A hybrid machine learning and enzyme-constrained metabolic model for ab initio prediction of proteome reallocation

Authors

Motamedian, E., Nikoloski, Z.

Abstract

High expression of heterologous proteins in microbial cell factories frequently triggers a severe burden due to reallocation of finite cellular proteome. Conventional constraint-based models struggle to predict these resource shifts ab initio without relying on condition-specific omics data. To bridge this gap, we developed the Hybrid Transcription-Translation (HyTT) framework, combining multivariate adaptive regression splines (MARS) with enzyme-constrained metabolic models by enforcing an 80S ribosome integrity constraint. Cast as a mixed-integer linear programming problem, HyTT mathematically couples macroscopic spatial boundaries with microscopic, sequence-derived translational costs based on a bisection search. Validation against steady-state chemostat quantitative proteomics data demonstrated the superior capability of HyTT over contenders in predicting system-wide resource (re)allocation in Saccharomyces cerevisiae. Operating ab initio, the framework doubled the predictive accuracy of protein abundances (Pearson r=0.501) compared to conventional models, successfully segregating the minimal essential proteome from the cellular reserve pool. Crucially, HyTT autonomously captures complex stress responses vital for metabolic engineering. Upon simulating a 15% recombinant protein burden, the framework accurately predicted systemic growth retardation, decrease of ribosomal portion of the proteome, and surge of ethanol production, in line with the Crabtree effect. System-level analysis uncovered that cells adapt to restricted proteomic capacity through non-uniform metabolic rerouting, downregulating respiratory complexes in favor of high-turnover glycolytic enzymes, and relying on ribosomal paralog switching to minimize sequence-specific assembly costs. Ultimately, HyTT provides a computationally agile, sequence-driven platform for decoding dynamic resource reallocation, offering a powerful predictive tool to navigate metabolic trade-offs and guide rational strain design without requiring condition-specific multi-omics inputs.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 22 Jul 2026.

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