[31] PHD: Predicting one-dimensional protein structure by profile-based neural networks

Publisher Summary The first step in a PHD prediction is generating a multiple sequence alignment. The second step involves feeding the alignment into a neural network system. Correctness of the multiple sequence alignment is as crucial for prediction accuracy as is the fact that the alignment contains a broad spectrum of homologous sequences. This chapter describes three prediction methods that use evolutionary information as input to neural network systems to predict secondary structure (PHDsec), relative solvent accessibility (PHDacc), and transmembrane helices (PHDhtm). It illustrates the possibilities and limitations in practical applications of these methods with results from careful cross-validation experiments on large sets of unique protein structures. All predictions are made available by an automatic Email prediction service. The baseline conclusion after some 30,000 requests to the service is that 1-D predictions have become accurate enough to be used as a starting point for the expert-driven modeling of protein structure.