Energetics of displacing water molecules from protein binding sites: consequences for ligand optimization.

By J. Michel, Julian Tirado-Rives, W. L. Jorgensen. 2009

237 citations recorded by the source corpus.

A strategy in drug design is to consider enhancing the affinity of lead molecules with structural modifications that displace water molecules from a protein binding site. Because success of the approach is uncertain, clarification of the associated energetics was sought in cases where similar structural modifications yield qualitatively different outcomes. Specifically, free energy perturbation calculations were carried out in the context of Monte Carlo statistical mechanics simulations to investigate ligand series that feature displacement of ordered water molecules in the binding sites of scytalone dehydratase, p38-αMAP kinase, and EGFR kinase. The change in affinity for a ligand modification is found to correlate with the ease of displacement of the ordered water molecule. However, as in the EGFR example, the binding affinity may diminish if the free energy increase due to the removal of the bound water molecule is not more than compensated by the additional interactions of the water-displacing moiety. For accurate computation of the effects of ligand modifications, a complete thermodynamic analysis is shown to be needed. It requires identification of the location of water molecules in the protein-ligand interface and evaluation of the free energy changes associated with their removal and with the introduction of the ligand modification. Direct modification of the ligand in free-energy calculations is likely to trap the ordered molecule and provide misleading guidance for lead optimization.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Computational Chemistry & Drug Discovery

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Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “Among these strategies one can highlight free energy perturbation calculations using Monte Carlo statistical mechanics simulations, which estimate the binding free energy for a given water molecule, allowing the discrimination between displaceable and strongly-bound structural water [110].”
Binding Affinity via Docking: Fact and Fiction (2018, background): “Water has an important role in ligand binding thermodynamics [40,41], even in the environment of a lipophilic binding cavity [42] and displacing specific water molecules from the binding site may play an important role in the ligand optimization process [43].”
Relative Binding Free Energy Calculations in Drug Discovery: Recent Advances and Practical Considerations (2017, methodology): “11 This allows for easy detection of sampling problems, 169,170 since the total free energy change in a closed thermodynamic cycle should sum to zero and deviations from zero reveal sampling errors along at least one leg of the cycle.”
Best Practices for Constructing, Preparing, and Evaluating Protein-Ligand Binding Affinity Benchmarks [Article v1.0] (2021, background): “…many reports demonstrating that RBFE calculations still struggle in scenarios [30] such as with scaﬀold modiﬁcations [31], ring expansion [32], water displacement [33–36], protein ﬂexibility [37–39], applications to GPCRs [40, 41], and the modelling of cofactors such as metal ions or heme [42, 43].”
Best practices for constructing, preparing, and evaluating protein-ligand binding aﬃnity benchmarks [Article v0.2] (2021, background): “…many reports demonstrating that RBFE calculations still struggle in scenarios [30] such as with scaﬀold modiﬁcations [31], ring expansion [32], water displacement [33–36], protein ﬂexibility [37–39], applications to GPCRs [40, 41], and the modelling of cofactors such as metal ions or heme [42, 43].”
Rapid and Accurate Prediction and Scoring of Water Molecules in Protein Binding Sites (2012, methodology): “[28] could switch the predicted sites ‘‘on’’ and ‘‘off’’.”
Approaches to efficiently estimate solvation and explicit water energetics in ligand binding: the use of WaterMap (2013, unknown): “showed that the results were sensitive to the initial water placement, and the Jorgensen lab necessarily applied their JAWS [49] to improve the water placement in the initial structures that feed FEP simulations [19].”