SLIDER: Super-fast predictor of proteins with Long Intrinsically DisordERed regions

Standalone version | References | Materials | Help | Acknowledgments | Disclaimer | Biomine

SLIDER webserver

SLIDER predicts whether a given protein sequence has long disordered segment(s), i.e., segment(s) with at least 30 consecutive disordered residues. The predictions is performed in high-throughput manner utilizing logistic regression model that takes amino acid composition, sequence complexity and selected physicochemical properties of amino acids as inputs.

Please follow the three steps below to make predictions:

1. Upload a file with protein sequences, or paste them into text area

Server accepts up to 75 000 (FASTA formated) protein sequences (40MB max file size limit). Either upload a file or enter each protein in a new line in the following text field (see Help for details):

2. Provide your e-mail address (required)

Please provide your e-mail address to be notified when results are ready.

3. Predict:

Click button to launch prediction.

Standalone version

Standalone version of the SLIDER method can be downloaded at SLIDER.tar.gz

References

Upon the usage the users are requested to use the following citation:

Materials

    Datasets used to design and evalaute SLIDER:
  • TRAINING dataset - Dataset used to develop classifier including feature selection and parameterization using 5-fold cross validation.
  • TEST dataset - Dataset used to perform out-of-sample evaluation of SLIDER.
    The format of these datasets is as follow:
  • Line 1: >protein ID
  • Line 2: protein sequence (one letter amino acid code)
  • Line 3: disorder annotations, where 0/1/X represents the ordered/disordered/unannotated residue

Help

SLIDER accepts either single or multiple protein sequences and the input is limited to 75 000 protein sequences at the time. The user should submit the protein sequence(s) in FASTA format.

Acknowledgments

We acknowledge, with thanks, that the following software was used as a part of this server:

  • SEG - Application for the prediction of low complexity regions (LCRs)
  • AAIndex - Representing the physicochemical properties of amino acids