The interactome: predicting the protein-protein interactions in cells.
ABSTRACT The term Interactome describes the set of all molecular interactions in cells, especially in the context of protein-protein interactions. These interactions are crucial for most cellular processes, so the full representation of the interaction repertoire is needed to understand the cell molecular machinery at the system biology level. In this short review, we compare various methods for predicting protein-protein interactions using sequence and structure information. The ultimate goal of those approaches is to present the complete methodology for the automatic selection of interaction partners using their amino acid sequences and/or three dimensional structures, if known. Apart from a description of each method, details of the software or web interface needed for high throughput prediction on the whole genome scale are also provided. The proposed validation of the theoretical methods using experimental data would be a better assessment of their accuracy.
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ABSTRACT: The kinetics of protein interactions are essential determinants in many cellular processes such as signal transduction and transcriptional regulation. Many proteins involved in these functions contain intrinsic disordered regions. This makes conformational flexibility become an unneglectable factor when studying the binding kinetic of these proteins. Compared with the binding of rigid proteins that is limited by diffusions, the binding mechanisms of proteins with internal flexibility are much more complicated. Using a small protein that contains two domains and a connecting loop as a testing system, we developed a multiscale simulation framework to study the role of flexible linkers in regulating kinetics of protein binding. The association and dissociation processes were implemented by a coarse-grained Monte-Carlo algorithm, while the conformational changes of the flexible linker were captured from all-atom molecular dynamic simulations. Our simulations illustrated that the presence of the extended domain linker can enhance the rate of protein association. On the other hand, the full-length flexible molecule is more difficult to dissociate than its two rigid domains but much easier than the molecule with a rigid linker. Overall, our studies demonstrated that both kinetics and thermodynamics of protein binding are closely modulated by the dynamic features of linker regions. © Proteins 2014;. © 2014 Wiley Periodicals, Inc.Proteins Structure Function and Bioinformatics 10/2014; 82(10). DOI:10.1002/prot.24614 · 2.92 Impact Factor
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ABSTRACT: Background The physical interactions between proteins constitute the basis of protein quaternary structures. They dominate many biological processes in living cells. Deciphering the structural features of interacting proteins is essential to understand their cellular functions. Similar to the space of protein tertiary structures in which discrete patterns are clearly observed on fold or sub-fold motif levels, it has been found that the space of protein quaternary structures is highly degenerate due to the packing of compact secondary structure elements at interfaces. Therefore, it is necessary to further decompose the protein quaternary structural space into a more local representation.ResultsHere we constructed an interface fragment pair library from the current structure database of protein complexes. After structural-based clustering, we found that more than 90% of these interface fragment pairs can be represented by a limited number of highly abundant motifs. These motifs were further used to guide complex assembly. A large-scale benchmark test shows that the native-like binding is highly likely in the structural ensemble of modeled protein complexes that were built through the library.Conclusions Our study therefore presents supportive evidences that the space of protein quaternary structures can be represented by the combination of a small set of secondary-structure-based packing at binding interfaces. Finally, after future improvements such as adding sequence profiles, we expect this new library will be useful to predict structures of unknown protein-protein interactions.BMC Bioinformatics 01/2015; 16(1):14. DOI:10.1186/s12859-014-0437-4 · 2.67 Impact Factor