MoRFMPM

MoRFMPM is a software tool for predicting molecular recognition features (MoRFs) in protein sequences. MoRFs are short disordered regions within longer intrinsically disordered regions that can undergo disorder-to-order transitions upon binding to interaction partners. Accurately predicting MoRFs computationally is essential, given their functional significance and the challenges in identifying them experimentally.

MoRFMPM employs a machine learning approach based on a multilayer perceptron model. It utilizes various sequence-based features to predict MoRFs, including:

1. Position-specific scoring matrix (PSSM) profiles
2. Predicted disorder probabilities
3. Predicted secondary structure probabilities
4. Physicochemical properties of amino acids

Topic

Protein interactions;Transcription factors and regulatory sites;Statistics and probability;Small molecules

Detail

  • Operation: Protein disorder prediction;Protein property calculation;Fold recognition

  • Software interface: Command-line interface

  • Language: MATLAB

  • License: Not stated

  • Cost: Free of charge

  • Version name: -

  • Credit: National Natural Science Foundation of China.

  • Input: -

  • Output: -

  • Contact: Jiaxiang Zhao zhaojx@nankai.edu.cn

  • Collection: -

  • Maturity: -

Publications

  • Computational prediction of MoRFs based on protein sequences and minimax probability machine.
  • He H, et al. Computational prediction of MoRFs based on protein sequences and minimax probability machine. Computational prediction of MoRFs based on protein sequences and minimax probability machine. 2019; 20:529. doi: 10.1186/s12859-019-3111-z
  • https://doi.org/10.1186/S12859-019-3111-Z
  • PMID: 31660849
  • PMC: PMC6819637

Download and documentation

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