A regression framework incorporating quantitative and negative interaction data improves prediction of quantitative PDZ domain-peptide interaction from primary sequences

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A regression framework incorporating quantitative and negative interaction data improves quantitative prediction of PDZ domain-peptide interaction from primary sequences

Xiaojian Shao1,2,5, Chris S. H. Tan2,3,5, Courtney Voss4, Shawn S. C. Li4, Naiyang Deng1*, Gary D. Bader2,3*

1. College of Science, China Agricultural University, Beijing, China,

2. Banting and Best Department of Medical Research, University of Toronto, Terrence Donnelly Centre for Cellular and Biomolecular Research, Toronto, Ontario, Canada

3. Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

4. Department of Biochemistry, University of Western Ontario, London, Ontario, Canada

5. These authors contributed equally to this work.

* To whom correspondence should be addressed. E-mail: gary.bader@utoronto.ca (GDB); dengnaiyang@cau.edu.cn (NYD)

Supplemental Materials

Supplementary Materials.doc - Supplementary Notes 1-4 and Figure S1-6 with legends

Supplemental Tables

Table S1 - Quantitative Mouse PDZ Domain-peptide Interaction Matrix
Table S2 - Performance on Different Encoding
Table S7 - Predicted Affinity Score for Putative Mouse PDZ Domain-peptide Pairs
Table S8 - Predicted Affinity Score for Putative Human PDZ Domain-peptide Pairs

Semi-quantitative Support Vector Regression Code

SemiSVR_Code.Zip - Matlab code for SemiSVR (compressed).

PDZ Sequence

Mouse PDZ sequence : MousePDZSequence.fasta
SemiSVR Predictable Human PDZ Sequence : SemiSVR_Predictable_HumanPDZSequence.fasta
SemiSVR Predictable Mouse PDZ Sequence : SemiSVR_Predictable_MousePDZSequence.fasta