MM/GBSA and LIE estimates of host–guest affinities: dependence on charges and solvation model

Genheden, Samuel

doi:10.1007/s10822-011-9486-1

Cited by 24 publications

(33 citation statements)

References 49 publications

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“…45,46,47,48 We have also tested such an approach for fXa and present the corresponding affinity estimates in Table 4. It can be seen that the omission of the entropy term makes all binding affinities much too negative (-165 to -182 kJ/mol, compared to the experimental estimates of -27 to -47 kJ/mol).…”

Section: Resultsmentioning

confidence: 99%

“…l We have tested to ignore the entropy term completely in MM/GBSA. Such an approach has been shown to improve the results in several studies, 45,46,47,48 although the results become worse in other cases. 16,46 For the fXa and HIV-PR test cases, it does not lead to any statistically significant changes in our quality measures, but for ferritin, the results become slightly worse.…”

Section: Discussionmentioning

confidence: 99%

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The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

Genheden

Kühn

Mikulskis

et al. 2012

J. Chem. Inf. Model.

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189

148

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We have performed a systematic study of the entropy term in the MM/GBSA (molecular mechanics combined with generalised Born and surface-area solvation) approach to calculate ligand-binding affinities. The entropies are calculated by a normal-mode analysis of harmonic frequencies from minimised snapshots of molecular dynamics simulations. For computational reasons, these calculations have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with distances larger than 8-16 Å to the ligand and including a 4 Å shell of fixed protein residues and water molecules, change the absolute entropies by 1-5 kJ/mol on average. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on average. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the whole protein within statistical uncertainty (1-2 kJ/mol). We have also tested to use a distance-dependent dielectric constant in the minimisation and frequency calculation (ε = 4r), but it typically gives slightly different entropies and poorer binding affinities. Therefore, we recommend entropies calculated with the smallest truncation radius (8 Å) and ε =1. Such an approach also gives an improved precision for the calculated binding free energies.Keywords: MM/GBSA, entropy, ligand-binding affinities, dielectric constant, factor Xa, ferritin, HIV-1 protease.Introduction MM/GBSA is an approximate method to estimate the absolute binding free energy, ΔG bind , of a ligand L, to a biomacromolecule, e.g. a protein, P, forming a complex PL. It estimates ΔG bind as the difference in free energy between PL, P, and L, viz. ΔG bind = G(PL) -G(P) -G(L). Each of these three free energies are estimated from the following sum 1,2 G = E int + E vdW + E ele + G solv + G np − TS MM ( 1) where the brackets indicate an average over snapshots from a molecular dynamics (MD) simulation. The first three terms on the right-hand side are the molecular mechanics (MM) internal (i.e. bonds, angles, and dihedral), van der Waals, and electrostatic energies, G solv and G np are the polar and nonpolar solvation free energies, and the last term is the absolute temperature multiplied by an entropy estimate. The latter estimate is usually taken as a combination of translational, rotational, and vibrational terms. All the terms are calculated on a system where water molecules from the simulation have been stripped off. Moreover, typically only the complex (PL) is simulated and the free energies of P and L are obtained from the same simulation by simply deleting the coordinates of the other species. 3,4 Thereby, the E int term cancels exactly and the precision is improved by a factor of ~5. 5,6 The most common method to estimate the vibrational entropy in the MM/GBSA method is to use frequencies from a normal-mode analysis (NMA...

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

Genheden

Kühn

Mikulskis

et al. 2012

J. Chem. Inf. Model.

Self Cite

189

148

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“…For the binding of eight hydroxamate inhibitors to gelatinase-A and the binding of fragment B of protein A to the Fc domain of immunoglobulin G, the two methods showed a similar performance [24,25]. Finally, for the binding of primary alcohols to cyclodextrin, LIE was more accurate than MM/GBSA and MM/PBSA, whereas for the binding of guests to cucurbitil [8] uril, LIE was less accurate than MM/GBSA and MM/PBSA [21].…”

Section: Introductionmentioning

confidence: 96%

“…Recently, we have evaluated the precision of the LIE method also and compared with the results of the MM/GBSA method [20]. In addition, we have investigated the effect of the force field and solvation model on the prediction of host-guest binding affinities [21]. Consequently, we have gained much experience with these methods.…”

