I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

Yang Li, Xiaogen Zhou, Wei Zheng, Robin Pearce, Chengxin Zhang, Eric W. Bell, Guijun Zhang, Yang Zhang

Published: 2022-08-04 DOI: 10.1038/s41596-022-00728-0

Multi-domain protein structure prediction

Deep-learning

I-TASSER-MTD

Automated pipeline

Function annotation

AI 解读

Generation of mouse–human chimeric embryos

Genome-wide quantification of transcription factor binding at single-DNA-molecule resolution using methyl-transferase footprinting

Generation of CRISPR–Cas9-mediated genetic knockout human intestinal tissue–derived enteroid lines by lentivirus transduction and single-cell cloning

A puromycin-dependent activity-based sensing probe for histochemical staining of hydrogen peroxide in cells and animal tissues

Decoding personal biotic and abiotic airborne exposome

A clinically relevant model of focal embolic cerebral ischemia by thrombus and thrombolysis in rhesus monkeys

Bioengineering human intestinal mucosal grafts using patient-derived organoids, fibroblasts and scaffolds

Biomimetic tail-to-head terpene cyclizations using the resorcin[4]arene capsule catalyst

Neuroscience robotics for controlled induction and real-time assessment of hallucinations

Scanorama: integrating large and diverse single-cell transcriptomic datasets

Single-cell RNA sequencing (scRNA-seq)（单细胞RNA测序）

Data integration（数据集成）

Heterogeneity（异质性）

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References

Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain
Introduction
Materials
- Equipment
- Software
Procedure
Troubleshooting
Timing
Anticipated results
- References
- References
References
ACKNOWLEDGMENTS

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I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction

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