Dataset Information

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

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).

SUBMITTER: Packiam KAR

PROVIDER: S-EPMC9201004 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:The use of yeast starter cultures consisting of a blend of Saccharomyces cerevisiae and non-Saccharomyces yeasts has increased in recent years as a mean to address consumers' demands for diversified wines. However, this strategy is currently limited by the lack of a comprehensive knowledge regarding the factors that determine the balance between the yeast-yeast interactions and their responses triggered in complex environments. Our previous studies demonstrated that the strain Hanseniaspora guilliermondii UTAD222 has potential to be used as an adjunct of S. cerevisiae in the wine industry due to its positive impact on the fruity and floral character of wines. To rationalize the use of this yeast consortium, this study aims to understand the influence of production factors such as sugar and nitrogen levels, fermentation temperature, and the level of co-inoculation of H. guilliermondii UTAD222 in shaping fermentation and wine composition. For that purpose, a Central Composite experimental Design was applied to investigate the combined effects of the four factors on fermentation parameters and metabolites produced. The patterns of variation of the response variables were analyzed using machine learning methods, to describe their clustered behavior and model the evolution of each cluster depending on the experimental conditions. The innovative data analysis methodology adopted goes beyond the traditional univariate approach, being able to incorporate the modularity, heterogeneity, and hierarchy inherent to metabolic systems. In this line, this study provides preliminary data and insights, enabling the development of innovative strategies to increase the aromatic and fermentative potential of H. guilliermondii UTAD222 by modulating temperature and the availability of nitrogen and/or sugars in the medium. Furthermore, the strategy followed gathered knowledge to guide the rational development of mixed blends that can be used to obtain a particular wine style, as a function of fermentation conditions.

Project description:High-quality human hepatocytes form the basis of drug safety and efficacy tests, cell-based therapies, and bridge-to-transplantation devices. Presently the only supply of cells derives from an inadequate pool of suboptimal disqualified donor livers. Here we evaluated whether machine perfusion could ameliorate ischemic injury that many of these livers experience prior to hepatocyte isolation.Non-heparinized female Lewis rat livers were exposed to an hour of warm ischemia (34°C) and then perfused for 3h. Five different perfusion conditions that utilized the cell isolation apparatus were investigated, namely: (1) modified Williams Medium E and (2) Lifor, both with active oxygenation (95%O(2)/5%CO(2)), as well as (3) Lifor passively oxygenated with ambient air (21%O(2)/0.04%CO(2)), all at ambient temperatures (20 ± 2°C). At hypothermic temperatures (5 ± 1°C) and under passive oxygenation were (4) University of Wisconsin solution (UW) and (5) Vasosol. Negative and positive control groups comprised livers that had ischemia (WI) and livers that did not (Fresh) prior to cell isolation, respectively.Fresh livers yielded 32 ± 9 million cells/g liver while an hour of ischemia reduced the cell yield to 1.6 ± 0.6 million cells/g liver. Oxygenated Williams Medium E and Lifor recovered yields of 39 ± 11 and 31 ± 2.3 million cells/g liver, respectively. The passively oxygenated groups produced 15 ± 7 (Lifor), 13 ± 7 (Vasosol), and 10 ± 6 (UW)million cells/g liver. Oxygenated Williams Medium E was most effective at sustaining pH values, avoiding the accumulation of lactate, minimizing edematous weight gain and producing bile during perfusion.Machine perfusion results in a dramatic increase in cell yields from livers that have had up to an hour of warm ischemia, but perfusate choice significantly impacts the extent of recovery. Oxygenated Williams Medium E at room temperature is superior to Lifor, UW and Vasosol, largely facilitated by its high oxygen content and low viscosity.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets