FRAGLER: A Fragment Recycler Application Enabling Rapid and Scalable Modular DNA Assembly

Öling, David; Lan-Chow-Wing, Olivier; Martella, Andrea; Gilberto, Samuel; Chi, Jordi; Cooper, Emily A.; Edström, Tora; Peng, Bo; Sumner, Dean; Karlsson, Fredrik; Volkov, P.; Webster, Cecelia; Roth, Robert

doi:10.1021/acssynbio.2c00106

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…New approaches to probe the mutational tolerance of proteins experimentally and computationally fuel interest in methods for efficient synthesis of combinatorial mutation libraries (Tretyachenko et al 2020;Wrenbeck et al 2016;Kirby et al 2021;Daffern et al 2023;Jacobs et al 2015;Shimko, Fordyce, and Orenstein 2020;Püllmann et al 2019;Yamamoto et al 2020;Alejaldre, Pelletier, and Quaglia 2021;Sidore et al 2020;Plesa et al 2018;Öling et al 2022;Lund et al 2024). GGAssembler exploits the fact that desired diversity in active sites is typically clustered in several contiguous epitopes (e.g., antibody CDRs or enzyme active-site loops) that are separated by immutable regions.…”

Section: Discussionmentioning

confidence: 99%

GGAssembler: precise and economical design and synthesis of combinatorial mutation libraries

Hoch,

Netzer,

Weinstein

et al. 2023

Preprint

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Golden Gate (GG) assembly is an effective tool for the seamless assembly of genes from DNA fragments. Its use for generating combinatorial mutagenic libraries is much less developed. We describe a new method called GGAssembler for designing gene libraries that encode combinatorial mutations through GG gene assembly. GGAssembler uses a graph-theoretical approach to find the most cost-effective way to assemble diversity from DNA fragments. We demonstrate the accuracy, efficiency, low bias, and cost-effectiveness of GGAssembler by encoding hundreds of thousands of variants of camelid single-domain antibodies. We envision that GGAssembler will advance protein science and engineering by enabling the construction of diverse protein libraries with precise control over mutations. Source code is freely available at: https://github.com/Fleishman-Lab/GGAssembler.

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Section: Discussionmentioning

confidence: 99%

GGAssembler: precise and economical design and synthesis of combinatorial mutation libraries

Hoch,

Netzer,

Weinstein

et al. 2023

Preprint

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“…This allows whole phage genomes to be assembled in a single step, bypassing the need to propagate sub‐assemblies that are likely to be highly toxic to bacterial hosts. Other published applications of DAD to design high‐complexity assemblies include the highly modular assembly of individual proteins for making high‐complexity or combinatorial libraries (Oling et al., 2022), the simplified assembly of highly repetitive DNA elements such as CRISPR arrays (Volke et al., 2022; Yuan et al., 2022), and the simplification of construction for large (10 kb+) plasmid expression systems in yeast (Szent‐Gyorgyi et al., 2022).…”

Section: Commentarymentioning

confidence: 99%

High‐Complexity One‐Pot Golden Gate Assembly

Sikkema,

Tabatabaei,

Lee

et al. 2023

Current Protocols

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Golden Gate Assembly is a flexible method of DNA assembly and cloning that permits the joining of multiple fragments in a single reaction through predefined connections. The method depends on cutting DNA using a Type IIS restriction enzyme, which cuts outside its recognition site and therefore can generate overhangs of any sequence while separating the recognition site from the generated fragment. By choosing compatible fusion sites, Golden Gate permits the joining of multiple DNA fragments in a defined order in a single reaction. Conventionally, this method has been used to join five to eight fragments in a single assembly round, with yield and accuracy dropping off rapidly for more complex assemblies. Recently, we demonstrated the application of comprehensive measurements of ligation fidelity and bias data using data‐optimized assembly design (DAD) to enable a high degree of assembly accuracy for very complex assemblies with the simultaneous joining of as many as 52 fragments in one reaction. Here, we describe methods for applying DAD principles and online tools to evaluate the fidelity of existing fusion site sets and assembly standards, selecting new optimal sets, and adding fusion sites to existing assemblies. We further describe the application of DAD to divide known sequences at optimal points, including designing one‐pot assemblies of small genomes. Using the T7 bacteriophage genome as an example, we present a protocol that includes removal of native Type IIS sites (domestication) simultaneously with parts generation by PCR. Finally, we present recommended cycling protocols for assemblies of medium to high complexity (12‐36 fragments), methods for producing high‐quality parts, examples highlighting the importance of DNA purity and fragment stoichiometric balance for optimal assembly outcomes, and methods for assessing assembly success. © 2023 New England Biolabs, Inc. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Assessing the fidelity of an overhang set using the NEBridge Ligase Fidelity ViewerBasic Protocol 2: Generating a high‐fidelity overhang set using the NEBridge GetSet ToolAlternate Protocol 1: Expanding an existing overhang set using the NEBridge GetSet ToolBasic Protocol 3: Dividing a genomic sequence with optimal fusion sites using the NEBridge SplitSet ToolBasic Protocol 4: One‐pot Golden Gate Assembly of 12 fragments into a destination plasmidAlternate Protocol 2: One‐pot Golden Gate Assembly of 24+ fragments into a destination plasmidBasic Protocol 5: One‐pot Golden Gate Assembly of the T7 bacteriophage genome from 12+ partsSupport Protocol 1: Generation of high‐purity amplicons for assemblySupport Protocol 2: Cloning assembly parts into a holding vectorSupport Protocol 3: Quantifying DNA concentration using a Qubit 4 fluorometerSupport Protocol 4: Visualizing large assemblies via TapeStationSupport Protocol 5: Validating phage genome assemblies via ONT long‐read sequencing

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GGAssembler: Precise and economical design and synthesis of combinatorial mutation libraries

Hoch,

Netzer,

Weinstein

et al. 2024

Protein Science

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Golden Gate assembly (GGA) can seamlessly generate full‐length genes from DNA fragments. In principle, GGA could be used to design combinatorial mutation libraries for protein engineering, but creating accurate, complex, and cost‐effective libraries has been challenging. We present GGAssembler, a graph‐theoretical method for economical design of DNA fragments that assemble a combinatorial library that encodes any desired diversity. We used GGAssembler for one‐pot in vitro assembly of camelid antibody libraries comprising >105 variants with DNA costs <0.007$ per variant and dropping significantly with increased library complexity. >93% of the desired variants were present in the assembly product and >99% were represented within the expected order of magnitude as verified by deep sequencing. The GGAssembler workflow is, therefore, an accurate approach for generating complex variant libraries that may drastically reduce costs and accelerate discovery and optimization of antibodies, enzymes and other proteins. The workflow is accessible through a Google Colab notebook at https://github.com/Fleishman-Lab/GGAssembler.

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FRAGLER: A Fragment Recycler Application Enabling Rapid and Scalable Modular DNA Assembly

Cited by 5 publications

References 24 publications

GGAssembler: precise and economical design and synthesis of combinatorial mutation libraries

GGAssembler: precise and economical design and synthesis of combinatorial mutation libraries

High‐Complexity One‐Pot Golden Gate Assembly

GGAssembler: Precise and economical design and synthesis of combinatorial mutation libraries

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