Workshop for AI-Powered Materials Discovery at Great Plains

AI-Driven Design of Methanotroph-Material Interfaces for Catalytic and Biomanufacturing Applications

Not scheduled

20m

University of South Dakota

AI-Driven Platforms and Digital Twins for Material Discovery and Manufacturing

Speaker

Dipayan Samanta (South Dakota School of Mines and Technology)

Description

Harnessing microbial systems for methane bioconversion necessitates a detailed understanding of enzyme architecture and microbial-surface interactions. Integrating microbial biotechnology, materials engineering, and artificial intelligence (AI) offers a transformative strategy for advancing sustainable methane bioconversion. In this study, we computationally remodeled the particulate methane monooxygenase (pMMO) of a model methanotroph, Methylosinus trichosporium OB3b, and identified multiple distinct active sites based on docking with methane homologs and tunnel analyses. Targeted mutagenesis of key residues, including B:Leu31Ser, B:Phe92Thr, B:Phe96Gly, B:Trp106Ala, and B:Tyr110Phe, significantly improved binding energies compared to the wild-type enzyme, suggesting the potential for enhanced catalytic rates. Furthermore, a comprehensive pangenomic analysis of 75 Type II methylotroph genomes revealed 256 exact core gene families and elucidated metabolic diversity across the group. We identified conserved biofilm-associated genes, including Type IVb pili genes (pilT, pilB, pilQ), quorum sensing regulators, and adhesion systems, underscoring genetic determinants critical for microbial attachment and colonization on surfaces. Moreover, methylotrophs differentially used conserved serine and ethylmalonyl-CoA (EMC) pathways. Unique adaptations, such as the acquisition of both glyoxylate and EMC pathway genes in Methylovirgula sp. 4MZ18, highlight the metabolic flexibility of these organisms under variable environments.
These discoveries lay the groundwork for engineering pMMO variants as immobilized catalysts on material surfaces, enabling the creation of efficient biointerfaces for methane oxidation. Building on this foundation, we aim to rationally design methanotroph-material systems that integrate enzymatic catalysis with microbial colonization dynamics. Realizing this vision will require AI and ML frameworks to predict enzyme-surface conjugation, model biofilm regulatory networks, and optimize material properties aligned with microbial functionalities. By integrating AI-driven predictive tools with experimental insights, we set the stage for next-generation smart catalytic surfaces, biomanufacturing platforms, and carbon capture technologies.