Energetic portrait of the amyloid beta transition state
Ontology highlight
ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE269461 | GEO | 2024/07/26
REPOSITORIES: GEO
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Project description:Alzheimer’s Disease (AD) is the most common form of dementia and a leading global cause of human mortality and morbidity. Amyloid plaques of the amyloid beta (Aß) peptide are a universal pathological hallmark of AD and rare mutations in Aß can cause familial forms of AD (fAD). However, hundreds of additional mutations in Aß of uncertain significance are likely to exist in the human population, making clinical interpretation of genetic variation a difficult challenge even in this short peptide. Here, we use deep mutagenesis to quantify the effects of all possible single nucleotide variants and thousands of double mutations on the ability of Aß to nucleate amyloid fibrils in vivo. This data provides the first comprehensive view of how mutations alter the nucleation of an amyloid, reveals a modular organisation of mutational effects in Aß, and demonstrates the importance of charge in preventing nucleation. Moreover, the in vivo nucleation data, unlike computational predictors, accurately discriminates the known dominant fAD mutations. These results illustrate how deep mutagenesis can be used to genetically validate assays as discovery platforms. They also further prioritize nucleation as the critical biochemical event to target in order to prevent and treat AD.
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Project description:Energetic portrait of the amyloid beta transition state
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Project description:More than 50 human diseases are characterized by the deposition of specific protein aggregates in the form of insoluble amyloid fibrils. However, only a very small number of proteins are known to form amyloids with high propensity, limiting our ability to understand, predict and engineer amyloid aggregation from sequence. Here we use a massively parallel assay to quantify the amyloid nucleation propensity of >100,000 random 20 amino acid sequences. Approximately 5% of assayed random sequences nucleate the formation of aggregates, generating a very large and diverse training dataset from which to train models to predict amyloid nucleation. We use this dataset to train CANYA, a convolution-attention hybrid neural network that predicts the propensity of any primary sequence to form amyloids. CANYA outperforms previous predictors of protein aggregation on additional random sequences and out-of-sample datasets including human disease-causing amyloids, with very stable performance across diverse prediction tasks. We adapt and extend recent advances in interpretability of genomic neural networks to elucidate CANYA’s decision-making process and learned grammar and to provide mechanistic insights into amyloid formation. Our results demonstrate the power of massive experimental random sequence-space exploration and provide an interpretable and robust neural network model for understanding, predicting and designing amyloid-forming proteins.
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