Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Panday, Shailesh Kumar; Alexov, Emil

doi:10.1021/acsomega.1c07037

Cited by 17 publications

(9 citation statements)

References 79 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be seen that the PBSA_E and GBSA_E estimates are very similar, which agrees with the implicit-solvent-independent behavior from analysis of weighting factors in Eq. (2). By contrast, the ELIE results obviously differ from those with the two pre-fitted regimes, which suggests the dissimilarities of the pre-fitted PBSA_E and GBSA_E regimes and the system-specific ELIE and provides an explanation of the better ranking reproduction of the ELIE scheme in Fig.…”

Section: Numerical Performance Of Educated End-point Regimesmentioning

confidence: 90%

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant

Liu

Zheng

Cong

et al. 2022

J Comput Aided Mol Des

View full text Add to dashboard Cite

End-point free energy calculations as a powerful tool have been widely applied in protein-ligand and protein-protein interactions. It is often recognized that these end-point techniques serve as an option of intermediate accuracy and computational cost compared with more rigorous statistical mechanic models (e.g., alchemical transformation) and coarser molecular docking. However, it is observed that this intermediate level of accuracy does not hold in relatively simple and prototypical host-guest systems. Specifically, in our previous work investigating a set of carboxylated-pillar[6]arene host-guest complexes, end-point methods provide free energy estimates deviating significantly from the experimental reference, and the rank of binding affinities is also incorrectly computed. These observations suggest the unsuitability and inapplicability of standard end-point free energy techniques in host-guest systems, and alteration and 2 / 41 development are required to make them practically usable. In this work, we consider two ways to improve the performance of end-point techniques. The first one is the PBSA_E regression that varies the weights of different free energy terms in the end-point calculation procedure, while the second one is considering the interior dielectric constant as an additional variable in the end-point equation. By detailed investigation of the calculation procedure and the simulation outcome, we prove that these two treatments (i.e., regression and dielectric constant) are manipulating the end-point equation in a somehow similar way, i.e., weakening the electrostatic contribution and strengthening the non-polar terms, although there are still many detailed differences between these two methods. With the trained end-point scheme, the RMSE of the computed affinities is improved from the standard ~12 kcal/mol to ~2.4 kcal/mol, which is comparable to another altered end-point method (ELIE) trained with system-specific data. This phenomenon along with the extremely efficient optimized-structure computation procedure suggests the regression (i.e., PBSA_E as well as its GBSA_E extension) as a practically applicable solution that brings end-point methods back into the library of usable tools for host-guest binding. However, the dielectric-constant-variable scheme cannot effectively minimize the experiment-calculation discrepancy for absolute binding affinities, but is able to improve the calculation of affinity ranks. This phenomenon is somehow different from the protein-ligand case and suggests the difference between host-guest and biomacromolecular (protein-ligand and proteinprotein) cases. Therefore, the spectrum of tools usable in protein-ligand cases could be unsuitable for hostguest binding, and numerical validations are necessary to screen out really workable solutions in these 'prototypical' situations.

show abstract

Section: Numerical Performance Of Educated End-point Regimesmentioning

confidence: 90%

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant

Liu

Zheng

Cong

et al. 2022

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…Panday and Alexov 58 have reported on a Gaussian-based method, termed f5-MM/PBSA/E, for calculating binding entropy in protein–protein interactions and incorporating it in MM-PBSA calculations. The model was validated using a data set consisting of 46 protein–protein binding cases, based on the same training set used in this work (consisting of 81).…”

Section: Resultsmentioning

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.

View full text Add to dashboard Cite

show abstract

“…101,124 Alteration of the implicit solvent model and the estimation of the entropic contribution could also be considered. [125][126][127] A further critical factor influencing the outcome is the exhaustiveness of the conformational sampling. As the end-point calculation is using configurations sampled with the unbiased technique, there could be several host-guest coordination patterns unexplored and thus not included in the ensemble average in Eq.…”

Section: Directions To Improve the Performance Of End-point Methodsmentioning

confidence: 99%

Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-guest Binding: I. Standard Procedure

Liu

Zheng

Qin

et al. 2022

Preprint

View full text Add to dashboard Cite

Despite the massive application of end-point free energy methods in protein-ligand and protein-protein interactions, computational understandings about their performance in relatively simple and prototypical host-guest systems are limited. In this work, we present a comprehensive benchmark calculation with standard end-point free energy techniques in a recent host-guest dataset containing 13 host-guest pairs involving the carboxylated-pillar[6]arene host. We first assess the charge schemes for solutes by comparing the charge-produced electrostatics with many ab initio references, in order to obtain a preliminary albeit detailed view of the charge quality. Then, we focus on four modelling details of end-point free energy calculations, including the docking procedure for the generation of initial condition, the charge scheme for host and guest molecules, the water model used in explicit-solvent sampling, and the end-point methods for free energy estimation. The binding thermodynamics obtained with different modelling schemes are compared with experimental references, and some practical guidelines on maximizing the performance of end-point methods in practical host-guest systems are summarized. Finally, we compare our simulation outcome with predictions in the grand challenge and discuss further developments to improve the prediction quality of end-point free energy methods.

show abstract

Protein–Protein Binding Free Energy Predictions with the MM/PBSA Approach Complemented with the Gaussian-Based Method for Entropy Estimation

Cited by 17 publications

References 79 publications

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant

Comprehensive evaluation of end-point free energy techniques in carboxylated-pillar[6]arene host–guest binding: II. regression and dielectric constant

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

Comprehensive Evaluation of End-Point Free Energy Techniques in Carboxylated-Pillar[6]arene Host-guest Binding: I. Standard Procedure

Contact Info

Product

Resources

About