Implicit ligand theory for relative binding free energies

Nguyen, Trung Hai; Minh, David D. L.

doi:10.1063/1.5017136

Cited by 8 publications

(8 citation statements)

References 37 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The receptor-ligand binding free energy can be computed using pathway methods such as the widely used alchemical pathway double decoupling method (DDM) [11][12][13] and physical pathway potential of mean force PMF 14 methods, and the more recently developed implicit ligand theory [15][16][17] and the alchemical transfer (ATM) method. 18 Alternatively, end-point methods such as MM-PB(GB)/SA, 19 LIE (Linear Interaction Energy), 20 and M2 (Mining Minima) 21 which need only consider the two end macrostates, are also popular in binding free energy calculations.…”

Section: Introductionmentioning

confidence: 99%

Developing end-point methods for absolute binding free energy calculation using the Boltzmann-quasiharmonic model

Wickstrom

Gallichio

Chen

et al. 2022

Phys. Chem. Chem. Phys.

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Developing end-point methods for absolute binding free energy calculation using the Boltzmann-quasiharmonic model

Wickstrom

Gallichio

Chen

et al. 2022

Phys. Chem. Chem. Phys.

View full text Add to dashboard Cite

show abstract

“…Implicit ligand theory (ILT) [8,9] is a framework that has the potential to enable faster and more scalable binding free energy calculations. According to ILT, the standard binding free energy may be expressed as an exponential average of the binding potential of mean force (BPMF) — the binding free energy between a flexible ligand and rigid receptor configuration.…”

Section: Introductionmentioning

confidence: 99%

“…If receptor configurations represent the unbound ensemble, then BPMFs may be used to compute absolute binding free energies [8]. If configurations are obtained from a bound ensemble, then BPMFs can be used to compute binding free energies relative to the ligand that defines the ensemble [9]. ILT-based free energy calculations have the potential to be faster and more scalable because they are based on BPMFs.…”

Section: Introductionmentioning

confidence: 99%

Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3

Xie

Minh

2018

J Comput Aided Mol Des

Self Cite

View full text Add to dashboard Cite

We participated in Subchallenges 1 and 2 of the Drug Design Data Resource (D3R) Grand Challenge 3. To prepare our submissions, we performed molecular docking with UCSF DOCK 6 and binding potential of mean force (BPMF) calculations-free energy calculations between flexible ligands and rigid receptors-using our open-source software package Alchemical Grid Dock (AlGDock). For each system, submissions were based on the minimum BPMF calculated for a selected set of crystal structures. In Subchallenge 1, our workflow performed poorly. Possible reasons for the poor performance include the neglect of cooperative ligands and limited sampling of ligand binding poses. In Subchallenge 2, our workflow led to some of most highly correlated submissions (Pearson R = 0.5) for vascular endothelial growth factor receptor 2. However, our results were poorly correlated for Janus Kinase 2 and Mitogen-activated protein kinase 14. Affinity prediction could potentially be improved by systematic selection of more diverse receptor configurations.

show abstract

“…molecular docking [10][11][12][13] and quantitative structure-activity relationship (QSAR) approaches. [10][11][12][13][14][15] The second group includes fast pulling of ligand (FPL), 16,17 umbrella sampling (US), 3,18,19 implicit ligand theory, 20,21 linear interaction energy, [22][23][24][25] and molecular mechanism/Poisson-Boltzmann surface area (MM/PBSA), [26][27][28] approaches. The last group contains free energy perturbation (FEP), 29,30 thermodynamics integration (TI), 31,32 and non-equilibrium molecular dynamics simulations (NEMD).…”

mentioning

confidence: 99%

Improving the Accuracy of AutoDock Vina by Changing the Empirical Parameters

Pham

Nguyen

Tâm

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

According to the previous benchmark, Autodock Vina (Vina) achieved a very high successful-docking rate, , but give a rather a low correlation coefficient, , for binding affinity with respect to experiment. This low correlation can be an obstacle for ranking of ligand-binding affinity, which is a main objective of docking simulations. The accuracy of Vina likely depends on the empirical parameters, which include the Gaussian steric interaction, repulsion, hydrophobic, hydrogen bond, and rotation metrics. In this context, we evaluated the dependence of Vina accuracy upon empirical parameters. Although changing of six parameters alters the obtained values, the gauss2 and rotation terms form more effects. The ̂ terms are sensitive to the alterations of the gauss1, gauss2, repulsion, and hydrogen bond parameters. Therefore, three sets of empirical parameters were proposed as well as more to modestly focus on R (set1), modestly focus on ̂ (set3), and keep a tradeoff between and . The testing study over 800 complexes indicated that the Vina with proposed sets of parameters can provide more accurate results since forming the larger value ( set1 = 0.556 ± 0.025) compared with the original one ( Default = 0.493 ± 0.028) and Vina version 1.2 ( Vina 1.2 = 0.503 ± 0.029). Besides, the testing study over 48 biological targets indicated that the modified Vina can be applied more widely compared with the default package. These newly proposed parameters achieved a higher correlation coefficient and reasonable correlation coefficients ( > 0.500) for at least 32 targets, whereas the default parameters provided by the original Vina gave only 31 targets with at least 0.500 correlation. In addition, validation calculations for 1315 complexes obtained from the version 2019 of PDBbind refined structures suggested that set1 of parameters are more appropriate than the other parameters ( set1 = 0.621 ± 0.016) compared with the default package ( Default = 0.552 ± 0.018) and Vina version 1.2 ( Vina 1.2 = 0.549 ± 0.017). The version of Vina with set1 of parameters can be downloaded at https://github.com/sontungngo/mvina.The outcomes probably enhance the ranking of ligand-binding affinity using Autodock Vina.The ligand-binding process is one of the most important issues in biology. 1 These processes are mostly associated with noncovalent chemical reactions between inhibitors and protein targets. 2 In particular, the process can be mimicked using computational approaches, 3,4 which plays a tremendous role in computer-aided drug design (CADD). 5 Accurate determination of ligand-binding affinity and pose of a small compound to an enzyme target are of great importance because they will reduce the cost and time for therapy development. [5][6][7] Therefore, numerous computational approaches were advanced to carry out these tasks. 8,9 In terms of accuracy and required computing resources, these approaches can be roughly arranged into three groups: low accuracy and small consumption of central processing unit (CPU) time; medium in both accuracy and re...

show abstract

Implicit ligand theory for relative binding free energies

Cited by 8 publications

References 37 publications

Developing end-point methods for absolute binding free energy calculation using the Boltzmann-quasiharmonic model

Developing end-point methods for absolute binding free energy calculation using the Boltzmann-quasiharmonic model

Alchemical Grid Dock (AlGDock) calculations in the D3R Grand Challenge 3

Improving the Accuracy of AutoDock Vina by Changing the Empirical Parameters

Contact Info

Product

Resources

About