About This Project
Methane removal at 1-100 MtCH₄ scale requires novel oxidation technologies, especially for atmospheric and dilute emissions. Methane monooxygenases (MMOs) enable biological CH₄ oxidation but face expression and activity challenges in heterologous hosts. Inspired by recently engineered miniaturized MMOs, we propose the computational design of de novo MMOs (dnMMOs), aiming for heterologously expressed, soluble, highly active enzymes that overcome current limitations and enhance MR efficiency.
Ask the Scientists
Join The DiscussionMotivating Factor
Novel technology for methane removal (MR) at 1-100 MtCH4 scale is needed, particularly for oxidizing atmospheric concentrations (2 ppm) [1][2] and emissions at 2-1000 ppm that are too dilute to be scalably oxidized with existing technology [3].
Several MR strategies have been proposed that would leverage biological CH4 oxidation in engineered contexts. One of such strategies is methane oxidation bioreactors based on using aerobic methanotrophs [4]. Such development would require heterologous expression, engineering, and/or context-specific characterization of methane monooxygenases (MMOs).
These MMOs exist in two forms: soluble (sMMOs) and particulate (pMMOs), with the former being better described at the structural and functional level [5]. Still, both types of MMOs impose significant challenges, particularly regarding heterologous expression in conventional host organisms, limiting their characterization and engineering [5] and preventing progress on technologies leveraging MMOs.
Specific Bottleneck
Expression of functional MMO in heterologous hosts faces many challenges. pMMO is a membrane-bound multi-subunit complex. The biophysical conditions and electron transfer pathways for high pMMO activity are undetermined, but thought to be strongly coupled to host biology [5]. Heterologously-expressed pMMO have shown low or no activity [6][7], and its PmoC subunit is toxic to cloning hosts [8], thus hindering efforts to engineer and study pMMO.
The sMMO is also a multi-subunit complex, for which functional heterologous expression is hindered by the need for chaperone proteins [9][10]. sMMO activity comparable to lower activities observed in its native context has been achieved in an optimized E. coli host [10], but those results are challenging to generalize due to host-specific biology.
An alternative pathway would be to pursue de novo engineering of an MMO that is readily heterologously expressible with high activity, thus circumventing the challenges associated with natural MMOs.
Actionable Goals
Recently, two engineered miniaturized soluble variants of sMMO (mini-sMMO) and pMMO (pMMO-mimic) from M. capsulatus (Bath) have been developed [11] [12]. They are built only from domains essential for or enhancing catalytic activity, scaffolded by human apoferritin (huHF). Strikingly, mini-sMMO exhibits an in vitro methanol turnover of 0.32 s−1 and leads to higher yield (3.0 g/L) and productivity (0.11 g/L/h) using recombinant E. coli in comparison to methanotrophs, under CH4 and air mixtures at 3:7 v/v [11]. While the turnover rate of pMMO-mimic is modest (0.084 s−1), at par with wt pMMO, it can be integrated into a hydrogel, greatly increasing their overall stability and cumulative production of methanol when compared to the non-scaffolded counterpart [12].
Building on this work, we propose to computational design de novo MMOs (dnMMOs), aiming to eliminate the need of a nanoparticle scaffold for soluble production and function and achieve higher methanol turnover in vitro.
Budget
The total budget considers the purchase of lab consumables and hiring personnel, as well as 10% of Institutional Overhead.
Meet the Team
Affiliates
Affiliates
Team Bio
The Khmelinskaia research group comprises 4 PhD students with experience in development of methods for computational protein engineering, one of which will be directly involved in this project. Likewise, The Ramirez-Sarmiento research group comprises 5 PhD students, one of which is being trained on computational and experimental enzyme engineering and will be directly involved in this project. Together, we are organizing an AI for Protein Design meeting in Chile in 2025.
Alena Khmelinskaia
Alena Khmelinskaia is a tenure-track Professor from Ludwig-Maximilians-Universität (LMU) München in Germany. She worked as a researcher at the Max Planck Institute of Biochemistry and obtained her doctorate in physics at LMU, before moving to the University of Washington in Seattle as a postdoctoral fellow. After building her own research group at the University of Bonn, she moved to LMU as a Professor of Biophysical Chemistry, where her group focuses on the de novo computational design of self-assembling protein materials to interface with biological systems, with emphasis on their dynamic and responsive properties. [Google Scholar]
Cesar A. Ramirez-Sarmiento
César A. Ramírez-Sarmiento is an Associate Professor at the Institute for Biological and Medical Engineering (IIBM) from Pontificia Universidad Católica de Chile and Adjunct Researcher at the Millenium Institute for Integrative Biology (iBio). He obtained his PhD in Molecular and Cellular Biology and Neurosciences at the University of Chile, and received doctoral training in biophysics and computational biology at the University of California San Diego. Using computational and experimental protein engineering and design tools, he currently works on the discovery, characterization, engineering, and design of bacterial enzymes that hydrolyze PET, a widely used plastic that accumulates as waste in landfills and natural environments at similar rates to its production. [Google Scholar]
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