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

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

doi:10.1016/j.csbj.2022.06.006

Cited by 9 publications

(10 citation statements)

References 39 publications

(45 reference statements)

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“…The methods based on personal experience and knowledge were popular for quick decisions in laboratories, and those according to the statistic and computational algorithms were used for improved development, e.g., response surface methodology (RSM) [19][20][21]. Since metabolism was a highly complex network, ML was employed to achieve more significant and efficient optimization [22][23][24][25]. Progress in well-established high-throughput technologies and automation primarily benefited the acquisition of large datasets [26,27], which are essential for ML.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

Aida

Uchida

Nagai

et al. 2023

Preprint

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The medium composition was crucial for achieving the best performance of synthetic construction. What and how medium components determined the production of the synthetic construction remained poorly investigated. To address the questions, a comparative survey with two genetically engineered Escherichia coli strains was performed. As a case study, the strains carried the synthetic pathways for producing the aromatic compounds of 4APhe or Tyr, which were common in the upstream but differentiated in the downstream metabolism. Bacterial growth and compound production were examined in hundreds of medium combinations that comprised 48 pure chemicals. The resultant data sets linking the medium composition to bacterial growth and production were subjected to machine learning for improved production. Intriguingly, the primary medium components determining the production of 4PheA and Tyr were differentiated, which were the initial resource (glucose) of the synthetic pathway and the inducer (IPTG) of the synthetic construction, respectively. Fine-tuning of the primary component significantly increased the yields of 4APhe and Tyr, indicating that a single component could be crucial for the performance of synthetic construction. Transcriptome analysis observed the local and global changes in gene expression for improved production of 4APhe and Tyr, respectively, revealing divergent metabolic strategies for producing the foreign and native metabolites. The study demonstrated that ML-assisted medium optimization could provide a novel point of view on how to make the synthetic construction meet the original design.

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Section: Introductionmentioning

confidence: 99%

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

Aida

Uchida

Nagai

et al. 2023

Preprint

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“…AI-based tools have been developed and deployed for various microbial expression systems such as E. coli , P. pastoris , S. cerevisiae and mammalian cell expression systems including CHO, HEK293, HeLa and MCF7 ( Linder et al., 2020 ; Van Brempt et al., 2020 ; Smiatek et al., 2021 ; Feng et al., 2022a ; Li et al., 2022a ; Packiam et al., 2022 ). Plant host system remains an unexplored arena for AI incorporation.…”

Section: Challenges and Current Limitationsmentioning

confidence: 99%

“…AI has been used in recombinant biologics production in host systems such as mammalian cells (CHO and HEK293), yeast ( Saccharomyces cerevisiae and Pichia pastoris ) and bacterial ( Escherichia coli and Bacillus subtilis ) systems ( Van Brempt et al., 2020 ; Smiatek et al., 2021 ; Feng et al., 2022a ; Li et al., 2022a ; Packiam et al., 2022 ). Application of AI or ML algorithms include protein engineering, protein-protein interaction, stability, localization, solubility, functional motif prediction and catalytic activity which increases the production and functionality of recombinant proteins ( Han et al., 2019 ; Jiang et al., 2021 ; Feng et al., 2022a ; LaFleur et al., 2022 ; Masson et al., 2022 ; Kalemati et al., 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Parthiban,

Vijeesh,

Gayathri

et al. 2023

Front. Plant Sci.

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Recombinant biopharmaceuticals including antigens, antibodies, hormones, cytokines, single-chain variable fragments, and peptides have been used as vaccines, diagnostics and therapeutics. Plant molecular pharming is a robust platform that uses plants as an expression system to produce simple and complex recombinant biopharmaceuticals on a large scale. Plant system has several advantages over other host systems such as humanized expression, glycosylation, scalability, reduced risk of human or animal pathogenic contaminants, rapid and cost-effective production. Despite many advantages, the expression of recombinant proteins in plant system is hindered by some factors such as non-human post-translational modifications, protein misfolding, conformation changes and instability. Artificial intelligence (AI) plays a vital role in various fields of biotechnology and in the aspect of plant molecular pharming, a significant increase in yield and stability can be achieved with the intervention of AI-based multi-approach to overcome the hindrance factors. Current limitations of plant-based recombinant biopharmaceutical production can be circumvented with the aid of synthetic biology tools and AI algorithms in plant-based glycan engineering for protein folding, stability, viability, catalytic activity and organelle targeting. The AI models, including but not limited to, neural network, support vector machines, linear regression, Gaussian process and regressor ensemble, work by predicting the training and experimental data sets to design and validate the protein structures thereby optimizing properties such as thermostability, catalytic activity, antibody affinity, and protein folding. This review focuses on, integrating systems engineering approaches and AI-based machine learning and deep learning algorithms in protein engineering and host engineering to augment protein production in plant systems to meet the ever-expanding therapeutics market.

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“…Those based on the statistical and computational approaches, e.g., response surface methodology (RSM) [19] , [20] , [21] , have been used for improved development. Since metabolism is a highly complex network, ML has been employed to achieve more significant and efficient optimization [22] , [23] , [24] , [25] . Progress in well-established high-throughput technologies and automation primarily benefited the acquisition of large datasets [26] , [27] , which are essential for ML.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

Aida¹,

Uchida²,

Nagai³

et al. 2023

Computational and Structural Biotechnology Journal

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

Cited by 9 publications

References 39 publications

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals

Machine learning-assisted medium optimization revealed the discriminated strategies for improved production of the foreign and native metabolites

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