title: Prediction of recombinant protein overexpression in Escherichia coli using a machine learning based model (RPOLP).
abstract: Recombinant protein overexpression, an important biotechnological process, is ruled by complex biological rules which are mostly unknown, is in need of an intelligent algorithm so as to avoid resource-intensive lab-based trial and error experiments in order to determine the expression level of the recombinant protein. The purpose of this study is to propose a predictive model to estimate the level of recombinant protein overexpression for the first time in the literature using a machine learning approach based on the sequence, expression vector, and expression host. The expression host was confined to Escherichia coli which is the most popular bacterial host to overexpress recombinant proteins. To provide a handle to the problem, the overexpression level was categorized as low, medium and high. A set of features which were likely to affect the overexpression level was generated based on the known facts (e.g. gene length) and knowledge gathered from related literature. Then, a representative sub-set of features generated in the previous objective was determined using feature selection techniques. Finally a predictive model was developed using random forest classifier which was able to adequately classify the multi-class imbalanced small dataset constructed. The result showed that the predictive model provided a promising accuracy of 80% on average, in estimating the overexpression level of a recombinant protein.
authors.affiliation: Department of Computer Engineering, Isfahan (Khorasgan) Branch, Islamic Azad University, Isfahan, Iran. Electronic address: narges.habibi@gmail.com.
authors.forename: Narjeskhatoon
authors.initials: N
authors.lastname: Habibi
authors.affiliation: Department of Electrical and Computer Engineering, Majlesi Branch, Islamic Azad University, Isfahan, Iran.
authors.forename: Alireza
authors.initials: A
authors.lastname: Norouzi
authors.affiliation: UTM Big Data Centre, Universiti Teknologi Malaysia, Johr, Malaysia.
authors.forename: Siti Z
authors.initials: SZ
authors.lastname: Mohd Hashim
authors.affiliation: Bioinformatics Research Group, Universiti Teknologi Malaysia, Johor, Malaysia.
authors.forename: Mohd Shahir
authors.initials: MS
authors.lastname: Shamsir
authors.affiliation: School of Biological Sciences, Universiti Sains Malaysia, Penang, Malaysia. Malaysia; Advanced Medical and Dental Institute, Universiti Sains Malaysia, Penang, Malaysia; Center for Chemical Biology, Universiti Sains Malaysia, Penang, Malaysia.
authors.forename: Razip
authors.initials: R
authors.lastname: Samian
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D030562:Databases, Protein; D004926:Escherichia coli / Q000378:metabolism*; D020869:Gene Expression Profiling / Q000379:methods; D015964:Gene Expression Regulation, Bacterial; D000069550:Machine Learning; D011994:Recombinant Proteins / Q000096:biosynthesis*; D015203:Reproducibility of Results
chemical_list: D011994:Recombinant Proteins
keywords: E. coli; Feature selection; Machine learning; Prediction; Protein overexpression level; Recombinant protein