KGSrna: efficient 3D kinematics-based sampling for nucleic acids
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Standard
KGSrna : efficient 3D kinematics-based sampling for nucleic acids. / Fonseca, Rasmus; van den Bedem, Henry; Bernauer, Julie.
Research in Computational Molecular Biology: 19th Annual International Conference, RECOMB 2015, Warsaw, Poland, April 12-15, 2015, Proceedings. ed. / Teresa Przytycka. Springer, 2015. p. 80-95 (Lecture Notes in Bioinformatics).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
Harvard
APA
Vancouver
Author
Bibtex
}
RIS
TY - GEN
T1 - KGSrna
T2 - Annual International Conference, RECOMB 2015
AU - Fonseca, Rasmus
AU - van den Bedem, Henry
AU - Bernauer, Julie
N1 - Conference code: 19
PY - 2015
Y1 - 2015
N2 - Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.
AB - Noncoding ribonucleic acids (RNA) play a critical role in a wide variety of cellular processes, ranging from regulating gene expression to post-translational modification and protein synthesis. Their activity is modulated by highly dynamic exchanges between three-dimensional conformational substates, which are difficult to characterize experimentally and computationally. Here, we present an innovative, entirely kinematic computational procedure to efficiently explore the native ensemble of RNA molecules. Our procedure projects degrees of freedom onto a subspace of conformation space defined by distance constraints in the tertiary structure. The dimensionality reduction enables efficient exploration of conformational space. We show that the conformational distributions obtained with our method broadly sample the conformational landscape observed in NMR experiments. Compared to normal mode analysis-based exploration, our procedure diffuses faster through the experimental ensemble while also accessing conformational substates to greater precision. Our results suggest that conformational sampling with a highly reduced but fully atomistic representation of noncoding RNA expresses key features of their dynamic nature.
U2 - 10.1007/978-3-319-16706-0_11
DO - 10.1007/978-3-319-16706-0_11
M3 - Article in proceedings
SN - 978-3-319-16705-3
T3 - Lecture Notes in Bioinformatics
SP - 80
EP - 95
BT - Research in Computational Molecular Biology
A2 - Przytycka, Teresa
PB - Springer
Y2 - 12 April 2015 through 15 April 2015
ER -
ID: 142175402