Knowledge of the detailed structure of a protein is crucial to our understanding 
of the biological functions of that protein. The gap between the number of 
solved protein structures and the number of protein sequences continues to widen 
rapidly in the post-genomics era due to long and expensive processes for solving 
structures experimentally. Computational prediction of structures from amino 
acid sequence has come to play a key role in narrowing the gap and has been 
successful in providing useful information for the biological research 
community. We have developed a prediction pipeline, PROSPECT-PSPP, an 
integration of multiple computational tools, for fully automated protein 
structure prediction. The pipeline consists of tools for (i) preprocessing of 
protein sequences, which includes signal peptide prediction, protein type 
prediction (membrane or soluble) and protein domain partition, (ii) secondary 
structure prediction, (iii) fold recognition and (iv) atomic structural model 
generation. The centerpiece of the pipeline is our threading-based program 
PROSPECT. The pipeline is implemented using SOAP (Simple Object Access 
Protocol), which makes it easier to share our tools and resources. The pipeline 
has an easy-to-use user interface and is implemented on a 64-node dual processor 
Linux cluster. It can be used for genome-scale protein structure prediction. The 
pipeline is accessible at http://csbl.bmb.uga.edu/protein_pipeline.