Base stacking classification via automated clustering method

1. Base stacking classification via automated clustering method Eli Hershkovits1, Xavier Le Faucheur1, Neocles Leontis2, Allen Tannenbaum1 1Georgia Institute of Technology, 2BGSU

2. Data Classification • Coordinate system and parameterization • Clustering of the data (“by eye” or Automated clustering)

3. Z2 Y2 Z1 Y1 r12 X2 X1 Base stackingRingCoordinate 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.)

4. w r r r f Base stackingRelativeCoordinate system • Relative rings coordinates are defined by the spherical coordinates r j and q.

5. 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.

6. Parameters relevant for clustering r w

7. Parameters relevant for clustering j r

8. Parameters relevant for clustering j q

9. 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 4*4*2 = 32 classes

10. Pyr - Pyr

11. Im - Pyr

12. Pyr-Im

13. Im-Im

14. ExamplesPyr-Pyr up up

15. ExamplesPyr-Pyr up down

16. ExamplesIm-Pyr up up

17. ExamplesIm-Pyr up down

18. ExamplesIm-Im up up

19. ExamplesIm-Im up down

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

21. 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.