There is a vast gulf between the two primary strategies for simulating protein-ligand interactions. Docking methods significantly limit or eliminate protein flexibility to gain great speed at the price of uncontrolled inaccuracy, whereas fully flexible atomistic molecular dynamics simulations are expensive and often suffer from limited sampling. We are developing a flexible docking approach geared especially for highly flexible or poorly resolved targets based on mixed-resolution Monte Carlo (MRMC), which is intended to offer a balance among speed, protein flexibility, and sampling power. The binding region of the protein is treated with a standard atomistic force field, while the remainder of the protein is modeled at the residue level with a Go model that permits protein flexibility while saving computational cost. Implicit solvation is used. In order to further enhance sampling, we combine this method with nonequilibrium candidate Monte Carlo (NCMC) (Nilmeier, J. et al. Proc. Natl. Acad. Sci. USA 2011, 108, E1009–E1018). We have been testing the MRMC method with known ligands of estrogen receptor α, an important cancer target known for its flexibility. The results are validated by comparing the final structure from each simulation with the known crystal structure. While MRMC docking takes substantially more CPU time for each individual ligand compared to standard docking, adding protein flexibility leads to significantly lower total energies and stronger interactions between protein and ligand. The addition of NCMC leads to substantially increased acceptance rates for large ligand translations and rotations. Both the addition of protein flexibility and the use of NCMC improve the performance of docking, as measured by ranking poses according to the protein-ligand interaction energy and computing the RMSD to the crystal structure.

'Middle-way' flexible docking: Combining mixed-resolution and non-equilibrium candidate Monte Carlo for improved pose prediction in estrogen receptor α

255th National Meeting (2018)

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