The Computational Modeling Component Project is charged with using the diverse data collected by other Projects within the Program Project, creating three dimensional models that summarize the data, and also offer insight into the data that suggest new experiments for clarification or testing of structural hypotheses. As such, it serves as a critical glue between the other Projects, and acts to summarize the overall interpretation of the data collected within the Program. However, computational modeling is an active research area, and this Project touches upon many open questions, including (a) the problem of generating ensembles of models to explain data sets that are not consistent with any single static conformation, and (b) the problem of using structural models (built based on both geometric and energetic criteria) to inform experimental design. Towards that end, the Computational Modeling Project has three specific aims: (1) to compute static three-dimensional models from the data collected by Component Projects, (2) to augment static models with ensemble and dynamic information as necessary to explain the experimental data, and (3) to use the resulting models to generate new experiments (in collaboration with other Projects) that either test or clarify the structural models that are built. The proposed Component Project will therefore conduct basic research in the uses of computational modeling to integrate diverse data sources and in methods for tightly coupling computational modeling with experimental design. The results of computational modeling will be stored and disseminated by the Bioinformatics Core, as for all data collected within this Program Project.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Program Projects (P01)
Project #
5P01GM066275-02
Application #
7551198
Study Section
Special Emphasis Panel (ZRG1)
Project Start
Project End
Budget Start
2004-06-01
Budget End
2005-05-31
Support Year
2
Fiscal Year
2004
Total Cost
$109,621
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Merriman, Dawn K; Yuan, Jiayi; Shi, Honglue et al. (2018) Increasing the length of poly-pyrimidine bulges broadens RNA conformational ensembles with minimal impact on stacking energetics. RNA 24:1363-1376
Gracia, Brant; Al-Hashimi, Hashim M; Bisaria, Namita et al. (2018) Hidden Structural Modules in a Cooperative RNA Folding Transition. Cell Rep 22:3240-3250
Zettl, Thomas; Das, Rhiju; Harbury, Pehr A B et al. (2018) Recording and Analyzing Nucleic Acid Distance Distributions with X-Ray Scattering Interferometry (XSI). Curr Protoc Nucleic Acid Chem 73:e54
Ganser, Laura R; Lee, Janghyun; Rangadurai, Atul et al. (2018) High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble. Nat Struct Mol Biol 25:425-434
Liu, Bei; Merriman, Dawn K; Choi, Seung H et al. (2018) A potentially abundant junctional RNA motif stabilized by m6A and Mg2. Nat Commun 9:2761
Denny, Sarah Knight; Bisaria, Namita; Yesselman, Joseph David et al. (2018) High-Throughput Investigation of Diverse Junction Elements in RNA Tertiary Folding. Cell 174:377-390.e20
Kimsey, Isaac J; Szymanski, Eric S; Zahurancik, Walter J et al. (2018) Dynamic basis for dG•dT misincorporation via tautomerization and ionization. Nature 554:195-201
Boyle, Evan A; Andreasson, Johan O L; Chircus, Lauren M et al. (2017) High-throughput biochemical profiling reveals sequence determinants of dCas9 off-target binding and unbinding. Proc Natl Acad Sci U S A 114:5461-5466
Bisaria, Namita; Jarmoskaite, Inga; Herschlag, Daniel (2017) Lessons from Enzyme Kinetics Reveal Specificity Principles for RNA-Guided Nucleases in RNA Interference and CRISPR-Based Genome Editing. Cell Syst 4:21-29
Gleitsman, Kristin R; Sengupta, Raghuvir N; Herschlag, Daniel (2017) Slow molecular recognition by RNA. RNA 23:1745-1753

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