Summary: Researchers have produced the first large-scale map showing how more than 140,000 mutations influence the formation of amyloid beta fibrils, revealing the energetic structure of the fleeting transition state that precedes plaque formation in Alzheimer’s disease. Using high-throughput DNA synthesis, engineered yeast assays, and machine learning, the team reconstructed a detailed energy landscape of Aβ42 aggregation, pinpointing the C-terminal region as the primary initiation site for fibril formation.
The findings highlight a concrete region of the amyloid beta peptide to target for future therapeutics and introduce a versatile experimental framework that can be adapted to study protein aggregation across other neurodegenerative diseases.
Key facts:
- Unprecedented scale: More than 140,000 variants of the Aβ42 peptide were assayed to map aggregation behavior across nearly all possible amino acid substitutions.
- Early molecular event located: Aggregation initiates in the hydrophobic C-terminal region of Aβ42, identifying a focal point for therapeutic intervention.
- Methodology transferable: The combined genomics, kinetic-selection, and machine learning approach can be applied to study transition states and protein aggregation in other diseases.
Source: Wellcome Trust Sanger Institute
Overview
A collaborative study from the Wellcome Sanger Institute, the Centre for Genomic Regulation (CRG), and the Institute for Bioengineering of Catalonia (IBEC) maps the energetic landscape of amyloid-β (Aβ42) nucleation, the critical early step that leads to toxic fibril and plaque formation in Alzheimer’s disease. Published on 11 June in Science Advances, the study combines massively parallel mutagenesis, a kinetic-selection assay performed in genetically engineered yeast, and machine learning to measure how sequence changes alter the rate and energetics of fibril nucleation.
Amyloid beta peptides are short chains of amino acids that can self-associate into amyloid fibrils. Over time these fibrils accumulate as plaques—hallmarks of Alzheimer’s and many other neurodegenerative conditions. The conversion from soluble peptide to ordered fibril requires overcoming an energetic barrier; the short-lived, high-energy intermediate immediately before fibril formation is called the transition state. Because this state is rare and transient, it has been difficult to study using traditional biochemical or structural approaches.
To address this, the research team systematically altered the Aβ42 sequence and measured how each mutation affected the nucleation rate. They combined high-throughput DNA synthesis to generate variant libraries with a yeast-based kinetic-selection assay that reports on aggregation rate and applied machine learning to integrate the data into a comprehensive model of the reaction’s energy landscape.
Measuring nucleation rates for over 140,000 Aβ42 variants allowed the authors to quantify changes in activation free energy for all possible single amino acid substitutions and to evaluate hundreds of energetic couplings between mutations. The resulting map reveals that only a limited set of interactions dominate the kinetics of nucleation: strong energetic couplings cluster in a short, hydrophobic C-terminal segment of Aβ42. This indicates that the transition state is structured primarily in that region, and that nucleation begins at the peptide’s C-terminus.
Because the C-terminal core appears to seed aggregation, the authors propose that therapeutics designed to disrupt interactions in this region—or to stabilize non-aggregating conformations—could be effective strategies to prevent the transition into fibril formation and, ultimately, slow plaque-driven neurodegeneration.
Beyond Alzheimer’s specifically, the paper establishes a broadly applicable pipeline for interrogating protein transition states at scale. The combination of combinatorial mutagenesis, kinetic selection, and machine learning can be adapted to study the energetic architecture of other amyloid transition states and, with suitable assays, additional protein folding or reaction transition states implicated in disease.
Dr Anna Arutyunyan, co-first author and Postdoctoral Fellow at the Wellcome Sanger Institute, commented that by measuring the effects of over 140,000 protein variants, the team produced the first high-resolution map of how individual mutations remodel the energy landscape of Aβ aggregation and reveal the transition state of a reaction central to Alzheimer’s disease.
Dr Benedetta Bolognesi, co-senior author and Group Leader at IBEC, noted two innovations: a kinetic-selection method that captures real rate-limiting steps for thousands of reactions in parallel, and the combinatorial mutation strategy that uncovers interactions between distant sequence positions as nucleation begins. Together, these advances enable a mechanistic view of the initiating events of protein aggregation.
Professor Ben Lehner, co-senior author and Head of Generative and Synthetic Genomics at the Wellcome Sanger Institute, emphasized that the study’s scale and approach open new possibilities for revealing transition state structures for other proteins implicated in neurodegeneration.
Funding: This research was part-funded by the La Caixa Research Foundation project ‘DeepAmyloids’. A full list of funders is provided in the publication’s acknowledgements.
About this genetics and Alzheimer’s disease research news
Author: Susannah Young
Source: Wellcome Trust Sanger Institute
Contact: Susannah Young – Wellcome Trust Sanger Institute
Image credit: Neuroscience News
Original research (open access): “Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state” by Anna Arutyunyan et al., Science Advances. DOI: 10.1126/sciadv.adv1422
Abstract
Massively parallel genetic perturbation suggests the energetic structure of an amyloid-β transition state
Amyloid aggregates are pathological hallmarks of many human diseases, but how soluble proteins nucleate to form amyloids is poorly understood. Here, we use combinatorial mutagenesis, a kinetic selection assay, and machine learning to massively perturb the energetics of the nucleation reaction of amyloid-β (Aβ42), the protein that aggregates in Alzheimer’s disease. In total, we measure the nucleation rates of >140,000 variants of Aβ42 to accurately quantify the changes in free energy of activation of the reaction for all possible amino acid substitutions in a protein and, in addition, to quantify >600 energetic interactions between mutations. Strong energetic couplings suggest that the Aβ42 nucleation reaction transition state is structured in a short C-terminal region, providing a structural model for the reaction that may initiate Alzheimer’s disease. Using this approach it should be possible to reveal the energetic structures of additional amyloid transition states and, in combination with additional selection assays, protein transition states more generally.