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A unified conformational selection and induced fit approach to protein-peptide docking - PubMed

A unified conformational selection and induced fit approach to protein-peptide docking

Mikael Trellet et al. PLoS One. 2013.

Abstract

Protein-peptide interactions are vital for the cell. They mediate, inhibit or serve as structural components in nearly 40% of all macromolecular interactions, and are often associated with diseases, making them interesting leads for protein drug design. In recent years, large-scale technologies have enabled exhaustive studies on the peptide recognition preferences for a number of peptide-binding domain families. Yet, the paucity of data regarding their molecular binding mechanisms together with their inherent flexibility makes the structural prediction of protein-peptide interactions very challenging. This leaves flexible docking as one of the few amenable computational techniques to model these complexes. We present here an ensemble, flexible protein-peptide docking protocol that combines conformational selection and induced fit mechanisms. Starting from an ensemble of three peptide conformations (extended, a-helix, polyproline-II), flexible docking with HADDOCK generates 79.4% of high quality models for bound/unbound and 69.4% for unbound/unbound docking when tested against the largest protein-peptide complexes benchmark dataset available to date. Conformational selection at the rigid-body docking stage successfully recovers the most relevant conformation for a given protein-peptide complex and the subsequent flexible refinement further improves the interface by up to 4.5 Å interface RMSD. Cluster-based scoring of the models results in a selection of near-native solutions in the top three for ∼75% of the successfully predicted cases. This unified conformational selection and induced fit approach to protein-peptide docking should open the route to the modeling of challenging systems such as disorder-order transitions taking place upon binding, significantly expanding the applicability limit of biomolecular interaction modeling by docking.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Protein-peptide benchmark characteristics.

(A) Distribution of positional backbone RMSDs between the bound form of the peptides present in the benchmark and an ideal extended conformation. These are classified into three categories (easy, medium and difficult) based on the amplitude of the conformational change upon binding. (B) Percentage of solvent accessible residues computed for all peptides in the crystal structures of the respective protein-peptide complexes.

Figure 2
Figure 2. Overall HADDOCK results for (A) bound/unbound (extended), (B) bound/unbound (3 conformations) and (C) bound/unbound (3 conformations) with enhanced flexibility.

The percentages of near-native and sub-angstrom resolution models at the various stages (rigid-body (it0), semi-flexible (it1) and water refinement (water)) are reported in the left panels and were calculated over the 400 final models generated by HADDOCK. The right panels show the percentages after water refinement as a function of the docking difficulty.

Figure 3
Figure 3. Performance of the conformational selection at the rigid-body stage of HADDOCK.

The top 400 models are selected from the 6000 models generated based on their HADDOCK score. (A) Selection details for the 19 helical peptide cases. (B) Fractions of extended cases (41) with a predominant selection (i.e. majority of the selected conformations) coming from either extended, helical or polyproline II peptides. (C) Fraction of other cases (37) with a predominant selection coming from either extended, helical or polyproline II peptides.

Figure 4
Figure 4. Unbound/unbound docking performance using the conformational selection/induced fit HADDOCK protocol.

The percentages of near-native and sub-angstrom resolution models (see Methods) at the various stages (rigid-body (it0), semi-flexible (it1) and water refinement (water)) are reported in the left panels and were calculated over the 400 final models generated by HADDOCK. The right panels show the percentages after water refinement as a function of the docking difficulty.

Figure 5
Figure 5. Success rate of unbound/unbound docking as a function of the number of top models considered.

A docking is defined as successful it at least one near-native model is present within the topXX selected models.

Figure 6
Figure 6. Clustering performance of HADDOCK in unbound/unbound docking onto acceptable cases (with at least one acceptable model) as a function of the number of clusters considered.

A cluster is considered near-native if one of its top four member is of near-native quality or better.

Figure 7
Figure 7. Examples of HADDOCK best models for the challenging unbound/unbound cases.

The PDB-id as well as difficulty, peptide length, rank and i-RMSD values are indicated for each case. The model selected for illustration is the acceptable model with the best rank at the end of the HADDOCK process. The model peptide is shown in purple together with the reference peptide in the crystal structure of the complex in black. Docking model and crystal structure were superimposed on backbone atoms of the protein. The protein (crystal structure) is shown in surface representation. (A) 1NX1, (B) 1CZY, (C) 1LVM and (D) 1D4T. Figure generated with PyMol .

Figure 8
Figure 8. Difference in interface-RMSD (i-RMSD) and fraction of native contacts (Fnat) between models from various stages of HADDOCK (it0/it1/water) for unbound/unbound docking using our 3 conformation/enhanced flexibility protocol.

The distributions are calculated from all generated models of the unbound/unbound docking benchmark. A negative i-RMSD difference value reflects an improvement (move toward the bound form) while a positive value indicates a deterioration of this i-RMSD. For Fnat this is reverse: a positive difference indicates an improvement. The impact of flexible refinement in torsion angle space (differences between rigid-body docking and flexible refinement (it1–it0)) is shown in A) i-RMSD diff and C) Fnat diff, and the impact of final water refinement (differences between flexible and water refinement (water-it1) is shown in B) i-RMSD diff and D) Fnat diff.

Figure 9
Figure 9. Impact of the (A) i-RMSD and (B) l-RMSD cutoffs defining a near-native solution on the docking performance.

In this analysis, a docking run is defined as successful if at least one near-native model (for the selected cutoff) is generated within the pool of 400 water-refined models. Results are presented for both bound/unbound (97, black) and unbound/unbound (62, gray) cases.

Figure 10
Figure 10. Comparison of the performance of HADDOCK and FlexPepDock.

Percentage of cases with sub-angstrom and near-native models quality assessed by ligand-interface RMSD. The results are given for the whole bound/unbound benchmark and the ‘non-helical’ subset, as reported by FlexPepDock.

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Grants and funding

This work was supported by the Netherlands Organization for Scientific Research (www.nwo.nl) (VICI grant 700.56.442 to A.M.J.J.B.) and the WeNMR project (European FP7 e-Infrastructure grant, contract no. 261572, www.wenmr.eu). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.