Computational design of an enzyme catalyst for a stereoselective bimolecular Diels-Alder reaction

By Justin B. Siegel, A. Zanghellini, H. Lovick, G. Kiss, A. Lambert, J. L. St.Clair, Jasmine L. Gallaher, Donald Hilvert, M. Gelb, Barry L. Stoddard and 3 more. 2010

884 citations recorded by the source corpus.

Biocatalytic Boost Enzymes tend to direct reactions toward specific products much more selectively than synthetic catalysts. Unfortunately, this selectivity has evolved for cellular purposes and may not promote the sorts of reactions chemists are seeking to enhance (see the Perspective by Lutz). Siegel et al. (p. 309) now describe the design of enzymes that catalyze the bimolecular Diels-Alder reaction, a carbon-carbon bond formation reaction that is central to organic synthesis but unknown in natural metabolism. The enzymes display high stereoselectivity and substrate specificity, and an x-ray structure of the most active enzyme confirms that the structure matches the design. Savile et al. (p. 305, published online 17 June) applied a directed evolution approach to modify an existing transaminase enzyme so that it recognized a complex ketone in place of its smaller native substrate, and could tolerate the high temperature and organic cosolvent necessary to dissolve this ketone. This biocatalytic reaction improved the production efficiency of a drug that treats diabetes. Synthetic enzymes catalyze a carbon-carbon bond-forming reaction with high stereoselectivity and substrate specificity. The Diels-Alder reaction is a cornerstone in organic synthesis, forming two carbon-carbon bonds and up to four new stereogenic centers in one step. No naturally occurring enzymes have been shown to catalyze bimolecular Diels-Alder reactions. We describe the de novo computational design and experimental characterization of enzymes catalyzing a bimolecular Diels-Alder reaction with high stereoselectivity and substrate specificity. X-ray crystallography confirms that the structure matches the design for the most active of the enzymes, and binding site substitutions reprogram the substrate specificity. Designed stereoselective catalysts for carbon-carbon bond-forming reactions should be broadly useful in synthetic chemistry.

Subfields: Organic Chemistry, Pharmacology

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Citations in the corpus

Protein sequence design with a learned potential (2022, background): “omputational protein design has emerged as a powerful tool for rational protein design, enabling signi ﬁ cant achievements in the engineering of therapeutics 1 – 3 , 4 – 6 , enzymes 7,8 , and more 9 – 11 .”
ProDCoNN: Protein design using a convolutional neural network (2019, background): “3,4,7,12 The design will usually start from a known structure, which will be kept in the process, and modify part of the sequence to achieve a new function.”
Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem (2022, neutral): “[32] Justin B Siegel, Alexandre Zanghellini, Helena M Lovick, Gert Kiss, Abigail R Lambert, Jennifer L StClair, Jasmine L Gallaher, Donald Hilvert, Michael H Gelb, Barry L Stoddard, Kendall N Houk, Forrest E Michael, and David Baker.”
Scaffolding protein functional sites using deep learning (2022, background): “…site geometries and amino acid identities which produce the desired activity--for enzymes this can be done using quantum chemistry calculations (1–3) and for protein binders by fragment docking calculations (4, 5); alternatively, functional sites can be extracted from a native protein having the…”
How to Hallucinate Functional Proteins (2019, background): “Like directed evolution, this approach has enjoyed wide success and has resulted in useful proteins designed from physical principals (15; 6; 3; 16; 17).”
Industrial applications of enzyme biocatalysis: Current status and future aspects. (2015, methodology): “zymes with new functions (Rothlisberger et al., 2008; Siegel et al., 2010), broadening the repertoire of enzymes.”
Masked Inverse Folding with Sequence Transfer for Protein Representation Learning (2023, background): “The ability to generate amino-acid sequences that fold into a desired structure is useful for developing novel therapeutics (Chevalier et al., 2017), biosensors (Quijano-Rubio et al., 2021), industrial enzymes (Siegel et al., 2010), and targeted small molecules (Lucas & Kortemme, 2020).”
Generative modeling for protein structures (2018, background): “Advancements in computational modeling methods have led to remarkable outcomes in protein design including the development of new therapies [1, 2], enzymes [3, 4, 5], small-molecule binders [6], and biosensors [7].”
Energy functions in de novo protein design: current challenges and future prospects. (2013, background): “Progress in structural specificity and stability was accompanied by novel proteins designed with functions ranging from protein binding (3, 40, 54, 59, 74, 93, 105) to catalytic activities (7, 29, 50, 62, 84, 106) to conformational switches (1, 2).”
Deep learning methods in protein structure prediction (2020, background): “The ability to disentangle this relationship can potentially be used to identify, or even design, proteins able to bind speciﬁc targets [3], catalyse novel reactions [4] or guide advances in biology, biotechnology and medicine [5], e.g. editing speciﬁc locations of the genome with CRISPR-Cas9 [6].”
Designing Peptides on a Quantum Computer (2019, background): “…protein topologies ( 2 , 3 ) , large macromolecular assemblies ( 4 – 7 ) , proteins with the ability to bind to toxic small molecules ( 8 , 9 ) or to other proteins of therapeutic interest ( 10 , 11 ) , and enzymes able to catalyze reactions that no known natural enzyme can catalyze ( 12 , 13 ) .”