Water Network Perturbation in Ligand Binding: Adenosine A_2AAntagonists as a Case Study

Abstract

Recent efforts in the computational evaluation of the thermodynamic properties of water molecules have resulted in the development of promising new in silico methods to evaluate the role of water in ligand binding. These methods include WaterMap, SZMAP, GRID/CRY probe, and Grand Canonical Monte Carlo simulations. They allow the prediction of the position and relative free energy of the water molecule in the protein active site and the analysis of the perturbation of an explicit water network (WNP) as a consequence of ligand binding. We have for the first time extended these approaches toward the prediction of kinetics for small molecules and of relative free energy of binding with a focus on the perturbation of the water network and application to large diverse data sets. Our results support a qualitative correlation between the residence time of 12 related triazine adenosine A(2A) receptor antagonists and the number and position of high energy trapped solvent molecules. From a quantitative viewpoint, we successfully applied these computational techniques as an implicit solvent alternative, in linear combination with a molecular mechanics force field, to predict the relative ligand free energy of binding (WNP-MMSA). The applicability of this linear method, based on the thermodynamics additivity principle, did not extend to 375 diverse A(2A) receptor antagonists. However, a fast but effective method could be enabled by replacing the linear approach with a machine learning technique using probabilistic classification trees, which classified the binding affinity correctly for 90% of the ligands in the training set and 67% in the test set.

Molecular Basis of Ligand Dissociation from the Adenosine A2A Receptor

“…Such a lid almost completely prevents the influx of water molecules to hydrate tiotropium, an essential step for ligand dissociation (Schmidtke et al, 2011;Bortolato et al, 2013). This is in accordance with tiotropium's long RT on the M 3 R (more than 24 hours) (Casarosa et al, 2009).…”

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia

“…Tightly bound water molecules maximize their number of interactions (up to four) and water networks are built around these -see for example the combined use of theory and experiment to explore such networks in a GPCR. 39 The complexity model separates the probability of a maximally correct binding match of possible interacting pharmacophore points from the separate probability of whether the number and type of these interactions releases sufficient free energy to be measured in a biophysical experiment ( Figure 5). range and fragment hit rates of 3% or more in NMR screening of fragments with an affinity threshold in the millimolar range.…”

Strategies to Calculate Water Binding Free Energies in Protein–Ligand Complexes

“…The chemical potential can be related directly to the binding free energy of the molecule, allowing both the location and affinity of water molecules to be found during a simulation. 31 One major drawback to the method lies in the poor acceptance rates, with water molecule insertions typically accepted with a probability of < 1 % even if cavity-biasing 32,33 and configurational bias 34 schemes are utilized.…”