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 journalArticle

14 Citations (Scopus)

Abstract

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 http://csbl.bmb.uga.edu/protein_pipeline.

Original languageEnglish (US)
JournalNucleic Acids Research
Volume32
Issue numberWEB SERVER ISS.
DOIs
StatePublished - Jul 1 2004
Externally publishedYes

Fingerprint

Proteins
Biota
Structural Models
Protein Sorting Signals
Amino Acid Sequence
Membrane Proteins
Genome
Research

ASJC Scopus subject areas

  • Genetics

Cite this

PROSPECT-PSPP : An automatic computational pipeline for protein structure prediction. / Guo, Jun Tao; Ellrott, Kyle; Chung, Won Jae; Xu, Dong; Passovets, Serguei; Xu, Ying.

In: Nucleic Acids Research, Vol. 32, No. WEB SERVER ISS., 01.07.2004.

Research output: Contribution to journalArticle

Guo, Jun Tao ; Ellrott, Kyle ; Chung, Won Jae ; Xu, Dong ; Passovets, Serguei ; Xu, Ying. / PROSPECT-PSPP : An automatic computational pipeline for protein structure prediction. In: Nucleic Acids Research. 2004 ; Vol. 32, No. WEB SERVER ISS.
@article{ac8cca727d5c4554bef10eedb426b32d,
title = "PROSPECT-PSPP: An automatic computational pipeline for protein structure prediction",
abstract = "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 http://csbl.bmb.uga.edu/protein_pipeline.",
author = "Guo, {Jun Tao} and Kyle Ellrott and Chung, {Won Jae} and Dong Xu and Serguei Passovets and Ying Xu",
year = "2004",
month = "7",
day = "1",
doi = "10.1093/nar/gkh414",
language = "English (US)",
volume = "32",
journal = "Nucleic Acids Research",
issn = "0305-1048",
publisher = "Oxford University Press",
number = "WEB SERVER ISS.",

}

TY - JOUR

T1 - PROSPECT-PSPP

T2 - An automatic computational pipeline for protein structure prediction

AU - Guo, Jun Tao

AU - Ellrott, Kyle

AU - Chung, Won Jae

AU - Xu, Dong

AU - Passovets, Serguei

AU - Xu, Ying

PY - 2004/7/1

Y1 - 2004/7/1

N2 - 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 http://csbl.bmb.uga.edu/protein_pipeline.

AB - 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 http://csbl.bmb.uga.edu/protein_pipeline.

UR - http://www.scopus.com/inward/record.url?scp=3242887524&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=3242887524&partnerID=8YFLogxK

U2 - 10.1093/nar/gkh414

DO - 10.1093/nar/gkh414

M3 - Article

VL - 32

JO - Nucleic Acids Research

JF - Nucleic Acids Research

SN - 0305-1048

IS - WEB SERVER ISS.

ER -