Free Energy Calculations to Estimate Ligand-Binding Affinities in Structure-Based Drug Design

Abstract

Post-genomic era has led to the discovery of several new targets posing challenges for structure-based drug design efforts to identify lead compounds. Multiple computational methodologies exist to predict the high ranking hit/lead compounds. Among them, free energy methods provide the most accurate estimate of predicted binding affinity. Pathway-based Free Energy Perturbation (FEP), Thermodynamic Integration (TI) and Slow Growth (SG) as well as less rigorous end-point methods such as Linear interaction energy (LIE), Molecular Mechanics-Poisson Boltzmann./Generalized Born Surface Area (MM-PBSA/GBSA) and λ-dynamics have been applied to a variety of biologically relevant problems. The recent advances in free energy methods and their applications including the prediction of protein-ligand binding affinity for some of the important drug targets have been elaborated. Results using a recently developed Quantum Mechanics (QM)/Molecular Mechanics (MM) based Free Energy Perturbation (FEP) method, which has the potential to provide a very accurate estimation of binding affinities to date has been discussed. A case study for the optimization of inhibitors for the fructose 1,6- bisphosphatase inhibitors has been described.

The effect of β-glucan and its potential analog on the structure of Dectin-1 receptor

“…Among them, binding energy provides extensive analysis of host-guest complexes will help improve the protocol used in predicting binding affinities for larger systems, such as protein-substrate compounds [34][35][36]. Binding energy estimation was revealed a deep insight about binding contribution per residues which were taking part with both ligands [37].…”

Structure-Based Predictions of Activity Cliffs

“…One possible approach could rely on pathway-based methods for free energy calculations. 21 Computational protocols based on docking, free energy perturbation (FEP), and molecular dynamics can lead to binding affinity predictions in good agreement with the experimental data. 22 In recent years, thanks to improvements in the quality of force fields, sampling methods and hardware solutions, the throughput of this kind of studies increased to up to thousands of molecules per year.…”

Variational Methods for Biomolecular Modeling

“…While S = 1 2 is usually chosen as the molecular surface, the three surfaces are very close due to the high resolution of the numerical method. The availability of the surface position and surface potential could significantly facilitate the analysis of binding affinity of protein-protein or protein-ligand systems, of which the electrostatic potential is an important component [119,5,63,34,87,108]. Numerically, this model can be computed by using both the Eulerian formulation, in which the solute boundary is embedded in the 3D Euclidean space so evaluation of the electrostatic potential can be carried out directly [27], and the Lagrangian formulation, wherein the solvent-solute interface is extracted as a sharp surface and subsequently used in solving the GPB equation for the electrostatic potential [26].…”