What is the best reference state for building statistical potentials in RNA 3D structure evaluation?

Abstract

Knowledge-based statistical potentials have been shown to be efficient in protein structure evaluation/prediction, and the core difference between various statistical potentials is attributed to the choice of reference states. However, for RNA 3D structure evaluation, a comprehensive examination on reference states is still lacking. In this work, we built six statistical potentials based on six reference states widely used in protein structure evaluation, including averaging, quasi-chemical approximation, atom-shuffled, finite-ideal-gas, spherical-noninteracting, and random-walk-chain reference states, and we examined the six reference states against three RNA test sets including six subsets. Our extensive examinations show that, overall, for identifying native structures and ranking decoy structures, the finite-ideal-gas and random-walkchain reference states are slightly superior to others, while for identifying near-native structures, there is only a slight difference between these reference states. Our further analyses show that the performance of a statistical potential is apparently dependent on the quality of the training set. Furthermore, we found that the performance of a statistical potential is closely related to the origin of test sets, and for the three realistic test subsets, the six statistical potentials have overall unsatisfactory performance. This work presents a comprehensive examination on the existing reference states and statistical potentials for RNA 3D structure evaluation.

Salt-Dependent RNA Pseudoknot Stability: Effect of Spatial Confinement

“…Although experimental methods, such as X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, can be used to determine the structures of RNAs including pseudoknots, the structures in Protein Data Bank (PDB; https://www.rcsb.org) are still limited due to the high cost of the experimental measurements (Hajdin et al, 2010;Rose et al, 2011;Shi et al, 2014b;Schlick and Pyle, 2017). To complement the experiments, some computational models/methods (e.g., FARNA, MC-Fold/MC-Sym, Vfold, iFoldRNA, 3dRNA, RNAComposer, SimRNA, oxRNA, HiRE-RNA, and pk3D) have been developed for predicting RNA 3D structures (Cao and Chen, 2005;Ding et al, 2008;Parisien and Major, 2008;Zhang et al, 2009;Das et al, 2010;Popenda et al, 2012;Zhao et al, 2012;He et al, 2013He et al, , 2015He et al, , 2019Kim et al, 2014;Liwo et al, 2014Liwo et al, , 2020Sulc et al, 2014;Cragnolini et al, 2015;Wang et al, 2015a,b;Boniecki et al, 2016;Dawson et al, 2016;Li et al, 2016Li et al, , 2018Tan et al, 2019). Most of these models/methods are primarily designed to predict folded structures and cannot predict the stability of RNAs, especially in ion solutions (Shi et al, 2014b;Dawson et al, 2016;Schlick and Pyle, 2017), whereas the structural stability of RNAs can be very sensitive to ion conditions due to their polyanionic nature (Das et al, 2005;Draper et al, 2005;Chen, 2007, 2011;Qiu et al, 2010;Lipfert et al, 2014;Wang et al, 2018<...>…”

Structure folding of RNA kissing complexes in salt solutions: predicting 3D structure, stability, and folding pathway

“…To better capture the geometry of helical parts, additional MC simulation of further structure refinement was performed based on the conformation at the end of the annealing process at the target temperature (e.g., 25°C), in which the parameters of bonded potentials were used for the base-pairing regions to more sufficiently depict the helical geometry of the stems (see also Para helical in Supplemental Table S1; Shi et al 2014aShi et al , 2015Shi et al , 2018Jin et al 2018). The predicted structure ensemble from the structure refinement can be evaluated by their rootmean-square deviation (RMSD) values calculated over all the CG beads to the corresponding atoms in the native structures in PDB (Rose et al 2017), since there is still no reliable scoring function to identify the nearest-native structure from the predicted CG structure ensemble for the all-atom structure conversion (Li et al 2018;Tan et al 2019). Since the CG beads were simplified from the structurally fundamental atom groups (phosphate, sugar, and base groups) and are located at the coordinates of key atoms of the groups (P, C4 ′ and N1/N9 atoms), the predicted CG structures of RNAs keep the important structure features of the RNAs, and consequently, the RMSDs from predicted CG structures can serve as an examination quantity for prediction reliability.…”

“…Although RNA2D3D can be used to manually construct the 3D structure of a kissing complex (Martinez et al 2008), its reliability strongly depends on the expert knowledge of users. In parallel, some coarsegrained (CG) models (Hyeon and Thirumalai 2011;Zhang et al 2012;He et al 2013;Kim et al 2014;Bian et al 2015;Dawson et al 2016;Li et al 2016;Boudard et al 2017;Jain and Schlick 2017;Sieradzan et al 2017;Uusitalo et al 2017) such as iFold (Ding et al 2008), NAST (Jonikas et al 2009), SimRNA (Boniecki et al 2015), HiRE-RNA (Cragnolini et al 2013), RACER (Xia et al 2013), and oxRNA (Šulc et al 2014) have been developed to predict RNA 3D structures by involving experimental thermodynamic parameters (Xia et al 1998) or/ and knowledge-based statistical potentials (Tan et al 2019). However, the structures of kissing complexes have not been involved in these 3D structure prediction models.…”

Robust RNA structure refinement by a nucleobase-centric sampling algorithm coupled with a backbone rotameric and quantum-mechanical-energy-scaled base-base knowledge-based potential.

“…Here, we build a knowledge-centric refinement energy score. Atomic-level knowledge-based energy functions, derived from known three-dimensional structures, have traditionally focused on distance dependence in protein structures 26 with similar statistical potentials developed for RNA [27][28][29] . Unlike these atomic knowledge-based scores and previous all-atom RNA refinement energy scores 22,23 , the statistical potential in this work is tailored specifically to RNA interactions that were dominant by orientation-dependent base-pairing and stacking interactions 30 with rotameric backbone 31 , in contrast to dominant distance-dependent hydrophobic interactions 32 with rotameric sidechains 33 in protein folding.…”