Röthlisberger, D. et al. Kemp elimination catalysts by computational enzyme design. Nature 453, 190–195 (2008).
Siegel, J. B. et al. Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction. Science 329, 309–313 (2010).
Privett, H. K. et al. Iterative approach to computational enzyme design. Proc. Natl Acad. Sci. USA 109, 3790–3795 (2012).
Jiang, L. et al. De novo computational design of retro-aldol enzymes. Science 319, 1387–1391 (2008).
Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).
Blomberg, R. et al. Precision is essential for efficient catalysis in an evolved Kemp eliminase. Nature 503, 418–421 (2013).
Khersonsky, O. et al. Bridging the gaps in design methodologies by evolutionary optimization of the stability and proficiency of designed Kemp eliminase KE59. Proc. Natl Acad. Sci. USA 109, 10358–10363 (2012).
Giger, L. et al. Evolution of a designed retro-aldolase leads to complete active site remodeling. Nat. Chem. Biol. 9, 494–498 (2013).
Patsch, D. et al. Enriching productive mutational paths accelerates enzyme evolution. Nat. Chem. Biol. 20, 1662–1669 (2024).
Korendovych, I. V. & DeGrado, W. F. Catalytic efficiency of designed catalytic proteins. Curr. Opin. Struct. Biol. 27, 113–121 (2014).
Lovelock, S. L. et al. The road to fully programmable protein catalysis. Nature 606, 49–58 (2022).
Listov, D., Goverde, C. A., Correia, B. E. & Fleishman, S. J. Opportunities and challenges in design and optimization of protein function. Nat. Rev. Mol. Cell Biol. 25, 639–653 (2024).
Marques, S. M., Planas-Iglesias, J. & Damborsky, J. Web-based tools for computational enzyme design. Curr. Opin. Struct. Biol. 69, 19–34 (2021).
Braun, M. et al. Computational design of highly active de novo enzymes. Preprint at bioRxiv https://doi.org/10.1101/2024.08.02.606416 (2024).
Lauko, A. et al. Computational design of serine hydrolases. Science https://doi.org/10.1126/science.adu2454 (2025).
Bar-Even, A. et al. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410 (2011).
Khare, S. D. & Fleishman, S. J. Emerging themes in the computational design of novel enzymes and protein-protein interfaces. FEBS Lett. 587, 1147–1154 (2013).
Baker, D. An exciting but challenging road ahead for computational enzyme design. Protein Sci. 19, 1817–1819 (2010).
Kries, H., Blomberg, R. & Hilvert, D. De novo enzymes by computational design. Curr. Opin. Chem. Biol. 17, 221–228 (2013).
Kiss, G., Çelebi-Ölçüm, N., Moretti, R., Baker, D. & Houk, K. N. Computational enzyme design. Angew. Chem. Int. Ed. Engl. 52, 5700–5725 (2013).
Goldenzweig, A. & Fleishman, S. J. Principles of protein stability and their application in computational design. Annu. Rev. Biochem. 87, 105–129 (2018).
Frushicheva, M. P., Cao, J., Chu, Z. T. & Warshel, A. Exploring challenges in rational enzyme design by simulating the catalysis in artificial Kemp eliminase. Proc. Natl Acad. Sci. USA 107, 16869–16874 (2010).
Otten, R. et al. How directed evolution reshapes the energy landscape in an enzyme to boost catalysis. Science 370, 1442–1446 (2020).
Nagel, Z. D. & Klinman, J. P. A 21st century revisionist’s view at a turning point in enzymology. Nat. Chem. Biol. 5, 543–550 (2009).
Vaissier Welborn, V. & Head-Gordon, T. Computational design of synthetic enzymes. Chem. Rev. 119, 6613–6630 (2019).
Chu, A. E., Lu, T. & Huang, P.-S. Sparks of function by de novo protein design. Nat. Biotechnol. 42, 203–215 (2024).
Lipsh-Sokolik, R. et al. Combinatorial assembly and design of enzymes. Science 379, 195–201 (2023).
Khersonsky, O. et al. Automated design of efficient and functionally diverse enzyme repertoires. Mol. Cell 72, 178–186.e5 (2018).
Goldenzweig, A. et al. Automated structure- and sequence-based design of proteins for high bacterial expression and stability. Mol. Cell 63, 337–346 (2016).
Rocklin, G. J. et al. Global analysis of protein folding using massively parallel design, synthesis, and testing. Science 357, 168–175 (2017).
Polizzi, N. F. & DeGrado, W. F. A defined structural unit enables de novo design of small-molecule-binding proteins. Science 369, 1227–1233 (2020).
Watson, J. L. et al. De novo design of protein structure and function with RFdiffusion. Nature 620, 1089–1100 (2023).
Frank, C. et al. Scalable protein design using optimization in a relaxed sequence space. Science 386, 439–445 (2024).
