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Predicts absolute per-residue fluctuation from a three-dimensional protein structure. FlexPred is designed to predict the fluctuations exhibited by a protein during a 10 ns MD simulation. It uses static features of a protein structure to predict MD residue fluctuation in 3D. Its predictions were evaluated using Pearson’s correlation coefficient. This tool is presented under two versions: a stand-alone and a web server.

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FlexPred forum

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FlexPred classification

FlexPred specifications

Software type:
Package
Restrictions to use:
None
Input format:
PDB
Output format:
CSV, PDB
Stability:
Stable
Interface:
Web user interface
Input data:
It takes a 3D protein structure as input.
Output data:
Predicts a fluctuation value for each residue of the protein. An image of the structure with high fluctuation residues, a graph of the predicted fluctuation for each residue number, a link to download file containing the predicted fluctuations.
Computer skills:
Basic

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0 user reviews

0 user reviews

No review has been posted.

FlexPred forum

No open topic.

FlexPred versioning

No versioning.

FlexPred classification

FlexPred specifications

Software type:
Standalone
Restrictions to use:
Academic users only
Input format:
PDB
Output format:
TXT
Programming languages:
Python
Stability:
Stable
Interface:
Command line interface
Input data:
It takes a 3D protein structure as input.
Output data:
Produces a text file containing the predicted fluctuations for each residue.
Operating system:
Unix/Linux
Computer skills:
Advanced

FlexPred support

Maintainer

Credits

Publications

  • (Peterson et al., 2017) Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods Mol Biol.
    PMID: 27787827
  • (Jamroz et al., 2012) Structural features that predict real-value fluctuations of globular proteins. Proteins.
    PMID: 22328193

Institution(s)

Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN, USA; Laboratory of Theory of Biopolymers, Faculty of Chemistry, University of Warsaw, Pasteura 1, Warszawa, Poland; Department of Biological Sciences, College of Science, Purdue University, West Lafayette, IN, USA; Department of Computer Science, College of Science, Purdue University, University Street, West Lafayette, IN, USA

Funding source(s)

This work was partly supported by the National Institute of General Medical Sciences of the National Institutes of Health (R01GM097528) and the National Science Foundation (IIS1319551, DBI1262189, IOS1127027).

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