Adaptive simulations, towards interactive protein-ligand modeling

Daniel Lecina,Victor Guallar,Joan F Gilabert

doi:10.1038/s41598-017-08445-5

Daniel Lecina, Victor Guallar + Show 1 more

Open Access

https://doi.org/10.1038/s41598-017-08445-5

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Abstract

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. Although in the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation. Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. Here we present a game-changing technology, opening up the way for fast reliable simulations of protein dynamics by combining an adaptive reinforcement learning procedure with Monte Carlo sampling in the frame of modern multi-core computational resources. We show remarkable performance in mapping the protein-ligand energy landscape, being able to reproduce the full binding mechanism in less than half an hour, or the active site induced fit in less than 5 minutes. We exemplify our method by studying diverse complex targets, including nuclear hormone receptors and GPCRs, demonstrating the potential of using the new adaptive technique in screening and lead optimization studies.

Highlights

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process
We plot here the results for the B-GPCR system, using 512 trajectories, but equivalent figures for the remaining systems are shown in the Supplementary Information
As seen in the root mean square deviation (RMSD) evolution plots, both the adaptive (Fig. 2a) and standard (Fig. 2c) Protein Energy Landscape Exploration (PELE) methods succeed in sampling native-like conformations, with RMSD values ~1 Å; analogous results are seen for all other systems (Supplementary Figs. 2 to 4)

Summary

Introduction

Modeling the dynamic nature of protein-ligand binding with atomistic simulations is one of the main challenges in computational biophysics, with important implications in the drug design process. In the past few years hardware and software advances have significantly revamped the use of molecular simulations, we still lack a fast and accurate ab initio description of the binding mechanism in complex systems, available only for up-to-date techniques and requiring several hours or days of heavy computation Such delay is one of the main limiting factors for a larger penetration of protein dynamics modeling in the pharmaceutical industry. These advances in sampling capabilities, when combined with an optimized force field for ligands, introduced significant improvements in ranking relative binding free energies[9] Despite these achievements, accurate (dynamical) modelling still requires several hours or days of dedicated heavy computation, being such a delay one of the main limiting factors for a larger penetration of these techniques in industrial applications. We present such a breakthrough tool: Adaptive-PELE, a combination of PELE with an adaptive reinforcement learning procedure

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Conclusion