Lu, T., Liu, M. H., Chen, Y., Kim, J. & Huang, P.-S. Assessing generative model coverage of protein structures with SHAPES. Preprint at bioRxiv https://doi.org/10.1101/2025.01.09.632260 (2025).
Kim, D. et al. Computational design of metallohydrolases. Preprint at bioRxiv https://doi.org/10.1101/2024.11.13.623507 (2024).
Copeland, R. A. Enzymes: A Practical Introduction to Structure, Mechanism, and Data Analysis (Wiley-VCH, 1996).
Sillitoe, I. et al. CATH: expanding the horizons of structure-based functional annotations for genome sequences. Nucleic Acids Res. 47, D280–D284 (2019).
Nagano, N., Orengo, C. A. & Thornton, J. M. One fold with many functions: the evolutionary relationships between TIM barrel families based on their sequences, structures and functions. J. Mol. Biol. 321, 741–765 (2002).
Huang, P. S. et al. De novo design of a four-fold symmetric TIM-barrel protein with atomic-level accuracy. Nat. Chem. Biol. 12, 29–34 (2016).
Eisenbeis, S. et al. Potential of fragment recombination for rational design of proteins. J. Am. Chem. Soc. 134, 4019–4022 (2012).
Lapidoth, G. et al. Highly active enzymes by automated combinatorial backbone assembly and sequence design. Nat. Commun. 9, 2780 (2018).
Lipsh‐Sokolik, R., Listov, D. & Fleishman, S. J. The AbDesign computational pipeline for modular backbone assembly and design of binders and enzymes. Protein Sci. 1, 151–159 (2021).
Zanghellini, A. et al. New algorithms and an in silico benchmark for computational enzyme design. Protein Sci. 15, 2785–2794 (2006).
Leaver-Fay, A. et al. ROSETTA3: an object-oriented software suite for the simulation and design of macromolecules. Methods Enzymol. 487, 545–574 (2011).
Warszawski, S., Netzer, R., Tawfik, D. S. & Fleishman, S. J. A ‘fuzzy’-logic language for encoding multiple physical traits in biomolecules. J. Mol. Biol. 426, 4125–4138 (2014).
Listov, D. et al. Assessing and enhancing foldability in designed proteins. Protein Sci. 31, e4400 (2022).
Na, J., Houk, K. N. & Hilvert, D. Transition state of the base-promoted ring-opening of isoxazoles. Theoretical prediction of catalytic functionalities and design of haptens for antibody production. J. Am. Chem. Soc. 118, 6462–6471 (1996).
Tantillo, D. J., Chen, J. & Houk, K. N. Theozymes and compuzymes: theoretical models for biological catalysis. Curr. Opin. Chem. Biol. 2, 743–750 (1998).
Hong, N. S. et al. The evolution of multiple active site configurations in a designed enzyme. Nat. Commun. 9, 3900 (2018).
Gutierrez-Rus, L. I. et al. Enzyme enhancement through computational stability design targeting NMR-determined catalytic hotspots. J. Am. Chem. Soc. https://doi.org/10.1021/jacs.4c09428 (2025).
Warshel, A. & Weiss, R. M. An empirical valence bond approach for comparing reactions in solutions and in enzymes. J. Am. Chem. Soc. 102, 6218–6226 (1980).
Hupfeld, E. et al. Conformational modulation of a mobile loop controls catalysis in the (βα)8-barrel enzyme of histidine biosynthesis HisF. JACS Au 4, 3258–3276 (2024).
Schlee, S. et al. Relationship of catalysis and active site loop dynamics in the (βα)8-barrel enzyme indole-3-glycerol phosphate synthase. Biochemistry 57, 3265–3277 (2018).
Goldsmith, M. et al. Overcoming an optimization plateau in the directed evolution of highly efficient nerve agent bioscavengers. Protein Eng. Des. Sel. 30, 333–345 (2017).
Kemp, D. S., Cox, D. D. & Paul, K. G. Physical organic chemistry of benzisoxazoles. IV. Origins and catalytic nature of the solvent rate acceleration for the decarboxylation of 3-carboxybenzisoxazoles. J. Am. Chem. Soc. 97, 7312–7318 (1975).
Li, W., Jaroszewski, L. & Godzik, A. Clustering of highly homologous sequences to reduce the size of large protein databases. Bioinformatics 17, 282–283 (2001).
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
Barber-Zucker, S. et al. Stable and functionally diverse versatile peroxidases designed directly from sequences. J. Am. Chem. Soc. 144, 3564–3571 (2022).
Richter, F., Leaver-Fay, A., Khare, S. D., Bjelic, S. & Baker, D. De novo enzyme design using Rosetta3. PLoS ONE 6, e19230 (2011).
Ribeiro, A. J. M. et al. Mechanism and Catalytic Site Atlas (M-CSA): a database of enzyme reaction mechanisms and active sites. Nucleic Acids Res. 46, D618–D623 (2018).
