Cell-free Biosynthesis of Peptidomimetics

Lee, Kanghun; Willi, Jessica; Cho, Namjin; Kim, Inseon; Jewett, Michael C.; Lee, Joongoo

doi:10.1007/s12257-022-0268-5

Cited by 7 publications

(3 citation statements)

References 200 publications

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“…These minimal systems provide a highly customizable, highly controllable environment to characterize proteins particularly by light microscopy-based techniques, and will hence be easy to adapt for other emergent protein functions. Furthermore, we show that cell-free protein expression systems, which have recently become a vital technique in synthetic biology as they can deliver various peptide/protein libraries 26 for prototyping in a rapid and easy manner, can significantly speed up the experimental screening process.…”

Section: Introductionmentioning

confidence: 99%

Machine learning-aided design and screening of an emergent protein function in synthetic cells

Kohyama,

Frohn,

Babl

et al. 2024

Nat Commun

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Recently, utilization of Machine Learning (ML) has led to astonishing progress in computational protein design, bringing into reach the targeted engineering of proteins for industrial and biomedical applications. However, the design of proteins for emergent functions of core relevance to cells, such as the ability to spatiotemporally self-organize and thereby structure the cellular space, is still extremely challenging. While on the generative side conditional generative models and multi-state design are on the rise, for emergent functions there is a lack of tailored screening methods as typically needed in a protein design project, both computational and experimental. Here we describe a proof-of-principle of how such screening, in silico and in vitro, can be achieved for ML-generated variants of a protein that forms intracellular spatiotemporal patterns. For computational screening we use a structure-based divide-and-conquer approach to find the most promising candidates, while for the subsequent in vitro screening we use synthetic cell-mimics as established by Bottom-Up Synthetic Biology. We then show that the best screened candidate can indeed completely substitute the wildtype gene in Escherichia coli. These results raise great hopes for the next level of synthetic biology, where ML-designed synthetic proteins will be used to engineer cellular functions.

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Section: Introductionmentioning

confidence: 99%

Machine learning-aided design and screening of an emergent protein function in synthetic cells

Kohyama,

Frohn,

Babl

et al. 2024

Nat Commun

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“…The rational design of new peptidomimetics is highly dependent on our knowledge of the structure-function relation of ncAA. In fact, very recent studies on the topic of peptidomimetics have reinforced the significance of ncAAs [ 17 , 18 , 19 , 20 ]. Do and Link, for instance, highlight their role with a focus on ribosomally synthesized and post-translationally modified peptides (RiPPs).…”

Section: Introductionmentioning

confidence: 99%

Non-Canonical Amino Acids as Building Blocks for Peptidomimetics: Structure, Function, and Applications

et al. 2023

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This review provides a fresh overview of non-canonical amino acids and their applications in the design of peptidomimetics. Non-canonical amino acids appear widely distributed in nature and are known to enhance the stability of specific secondary structures and/or biological function. Contrary to the ubiquitous DNA-encoded amino acids, the structure and function of these residues are not fully understood. Here, results from experimental and molecular modelling approaches are gathered to classify several classes of non-canonical amino acids according to their ability to induce specific secondary structures yielding different biological functions and improved stability. Regarding side-chain modifications, symmetrical and asymmetrical α,α-dialkyl glycines, Cα to Cα cyclized amino acids, proline analogues, β-substituted amino acids, and α,β-dehydro amino acids are some of the non-canonical representatives addressed. Backbone modifications were also examined, especially those that result in retro-inverso peptidomimetics and depsipeptides. All this knowledge has an important application in the field of peptidomimetics, which is in continuous progress and promises to deliver new biologically active molecules and new materials in the near future.

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“…Numerous strategies and methodologies are involved in the design and development of peptide-analogs such as mutation or substitution (canonical and non-canonical amino acids), modification (D and L isomerization), addition (cell-penetrating peptides (CPPs) and other chemical additives), etc. [34][35][36] In this study, we have adopted the design and development of peptide-analogs that mimic the peptide-substrate by using canonical amino acids. 37 Smaller peptides (o20 amino acids) are generally bioactive and may act as an inhibitor for numerous other protein drug targets.…”

mentioning

confidence: 99%

De novo design of potential peptide analogs against the main protease of Omicron variant using in silico studies

M. L.,

Kumari,

Martinek

et al. 2024

Phys. Chem. Chem. Phys.

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Cell-free Biosynthesis of Peptidomimetics

Cited by 7 publications

References 200 publications

Machine learning-aided design and screening of an emergent protein function in synthetic cells

Machine learning-aided design and screening of an emergent protein function in synthetic cells

Non-Canonical Amino Acids as Building Blocks for Peptidomimetics: Structure, Function, and Applications

De novo design of potential peptide analogs against the main protease of Omicron variant using in silico studies

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