TY - JOUR
T1 - The INSIGHT platform
T2 - Enhancing NAD(P)-dependent specificity prediction for co-factor specificity engineering
AU - Ye, Yilin
AU - Jiang, Haoran
AU - Xu, Ran
AU - Wang, Sheng
AU - Zheng, Liangzhen
AU - Guo, Jingjing
N1 - Publisher Copyright:
© 2024 Elsevier B.V.
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
KW - Cofactor
KW - Deep learning
KW - Enzyme screening
KW - NAD(P)H-dependent enzymes
KW - Protein language model
UR - http://www.scopus.com/inward/record.url?scp=85202355303&partnerID=8YFLogxK
U2 - 10.1016/j.ijbiomac.2024.135064
DO - 10.1016/j.ijbiomac.2024.135064
M3 - Article
C2 - 39182884
AN - SCOPUS:85202355303
SN - 0141-8130
VL - 278
JO - International Journal of Biological Macromolecules
JF - International Journal of Biological Macromolecules
M1 - 135064
ER -