Modeling and design by hierarchical natural moves.
We develop a unique algorithm implemented in the program MOSAICS (Methodologies for Optimization and Sampling in Computational Studies) that is capable of nanoscale modeling without compromising the resolution of interest. This is achieved by modeling with customizable hierarchical degrees of freedom, thereby circumventing major limitations of conventional molecular modeling. With the emergence of RNA-based nanotechnology, large RNAs in all-atom representation are used here to benchmark our algorithm. Our method locates all favorable structural states of a model RNA of significant complexity while improving sampling accuracy and increasing speed many fold over existing all-atom RNA modeling methods. We also modeled the effects of sequence mutations on the structural building blocks of tRNA-based nanotechnology. With its flexibility in choosing arbitrary degrees of freedom as well as in allowing different all-atom energy functions, MOSAICS is an ideal tool to model and design biomolecules of the nanoscale.
Department of Applied Physics, Stanford University, Stanford, CA 94305, USA.
Adelene Y L
AY
Sim
Michael
M
Levitt
Peter
P
Minary
D000465:Algorithms; D001483:Base Sequence; D008958:Models, Molecular; D008969:Molecular Sequence Data; D009154:Mutation / Q000235:genetics; D049329:Nanostructures; D009690:Nucleic Acid Conformation; D018583:Pliability; D012313:RNA / Q000737:chemistry / Q000235:genetics
D012313:RNA