[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] RNA (ribonucleic acid) molecules play a variety of crucial roles in cellular functions at the level of transcription, translation and gene regulation. RNA functions are tied to structures. We aim to develop a novel free energy-based model for RNA structures, especially for RNA loops and junctions. In the first project, we develop a new conformational entropy model for RNA structures consisting of multiple helices connected by cross-linked loops. The basic strategy of our approach is to decompose the whole structure into a number of three-body building blocks, where each building block consists of a loop and two helices that are directly connected to the two ends of the loop. Assembly of the building blocks gives the entropy of the whole structure. The method provide a solid first step toward a systematic development of an entropy and free energy model for complex tertiary folds for RNA and other biopolymer. In the second project, based on the survey of all the known RNA structures, we derive a set of virtual bond-based scoring functions for the different types of dinucleotides. To circumvent the problem of reference state selection, we apply an iterative method to extract the effective potential, based on the complete conformational ensemble. With such a set of knowledge-based energy parameters, for a given sequence, we can successfully identify the native structure (the best-scored structure) from a set of structural decoys.
Corrigendum to “All-Atom Four-Body Knowledge-Based Statistical Potentials to Distinguish Native Protein Structures from Nonnative Folds”