Abstract
Molecular docking is the methodology of choice for studying in silico protein-ligand binding and for prioritizing compounds to discover new lead candidates. Traditional docking simulations suffer from major limitations, mostly related to the static or semi-flexible treatment of ligands and targets. They also neglect solvation and entropic effects, which strongly limits their predictive power. During the last decade, methods based on full atomistic molecular dynamics (MD) have emerged as a valid alternative for simulating macromolecular complexes. In principle, compared to traditional docking, MD allows the full exploration of drug-target recognition and binding from both the mechanistic and energetic points of view (dynamic docking). Binding and unbinding kinetic constants can also be determined. While dynamic docking is still too computationally expensive to be routinely used in fast-paced drug discovery programs, the advent of faster computing architectures and advanced simulation methodologies are changing this scenario. It is feasible that dynamic docking will replace static docking approaches in the near future, leading to a major paradigm shift in in silico drug discovery. Against this background, we review the key achievements that have paved the way for this progress.
Highlights
Nowadays, molecular docking programs are extensively and routinely used in computer-aided drug discovery, mostly in the framework of virtual-library screening (VS) campaigns [1,2]
Molecular docking programs are extensively and routinely used in computer-aided drug discovery, mostly in the framework of virtual-library screening (VS) campaigns [1,2]. This is the first critical step in structure-based drug discovery (SBDD), where the new drug is identified as the ligand that fits best into the binding pocket of the protein target [2]
Provided that adequate sampling is achieved, kinetic observables such as the association and dissociation constant can be determined. This is a crucial aspect of unbiased dynamic docking because it expands the predictive power of computational methods applied to SBDD
Summary
Molecular docking programs are extensively and routinely used in computer-aided drug discovery, mostly in the framework of virtual-library screening (VS) campaigns [1,2]. In the past two decades, researchers have produced a wealth of structural data, and constantly improved the protocols for molecular calculations, allowing for a rapid screening of libraries containing hundreds of thousands of compounds This computational speed comes at the cost of accuracy, especially when target rearrangements are required upon ligand binding [3,4]. Several research groups have addressed these limitations using enhanced sampling methods [8] This class of MD-based simulative approaches allows one to accelerate the observation of events, such as drug-target (un)binding, by biasing the forces or altering the potential energy function. We emphasize the benefits of a dynamic description compared to the static view of binding provided by conventional docking methods
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