Publication detail

SoluProt: Prediction of Protein Solubility

HON, J. MARUŠIAK, M. MARTÍNEK, T. ZENDULKA, J. BEDNÁŘ, D. DAMBORSKÝ, J.

Original Title

SoluProt: Prediction of Protein Solubility

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Protein solubility poses a major bottleneck in production of many therapeutic and industrially attractive proteins. Experimental solubilization attempts are plagued by relatively low success rates and often lead to the loss of biological activity. Therefore, any advance in computational prediction of protein solubility may reduce the cost of experimental studies significantly. Here, we propose a novel software tool SoluProt for prediction of solubility from protein sequence based on machine learning and TargetTrack database. SoluProt achieved the best accuracy 58.2% and AUC 0.61 of all available tools at an independent balanced test set derived from NESG database. While the absolute prediction performance is rather low, SoluProt can still help to reduce costs of experimental studies significantly by efficient prioritization of protein sequences. The main SoluProt contribution lies in improved preprocessing of noisy training data and sensible selection of sequence features included in the prediction model.

Keywords

protein, solubility, prediction, machine-learning

Authors

HON, J.; MARUŠIAK, M.; MARTÍNEK, T.; ZENDULKA, J.; BEDNÁŘ, D.; DAMBORSKÝ, J.

Released

17. 8. 2018

Publisher

Brno University of Technology

Location

Brno

ISBN

978-80-214-5679-2

Book

DAZ & WIKT 2018 Proceedings

Pages from

261

Pages to

265

Pages count

5

URL

BibTex

@inproceedings{BUT155085,
  author="Jiří {Hon} and Martin {Marušiak} and Tomáš {Martínek} and Jaroslav {Zendulka} and David {Bednář} and Jiří {Damborský}",
  title="SoluProt: Prediction of Protein Solubility",
  booktitle="DAZ & WIKT 2018 Proceedings",
  year="2018",
  pages="261--265",
  publisher="Brno University of Technology",
  address="Brno",
  isbn="978-80-214-5679-2",
  url="https://www.fit.vut.cz/research/publication/11808/"
}