EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Mu, X.; Yang, Y.; Wang, Q.; Chen, Z.; Luo, B.; Huang, Z.; Lin, X.; Xu, L.; Li, X.; Qu, Y.; Xiao, J.; Wang, Z.; Shi, B.; Ou, Q.; Yao, B.; Yan, J.; Zhuang, Y.; Zhang, Y.; Shi, R.; Xu, Y.

doi:10.64898/2026.04.28.721430

bioRxiv · 10.64898/2026.04.28.721430

EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics

Mu, X.·Yang, Y.·Wang, Q.·Chen, Z.·Luo, B.·Huang, Z.·Lin, X.·Xu, L.·Li, X.·Qu, Y.·Xiao, J.·Wang, Z.·Shi, B.·Ou, Q.·Yao, B.·Yan, J.·Zhuang, Y.·Zhang, Y.·Shi, R.·Xu, Y.

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Abstract

Enzyme kinetic parameters underpin mechanistic biology but remain sparse in physiological context. We present EnzCast, a multi-modal framework jointly predicting Km, kcat, kcat/Km, and Ki from protein sequence, 3D structure, substrate chemistry, and experimental conditions, paired with IsoKin, an isoform-resolved Bayesian framework converting EnzCast priors into patient-specific in vivo kinetics. Trained on KinBench, the largest curated kinetics database, task-adaptive EnzCast achieved R2 = 0.413, 0.455, 0.227, and 0.105 for Km, Ki, kcat, and kcat/Km, surpassing all baselines on catalytic tasks. Systematic condition scans recovered compartment-specific pH direction inversion and pathway-level temperature responses. In a 20-patient colorectal cancer single-cell cohort, IsoKin reduced posterior uncertainty by 73.3% and 77.3%, revealing cell-type-specific rewiring. Orthogonal validation--scFEA flux, DepMap essentiality (permutation P = 0.0008) and TCGA survival-- provided mixed but directionally consistent support. Together, EnzCast and IsoKin bridge in vitro prediction, condition-aware biochemical interrogation and patient-resolved in vivo inference.

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Mu, X., Yang, Y., Wang, Q., Chen, Z., Luo, B., Huang, Z., Lin, X., Xu, L., Li, X., Qu, Y., Xiao, J., Wang, Z., Shi, B., Ou, Q., Yao, B., Yan, J., Zhuang, Y., Zhang, Y., Shi, R., Xu, Y.. 2026-04-30. EnzCast: Prediction of Patient-Specific Enzymatic Kinetics through Multi-Modal Deep Learning and Isoform-Resolved Bayesian Inference based on Single-Cell Transcriptomics. https://doi.org/10.64898/2026.04.28.721430

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