Fast Dynamic Docking Guided by Adaptive Electrostatic Bias: The MD-Binding Approach

Engineering chemical entities to
modify how pharmaceutical targets
function, as it is done in drug design, requires a good understanding
of molecular recognition and binding. In this context, the limitations
of statically describing bimolecular recognition, as done in docking/scoring,
call for insightful and efficient dynamical investigations. On the
experimental side, the characterization of dynamical binding processes
is still in its infancy. Thus, computer simulations, particularly
molecular dynamics (MD), are compelled to play a prominent role, allowing
a deeper comprehension of the binding process and its causes and thus
a more informed compound selection, making more significant the computational
contribution to drug discovery (Carlson, H. A. Curr. Opin.
Chem. Biol. 2002, 6, 447−452). Unfortunately,
MD-based approaches cannot yet describe complex events without incurring
prohibitive time and computational costs. Here, we present a new method
for fully and dynamically simulating drug–target-complex formations,
tested against a real world and pharmaceutically relevant benchmark
set. The method, based on an adaptive, electrostatics-inspired bias,
envisions a campaign of trivially parallel short MD simulations and
a strategy to identify a near native binding pose from the sampled
configurations. At an affordable computational cost, this method provided
predictions of good accuracy also when the starting protein conformation
was different from that of the crystal complex, a known hurdle for
traditional molecular docking (Lexa, K. W.; Carlson, H. A. Q. Rev. Biophys. 2012, 45, 301−343).
Moreover, along the observed binding routes, it identified some key
features also found by much more computationally expensive plain-MD
simulations. Overall, this methodology represents significant progress
in the description of binding phenomena.