1. Methods Used to Determine RNA Conformational Classes Bohdan Schneider Institute of Organic Chemistry and Biochemistry Academy of Sciences of the Czech Republic, Prague, Czech Republic  bohdan@uochb.cas.cz David Micallef John Westbrook Helen M. Berman Department of Chemistry and Biological Chemistry, Rutgers University, Piscataway, NJ, USA in collaboration with Laura Murray and Jane Richardson Duke University, Durham NC, USA Supported by the NSF grant DBI 0110076 to the NDB and grant LC512 from Ministry of Education of the Czech Republic

2. Unit of Analysis  Nucleotide-like  Largest variability at the phosphodiester link  A unit for analysis  dinucleotide  “suite” (ribose-to-ribose)  (P i – P i+1 – P i+2 )  Challenge  dimensionality nucleotide has 7 torsions  noise of experimental data

3. Datasets  Original analysis done on crystal structure of 50S rRNAs: Ban et al., Science, 905 (2000), PDB code 1JJ2 analyzed ~2700 dinucleotides  Repeated using filtered data supplied by the Richardson group ~4000 “suites” from ~100 crystal structures

4. 1D, 2D, 3D Distributions  Simple analysis in 1D and 2D indicates possible clustering, directs further analysis  A few torsions bear most variability  Histograms – hints for clustering

5. Scattergrams  Highest variability at phosphodiester link  i –  i+1  Other important distributions:  i  i  i  i

6. Analysis of 3D torsion distributions  Combine key 2D distributions, as  i –  i+1 or  i  i, with other torsions:  i,  i+1,  i+1,  i,  i,  i+1,  i  In the current analysis:  used ~4,000 filtered “suites”  calculated 17 3D maps in all 17, fitted peaks, assigned fragments to peaks

7. Analysis of 3D distributions by Fourier averaging Point distributionFourier average map  i -  i+1 -  i A-RNA

8. Clustering  Peaks in 3D maps fitted  Nearby data points labeled in all analyzed maps  Fragments clustered by alphabetical sorting  6 primary maps for clustering  5 to monitor quality of proposed clusters  6 more or less ignored in the analysis

9. 3'RESIDUE 5'  GA1448UH3H1B.??D.?? GU1985UH3H1G.??D.?? AC2021CH3H1B.??D.?? UA327GH2 AAG1??C. UA397UH2 A3??F.?? AG873UH2 AAG1??C. CC1651CH2 AA?? UC558CH1J.AA?? AB GG680GH1J.AAG2D.AB AG775CH1J.A1??D.AB GU374GH1 AAG2D.AB CA449GH1 A4??D.?? Clustering by Peak Names

10. Torsional Space  Real Space  To check if clusters represent typical conformations:  Cartesian coordinates were determined for all clusters using standard valence geometry  Members of a cluster were overlapped over the average  resulting rmsd values were analyzed, outliers excluded  Result is a conformational family

11. Results  Ribosome: 32 clusters of dinucleotides  Filtered data: 38 clusters of “suites”  For the atoms common to both fragments, “Ribosome” and “Filtered” clusters overlap well more clusters were discovered with the filtered data  Both FT analyses monitored  during clustering

12. Protocol  Selection of fragments for analysis 23S and 5S rRNA from 1JJ2 filtered “suite” fragments from ~100 crystals  Put torsion angles into data matrix  Fourier-average 3D distributions of torsions  Localize and name peaks in all maps  Name data points by nearby peaks  Cluster fragments by their names  Check clusters by overlap in real 3D  Well overlapping fragments within a cluster form conformational family

13. A-RNA low rise open, stretchedA-like, intercalationZ-like U-/S-shape