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. 2004;5(2):R11.
doi: 10.1186/gb-2004-5-2-r11. Epub 2004 Jan 16.

Predicting specificity in bZIP coiled-coil protein interactions

Jessica H Fong  1 , Amy E Keating, Mona Singh
Affiliations

Predicting specificity in bZIP coiled-coil protein interactions

Jessica H Fong et al. Genome Biol. 2004.

Abstract

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.

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Figures

Figure 1
Figure 1
Cartoon of a parallel two-stranded coiled coil. (a) Side view and (b) top view. The interface between the α-helices in a coiled-coil structure is formed by residues at the core positions a, d, e and g. Positions in the two helices are distinguished by the prime notation; for example, a and a' are analogous positions in the two helices. N, amino terminus; C, carboxy terminus.
Figure 2
Figure 2
Histogram of scores using base-optimized weights. Non-interactions are shown in white and strong interactions are shown in black. Bins are of size 2.5.
Figure 3
Figure 3
Prediction of strong interactions and non-interactions. Predictions are shown in red for the base-optimized weights, in blue for the coupling-energy weights, and in green for the simple electrostatic weights. (a) The fraction of strong coiled-coil interactions correctly identified as interactions (TP/(TP+FN)) as a function of the number of non-interactions incorrectly identified as interactions (FP). The second y-axis shows the number of strong coiled-coil interactions (TP). (b) The fraction of non-interactions correctly identified (TN/(TN+FP)), as a function of the number of strong interactions incorrectly identified as non-interactions (FN). The second y-axis shows the number of non-interactions (TN).
Figure 4
Figure 4
Interaction scores for each protein. Each column shows the interaction scores using the base-optimized weights for one sequence's strong interactions, shown in red, and non-interactions, shown in blue. The 58 sequences are grouped by similarity, and ordered as in Table 1 of Additional data file 1. The blue horizontal lines mark off high-confidence predictions of interactions (more than 32.8) and non-interactions (less than 27.8). Note that all heterodimer interactions appear twice on the graph.
Figure 5
Figure 5
Full grid depiction of high-confidence predictions. Correct predictions are colored green (TP) or grey (TN) whereas incorrect predictions are colored yellow (FN) or blue (FP). White boxes represent pairs of sequences that are not classified as strong interactions or non-interactions. A point is 'positive' if its score using the base-optimized weights is greater than 32.8. A point is 'negative' if its score is lower than 27.8. Strong interactions and non-interactions are as defined in Methods. The sequences are grouped by similarity, and are numbered as in Table 1 of Additional data file 1. The families, separated by the gridlines, are ordered as follows: C/EBP, sequences 1-7; CREB, 8-11; OASIS, 12-15; ATF-6, 16-17; XBP, 18; E4BP4, 19; ATF-2, 20-22; JUN, 23-25; FOS, 26-29; ATF-3, 30; ATF-4, 31-33; B-ATF, 34-35; PAR, 36-38; smMAF, 39-40; lgMAF, 41-42; CNC, 43-48; and YEAST, 49-58.
Figure 6
Figure 6
Accuracy in predicting relative strengths of interactions. Percent of comparisons correct using base-optimized weights, as a function of separation of raw fluorescence values. Labels on points show the number of comparisons with consistent experimental data and the given level of separation.
Figure 7
Figure 7
Cross-validation testing. The average fraction of correctly identified coiled-coil interactions as a function of the number of FP. Solid lines give averages computed using the appropriate cross-validation weights, and dotted lines give averages computed using the base-optimized weights. The TP rates shown were averaged over all human bZIP sequences, shown in purple, over human bZIP sequences with CV-similarity equal to or greater than 50%, shown in red, and over human bZIP sequences with CV-similarity less than 50%, shown in blue.
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