M-DeepAssembly provides a distinctive multi-domain assembly algorithm. To some extent, M-DeepAssembly can alleviate the current challenges of weak evolutionary signals and large structures by forming diverse ensembles through the use of multi-objective protein conformation space sampling algorithm. Furthermore, this method not only provides new insights for exploring the functions of multi-domain protein but also offers the possibility for multiple conformations studies based on the obtained ensembles.

• 2022/05/21: We developed DeepAssembly for multi-domain protein structure assembly
• 2022/05/28: DeepAssembly was randked No. 1 for the protein structure prediction in the 1 week CAMEO blind test.
• 2023/05/03: The server adds protein complex structure assembly functioin.
• 2024/01/24: DeepAssembly Enhanced Version M-DeepAssembly is released.

• Xinyue Cui, Yuhao Xia, Minghua Hou, Xuanfeng Zhao, Suhui Wang and Guijun Zhang*. M-DeepAssembly:Enhanced DeepAssembly based on multi-objective protein conformation sampling.
• Yuhao Xia^✝,Kailong Zhao^✝,Dong Liu,Xiaogen Zhou,Guijun Zhang*. Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning. Communications biology, 6, 1221 (2023). DOI: https://www.nature.com/articles/s42003-023-05610-7.