Simulation of Reversible Protein–Protein Binding and Calculation of Binding Free Energies Using Perturbed Distance Restraints

Perthold, Jan Walther; Oostenbrink, Chris

doi:10.1021/acs.jctc.7b00706

Cited by 37 publications

(59 citation statements)

References 48 publications

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“…One of the major challenges in modern computational biochemistry is the accurate and reliable calculation of affinities between proteins and associated binding partners . A very prominent method to calculate free‐energy changes upon a ligand binding to a host protein or DNA is to simulate a nonphysical path where the ligand is alchemically perturbed in the protein and free in solution .…”

Section: Introductionmentioning

confidence: 99%

“…One of the major challenges in modern computational biochemistry is the accurate and reliable calculation of affinities between proteins and associated binding partners. [1][2][3][4][5][6][7][8] A very prominent method to calculate free-energy changes upon a ligand binding to a host protein or DNA is to simulate a nonphysical path where the ligand is alchemically perturbed in the protein and free in solution. [9] In combination with the employment of a thermodynamic cycle, the free-energy change along the nonphysical path is the same as the free-energy change along the physical path.…”

Section: Introductionmentioning

confidence: 99%

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Correcting electrostatic artifacts due to net‐charge changes in the calculation of ligand binding free energies

et al. 2020

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Alchemically derived free energies are artifacted when the perturbed moiety has a nonzero net charge. The source of the artifacts lies in the effective treatment of the electrostatic interactions within and between the perturbed atoms and remaining (partial) charges in the simulated system. To treat the electrostatic interactions effectively, lattice‐summation (LS) methods or cutoff schemes in combination with a reaction‐field contribution are usually employed. Both methods render the charging component of the calculated free energies sensitive to essential parameters of the system like the cutoff radius or the box side lengths. Here, we discuss the results of three previously published studies of ligand binding. These studies presented estimates of binding free energies that were artifacted due to the charged nature of the ligands. We show that the size of the artifacts can be efficiently calculated and raw simulation data can be corrected. We compare the corrected results with experimental estimates and nonartifacted estimates from path‐sampling methods. Although the employed correction scheme involves computationally demanding continuum‐electrostatics calculations, we show that the correction estimate can be deduced from a small sample of configurations rather than from the entire ensemble. This observation makes the calculations of correction terms feasible for complex biological systems. To show the general applicability of the proposed procedure, we also present results where the correction scheme was used to correct independent free energies obtained from simulations employing a cutoff scheme or LS electrostatics. In this work, we give practical guidelines on how to apply the appropriate corrections easily.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Correcting electrostatic artifacts due to net‐charge changes in the calculation of ligand binding free energies

et al. 2020

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“…The method produces comparable results to alternative methods that pursue free energy calculations based on a geometrical route . Other methods were able to achieve more full transitions from the unbound state to the bound state in previous studies . These methods efficiently performed the enhanced sampling, but they were applied to other protein systems different in molecular size from the proteins considered in this study.…”

Section: Discussionmentioning

confidence: 91%

“…[16,17,35,38,39] Other methods were able to achieve more full transitions from the unbound state to the bound state in previous studies. [40,41] These methods efficiently performed the enhanced sampling, but they were applied to other protein systems different in molecular size from the proteins considered in this study.…”

Section: Discussionmentioning

confidence: 99%

Specific Residue Interactions Regulate the Binding of Dengue Antigens to Broadly Neutralizing EDE Antibodies

2018

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Antibodies binding to antigens present on the dengue virus (DENV) represent the main defense mechanism of the host organism against the pathogen. Among the antibodies elicited by DENV and that bind to DII of protein E, EDE1‐C8 can bind all DENV serotypes. Our analysis reveals the key residues in this interaction as well as structurally conserved hydrogen bonds located at the binding interface. They stabilize the dengue antigen–antibody complex among the EDE1 group of antibodies (Abs). Combining structural alignments with molecular dynamics simulations in the EDE1 Abs, we identified the critical elements that provide a major energetic contribution to the association of antigens from protein E with Abs. We discuss possible molecular insights into the binding mechanism by using a surrogate molecular entity resembling the protein E that forms native salt bridges and hydrogen bonds, including inferences on the light of high‐resolution crystal structures of dengue Fab complexes. Finally, the molecular determinants, the free energy profile, and the binding mechanism provide inspiration for potential strategies in protein engineering to design novel immunogens of protein E against DENV.

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“…Computational methods for ΔΔG prediction can be largely grouped into three main strategies: (1) Rigorous methods, such as thermodynamic integration and free energy perturbation, 4,5 (2) empirical energy-based methods, based for example on classical mechanics or statistical potentials [6][7][8][9][10] (typically in linear forms), and (3) machine learning-based methods which can exploit a large variety of energetics and non-energetics (eg, geometric, evolutionary) features. [11][12][13] The rigorous methods can be accurate but they are computationally highly demanding.…”

Section: Introductionmentioning

confidence: 99%

iSEE: Interface structure, evolution, and energy‐based machine learning predictor of binding affinity changes upon mutations

Geng

Vangone²,

Folkers

et al. 2018

Proteins

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Quantitative evaluation of binding affinity changes upon mutations is crucial for protein engineering and drug design. Machine learning‐based methods are gaining increasing momentum in this field. Due to the limited number of experimental data, using a small number of sensitive predictive features is vital to the generalization and robustness of such machine learning methods. Here we introduce a fast and reliable predictor of binding affinity changes upon single point mutation, based on a random forest approach. Our method, iSEE, uses a limited number of i nterface S tructure, E volution, and E nergy‐based features for the prediction. iSEE achieves, using only 31 features, a high prediction performance with a Pearson correlation coefficient (PCC) of 0.80 and a root mean square error of 1.41 kcal/mol on a diverse training dataset consisting of 1102 mutations in 57 protein‐protein complexes. It competes with existing state‐of‐the‐art methods on two blind test datasets. Predictions for a new dataset of 487 mutations in 56 protein complexes from the recently published SKEMPI 2.0 database reveals that none of the current methods perform well (PCC < 0.42), although their combination does improve the predictions. Feature analysis for iSEE underlines the significance of evolutionary conservations for quantitative prediction of mutation effects. As an application example, we perform a full mutation scanning of the interface residues in the MDM2–p53 complex.

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Simulation of Reversible Protein–Protein Binding and Calculation of Binding Free Energies Using Perturbed Distance Restraints

Cited by 37 publications

References 48 publications

Correcting electrostatic artifacts due to net‐charge changes in the calculation of ligand binding free energies

Correcting electrostatic artifacts due to net‐charge changes in the calculation of ligand binding free energies

Specific Residue Interactions Regulate the Binding of Dengue Antigens to Broadly Neutralizing EDE Antibodies

iSEE: Interface structure, evolution, and energy‐based machine learning predictor of binding affinity changes upon mutations

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