Gene

Random forest

Engineering

Biology

Solubility

Prioritization

Test set

Biochemistry

Data mining

Computer science

Protein sequencing

Management science

Computational biology

Boosting (machine learning)

Organic chemistry

Database

Protein expression

Escherichia coli

Gradient boosting

Artificial intelligence

Chemistry

Peptide sequence

Machine learning

Bioinformatics

Motivation Poor protein solubility hinders the production of many therapeutic and industrially useful proteins. Experimental efforts to increase solubility are plagued by low success rates and often reduce biological activity. Computational prediction of protein expressibility and solubility in Escherichia coli using only sequence information could reduce the cost of experimental studies by enabling prioritization of highly soluble proteins. Results A new tool for sequence-based prediction of soluble protein expression in E.coli, SoluProt, was created using the gradient boosting machine technique with the TargetTrack database as a training set. When evaluated against a balanced independent test set derived from the NESG database, SoluProt’s accuracy of 58.5% and AUC of 0.62 exceeded those of a suite of alternative solubility prediction tools. There is also evidence that it could significantly increase the success rate of experimental protein studies. SoluProt is freely available as a standalone program and a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/. Availability and implementation https://loschmidt.chemi.muni.cz/soluprot/. Supplementary information Supplementary data are available at Bioinformatics online.

SoluProt: prediction of soluble protein expression in <i>Escherichia coli</i>

SoluProt: prediction of soluble protein expression in Escherichia coli