Yeh, A. H.-W. et al. De novo design of luciferases using deep learning. Nature 614, 774–780 (2023).
Article CAS PubMed PubMed Central Google Scholar
Tiessen, A., Pérez-Rodríguez, P. & Delaye-Arredondo, L. J. Mathematical modeling and comparison of protein size distribution in different plant, animal, fungal and microbial species reveals a negative correlation between protein size and protein number, thus providing insight into the evolution of proteomes. BMC Res. Notes 5, 85 (2012).
Article CAS PubMed PubMed Central Google Scholar
Mandecki, W. A method for construction of long randomized open reading frames and polypeptides. Protein Eng. 3, 221–226 (1990).
Article CAS PubMed Google Scholar
Prijambada, I. D. et al. Solubility of artificial proteins with random sequences. FEBS Lett. 382, 21–25 (1996).
Article CAS PubMed Google Scholar
Keefe, A. D. & Szostak, J. W. Functional proteins from a random-sequence library. Nature 410, 715–718 (2001).
Article CAS PubMed PubMed Central Google Scholar
Jensen, R. A. Enzyme recruitment in evolution of new function. Annu. Rev. Microbiol. 30, 409–425 (1976).
Article CAS PubMed Google Scholar
O’Brien, P. J. & Herschlag, D. Catalytic promiscuity and the evolution of new enzymatic activities. Chem. Biol. 6, R91–R105 (1999).
Colin, P.-Y. et al. Ultrahigh-throughput discovery of promiscuous enzymes by picodroplet functional metagenomics. Nat. Commun. 6, 10008 (2015).
Article CAS PubMed Google Scholar
Seelig, B. & Szostak, J. W. Selection and evolution of enzymes from a partially randomized non-catalytic scaffold. Nature 448, 828–831 (2007).
Article CAS PubMed PubMed Central Google Scholar
Chao, F.-A. et al. Structure and dynamics of a primordial catalytic fold generated by in vitro evolution. Nat. Chem. Biol. 9, 81–83 (2013).
Article CAS PubMed Google Scholar
Hilvert, D. Design of protein catalysts. Annu. Rev. Biochem. 82, 447–470 (2013).
Article CAS PubMed Google Scholar
Eck, R. V. & Dayhoff, M. O. Evolution of the structure of ferredoxin based on living relics of primitive amino acid sequences. Science 152, 363–366 (1966).
Article CAS PubMed Google Scholar
Romero Romero, M. L., Rabin, A. & Tawfik, D. S. Functional proteins from short peptides: Dayhoff’s hypothesis turns 50. Angew. Chem. Int. Ed. 55, 15966–15971 (2016).
Wei, Y., Kim, S., Fela, D., Baum, J. & Hecht, M. H. Solution structure of a de novo protein from a designed combinatorial library. Proc. Natl Acad. Sci. USA 100, 13270–13273 (2003).
Article CAS PubMed PubMed Central Google Scholar
Wei, Y. et al. Stably folded de novo proteins from a designed combinatorial library. Protein Sci. 12, 92–102 (2003).
Article CAS PubMed PubMed Central Google Scholar
Ferris, J. P. Catalysis and prebiotic RNA synthesis. Orig. Life Evol. Biosph. 23, 307–315 (1993).
Article CAS PubMed Google Scholar
Bray, M. S. et al. Multiple prebiotic metals mediate translation. Proc. Natl Acad. Sci. USA 115, 12164–12169 (2018).
Article CAS PubMed PubMed Central Google Scholar
Muchowska, K. B. et al. Metals promote sequences of the reverse Krebs cycle. Nat. Ecol. Evol. 1, 1716–1721 (2017).
Article PubMed PubMed Central Google Scholar
Kamtekar, S., Schiffer, J. M., Xiong, H., Babik, J. M. & Hecht, M. H. Protein design by binary patterning of polar and nonpolar amino acids. Science 262, 1680–1685 (1993).
