DUAL SCALE COMPUTING WITH RNA

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Our understanding of the role of RNA in biology has expanded enormously over the last two decades. Originally, RNA was understood to participate in protein expression as a passive carrier of genetic information (mRNA) and as an adapter molecule (tRNA) for reading the code. Then RNA was discovered to catalyze reactions, first self-splicing, then phosphodiester bond cleavage, and, recently, peptide bond formation. RNA is now known to play important functions in many diverse cellular processes, such as development, immunity, RNA editing and modification, and post-transcriptional gene regulation. RNA is also an important player in many diseases, including Prader-Willi, b-thalassemia, and myotonic dystrophy. With the increasing awareness of RNAs biological diversity, the ability to harness RNA as a tool has increased. RNA sequences can be evolved in vitro to catalyze many reactions that are not part of the natural repertoire. Antisense and RNAi can be used to modulate gene expression. Our group is interested in developing new computational biology tools and applying these tools to understanding RNA structure and function. We are currently working on four projects with extensive computational requirements: 1) We have developed a method for finding novel non-coding RNA (ncRNA) genes in genomic sequence. These are RNA sequences that function directly, without coding for a protein. Our method relies on our Dynalign algorithm to determine the lowest free energy structure common to two, unaligned sequences (Mathews, 2005; Mathews & Turner, 2002). Folding free energies, as determined by nearest neighbor parameters (Mathews et al., 2004; Mathews et al., 1999), of two ncRNA sequences are significantly lower than folding free energies of random sequences of identical dinucleotide-frequency content. Dynalign requires no sequence identity to find the common structure. We have therefore found that we can discover homologous ncRNA genes in crudely aligned genome fragments with greater sensitivity than other methods, especially at low sequence identity. 2) We have incorporated nudged elastic band (NEB) (Jnsson et al., 1998) into the AMBER molecular dynamics package (Pearlman et al., 1995) in collaboration with Dr. David Case, The Scripps Research Institute. NEB provides a time-scale independent method for finding low-energy pathways for conformational changes. We are currently applying NEB to understanding strand invasion, by which intermolecular base pairs displace intramolecular base pairs. This is important for understanding RNAi and antisense mechanisms. 3) We are using free energy calculations to understand the nature of SHAPE mapping of RNA structure. SHAPE maps RNA structures using N-methylisatoic anhydride to acylate the 2 OH of flexible RNA nucleotides (Merino et al., 2005; Wilkinson et al., 2005). It is known that the acylation reaction rate is dependent on the pKa of the 2 OH, but it is unclear why flexible nucleotides have lower pKas. Our simulations with AMBER using tRNA structures will be used to determine the connection. 4) We are developing new methods based on the Jarzynski equality to determine unfolding free energies of RNA hairpins on reasonable timescales (Jarzynski, 1997). Our goal is to test whether the AMBER forcefield can reproduce unfolding free energies found experimentally by mechanical pulling (Liphardt et al., 2002; Liphardt et al., 2001). Ultimately, we will model the molecular-level details of mechanical unfolding of RNA. For this Development Application to Teragrid, we propose to use most of the allocation in finding non-coding RNA sequences (project 1 above). To start, we will scan an alignment of the E. coli genome to S. Typhi that we have constructed using MUMmer 2 (Kurtz et al., 2004). We estimate that this will require 6,000 CPU hours on a 3 GHz Intel Pentium 4. We also plan use the balance of the allocation to benchmark our methods outlined in projects 2-4 and to start the scan of another genome for ncRNA genes. This will allow us to prepare a Medium Resource Allocation for Teragrid resources in the next 4 to 6 months. References: Jarzynski, C. (1997). Nonequilibrium equality for free energy differences. Phys. Rev. Lett. 78, 2690-2693. Jnsson, H., Mills, G. & Jacobsen, K. W. (1998). Nudged elastic band method for finding minimum energy paths of transitions. In Classical and Quantum Dynamics in Condensed Phase Simulations (Berne, B. J., Ciccoti, G. & Coker, D. F., eds.), pp. 385-404. World Scientific, Singapore. Kurtz, S., Phillippy, A., Delcher, A. L., Smoot, M., Shumway, M., Antonescu, C., et al. (2004). Versatile and open software for comparing large genomes. Genome Biol 5, R12. Liphardt, J., Dumont, S., Smith, S. B., Tinoco, I., Jr. & Bustamante, C. (2002). Equilibrium information from nonequilibrium measurements in an experimental test of Jarzynski's equality. Science 296, 1832-5. Liphardt, J., Onoa, B., Smith, S. B., Tinoco, I. J. & Bustamante, C. (2001). Reversible unfolding of single RNA molecules by mechanical force. Science 292, 733-7. Mathews, D. H. (2005). Predicting a set of minimal free energy RNA secondary structures common to two sequences. Bioinformatics 21, 2246-2253. Mathews, D. H., Disney, M. D., Childs, J. L., Schroeder, S. J., Zuker, M. & Turner, D. H. (2004). Incorporating chemical modification constraints into a dynamic programming algorithm for prediction of RNA secondary structure. Proc. Natl. Acad. Sci. USA 101, 7287-7292. Mathews, D. H., Sabina, J., Zuker, M. & Turner, D. H. (1999). Expanded sequence dependence of thermodynamic parameters provides improved prediction of RNA Secondary Structure. J. Mol. Biol. 288, 911-940. Mathews, D. H. & Turner, D. H. (2002). Dynalign: An algorithm for finding the secondary structure common to two RNA sequences. J. Mol. Biol. 317, 191-203. Merino, E. J., Wilkinson, K. A., Coughlan, J. L. & Weeks, K. M. (2005). RNA structure analysis at single nucleotide resolution by selective 2'-hydroxyl acylation and primer extension (SHAPE). J Am Chem Soc 127, 4223-31. Pearlman, D. A., Case, D. A., Caldwell, J. W., Ross, W. S., Cheatham, T. E., III, DeBolt, S., et al. (1995). AMBER, a package of computer programs for applying molecular dynamics and free energy calculations to simulate the structural and energetic properties of molecules. Comp. Phys. Commun. 91, 1-41. Wilkinson, K. A., Merino, E. J. & Weeks, K. M. (2005). RNA SHAPE chemistry reveals nonhierarchical interactions dominate equilibrium structural transitions in tRNA(Asp) transcripts. J Am Chem Soc 127, 4659-67.