About This Project

AI-driven protein design is undergoing a transformation, driven by recent breakthroughs such as AlphaFold. However, the application of AI for engineering enzymes for climate-related applications like carbonic anhydrase remains a challenge that necessitates the design of an intelligent pipeline and computational metrics. In this project, we will leverage existing AI architectures, propose new AI architectures and MD tools to engineer more stable variants of CAs.

Ask the Scientists

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:

• temperature stability
  • 203-day half-life at 60 ˚C (7)
  • 73% activity retention after 24 hrs day at 80˚ C (4,8)
• pH stability
  • 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.