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

Kulandai Arockia Rajesh Packiam, Chien Wei Ooi, Fuyi Li, Shutao Mei, Beng Ti Tey, Huey Fang Ong, Jiangning Song, Ramakrishnan Nagasundara Ramanan

Research output: Contribution to journalArticleResearchpeer-review

6 Citations (Scopus)

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 (http://periscope-opt.erc.monash.edu).

Original languageEnglish
Pages (from-to)2909-2920
Number of pages12
JournalComputational and Structural Biotechnology Journal
Volume20
DOIs
Publication statusPublished - 2022

Keywords

  • Escherichia coli
  • Machine learning
  • Optimization
  • Periplasmic expression
  • Prediction model
  • Recombinant protein production

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