Protein Complex Prediction

Participants: Hon Nian Chua, Joanne Lee, Kevin Lim, Guimei Liu, Limsoon Wong, Chern Han Yong


Progress in high-throughput experimental techniques in the past decade has resulted in a rapid accumulation of protein-protein interaction (PPI) data. However, recent surveys reveal that interaction data obtained by the popular high-throughput assays such as yeast-two-hybrid experiments may contain as much as 50% false positives and false negatives. As a result, further carefully-focused small-scale experiments are often needed to complement the large-scale methods to validate the detected interactions. However, the vast interactomes require much more scalable and inexpensive approaches. Thus it would be useful if the list of protein-protein interactions detected by such high-throughput assays could be prioritized in some way.

Furthermore, the PPI networks resulting from these assays are still essentially an in vitro scaffold. Further progress in computational analyses techniques and experimental methods is needed to reliably deduce in vivo protein interactions, to distinguish between permanent and transient interactions, to distinguish between direct protein binding from membership in the same protein complex and to distinguish protein complexes from functional modules.


In a past project, we studied techniques for assessing the reliability of PPIs. In this project, we aim to advance computational techniques for:

At the end of the project, we expect to have developed a robust and powerful system to postprocessing results of high-throughput PPI assays, as well as integrating extensive annotation information, yielding a more informative protein interactome beyond a mere in vitro scaffold.

Selected Publications


Selected Presentations


This project is supported in part by two A*STAR AGS scholarships (Chua: 8/03 - 7/07, Yong: 1/09 - ), a URC grant R-252-000-274-112 (Liu, Wong: 10/06 - 9/09), and a NRF CRP grant NRF-G-CRP-2997-04-082(d) (Wong: 4/08 - 3/13).

Last updated: 8/6/2017, Limsoon Wong.