Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

The binding of small-molecule ligands
to protein or nucleic acid
targets is important to numerous biological processes. Accurate prediction
of the binding modes between a ligand and a macromolecule is of fundamental
importance in structure-based structure–function exploration.
When multiple ligands with different sizes are docked to a target
receptor, it is reasonable to assume that the residues in the binding
pocket may adopt alternative conformations upon interacting with the
different ligands. In addition, it has been suggested that the entropic
contribution to binding can be important. However, only a few attempts
to include the side chain conformational entropy upon binding within
the application of flexible receptor docking methodology exist. Here,
we propose a new physics-based scoring function that includes both
enthalpic and entropic contributions upon binding by considering the
conformational variability of the flexible side chains within the
ensemble of docked poses. We also describe a novel hybrid searching
algorithm that combines both molecular dynamics (MD)-based simulated
annealing and genetic algorithm crossovers to address the enhanced
sampling of the increased search space. We demonstrate improved accuracy
in flexible cross-docking experiments compared with rigid cross-docking.
We test our developments by considering five protein targets, thrombin,
dihydrofolate reductase­(DHFR), T4 L99A, T4 L99A/M102Q, and PDE10A,
which belong to different enzyme classes with different binding pocket
environments, as a representative set of diverse ligands and receptors.
Each target contains dozens of different ligands bound to the same
binding pocket. We also demonstrate that this flexible docking algorithm
may be applicable to RNA docking with a representative riboswitch
example. Our findings show significant improvements in top ranking
accuracy across this set, with the largest improvement relative to
rigid, 23.64%, occurring for ligands binding to DHFR. We then evaluate
the ability to identify lead compounds among a large chemical space
for the proposed flexible receptor docking algorithm using a subset
of the DUD-E containing receptor targets MCR, GCR, and ANDR. We demonstrate
that our new algorithms show improved performance in modeling flexible
binding site residues compared to DOCK. Finally, we select the T4
L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and
experimentally validated nonbinders, to test our approach in distinguishing
binders from nonbinders. We illustrate that our new algorithms for
searching and scoring have superior performance to rigid receptor
CDOCKER as well as AutoDock Vina. Finally, we suggest that flexible
CDOCKER is sufficiently fast to be utilized in high-throughput docking
screens in the context of hierarchical approaches.