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DGMFold is fast protein structure prediction algorithm by iterative inter-residue geometries prediction and model quality assessment using deep learning. Two different deep residual neural networks are designed to predict the inter-residue geometries and to evaluate the quality of the predicted structure models. The results of the model assessment are fed back to the geometries prediction network to modify the predicted geometries and structural models.

  DGMFold On-line Server [View example of output]

Please input the protein sequence (mandatory, Click for an example input) below.
Or upload the protein squence file:
Email: (mandatory, where results will be sent to)

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

References

  • Jun Liu, Guangxing He, Kailong Zhao, and Guijun Zhang*. De novo protein structure prediction by incremental inter-residue geometries prediction and model quality assessment using deep learning. bioRxiv, doi: https://doi.org/10.1101/2022.01.11.475831.