Academic Journal

Rapid protein stability prediction using deep learning representations.

التفاصيل البيبلوغرافية
العنوان: Rapid protein stability prediction using deep learning representations.
المؤلفون: Blaabjerg, Lasse M, Kassem, Maher M, Good, Lydia L, Jonsson, Nicolas, Cagiada, Matteo, Johansson, Kristoffer E, Boomsma, Wouter, Stein, Amelie, Lindorff-Larsen, Kresten
بيانات النشر: eLife Sciences Publications, Ltd
Yusuf Hamied Department of Chemistry student
//doi.org/10.7554/elife.82593
Elife
سنة النشر: 2023
المجموعة: Apollo - University of Cambridge Repository
مصطلحات موضوعية: biophysics, computational biology, genomic variants, machine learning, molecular biophysics, none, protein stability, structural biology, systems biology, Humans, Deep Learning, Proteins, Mutagenesis, Amino Acids
الوصف: Predicting the thermodynamic stability of proteins is a common and widely used step in protein engineering, and when elucidating the molecular mechanisms behind evolution and disease. Here, we present RaSP, a method for making rapid and accurate predictions of changes in protein stability by leveraging deep learning representations. RaSP performs on-par with biophysics-based methods and enables saturation mutagenesis stability predictions in less than a second per residue. We use RaSP to calculate ∼ 230 million stability changes for nearly all single amino acid changes in the human proteome, and examine variants observed in the human population. We find that variants that are common in the population are substantially depleted for severe destabilization, and that there are substantial differences between benign and pathogenic variants, highlighting the role of protein stability in genetic diseases. RaSP is freely available-including via a Web interface-and enables large-scale analyses of stability in experimental and predicted protein structures.
نوع الوثيقة: article in journal/newspaper
وصف الملف: application/pdf
اللغة: English
Relation: https://www.repository.cam.ac.uk/handle/1810/354206
الاتاحة: https://www.repository.cam.ac.uk/handle/1810/354206
Rights: Attribution 4.0 International ; https://creativecommons.org/licenses/by/4.0/
رقم الانضمام: edsbas.765CD015
قاعدة البيانات: BASE