AMBER Drug Discovery Boost Tools: Automated Workflow for Production Free-Energy Simulation Setup and Analysis (ProFESSA)

Abstract

We report an automated workflow for production free-energy simulation setup and analysis (ProFESSA) using the GPU-accelerated AMBER free-energy engine with enhanced sampling features and analysis tools, part of the AMBER Drug Discovery Boost package that has been integrated into the AMBER22 release. The workflow establishes a flexible, end-to-end pipeline for performing alchemical free-energy simulations that brings to bear technologies, including new enhanced sampling features and analysis tools, to practical drug discovery problems. ProFESSA provides the user with top-level control of large sets of free-energy calculations and offers access to the following key functionalities: (1) automated setup of file infrastructure; (2) enhanced conformational and alchemical sampling with the ACES method; and (3) network-wide free-energy analysis with the optional imposition of cycle closure and experimental constraints. The workflow is applied to perform absolute and relative solvation free-energy and relative ligand–protein binding free-energy calculations using different atom-mapping procedures. Results demonstrate that the workflow is internally consistent and highly robust. Further, the application of a new network-wide Lagrange multiplier constraint analysis that imposes key experimental constraints substantially improves binding free-energy predictions.

Modern Alchemical Free Energy Methods for Drug Discovery Explained

“…The following are some general recommendations for setting up and running simulations (further details are outlined in a recently reported workflow): Carefully equilibrate each end state (node) prior to setting up an alchemical transformation (edge) simulation between the nodes. Use the same equilibrated structures of a given node consistently for any edge containing the node. Construct “dense” thermodynamic graphs that have sufficient redundancy to enable network-wide analysis of cycle closure conditions and yield meaningful internal consistency checks. Use optimized alchemical transformation pathways, including smoothstep softcore potentials and, if necessary, advanced λ scheduling features in order to maximize phase space overlap between windows. Apply robust enhanced sampling methods such as ACES that leverage an HREMD framework to ensure that different λ-windows are kept in equilibrium. Simulation results should be tested for convergence through block analysis, and statistical error estimates should be made ,,,− that include multiple independent trials of ACES runs.…”

“…With the concurrent advancement of new AFE methods, high-performance computing hardware, and efficient software implementations, this once established paradigm is now being revisited to some degree. As will be discussed in more detail below, the construction of more “dense” thermodynamic graph networks enables analysis of cycle closure conditions as well as integration of experimental free energy contraints for known ligands. Network AFE simulations can be performed adaptively with resources allocated dynamically in order to improve precision of the free energy predictions. , Further, the choice of atoms treated by softcore potentials and using a dual topology are leveraged in alchemical enhanced sampling methods such as ACES .…”

“…RBFE calculations, on the other hand, enable dense thermodynamic graphs to be constucted and cycle closure conditions to be leveraged to increase overall calculation precision and potentially accuracy as well in the case of inclusion of experimental free energy constraints for known ligands. , When RBFE calculations are being performed optimally, there is only modest rearrangement of the environment along the alchemical transformation pathway and minimal generation of void space that needs to be occupied by the environment. Moreover, RBFE simulations do not have the same issues as ABFE simulations of maintaining position in the binding pocket when the ligand is in the pure dummy atom state, as such a state does not exist for the RBFE case where there is a shared “common core” of atoms .…”

AmberTools

“…Free energy methods have been a mainstay of Amber for decades. , Besides our existing free energy technology base this latest release of AmberTools includes a collection of new software tools for the robust analysis of free energy simulations ( FE-ToolKit ) , as well as workflow tools for production free energy simulation setup and analysis ( ProFESSA ) using the GPU-accelerated Amber free energy engine with enhanced sampling features. This software is part of the Amber Drug Discovery Boost package …”

End-to-End Differentiable Force Field Generator with Crystal Structure Differentiation and Matching

“…Molecular dynamics (MD) is an essential tool for atomic and molecular modeling of small organic molecules [1][2][3]. In structure-based drug design, the characterization of intermolecular interactions is crucial for predicting ligand activity [4][5][6][7]. MD provides vital insights into the fundamental mechanisms governing the condensed matter properties of a diverse range of materials [8][9][10].…”