Breaker, R. R. DNA enzymes. Nat. Biotechnol. 15, 427–431 (1997).
Google Scholar
Knowles, J. R. Enzyme catalysis: not totally different, simply higher. Nature 350, 121–124 (1991).
Google Scholar
Khosla, C. & Harbury, P. B. Modular enzymes. Nature 409, 247–252 (2001).
Google Scholar
Chen, Y. & Li, F. Metabolomes evolve quicker than metabolic community constructions. Proc. Natl Acad. Sci. USA 121, e2400519121 (2024).
Google Scholar
Kraut, J. How do enzymes work? Science 242, 533–540 (1988).
Google Scholar
Murakami, Y., Kikuchi, J.-i, Hisaeda, Y. & Hayashida, O. Synthetic enzymes. Chem. Rev. 96, 721–758 (1996).
Google Scholar
Klibanov, A. M. Bettering enzymes by utilizing them in natural solvents. Nature 409, 241–246 (2001).
Google Scholar
Copeland, R. A. Enzymes: A Sensible Introduction to Construction, Mechanism, and Knowledge Aanalysis (Wiley, 2023).
Nielsen, J. E. & McCammon, J. A. Calculating pKa values in enzyme lively websites. Protein Sci. 12, 1894–1901 (2003).
Google Scholar
Eisenmesser, E. Z. et al. Intrinsic dynamics of an enzyme underlies catalysis. Nature 438, 117–121 (2005).
Google Scholar
Noraini, M., Ong, H. C., Badrul, M. J. & Chong, W. A evaluate on potential enzymatic response for biofuel manufacturing from algae. Renew. Maintain. Vitality Rev. 39, 24–34 (2014).
Google Scholar
Ding, Okay. et al. Machine learning-guided co-optimization of health and variety facilitates combinatorial library design in enzyme engineering. Nat. Commun. 15, 6392 (2024).
Google Scholar
Bateman, A. et al. UniProt: the Common protein knowledgebase in 2025. Nucleic Acids Res. 53, D609–D617 (2025).
Google Scholar
Bansal, P. et al. Rhea, the response knowledgebase in 2022. Nucleic Acids Res. 50, D693–D700 (2022).
Google Scholar
Caspi, R. et al. The MetaCyc database of metabolic pathways and enzymes—a 2019 replace. Nucleic Acids Res. 48, D445–D453 (2020).
Google Scholar
Rudroff, F. et al. Alternatives and challenges for combining chemo- and biocatalysis. Nat. Catal. 1, 12–22 (2018).
Google Scholar
Li, W.-L. & Head-Gordon, T. Catalytic ideas from pure enzymes and translational design methods for artificial catalysts. ACS Cent. Sci. 7, 72–80 (2020).
Google Scholar
Vogt, C. & Weckhuysen, B. M. The idea of lively web site in heterogeneous catalysis. Nat. Rev. Chem. 6, 89–111 (2022).
Google Scholar
Lonsdale, R., Harvey, J. N. & Mulholland, A. J. A sensible information to modelling enzyme-catalysed reactions. Chem. Soc. Rev. 41, 3025–3038 (2012).
Google Scholar
Hay, S. & Scrutton, N. S. Good vibrations in enzyme-catalysed reactions. Nat. Chem. 4, 161–168 (2012).
Google Scholar
Li, F. et al. Deep learning-based okcat prediction allows improved enzyme-constrained mannequin reconstruction. Nat. Catal. 5, 662–672 (2022).
Google Scholar
Martín, A. J., Mitchell, S., Mondelli, C., Jaydev, S. & Pérez-Ramírez, J. Unifying views on catalyst deactivation. Nat. Catal. 5, 854–866 (2022).
Google Scholar
Goldman, S., Das, R., Yang, Okay. Okay. & Coley, C. W. Machine studying modeling of household large enzyme-substrate specificity screens. PLoS Comput. Biol. 18, e1009853 (2022).