Article CAS PubMed Google Scholar
Karas, C. & Hecht, M. A strategy for combinatorial cavity design in de novo proteins. Life 10, 9 (2020).
Article CAS PubMed PubMed Central Google Scholar
Colin, P.-Y., Zinchenko, A. & Hollfelder, F. Enzyme engineering in biomimetic compartments. Curr. Opin. Struct. Biol. 33, 42–51 (2015).
Article CAS PubMed Google Scholar
Gantz, M., Aleku, G. A. & Hollfelder, F. Ultrahigh-throughput screening in microfluidic droplets: a faster route to new enzymes. Trends Biochem. Sci. 47, 451–452 (2022).
Article CAS PubMed Google Scholar
Baret, J.-C. et al. Fluorescence-activated droplet sorting (FADS): efficient microfluidic cell sorting based on enzymatic activity. Lab Chip 9, 1850–1858 (2009).
Fowler, D. M. et al. High-resolution mapping of protein sequence-function relationships. Nat. Methods 7, 741–746 (2010).
Article CAS PubMed PubMed Central Google Scholar
Hietpas, R. T., Jensen, J. D. & Bolon, D. N. A. Experimental illumination of a fitness landscape. Proc. Natl Acad. Sci. USA 108, 7896–7901 (2011).
Article CAS PubMed PubMed Central Google Scholar
Larsen, A. C. et al. A general strategy for expanding polymerase function by droplet microfluidics. Nat. Commun. 7, 11235 (2016).
Article CAS PubMed PubMed Central Google Scholar
Check Hayden, E. Chemistry: designer debacle. Nature 453, 275–278 (2008).
O’Brien, P. J. & Herschlag, D. Functional interrelationships in the alkaline phosphatase superfamily: phosphodiesterase activity of Escherichia coli alkaline phosphatase. Biochemistry 40, 5691–5699 (2001).
Nielsen, L. D., Monard, D. & Rickenberg, H. V. Cyclic 3′,5′-adenosine monophosphate phosphodiesterase of Escherichia coli. J. Bacteriol. 116, 857–866 (1973).
Article CAS PubMed PubMed Central Google Scholar
Imamura, R. et al. Identification of the cpdA gene encoding cyclic 3ʹ,5ʹ-adenosine monophosphate phosphodiesterase in Escherichia coli. J. Biol. Chem. 271, 25423–25429 (1996).
Article CAS PubMed Google Scholar
Schwer, B., Khalid, F. & Shuman, S. Mechanistic insights into the manganese-dependent phosphodiesterase activity of yeast Dbr1 with bis-p-nitrophenylphosphate and branched RNA substrates. RNA 22, 1819–1827 (2016).
Article CAS PubMed PubMed Central Google Scholar
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
Article CAS PubMed PubMed Central Google Scholar
Evans, R. et al. Protein complex prediction with AlphaFold-Multimer. Preprint at bioRxiv https://doi.org/10.1101/2021.10.04.463034 (2022).
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
Article CAS PubMed PubMed Central Google Scholar
Lin, Z. et al. Evolutionary-scale prediction of atomic-level protein structure with a language model. Science 379, 1123–1130 (2023).
Article CAS PubMed Google Scholar
Hou, M. et al. Protein multiple conformation prediction using multi-objective evolution algorithm. Interdiscip. Sci. Comput. Life Sci. https://doi.org/10.1007/s12539-023-00597-5 (2024).
Bar-Even, A. et al. The moderately efficient enzyme: evolutionary and physicochemical trends shaping enzyme parameters. Biochemistry 50, 4402–4410 (2011).
Article CAS PubMed Google Scholar
Copley, S. D., Newton, M. S. & Widney, K. A. How to recruit a promiscuous enzyme to serve a new function. Biochemistry 62, 300–308 (2023).
Article CAS PubMed Google Scholar
Radzicka, A. & Wolfenden, R. A proficient enzyme. Science 267, 90–93 (1995).
留言 (0)