Algorithm for detecting potential structural interactions among proteins

This technology is an algorithm that leverages genomic and structural information to detect potential interactions between proteins.

Unmet Need: Identifying potential protein structural interactions from sequence information

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.

The Technology: Algorithm identifies potential protein-protein interactions without time-consuming experimental studies

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.

Applications:

• Predicting protein-protein interactions
• Tool for improved drug development
• Research tool for determining protein interactomes

Advantages:

• Improved protein-protein interaction predictions
• Predicts interactions without time-consuming experimental studies
• Outperforms related methods with lower false positive rate and higher true positive rate

Lead Inventor:

Barry Honig, Ph.D.

Related Publications:

• Zhang QF, Petrey D, Deng L, Qiang L, Shi Y, Thu CA, Bisikirska B, Lefebvre C, Accili D, Hunter T, Maniatis T, Califano A, Honig B. “Structure-based prediction of protein–protein interactions on a genome-wide scale.” Nature 2012 Oct 25;490:556-560.

Tech Ventures Reference:

• IR CU12266

• Licensing Contact: Ron Katz