Publication Date
Fall 2016
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Chemistry
Advisor
Brooke Lustig
Keywords
descriptor-based, hydrogen bonding, knowledge-base, protein, RNA, statistical potential
Subject Areas
Chemistry; Bioinformatics; Biophysics
Abstract
Computational modeling of RNA-protein interactions remains an important endeavor. However, exclusively all-atom approaches that model RNA-protein interactions via molecular dynamics are often problematic in their application. One possible alternative is the implementation of hierarchical approaches, first efficiently exploring configurational space with a coarse-grained representation of the RNA and protein. Subsequently, the lowest energy set of such coarse-grained models can be used as scaffolds for all-atom placements, a standard method in modeling protein 3D-structure. However, the coarse-grained modeling likely will require improved ribonucleotide-amino acid potentials as applied to coarse-grained structures. As a first step we downloaded 1,345 PDB files and clustered them with PISCES to obtain a non-redundant complex data set. The contacts were divided into nine types with DSSR according to the 3D structure of RNA and then 9 sets of potentials were calculated. The potentials were applied to score fifty thousand poses generated by FTDock for twenty-one standard RNA-protein complexes. The results compare favorably to existing RNA-protein potentials. Future research will optimize and test such combined potentials.
Recommended Citation
Kimura, Takayuki, "RNA-Protein Structure Classifiers Incorporated into Second-Generation Statistical Potentials" (2016). Master's Theses. 4760.
DOI: https://doi.org/10.31979/etd.ykec-6u66
https://scholarworks.sjsu.edu/etd_theses/4760
