ProTstab2 for Prediction of Protein Thermal Stabilities
Yang, Yang ; Zhao, Jianjun ; Zeng, Lianjie and Vihinen, Mauno LU
(2022)
In International Journal of Molecular Sciences
23(18).
- Abstract
The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance... (More)
The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.
(Less)
- author
- Yang, Yang
; Zhao, Jianjun
; Zeng, Lianjie
and Vihinen, Mauno
LU
- organization
- publishing date
- 2022-09
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- artificial intelligence, gradient boosting, machine learning predictor, protein cellular stability, protein property, stability prediction
- in
- International Journal of Molecular Sciences
- volume
- 23
- issue
- 18
- article number
- 10798
- publisher
- MDPI AG
- external identifiers
-
- pmid:36142711
- scopus:85138929564
- ISSN
- 1661-6596
- DOI
- 10.3390/ijms231810798
- language
- English
- LU publication?
- yes
- id
- 9c1cbecf-9c90-476c-ab5c-111a9514fdbf
- date added to LUP
- 2022-12-12 10:53:36
- date last changed
- 2024-09-20 06:39:35
@article{9c1cbecf-9c90-476c-ab5c-111a9514fdbf,
abstract = {{<p>The stability of proteins is an essential property that has several biological implications. Knowledge about protein stability is important in many ways, ranging from protein purification and structure determination to stability in cells and biotechnological applications. Experimental determination of thermal stabilities has been tedious and available data have been limited. The introduction of limited proteolysis and mass spectrometry approaches has facilitated more extensive cellular protein stability data production. We collected melting temperature information for 34,913 proteins and developed a machine learning predictor, ProTstab2, by utilizing a gradient boosting algorithm after testing seven algorithms. The method performance was assessed on a blind test data set and showed a Pearson correlation coefficient of 0.753 and root mean square error of 7.005. Comparison to previous methods indicated that ProTstab2 had superior performance. The method is fast, so it was applied to predict and compare the stabilities of all proteins in human, mouse, and zebrafish proteomes for which experimental data were not determined. The tool is freely available.</p>}},
author = {{Yang, Yang and Zhao, Jianjun and Zeng, Lianjie and Vihinen, Mauno}},
issn = {{1661-6596}},
keywords = {{artificial intelligence; gradient boosting; machine learning predictor; protein cellular stability; protein property; stability prediction}},
language = {{eng}},
number = {{18}},
publisher = {{MDPI AG}},
series = {{International Journal of Molecular Sciences}},
title = {{ProTstab2 for Prediction of Protein Thermal Stabilities}},
url = {{http://dx.doi.org/10.3390/ijms231810798}},
doi = {{10.3390/ijms231810798}},
volume = {{23}},
year = {{2022}},
}