Effective Estimation of Ligand-Binding Affinity Using Biased Sampling Method.

Son Tung Ngo,Le Minh Bui,Khanh B Vu,Van V Vu

doi:10.1021/acsomega.8b03258

Son Tung Ngo, Le Minh Bui + Show 2 more

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https://doi.org/10.1021/acsomega.8b03258

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Abstract

The binding between two biomolecules is one of the most critical factors controlling many bioprocesses. Therefore, it is of great interest to derive a reliable method to calculate the free binding energy between two biomolecules. In this work, we have demonstrated that the binding affinity of ligands to proteins can be determined through biased sampling simulations. The umbrella sampling (US) method was applied on 20 protein–ligand complexes, including the cathepsin K (CTSK), type II dehydroquinase (DHQase), heat shock protein 90 (HSP90), and factor Xa (FXa) systems. The ligand-binding affinity was evaluated as the difference between the largest and smallest values of the free-energy curve, which was obtained via a potential of mean force analysis. The calculated affinities differ sizably from the previously reported experimental values, with an average difference of ∼3.14 kcal/mol. However, the calculated results are in good correlation with the experimental data, with correlation coefficients of 0.76, 0.87, 0.96, and 0.97 for CTSK, DHQase, HSP90, and FXa, respectively. Thus, the binding free energy of a new ligand can be reliably estimated using our US approach. Furthermore, the root-mean-square errors (RMSEs) of binding affinity of these systems are 1.13, 0.90, 0.37, and 0.25 kcal/mol, for CTSK, DHQase, HSP90, and FXa, respectively. The small RMSE values indicate the good precision of the biased sampling method that can distinguish the ligands exhibiting similar binding affinities.

Highlights

A large number of biological processes involve the binding of two or more biomolecules, which is often evaluated through Gibbs free-energy difference.[1]
We have demonstrated that biased sampling simulation is a highly appropriate approach for ranking the affinity between ligand and enzyme for the cathepsin K (CTSK), DHQase, heat shock protein 90 (HSP90), and factor Xa (FXa) complexes
The computational binding affinity of the inhibitor was evaluated as the difference between the largest and smallest values of the free-energy curve obtained with potential of mean force (PMF) calculation

Summary

Introduction

A large number of biological processes involve the binding of two or more biomolecules, which is often evaluated through Gibbs free-energy difference.[1] The accurate determination or ranking of the binding affinity is a prerequisite for the synthesis of potential inhibitors that would allow for the reduction of therapeutic development and medication cost.[2] Several schemes have been developed, including molecular docking,[3−6] quantitative structure−activity relationship,[7−10] linear interaction energy,[11,12] molecular mechanism/Poisson−Boltzmann surface area (MM-PBSA),[13−15] fast pulling of ligand,[16,17] freeenergy perturbation,[18,19] thermodynamic integration,[20,21] and nonequilibrium molecular dynamics (MD) simulations.[22] Many studies in this area have been published in recent years.[23−26] precise prediction of binding affinity yet remains elusive. The PMF values are determined using the weighted histogram analysis method (WHAM)[32] calculation

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Results

Conclusion