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Machine learning approach to classifying kinase crystal structure conformations.

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KinConform -- Kinase conformation classification

DOI

Classify kinase structures as active/inactive.

Description of methods


KinConform takes any number of input structures, separates their chains and generates a fasta file of sequences. This fasta file is aligned (using MAPGAPS) to kinase profiles, identifying which chains are kinases. A series of measurements are then taken for each kinase chain and used as input to a machine learning classifier.

The output is tab-delimited and displays the conformation (active/inactive) for each input kinase chain.

Usage


./kinconform XXX.pdb YYY.pdb ZZZ.pdb > a.out

To run test structures, simply cd test and make all.

Note: the profile and model directories should be colocated with kinconform. To install, add a symbolic link to kinconform from your bin/ directory.

Dependencies


Please ensure the following software is installed:

  • NumPy <http://www.numpy.org>
  • MDAnalysis <http://www.mdanalysis.org>
  • Biocma <https://github.com/etal/biocma>
  • MAPGAPS <http://mapgaps.igs.umaryland.edu>

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Machine learning approach to classifying kinase crystal structure conformations.

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