Flexible Topology: A New Method for Dynamic Drug Design

Abstract

Ligand-induced conformational changes form the underpinnings of most essential biomolecular processes, however they are often neglected in screening and design applications, due to the high computational cost. We propose a method called “Flexible Topology”, where a ligand is comprised of a set of shapeshifting “ghost” atoms, whose atomic identities and connectivity can dynamically change over the course of a simulation. Ghost atoms are guided toward their target positions using a translation-, rotation-, and index-invariant restraint potential. When implemented with a large set possible targets, this can simulate trajectories in a coupled chemical-conformational space that allows solutions to molecular design problems to simply emerge over the course of a trajectory. It also provides a mechanism for observing ligand-induced conformational change, by allowing for the surrounding environment and the ghost atoms to respond to each other during the design process. This builds on a substantial history of alchemy in the field of molecular dynamics simulation, including the Lambda dynamics method developed by Brooks and coworkers [X. Kong and C.L. Brooks III, J. Chem. Phys. 105, 2414 (1996)], but takes it to an extreme by associating a set of four dynamical variables with each shapeshifting atom that control not only its presence but its atomic identity. Here we outline the theoretical details of this method, its implementation using the OpenMM simulation package, and some preliminary studies of ghost particle assembly simulations. We show that while Flexible Topology is able to consistently assemble molecules up to 50 atoms in size, modifications are needed before it can reliably predict bound poses in heterogeneous environments.

Flexible Topology: A new method for dynamic drug design

“…Derivatives of the loss function are used to define atomic forces that are added to an OpenMM MD simulation, implemented using a PyTorch model that incorporates features from the TorchANI 82 package. This was achieved with an external force plugin for the OpenMM tool, referred to as "MLForce", 78 which has already been made freely available on Github. MLForce is responsible for loading the TorchScript file of a TorchANI model and computing the internal force on the ghost This approach is agnostic to the content of the TorchScript file; it can be easily swapped out with a model that computes different AEVs, or with a model that implements a different loss function that involves a deep neural network.…”

“…The goal of the Flexible Topology method is to model a set of atoms that can continuously transform between different molecules. To implement this method, we use MD simulations that are coupled with an ML-based external force (MLForce 78 ) that slowly nudges a set of ghost particles to become a drug-like molecule (Figure 1). MLForce consists of three components: a molecular representation model, an assignment algorithm and a loss function.…”

Flexible Topology: A Dynamic Model of a Continuous Chemical Space

“…Derivatives of the loss function are used to define atomic forces that are added to an OpenMM MD simulation, implemented using a PyTorch model that incorporates features from the TorchA-NI 40 package. This was achieved with an external force plugin for the OpenMM tool, referred to as "MLForce", 36 which has already been made freely available on Github. MLForce is responsible for loading the TorchScript file of a TorchANI model and computing the internal force on the ghost particles.…”

“…The goal of the Flexible Topology method is to model a set of atoms that can continuously transform between different molecules. To implement this method, we use MD simulations that are coupled with an ML-based external force (MLForce) 36 that slowly nudges a set of ghost particles to become a drug-like molecule (Figure 1).…”