A Unified Conformational Selection and Induced Fit Approach to Protein-Peptide Docking

By Mikael E. Trellet, Adrien S. J. Melquiond, Alexandre M. J. J. Bonvin. 2013

182 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Molecular Biology

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Citations in the corpus

Efficient Flexible Backbone Protein-Protein Docking for Challenging Targets (2017, background): “…2010; Moal and Bates, 2010; Venkatraman and Ritchie, 2012) or interface residues (Schindler et al. , 2015; Wang et al. , 2005) or by docking a small number of backbone conformations of the two partners (Chaudhury and Gray, 2008; Gru¨nberg et al. , 2004; Trellet et al. , 2013; Zhang et al. , 2017).”
CABS-dock web server for the flexible docking of peptides to proteins without prior knowledge of the binding site (2015, unknown): “The promising CABS-dock extensions include: for example, adding a refinement step (19,23); incorporation of experimental data; increasing the flexibility of appropriate receptor fragments (e.”
HPEPDOCK: a web server for blind peptide–protein docking based on a hierarchical algorithm (2018, methodology): “The test set for global peptide docking was constructed based on the 80 unbound / unbound protein–peptide complexes from the peptiDB docking benchmark (8,11,37) with two criteria.”
A Comprehensive Review on Current Advances in Peptide Drug Development and Design (2019, methodology): “Recently, HADDOCK method (HADDOCK peptide docking) was used to hypothesize the unnecessity of prior backbone information in local docking using secondary structure: α-helix, extended or polyproline-II helix as an ensemble of canonical conformation constrained to a defined binding site [97].”
GalaxyPepDock: a protein–peptide docking tool based on interaction similarity and energy optimization (2015, background): “When experimental or predicted data on binding site residues are available, such information can be used to constrain the docking to local regions of the protein surface (12).”