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Nature

a CAS Framework for Predicting the Synthetic Evolution of Anti-Plastic Enzymes

3 Juni 2025   17:54 Diperbarui: 4 Juni 2025   09:05 930
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(Tabel CAS Variables in Nonlinear Dynamic (Sumber: Pribadi))

When fully implemented, such a platform could reduce discovery time, expand accessible functional space, and democratize enzyme engineering, especially for critical sustainability challenges such as plastic degradation, toxic waste neutralization, or CO fixation.

List of References

1. Jumper, J., Evans, R., Pritzel, A., et al. (2021). Highly accurate protein structure prediction with AlphaFold. Nature, 596(7873), 583--589. https://doi.org/10.1038/s41586-021-03819-2

2. Arnold, F. H. (2018). Directed Evolution: Bringing New Chemistry to Life. Angewandte Chemie International Edition, 57(16), 4143--4148. https://doi.org/10.1002/anie.201708408

3. Holland, J. H. (2006). Studying complex adaptive systems. Journal of Systems Science and Complexity, 19, 1--8. https://doi.org/10.1007/s11424-006-0001-z

4. Lehner, B. (2011). Molecular mechanisms of epistasis within and between genes. Trends in Genetics, 27(8), 323--331. https://doi.org/10.1016/j.tig.2011.05.007

5. Tokuriki, N., & Tawfik, D. S. (2009). Stability effects of mutations and protein evolvability. Current Opinion in Structural Biology, 19(5), 596--604. https://doi.org/10.1016/j.sbi.2009.08.003

6. Yang, K. K., Wu, Z., & Arnold, F. H. (2019). Machine-learning-guided directed evolution for protein engineering. Nature Methods, 16(8), 687--694. https://doi.org/10.1038/s41592-019-0496-6

7. Ryu, J. Y., Kang, J. H., & Park, S. J. (2021). Enzyme Engineering for Plastic Degradation. Trends in Biotechnology, 39(9), 874--885. https://doi.org/10.1016/j.tibtech.2021.03.009

8. Wang, X., & Zhang, J. (2020). Predicting the evolution of protein--protein interaction networks using reinforcement learning. Bioinformatics, 36(14), 4072--4078. https://doi.org/10.1093/bioinformatics/btaa260

9. Barabsi, A.-L. (2016). Network Science. Cambridge University Press.

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