E2EDA
Zhang Lab Services Zhejiang University of Technology


E2EDA is an End-To-End Domain Assembly method based on deep learning. In E2EDA, we first develop an EfficientNetV2-based deep learning model (RMNet), which is dedicated to predicting inter-domain orientations. The RMNet uses an attention mechanism to predict inter-domain rigid motion by fusing sequence features, multiple template features, and single-domain features. Then, the predicted rigid motions are converted into inter-domain spatial transformations to directly assemble full-chain models of multi-domain proteins without time-consuming simulation processes. Finally, a scoring strategy, RMscore, is designed to select the best model from multiple assembled models to improve assembly accuracy.

E2EDA 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)


                         


References:

  • Haitao Zhu, Yuhao Xia and Guijun Zhang*. E2EDA: Protein domain assembly based on end-to-end deep learning. Submitted.