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I-TASSER (Iterative Threading ASSEmbly Refinement)
is a hierarchical approach to protein structure prediction
and structure-based function annotation.
It first identifies structural templates from the PDB by
multiple threading approach
LOMETS,
with full-length atomic models constructed by
iterative template-based fragment assembly simulations.
Function insights of the target are then derived by
re-threading the 3D models through protein function database
BioLiP.
I-TASSER (as 'Zhang-Server')
was ranked as the No 1 server for protein structure prediction
in recent community-wide
CASP7,
CASP8,
CASP9,
CASP10,
CASP11,
CASP12,
CASP13,
and
CASP14
experiments.
It was also ranked the best for function prediction in
CASP9.
The server is in active development with
the goal to provide the most accurate protein structure and function predictions
using state-of-the-art algorithms.
The server is only for non-commercial use.
Please report problems and questions at
I-TASSER message board and our developers will study and answer the questions accordingly.
(>> More about
the server ...)
D-I-TASSER: A new deep learning–based method outperforming AlphaFold2 and
AlphaFold3
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[FAQ]
Due to a webserver outage, the I-TASSER service is temporarily unavailable.
Our team is actively working to restore the system, and we expect it to be
back online by the last week of October. We sincerely apologize for any
inconvenience this may cause and appreciate your patience.
I-TASSER On-line Server (View an example of I-TASSER output):
I-TASSER Suite:
I-TASSER News:
References:
- W Zheng, Q Wuyun, Y Li, Q Liu, X Zhou, C Peng, Y Zhu, L Freddolino, Y Zhang.
Deep-learning-based single-domain and multidomain protein structure prediction with D-I-TASSER.
Nature Biotechnology, https://doi.org/10.1038/s41587-025-02654-4 (2025).
(PDF and Support Information).
- X Zhou, W Zheng, Y Li, R Pearce, C Zhang, EW Bell, G Zhang, Y Zhang.
I-TASSER-MTD: A deep-learning based platform for multi-domain protein structure and function prediction.
Nature Protocols, 17: 2326-2353 (2022).
(PDF and Support Information).
- W Zheng, C Zhang, Y Li, R Pearce, EW Bell, Y Zhang.
Folding non-homology proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.
Cell Reports Methods, 1: 100014 (2021).
(PDF and Support Information).