There are multiple docking platforms suitable

There are multiple docking platforms suitable for use with nucleic 543 receptors. These include: DOCK v4-6 (UCSF) [[68], [69], [70]], AutoDock (Scripps) [71], AutoDock Vina (Scripps) [72], GOLD (Cambridge Crystallographic Data Centre) [73], Surflex-DOCK (BioPharmics) [74], Glide (Schrӧdinger) [75], and ICM (Molsoft) [76]. Many of these software have been compared elsewhere in the context of protein docking [77]. The author has also compared two of these platforms, Surflex-DOCK and AutoDock, in the context of nucleic acids [78]. Both platforms performed equally well with Surflex being slightly faster and more easily scalable. DOCK, AutoDock, AutoDock Vina, and GOLD are all freely available to academic institutions. Each docking platform varies with respect to sampling algorithms and scoring functions. A sampling algorithm is a systematic way to sample from a population of possible molecular conformations and binding modes without exhausting all possibilities. The primary hurdle in docking is the vast number of potential docked positions for a given set of molecules. Minimizing the computational time necessary for each docking run is of prime importance for high-throughput screening. Strategies to minimize computational time include: library optimization (reduced size, generation of tautomers, protonation, filtering), robust computational infrastructure (computing grids), and selection of the appropriate sampling algorithm(s). Such sampling algorithms include: geometric matching algorithms (GM), incremental construction methods (IC), Monte Carlo (MC) searches, genetic algorithms (GA), and molecular dynamics (MD) (see Ref. [79] for an overview). Table 1 lists the various algorithms employed by the docking platforms discussed here.
Scoring functions Docking algorithms attempt to find ‘solutions’ to the orientation and ranking of ligand-receptor interactions. In doing so they must have a way to order the thousands or millions of complexes. This is achieved by scoring, which approximates the binding affinity (ΔGbind). Relative binding free energies can be approximated by free energy perturbation methods using molecular dynamics simulations [99]; however, these methods are far too computationally expensive for routine docking, and so more approximate solutions have been devised. The first type of free energy approximation is the “empirical” [107] scoring function, which is an additive equation derived from each of the different modes of interaction of system [101,108]. As implied by the name, empirical score values are derived from a set of known ligand-receptor complexes. As an example (as adapted from Ref. [101]):where would be the total docking score based on the additive scores from H-bonds (hb), ionic interactions (ionic), rotational constraints of constituent groups (rot), and Van der Waals (VDW) interactions. These terms can also be modified by the user with weighting to favor or disfavor interactions depending on the system in question. Similarly, there are modifier (or “penalty”) terms which can be applied to disfavor improper H-bond angles, distance restraints, hydrophobic interactions, and torsions. Autodock 4, DOCK v4-6, GOLD, Surflex-Dock, and Autodock Vina use empirical scoring terms. There are also force-field (FF) based scoring functions. These functions implement current molecular mechanics (MM) force fields (e.g. AMBER, CHARMM) to estimate enthalpy of binding from VDW and electrostatic interactions, strain energies, and solvation effects. The latter is typically estimated by calculating the desolvation energy using MM/PBSA (Poisson-Boltzmann surface area) or MM/GBSA (generalized Born surface area) methods. However, MM/PBSA and MM/GBSA are too computationally expensive to be used in high throughput screening [109]. FF scoring is achieved by pair-wise evaluation of each non-bonded interaction, with the following general format (example taken from Autodock v4.2's manual [110]):where L is the ligand, P is the receptor, and V is the calculated potential term from MD force fields. Eq. (2) shows the 6 pair-wise evaluations and entropy term to account for any changes in conformational entropy. The force field potentials used here are comparable to that used in the Amber, CHARMM, or GROMACS force fields but can be modified by the user if desired. Glide, ICM, and early versions of DOCK and Autodock use FF based scoring functions with empirical weighting.