A. Malhotra et al., MODELING LARGE RNAS AND RIBONUCLEOPROTEIN-PARTICLES USING MOLECULAR MECHANICS TECHNIQUES, Biophysical journal, 66(6), 1994, pp. 1777-1795
There is a growing body of low-resolution structural data that can be
utilized to devise structural models for large RNAs and ribonucleoprot
eins. These models are routinely built manually. We introduce an autom
ated refinement protocol to utilize such data for building low-resolut
ion three-dimensional models using the tools of molecular mechanics. I
n addition to specifying the positions of each nucleotide, the protoco
l provides quantitative estimates of the uncertainties in those positi
ons, i.e., the resolution of the model. In typical applications, the r
esolution of the models is about 10-20 Angstrom. Our method uses reduc
ed representations and allows us to refine three-dimensional structure
s of systems as big as the 16S and 23S ribosomal RNAs, which are about
one to two orders of magnitude larger than nucleic acids that can be
examined by traditional all-atom modeling methods. Nonatomic resolutio
n structural data-secondary structure, chemical cross-links, chemical
and enzymatic footprinting patterns, protein positions, solvent access
ibility, and so on-are combined with known motifs in RNA structure to
predict low-resolution models of large RNAs. These structural constrai
nts are imposed on the RNA chain using molecular mechanics-type potent
ial functions with parameters based on the quality of experimental dat
a. Surface potential functions are used to incorporate shape and posit
ional data from electron microscopy image reconstruction experiments i
nto our models. The structures are optimized using techniques of energ
y refinement to get RNA folding patterns. In addition to providing a c
onsensus model, the method finds the range of models consistent with t
he data, which allows quantitative evaluation of the resolution of the
model. The method also identifies conflicts in the experimental data.
Although our protocol is aimed at much larger RNAs, we illustrate the
se techniques using the tRNA structure as an example and test-bed.