The benefit of intrinsic disorder information in neural network prediction of calmodulin binding targets

Calmodulin is an important calcium dependent signaling protein found in all eukaryotic cells. Binding calcium enables calmodulin to bind its targets: basic, amphipathic cc-helices. Such binding regulates the activities of many proteins. Because calmodulin wraps completely around the target helix upon binding, it is hypothesized that disorder of a target helix is an important feature of this process. We have used several sequence derived features of calmodulin binding targets (CBT's), including intrinsic order/disorder predictions, to construct neural networks based on permutations of three or more of these features. The resulting networks demonstrate that the addition of intrinsic order/disorder information always increases the performance of a given neural network predictor. The best predictor generated has a performance of 87.8% true positive prediction and 87.2% true negative prediction.