An affinity-based scoring scheme for predicting DNA-binding activities of modularly assembled zinc-finger proteins

Zinc-finger proteins (ZFPs) have long been recognized for their potential to manipulate genetic information because they can be engineered to bind novel DNA targets. Individual zinc-finger domains (ZFDs) bind specific DNA triplet sequences; their apparent modularity has led some groups to propose methods that allow virtually any desired DNA motif to be targeted in vitro. In practice, however, ZFPs engineered using this modular assembly approach do not always function well in vivo. Here we report a modular assembly scoring strategy that both identifies combinations of modules least likely to function efficiently in vivo and provides accurate estimates of their relative binding affinities in vitro. Predicted binding affinities for 53 three-finger ZFPs, computed based on energy contributions of the constituent modules, were highly correlated (r 0.80) with activity levels measured in bacterial two-hybrid assays. Moreover, K(d) values for seven modularly assembled ZFPs and their intended targets, measured using fluorescence anisotropy, were also highly correlated with predictions (r 0.91). We propose that success rates for ZFP modular assembly can be significantly improved by exploiting the score-based strategy described here.