InSite: a computational method for identifying protein-protein interaction binding sites on a proteome-wide scale

Haidong Wang¹ , Eran Segal² , Asa Ben-Hur³ , Qian-Ru Li⁴ , Marc Vidal⁴ and Daphne Koller¹

¹Computer Science Department, Stanford University, Serra Mall, Stanford, CA 94305, USA

²Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 76100, Israel

³Computer Science Department, Colorado State University, South Howes Street, Fort Collins, CO 80523, USA

⁴Center for Cancer Systems Biology (CCSB) and Department of Cancer Biology, Dana-Farber Cancer Institute, and Department of Genetics, Harvard Medical School, Binney Street, Boston, MA 02115, USA

author email corresponding author email

Genome Biology 2007, 8:R192doi:10.1186/gb-2007-8-9-r192


Published:	14 September 2007

Subject areas: Bioinformatics, Biochemistry and structural biology, Genetics

Additional files

Additional data file 1:

Figure S1 shows motif coverage of protein sequences compared with coverage of the protein-protein interaction binding sites. Figure S2 is an illustration of the Bayesian network used in the first phase of InSite. Figure S3 is a schematic illustration of our EM-based learning algorithm. Figure S4 is an illustration of the Bayesian network used in the second phase of InSite. Figure S5 shows the number of motifs and their lengths on each protein. Figure S6 shows the number of occurrences for each pair of motif types. Figure S7 evaluates motif-protein binding site predictions with or without indirect evidence relative to solved PDB structures. Figure S8 shows the statistics for each protein-protein interaction dataset.

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Additional data file 2:

Supplementary material on the EM algorithm for the spurious binding variable and captions for supplementary figures

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