Recent developments in multiscale free energy simulations

Barros, Emília P.; Ries, B.; Böselt, Lennard; Champion, Candide; Riniker, Sereina

doi:10.1016/j.sbi.2021.08.003

Cited by 7 publications

(7 citation statements)

References 63 publications

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“…2,8−11 To evaluate the potency of potential drug candidates in silico, free energy calculation methods, including simulations on the theoretical level of molecular mechanics or quantum mechanics, are currently the state-of-the-art that promise the most accurate estimates. 7,12 Furthermore, large efforts are undertaken to automatize such approaches. 13−22 In order to rank the most promising drug candidates by potency with in silico methods, ligands are typically ranked based on their calculated binding free energies.…”

Section: ■ Introductionmentioning

confidence: 99%

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Ries,

Alibay,

Swenson

et al. 2024

J. Chem. Theory Comput.

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Relative binding free energy (RBFE) calculations have emerged as a powerful tool that supports ligand optimization in drug discovery. Despite many successes, the use of RBFEs can often be limited by automation problems, in particular, the setup of such calculations. Atom mapping algorithms are an essential component in setting up automatic large-scale hybrid-topology RBFE calculation campaigns. Traditional algorithms typically employ a 2D subgraph isomorphism solver (SIS) in order to estimate the maximum common substructure. SIS-based approaches can be limited by time-intensive operations and issues with capturing geometry-linked chemical properties, potentially leading to suboptimal solutions. To overcome these limitations, we have developed Kartograf, a geometric-graph-based algorithm that uses primarily the 3D coordinates of atoms to find a mapping between two ligands. In free energy approaches, the ligand conformations are usually derived from docking or other previous modeling approaches, giving the coordinates a certain importance. By considering the spatial relationships between atoms related to the molecule coordinates, our algorithm bypasses the computationally complex subgraph matching of SIS-based approaches and reduces the problem to a much simpler bipartite graph matching problem. Moreover, Kartograf effectively circumvents typical mapping issues induced by molecule symmetry and stereoisomerism, making it a more robust approach for atom mapping from a geometric perspective. To validate our method, we calculated mappings with our novel approach using a diverse set of small molecules and used the mappings in relative hydration and binding free energy calculations. The comparison with two SIS-based algorithms showed that Kartograf offers a fast alternative approach. The code for Kartograf is freely available on GitHub (https://github.com/ OpenFreeEnergy/kartograf). While developed for the OpenFE ecosystem, Kartograf can also be utilized as a standalone Python package.

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Section: ■ Introductionmentioning

confidence: 99%

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Ries,

Alibay,

Swenson

et al. 2024

J. Chem. Theory Comput.

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“…All MM calculations were carried out with the OpenMM [72] GPU acceleration available in CHARMM. 4 2.5. Overview of calculated free energy differences and paths:…”

Section: Xsolvmentioning

confidence: 99%

“…Since the first applications reported shortly before 1990 [1,2], so-called alchemical free energy simulations (FES) have become an essential tool of computational chemists in academia and industry alike [3][4][5][6]. The free energy difference determines the spontaneity of chemical or biochemical processes and, hence, makes it possible to predict, e.g., binding affinities of potential drugs [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Exploring Routes to Enhance the Calculation of Free Energy Differences via Nonequilibrium Work SQM/MM Switching Simulations by Using Hybrid Charge Intermediates Between MM and SQM Level of Theory or Non-linear Switching Schemes

Schöller¹,

Woodcock²,

Boresch³

2023

Preprint

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Nonequilibrium work switching simulations and Jarzynski’s equation are a reliable method to compute free energy differences, ΔAlow→high, between two levels of theory, such as a pure molecular mechanical (MM) and a quantum mechanical/molecular mechanical (QM/MM) description of a system of interest. Despite the inherent parallelism, the computational cost of this approach can quickly get very high. This is particularly true for systems where the core region, the part of the system to be described at different levels of theory, is embedded in an environment, such as explicit solvent water. We find that even for relatively simple solute–water systems, switching lengths of at least 5 ps are necessary to compute ΔAlow→high reliably. In this study, we investigate two approaches towards an affordable protocol, with emphasis on keeping the switching length well below 5 ps. Inserting a hybrid charge intermediate state with modified partial charges which resembles the charge distribution of the desired high level makes it possible to obtain reliable calculations with 2 ps switches. Attempts using step-wise linear switching paths, on the other hand, did not lead to improvement, i.e., faster convergence for all systems. To understand these findings, we analyzed the solutes’ properties as a function of partial charges used, the number of waters in direct contact with the solute, and studied the time needed for water molecules to reorient themselves upon a change in the solute’s charge distribution.

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“…Although the DG of binding is a central concern for protein modeling and design, and the calculations by computational means have been an active area of development since the beginning of the 1980s, 12,13 obtaining it accurately at a low computational cost remains a challenge for the molecular simulation community. 14 This difficulty is even more pronounced when trying to obtain the DG for liquids and flexible macromolecules, mainly due to insufficient sampling and inaccuracies in the description of the potential energy surface.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties

Ferraz

Soares

Lins

et al. 2023

Phys. Chem. Chem. Phys.

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Recent developments in multiscale free energy simulations

Cited by 7 publications

References 63 publications

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Kartograf: A Geometrically Accurate Atom Mapper for Hybrid-Topology Relative Free Energy Calculations

Exploring Routes to Enhance the Calculation of Free Energy Differences via Nonequilibrium Work SQM/MM Switching Simulations by Using Hybrid Charge Intermediates Between MM and SQM Level of Theory or Non-linear Switching Schemes

An artificial neural network model to predict structure-based protein–protein free energy of binding from Rosetta-calculated properties

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