PERISCOPE-Opt: machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli

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dc.contributor.author Packiam, Kulandai Arockia Rajesh
dc.contributor.author Ooi, Chien Wei
dc.contributor.author Li, Fuyi
dc.contributor.author Mei, Shutao
dc.contributor.author Tey, Beng Ti
dc.contributor.author Ong, Huey Fang
dc.contributor.author Song, Jiangning
dc.contributor.author Ramanan, Ramakrishnan Nagasundara
dc.coverage.spatial United States of America
dc.date.accessioned 2022-06-16T10:35:57Z
dc.date.available 2022-06-16T10:35:57Z
dc.date.issued 2022-06
dc.identifier.citation Packiam, Kulandai Arockia Rajesh; Ooi, Chien Wei; Li, Fuyi; Mei, Shutao; Tey, Beng Ti; Ong, Huey Fang; Song, Jiangning and Ramanan, Ramakrishnan Nagasundara, "PERISCOPE-Opt: machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli", Computational and Structural Biotechnology Journal, DOI: 10.1016/j.csbj.2022.06.006, vol. 20, Jun. 2022. en_US
dc.identifier.issn 2001-0370
dc.identifier.uri https://doi.org/10.1016/j.csbj.2022.06.006
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/7812
dc.description.abstract Optimization of the fermentation process for recombinant protein production (RPP) is often resource-intensive. Machine learning (ML) approaches are helpful in minimizing the experimentations and find vast applications in RPP. However, these ML-based tools primarily focus on features with respect to amino-acid-sequence, ruling out the influence of fermentation process conditions. The present study combines the features derived from fermentation process conditions with that from amino acid-sequence to construct an ML-based model that predicts the maximal protein yields and the corresponding fermentation conditions for the expression of target recombinant protein in the Escherichia coli periplasm. Two sets of XGBoost classifiers were employed in the first stage to classify the expression levels of the target protein as high (>50 mg/L), medium (between 0.5 and 50 mg/L), or low (<0.5 mg/L). The second-stage framework consisted of three regression models involving support vector machines and random forest to predict the expression yields corresponding to each expression-level-class. Independent tests showed that the predictor achieved an overall average accuracy of 75% and a Pearson coefficient correlation of 0.91 for the correctly classified instances. Therefore, our model offers a reliable substitution of numerous trial-and-error experiments to identify the optimal fermentation conditions and yield for RPP. It is also implemented as an open-access webserver, PERISCOPE-Opt
dc.description.statementofresponsibility by Kulandai Arockia Rajesh Packiam, Chien Wei Ooi, Fuyi Li, Shutao Mei, Beng Ti Tey, Huey Fang Ong, Jiangning Song and Ramakrishnan Nagasundara Ramanan
dc.format.extent vol. 20
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject Optimization en_US
dc.subject Machine learning en_US
dc.subject Recombinant protein production en_US
dc.subject Periplasmic expression en_US
dc.subject Prediction model en_US
dc.title PERISCOPE-Opt: machine learning-based prediction of optimal fermentation conditions and yields of recombinant periplasmic protein expressed in Escherichia coli en_US
dc.type Article en_US
dc.relation.journal Computational and Structural Biotechnology Journal


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