Toward Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS)

Perthold, Jan Walther; Petrov, Dražen; Oostenbrink, Chris

doi:10.1021/acs.jcim.0c00456

Cited by 30 publications

(61 citation statements)

References 59 publications

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“…This in turn highlights the importance of adequate sampling and enhanced sampling techniques. 11,44,63,64 Interestingly, making pairwise comparisons between these different estimators shows that some pairs correlate almost perfectly with each other, while some show anti-correlation or no correlation (Figure S5 and 6). This suggests that the examined estimators rely and report on different aspects of convergence behavior of the simulations.…”

Section: Application Of the Tool On A Set Of Histone-related Post-translational Modifications And Convergence Analysis Of The Perturbatiomentioning

confidence: 98%

“…or polycycles as shown in Figure 2. In addition to these simplified test cases, in a recently published work, 44 EDS topologies were generated using this tool based on automatically recognized scaffold among different sets of molecules. Note that an EDS topology is a single topology (defining reference state Hamiltonian) that can represent multiple molecules by switching atom types, where the free energy differences between the molecules are calculated using a one step perturbation approach from the reference state.…”

Section: Perturbation Topology Buildermentioning

confidence: 99%

“…However, such sets of compounds are arguably also not relevant in the context of the EDS methodology or evaluation of the cycle closure, since the number of states / perturbation lags would be too large for meaningful calculations. On the other hand, this toolkit offers an alternative way of tackling a large set of compounds by employing pairwise generation of dual perturbation topologies, potentially in combination with utilizing EDS techniques [42][43][44] on small subgroups of similar compounds or by optimizing the choices of pairwise perturbations as proposed by Liu et al 60…”

Section: Perturbation Topology Buildermentioning

confidence: 99%

“…Importantly, this algorithm can also be simultaneously applied on a set of multiple topologies, where the resulting match represents the minimum structure of which each individual compound is a substructure, or simply put a common scaffold. This can be used to perform Enveloping Distribution Sampling (EDS) [42][43][44] or generate closed thermodynamic cycles on a set of multiple compounds.…”

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Perturbation Free-Energy Toolkit: Automated Alchemical Topology Builder and Optimized Simulation Update Scheme

Petrov

2021

Preprint

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Free-energy calculations play an important role in the application of computational chemistry to a range of fields, including protein biochemistry, rational drug design or material science. Importantly, the free energy difference is directly related to experimentally measurable quantities such as partition and adsorption coefficients, water activity and binding affinities. Among several techniques aimed at predicting the free-energy differences, perturbation approaches, involving alchemical transformation of one molecule into another through intermediate states, stand out as rigorous methods based on statistical mechanics. However, despite the importance of efficient and accurate free energy predictions, applicability of the perturbation approaches is still largely impeded by a number of challenges. This study aims at addressing two of them: 1) the definition of the perturbation path, i.e., alchemical changes leading to the transformation of one molecule to the other, and 2) determining the amount of sampling along the path to reach desired convergence. In particular, an automatic perturbation builder based on a graph matching algorithm is developed, that is able to identify the maximum common substructure of two molecules and provide the perturbation topologies suitable for free-energy calculations using GROMOS and GROMACS simulation packages. Moreover, it was used to calculate the changes in free energy of a set of post-translational modifications and analyze their convergence behavior. Different methods were tested, which showed that MBAR and extended thermodynamic integration (TI) in combination with MBAR show better performance as compared to BAR, extended TI with linear interpolation and plain TI. Also, a number of error estimators were explored and how they relate to the true error, estimated as the difference in free energy from an extensive set of simulation data. This analysis shows that most of the estimators provide only a qualitative agreement to the true error, with little quantitative predictive power. This notwithstanding, the preformed analyses provided insight into the convergence of free-energy calculations, which allowed for development of an iterative update scheme for perturbation simulations that aims at minimizing the simulation time to reach the convergence, i.e., optimizing the efficiency. Importantly, this toolkit is made available online as an open-source python package (https://github.com/drazen-petrov/SMArt).

