Computational Proteomics: Protein Interaction Prediction
Project Award Date: 09-01-2004
Proteins perform biological functions by interacting with other molecules. During the protein-protein interaction, the conserved domains physically interact with each other. Thus, understanding protein interactions at domain level gives detailed functional insights upon proteins that are either characterized or newly discovered. However, unlike protein-protein interactions that can be discovered by some high throughput technologies, domain-domain interactions largely remain unknown. This project addresses this issue by developing computational models to infer domain-domain interactions from protein-protein interactions; the model can then be used to validate and predict unknown protein interactions.
The long-term objectives of this research include better understanding protein functions based on their domain structures and predicting protein domains in terms of their functions. Specific aims include:
(1) The development of new computational models for inferring domain-domain interactions and for predicting protein-protein interactions. Kernel-based learning models will be developed to extract information from known protein-protein interactions, which is then used to infer the probabilities of domain-domain interactions. The newly developed computational models allow us to (i) predict the undiscovered protein-protein interactions, (ii) identify protein domains in terms of protein functions, and (iii) validate the newly discovered protein-protein interactions through biological experiments or other means.
(2) The development of an online system based on the computational models. This system will allow users to find the possible proteins that will interact with newly discovered proteins, validate protein-protein interactions, and identify protein domains.
In collaboration with Higuchi Biosciences Center
Primary Sponsor(s): National Institutes of Health