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Background: Protein-protein interactions (PPI) play a key role in an investigation of various biochemical processes, and their identification is thus of great importance. Although computational prediction of which amino acids take part in a PPI has been an active field of research for some time, the quality of in-silico methods is still far from perfect. Results: We have developed a novel prediction method called INSPiRE which benefits from a knowledge base built from data available in Protein Data Bank. All proteins involved in PPIs were converted into labeled graphs with nodes corresponding to amino acids and edges to pairs of neighboring amino acids. A structural neighborhood of each node was then encoded into a bit string and stored in the knowledge base. When predicting PPIs, INSPiRE labels amino acids of unknown proteins as interface or non-interface based on how often their structural neighborhood appears as interface or non-interface in the knowledge base. We evaluated INSPiRE's behavior with respect to different types and sizes of the structural neighborhood. Furthermore, we examined the suitability of several different features for labeling the nodes. Our evaluations showed that INSPiRE clearly outperforms existing methods with respect to Matthews correlation coefficient. Conclusion: In this paper we introduce a new knowledge-based method for identification of protein-protein interaction sites called INSPiRE. Its knowledge base utilizes structural patterns of known interaction sites in the Protein Data Bank which are then used for PPI prediction. Extensive experiments on several well-established datasets show that INSPiRE significantly surpasses existing PPI approaches. [ABSTRACT FROM AUTHOR]

Background: LRP6 is a membrane protein crucial in the initiation of canonical Wnt/b-catenin signalling. Its function is dependent on its proline-serine rich intracellular domain. LRP6 has five PPP(S/T)P motifs that are phosphorylated during activation, starting with the site closest to the membrane. Like all long proline rich regions, there is no stable 3D structure for this isolated, contiguous region. Results: In our study, we use a computational simulation tool to sample the conformational space of the LRP6 intracellular domain, under the spatial constraints imposed by (a) the membrane and (b) the close approach of the neighboring intracellular molecular complex, which is assembled on Frizzled when Wnt binds to both LRP6 and Frizzled on the opposite side of the membrane. We observe that an elongated form dominates in the LRP6 intracellular domain structure ensemble. This elongation could relieve conformational auto-inhibition of the PPP(S/ T)PX(S/T) motif binding sites and allow GSK3 and CK1 to approach their phosphorylation sites, thereby activating LRP6 and the downstream pathway. Conclusions: We propose a model in which the conformation of the LRP6 intracellular domain is elongated before activation. This is based on the intrusion of the Frizzled complex into the ensemble space of the proline rich region of LRP6, which alters the shape of its available ensemble space. To test whether this observed ensemble conformational change is sequence dependent, we did a control simulation with a hypothetical sequence with 50% proline and 50% serine in alternating residues. We confirm that this ensemble neighbourhood-based conformational change is independent of sequence and conclude that it is likely found in all proline rich sequences. These observations help us understand the nature of proline rich regions which are both unstructured and which seem to evolve at a higher rate of mutation, while maintaining sequence composition. [ABSTRACT FROM AUTHOR]