arXiv subjects

Anika J. Friedman

Publications and source records attributed to Anika J. Friedman.

3 recordsLinked to original sources

arXiv2026

Improving sampling of binding free energy differences between covalently bound ligands in alternate binding pockets using MT-REXEE

The primary limitation for the application of alchemical free energy methods to a wider variety of complex molecular systems is achieving reasonable sampling. Flexible binding complexes often have high free energy barriers, which require prohibitively long simulations or carefully tuned enhanced sampling methods in order to gather sufficient uncorrelated samples to obtain reliable free energy estimates. An example of such a flexible system is the complex formed between FabB, an elongating \b{eta}-ketoacyl-acyl carrier protein (ACP) synthase (KS) from Escherichia coli, and ACP, which carries acyl chains of varying lengths. Previous experimental evidence suggests that growing acyl chains can bind to at least two pockets in FabB. With the multiple topology replica exchange of expanded ensemble (MT-REXEE) enhanced sampling approach, we can obtain highly efficient sampling of both pockets by adaptively growing and shrinking the chains in the simulation ensemble, allowing each simulation to visit chain lengths where transitions between the pockets occur. This enables unbiased sampling of alternate configurational states for large complex systems without prior pocket definitions, as collective-variable based enhanced sampling methods would require. Using the new swapping approach gives significantly enhanced sampling even for this simpler problem, as demonstrated by faster convergence of free energy estimates of relative binding affinity between kinetically separated binding pockets. This case study demonstrates the utility of MT-REXEE and its open-source implementation for systems that feature high free energy barriers for a subset of ligands of interest, demonstrating a valuable addition to the existing stable of enhanced sampling methods.

physics.bio-ph↗

arXiv2024

Multiple Topology Replica Exchange of Expanded Ensembles (MT-REXEE) for Multidimensional Alchemical Calculations

Relative free energy calculations are now widely used in academia and industry, but the accuracy is often limited by poor sampling of the complexes conformational ensemble. To address this, we have developed a novel method termed Multi-Topology Replica Exchange of Expanded Ensembles (MT-REXEE). This method enables parallel expanded ensemble calculations, facilitating iterative relative free energy computations while allowing conformational exchange between parallel transformations. These iterative transformations are adaptable to any set of systems with a common backbone or central substructure. We demonstrate that the MT-REXEE method maintains thermodynamic cycle closure to the same extent as standard expanded ensemble for both solvation free energy and relative binding free energy. The transformations tested involve simple systems that incorporate diverse heavy atoms and multi-site perturbations of a small molecule core resembling multi-site $λ$ dynamics, without necessitating modifications to the MD code, which in our initial implementation is GROMACS. We outline a systematic approach for topology set-up and provide instructions on how to perform inter-replicate coordinate modifications. This work shows that MT-REEXE can be used to perform accurate and reproducible free energy estimates and prompts expansion to more complex test systems and other molecular dynamics simulation infrastructures.

physics.chem-ph↗

arXiv2023

Machine-learned molecular mechanics force field for the simulation of protein-ligand systems and beyond

The development of reliable and extensible molecular mechanics (MM) force fields -- fast, empirical models characterizing the potential energy surface of molecular systems -- is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, \texttt{espaloma-0.3}, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1M energy and force calculations, \texttt{espaloma-0.3} reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

physics.chem-ph↗