ATP-binding site as a further application of neural networks to residue level prediction

Similar neural network models based on single sequence and evolutionary profiles of residues have been successfully used in the past for predicting secondary structure, solvent accessibility, protein-, DNA- and carbohydrate- binding sites. ATP is a ubiquitous ligand in all living-systems, involved in most biological functions requiring energy and charge transfer. Prediction of ATP-binding site from single sequences and their evolutionary profiles at a high throughput rate can be used at genomic level as well as quick clues for site-directed mutagenesis experiments. We have developed a method for such predictions to demonstrate yet another application of sequence-base prediction algorithms using neural networks. This method can achieve 81% sensitivity and 69% specificity which are mutually adjustable in a wide range on a three-fold cross-validation data set.