Literature DB >> 15561139

Analysis of void volumes in proteins and application to stability of the p53 tumour suppressor protein.

Alison L Cuff1, Andrew C R Martin.   

Abstract

We have developed a new method for the analysis of voids in proteins (defined as empty cavities not accessible to solvent). This method combines analysis of individual discrete voids with analysis of packing quality. While these are different aspects of the same effect, they have traditionally been analysed using different approaches. The method has been applied to the calculation of total void volume and maximum void size in a non-redundant set of protein domains and has been used to examine correlations between thermal stability and void size. The tumour-suppressor protein p53 has then been compared with the non-redundant data set to determine whether its low thermal stability results from poor packing. We found that p53 has average packing, but the detrimental effects of some previously unexplained mutations to p53 observed in cancer can be explained by the creation of unusually large voids.

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Year:  2004        PMID: 15561139     DOI: 10.1016/j.jmb.2004.10.015

Source DB:  PubMed          Journal:  J Mol Biol        ISSN: 0022-2836            Impact factor:   5.469


  22 in total

1.  Void distributions reveal structural link between jammed packings and protein cores.

Authors:  John D Treado; Zhe Mei; Lynne Regan; Corey S O'Hern
Journal:  Phys Rev E       Date:  2019-02       Impact factor: 2.529

2.  BetaCavityWeb: a webserver for molecular voids and channels.

Authors:  Jae-Kwan Kim; Youngsong Cho; Mokwon Lee; Roman A Laskowski; Seong Eon Ryu; Kokichi Sugihara; Deok-Soo Kim
Journal:  Nucleic Acids Res       Date:  2015-04-22       Impact factor: 16.971

3.  Gaia: automated quality assessment of protein structure models.

Authors:  Pradeep Kota; Feng Ding; Srinivas Ramachandran; Nikolay V Dokholyan
Journal:  Bioinformatics       Date:  2011-06-23       Impact factor: 6.937

4.  Expanded explorations into the optimization of an energy function for protein design.

Authors:  Yao-Ming Huang; Christopher Bystroff
Journal:  IEEE/ACM Trans Comput Biol Bioinform       Date:  2013 Sep-Oct       Impact factor: 3.710

5.  QM-MM simulations on p53-DNA complex: a study of hot spot and rescue mutants.

Authors:  Shruti Koulgi; Archana Achalere; Neeru Sharma; Uddhavesh Sonavane; Rajendra Joshi
Journal:  J Mol Model       Date:  2013-11-21       Impact factor: 1.810

6.  Structure of Cytochrome P450 2C9*2 in Complex with Losartan: Insights into the Effect of Genetic Polymorphism.

Authors:  Sonia J Parikh; Chiara M Evans; Juliet O Obi; Qinghai Zhang; Keiko Maekawa; Karen C Glass; Manish B Shah
Journal:  Mol Pharmacol       Date:  2020-09-16       Impact factor: 4.436

7.  All-codon scanning identifies p53 cancer rescue mutations.

Authors:  Roberta Baronio; Samuel A Danziger; Linda V Hall; Kirsty Salmon; G Wesley Hatfield; Richard H Lathrop; Peter Kaiser
Journal:  Nucleic Acids Res       Date:  2010-06-25       Impact factor: 16.971

8.  Glutamate optimizes enzymatic activity under high hydrostatic pressure in Desulfovibrio species: effects on the ubiquitous thioredoxin system.

Authors:  H Gaussier; M Nouailler; E Champaud; E B Garcin; C Sebban-Kreuzer; O Bornet; M Garel; C Tamburini; L Pieulle; A Dolla; N Pradel
Journal:  Extremophiles       Date:  2021-07-01       Impact factor: 2.395

9.  Predicting positive p53 cancer rescue regions using Most Informative Positive (MIP) active learning.

Authors:  Samuel A Danziger; Roberta Baronio; Lydia Ho; Linda Hall; Kirsty Salmon; G Wesley Hatfield; Peter Kaiser; Richard H Lathrop
Journal:  PLoS Comput Biol       Date:  2008-09-04       Impact factor: 4.475

10.  RHYTHM--a server to predict the orientation of transmembrane helices in channels and membrane-coils.

Authors:  Alexander Rose; Stephan Lorenzen; Andrean Goede; Björn Gruening; Peter W Hildebrand
Journal:  Nucleic Acids Res       Date:  2009-05-22       Impact factor: 16.971

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