Machine Learning-Guided Optimization of p-Coumaric Acid Production in Yeast.

Industrial biotechnology uses Design-Build-Test-Learn (DBTL) cycles to accelerate the development of microbial cell factories, required for the transition to a biobased economy. To use them effectively, appropriate connections between the phases of the cycle are crucial. Using p-coumaric acid (pCA) production in Saccharomyces cerevisiae as a case study, we propose the use of one-pot library generation, random screening, targeted sequencing, and machine learning (ML) as links during DBTL cycles. We showed that the robustness and flexibility of the ML models strongly enable pathway optimization and propose feature importance and Shapley additive explanation values as a guide to expand the design space of original libraries. This approach allowed a 68% increased production of pCA within two DBTL cycles, leading to a 0.52 g/L titer and a 0.03 g/g yield on glucose.

Signal Peptide Efficiency: From High-Throughput Data to Prediction and Explanation.

The passage of proteins across biological membranes via the general secretory (Sec) pathway is a universally conserved process with critical functions in cell physiology and important industrial applications. Proteins are directed into the Sec pathway by a signal peptide at their N-terminus. Estimating the impact of physicochemical signal peptide features on protein secretion levels has not been achieved so far, partially due to the extreme sequence variability of signal peptides. To elucidate relevant features of the signal peptide sequence that influence secretion efficiency, an evaluation of ∼12,000 different designed signal peptides was performed using a novel miniaturized high-throughput assay. The results were used to train a machine learning model, and a post-hoc explanation of the model is provided. By describing each signal peptide with a selection of 156 physicochemical features, it is now possible to both quantify feature importance and predict the protein secretion levels directed by each signal peptide. Our analyses allow the detection and explanation of the relevant signal peptide features influencing the efficiency of protein secretion, generating a versatile tool for the de novo design and in silico evaluation of signal peptides.

To divide or not to divide: a key role of Rim15 in calorie-restricted yeast cultures.

The PAS kinase Rim15 is proposed to integrate signals from different nutrient-sensing pathways and to control transcriptional reprogramming of Saccharomyces cerevisiae upon nutrient depletion. Despite this proposed role, previous transcriptome analyses of rim15 mutants solely focused on growing cultures. In the present work, retentostat cultivation enabled analysis of the role of Rim15 under severely calorie-restricted, virtually non-growing conditions. Under these conditions, deletion of RIM15 affected transcription of over 10-fold more genes than in growing cultures. Transcriptional responses, metabolic rates and cellular morphology indicated a key role of Rim15 in controlled cell-cycle arrest upon nutrient depletion. Moreover, deletion of rim15 reduced heat-shock tolerance in non-growing, but not in growing cultures. The failure of rim15 cells to adapt to calorie restriction by entering a robust post-mitotic state resembles cancer cell physiology and shows that retentostat cultivation of yeast strains can provide relevant models for healthy post-mitotic and transformed human cells.