Incorporating prior knowledge to seeds of adaptive sampling molecular dynamics simulations of ligand transport in enzymes with buried active sites

Abstract

Given that most proteins have buried active sites, protein tunnels or channels play a crucial role in mitigating the transport of small molecules to the buried cavity for enzymatic catalysis. Tunnels can critically modulate the biological process of protein-ligand recognition. Various molecular dynamics methods have been developed for exploring and exploiting the protein-ligand conformational space to extract high-resolution details of the binding processes, one of the most recent represented by energetically unbiased high-throughput adaptive sampling simulations. The current study systematically contrasts the role of integrating prior knowledge while generating useful initial protein-ligand configurations, called seeds, for these simulations. Using a non-trivial system of haloalkane dehalogenase mutant with multiple transport tunnels leading to a deeply buried active site, these simulations were employed to derive kinetic models describing the process of association and dissociation of the substrate molecule. The more knowledge-based seed generation enabled high-throughput simulations that could more consistently capture the entire transport process, effectively explore the complex network of transport tunnels, and predict equilibrium dissociation constants, koff/kon, on the same order of magnitude as experimental measurements. Overall, the infusion of more knowledge into the initial seeds of adaptive sampling simulations could render analyses of transport mechanisms in enzymes more consistent even for very complex biomolecular systems, thereby promoting the rational design of enzymes with buried active sites and drug development efforts.