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Section: Application Of the Tool On A Set Of Histone-related Post-translational Modifications And Convergence Analysis Of The Perturbatiomentioning

confidence: 98%

Section: Perturbation Topology Buildermentioning

confidence: 99%

Section: Perturbation Topology Buildermentioning

confidence: 99%

mentioning

confidence: 99%

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Perturbation Free-Energy Toolkit: Automated Alchemical Topology Builder and Optimized Simulation Update Scheme

Petrov

2021

Preprint

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“…Alchemical free energy calculations employ an unphysical path that connects two physical end states in order to obtain free energy differences. [11][12][13][14][15][16][17] Let us consider for example the process of turning the interactions of a ligand off in the binding site. The initial state in this case is the fully interacting ligand bound to the protein and the final state is the decoupled ligand, not interacting with its surroundings, and a sepa-rate protein and solvent box.…”

Section: Introductionmentioning

confidence: 99%

Challenges Encountered Applying Equilibrium and Non-Equilibrium Binding Free Energy Calculations

Baumann¹,

Gapsys²,

Groot³

et al. 2021

Preprint

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<div>Binding free energy calculations have become increasingly valuable to drive decision making in drug discovery projects. </div><div>However, among other issues, inadequate sampling can reduce accuracy, limiting the value of the technique.</div><div>In this paper we apply absolute binding free energy calculations to ligands binding to T4 lysozyme L99A and HSP90 using equilibrium and non-equilibrium approaches. We highlight sampling problems encountered in these systems, such as slow side chain rearrangements and slow changes of water placement upon ligand binding. These same types of challenges are likely to show up in other protein-ligand systems as well and we propose some strategies to diagnose and test for such problems in alchemical free energy calculations. We also explore similarities and differences in how the equilibrium and the non-equilibrium approaches handle these problems. Our results show the large amount of work still to be done to make free energy calculations robust and reliable and provide insight for future research in this area. </div>

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Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure

Ries

Normak

Weiß

et al. 2022

J Comput Aided Mol Des

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The calculation of relative free-energy differences between different compounds plays an important role in drug design to identify potent binders for a given protein target. Most rigorous methods based on molecular dynamics simulations estimate the free-energy difference between pairs of ligands. Thus, the comparison of multiple ligands requires the construction of a “state graph”, in which the compounds are connected by alchemical transformations. The computational cost can be optimized by reducing the state graph to a minimal set of transformations. However, this may require individual adaptation of the sampling strategy if a transformation process does not converge in a given simulation time. In contrast, path-free methods like replica-exchange enveloping distribution sampling (RE-EDS) allow the sampling of multiple states within a single simulation without the pre-definition of alchemical transition paths. To optimize sampling and convergence, a set of RE-EDS parameters needs to be estimated in a pre-processing step. Here, we present an automated procedure for this step that determines all required parameters, improving the robustness and ease of use of the methodology. To illustrate the performance, the relative binding free energies are calculated for a series of checkpoint kinase 1 inhibitors containing challenging transformations in ring size, opening/closing, and extension, which reflect changes observed in scaffold hopping. The simulation of such transformations with RE-EDS can be conducted with conventional force fields and, in particular, without soft bond-stretching terms.

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Toward Automated Free Energy Calculation with Accelerated Enveloping Distribution Sampling (A-EDS)

Cited by 30 publications

References 59 publications

Perturbation Free-Energy Toolkit: Automated Alchemical Topology Builder and Optimized Simulation Update Scheme

Perturbation Free-Energy Toolkit: Automated Alchemical Topology Builder and Optimized Simulation Update Scheme

Challenges Encountered Applying Equilibrium and Non-Equilibrium Binding Free Energy Calculations

Relative free-energy calculations for scaffold hopping-type transformations with an automated RE-EDS sampling procedure

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