Lignin-Degrading Enzymes for Biomass Valorization | Slide
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Initially, I was planning to design new enzymes for plastic degradation and integrating them to organisms. But, then, I saw a lot of other people also following kind of same motivation, and so I thought maybe I can change the base, with keeping all the implementation plans intact— and came up with it after some brainstorming.
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Lignin, a complex and recalcitrant aromatic polymer, presents one of the biggest challenges in biomass valorization due to its resistance to breakdown. Current degradation methods, both chemical and enzymatic, are limited by inefficiency, lack of specificity, or instability under industrial conditions. This project explores the in-silico design of novel lignin-degrading enzymes using recent advances in AI-driven protein engineering. My goal is to generate 10–20 enzyme sequences optimized for lignin-derived substrates, specifically guaiacol and syringol, through a pipeline that includes backbone generation with RFDiffusion, sequence optimization via ProteinMPNN, and validation using molecular docking and dynamics simulations. This project combines sustainability goals with cutting-edge computational tools, aiming to develop enzymes that could one day be integrated into green industrial workflows for biofuel and chemical production.
Lignin is an abundant, heterogeneous, and highly cross-linked aromatic polymer that forms a protective matrix around cellulose in plant cell walls. Its recalcitrant nature makes it one of the most challenging components of biomass to break down.
Current methods for lignin degradation—such as chemical or thermal treatments—are not ideal. They require a lot of energy, are non-specific, and often produce low-value byproducts. Natural enzymes like laccases and peroxidases show potential, but they come with their own limitations, such as moderate catalytic efficiency, instability in industrial conditions, and a limited substrate scope.
This got me thinking—what if we could design better enzymes from scratch? Could we create more efficient, robust lignin-degrading enzymes using recent advancements in protein engineering? If successful, this could open new doors for biomass valorization, making biofuel production and green chemistry more viable.
The real appeal of this project lies in two key areas. First, from a sustainability perspective, efficient lignin degradation could help turn agricultural and forestry waste into valuable biofuels and chemicals. This could reduce dependence on fossil fuels and promote circular economy models. Second, there’s the exciting potential of advancing protein engineering. With the rise of AI-driven protein design, I feel like we’re entering a new era of enzyme engineering. I’d love to explore how tools like diffusion-based backbone generation and sequence optimization can help us create enzymes that are tailor-made for industrial applications. This is still an exploratory idea, but I’m eager to see how far I can push it.
The current state of knowledge around enzymatic lignin degradation highlights both the incredible complexity of lignin and the persistent challenges in breaking it down efficiently for industrial use. Lignin is an irregular, highly branched aromatic polymer, making up 15–30% of lignocellulosic biomass, and its recalcitrance poses a major barrier in accessing fermentable sugars from biomass [Gałązka et al., 2024]. Naturally, white-rot fungi and some bacteria have evolved oxidoreductases—particularly laccases and various peroxidases (LiP, MnP, VP)—to degrade lignin using oxidative radical chemistry [Bugg et al., 2024]. However, these enzymes often lack robustness under industrial conditions. They degrade poorly in high temperatures, extreme pH levels, or in the presence of lignin-derived inhibitors. Peroxidases also require hydrogen peroxide, adding complexity, while laccases often need costly mediators to access non-phenolic lignin structures [Pollegioni et al., 2015; Roth & Spiess, 2015].
To address these issues, significant effort has gone into engineering natural enzymes through rational design and directed evolution. However, these approaches are constrained by the limitations of the original enzyme scaffolds. This has led to growing interest in computational enzyme design. Traditionally, such efforts involved modifying existing structures using homology modeling, but recent advances are shifting the focus to de novo enzyme design—building entirely new enzymes from scratch [Zanghellini, A., 2014]. The emergence of AI-based tools like RFDiffusion has accelerated this shift, enabling more accurate and functional protein design by leveraging deep learning models trained on massive structural datasets. This project builds on that frontier, aiming to develop and evaluate novel lignin-degrading enzymes using modern AI-assisted de novo design pipelines.
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