Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller

Bj�rn Wallner; Huisheng Fang; Arne Elofsson

doi:10.1002/prot.10536

journal article Jan 01, 2003

Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller

Bj�rn Wallner Huisheng Fang Arne Elofsson

Proteins: Structure, Function, and Bioinformatics Vol. 53 No. S6 pp. 534-541 · Wiley

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References

26

[1]

Pcons: A neural‐network–based consensus predictor that improves fold recognition

Jesper Lundström, Leszek Rychlewski, Janusz Bujnicki et al.

Protein Science 2001 10.1110/ps.08501

[2]

CAFASP2: The second critical assessment of fully automated structure prediction methods

Daniel Fischer, Arne Elofsson, Leszek Rychlewski et al.

Proteins: Structure, Function, and Bioinformatics 2001 10.1002/prot.10036

[3]

Fischer Proteins (2003)

[4]

Ginalski Bioinformatics (2003)

[5]

LiveBench‐1: Continuous benchmarking of protein structure prediction servers

Janusz M. Bujnicki, Arne Elofsson, Daniel Fischer et al.

Protein Science 2001 10.1110/ps.40501

[6]

Bujnicki Proteins (2001) 10.1002/prot.10039

[7]

Critical assessment of methods of protein structure prediction (CASP): Round II

John Moult, Tim Hubbard, Stephen H. Bryant et al.

Proteins: Structure, Function, and Bioinformatics 1997 10.1002/(sici)1097-0134(1997)1+<2::aid-prot2>3.0.co;2-t

[8]

CAFASP-1: Critical assessment of fully automated structure prediction methods

Daniel Fischer, Christian Barret, Kevin Bryson et al.

Proteins: Structure, Function, and Bioinformatics 1999 10.1002/(sici)1097-0134(1999)37:3+<209::aid-prot27>3.0.co;2-y

[9]

GenTHREADER: an efficient and reliable protein fold recognition method for genomic sequences

David T. Jones

Journal of Molecular Biology 1999 10.1006/jmbi.1999.2583

[10]

A new approach to protein fold recognition

David T. Jones, W. R. Taylort, J. M. Thornton

Nature 1992 10.1038/358086a0

[11]

Enhanced genome annotation using structural profiles in the program 3D-PSSM 1 1Edited by J. Thornton

Lawrence A Kelley, Robert M MacCallum, Michael J.E Sternberg

Journal of Molecular Biology 2000 10.1006/jmbi.2000.3741

[12]

Can correct protein models be identified?

Björn Wallner, Arne Elofsson

Protein Science 2003 10.1110/ps.0236803

[13]

Protein structure prediction: bioinformatics approach. In: editor. La Jolla, CA: International University Line Biotechnology Series; 2002.

[14]

Rosetta in CASP4: Progress in ab initio protein structure prediction

Richard Bonneau, Jerry Tsai, Ingo Ruczinski et al.

Proteins: Structure, Function, and Bioinformatics 2001 10.1002/prot.1170

[15]

Improving the quality of twilight‐zone alignments

Lukasz Jaroszewski, Adam Godzik, Leszek Rychlewski

Protein Science 2000 10.1110/ps.9.8.1487

[16]

Hybrid fold recognition: combining sequence derived properties with evolution-ary information. In Pacific symposium on biocomputing. editors. Singapore: World Scientific; 2000; 5: 116-127.

[17]

Gapped BLAST and PSI-BLAST: a new generation of protein database search programs

S. Altschul

Nucleic Acids Research 1997 10.1093/nar/25.17.3389

[18]

What is the value added by human intervention in protein structure prediction?

Kevin Karplus, Rachel Karchin, Christian Barrett et al.

Proteins: Structure, Function, and Bioinformatics 2001 10.1002/prot.10021

[19]

FUGUE: sequence-structure homology recognition using environment-specific substitution tables and structure-dependent gap penalties11Edited by B. Honig

Jiye Shi, Tom L Blundell, Kenji Mizuguchi

Journal of Molecular Biology 2001 10.1006/jmbi.2001.4762

[20]

Cristobal BMC Bioinformatics (2001)

[21]

MaxSub: an automated measure for the assessment of protein structure prediction quality

Naomi Siew, Arne Elofsson, Leszek Rychlewski et al.

Bioinformatics 2000 10.1093/bioinformatics/16.9.776

[22]

Comparative Protein Modelling by Satisfaction of Spatial Restraints

Andrej Šali, Tom L. Blundell

Journal of Molecular Biology 1993 10.1006/jmbi.1993.1626

[23]

Protein secondary structure prediction based on position-specific scoring matrices 1 1Edited by G. Von Heijne

David T. Jones

Journal of Molecular Biology 1999 10.1006/jmbi.1999.3091

[24]

Processing and analysis of CASP3 protein structure predictions

Adam Zemla, ?eslovas Venclovas, John Moult et al.

Proteins: Structure, Function, and Bioinformatics 1999 10.1002/(sici)1097-0134(1999)37:3+<22::aid-prot5>3.0.co;2-w

[25]

LiveBench-6: Large-scale automated evaluation of protein structure prediction servers

Leszek Rychlewski, Daniel Fischer, Arne Elofsson

Proteins: Structure, Function, and Bioinformatics 2003 10.1002/prot.10535

[26]

Bujnicki Bioinformatics (2001) 10.1093/bioinformatics/17.8.750

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Citations

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References

Details

Published: Jan 01, 2003
Vol/Issue: 53(S6)
Pages: 534-541
License: View

Authors

Stockholm University

Cite This Article

Bj�rn Wallner, Huisheng Fang, Arne Elofsson (2003). Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller. Proteins: Structure, Function, and Bioinformatics, 53(S6), 534-541. https://doi.org/10.1002/prot.10536

Automatic consensus-based fold recognition using Pcons, ProQ, and Pmodeller

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