In protein-ligand binding, only a few residues contribute significantly to the ligand binding. Quantitative characterization of binding free energies of specific residues in protein-ligand binding is extremely useful in our understanding of drug resistance and rational drug design. In this paper, we present an alanine scanning approach combined with an efficient interaction entropy method to compute residue-specific protein-ligand binding free energies in protein-drug binding. In the current approach, the entropic components in the free energies of all residues binding to the ligand are explicitly computed from just a single trajectory MD simulation by using the interaction entropy method. In this approach the entropic contribution to binding free energy is determined from fluctuations of individual residue-ligand interaction energies contained in the MD trajectory. The calculated residue-specific binding free energies give relative values between those for ligand binding to the wild type protein and those to the mutants when specific results mutated to alanine. Computational study for the binding of two classes of drugs (first and second generation drugs) to target protein ALK and its mutant was performed. Important or hot spot residues with large contributions to the total binding energy are quantitatively characterized and the mutation effect for the loss of binding affinity for the first generation drug is explained. Finally, it is very interesting to note that the sum of those individual residue-specific binding free energies are in quite good agreement with the experimentally measured total binding free energies for this protein-ligand system.
Accurate and efficient computation of protein−protein binding free energy remains a grand challenge. In this study, we develop a new strategy to achieve efficient calculation for total protein−protein binding free energies with improved accuracy. The new method combines the recently developed interaction entropy method for efficient computation of entropic change together with the use of residue type-specific dielectric constants in the framework of MM/GBSA to achieve optimal result for protein−protein binding free energies. The new strategy is shown to be computationally efficient and accurate than that using standard MM/GBSA methods in which the entropic computation is performed by the normal model approach and the protein interior is represented by the standard dielectric constant (typically set to 1), both in terms of accuracy and computational efficiency. Our study using the new strategy on a set of randomly selected 20 protein−protein binding systems produced an optimal dielectric constant of 2.7 for charged residues and 1.1 for noncharged residues. Using this new strategy, the mean absolute error in computed binding free energies for these 20 selected protein−protein systems is significantly reduced by more than 3-fold while the computational cost is reduced by more than 2 orders of magnitude, compared to the result using standard MM/GBSA method with the normal mode approach. A similar improvement in accuracy is confirmed for a test set consisting of 10 protein−protein systems.
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