SoluProt: Prediction of Protein Solubility

article in a collection out of WoS and Scopus

English

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.

protein, solubility, prediction, machine-learning

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

17. 8. 2018

Brno University of Technology

Brno

978-80-214-5679-2

DAZ & WIKT 2018 Proceedings

261

265

5

https://www.fit.vut.cz/research/publication/11808/

DAZ_S9_Jiri_Hon_v2.pdf