R.B. Leveson-Gower

Nature Catalysis, Published online: 20 July 2023; doi:10.1038/s41929-023-00986-5

Controlling the stereoselectivity in free-radical-mediated reactions is challenging. Now, a metalloredox biocatalysis strategy is reported that uses engineered cytochrome P450 enzymes for the unnatural asymmetric radical cyclization of α-haloesters to arenes.

Proceedings of the National Academy of Sciences, Volume 120, Issue 29, July 2023.

Nature Communications, Published online: 13 July 2023; doi:10.1038/s41467-023-39819-1

Viral occlusion bodies are robust protein crystals that encapsulate virions of some insect viruses. Here, the authors determine the nudivirus occlusion body structure and describe common principles of occlusion body structure.

Nature, Published online: 11 July 2023; doi:10.1038/s41586-023-06415-8

De novo design of protein structure and function with RFdiffusion

Nature Chemical Biology, Published online: 13 July 2023; doi:10.1038/s41589-023-01387-2

Tian et al. developed a bacterial orthogonal DNA replication system by harnessing the temperate phage GIL16 DNA replication machinery, which provides a powerful tool for continuous evolution in prokaryotic cells.

Nature Catalysis, Published online: 13 July 2023; doi:10.1038/s41929-023-00979-4

The direct regioselective oxidation of internal alkenes to ketones poses an important synthetic challenge. Now, directed evolution of a cytochrome P450 enzyme affords a ketone synthase that can efficiently oxidize internal arylalkenes directly to ketones with high chemo- and regioselectivity.

“My most important contribution to open science is perhaps our ongoing work advocating for the use of scientific color maps … I advise my students to expect that an experiment might take at least three attempts: One to fail and learn from. One to improve and get a grip on things. One to get it halfway right and acquire meaningful data.” Find out more about Felix Kaspar in his Introducing … Profile.

Nature, Published online: 05 July 2023; doi:10.1038/s41586-023-06288-x

An engineered minimal cell evolves to escape the negative consequences of genome streamlining.

Selective, one-step C-H activation of fatty acids from biomass is an attractive concept in sustainable chemistry. Biocatalysis has shown promise for generating high-value hydroxy acids but to date enzyme discovery has relied on laborious screening and produced limited hits, which predominantly oxidise the sub-terminal positions of fatty acids. Here we show that ancestral sequence reconstruction (ASR) is an effective tool to explore the sequence-activity landscape of a family of multi-domain, self-sufficient P450 monooxygenases. We resurrected eleven catalytically active CYP116B ancestors, each with a unique regioselectivity fingerprint that varied from sub-terminal in the older ancestors to mid-chain in the lineage leading to the extant, P450-TT. In lineages leading to extant enzymes in thermophiles, thermostability increased from ancestral to extant forms, as expected if thermophily had arisen de novo. Our studies show that ASR can be applied to multi-domain enzymes to develop active, self- sufficient monooxygenases as regioselective biocatalysts for fatty acid hydroxylation.

8-Azido-3,8-dideoxy-α/β-ᴅ-manno-oct-2-ulosonic acid (Kdo-8-N3) is a Kdo derivative used in metabolic labeling of lipopolysaccharide (LPS) structures found on the cell membrane of Gram-negative bacteria. Several studies have reported successful labeling of LPS using Kdo-8-N3 and visualization of LPS by a fluorescent reagent through click chemistry on a selection of Gram-negative bacteria such as Escherichia coli strains, Salmonella typhimurium, and Myxococcus xanthus. Motivated by the large promise of Kdo-8-N3 to be useful in the investigation of LPS biosynthesis and cell surface labeling across different strains, we set out to explore the variability in nature and efficiency of LPS labeling using Kdo-8-N3 in a variety of E. coli strains and serotypes. We optimized the chemical synthesis of Kdo-8-N3 and subsequently used Kdo-8-N3 to metabolically label pathogenic E. coli strains from commercial and clinical origin. Interestingly, different extents of labeling were observed in different E. coli strains, which seemed to be dependent also on growth media, and the majority of labeled LPS appears to be of the ‘rough’ LPS variant, as visualized using SDS-PAGE and fluorescence microscopy. This knowledge is important for future application of Kdo-8-N3 in the study of LPS biosynthesis and dynamics, especially when working with clinical isolates.

Protein–protein interactions (PPIs) are intriguing targets in drug discovery and development. Peptides are well suited to target PPIs, which typically present with large surface areas lacking distinct features and deep binding pockets. To improve binding interactions to these topologies by PPI-focused therapeutics and advance their development, potential ligands can be equipped with electrophilic groups to enable binding through covalent mechanisms of action. We report a strategy termed electrophile scanning to identify reactivity hotspots in a known peptide ligand. Cysteine mutants of the ligand are used to install protein-reactive modifiers via a palladium oxidative addition complex (Pd-OAC). Reactivity hotspots are revealed by cross-linking reactions with the target protein under physiological conditions. In a system with the 9-mer peptide antigen VL9 and MHC class I receptor HLA-E, we identify two reactivity hotspots that afford up to 87% conversion to the protein–peptide conjugate within 4 hours. The reactions are specific to the target protein in vitro and dependent on the peptide sequence. Moreover, the cross-linked peptide successfully inhibits molecular recognition of HLA-E by CD94─NKG2A possibly due to structural changes enacted at the PPI interface. The results illustrate the potential of electrophile scanning as a tool for rapid discovery and development of covalent peptide binders.

Directed evolution facilitates enzyme engineering via iterative rounds of mutagenesis. Despite the wide applications of high-throughput screening, building “smart libraries” to effectively identify beneficial variants remains a major challenge in the community. Here, we developed a new computational directed evolution protocol based on EnzyHTP, a software we have previously reported to automate enzyme modeling. To enhance the throughput efficiency, we implemented an adaptive resource allocation strategy that dynamically allocates different types of computing resources (e.g., GPU/CPU) based on the specific need of an enzyme modeling sub-task in the workflow. We implemented the strategy as a Python library and tested the library using fluoroacetate dehalogenase as a model enzyme. The results show that comparing to fixed resource allocation where both CPU and GPU are on-call for use during the entire workflow, applying adaptive resource allocation can save 87% CPU hours and 14% GPU hours. Furthermore, we constructed a computational directed evolution protocol under the framework of adaptive resource allocation. The workflow was tested against two rounds of mutational screening in the directed evolution experiments of Kemp eliminase with a total of 184 mutants. Using folding stability and electrostatic stabilization energy as computational readout, we reproduced three out of the four experimentally-observed target variants. Enabled by the workflow, the entire computation task (i.e., 18.4 μs MD and 18,400 QM single point calculations) completes in three days of wall clock time using ~30 GPUs and ~1000 CPUs.

Nature Chemistry, Published online: 19 June 2023; doi:10.1038/s41557-023-01262-6

Protein translation is the ultimate paradigm for sequence-defined polymer synthesis. To introduce non-canonical monomers into the genetic code of living organisms, pairs of biomolecules known as aminoacyl-tRNA synthetases (aaRSs) and transfer RNAs (tRNAs) are required. The discovery and engineering of five such pairs, that do not interfere with each other or the aaRS–tRNA pairs of a bacterial host, sets the stage for highly modified genetically encoded biopolymers.