دورية أكاديمية
أعرض في EDS

Rapid protein stability prediction using deep learning representations.

التفاصيل البيبلوغرافية
العنوان: Rapid protein stability prediction using deep learning representations.
المؤلفون: Blaabjerg LM; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark., Kassem MM; Center for Basic Machine Learning Research in Life Science, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark., Good LL; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark., Jonsson N; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark., Cagiada M; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark., Johansson KE; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark., Boomsma W; Center for Basic Machine Learning Research in Life Science, Department of Computer Science, University of Copenhagen, Copenhagen, Denmark., Stein A; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark., Lindorff-Larsen K; Linderstrøm-Lang Centre for Protein Science, Department of Biology, University of Copenhagen, Copenhagen, Denmark.
المصدر: ELife [Elife] 2023 May 15; Vol. 12. Date of Electronic Publication: 2023 May 15.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: eLife Sciences Publications, Ltd Country of Publication: England NLM ID: 101579614 Publication Model: Electronic Cited Medium: Internet ISSN: 2050-084X (Electronic) Linking ISSN: 2050084X NLM ISO Abbreviation: Elife Subsets: MEDLINE
أسماء مطبوعة: Original Publication: Cambridge, UK : eLife Sciences Publications, Ltd., 2012-
مواضيع طبية MeSH: Deep Learning*, Humans ; Proteins/metabolism ; Mutagenesis ; Amino Acids/genetics ; Protein Stability ; Computational Biology/methods
مستخلص: 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.
Competing Interests: LB, MK, LG, NJ, MC, KJ, WB, AS, KL No competing interests declared
(© 2023, Blaabjerg et al.)
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فهرسة مساهمة: Keywords: biophysics; computational biology; genomic variants; machine learning; molecular biophysics; none; protein stability; structural biology; systems biology
المشرفين على المادة: 0 (Proteins)
0 (Amino Acids)
تواريخ الأحداث: Date Created: 20230515 Date Completed: 20230615 Latest Revision: 20230617
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC10266766
DOI: 10.7554/eLife.82593
PMID: 37184062
قاعدة البيانات: MEDLINE
الوصف
تدمد:2050-084X
DOI:10.7554/eLife.82593