Table 4b.

Docking studies that applied discrete sampling of flexibility through panels of receptor structures during the performance of IFD

Method	Target	Flexibility	Results	Caveats	Author
Flex X-Ensemble	105 cases	Averaged highly conserved regions Retained orientations of flexible regions as a set	FlexX-Ensemble yielded 66.7% success compared to 63.3% with FlexX within an RMSD of <2 Å for the first ten solutions (out of 40 hits) Scoring could not always identify the lowest RMSD complex within the top ten ranked solutions	May find ligands that are not compatible with the ‘real’ ensemble	Claussen et al. (2001)
Modified DOCK	T4 lysozyme mutant	Created a composite receptor, including rigid and flexible regions Calculated interaction energy between ligand and flexible regions independently Created average receptor representation for VS from results	Found 18 new hits Internal energy of conformer important for final ranking Conformational ensemble retrieved 79% of ligands in the top 1.5% of the database, compared to the single holo conformation that retrieved 77%, and the apo conformation that retrieved 54% Found that the receptor conformational energy was important to success	Conformational changes caused by ligand binding not fully predicted by method	Wei et al. (2004)
Ensemble as docking variable	10 cases	Conformational ensemble included as a variable with DOCK4.0 Representative for each side chain within the active site was based on the greatest distance from the reference sphere points by SPHGEN Optimized bound complex with SIMPLEX	Success defined as RMSD from crystal pose of ≤2.5 Å and an energy score > native docking Ensemble docking had the same speed as rigid docking Ensemble docking had 67–100% success, rigid docking had 23–87% success, sequential docking had 33–100% success based on pose and score	Use of a unified representation of the receptor can result in the identification of high-scoring false positives	Huang & Zou (2007)
Fleksy	35 cases	Generated ensemble from backbone-dependent rotamer exploration with a soft potential Used FlexX-Ensemble with soft docking Flexible optimization of complex with Yasara	Cross-docking with Fleksy gave RMSD to the crystal pose (rank 1) of 0.5–5.3 Å and FlexX found 1.2–9.3 Å Successful cross-docking by Fleksy in 78% of all three sets, compared to 44% success for FlexX (rigid docking) Docking failures related to sampling problems in ligand placement, rotatable bonds, and receptor conformation	Cannot handle large changes in backbone conformation No consideration of solvent effects	Nabuurs et al. (2007)
Flip DOCK	HIVp, Protein Kinase A	Flexibility Tree data structure represented conformational subspace Docking performed with AutoDock Divide-and-conquer genetic algorithm (GA) search performed substantially better than simple GA in best of 10 and average of 10 docking runs to 2 HIVp structures Also critical side chains in the active site were sampled (based on a rotamer library) during optimization	Performed 400 dockings (5 runs per complex) with 96.25% success (RMSD <2.0 Å) In a later paper (2008), the authors successfully docked 22/25 ‘tough’ cross-docking cases (those that had failed rigid docking) from four proteins by making 3–6 side chains flexible in FLIPDock Failure possibly caused by sampling, side chains could adopt a less than favorable position due to a steric clash with the ligand Failure also attributed to inability of scoring function to distinguish alternate binding modes	Each degree of freedom must be selected by hand Side chains selected as critical for binding were not sufficient for complete success	Zhao & Sanner (2007)
FITTED 2.6	18 cases	Docked against rigid protein, MRC, or flexible protein with a modified GA for the receptor chromosome Allowed switching between conformations, side chains in binding site, and/or water positions	Success rate based on RMSD of docked pose to within 2.5 Å of crystal structure: 79% for rigid native-, 56% for rigid cross-, 67% for flexible-docking, and 67% for MRC Notable speed increase over previous versions	Accuracy decreased between FITTED 1.5 and 2.6 Explained as being due to omega-generated ligand structures and a different test set	Corbeil & Moitessier (2009) (earlier versions: Corbeil et al. 2007, 2008)
4D Docking	267 nonredundant structures	Generated MRC through EN-NMA Ensemble assembled onto 4D grid based on binding potential and superposition During docking, could switch receptor conformation as well as ligand conformation	4D docking 73% success rate with 3–8 conformers when the cognate receptor was not included For the same scenario, MRC had 71.1% success rate 4D docking was 4x faster than MRC Inadequate sampling occurred in 12.4% of the set with 4D docking, compared to 2.7% of the time for MRC Incorrect scoring resulted in the wrong pose being top-scored in 17.67% of the cases for MRC, and 10.3% for 4D docking	Performance decreased with >8 conformers 4D docking marginally less successful than MRC	Bottegoni et al. (2009)