DeepIDDP
Zhang Lab Services SADA Zhejiang University of Technology


DeepIDDP is a deep learning based method for inter-domain distance prediction. In DeepIDDP, we firstly construct a non-redundant multi-domain training set from the MPDB library, and perform MSA search and DPMSA data enhancement based on the single-domain structure and full-length sequences; secondly, we extract three major features including inter-domain features, single-domain features and MSA features from the single-domain structure and MSA data. Then, a neural network with attention mechanism and deep residual blocks was designed to predict the distance distribution of inter-domain residual pairs. Finally, we integrate DeepIDDP into the previously developed SADA assembly method, which can further improve the model accuracy.

DeepIDDP On-line Sever

    Input the full-chaim sequence:
  • Please copy and paste your sequence file here in FASTA format (mandatory). Sample input

    Or upload the sequence file:

    • Input the domain structures in order:
  • Input the structure of domain 1 in PDB format (mandatory):
    Please copy and paste your structure file here. Sample input

    Or upload the seqence file:

  • Input the structure of domain 2 in PDB format (mandatory):
    Please copy and paste your structure file here. Sample input

    Or upload the seqence file:

    • User information
    Email: (mandatory, where results will be sent to)

    Job name: (optional, your given name to this job)


                         


SADA domain assembly

  • If you want to use the structural analogue-based protein structure domain assembly method SADA. Please click here

References:

  • Fengqi Ge✝, Chunxiang Peng✝, Xinyue Cui, Yuhao Xia and Gui-jun Zhang*. Inter-domain distance prediction based on deep learning for domain assembly. Briefings in Bioinformatics , 2023, bbad100.

Contact us:

  • guijunzhanglab@163.com