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PON-Sol : Prediction of effects of amino acid substitutions on protein solubility

Yang, Yang LU ; Niroula, Abhishek LU ; Shen, Bairong and Vihinen, Mauno LU (2016) In Bioinformatics 32(13). p.2032-2034
Abstract

Motivation: Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases. Results: We collected from literature the largest experimentally verified solubility affecting amino acid substitution (AAS) dataset and used it to train a predictor called PON-Sol. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PONSol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be... (More)

Motivation: Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases. Results: We collected from literature the largest experimentally verified solubility affecting amino acid substitution (AAS) dataset and used it to train a predictor called PON-Sol. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PONSol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be superior. PON-Sol can be used for the prediction of effects of disease-related substitutions, effects on heterologous recombinant protein expression and enhanced crystallizability. One application is to investigate effects of all possible AASs in a protein to aid protein engineering.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Bioinformatics
volume
32
issue
13
pages
2032 - 2034
publisher
Oxford University Press
external identifiers
  • scopus:85007247074
  • wos:000379761500017
ISSN
1367-4803
DOI
10.1093/bioinformatics/btw066
language
English
LU publication?
yes
id
5d608096-9b09-48b8-8b71-991a6405d231
date added to LUP
2017-01-18 12:32:08
date last changed
2017-09-24 05:05:59
@article{5d608096-9b09-48b8-8b71-991a6405d231,
  abstract     = {<p>Motivation: Solubility is one of the fundamental protein properties. It is of great interest because of its relevance to protein expression. Reduced solubility and protein aggregation are also associated with many diseases. Results: We collected from literature the largest experimentally verified solubility affecting amino acid substitution (AAS) dataset and used it to train a predictor called PON-Sol. The predictor can distinguish both solubility decreasing and increasing variants from those not affecting solubility. PONSol has normalized correct prediction ratio of 0.491 on cross-validation and 0.432 for independent test set. The performance of the method was compared both to solubility and aggregation predictors and found to be superior. PON-Sol can be used for the prediction of effects of disease-related substitutions, effects on heterologous recombinant protein expression and enhanced crystallizability. One application is to investigate effects of all possible AASs in a protein to aid protein engineering.</p>},
  author       = {Yang, Yang and Niroula, Abhishek and Shen, Bairong and Vihinen, Mauno},
  issn         = {1367-4803},
  language     = {eng},
  month        = {07},
  number       = {13},
  pages        = {2032--2034},
  publisher    = {Oxford University Press},
  series       = {Bioinformatics},
  title        = {PON-Sol : Prediction of effects of amino acid substitutions on protein solubility},
  url          = {http://dx.doi.org/10.1093/bioinformatics/btw066},
  volume       = {32},
  year         = {2016},
}