Reinforced molecular dynamics: Physics-infused generative machine learning model explores CRBN activation process

Abstract

We propose a simple and practical machine learning-based desktop solution for modeling biologically relevant protein motions. We termed our technology reinforced molecular dynamics (rMD) combining MD trajectory data and free-energy (FE) map data to train a dual-loss function autoencoder network that can explore conformational space more efficiently than the underlying MD simulation. The key insight of rMD is that it effectively replaces the latent space with an FE map, thus infusing the autoencoder network with a physical context. The FE map is computed from an MD simulation over a low-dimensional collective variable space that captures some biological function. One can directly use then the FE map for example, to generate more protein structures in poorly sampled regions, follow paths on the FE map to explore conformational transitions, etc. The rMD technology is entirely self-contained, does not rely on any pre-trained model, and can be run on a single GPU desktop computer. We present our rMD computations in a key area of molecular-glue degraders aimed at a deeper understanding of the structural transition from open to closed conformations of CRBN.