Literature DB >> 20709078

Proteome-level interplay between folding and aggregation propensities of proteins.

Gian Gaetano Tartaglia1, Michele Vendruscolo.   

Abstract

With the advent of proteomics, there is an increasing need of tools for predicting the properties of large numbers of proteins by using the information provided by their amino acid sequences, even in the absence of the knowledge of their structures. One of the most important types of predictions concerns whether proteins will fold or aggregate. Here, we study the competition between these two processes by analyzing the relationship between the folding and aggregation propensity profiles for the human and Escherichia coli proteomes. These profiles are calculated, respectively, using the CamFold method, which we introduce in this work, and the Zyggregator method. Our results indicate that the kinetic behavior of proteins is, to a large extent, determined by the interplay between regions of low folding and high aggregation propensities.
Copyright © 2010. Published by Elsevier Ltd.

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Year:  2010        PMID: 20709078     DOI: 10.1016/j.jmb.2010.08.013

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


  20 in total

1.  Understanding the frustration arising from the competition between function, misfolding, and aggregation in a globular protein.

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Journal:  Proc Natl Acad Sci U S A       Date:  2014-09-16       Impact factor: 11.205

Review 2.  Protein folding in the cell: challenges and progress.

Authors:  Anne Gershenson; Lila M Gierasch
Journal:  Curr Opin Struct Biol       Date:  2010-11-26       Impact factor: 6.809

3.  Fibrillization propensity for short designed hexapeptides predicted by computer simulation.

Authors:  Victoria A Wagoner; Mookyung Cheon; Iksoo Chang; Carol K Hall
Journal:  J Mol Biol       Date:  2011-12-29       Impact factor: 5.469

4.  The cotranslational function of ribosome-associated Hsp70 in eukaryotic protein homeostasis.

Authors:  Felix Willmund; Marta del Alamo; Sebastian Pechmann; Taotao Chen; Véronique Albanèse; Eric B Dammer; Junmin Peng; Judith Frydman
Journal:  Cell       Date:  2013-01-17       Impact factor: 41.582

Review 5.  Folding the proteome.

Authors:  Esther Braselmann; Julie L Chaney; Patricia L Clark
Journal:  Trends Biochem Sci       Date:  2013-06-11       Impact factor: 13.807

6.  An evolutionary trade-off between protein turnover rate and protein aggregation favors a higher aggregation propensity in fast degrading proteins.

Authors:  Greet De Baets; Joke Reumers; Javier Delgado Blanco; Joaquin Dopazo; Joost Schymkowitz; Frederic Rousseau
Journal:  PLoS Comput Biol       Date:  2011-06-23       Impact factor: 4.475

7.  Exome-sequencing confirms DNAJC5 mutations as cause of adult neuronal ceroid-lipofuscinosis.

Authors:  Bruno A Benitez; David Alvarado; Yefei Cai; Kevin Mayo; Sumitra Chakraverty; Joanne Norton; John C Morris; Mark S Sands; Alison Goate; Carlos Cruchaga
Journal:  PLoS One       Date:  2011-11-04       Impact factor: 3.240

8.  Widespread aggregation and neurodegenerative diseases are associated with supersaturated proteins.

Authors:  Prajwal Ciryam; Gian Gaetano Tartaglia; Richard I Morimoto; Christopher M Dobson; Michele Vendruscolo
Journal:  Cell Rep       Date:  2013-10-31       Impact factor: 9.423

9.  Machine learning methods can replace 3D profile method in classification of amyloidogenic hexapeptides.

Authors:  Jerzy Stanislawski; Malgorzata Kotulska; Olgierd Unold
Journal:  BMC Bioinformatics       Date:  2013-01-17       Impact factor: 3.169

10.  Aggregation is a Context-Dependent Constraint on Protein Evolution.

Authors:  Michele Monti; Alexandros Armaos; Marco Fantini; Annalisa Pastore; Gian Gaetano Tartaglia
Journal:  Front Mol Biosci       Date:  2021-06-18
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