will derive three pharmacophore hypotheses, emphasizing critical features necessary for binding, such as interactions with the aromatic cages and accommodation of methylated or acetylated lysine sidechains. Structure-based protein-ligand models will generate three additional pharmacophore hypotheses, identifying key interaction hotspots within the histone binding groove, including the aromatic cages and the acetylated lysine cavity essential for binding and activity.

A subset of compounds from Enamine will be curated to generate a database of potential hit compounds for this study. This sub-library will include only PAINS-free molecules to minimize the risk of false positives and ensure good ADMET properties, such as favorable cell permeability, acceptable aqueous solubility, and the exclusion of compounds with potential toxicophores. Each pharmacophore hypothesis will be scanned against the Enamine database to identify initial hits from our curated library. Key parameters such as fitness score, site score, vector score, volume score, and alignment score will be calculated to determine the precision of the match between database compounds and the hypotheses.

The initial stages of our computational pipeline will involve a virtual screening workflow (VSW) using Glide to filter potential hits from the databases. Following this, molecular docking will be performed on the top 10% of output hits using the standard precision (SP) Glide docking score. The top 500 docked compounds from each pharmacophore hypothesis, 3000 in total, will be re-docked using extra-precision (XP) Glide docking score, including strain energy corrections. The top hits from the virtual screening will be clustered based on pharmacophore hypotheses. Subsequently, all-atom MD simulations will be conducted to examine the relative stability of receptor-ligand interactions of the top hits. Finally, to prioritize compounds for in vitro testing, rigorous and cost-intensive alchemical free energy perturbation (FEP) calculations will be employed (following the protocol by Jiang et al., DOI: 10.1021/acs.jcim.9b00362) to accurately predict the potency of the top binders. This comprehensive in silico protocol is expected to yield potent and novel candidates targeting SETDB1 that can be experimentally validated and optimized through biochemical assays.