A MESOSCOPIC LATTICE MODEL FOR STUDYING NUCLEIC ACID FOLDING DYNAMICS

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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. Ribonucleic acids (RNA) play an essential role in vital cell processes such as transcription and translation. At present, the folding mechanisms and pathways of most RNA strands have not been fully elucidated. With our Monte Carlo lattice model, we aim to predict the folded structure of RNA strands less than 100 nucleotides long and to study their folding dynamics. Most current models offer little insight into the folding processes that lead to the varied RNA tertiary structures observed in nature. We postulate that insightful mechanistic models should retain sequence-specific information relevant over the nanosecond to millisecond time-scales appropriate for folding. Although atomistic models offer a high resolution, simulating a ten nucleotide strand for longer than ten nanoseconds is computationally intractable. Thin rod models on the other hand, can handle strands longer than 1000 nucleotides, but discard sequence-specific details and are limited to a minimum time-scale on the order of milliseconds. We aim to build a coarse grained, mesoscopic model that incorporates sequence effects in a discretized space. We will use molecular dynamics simulations to find the degrees of freedom relevant to our time-scale. We are using NAMD 2.6, a software developed by the Theoretical and Computational Biophysics Group at the University of Illinois at Urbana-Champaign. On a single node in our laboratory, equilibrating a solvated three nucleotide-long toy structure takes 25 days. Using the 200000 SUs start up allocation on Teragrid will allow us to simulate a more relevant ten nucleotide strand and enable us to validate our model.