Cystic Fibrosis. Edited by Marcus A. Mall and J. Stuart Elborn

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doi:10.1007/s10897-015-9855-3

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“…The Caucasian population is most at risk of CF, although other groups are still at risk (and likely at a higher rate the official numbers would suggest due to under reporting (4)). The disease's etiology is caused by mutations to the cystic fibrosis transmembrane conductance regulator (CFTR) gene which causes an alteration in the chloride and bicarbonate transport channel regulated by cyclic adenosine monophosphate (cAMP) (5). This can lead to a pathological build up of mucus in the organs, with the most deleterious impact being on the lungs and the pancreas.…”

Section: Introductionmentioning

confidence: 99%

A multitask neural network trained on embeddings from ESMFold can accurately rank order clinical outcomes for different cystic fibrosis mutations

Drysdale

2023

Preprint

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Advancements in AI-based protein structure prediction have opened new research avenues in computational biology. Previous work has shown mixed results when the predicted 3D structure or model embeddings have been used as features for predicting phenotypes. Many existing tools which predict the pathogenicity of variants (e.g. SIFT 4G, Polyphen-2, REVEL, LYRUS) have incorporated structural protein features, but have shown mixed success when applied to mutations on the CFTR gene. This paper explores whether embeddings from the ESM-Fold model, which predicts the 3D structure of a protein from an amino acid sequence, can be used to predict clinical outcomes for different Cystic Fibrosis (CF) genotypes found in the CFTR2 database. A neural network model trained on these embeddings is able to obtain both non-trivial and statistically significant correlations on CF-related phenotypes (sweat chloride, pancreatic insufficiency, and infection rates). Overall, AI-based protein structure prediction models show a promising ability to assess the relative severity of CFTR-based mutations on key phenotypic outcomes associated with CF. The entire processing and analysis pipeline for this work can be found at thisrepo.

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Section: Introductionmentioning

confidence: 99%

A multitask neural network trained on embeddings from ESMFold can accurately rank order clinical outcomes for different cystic fibrosis mutations

Drysdale

2023

Preprint

View full text Add to dashboard Cite

show abstract

Cystic Fibrosis. Edited by Marcus A. Mall and J. Stuart Elborn

Cited by 1 publication

References 0 publications

A multitask neural network trained on embeddings from ESMFold can accurately rank order clinical outcomes for different cystic fibrosis mutations

A multitask neural network trained on embeddings from ESMFold can accurately rank order clinical outcomes for different cystic fibrosis mutations

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