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.
(Less)
- author
- Yang, Yang
LU
; Niroula, Abhishek
LU
; Shen, Bairong
and Vihinen, Mauno
LU
- organization
- publishing date
- 2016-07-01
- 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
- pmid:27153720
- 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
- 2024-06-16 00:58:12
@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}},
doi = {{10.1093/bioinformatics/btw066}},
volume = {{32}},
year = {{2016}},
}