Limits in accuracy and a strategy of RNA structure prediction using experimental information

Abstract

RNA structural complexity and flexibility present a challenge for computational modeling efforts. Experimental information and bioinformatics data can be used as restraints to improve the accuracy of RNA tertiary structure prediction. Regarding utilization of restraints, the fundamental questions are: (i) What is the limit in prediction accuracy that one can achieve with arbitrary number of restraints? (ii) Is there a strategy for selection of the minimal number of restraints that would result in the best structural model? We address the first question by testing the limits in prediction accuracy using native contacts as restraints. To address the second question, we develop an algorithm based on the distance variation allowed by secondary structure (DVASS), which ranks restraints according to their importance to RNA tertiary structure prediction. We find that due to kinetic traps, the greatest improvement in the structure prediction accuracy is achieved when we utilize only 40-60% of the total number of native contacts as restraints. When the restraints are sorted by DVASS algorithm, using only the first 20% ranked restraints can greatly improve the prediction accuracy. Our findings suggest that only a limited number of strategically selected distance restraints can significantly assist in RNA structure modeling.

Figure 1.

Design of RNA Native Contacts, Gō Potential Restraints and the DVASS Algorithm. (A) Distances between C1′ atoms in RNA fall under 15 Å for many common base interactions such as A-U Watson–Crick base pairs (10.2 Å) and G-C Watson–Crick base pairs (10.7 Å) or stacking interactions with adjacent nucleotides (5.4–10.7 Å). Native restraints are modeled using a step-wise function in DMD simulations. Potential energy values for the well reach −1.0 kcal/mol/K and the walls of the restraints span from 3.75 to 15 Å. (B) The workflow of DVASS algorithm. (C) The helix geometry parameters and the chain connectivity parameters. (D) The triangle inequality.

Figure 2.

Relationship between the normalized RMSD and the percentage of imposed restraints. (A) The average normalized RMSD of the 22 RNAs when using 0%, 5%, 10%, 20%, 40%, 60%, 80% and 100% restraints, respectively. (B) The RMSD of 3LA5 when using different fractions of restraints. (C) The RMSD of 2L1V when using different fractions of restraints. (D) The secondary structure of 3LA5. (E) The secondary structure of 2L1V.

Figure 3.

Thermodynamic analyses of a three-way junction in Varkud Satellite Ribozyme. (A, B and C) The free energy landscapes of 2MTJ obtained by imposing no restraints (A), 40% restraints (B) and 80% restraints (C), respectively. NN refers to the near-native structure, and DN refers to the distal-native structure. The white box refers to a free energy barrier that impedes the inter-conversion of the NN and DN states. The landscapes are derived from the potential of mean force of RMSD and iFoldRNA energies. The color bar represents the relative Helmholtz free energy in kcal/mol. (D) Overlap of the crystal structure (green) of 2MTJ and the NN structure (olive) with an RMSD of 3.37 Å. The red color refers to the red boxed region in (F). (E) Overlap of the crystal structure (green) of 2MTJ and the DN structure (blue) with an RMSD of 9.96 Å. The red color refers to the red boxed region in (F). (F) The comparison of the contacts in the native structure (grey square), the NN structure (olive circle) and the DN structure (blue circle) of 2MTJ.

Figure 4.

DVASS Algorithm Test Results in the 22 RNAs Dataset. (A) The average normalized RMSD of the 22 RNAs when using 0%, 0–20%, 20–40%, 40–60%, 60–80% and 80–100% top ranked restraints. (B) Predicted number of contacts versus the number of native contacts. The green line refers to the linear regression of the correlation between the predicted and native contacts, which is: N_predicted = 0.989 × N_native − 13.9, where N_predicted is the predicted number of contacts and N_native is the genuine number of contacts. The Pearson Correlation Coefficient is 0.962. The P-value is 3.54 × 10⁻¹². (C) The RMSD of 3RG5 obtained by imposing 0%, 5%, 10%, 20%, 40%, 60%, 80% and 100% unsorted restraints. (D) The secondary structure of 3RG5. Restraints in residue pairs colored by red, green, light green, pink, blue and cyan are predicted to be located in top 0–20%, 20–40%, 40–60%, 60–80%, 80–100% and 80–100% ranked area, respectively. The brown dashed line refers to the pseudoknot. (E) The average RMSD of 3RG5 obtained by imposing 0%, 0–20%, 20–40%, 40–60%, 60–80% and 80–100% top ranked restraints.