Literature DB >> 22172487

Sequence-based prediction of protein solubility.

Federico Agostini1, Michele Vendruscolo, Gian Gaetano Tartaglia.   

Abstract

In order to investigate the relationship between the thermodynamics and kinetics of protein aggregation, we compared the solubility of proteins with their aggregation rates. We found a significant correlation between these two quantities by considering a database of protein solubility values measured using an in vitro reconstituted translation system containing about 70% of Escherichia coli proteins. The existence of such correlation suggests that the thermodynamic stability of the native states of proteins relative to the aggregate states is closely linked with the kinetic barriers that separate them. In order to create the possibility of conducting computational studies at the proteome level to investigate further this concept, we developed a method of predicting the solubility of proteins based on their physicochemical properties. Crown
Copyright © 2011. Published by Elsevier Ltd. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 22172487     DOI: 10.1016/j.jmb.2011.12.005

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


  28 in total

Review 1.  Advanced protein formulations.

Authors:  Wei Wang
Journal:  Protein Sci       Date:  2015-05-01       Impact factor: 6.725

Review 2.  Critical evaluation of bioinformatics tools for the prediction of protein crystallization propensity.

Authors:  Huilin Wang; Liubin Feng; Geoffrey I Webb; Lukasz Kurgan; Jiangning Song; Donghai Lin
Journal:  Brief Bioinform       Date:  2018-09-28       Impact factor: 11.622

3.  SODA: prediction of protein solubility from disorder and aggregation propensity.

Authors:  Lisanna Paladin; Damiano Piovesan; Silvio C E Tosatto
Journal:  Nucleic Acids Res       Date:  2017-07-03       Impact factor: 16.971

4.  PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine.

Authors:  Reda Rawi; Raghvendra Mall; Khalid Kunji; Chen-Hsiang Shen; Peter D Kwong; Gwo-Yu Chuang
Journal:  Bioinformatics       Date:  2018-04-01       Impact factor: 6.937

5.  Dynamic transcriptional response of Escherichia coli to inclusion body formation.

Authors:  Faraz Baig; Lawrence P Fernando; Mary Alice Salazar; Rhonda R Powell; Terri F Bruce; Sarah W Harcum
Journal:  Biotechnol Bioeng       Date:  2014-01-30       Impact factor: 4.530

6.  DeepSol: a deep learning framework for sequence-based protein solubility prediction.

Authors:  Sameer Khurana; Reda Rawi; Khalid Kunji; Gwo-Yu Chuang; Halima Bensmail; Raghvendra Mall
Journal:  Bioinformatics       Date:  2018-08-01       Impact factor: 6.937

7.  The effects of protein solubility on the RNA Integrity Number (RIN) for recombinant Escherichia coli.

Authors:  Mary Alice Salazar; Lawrence P Fernando; Faraz Baig; Sarah W Harcum
Journal:  Biochem Eng J       Date:  2013-10-15       Impact factor: 3.978

8.  Distinguishing crystal-like amyloid fibrils and glass-like amorphous aggregates from their kinetics of formation.

Authors:  Yuichi Yoshimura; Yuxi Lin; Hisashi Yagi; Young-Ho Lee; Hiroki Kitayama; Kazumasa Sakurai; Masatomo So; Hirotsugu Ogi; Hironobu Naiki; Yuji Goto
Journal:  Proc Natl Acad Sci U S A       Date:  2012-08-20       Impact factor: 11.205

9.  Assessment of Therapeutic Antibody Developability by Combinations of In Vitro and In Silico Methods.

Authors:  Adriana-Michelle Wolf Pérez; Nikolai Lorenzen; Michele Vendruscolo; Pietro Sormanni
Journal:  Methods Mol Biol       Date:  2022

10.  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

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.