Alford, R. F. et al. The Rosetta all-atom energy function for macromolecular modeling and design. J. Chem. Theory Comput. 13, 3031–3048 (2017).
Elmsley, P., Lohkamp, B., Scott, W. G. & Cowtan, K. Features and development of Coot. Acta Crystallogr. D Biol. Crystallogr. 66, 486–501 (2010).
Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D Biol. Crystallogr. 66, 213–221 (2010).
Williams, C. J. et al. MolProbity: more and better reference data for improved all-atom structure validation. Protein Sci. 27, 293–315 (2018).
Woods, R. J. & Chappelle, R. Electrostatic potential atomic partial charges for condensed-phase simulations of carbohydrates. J. Mol. Struct. 527, 149–156 (2000).
Wang, J., Wang, W., Kollman, P. A. & Case, D. A. Automatic atom type and bond type perception in molecular mechanical calculations. J. Mol. Graph. Model. 25, 247–260 (2006).
Frisch, M. J. et al. Gaussian 16, Revision B.01 (Gaussian, 2016).
Wang, J., Wolf, R. M., Caldwell, J. W., Kollman, P. A. & Case, D. A. Development and testing of a general amber force field. J. Comput. Chem. 25, 1157–1174 (2004).
Olsson, M. H. M., Søndergaard, C. R., Rostkowski, M. & Jensen, J. H. PROPKA3: consistent treatment of internal and surface residues in empirical pKa predictions. J. Chem. Theory Comput. 7, 525–537 (2011).
Søndergaard, C. R., Olsson, M. H. M., Rostkowski, M. & Jensen, J. H. Improved treatment of ligands and coupling effects in empirical calculation and rationalization of pKa values. J. Chem. Theory Comput. 7, 2284–2295 (2011).
Izadi, S., Anandakrishnan, R. & Onufriev, A. V. Building Water Models: A Different Approach. J. Phys. Chem. Lett. 5, 3863–3871 (2014).
Listov, D. et al. Complete computational design of high-efficiency Kemp elimination enzymes. Zenodo https://doi.org/10.5281/zenodo.14563437 (2025).
Case, D. A. et al. Amber 2025 (Univ. California, San Francisco, 2025).
Tian, C. et al. Ff19SB: amino-acid-specific protein backbone parameters trained against quantum mechanics energy surfaces in solution. J. Chem. Theory Comput. 16, 528–552 (2020).
Schneider, T. & Stoll, E. Molecular-dynamics study of a three-dimensional one-component model for distortive phase transitions. Phys. Rev. B 17, 1302–1322 (1978).
Berendsen, H. J. C., Postma, J. P. M., Van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).
Ryckaert, J.-P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the Cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977).
Hopkins, C. W., Le Grand, S., Walker, R. C. & Roitberg, A. E. Long-time-step molecular dynamics through hydrogen mass repartitioning. J. Chem. Theory Comput. 11, 1864–1874 (2015).
Bauer, P. et al. Q6: a comprehensive toolkit for empirical valence bond and related free energy calculations. SoftwareX 7, 388–395 (2018).
Kaminski, G. A., Friesner, R. A., Tirado-Rives, J. & Jorgensen, W. L. Evaluation and reparametrization of the OPLS-AA force field for proteins via comparison with accurate quantum chemical calculations on peptides. J. Phys. Chem. B 105, 6474–6487 (2001).
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).
King, G. & Warshel, A. A surface constrained all-atom solvent model for effective simulations of polar solvents. J. Chem. Phys. 91, 3647–3661 (1989).
Lee, F. S. & Warshel, A. A local reaction field method for fast evaluation of long-range electrostatic interactions in molecular simulations. J. Chem. Phys. 97, 3100–3107 (1992).
McGibbon, R. T. et al. MDTraj: a modern open library for the analysis of molecular dynamics trajectories. Biophys. J. 109, 1528–1532 (2015).
Roe, D. R. & Cheatham, T. E. 3rd PTRAJ and CPPTRAJ: software for processing and analysis of molecular dynamics trajectory data. J. Chem. Theory Comput. 9, 3084–3095 (2013).
Case, D. A. et al. AmberTools. J. Chem. Inf. Model. 63, 6183–6191 (2023).
Schmidtke, P., Bidon-Chanal, A., Luque, F. J. & Barril, X. MDpocket: open-source cavity detection and characterization on molecular dynamics trajectories. Bioinformatics 27, 3276–3285 (2011).
Wagner, J. R. et al. POVME 3.0: software for mapping binding pocket flexibility. J. Chem. Theory Comput. 13, 4584–4592 (2017).
Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).
Fleishman, S. J. et al. RosettaScripts: a scripting language interface to the Rosetta macromolecular modeling suite. PLoS ONE 6, e20161 (2011).
