The INSIGHT platform: Enhancing NAD(P)-dependent specificity prediction for co-factor specificity engineering

Yilin Ye, Haoran Jiang, Ran Xu, Sheng Wang, Liangzhen Zheng, Jingjing Guo

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number135064
JournalInternational Journal of Biological Macromolecules
Volume278
DOIs
Publication statusPublished - Oct 2024

Keywords

  • Cofactor
  • Deep learning
  • Enzyme screening
  • NAD(P)H-dependent enzymes
  • Protein language model

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