Boosting(机器学习)
可扩展性
NAD+激酶
生化工程
计算机科学
辅因子
校长(计算机安全)
酶
计算生物学
化学
机器学习
工程类
生物化学
生物
数据库
操作系统
作者
Yilin Ye,Haoran Jiang,Ran Xu,Sheng Wang,Liangzhen Zheng,Jingjing Guo
标识
DOI:10.1016/j.ijbiomac.2024.135064
摘要
Enzyme specificity towards cofactors like NAD(P)H is crucial for applications in bioremediation and eco-friendly chemical synthesis. Despite their role in converting pollutants and creating sustainable products, predicting enzyme specificity faces challenges due to sparse data and inadequate models. To bridge this gap, we developed the cutting-edge INSIGHT platform to enhance the prediction of coenzyme specificity in NAD(P)-dependent enzymes. INSIGHT integrates extensive data from principal bioinformatics resources, concentrating on both NADH and NADPH specificities, and utilizes advanced protein language models to refine the predictions. This integration not only strengthens computational predictions but also meets the practical demands of high-throughput screening and optimization. Experimental validation confirms INSIGHT's effectiveness, boosting our ability to engineer enzymes for efficient, sustainable industrial and environmental processes. This work advances the practical use of computational tools in enzyme research, addressing industrial needs and offering scalable solutions for environmental challenges.
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