Dissertation/Thesis Abstract

RNA-protein structure classifiers incorporated into second-generation statistical potentials
by Kimura, Takayuki, M.S., San Jose State University, 2016, 145; 10241445
Abstract (Summary)

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.

Indexing (document details)
Advisor: Lustig, Brooke, Eggers, Daryl K.
Commitee: Rascon, Alberto A.
School: San Jose State University
Department: Chemistry
School Location: United States -- California
Source: MAI 56/03M(E), Masters Abstracts International
Subjects: Biochemistry, Bioinformatics, Biophysics
Keywords: Descriptor-based, Hydrogen bonding, Knowledge-base, Protein, RNA, Statistical potential
Publication Number: 10241445
ISBN: 978-1-369-56963-6
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