Computational Prediction of Recombinant Expression Efficiency for Industrial Enzymes: An Integrated Co-Translational Sequence Analysis Platform

Abstract

Industrial biocatalysis relies on the cost effective production of active enzymes in heterologous hosts, yet a substantial fraction of candidate sequences fails during recombinant expression because sequence-level determinants of translational efficiency are not captured by conventional codon optimization alone. Here we present an integrated computational platform for pre-clinical assessment of industrial enzyme expression potential that combines baseline sequence metrics with co-translational modeling of ribosome stalling, translation rhythm, codon harmonization, folding bottlenecks, solubility proxies, and proteolysis risk. The platform accepts DNA, protein, FASTA, and PDB inputs and returns interpretable expression risk assessments, component-level diagnostics, variant design proposals, and actionable engineering recommendations. Analyses are parameterized for industrial expression hosts (Escherichia coli, Saccharomyces cerevisiae, Bacillus subtilis, and mammalian cells) and for native enzyme sources including Streptomyces, Bacillus, filamentous fungi, actinomycetes, and extremophiles. All analyses execute locally without dependence on external databases, enabling rapid and secure screening of proprietary enzyme candidates prior to gene synthesis and fermenter scale-up. We report three layers of preliminary validation on small cohorts: (i) a retrospective public industrial-enzyme ranking benchmark (n = 20; rank subset n = 10); (ii) calibration of the solubility module on held-out eSOL enzymes with quantitative experimental solubility; and (iii) an independent collaborator wet-lab construct set retained as a true external-test holdout and never used for model fitting (n = 38 labeled constructs). On the external holdout, solubility calls achieved accuracy 68.4% (95% CI 52.5 - 80.9%), sensitivity 56.5% (36.8 - 74.4%), specificity 86.7% (62.1 - 96.3%), balanced accuracy 71.6% (bootstrap 95% CI 57.4 - 84.4%), and MCC 0.43 (0.15 - 0.69). Sensitivity remains close to chance on this small set, whereas specificity is higher; intervals are wide and the evidence should be read as directional rather than definitive. A directional arginine-surface redesign example (SrMilN insoluble parent to SrMilN_SASAR partially soluble variant; incomplete rescue with multiple soluble bands) was recovered. These findings support preliminary screening utility and transparent experimental agreement, but do not constitute high-accuracy prediction of industrial fermenter titre. Offline EC-class heuristics are retained only as optional provisional labels and are not presented as a validated platform capability (agreement 3/20 = 15%, 95% CI 5.2 - 36%; chance for six classes is 16.7%).