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Finding biologically relevant protein domain interactions: conserved binding mode analysis.

ABSTRACT: Proteins evolved through the shuffling of functional domains, and therefore, the same domain can be found in different proteins and species. Interactions between such conserved domains often involve specific, well-determined binding surfaces reflecting their important biological role in a cell. To find biologically relevant interactions we developed a method of systematically comparing and classifying protein domain interactions from the structural data. As a result, a set of conserved binding modes (CBMs) was created using the atomic detail of structure alignment data and the protein domain classification of the Conserved Domain Database. A conserved binding mode is inferred when different members of interacting domain families dock in the same way, such that their structural complexes superimpose well. Such domain interactions with recurring structural themes have greater significance to be biologically relevant, unlike spurious crystal packing interactions. Consequently, this study gives lower and upper bounds on the number of different types of interacting domain pairs in the structure database on the order of 1000-2000. We use CBMs to create domain interaction networks, which highlight functionally significant connections by avoiding many infrequent links between highly connected nodes. The CBMs also constitute a library of docking templates that may be used in molecular modeling to infer the characteristics of an unknown binding surface, just as conserved domains may be used to infer the structure of an unknown protein. The method's ability to sort through and classify large numbers of putative interacting domain pairs is demonstrated on the oligomeric interactions of globins.

SUBMITTER: Shoemaker BA

PROVIDER: S-EPMC1855242 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Understanding protein-protein interactions (PPIs) at the molecular level may lead to innovations in medicine and biochemistry. The assumption that there are certain "hot spots" on protein surfaces that mediate their interactions with other proteins has led to a search for specific sequences involved in protein-protein contacts. In this work, we analyze sequential amino acid motifs, both at the single motif and at the motif-motif level, across a large and diverse dataset of biologically relevant protein-protein interfaces retrieved from the PDB, comparing their presence at interfaces and surfaces in a statistically rigorous manner. At the single motif level, our results indicate statistically significant over-presence of hydrophobic and in particular aromatic residues and under-presence of charged residues at protein-protein interfaces. Certain PPI-mediating motifs reported in the literature (e.g., the Tyrosine-based Motif YxxΦ and the PDZ-Binding Motif X-S/T-X-V/I) were confirmed to have a significant presence at interfaces. In addition, multiple PPI-mediating motifs were reported in the ELM database and from those present in our dataset, half were confirmed to have a statistically significant presence at interfaces whereas others were not. At the single residue, motif-motif level, Cysteine-Cysteine contacts were found to be the most abundant ones followed by interactions involving aromatic/hydrophobic residues. Top ranking, longer motif-motif pairs show predominance of Leucine and aromatic residues. Finally, preliminary energy calculations (using the MM/GBSA procedure) indicate a partial correlation between the probability of motifs-pair to be a part of a protein-protein interface and the strength of the interactions between the motifs. In conclusion, this study points to specific characteristics of motifs that have a higher probability to mediate protein-protein interactions. Prominent motifs identified in this study may be used in the future as possible components in protein engineering.

Project description:BACKGROUND:Protein-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. RESULTS:In 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. CONCLUSIONS:An approach using arbitrary docking, and based solely on physical properties, can successfully identify biologically pertinent protein interfaces.

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Finding biologically relevant protein domain interactions: conserved binding mode analysis.

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