Application of multiple sequence alignment profiles to improve protein secondary structure prediction - PubMed (original) (raw)
Application of multiple sequence alignment profiles to improve protein secondary structure prediction
J A Cuff et al. Proteins. 2000.
Abstract
The effect of training a neural network secondary structure prediction algorithm with different types of multiple sequence alignment profiles derived from the same sequences, is shown to provide a range of accuracy from 70.5% to 76.4%. The best accuracy of 76.4% (standard deviation 8.4%), is 3.1% (Q(3)) and 4.4% (SOV2) better than the PHD algorithm run on the same set of 406 sequence non-redundant proteins that were not used to train either method. Residues predicted by the new method with a confidence value of 5 or greater, have an average Q(3) accuracy of 84%, and cover 68% of the residues. Relative solvent accessibility based on a two state model, for 25, 5, and 0% accessibility are predicted at 76.2, 79.8, and 86. 6% accuracy respectively. The source of the improvements obtained from training with different representations of the same alignment data are described in detail. The new Jnet prediction method resulting from this study is available in the Jpred secondary structure prediction server, and as a stand-alone computer program from: http://barton.ebi.ac.uk/. Proteins 2000;40:502-511.
Copyright 2000 Wiley-Liss, Inc.
Similar articles
- The Jpred 3 secondary structure prediction server.
Cole C, Barber JD, Barton GJ. Cole C, et al. Nucleic Acids Res. 2008 Jul 1;36(Web Server issue):W197-201. doi: 10.1093/nar/gkn238. Epub 2008 May 7. Nucleic Acids Res. 2008. PMID: 18463136 Free PMC article. - Real value prediction of solvent accessibility in proteins using multiple sequence alignment and secondary structure.
Garg A, Kaur H, Raghava GP. Garg A, et al. Proteins. 2005 Nov 1;61(2):318-24. doi: 10.1002/prot.20630. Proteins. 2005. PMID: 16106377 - Enhanced recognition of protein transmembrane domains with prediction-based structural profiles.
Cao B, Porollo A, Adamczak R, Jarrell M, Meller J. Cao B, et al. Bioinformatics. 2006 Feb 1;22(3):303-9. doi: 10.1093/bioinformatics/bti784. Epub 2005 Nov 17. Bioinformatics. 2006. PMID: 16293670 - A neural network method for prediction of beta-turn types in proteins using evolutionary information.
Kaur H, Raghava GP. Kaur H, et al. Bioinformatics. 2004 Nov 1;20(16):2751-8. doi: 10.1093/bioinformatics/bth322. Epub 2004 May 14. Bioinformatics. 2004. PMID: 15145798 - JPred: a consensus secondary structure prediction server.
Cuff JA, Clamp ME, Siddiqui AS, Finlay M, Barton GJ. Cuff JA, et al. Bioinformatics. 1998;14(10):892-3. doi: 10.1093/bioinformatics/14.10.892. Bioinformatics. 1998. PMID: 9927721
Cited by
- Classification of likely functional class for ligand binding sites identified from fragment screening.
Utgés JS, MacGowan SA, Ives CM, Barton GJ. Utgés JS, et al. Commun Biol. 2024 Mar 13;7(1):320. doi: 10.1038/s42003-024-05970-8. Commun Biol. 2024. PMID: 38480979 Free PMC article. - Some Structural Elements of Bacterial Protein MF3 That Influence Its Ability to Induce Plant Resistance to Fungi, Viruses, and Other Plant Pathogens.
Erokhin D, Popletaeva S, Sinelnikov I, Rozhkova A, Shcherbakova L, Dzhavakhiya V. Erokhin D, et al. Int J Mol Sci. 2023 Nov 15;24(22):16374. doi: 10.3390/ijms242216374. Int J Mol Sci. 2023. PMID: 38003563 Free PMC article. - Naive Prediction of Protein Backbone Phi and Psi Dihedral Angles Using Deep Learning.
Broz M, Jukič M, Bren U. Broz M, et al. Molecules. 2023 Oct 12;28(20):7046. doi: 10.3390/molecules28207046. Molecules. 2023. PMID: 37894526 Free PMC article. - AttSec: protein secondary structure prediction by capturing local patterns from attention map.
Kim Y, Kwon J. Kim Y, et al. BMC Bioinformatics. 2023 May 4;24(1):183. doi: 10.1186/s12859-023-05310-3. BMC Bioinformatics. 2023. PMID: 37142993 Free PMC article. - Machine learning for the identification of respiratory viral attachment machinery from sequences data.
Walker KC, Shwarts M, Demidikin S, Chakravarty A, Joseph-McCarthy D. Walker KC, et al. PLoS One. 2023 Mar 2;18(3):e0281642. doi: 10.1371/journal.pone.0281642. eCollection 2023. PLoS One. 2023. PMID: 36862685 Free PMC article.
MeSH terms
Substances
LinkOut - more resources
Full Text Sources
Other Literature Sources