PROSPECT-PSPP: An automatic computational pipeline for protein structure prediction

Jun Tao Guo, Kyle Ellrott, Won Jae Chung, Dong Xu, Serguei Passovets, Ying Xu

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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 postgenomics 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

Original languageEnglish (US)
Pages (from-to)W522-W525
JournalNucleic acids research
Issue numberWEB SERVER ISS.
StatePublished - Jul 1 2004
Externally publishedYes

ASJC Scopus subject areas

  • Genetics


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