TY - JOUR
T1 - F-RAG
T2 - Generating Atomic Coordinates from RNA Graphs by Fragment Assembly
AU - Jain, Swati
AU - Schlick, Tamar
N1 - Funding Information:
We thank current and previous members of the Schlick lab for helpful comments and discussions, and Shereef Elmetwaly for technical assistance. This work has been supported by the National Institute of General Medical Sciences, National Institutes of Health (Grants Nos. GM100469 , GM081410 , and R35GM122562 to T.S.). The funding body listed above did not play any role in the study or conclusions of this study. Conflict of Interest Statement: None declared. Appendix A
Funding Information:
We thank current and previous members of the Schlick lab for helpful comments and discussions, and Shereef Elmetwaly for technical assistance. This work has been supported by the National Institute of General Medical Sciences, National Institutes of Health (Grants Nos. GM100469, GM081410, and R35GM122562 to T.S.). The funding body listed above did not play any role in the study or conclusions of this study.
Publisher Copyright:
© 2017 Elsevier Ltd
PY - 2017/11/24
Y1 - 2017/11/24
N2 - Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.
AB - Coarse-grained models represent attractive approaches to analyze and simulate ribonucleic acid (RNA) molecules, for example, for structure prediction and design, as they simplify the RNA structure to reduce the conformational search space. Our structure prediction protocol RAGTOP (RNA-As-Graphs Topology Prediction) represents RNA structures as tree graphs and samples graph topologies to produce candidate graphs. However, for a more detailed study and analysis, construction of atomic from coarse-grained models is required. Here we present our graph-based fragment assembly algorithm (F-RAG) to convert candidate three-dimensional (3D) tree graph models, produced by RAGTOP into atomic structures. We use our related RAG-3D utilities to partition graphs into subgraphs and search for structurally similar atomic fragments in a data set of RNA 3D structures. The fragments are edited and superimposed using common residues, full atomic models are scored using RAGTOP's knowledge-based potential, and geometries of top scoring models is optimized. To evaluate our models, we assess all-atom RMSDs and Interaction Network Fidelity (a measure of residue interactions) with respect to experimentally solved structures and compare our results to other fragment assembly programs. For a set of 50 RNA structures, we obtain atomic models with reasonable geometries and interactions, particularly good for RNAs containing junctions. Additional improvements to our protocol and databases are outlined. These results provide a good foundation for further work on RNA structure prediction and design applications.
KW - RNA atomic models
KW - RNA graph partitioning
KW - RNA graphs
KW - RNA motif search
KW - fragment assembly
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U2 - 10.1016/j.jmb.2017.09.017
DO - 10.1016/j.jmb.2017.09.017
M3 - Article
C2 - 28988954
AN - SCOPUS:85031710274
SN - 0022-2836
VL - 429
SP - 3587
EP - 3605
JO - Journal of Molecular Biology
JF - Journal of Molecular Biology
IS - 23
ER -