Methods and applications of RNA contact prediction

doi:10.1088/1674-1056/abb7f3

Abstract

The RNA tertiary structure is essential to understanding the function and biological processes. Unfortunately, it is still challenging to determine the large RNA structure from direct experimentation or computational modeling. One promising approach is first to predict the tertiary contacts and then use the contacts as constraints to model the structure. The RNA structure modeling depends on the contact prediction accuracy. Although many contact prediction methods have been developed in the protein field, there are only several contact prediction methods in the RNA field at present. Here, we first review the theoretical basis and test the performances of recent RNA contact prediction methods for tertiary structure and complex modeling problems. Then, we summarize the advantages and limitations of these RNA contact prediction methods. We suggest some future directions for this rapidly expanding field in the last.

Keywords: RNA structure contact prediction direct coupling analysis network machine learning

Received: 30 April 2020
Revised: 07 July 2020
Accepted manuscript online: 14 September 2020

PACS:	87.14.gn	(RNA)
	87.15.K-	(Molecular interactions; membrane-protein interactions)
	87.10.Ca	(Analytical theories)
	87.15.A-	(Theory, modeling, and computer simulation)

Corresponding Authors: ^†Corresponding author. E-mail: yjzhaowh@mail.ccnu.edu.cn

About author:

†Corresponding author. E-mail: yjzhaowh@mail.ccnu.edu.cn

* Project supported by the National Natural Science Foundation of China (Grant No. 11704140) and Self-determined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE (Grant No. CCNU20TS004).

Cite this article:

Huiwen Wang(王慧雯) and Yunjie Zhao(赵蕴杰)† Methods and applications of RNA contact prediction 2020 Chin. Phys. B 29 108708

Table 1.

A list of RNA contact prediction methods.

Fig. 1.

The direct and indirect contacts in RNA structure. The interactions of i–j, j–k, and k–m are direct contacts because they are in close distance. The interaction of i–m is indirect contact due to the transitive correlation from the tandem direct contacts. The yellow dot, red line, and blue line represent a nucleotide, direct contact, and indirect contact, respectively. The HIV-1 RNA molecule is colored in yellow with a cartoon representation (PDB code: 5L1Z. N chain).^[92]

Fig. 2.

The co-evolution based RNA contact prediction. The co-evolution based contact prediction can identify the RNA intramolecular contacts from the homologous sequence across different species. The solid black lines, red dots, and green dotted lines represent RNA sequences, nucleotides, and RNA contacts, respectively. The HIV-1 RNA (PDB code: 5L1Z, N chain)^[92] is colored in yellow with a cartoon representation.

Fig. 3.

The accuracy of RNA intramolecular contact prediction methods. The accuracy (positive predictive value, PPV) of MI, mfDCA, plmDCA, and DIRECT are 0.26, 0.28, 0.31, and 0.34, respectively.

Fig. 4.

The accuracy of RNA intermolecular contact prediction methods. The accuracy (positive predictive value, PPV) of Rsite, Rsite2, RBind, and PRIdictor are 0.62, 0.64, 0.67, and 0.62, respectively.

Fig. 5.

The structure-based RNA contact prediction. The RNA or RNA complex structural characteristic patterns can be used for the RNA contact prediction by using machine learning. The red dots, blue dots, and green dotted lines represent nucleotides, residues, and predicted RNA contacts, respectively. The HIV-1 RNA (PDB code: 5L1Z, N chain) and HIV-1 Tat protein (PDB code: 5L1Z, D chain)^[92] are colored in yellow and cyan with a cartoon representation, respectively.

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