Google Scholar
Kroll, A., Ranjan, S., Engqvist, M. Okay. & Lercher, M. J. A common mannequin to foretell small molecule substrates of enzymes based mostly on machine and deep studying. Nat. Commun. 14, 2787 (2023).
Google Scholar
González-Granda, S., Albarrán-Velo, J., Lavandera, I. & Gotor-Fernández, V. Increasing the artificial toolbox by means of steel–enzyme cascade reactions. Chem. Rev. 123, 5297–5346 (2023).
Google Scholar
Hua, C. et al. Reactzyme: a benchmark for enzyme–response prediction. Adv. Neural Inf. Course of. Syst. 37, 26415–26442 (2024).
Ryu, J. Y., Kim, H. U. & Lee, S. Y. Deep studying allows high-quality and high-throughput prediction of enzyme fee numbers. Proc. Natl Acad. Sci. USA 116, 13996–14001 (2019).
Google Scholar
Sanderson, T., Bileschi, M. L., Belanger, D. & Colwell, L. J. ProteInfer, deep neural networks for protein purposeful inference. eLife 12, e80942 (2023).
Google Scholar
Li, Y. et al. DEEPre: sequence-based enzyme EC quantity prediction by deep studying. Bioinformatics 34, 760–769 (2018).
Google Scholar
Dalkiran, A. et al. ECPred: a device for the prediction of the enzymatic features of protein sequences based mostly on the EC nomenclature. BMC Bioinformatics 19, 1–13 (2018).
Google Scholar
Gligorijević, V. et al. Construction-based protein perform prediction utilizing graph convolutional networks. Nat. Commun. 12, 3168 (2021).
Google Scholar
Yu, T. et al. Enzyme perform prediction utilizing contrastive studying. Science 379, 1358–1363 (2023).
Google Scholar
Xing, H. et al. Excessive-throughput prediction of enzyme promiscuity based mostly on substrate–product pairs. Transient. Bioinform. 25, bbae089 (2024).
Google Scholar
Mikhael, P. G., Chinn, I. & Barzilay, R. CLIPZyme: reaction-conditioned digital screening of enzymes. Int. Conf. Mach. Be taught. 235, 35647–35663 (2024).
Yang, J. et al. CARE: a benchmark suite for the classification and retrieval of enzymes. Adv. Neural Inf. Course of. Syst. 37, 3094–3121 (2024).
Rappoport, D. & Jinich, A. Enzyme substrate prediction from three-dimensional characteristic representations utilizing space-filling curves. J. Chem. Inf. Mannequin. 63, 1637–1648 (2023).
Google Scholar
Salas-Nuñez, L. F. et al. Machine studying to foretell enzyme–substrate interactions in elucidation of synthesis pathways: a evaluate. Metabolites 14, 154 (2024).
Google Scholar
Li, F., Chen, Y., Anton, M. & Nielsen, J. GotEnzymes: an in depth database of enzyme parameter predictions. Nucleic Acids Res. 51, D583–D586 (2023).
Google Scholar
Hua, C. et al. EnzymeFlow: producing reaction-specific enzyme catalytic pockets by means of stream matching and co-evolutionary dynamics. Preprint at (2024).
Carbonell, P. et al. Selenzyme: enzyme choice device for pathway design. Bioinformatics 34, 2153–2154 (2018).
Google Scholar
Altschul, S. F. et al. Gapped BLAST and PSI-BLAST: a brand new era of protein database search packages. Nucleic Acids Res. 25, 3389–3402 (1997).
Google Scholar
Tian, W. & Skolnick, J. How properly is enzyme perform conserved as a perform of pairwise sequence identification? J. Mol. Biol. 333, 863–882 (2003).
Google Scholar
Ma, F. et al. Sequence homolog-based molecular engineering for shifting the enzymatic pH optimum. Synth. Syst. Biotechnol. 1, 195–206 (2016).
Google Scholar
Wang, J., Wu, Y., Solar, X., Yuan, Q. & Yan, Y. De novo biosynthesis of glutarate by way of α-keto acid carbon chain extension and decarboxylation pathway in Escherichia coli. ACS Synth. Biol. 6, 1922–1930 (2017).
