Literature DB >> 10436080

Role of structural and sequence information in the prediction of protein stability changes: comparison between buried and partially buried mutations.

M M Gromiha1, M Oobatake, H Kono, H Uedaira, A Sarai.   

Abstract

Predicting mutation-induced changes in protein stability is one of the greatest challenges in molecular biology. In this work, we analyzed the correlation between stability changes caused by buried and partially buried mutations and changes in 48 physicochemical, energetic and conformational properties. We found that properties reflecting hydrophobicity strongly correlated with stability of buried mutations, and there was a direct relation between the property values and the number of carbon atoms. Classification of mutations based on their location within helix, strand, turn or coil segments improved the correlation of mutations with stability. Buried mutations within beta-strand segments correlated better than did those in alpha-helical segments, suggesting stronger hydrophobicity of the beta-strands. The stability changes caused by partially buried mutations in ordered structures (helix, strand and turn) correlated most strongly and were mainly governed by hydrophobicity. Due to the disordered nature of coils, the mechanism underlying their stability differed from that of the other secondary structures: the stability changes due to mutations within the coil were mainly influenced by the effects of entropy. Further classification of mutations within coils, based on their hydrogen-bond forming capability, led to much stronger correlations. Hydrophobicity was the major factor in determining the stability of buried mutations, whereas hydrogen bonds, other polar interactions and hydrophobic interactions were all important determinants of the stability of partially buried mutations. Information about local sequence and structural effects were more important for the prediction of stability changes caused by partially buried mutations than for buried mutations; they strengthened correlations by an average of 27% among all data sets.

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Year:  1999        PMID: 10436080     DOI: 10.1093/protein/12.7.549

Source DB:  PubMed          Journal:  Protein Eng        ISSN: 0269-2139


  28 in total

1.  ProTherm, version 2.0: thermodynamic database for proteins and mutants.

Authors:  M M Gromiha; J An; H Kono; M Oobatake; H Uedaira; P Prabakaran; A Sarai
Journal:  Nucleic Acids Res       Date:  2000-01-01       Impact factor: 16.971

2.  On the thermal unfolding character of globular proteins.

Authors:  R Muthusamy; M M Gromiha; P K Ponnuswamy
Journal:  J Protein Chem       Date:  2000-01

3.  Role of hydrophobic clusters and long-range contact networks in the folding of (alpha/beta)8 barrel proteins.

Authors:  S Selvaraj; M Michael Gromiha
Journal:  Biophys J       Date:  2003-03       Impact factor: 4.033

4.  ProTherm, version 4.0: thermodynamic database for proteins and mutants.

Authors:  K Abdulla Bava; M Michael Gromiha; Hatsuho Uedaira; Koji Kitajima; Akinori Sarai
Journal:  Nucleic Acids Res       Date:  2004-01-01       Impact factor: 16.971

5.  Real value prediction of protein folding rate change upon point mutation.

Authors:  Liang-Tsung Huang; M Michael Gromiha
Journal:  J Comput Aided Mol Des       Date:  2012-03-18       Impact factor: 3.686

6.  Structural stability studies in adhesion molecules--role of cation-π interactions.

Authors:  K Sophiya; Anand Anbarasu
Journal:  Protoplasma       Date:  2010-10-27       Impact factor: 3.356

7.  Computational predictions of the mutant behavior of AraC.

Authors:  Monica Berrondo; Jeffrey J Gray; Robert Schleif
Journal:  J Mol Biol       Date:  2010-03-23       Impact factor: 5.469

8.  Sequence analysis and rule development of predicting protein stability change upon mutation using decision tree model.

Authors:  Liang-Tsung Huang; M Michael Gromiha; Shinn-Ying Ho
Journal:  J Mol Model       Date:  2007-03-30       Impact factor: 1.810

9.  Anion-π interactions in complexes of proteins and halogen-containing amino acids.

Authors:  Sunčica Z Borozan; Mario V Zlatović; Srđan Đ Stojanović
Journal:  J Biol Inorg Chem       Date:  2016-02-24       Impact factor: 3.358

10.  Inferring stabilizing mutations from protein phylogenies: application to influenza hemagglutinin.

Authors:  Jesse D Bloom; Matthew J Glassman
Journal:  PLoS Comput Biol       Date:  2009-04-17       Impact factor: 4.475

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