Literature DB >> 25447878

Predicting the solubility of recombinant proteins in Escherichia coli.

Roger G Harrison1, Miguel J Bagajewicz.   

Abstract

We describe a statistical model that uses binomial logistic regression for predicting the solubility of heterologous proteins expressed in E. coli. The model is based on a set of proteins reported to have been expressed in E. coli in either soluble or insoluble form. The 22 parameters used in the final model based on proteins' amino acid composition are discussed. The overall accuracy of the model developed is 94%. The way to use this model on the website http://www.ou.edu/ for the prediction of protein solubility is explained.

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Year:  2015        PMID: 25447878     DOI: 10.1007/978-1-4939-2205-5_23

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  5 in total

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2.  Global Landscapes of the Na+/H+ Antiporter (NHX) Family Members Uncover their Potential Roles in Regulating the Rapeseed Resistance to Salt Stress.

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3.  MPEPE, a predictive approach to improve protein expression in E. coli based on deep learning.

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Journal:  Comput Struct Biotechnol J       Date:  2022-03-01       Impact factor: 7.271

4.  Omics Analysis Reveals the Mechanism of Enhanced Recombinant Protein Production Under Simulated Microgravity.

Authors:  Jie Huangfu; Hye Su Kim; Ke Xu; Xiaoyu Ning; Lei Qin; Jun Li; Chun Li
Journal:  Front Bioeng Biotechnol       Date:  2020-02-20

5.  Genome-wide identification of the amino acid permease genes and molecular characterization of their transcriptional responses to various nutrient stresses in allotetraploid rapeseed.

Authors:  Ting Zhou; Cai-Peng Yue; Jin-Yong Huang; Jia-Qian Cui; Ying Liu; Wen-Ming Wang; Chuang Tian; Ying-Peng Hua
Journal:  BMC Plant Biol       Date:  2020-04-08       Impact factor: 4.215

  5 in total

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