kinGEMs 框架结合深度学习预测的动力学参数构建酶约束基因组尺度模型
kinGEMs: A robust and scalable framework for resource-constraint models through stochastic tuning of deep learning-predicted kinetic parameters
Rana A. Barghout 等人提出 kinGEMs 框架，将深度学习模型 CPI-Pred 从序列和化合物嵌入预测的 kcat 参数整合进酶约束基因组尺度模型（ecGEMs），用于代谢优化。CPI-Pred 输出的参数带有不确定性，框架在整合时显式考虑这一不确定性，并利用序列、结构和动力学等多尺度表征提升模型的泛化性和准确性。基准评测显示，该框架预测的生长速率和通量与实验观测及实测蛋白质组分数一致，通量中位变异性降低数倍，模型约束更紧。
来源：PLOS Computational Biology · journals.plos.org
by Rana A. Barghout, Lya Chiñas Serrano, Benjamin Sanchez-Lengeling, Radhakrishnan Mahadevan
The construction of accurate enzyme-constrained genome-scale models (ecGEMs) remains a critical challenge in systems biology, limited by sparse kinetic data and the need for biologically meaningful representations. This work presents an integrated framework combining CPI-Pred, a deep learning model to predict kinetic parameters (kcat) from sequence and compound embeddings, with kinGEMs, a pipeline to incorporate these uncertainty-aware parameters into ecGEMs for metabolic optimization. By leveraging representations at multiple scales, the approach captures sequence, structure, and kinetic data to enhance model generalizability and accuracy. Rigorous benchmarking demonstrates the framework’s capability to predict growth rates and fluxes that are consistent with experimental observations and measured proteome fractions, reduce median flux variability by several folds, and yield better-constrained metabolic models by explicitly accounting for the uncertainty inherent in ML-predicted kinetic parameters. kinGEMs successfully generated ecGEMs for 93 models spanning a phylogenetically diverse set of organisms including Gram-negative and Gram-positive bacteria, mycobacteria, parasitic protists, fungi, and mammalian cell lines. These innovations breakdown the barrier to application of ecGEMs to non-model organisms and open new avenues for metabolic engineering and synthetic biology in more industrially relevant hosts.