Predicting specificity in bZIP coiled-coil protein interactions.
We present a method for predicting protein-protein interactions mediated by the coiled-coil motif. When tested on interactions between nearly all human and yeast bZIP proteins, our method identifies 70% of strong interactions while maintaining that 92% of predictions are correct. Furthermore, cross-validation testing shows that including the bZIP experimental data significantly improves performance. Our method can be used to predict bZIP interactions in other genomes and is a promising approach for predicting coiled-coil interactions more generally.
Fong
Jessica H
JH
Computer Science Department and Lewis-Sigler Institute for Integrative Genomics, Princeton University, Olden Street, Princeton, NJ 08544, USA.
Keating
Amy E
AE
Singh
Mona
M
D020816:Amino Acid Motifs; D050976:Basic-Leucine Zipper Transcription Factors; D019295:Computational Biology / Q000379:methods*; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000378:metabolism; D005656:Fungal Proteins / Q000737:chemistry / Q000378:metabolism; D051790:G-Box Binding Factors; D006801:Humans; D008958:Models, Molecular; D011485:Protein Binding; D017433:Protein Structure, Secondary; D015203:Reproducibility of Results; D055672:Static Electricity; D014157:Transcription Factors / Q000737:chemistry* / Q000378:metabolism
D050976:Basic-Leucine Zipper Transcription Factors; D004268:DNA-Binding Proteins; D005656:Fungal Proteins; D051790:G-Box Binding Factors; D014157:Transcription Factors