Computer simulations of biomolecules have been improving at a pace that is faster
than Mooreâs law for microprocessors in the last few decades. Thanks to advances
in theory, hardware, and algorithms it is increasingly possible to study biological
processes at relevant spatial and temporal resolutions, and to exploit simulation
for quantitative predictions. One area that can potentially benefit greatly from
such computational predictions is that of drug discovery. Since the inception
of the concept of rational drug design, the prediction of how tightly an organic
molecule binds to a macromolecular partner has been one of the chief objectives
of computational chemistry. Computers already play a fundamental support role
during the drug discovery process, and today many novel approaches that aim
at studying the details of drug binding and predicting binding affinity are being
actively investigated. In this thesis, I report a series of studies that aim to evaluate
the potential utility of free energy calculations based on molecular simulations for
drug design. In particular, I focus on the prediction of small-molecule binding
affinities to the epigenetic target of bromodomains. Bromodomains are small protein
modules that have been found in 46 human proteins involved in gene regulation.
Given their role in various diseases, in particular cancer and inflammation, a
number of bromodomain inhibitors are currently being investigated both in the
laboratory and the clinic. Here, it is shown how thorough calculations based
on explicit-solvent simulations and all-atom force fields can accurately reproduce
binding free energies for this protein family. Rigorous free energy calculations
are also compared to more approximate methods based on the post-processing
of the simulation trajectories in implicit solvent. Finally, a recently proposed
method for the estimation of water binding free energy is employed to study water
displaceability from bromodomain binding pockets.</p