Literature DB >> 2390062

The distribution of physical, chemical and conformational properties in signal and nascent peptides.

M Prabhakaran1.   

Abstract

Signal peptides play a major role in an as-yet-undefined way in the translocation of proteins across membranes. The sequential arrangement of the chemical, physical and conformational properties of the signal and nascent amino acid sequences of the translocated proteins has been compiled and analysed in the present study. The sequence data of 126 signal peptides of length between 18 and 21 residues form the basis of this study. The statistical distribution of the following properties was studied hydrophobicity, Mr, bulkiness, chromatographic index and preference for adopting alpha-helical, beta-sheet and turn structures. The contribution of each property to the sequence arrangement was derived. A hydrophobic core sequence was found in all signal peptides investigated. The structural arrangement of the cleavage site was also clearly revealed by this study. Most of the physical properties of the individual sequences correlated (correlation coefficient approximately 0.4) very well with the average distribution. The preferred occupancy of amino acid residues in the signal and nascent sequences was also calculated and correlated with their property distribution. The periodic behaviour of the signal and nascent chains was revealed by calculating their hydrophobic moments for various repetitive conformations. A graphical analysis of average hydrophobic moments versus average hydrophobicity of peptides revealed the transmembrane characteristics of signal peptides and globular characteristics of the nascent peptides.

Mesh:

Substances:

Year:  1990        PMID: 2390062      PMCID: PMC1131643          DOI: 10.1042/bj2690691

Source DB:  PubMed          Journal:  Biochem J        ISSN: 0264-6021            Impact factor:   3.857


  16 in total

1.  A putative signal peptidase recognition site and sequence in eukaryotic and prokaryotic signal peptides.

Authors:  D Perlman; H O Halvorson
Journal:  J Mol Biol       Date:  1983-06-25       Impact factor: 5.469

2.  Analysis of membrane and surface protein sequences with the hydrophobic moment plot.

Authors:  D Eisenberg; E Schwarz; M Komaromy; R Wall
Journal:  J Mol Biol       Date:  1984-10-15       Impact factor: 5.469

3.  On the possible participation of acid phospholipids in the translocation of secreted proteins through the bacterial cytoplasmic membrane.

Authors:  M A Nesmeyanova
Journal:  FEBS Lett       Date:  1982-06-07       Impact factor: 4.124

4.  Signal sequences. The limits of variation.

Authors:  G von Heijne
Journal:  J Mol Biol       Date:  1985-07-05       Impact factor: 5.469

5.  Membrane proteins: the amino acid composition of membrane-penetrating segments.

Authors:  G von Heijne
Journal:  Eur J Biochem       Date:  1981-11

6.  How signal sequences maintain cleavage specificity.

Authors:  G von Heijne
Journal:  J Mol Biol       Date:  1984-02-25       Impact factor: 5.469

7.  Trans-membrane translocation of proteins. The direct transfer model.

Authors:  G von Heijne; C Blomberg
Journal:  Eur J Biochem       Date:  1979-06

8.  The hydrophobic moment detects periodicity in protein hydrophobicity.

Authors:  D Eisenberg; R M Weiss; T C Terwilliger
Journal:  Proc Natl Acad Sci U S A       Date:  1984-01       Impact factor: 11.205

9.  Prediction of the three-dimensional structure of the leader sequence of pre-kappa light chain, a hexadecapeptide.

Authors:  M R Pincus; R D Klausner
Journal:  Proc Natl Acad Sci U S A       Date:  1982-06       Impact factor: 11.205

10.  Transfer of proteins across membranes. II. Reconstitution of functional rough microsomes from heterologous components.

Authors:  G Blobel; B Dobberstein
Journal:  J Cell Biol       Date:  1975-12       Impact factor: 10.539

View more
  13 in total

1.  Transmembrane helix predictions revisited.

Authors:  Chien Peter Chen; Andrew Kernytsky; Burkhard Rost
Journal:  Protein Sci       Date:  2002-12       Impact factor: 6.725

2.  Proteasomal cleavage site prediction of protein antigen using BP neural network based on a new set of amino acid descriptor.

Authors:  Yuanqiang Wang; Yong Lin; Mao Shu; Rui Wang; Yong Hu; Zhihua Lin
Journal:  J Mol Model       Date:  2013-04-13       Impact factor: 1.810

3.  Boosting the prediction and understanding of DNA-binding domains from sequence.

Authors:  Robert E Langlois; Hui Lu
Journal:  Nucleic Acids Res       Date:  2010-02-15       Impact factor: 16.971

4.  Identification of 13 new mutations in the vasopressin-neurophysin II gene in 17 kindreds with familial autosomal dominant neurohypophyseal diabetes insipidus.

Authors:  S Rittig; G L Robertson; C Siggaard; L Kovács; N Gregersen; J Nyborg; E B Pedersen
Journal:  Am J Hum Genet       Date:  1996-01       Impact factor: 11.025

5.  New user-friendly approach to obtain an Eisenberg plot and its use as a practical tool in protein sequence analysis.

Authors:  Rob C A Keller
Journal:  Int J Mol Sci       Date:  2011-08-30       Impact factor: 5.923

6.  Mu-8: visualizing differences between proteins and their families.

Authors:  Johnathan D Mercer; Balaji Pandian; Alexander Lex; Nicolas Bonneel; Hanspeter Pfister
Journal:  BMC Proc       Date:  2014-08-28

7.  Designing of peptides with desired half-life in intestine-like environment.

Authors:  Arun Sharma; Deepak Singla; Mamoon Rashid; Gajendra Pal Singh Raghava
Journal:  BMC Bioinformatics       Date:  2014-08-20       Impact factor: 3.169

8.  UMAP-DBP: An Improved DNA-Binding Proteins Prediction Method Based on Uniform Manifold Approximation and Projection.

Authors:  Jinyue Wang; Shengli Zhang; Huijuan Qiao; Jiesheng Wang
Journal:  Protein J       Date:  2021-06-27       Impact factor: 2.371

9.  Identification of properties important to protein aggregation using feature selection.

Authors:  Yaping Fang; Shan Gao; David Tai; C Russell Middaugh; Jianwen Fang
Journal:  BMC Bioinformatics       Date:  2013-10-28       Impact factor: 3.169

10.  The cleverSuite approach for protein characterization: predictions of structural properties, solubility, chaperone requirements and RNA-binding abilities.

Authors:  Petr Klus; Benedetta Bolognesi; Federico Agostini; Domenica Marchese; Andreas Zanzoni; Gian Gaetano Tartaglia
Journal:  Bioinformatics       Date:  2014-02-03       Impact factor: 6.937

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.