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. 2008 Sep 12;381(4):1068-87.
doi: 10.1016/j.jmb.2008.05.042. Epub 2008 May 24.

Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles

Sidhartha Chaudhury  1 , Jeffrey J Gray
Affiliations

Conformer selection and induced fit in flexible backbone protein-protein docking using computational and NMR ensembles

Sidhartha Chaudhury et al. J Mol Biol. .

Abstract

Accommodating backbone flexibility continues to be the most difficult challenge in computational docking of protein-protein complexes. Towards that end, we simulate four distinct biophysical models of protein binding in RosettaDock, a multiscale Monte-Carlo-based algorithm that uses a quasi-kinetic search process to emulate the diffusional encounter of two proteins and to identify low-energy complexes. The four binding models are as follows: (1) key-lock (KL) model, using rigid-backbone docking; (2) conformer selection (CS) model, using a novel ensemble docking algorithm; (3) induced fit (IF) model, using energy-gradient-based backbone minimization; and (4) combined conformer selection/induced fit (CS/IF) model. Backbone flexibility was limited to the smaller partner of the complex, structural ensembles were generated using Rosetta refinement methods, and docking consisted of local perturbations around the complexed conformation using unbound component crystal structures for a set of 21 target complexes. The lowest-energy structure contained >30% of the native residue-residue contacts for 9, 13, 13, and 14 targets for KL, CS, IF, and CS/IF docking, respectively. When applied to 15 targets using nuclear magnetic resonance ensembles of the smaller protein, the lowest-energy structure recovered at least 30% native residue contacts in 3, 8, 4, and 8 targets for KL, CS, IF, and CS/IF docking, respectively. CS/IF docking of the nuclear magnetic resonance ensemble performed equally well or better than KL docking with the unbound crystal structure in 10 of 15 cases. The marked success of CS and CS/IF docking shows that ensemble docking can be a versatile and effective method for accommodating conformational plasticity in docking and serves as a demonstration for the CS theory--that binding-competent conformers exist in the unbound ensemble and can be selected based on their favorable binding energies.

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Figures

Figure 1
Figure 1
Ligand ensemble generation and conformer selection. (A) Crystal structures are idealized and then relaxed in low and high-resolution to generate an ensemble of 10 structures. (B) Conformers in an NMR ensemble are idealized and refined in high-resolution only. (C) During low-resolution docking, conformers are superposed along the current conformer’s interface residues, and a conformer is selected from a partition function using Boltzmann-weighted energies.
Figure 2
Figure 2
The flexible docking algorithm. The low-resolution phase models the formation of an encounter complex and the high-resolution phase models its transition to a bound complex. CS and CS/IF-docking include the conformer selection step (green box). IF and CS/IF-docking include backbone minimization (orange box).
Figure 3
Figure 3
(A) The frequency of selecting a particular conformer during the conformer selection step vs. the internal energy for each conformer when using total energy (red) and binding energy (blue) for 1BRC. (B) Total energy vs. L_rmsd and binding energy vs. L_rmsd for 1BRC. CAPRI criteria high-quality decoys are shown in brown, medium-quality in orange and acceptable-quality in tan; note that high-quality decoys sometimes meet the CAPRI I_rmsd criterion rather than the L_rmsd criterion.
Figure 4
Figure 4
Backbone variability of FAS2. (A) Bound (red) and unbound (blue) FAS2 conformations. (B) Ensemble generated by Rosetta from the unbound FAS2 conformation. (C) Cα mean-squared fluctuation (MSF) for the Rosetta ensemble (black circles), the Cα MSF between the bound and unbound FAS2 conformation (red squares), and the Cα MSF calculated from the crystallographic B-factors45 from 1FSC (green triangles).
Figure 5
Figure 5
Binding energy vs. L_rmsd, binding energy vs. fnat, and the top-ranked decoy (blue) superposed along the receptor (green) with the crystal structure of the bound ligand (red) for AChe binding to FAS2 (1FSS). (A) KL-docking using the bound FAS2 structure, (B-E) KL, CS, IF, and CS/IF-docking, respectively, using the unbound FAS2 structure. The unbound AChe structure (1FSC) is used in all cases. CAPRI criteria high quality decoys are shown in brown, medium quality in orange, and acceptable quality in tan.
Figure 6
Figure 6
Detail of the lowest energy structure ligand (cyan) and receptor (green) for the CS/IF method for 1FSS superposed with the native structure (gray) along the receptor. This decoy has an L_rmsd of 2.5 Å, an I_rmsd of 0.94 Å, and an fnat of 0.61. Met33 of FAS2 and Trp239 of AChe are shown as spheres.
Figure 7
Figure 7
Backbone sampling and discrimination. (A) The BB_rmsd and UB_rmsd in each decoy output by CS, IF and CS/IF-docking respectively. Red lines show the BB_rmsd and UB_rmsd of the unbound and bound conformations respectively. (B) Binding energy vs. BB_rmsd, for CS, IF, and CS/IF-docking, respectively. CAPRI criteria high quality decoys are shown in brown, medium quality in orange, and acceptable quality in tan.
Figure 8
Figure 8
Histogram of hit quality (quality of the top-ranked decoy for all runs with at least 5 of the 10 top-scoring decoys of medium or high quality) for each docking method for (A) crystal structure targets and (B) NMR targets. CAPRI criteria high-quality is shown in brown, medium-quality in orange.
Figure 9
Figure 9
NMR docking results for CS-docking of 1ACB. (A) Binding energy vs. L_rmsd. (B) Binding energy vs. BB_rmsd. (C) Rosetta-refined NMR ensemble. (D) Lowest energy conformer from CS-docking (purple) superposed on the bound structure (red) and the first model in the NMR ensemble (blue). (E) Lowest energy structure from the CS method (receptor, green; ligand, purple) superposed on the native complex (gray).
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