Literature DB >> 9655341

Deciphering the structural code for proteins: helical propensities in domain classes and statistical multiresidue information in alpha-helices.

J A Negrete1, Y Viñuales, J Palau.   

Abstract

We made several statistical analyses in a large sample of nearly 4,000 helices (from 546 redundancy-controlled PDB protein subunits), which give new insights into the helical properties of globular proteins. In a first experiment, the amino acid composition of the whole sample was compared with the composition of two helical sample subgroups (the "mainly-alpha" and the "(alpha/beta)8 barrel" domain classes); we reached the conclusion that composition-based helical propensities for secondary structure prediction do not depend on the structural class. Running a five-residue window through the whole sample, the positional composition revealed that positive and negative residues are located throughout the helices and tend to neutralize the macrodipole effect. On this basis, we analyzed charged triplets using a running five-residue window. The conclusion was that only mixed charged residues [positive (+) and negative (-)] located at positions 1-2-5 and 1-4-5 are clearly favored. In these locations the most abundant are (- -..+) and (-..+ +), and this shows the existence of side chain microdipoles, which neutralize the large macrodipole of the helix. We made a systematic statistical analysis of charged, dipolar, and hydrophobic + aromatic residues, which enabled us to work out rules that should be useful for modeling and design purposes. Finally, we analyzed the relative abundance of all the different amphipathic double-arcs that are present in helices formed by octapeptides (8) and nonapeptides (18). All of the double-arcs that make up Schiffer and Edmundson's classical helical wheel are found in abundance in the sample.

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Year:  1998        PMID: 9655341      PMCID: PMC2144023          DOI: 10.1002/pro.5560070613

Source DB:  PubMed          Journal:  Protein Sci        ISSN: 0961-8368            Impact factor:   6.725


  23 in total

1.  Highly restricted distributions of hydrophobic and charged amino acids in longitudinal quadrants of alpha-helices.

Authors:  R R Torgerson; R A Lew; V E Reyes; L Hardy; R E Humphreys
Journal:  J Biol Chem       Date:  1991-03-25       Impact factor: 5.157

2.  Use of helical wheels to represent the structures of proteins and to identify segments with helical potential.

Authors:  M Schiffer; A B Edmundson
Journal:  Biophys J       Date:  1967-03       Impact factor: 4.033

3.  Amino acid preferences for specific locations at the ends of alpha helices.

Authors:  J S Richardson; D C Richardson
Journal:  Science       Date:  1988-06-17       Impact factor: 47.728

4.  Structural principles of the globular organization of protein chains. A stereochemical theory of globular protein secondary structure.

Authors:  V I Lim
Journal:  J Mol Biol       Date:  1974-10-05       Impact factor: 5.469

5.  Algorithms for prediction of alpha-helical and beta-structural regions in globular proteins.

Authors:  V I Lim
Journal:  J Mol Biol       Date:  1974-10-05       Impact factor: 5.469

6.  The structural code for proteins: zonal distribution of amino acid residues and stabilization of helices by hydrophobic triplets.

Authors:  J Palau; P Puigdoménech
Journal:  J Mol Biol       Date:  1974-09-15       Impact factor: 5.469

7.  Electrostatic stabilization in four-helix bundle proteins.

Authors:  C R Robinson; S G Sligar
Journal:  Protein Sci       Date:  1993-05       Impact factor: 6.725

8.  Prediction of alpha-helices in proteins with the hydrophobic strip-of-helix template and distributions of other amino acids around the hydrophobic strip.

Authors:  S R Vazquez; D Z Kuo; M Salomon; L Hardy; R A Lew; R E Humphreys
Journal:  Arch Biochem Biophys       Date:  1993-09       Impact factor: 4.013

9.  Protein secondary structure. Studies on the limits of prediction accuracy.

Authors:  J Palau; P Argos; P Puigdomenech
Journal:  Int J Pept Protein Res       Date:  1982-04

10.  Amino acid distribution in protein secondary structures.

Authors:  P Argos; J Palau
Journal:  Int J Pept Protein Res       Date:  1982-04
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  1 in total

1.  Refinement of modelled structures by knowledge-based energy profiles and secondary structure prediction: application to the human procarboxypeptidase A2.

Authors:  P Aloy; J M Mas; M A Martí-Renom; E Querol; F X Avilés; B Oliva
Journal:  J Comput Aided Mol Des       Date:  2000-01       Impact factor: 3.686

  1 in total

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