DMBS is an accurate method for delterious nsSNP prediciton. The design of DMBS relies on the observation that the deleterious mutations are likely to occur at highly conserved and functional important positions in the input protein sequence. Correspondingly, we introduce two innovative components. First, we improve the estimates of the conservation computed from the multiple sequence profiles. Second, we utilize putative annotations of functional/binding residues produced by two state-of-the-art sequence-based methods (NucBind and the S-SITE in COACH-D). These inputs are processed by a Random Forest model that provides favorable predictive performance when empirically compared against five other machine learning algorithms. Read more about the DMBS algorithm...


  • Provide the protein data (mandatory)
  • Input your protein sequence in FASTA format: (Click for an example FASTA input)
    Or upload the sequence file:

  • Provide the mutation information (mandatory)
  • The mutated position
    Select one or more amino acids to be mutated to:

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

    ID: (Optional, your given name to this protein)

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  • Song R, Cao B, Peng Z, Oldfield CJ, Kurgan L, Wong K-C, Yang J. Accurate Sequence-Based Prediction of Deleterious nsSNPs with Multiple Sequence Profiles and Putative Binding Residues. Biomolecules. 2021; 11(9):1337.