Fragment Pose Prediction Using Non-equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

Abstract

Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures. We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions between binding modes in our study. Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.

Temperature artifacts in protein structures bias ligand-binding predictions

“…To analyze our binding-mode simulations, we used time-lagged independent component analysis and perron-cluster cluster analysis tools, as described in Lim et al 2019. 96 More details about the BLUES simulations are provided in the ESI Appendix and ESI Methods. †…”

A Computational Workflow for the Identification of Novel Fragments Acting as Inhibitors of the Activity of Protein Kinase CK1δ

“…More advanced protocols have also been developed. Nonequilibrium candidate Monte Carlo (NCMC) is an algorithm that has been applied to enhance the sampling of fragment binding modes [ 38 ]; this method has been successfully applied to FBDD [ 39 ]. Another promising approach is the application of Markov state models to MD simulations, which has proved its potential to FBDD [ 40 ].…”

Combining Machine Learning and Enhanced Sampling Techniques for Efficient and Accurate Calculation of Absolute Binding Free Energies

“…To test the chosen methods, we have selected a complex and realistic target, the human soluble epoxide hydrolase (sEH) and a number of noncongeneric ligands spanning a wide range of affinities and sizes. This enzyme has enduring pharmaceutical and computational , significance, and a proof of that is the number of inhibitors that have been synthesized . The first generation of compounds mostly contained urea-like motifs that participate in H-bonding interactions with the active-site residues.…”