Results for “aminolevulinic acid synthase”

DOE OSTI2023

The yeast ALA synthase C‐terminus positively controls enzyme structure and function

Abstract 5‐Aminolevulinic acid synthase (ALAS) is a pyridoxal 5′‐phosphate (PLP)‐dependent enzyme that catalyzes the first and rate‐limiting step of heme biosynthesis in α‐proteobacteria and several non‐plant eukaryotes. All ALAS homologs contain a highly conserved catalytic core, but eukaryotes also have a unique C‐terminal extension that plays a role in enzyme regulation. Several mutations in this region are implicated in multiple blood disorders in humans. In Saccharomyces cerevisiae ALAS (Hem1), the C‐terminal extension wraps around the homodimer core to contact conserved ALAS motifs proximal to the opposite active site. To determine the importance of these Hem1 C‐terminal interactions, we determined the crystal structure of S. cerevisiae Hem1 lacking the terminal 14 amino acids (Hem1 ΔCT). With truncation of the C‐terminal extension, we show structurally and biochemically that multiple catalytic motifs become flexible, including an antiparallel β‐sheet important to Fold‐Type I PLP‐dependent enzymes. The changes in protein conformation result in an altered cofactor microenvironment, decreased enzyme activity and catalytic efficiency, and ablation of subunit cooperativity. These findings suggest that the eukaryotic ALAS C‐terminus has a homolog‐specific role in mediating heme biosynthesis, indicating a mechanism for autoregulation that can be exploited to allosterically modulate heme biosynthesis in different organisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Design of diverse, functional mitochondrial targeting sequences across eukaryotic organisms using variational autoencoder

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Data for "Design of Diverse, Functional Mitochondrial Targeting Sequences Across Eukaryotic Organisms Using Variational Autoencoder"

Mitochondria play a key role in energy production and metabolism, making them a promising target for metabolic engineering and disease treatment. However, despite the known influence of passenger proteins on localization efficiency, only a few protein-localization tags have been characterized for mitochondrial targeting. To address this limitation, we leverage a Variational Autoencoder to design novel mitochondrial targeting sequences. In silico analysis reveals that a high fraction of the generated peptides (90.14%) are functional and possess features important for mitochondrial targeting. We characterize artificial peptides in four eukaryotic organisms and, as a proof-of-concept, demonstrate their utility in increasing 3-hydroxypropionic acid titers through pathway compartmentalization and improving 5-aminolevulinate synthase delivery by 1.62-fold and 4.76-fold, respectively. Moreover, we employ latent space interpolation to shed light on the evolutionary origins of dual-targeting sequences. Overall, our work demonstrates the potential of generative artificial intelligence for both fundamental research and practical applications in mitochondrial biology.

AI/ML↗