人工智能系统生成的蛋白质有多“新”——超越自然界了

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1. Cross-modality deep learning-based prediction of TAP binding and naturally processed peptide.

Hanan Besser,Yoram Louzoun

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

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https://pubmed.ncbi.nlm.nih.gov/29492592/

Abstract

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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MeSH terms

• 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

Substances

• 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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