• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of prosciprotein sciencecshl presssubscriptionsetoc alertsthe protein societyjournal home
Protein Sci. Apr 1994; 3(4): 557–566.
PMCID: PMC2142860

Analysis of protein transmembrane helical regions by a neural network.


Neural networks were used to generalize common themes found in transmembrane-spanning protein helices. Various-sized databases were used containing nonoverlapping sequences, each 25 amino acids long. Training consisted of sorting these sequences into 1 of 2 groups: transmembrane helical peptides or nontransmembrane peptides. Learning was measured using a test set 10% the size of the training set. As training set size increased from 214 sequences to 1,751 sequences, learning increased in a nonlinear manner from 75% to a high of 98%, then declined to a low of 87%. The final training database consisted of roughly equal numbers of transmembrane (928) and nontransmembrane (1,018) sequences. All transmembrane sequences were entered into the database with respect to their lipid membrane orientation: from inside the membrane to outside. Generalized transmembrane helix and nontransmembrane peptides were constructed from the maximally weighted connecting strengths of fully trained networks. Four generalized transmembrane helices were found to contain 9 consensus residues: a K-R-F triplet was found at the inside lipid interface, 2 isoleucine and 2 other phenylalanine residues were present in the helical body, and 2 tryptophan residues were found near the outside lipid interface. As a test of the training method, bacteriorhodopsin was examined to determine the position of its 7 transmembrane helices.

Full Text

The Full Text of this article is available as a PDF (1.7M).

Selected References

These references are in PubMed. This may not be the complete list of references from this article.
  • Bohr H, Bohr J, Brunak S, Cotterill RM, Lautrup B, Nørskov L, Olsen OH, Petersen SB. Protein secondary structure and homology by neural networks. The alpha-helices in rhodopsin. FEBS Lett. 1988 Dec 5;241(1-2):223–228. [PubMed]
  • Dohlman HG, Thorner J, Caron MG, Lefkowitz RJ. Model systems for the study of seven-transmembrane-segment receptors. Annu Rev Biochem. 1991;60:653–688. [PubMed]
  • Dubchak I, Holbrook SR, Kim SH. Prediction of protein folding class from amino acid composition. Proteins. 1993 May;16(1):79–91. [PubMed]
  • Findlay J, Eliopoulos E. Three-dimensional modelling of G protein-linked receptors. Trends Pharmacol Sci. 1990 Dec;11(12):492–499. [PubMed]
  • Hayward S, Collins JF. Limits on alpha-helix prediction with neural network models. Proteins. 1992 Nov;14(3):372–381. [PubMed]
  • Hirst JD, Sternberg MJ. Prediction of structural and functional features of protein and nucleic acid sequences by artificial neural networks. Biochemistry. 1992 Aug 18;31(32):7211–7218. [PubMed]
  • Holley LH, Karplus M. Protein secondary structure prediction with a neural network. Proc Natl Acad Sci U S A. 1989 Jan;86(1):152–156. [PMC free article] [PubMed]
  • Jones MK, Anantharamaiah GM, Segrest JP. Computer programs to identify and classify amphipathic alpha helical domains. J Lipid Res. 1992 Feb;33(2):287–296. [PubMed]
  • Kneller DG, Cohen FE, Langridge R. Improvements in protein secondary structure prediction by an enhanced neural network. J Mol Biol. 1990 Jul 5;214(1):171–182. [PubMed]
  • Kyte J, Doolittle RF. A simple method for displaying the hydropathic character of a protein. J Mol Biol. 1982 May 5;157(1):105–132. [PubMed]
  • Ladunga I, Czakó F, Csabai I, Geszti T. Improving signal peptide prediction accuracy by simulated neural network. Comput Appl Biosci. 1991 Oct;7(4):485–487. [PubMed]
  • Levitt M, Greer J. Automatic identification of secondary structure in globular proteins. J Mol Biol. 1977 Aug 5;114(2):181–239. [PubMed]
  • Liao CF, Themmen AP, Joho R, Barberis C, Birnbaumer M, Birnbaumer L. Molecular cloning and expression of a fifth muscarinic acetylcholine receptor. J Biol Chem. 1989 May 5;264(13):7328–7337. [PubMed]
  • McGregor MJ, Flores TP, Sternberg MJ. Prediction of beta-turns in proteins using neural networks. Protein Eng. 1989 May;2(7):521–526. [PubMed]
  • Qian N, Sejnowski TJ. Predicting the secondary structure of globular proteins using neural network models. J Mol Biol. 1988 Aug 20;202(4):865–884. [PubMed]
  • Mohana Rao JK, Argos P. A conformational preference parameter to predict helices in integral membrane proteins. Biochim Biophys Acta. 1986 Jan 30;869(2):197–214. [PubMed]
  • Sasagawa F, Tajima K. Prediction of protein secondary structures by a neural network. Comput Appl Biosci. 1993 Apr;9(2):147–152. [PubMed]
  • von Heijne G. Membrane proteins: the amino acid composition of membrane-penetrating segments. Eur J Biochem. 1981 Nov;120(2):275–278. [PubMed]
  • von Heijne G. Membrane protein structure prediction. Hydrophobicity analysis and the positive-inside rule. J Mol Biol. 1992 May 20;225(2):487–494. [PubMed]
  • Williams KA, Deber CM. Proline residues in transmembrane helices: structural or dynamic role? Biochemistry. 1991 Sep 17;30(37):8919–8923. [PubMed]

Articles from Protein Science : A Publication of the Protein Society are provided here courtesy of The Protein Society


Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...


  • PubMed
    PubMed citations for these articles
  • Substance
    PubChem Substance links

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...