Design of strictly orthogonal biosensors for maximizing renewable biofuel overproduction.

INTRODUCTION: Transcription factors (TFs) activate transcriptional initiation by binding specific signal molecules (SMs), yet designing TFs to precisely target non-natural SMs remains challenging. OBJECTIVES: Using transcriptional activator BmoR as an example, a machine-learning based model named BT to predict three crucial residue regions (CRRs) was generated. This study aimed to achieve BmoR with strict SM orthogonality (SSO). METHODS: Random Forest Algorithm was used to generate a model BT that pinpointed CRRs. The BmoR-SM complexes in the prediction dataset of Model BT were batch-simulated using a computational pipeline via Discovery Studio. Semi-rational engineering of the residues in the CRRs generated BmoR mutants with SSO, validated through MicroScale Thermophoresis (MST) affinity assays. The SSO-enabled BmoR-based biosensor was used to screen microbial overproducers for 3-L fed-batch fermentation. RESULTS: The transcription activation effects of 245 TF-SM complexes were experimentally verified, providing the training and test dataset to generate a machine-learning based Model BT with 88.5 % accuracy. The binding between 5,700 BmoR mutants and four SMs was simulated by Discovery Studio, generating 22,800 complexes to output BmoR-SM hydrogen bond (BSH) counts. BSH counts combined with supplementary parameters to form a prediction dataset. The CRRs containing totally 36 residues were successfully predicted by Model BT. The CRRs were semi-rational modified to obtain BmoR mutants with SSO. The SSO-enabled BmoR-based biosensor effectively screened a strain yielding 12.6 g/L isopentanol. CONCLUSION: By demonstrating the dominant role of the BSH counts in TF-SM interactions and establishing a machine learning-guided framework for TF evolution, this work advances rational design principles for engineering TFs with precise molecular recognition, offering broad applications in synthetic biology and metabolic engineering.