Abstract
Simulations of molecular dynamics (MD) are playing an increasingly important role in structure-based drug discovery (SBDD). Here we review the use of MD for proteins in aqueous solvation, organic/aqueous mixed solvents (MDmix) and with small ligands, to the classic SBDD problems: Binding mode and binding free energy predictions. The simulation of proteins in their condensed state reveals solvent structures and preferential interaction sites (hot spots) on the protein surface. The information provided by water and its cosolvents can be used very effectively to understand protein ligand recognition and to improve the predictive capability of well-established methods such as molecular docking. The application of MD simulations to the study of the association of proteins with drug-like compounds is currently only possible for specific cases, as it remains computationally very expensive and labor intensive. MDmix simulations on the other hand, can be used systematically to address some of the common tasks in SBDD. With the advent of new tools and faster computers we expect to see an increase in the application of mixed solvent MD simulations to a plethora of protein targets to identify new drug candidates.
Highlights
The first revolution in structural biology, in the early 1990’s, increased the available structural information by 20-fold in a decade, creating a high expectation for computational methods that could turn this information into drug candidates
Simulation of molecular dynamics in an explicit solvent are needed for accurate drug design
As the thermodynamics of the solvent reorganization upon drug binding is a key contribution to the complex formation free energy and to the ligand binding affinity
Summary
The first revolution in structural biology, in the early 1990’s, increased the available structural information by 20-fold in a decade, creating a high expectation for computational methods that could turn this information into drug candidates. We will discuss how MD simulations of proteins in water and mixed solvents can be used to identify key interactions on their surface, and how these can be incorporated into computational docking, to identify better drug candidates. Solvation affects the bound state and binding pathways, the gold standard for computational methods is to recapitulate the binding process of a ligand to its target by means of molecular simulations that consider the solvent explicitly. Except for the crucial difference of including explicit solvation in all the computational procedures, MDmix-type simulations can trace their roots to Goodford’s GRID [8], Karplus’ MCSS [9] or the more recent FTmap [10] All such methods assume that the behavior of the probe is transferable to bigger molecules. We will conclude by discussing the practical limitations and future perspectives for the application of these methods in drug discovery
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