Uncovering the Structural Basis of Protein Interactions with Efficient Clustering of 3-D Interaction Interfaces

Zeyar Aung*, Soon-Heng Tan, See-Kiong Ng, Kian-Lee Tan

Institute for Infocomm Research, 21 Heng Mui Keng Terrace, Singapore 119613, Singapore. azeyar@i2r.a-star.edu.sg

Proc LSS Comput Syst Bioinform Conf. August, 2007. Vol. 6, p. 287-297. Full-Text PDF

*To whom correspondence should be addressed.

The biological mechanisms with which proteins interact with one another are best revealed by studying the structural interfaces between interacting proteins. Protein–protein interfaces can be extracted from 3-D structural data of protein complexes and then clustered to derive biological insights. However, conventional protein interface clustering methods lack computational scalability and statistical support. In this work, we present a new method named "PPiClust" to systematically encode, cluster and analyze similar 3-D interface patterns in protein complexes efficiently. Experimental results showed that our method is effective in discovering visually consistent and statistically significant clusters of interfaces, and at the same time sufficiently time-efficient to be performed on a single computer. The interface clusters are also useful for uncovering the structural basis of protein interactions. Analysis of the resulting interface clusters revealed groups of structurally diverse proteins having similar interface patterns. We also found, in some of the interface clusters, the presence of well-known linear binding motifs which were non-contiguous in the primary sequences. These results suggest that PPiClust can discover not only statistically significant but also biologically significant protein interface clusters from protein complex structural data.