Google Scholar
Reynolds, E. et al. Elucidation of gene clusters underlying withanolide biosynthesis in ashwagandha by means of yeast metabolic engineering. Preprint at bioRxiv (2024).
Hekkelman, M. L., de Vries, I., Joosten, R. P. & Perrakis, A. AlphaFill: enriching AlphaFold fashions with ligands and cofactors. Nat. Strategies 20, 205–213 (2023).
Google Scholar
ESM Group. ESM Cambrian: revealing the mysteries of proteins with unsupervised studying. EvolutionaryScale (2024).
Probst, D., Schwaller, P. & Reymond, J.-L. Response classification and yield prediction utilizing the differential response fingerprint DRFP. Digit. Discov. 1, 91–97 (2022).
Google Scholar
Kingma, D. P. Adam: a technique for stochastic optimization. Preprint at (2014).
Steinegger, M. & Söding, J. MMseqs2 allows delicate protein sequence looking for the evaluation of huge information units. Nat. Biotechnol. 35, 1026–1028 (2017).
Google Scholar
Track, Y. et al. Precisely predicting enzyme features by means of geometric graph studying on ESMFold-predicted constructions. Nat. Commun. 15, 8180 (2024).
Google Scholar
Järvelin, Okay. & Kekäläinen, J. Cumulated gain-based analysis of IR strategies. ACM Trans. Inf. Syst. 20, 422–446 (2002).
Google Scholar
Chen, H., Lyne, P. D., Giordanetto, F., Lovell, T. & Li, J. On evaluating molecular-docking strategies for pose prediction and enrichment components. J. Chem. Inf. Mannequin. 46, 401–415 (2006).
Google Scholar
Van der Maaten, L. & Hinton, G. Visualizing information utilizing t-SNE. J. Mach. Be taught. Res. 9, 2579–2605 (2008).
Ribeiro, A. J. M. et al. Mechanism and Catalytic Web site Atlas (M-CSA): a database of enzyme response mechanisms and lively websites. Nucleic Acids Res. 46, D618–D623 (2018).
Google Scholar
Zhang, Y. et al. P450Rdb: a manually curated database of reactions catalyzed by cytochrome P450 enzymes. J. Adv. Res. 63, 35–42 (2024).
Google Scholar
Samusevich, R. et al. Discovery and characterization of terpene synthases powered by machine studying. Preprint at (2024).
Huang, H. et al. Panoramic view of a superfamily of phosphatases by means of substrate profiling. Proc. Natl Acad. Sci. USA 112, E1974–E1983 (2015).
Google Scholar
Zheng, S. et al. Deep studying pushed biosynthetic pathways navigation for pure merchandise with BioNavi-NP. Nat. Commun. 13, 3342 (2022).
Google Scholar
Zeng, T., Jin, Z., Zheng, S., Yu, T. & Wu, R. Growing BioNavi for hybrid retrosynthesis planning. JACS Au 4, 2492–2502 (2024).
Google Scholar
van Beusekom, B. et al. Homology-based hydrogen bond data improves crystallographic constructions within the PDB. Protein Sci. 27, 798–808 (2018).
Google Scholar
Krivák, R. & Hoksza, D. P2Rank: machine studying based mostly device for fast and correct prediction of ligand binding websites from protein construction. J. Cheminform. 10, 39 (2018).
Google Scholar
van Kempen, M. et al. Quick and correct protein construction search with Foldseek. Nat. Biotechnol. 42, 243–246 (2024).
Google Scholar
Jing, B. et al. Studying from protein construction with geometric vector perceptrons. Int. Conf. Be taught. Characterize. (2021).
Schütt, Okay. et al. Schnet: a continuous-filter convolutional neural community for modeling quantum interactions. Adv. Neural Inf. Course of. Syst. 30 (2017).
Vaswani, A. Consideration is all you want. Adv. Neural Inf. Course of. Syst. 30 (2017).



