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人工智能系统生成的蛋白质有多“新”——超越自然界了
https://www.ebiotrade.com/newsf/2023-4/20230421002326881.htm
1. Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide.
Department of Mathematics and Gonda Brain Research Center, Bar-Ilan University, 52900, Ramat Gan, Israel.
louzouy@math.biu.ac.il
Immunogenetics (P 1432-1211 E 0093-7711) H指数:85 2018 年 70 卷 7 期 419-428 页
PMID:29492592 相似文献
http://www.pubmedplus.cn/P/SearchQuickResult?wd=ac5fedd8-979f-469b-a182-dbc52428befc
01. | Histocompatibility Antigens Class I | 48 篇 | 82.759% |
02. | ATP-Binding Cassette Transporters | 46 篇 | 79.310% |
03. | Antigen Presentation | 41 篇 | 70.690% |
04. | Humans | 41 篇 | 70.690% |
05. | Animals | 27 篇 | 46.552% |
06. | Proteasome Endopeptidase Complex | 26 篇 | 44.828% |
07. | ATP Binding Cassette Transporter, Subfamily B, Member 2 | 21 篇 | 36.207% |
08. | Peptides | 19 篇 | 32.759% |
09. | Mice | 14 篇 | 24.138% |
10. | ATP Binding Cassette Transporter, Subfamily B, Member 3 | 13 篇 | 22.414% |
https://pubmed.ncbi.nlm.nih.gov/29492592/
Immunogenetics
. 2018 Jul;70(7):419-428.
doi: 10.1007/s00251-018-1054-6. Epub 2018 Feb 28.
Affiliations expand
PMID: 29492592
Epitopes presented on MHC class I molecules pass multiple processing stages before their presentation on MHC molecules, the main ones being proteasomal cleavage and TAP binding. Transporter associated with antigen processing (TAP) binding is a necessary stage for most, but not all, MHC-I-binding peptides. The molecular determinants of TAP-binding peptides can be experimentally estimated from binding experiments and from the properties of peptides inducing a CD8 T cell response. We here propose novel optimization formalisms to combine binding and activation experimental results to produce a classifier for TAP binding using dual-output kernel and deep learning approaches. The application of these algorithms to the human and murine TAP binding leads to predictors that are much more precise than current state of the art methods. Moreover, the computed score is highly correlated with the observed binding energy. The new predictors show that TAP binding may be much more selective than previously assumed in humans and mice and sensitive to the properties of most positions of the peptides. Beyond the improved precision for TAP binding, we propose that the same approach holds in most molecular binding problems, where functional and binding measures are simultaneously available, and can be used to significantly improve the precision of binding prediction algorithms in general and immune system molecules specifically.
Keywords: Deep learning; Dual output; Prediction; TAP.
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Show all 31 references
ATP-Binding Cassette Transporters / classification
ATP-Binding Cassette Transporters / physiology*
Algorithms
Animals
Antigen Presentation / immunology
Computer Simulation
Deep Learning
Epitopes / classification
Forecasting
Histocompatibility Antigens Class I / immunology*
Histocompatibility Antigens Class I / physiology
Humans
Membrane Transport Proteins
Peptides / immunology
Proteasome Endopeptidase Complex / metabolism
ATP-Binding Cassette Transporters
Epitopes
Histocompatibility Antigens Class I
Membrane Transport Proteins
Peptides
transporter associated with antigen processing (TAP)
Proteasome Endopeptidase Complex
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