This technology is an algorithm that leverages genomic and structural information to detect potential interactions between proteins.
The identification of pairs of interacting proteins is essential for understanding cell regulatory mechanisms. However, most known protein-protein interactions have been deduced from exhaustive and time-consuming experimental studies such as the yeast two-hybrid assay, affinity purification, and manual curation. Additionally, while computational approaches have been developed to infer protein-protein interactions, they have failed to yield reliable genome-wide information.
This technology is an algorithm that combines known information on the structure and sequence of proteins to predict new protein-protein interactions. Nicknamed PrePPI (predicting protein-protein interactions), this technology compares protein sequences to create structure-based sequence alignments that can be used to create interaction models between each pair of proteins in a dataset. These predicted protein pairs are then scored by the algorithm to yield the probability that the predicted interaction pair is true. By predicting the most likely protein-protein interactions, this technology bypasses the need for exhaustive experimentation, allowing researchers to selectively validate predicted pairs.
A benchmark study revealed that PrePPI provides superior information to that obtained from structural and non-structural evidence alone.
IR CU12266
Licensing Contact: Ron Katz