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Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

ABSTRACT: Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

PROVIDER: PRJNA1115911 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR29155203_1.fastq.gz	Fastqsanger.gz
	SRR29155203_2.fastq.gz	Fastqsanger.gz
	SRR29155204_1.fastq.gz	Fastqsanger.gz
	SRR29155204_2.fastq.gz	Fastqsanger.gz
	SRR29155205_1.fastq.gz	Fastqsanger.gz

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Interpretably deep learning amyloid nucleation by massive experimental quantification of random sequences

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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The genetic landscape for amyloid beta nucleation accurately discriminates familial Alzheimer’s disease mutations

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An atlas of amyloid aggregation: the impact of substitutions, insertions, deletions and truncations on amyloid beta fibril nucleation

Project description:Multiplexed assays of variant effects (MAVEs) guide clinical variant interpretation and reveal disease mechanisms. To date, MAVEs have focussed on a single mutation type - amino acid (AA) substitutions - despite the diversity of coding variants that cause disease. Here we use Deep Indel Mutagenesis (DIM) to generate the first comprehensive atlas of diverse variant effects for a disease protein, amyloid beta (Aß) that aggregates in Alzheimer’s disease (AD) and is mutated in familial AD (fAD). The atlas identifies known fAD variants and many mutations beyond substitutions that accelerate Aß aggregation. Truncations, substitutions, insertions, single- and multi-AA deletions differ in their propensity to enhance or impair aggregation, but likely pathogenic variants from all classes are strongly enriched in the polar N-terminus of Aß. This first comparative atlas for any disease gene highlights the importance of including diverse mutation types in MAVEs and provides important mechanistic insights into amyloid nucleation.

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Project description:Different forms of dementia are caused by stop-loss mutations in the ITM2B gene, also known as Bri2, which result in the expression of 34 amino acid long peptides that accumulate as amyloids in human brains. In order to gather mechanistic insights into the formation of amyloids by two of these peptides, ADan and ABri - hallmarks of Danish and British dementia respectively - we employed saturation mutagenesis combined to a massively parallel selection assay that reports on amyloid nucleation. Our results reveal that ADan aggregates into amyloids remarkably faster than both the unextended peptide Bri2 and the extended ABri sequence. The complete mutational landscape of ADan reveals asparagines and charged residues as key players in the nucleation process in addition to aliphatic residues within positions 20-25. What is more, we show that extending Bri2 with just two specific residues is enough to generate a novel amyloid core which we suggest builds the structured core of ADan fibrils. On the other hand, only a handful of mutations can boost the ability of ABri to nucleate amyloids, including a SNV replacing the Bri2 stop codon by a Cys codon. Overall, the remarkably different aggregation profiles and mutational landscapes for the two peptides suggest that different disease mechanisms underlie disease in Danish and British dementia and highlight the importance of accurately measuring the impact of stop extension mutations for these and other sequences across the genome.

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Amyloids “at the border”: deep mutagenesis and random sequence extension reveal an incomplete amyloid-forming motif in Bri2 that turns amyloidogenic upon C-terminal extension

Project description:Stop-loss mutations cause over twenty different diseases. The effects of stop-loss mutations can have multiple consequences that are, however, hard to predict. Stop-loss in ITM2B/BRI2 results in C-terminal extension of the encoded protein and, upon furin cleavage, in the production of two 34 amino acid long peptides, ADan and ABri, that accumulate as amyloids in the brains of patients affected by familial Danish and British Dementia. To systematically explore the consequences of Bri2 C-terminal extension, here, we measure amyloid formation for 676 ADan substitutions and identify the region that forms the putative amyloid core of ADan fibrils, located between positions 20 and 26, where stop-loss occurs. Moreover, we measure amyloid formation for ~18,000 random C-terminal extensions of Bri2 and find that ~32% of these sequences can nucleate amyloids. We find that the amino acid composition of these nucleating sequences varies with peptide length and that short extensions of 2 specific amino acids (Aliphatics, Aromatics and Cysteines) are sufficient to generate novel amyloid cores. Overall, our results show that the C-terminus of Bri2 contains an incomplete amyloid motif that can turn amyloidogenic upon extension. C-terminal extension with de novo formation of amyloid motifs may thus be a widespread pathogenic mechanism resulting from stop-loss, highlighting the importance of determining the impact of these mutations for other sequences across the genome.

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