Base stacking classification via automated clustering method

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Base stacking classification via automated
clustering method

• Eli Hershkovits1, Xavier Le Faucheur1, Neocles
  Leontis2, Allen Tannenbaum1
• 1Georgia Institute of Technology, 2BGSU

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Data Classification

• Coordinate system and parameterization
• Clustering of the data (by eye or Automated
  clustering)

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Base stackingRing Coordinate system

• the three orthogonal directions are calculated
  with Cremer and Pople method.
• The coordinates y1 and y2 can be used to define
  face of the ring (up or down.)

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Base stackingRelative Coordinate system

• Relative rings coordinates are defined by the
  spherical coordinates r j and q.

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Primary Classification

• For each base stacking candidate the two closest
  rings are chosen to represent the pair. This
  choice gives a classification to four groups
  Pyrimidine-pyrimidine Pyrimidine-imidazole,
  Imadizole-pyrimidine and Imidazole-imidazole.
• There are four possible combinations of face-face
  interactions Up-up, Up down, Down-up, Down,down.

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Parameters relevant for clustering
r
w
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Parameters relevant for clustering
j
r
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Parameters relevant for clustering
j
q
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Secondary classification

• The polar coordinates r , j and q are
  correlated and show distinction to two clusters
  Proper stacking and improper stacking.
• Those classifications give 442 32 classes

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Pyr - Pyr
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Im - Pyr
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Pyr-Im
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Im-Im
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ExamplesPyr-Pyr up up
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ExamplesPyr-Pyr up down
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ExamplesIm-Pyr up up
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ExamplesIm-Pyr up down
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ExamplesIm-Im up up
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ExamplesIm-Im up down
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Possible problems

• For stacking of residues that are not neighbors
  the distribution of w is broad.
• Possible overlap between clusters.

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Stacking of RNA on protein

• Stacking interactions between nucleic acids and
  amino acids are not abundant (9 for the large
  subunit RR0033.)
• Most of the stacking interactions are with
  Histidine (6.) From the staking cases 5 are with
  the pyrimidine ring.