Computational RNA structure prediction is currently limited to secondary structure at the exception of few attempts to predict its 3D conformation. Thus, the problem of predicting the structure of RNA is still unsolved and is part of a major challenge in structural biology: to derive the rules of RNA molecular evolution.
This proposal focus on characterizing the underlying principles for comparative modeling of RNA structures at atomic resolution, implementing those principles in a method for comparative modeling, and illustrating its utility. RNA is more like a protein than a DNA because the size, complexity and specific detail of its structure determine its function.
Protein comparative modeling is possible because structure is evolutionary more conserved than sequence and because the relationship between sequence and structure can be quantified. At the same time, the number of available protein structures is increasing and sufficient for the derivation of statistical rules for comparative modeling. These requirements are currently meet for comparative modeling of RNA structures.
Thus, the challenge for structural computational biologists is to derive the basis for developing an automated, accurate and readily available method for comparative modeling of RN A structures at atomic resolution. The result of this proposal should be such a method, which will be implemented in the MODELLER program currently used at more than 6,000 research institutions worldwide.
The application of the method to a large-scale prediction will increase the number of available structures of RNA molecules. Thus, advancing our fundamental knowledge of the relationship between sequence, structure and function of RNA molecules.
This is likely to impact various fields of biomedicine, including but not limited to the design of new drugs to interact with RNA molecules and the study of RNA molecules that regulate cellular processes
Call for proposal
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