Section: Introductionmentioning

confidence: 99%

Binding affinities in the SAMPL3 trypsin and host–guest blind tests estimated with the MM/PBSA and LIE methods

Mikulskis

Genheden

Rydberg

et al. 2011

J Comput Aided Mol Des

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We have estimated affinities for the binding of 34 ligands to trypsin and nine guest molecules to three different hosts in the SAMPL3 blind challenge, using the MM/PBSA, MM/GBSA, LIE, continuum LIE, and Glide score methods. For the trypsin challenge, none of the methods were able to accurately predict the experimental results. For the MM/GB(PB)SA and LIE methods, the rankings were essentially random and the mean absolute deviations were much worse than a null hypothesis giving the same affinity to all ligand. Glide scoring gave a Kendall's τ index better than random, but the ranking is still only mediocre, τ = 0.2. However, the range of affinities is small and most of the pairs of ligands have an experimental affinity difference that is not statistically significant. Removing those pairs improves the ranking metric to 0.4-1.0 for all methods except CLIE. Half of the trypsin ligands were non-binders according to the binding assay. The LIE methods could not separate the inactive ligands from the active ones better than a random guess, whereas MM/GBSA and MM/PBSA were slightly better than random (area under the receiver-operating-characteristic curve, AUC = 0.65-0.68), and Glide scoring was even better (AUC = 0.79). For the first host, MM/GBSA and MM/PBSA reproduce the experimental ranking fairly good, with τ = 0.6 and 0.5, respectively, whereas the Glide scoring was considerably worse, with a τ = 0.4, highlighting that the success of the methods is system-dependent.

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“…Although MM‐P(G)BSA methods have been used successfully in both virtual screening and lead optimization programs, it has been shown that the results are sensitive to atomic charges, simulation length, entropy calculations, and sampling protocols which can lead to dramatic differences in affinity predictions using the same study system . Studies have also suggested that prediction results of MM‐GBSA methods might be influenced by radii settings .…”

Section: Introductionmentioning

confidence: 99%

Comparison of radii sets, entropy, QM methods, and sampling on MM‐PBSA, MM‐GBSA, and QM/MM‐GBSA ligand binding energies of F. tularensis enoyl‐ACP reductase (FabI)

et al. 2015

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To validate a method for predicting the binding affinities of FabI inhibitors, three implicit solvent methods, MM-PBSA, MM-GBSA and QM/MM GBSA were carefully compared using sixteen benzimidazole inhibitors in complex with F. tularensis FabI. The data suggests that the prediction results are sensitive to radii sets, GB methods, QM Hamiltonians, sampling protocols, and simulation length, if only one simulation trajectory is used for each ligand. In this case, QM/MM-GBSA using 6 ns MD simulation trajectories together with GBneck2, PM3, and the mbondi2 radii set, generate the closest agreement with experimental values (r2= 0.88). However, if the three implicit solvent methods are averaged from six 1 ns MD simulations for each ligand (called “multiple independent sampling”), the prediction results are relatively insensitive to all the tested parameters. Moreover, MM/GBSA together with GBHCT and mbondi, using 600 frames extracted evenly from six 0.25 ns MD simulations, can also provide accurate prediction to experimental values (r2 = 0.84). Therefore, the multiple independent sampling method can be more efficient than a single, long simulation method. Since future scaffold expansions may significantly change the benzimidazole's physiochemical properties (charges, etc.) and possibly binding modes, which may affect the sensitivities of various parameters, the relatively insensitive “multiple independent sampling method” may avoid the need of an entirely new validation study. Moreover, due to large fluctuating entropy values, (QM/)MM-P(G)BSA were limited to inhibitors’ relative affinity prediction, but not the absolute affinity. The developed protocol will support an ongoing benzimidazole lead optimization program.

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MM/GBSA and LIE estimates of host–guest affinities: dependence on charges and solvation model

Cited by 24 publications

References 49 publications

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

Binding affinities in the SAMPL3 trypsin and host–guest blind tests estimated with the MM/PBSA and LIE methods

Comparison of radii sets, entropy, QM methods, and sampling on MM‐PBSA, MM‐GBSA, and QM/MM‐GBSA ligand binding energies of F. tularensis enoyl‐ACP reductase (FabI)

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