DEMO (Domain Enhanced MOdeling, version 2) is an significantly improved version
for automated assembly of full-length structural models of multi-domain proteins
by integrating analogous template alignments with deep-learning predicted
inter-domain spatial restraints.
Starting from individual domain structures, quaternary structure templates that have
similar component domains are identified by domain-level structural alignments
Meanwhile, inter-domain spatial restraints are predicted by the
deep residual neural-network-based predictor DeepPotential. Full-length
models are then created by a fast quasi-Newton optimization for rigid-body
domain structure assembly,
which are guided by the DeepPotential predicted inter-domain restraints,
inter-domain distance profiles collected from the top-ranked quaternary templates,
and physics-based steric potentials. The final models are selected from the
low energy conformations and further refined with fragment-guided molecule dynamics
simulations. Large-scaled benchmark tests showed that the performance is
significantly beyond its predecessor. (>>more about the server ...
DEMO On-line Server
[View an example output
We have updated the program to significantly reduce the processing time.
DEMO has been updated to DEMO2 by integrating analogous template alignments with deep-learning
spatial restraints for high-accuracy domain structure assembly.
DEMO was used to assemble structures of all multi-domain targets of 'Zhang-Server' which was
ranked as the No. 1 protein structure prediction server in the
DEMO assembled structures of all multi-domain proteins encoded by
the genome of
- Xiaogen Zhou, Chunxiang Peng, Wei Zheng, Yang Li, Guijun Zhang, Yang Zhang.
DEMO2: Assemble multi-domain protein structures by coupling analogous template alignments
with deep-learning inter-domain restraint prediction.
Nucleic Acids Research, 50(W1): W235-W245 (2022). [PDF] [Support Information]
- Xiaogen Zhou, Jun Hu, Chengxin Zhang, Guijun Zhang, Yang Zhang.
Assembling multidomain protein structures through analogous global structural alignments.
Proceedings of the National Academy of Sciences, 116: 15930-15938 (2019). [PDF] [Support Information]