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

Author Correction: Full-length sequencing of circular DNA viruses and extrachromosomal circular DNA using CIDER-Seq

Generation of heart-forming organoids from human pluripotent stem cells

Design and fabrication of wearable electronic textiles using twisted fiber-based threads

Activation of mechanoluminescent nanotransducers by focused ultrasound enables light delivery to deep-seated tissue in vivo

Chromatin accessibility profiling by ATAC-seq

Deoxyfluorination of phenols for chemoselective18F-labeling of peptides

Author Correction: cfSNV: a software tool for the sensitive detection of somatic mutations from cell-free DNA

Human primordial germ cell-like cells specified from resetting precursors develop in human hindgut organoids

An inexpensive semi-automated sample processing pipeline for cell-free RNA extraction

Application of CHyMErA Cas9-Cas12a combinatorial genome-editing platform for genetic interaction mapping and gene fragment deletion screening

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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
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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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