MapPred is a web server for deep learning-based prediciton of protein inter-residue contacts and distances. For each query sequence, a multiple sequence alignment (MSA) was constructed. Deep residual neural networks are used as the engine for training and prediction, with covariance features derived from the MSA. MapPred is shown to be comparable or superior to the state-of-the-art methods. The output inlcudes the predicted contact map, distance maps and the distance distribtuion.


The server has been moved to: Please submit jobs at the new site.

  • Provide the protein data (mandatory)
  • Input the protein sequence data below: (example sequence, please submit one sequence each time).
    Or upload the protein squence file:

  • Other information (optional)
  • Email: (Optional, where the results will be sent to)

    Target name: (Optional, your given name to this target)

    Keep my results private (check this box if you want to keep your job private. A key will be assigned for you to access the results)


  • Q Wu, Z Peng, I Anishchenko, Q Cong, D Baker, J Yang, Protein contact prediction using metagenome sequence data and residual neural networks, Bioinformatics, 36: 41-48 (2020).