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Title:	PHD: predicting 1D protein structure by profile based neural networks
Author:	Burkhard Rost
Quote:	In: Doolittle, R (ed.) "Computer Methods for Macromolecular Sequence Analysis"Methods in Enzymology, 266, 525-539 (1996)

Introduction

We still cannot predict protein three-dimensional (3D) structure from sequence alone. But, we can predict 3D structure for one fourth of the known protein sequences (SWISSPROT) by homology modelling based on significant sequence identity (>25%) to known 3D structures (PDB). For the remaining, about 30,000 known sequences, the prediction problem has to be simplified. An extreme simplification is to try to predict projections of 3D structure, e.g., 1D secondary structure, solvent accessibility, or transmembrane location assignments for each residue.

Despite the extreme simplification, the success of 1D predictions has been limited as segments from single sequences (used as input) do not contain sufficient global information about 3D structures. Patterns of amino acid substitutions within sequence families are highly specific for the 3D structure of that family. Using such evolutionary information is the key to a significant improvement of 1D predictions.

In this review I describe 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). I shall also illustrate 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 (see Availability). The baseline conclusion after some 30,000 requests to the service is that 1D predictions have become accurate enough to be used as a starting point for expert-driven modelling of protein structure.

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