Arbitrary protein−protein docking targets biologically relevant interfaces

Abstract

BackgroundProtein-protein recognition is of fundamental importance in the vast majority of biological processes. However, it has already been demonstrated that it is very hard to distinguish true complexes from false complexes in so-called cross-docking experiments, where binary protein complexes are separated and the isolated proteins are all docked against each other and scored. Does this result, at least in part, reflect a physical reality? False complexes could reflect possible nonspecific or weak associations.ResultsIn this paper, we investigate the twilight zone of protein-protein interactions, building on an interesting outcome of cross-docking experiments: false complexes seem to favor residues from the true interaction site, suggesting that randomly chosen partners dock in a non-random fashion on protein surfaces. Here, we carry out arbitrary docking of a non-redundant data set of 198 proteins, with more than 300 randomly chosen "probe" proteins. We investigate the tendency of arbitrary partners to aggregate at localized regions of the protein surfaces, the shape and compositional bias of the generated interfaces, and the potential of this property to predict biologically relevant binding sites. We show that the non-random localization of arbitrary partners after protein-protein docking is a generic feature of protein structures. The interfaces generated in this way are not systematically planar or curved, but tend to be closer than average to the center of the proteins. These results can be used to predict biological interfaces with an AUC value up to 0.69 alone, and 0.72 when used in combination with evolutionary information. An appropriate choice of random partners and number of docking models make this method computationally practical. It is also noted that nonspecific interfaces can point to alternate interaction sites in the case of proteins with multiple interfaces. We illustrate the usefulness of arbitrary docking using PEBP (Phosphatidylethanolamine binding protein), a kinase inhibitor with multiple partners.ConclusionsAn approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.

Inferring processes underlying B-cell repertoire diversity

“…Out-of-frame sequences have similarly been used as a baseline to study the properties of BCR naive repertoires [16], or to estimate selective table 3.3 [29]). Residues that are exposed to solvent in protein-protein complexes (following definitions and data from [30]) are divided into three groups: surface (interface) residues that have unchanged accessibility area when the interaction partner is present (e), rim (interface) residues that have changed accessibility area, but no atoms with zero accessibility in the complex (f ) and core (interface) residues that have changed accessibility area and at least one atom with zero accessibility in the complex (g). Finally, we plot the basic biochemical amino acid properties (http://en.wikipedia.org/wiki/Amino_acid; http://en.wikipedia.org/wiki/Proteino genic_amino_acid): charge (h), pH (i), polarity ( j ), hydrophobicity (k) and volume (l ).…”

Quantifying selection in immune receptor repertoires

“…21, 2014; Fraction of positions Table 3.3 [17]). Residues that are exposed to solvent in protein-protein complexes (following definitions and data from [18]) are divided intothree groups: surface (interface) residues that have unchanged accessibility area when the interaction partner is present (D), rim (interface) residues that have changed accessibility area, but no atoms with zero accessibility in the complex (E) and core (interface) residues that have changed accessibility area and at least one atom with zero accessibility in the complex (F). Rim residues roughly correspond to the periphery of the interface region, and core residues correspond to the center.…”

Protein social behavior makes a stronger signal for partner identification than surface geometry

“…Large‐scale modeling experiments dealing with hundreds of proteins can be computationally highly demanding and scaling up to thousands or tens of thousands of proteins asks for drastically reducing the computing time associated to molecular docking. In this respect, rigid‐body geometrical docking algorithms have been used to efficiently generate and rank candidate complex conformations . It has been suggested that docking scores based only on the geometric complementarity of the two molecular surfaces can be used to identify binding partners .…”

“…Some of these methods already provide very accurate predictions, without any knowledge of a protein's partner(s). Likewise, several docking studies showed that some regions at the surface of a protein are preferentially targeted by any protein (partner or not) . Nevertheless, a few recent works demonstrated that protein binding site predictions can be improved by including information about the native partner, provided that reliable structural data are available, and highlighted the specificity of interfaces involved in transient interactions .…”