In a groundbreaking study published in Nature Chemical Biology, researchers have employed artificial intelligence (AI) tools to uncover how enzymes responsible for producing lasso peptides function. These lasso peptides, naturally occurring molecules produced by bacteria, possess an intriguing knot-like structure that renders them incredibly stable and potentially useful in a wide range of therapeutic applications, from antibacterial to anti-cancer treatments.
Lasso peptides are created through a ribosomal process, where amino acids are strung together into a linear form. Two key enzymes, a peptidase and a cyclase, then collaborate to convert this linear peptide into the characteristic knotted structure. Despite decades of research, understanding how cyclase enzymes achieve this complex folding has been elusive due to challenges with purifying and studying the enzymes.
One success story in this field involved the enzyme FusC, which the Mitchell lab at the University of Illinois managed to purify and study in 2019. However, the exact structural details of FusC remained unknown, hindering a full understanding of its function. Enter artificial intelligence—using the powerful AlphaFold system, the team was able to predict the structure of FusC and, with the help of other AI-based tools like RODEO, they identified critical regions of the enzyme’s active site essential for its interaction with lasso peptides.
Computational Simulations and Their Impact
The study didn’t stop at structural predictions. Researchers also employed molecular dynamics simulations, which allowed them to visualize the atomic-level interactions between the enzyme and the peptide. This approach, made possible through the Folding@home project, represents the first time these simulations have been used to study lasso peptides. The insights gained from this method provided crucial information about which regions of the enzyme, particularly the backwall of the active site, are key to folding the lasso peptide.
Notably, the team identified that a particular region of the enzyme, known as helix 11, played an important role in the folding process. By introducing mutations in this region, they were able to create variants of FusC capable of folding lasso peptides that the original enzyme could not. This experimental validation not only confirmed the computational model but also opened the door to engineering cyclases that could produce new lasso peptides with novel properties.
Broad Implications for Therapeutic Applications
Lasso peptides have long attracted interest due to their stability and therapeutic potential. Their slipknot-like structure makes them resistant to heat, enzymatic degradation, and other extreme conditions, making them excellent candidates for drug development. Researchers demonstrated that their newly engineered enzymes could create lasso peptides that otherwise couldn’t be made, significantly expanding the scope of potential therapeutic molecules.
In collaboration with Lassogen, a biotech company based in San Diego, the researchers engineered a different cyclase, McjC, to produce a potent inhibitor of cancer-promoting integrins. This proof-of-concept suggests that with further engineering, cyclases could be tailored to create a wide variety of lasso peptides with diverse therapeutic applications, including in cancer and infectious disease treatments.
AI and Interdisciplinary Collaborations: A New Era for Enzyme Engineering
This study showcases how artificial intelligence, combined with advanced computational methods, is changing the landscape of enzyme engineering. "Our work would not have been possible without access to powerful computing and recent advances in artificial intelligence and cell-free biosynthetic methods," noted Douglas Mitchell, a professor of chemistry involved in the study. The collaboration between AI experts and biochemists highlights the potential of interdisciplinary approaches to tackle complex biochemical problems.
Looking forward, the insights gained from this research could be applied to engineer enzymes for various peptide-based drugs, which may revolutionize pharmaceutical development. By optimizing the process of lasso peptide production, these AI-driven methods offer a promising avenue for generating new therapeutics that are more stable, potent, and targeted than traditional drugs.
This research marks a pivotal moment in the understanding of enzyme mechanisms and the broader potential of AI in biochemical studies. As computational power continues to grow and AI tools become more sophisticated, we can expect even more groundbreaking discoveries in the field of enzyme engineering and beyond.
Credit: Nature Chemical Biology (2024). DOI: 10.1038/s41589-024-01727-w
Source: Susanna E. Barrett et al, Substrate interactions guide cyclase engineering and lasso peptide diversification, Nature Chemical Biology (2024). DOI: 10.1038/s41589-024-01727-w
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