Abstract
Conserved water molecules are of interest in drug design, as displacement of such waters can lead to higher affinity ligands, and in some cases, contribute towards selectivity. Bromodomains, small protein domains involved in the epigenetic regulation of gene transcription, display a network of four conserved water molecules in their binding pockets and have recently been the focus of intense medicinal chemistry efforts. Understanding why certain bromodomains have displaceable water molecules and others do not is extremely challenging, and it remains unclear which water molecules in a given bromodomain can be targeted for displacement. Here we estimate the stability of the conserved water molecules in 35 bromodomains via binding free energy calculations using all-atom grand canonical Monte Carlo simulations. Encouraging quantitative agreement to the available experimental evidence is found. We thus discuss the expected ease of water displacement in different bromodomains and the implications for ligand selectivity.
Highlights
Conserved water molecules are of interest in drug design, as displacement of such waters can lead to higher affinity ligands, and in some cases, contribute towards selectivity
Even more arduous is the quantitative estimation of water stability, so that one cannot differentiate between a stable water molecule carrying a large free energy penalty for its displacement from a relatively unstable one
A new approach based on Monte Carlo (MC) simulation in the grand canonical (GC) ensemble was proposed by Ross et al[20,21]
Summary
While bearing this in mind, the data in this study may be used to estimate the contribution of differential water stability in bromodomains towards the selectivity of a hypothetical ligand displacing one or more of the four conserved water molecules Based on this argument, plots where bromodomains are ranked according to their predicted water binding free energies We anticipate that full binding free energy calculations of the individual ligands with the bromodomains in combination with GCMC will improve the quantitative accuracy of the relative affinity predictions
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