Motivation: Poor proteinsolubility hinders the production of many therapeutic and industrially useful proteins.Experimental efforts to increase solubility are plagued by low success ratesand often reduce biological activity. Computational prediction of proteinexpressibility and solubility in Escherichiacoli using only sequence information could reduce the cost of experimentalstudies by enabling prioritisation of highly soluble proteins. Results: A newtool for sequence-based prediction of soluble protein expression in Escherichia coli, SoluProt, was createdusing the gradient boosting machine technique with the TargetTrack database asa training set. When evaluated against a balanced independent test set derivedfrom the NESG database, SoluProt’s accuracy of 58.4% and AUC of 0.60 exceeded those of a suite of alternative solubility prediction tools.There is also evidence that it could significantly increase the success rate ofexperimental protein studies. SoluProt is freely available as a standalone programand a user-friendly webserver at https://loschmidt.chemi.muni.cz/soluprot/. Availabilityand Implementation: https://loschmidt.chemi.muni.cz/soluprot/ Contact:jiri@chemi.muni.cz Supplementary Information: Supplementary data areavailable at Bioinformatics online
SoluProt：大肠杆菌中可溶性蛋白表达的预测,ChemRxiv 大肠杆菌自身表达2000种以上的蛋白质
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