New metrics for comparing and assessing discrepancies between RNA 3D structures and models.

By Marc-André Parisien, José Almeida Cruz, Eric Westhof, François Major. 2009

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In Molecular Biology

Citations in the corpus

Automated 3D structure composition for large RNAs (2012, methodology): “The ones assembled from structural elements with high-sequence identity show high accuracy, as reﬂected by both the r.m.s.d. and interaction network ﬁdelity parameters (33) (INF all and INF cbp ), between the predicted and crystal structures (Table 1).”
RNA-Puzzles toolkit: a computational resource of RNA 3D structure benchmark datasets, structure manipulation, and evaluation tools (2019, methodology): “The Interaction Network Fidelity (INF) is defined as the Matthews correlation coefficient (MCC) between the interactions of the reference structure and that of the predicted structure.”
RNA-Puzzles: a CASP-like evaluation of RNA three-dimensional structure prediction. (2012, methodology): “It is computed using the ‘‘dp.py’’ command from the ‘‘SIMINDEX’’ package (Parisien et al. 2009).”
Integrating end-to-end learning with deep geometrical potentials for ab initio RNA structure prediction (2022, unknown): “In Table S1, we summarize the base interaction network ﬁ delity (INF) 26,27 and deformation index (DI) 28 scores of the models generated by DRfold and the control methods, which were calculated using the RNA-Puzzles toolkit 29 In Table S2, we further list the mean of circular quantity (MCQ) 30 and…”
RNA-Puzzles Round IV: 3D structure predictions of four ribozymes and two aptamers (2020, methodology): “Although most of the predicted structures show very low Clash scores, still some outliers exist in the distribution, Figure 1C. Supplemental Figure S4 displays the various metrics as a function of RMSD. Deformation Index shows a distribution proportional to RMSD.”
RNA-Puzzles Round III: 3D RNA structure prediction of five riboswitches and one ribozyme (2017, methodology): “…accuracy of the nucleotide interactions, while the interaction network fidelity (INF) assesses the interaction accuracies at different levels (Parisien et al. 2009); the Clash score evaluated by MolProbity (Chen et al. 2010) assesses the atomic harmony of the structure, and the mean of…”
iFoldRNA v2: folding RNA with constraints (2015, methodology): “The iFoldRNA v2 web-server is freely accessible at http://iFoldRNA.dokhlab.org for academic and non-profit users.”
RNA-Puzzles Round II: assessment of RNA structure prediction programs applied to three large RNA structures (2015, methodology): “…predicted models with the experi- mental structures, we used the Root Mean Square Deviation (RMSD)measure, the Deformation Index (DI), and the complete Deformation Profile matrix (DP) which provides an evaluation of the predictive quality of a model at multiple scales (Parisien et al. 2009).”
Pairing a high-resolution statistical potential with a nucleobase-centric sampling algorithm for improving RNA model refinement (2021, background): “The lowest RMSD model from all BRiQ predicted Another metric for describing structural difference is deformation index (DI) proposed by Parisien et al. 36 , which emphasized on similarity in the base – base interaction network.”
SimRNA: a coarse-grained method for RNA folding simulations and 3D structure prediction (2015, methodology): “The assessment of RNA structures requires analysis of both the global conformation, and the local features such as interaction patterns (41).”
New prediction categories in CASP15 (2023, methodology): “We additionally consider an RNA-specific measure, Interaction Network Fidelity (INF), 13,20 which evaluates different types of RNA interactions in models.”