About This Project
Engineering an ultrastable carbonic anhydrase (CA) is an important challenge in efforts to make large-scale carbon capture tech cost-effective. While achieving much success, previous methods for CA engineering have been limited in many regards. This project sets out to overcome those limitations via the use of modern protein AI models, in-vivo continuous directed evolution, and untested rational design methods to push CA to its stability limits and develop climate-valuable CA variants.
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What is the context of this research?
Atmospheric CO2 removal (CDR) and point-source capture (PSC) of CO2 are well-accepted as being necessary for successfully decarbonizing within climate goals (1). Direct air capture (DAC) is a CDR pathway with ideal verifiability and durability. Both DAC and PSC are cost constrained, primarily by the CapEx of the gas contactor and the energy required to drive large swings in temperature or pH to regenerate CO2 from the capture material (2).
Those high cost and energy requirements are driven by a thermodynamic trade-off between the rate of CO2 absorption and the CO2 regeneration energy: CO2 capture materials with high absorption rate, which reduce cost by reducing the gas contactor size, typically have high CO2 regeneration energy, and vice versa (3).
What is the significance of this project?
Carbonic anhydrases (CAs) catalyze fast CO2 absorption in solvents with low CO2 regeneration energy, resolving the tradeoff described above (4). CA could reduce DAC and PSC cost by reducing parameter swing size or gas contactor size, if it were stable in DAC or PSC processes that may include high pH, temperature, or ionic strength. E.g., thermostable CA via protein engineering (PE) can already reduce PSC cost >30% (5,3).
AI-driven PE and screens of many natural variants are revolutionizing PE but haven’t been applied to CA. Ultrastable CAs produced using those tools likely could reduce DAC and PSC cost substantially. While modeling is needed to quantify application-specific benefits and target CA properties, PE for ultrastable CA can begin now and later be adapted to specific uses.
What are the goals of the project?
While modeling analyses are ultimately required to provide target properties for ultrastable CAs to be used in development of novel CA-enhanced DAC and PSC, initial efforts to use AI-based PE and screens of many variants should target many-fold CA stability improvements compared to the state-of-the-art while retaining high activity (kcat/kM ~108 M-1s-1). For a comprehensive discussion of state-of-the art CA engineering and performance, see (6) and (4).
Examples of the state of the art are:
90% activity retention after 24 hrs at pH 11.0 (9)
Stability demonstrations should be performed in solvents relevant to DAC and PSC, such as 10-20% K2CO3.
Details in solution statement.
Details in solution statement.
Dec 01, 2024
Meet the Team
I am an independent early-career researcher with a background in bioengineering. I have gained my skills and experience as a wet lab and dry lab researcher while having worked on a wide variety of molecular and computational biology research projects. The most relevant of those projects include in-vitro mutagenesis library screening of enhanced fluorescent and luminescent proteins, development of programmable genetic switches in E.coli for the iGEM synthetic biology competition, in-silico affinity maturation of antibodies, and de novo design of novel CD20 binders with RF Diffusion. My introduction to carbonic anhydrase (CA) protein engineering occurred during my time at a climate biotech startup, where I was tasked with a deep literature search on state-of-the-art CO2 capture biotechnologies and learned about how useful CA is as a biocatalyst in large-scale CO2 capture technologies. While I have carried out mutagenesis assays and designed de novo proteins in-silico, I have not yet worked with CA’s before and have not engineered proteins for thermostability. Overtime I have built out a molecular biology lab where I can carry out small-scale experiments independently. For this project, I plan to outsource all synthesis and cloning steps, while taking on the in-silico design, the CA assay of variants, and the small scale in-vivo continuous directed evolution steps myself for ultrastable CA discovery.
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