Department of Electrical Engineering and Computer Science
University of Kansas
Computational Proteomics: Protein Interaction Prediction (2004-2007)
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 such as two-hybrid systems, 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.
Dr. Chen and his research group first developed new computational models for inferring domain-domain interactions and for predicting protein-protein interactions. The newly developed computational models allowed them to:
- predict the undiscovered protein-protein interactions,
- identify protein domains in terms of protein functions, and
- validate the newly discovered protein-protein interactions through biological experiments or other means.
Second, they will develop 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.
Researchers have acquired protein-protein interaction data from several public domain databases, including the Database of Interacting Proteins, the Biomolecular Interaction Network Database and Human Protein Reference Database. The research group has written software scripts to retrieve from remote servers the sequences of proteins involved and to scan these sequences against the Pfam domain database to determine domains each protein may have. They have also designed and implemented a relational database to save collected data, protein sequences, and derived domain matching information. Work continues on the completion of the database collection and computational modeling methods.
Xue-Wen Chen received a CAREER award from the National Science Foundation in 2007. He was promoted to Professor of Electrical Engineering and Computer Science at the University of Kansas. Dr. Chen is now a professor in the Department of Computer Science and the Co-Director, Big Data & Analytics Group at Wayne State University, Detroit, MI.