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.
* 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
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.
[1]
Wang J, Zhao Y, Zhu C, Xiao Y 2015 Nucleic Acids Res. 43 e63
[2]
Wang J, Mao K, Zhao Y, Zeng C, Xiang J, Zhang Y, Xiao Y 2017 Nucleic Acids Res. 45 6299
[3]
Zelinger L, Swaroop A 2018 Trends Genet. 34 341
[4]
Lu D, Thum T 2019 Nat. Rev. Cardiol. 16 661
[5]
Huang Y, Li H, Xiao Y 2018 Bioinformatics 34 1238
[6]
Zhang J, Zhang Y J, Wang W 2010 Chin. Phys. Lett. 27 118702
[7]
Nithin C, Ghosh P, Bujnicki J M 2018 Genes 9 432
[8]
Wang H, Wang K, Guan Z, Jian Y, Jia Y, Kashanchi F, Zeng C, Zhao 2017 Chin. Phys. B 26 128702
[9]
Yan Y, Huang S Y 2018 Bioinformatics 34 453
[10]
Yan Y, Zhang D, Zhou P, Li B, Huang S Y 2017 Nucleic Acids Res. 45 W365
[11]
Zhao Y, Jian Y, Liu Z, Liu H, Liu Q, Chen C, Li Z, Wang L, Huang H H, Zeng C 2017 Sci. Rep. 7 2876
[12]
Yan Y, Wen Z, Zhang D, Huang S Y 2018 Nucleic Acids Res. 46 e56
[13]
Bao L, Wang J, Xiao Y 2019 Phys. Rev. E 100 022412
[14]
Wang H, Qiu J, Liu H, Jian Y, Xu Y, Jia Y, Kashanchi F, Zeng C, Zhao Y 2019 BMC Bioinformatics 20 617
[15]
Karn J, Keen N J, Churcher M J, Aboul-ela F, Varani G, Hamy F, Felder E R, Heizmann G, Klimkait T 1998 Pharmacochemistry Library 29 121
[16]
Abulwerdi F A, Grice S F J L 2017 Curr. Pharm. Des. 23 4112
[17]
Zhao Y, Chen H, Du C, Jian Y, Li H, Xiao Y, Saifuddin M, Kashanchi F, Zeng C 2018 Int. J. Pept. Res. Ther. 25 807
[18]
Romby P, Charpentier E 2010 Cell. Mol. Life Sci. 67 217
[19]
Zhou T, Wang H, Song L, Zhao Y 2020 J. Theor. Comput. Chem. 19 2040001
[20]
Lou Y, Chen B, Zhou J, Sintim H O, Dayie T K 2014 Mol.Biosyst. 10 384
[21]
Kang M, Eichhorn C D, Feigon J 2014 Proc. Natl. Acad. Sci. USA 111 E663
[22]
Heroven A K, Nuss A M, Dersch P 2017 RNA Biol. 14 471
[23]
Wang H, Guan Z, Qiu J, Jia Y, Zeng C, Zhao Y 2020 RSC. Adv. 10 2004
[24]
Jiang L, Schaffitzel C, Bingel-Erlenmeyer R, Ban N, Korber P, Koning R I, de Geus Dd C, Plaisier J R, Abrahams J P 2009 J. Mol. Biol. 386 1357
Latham M P, Brown D J, McCallum S A, Prodi A 2005 Chembiochem 6 1492
[27]
Zhao Y, Wang J, Zeng C, Xiao Y 2018 Biophys. Rep. 4 123
[28]
Tang Y, Liu D, Wang Z, Wen T, Deng L 2017 BMC Bioinformatics 18 465
[29]
Su H, Liu M, Sun S, Peng Z, Yang J 2019 Bioinformatics 35 930
[30]
Duss O, Yulikov M, Jeschke G, Allain F H 2014 Nat. Commun. 5 3669
[31]
Duss O, Yulikov M, Allain F H T, Jeschke G 2015 Method. Enzymol. 558 279
[32]
Cheong H K, Hwang E, Lee C, Choi B S, Cheong C 2004 Nucleic Acids Res. 32 e84
[33]
Zhao Y, Huang Y, Zhou G, Wang Y, Man J, Xiao Y 2012 Sci. Rep. 2 734
[34]
Wang J, Xiao Y 2017 Current Protocols in Bioinformatics 57 5.9.1
[35]
Wang J, Wang J, Huang Y, Xiao Y 2019 Int. J. Mol. Sci. 20 4116
[36]
Gong Z, Zhao Y, Xiao Y 2010 J. Biomol. Struc. Dyn. 28 431
[37]
Zhao Y, Zhou G, Xiao Y 2011 J. Biomol. Struc. Dyn. 28 815
[38]
Sharma S, Ding F, Dokholyan N V 2008 Bioinformatics 24 1951
[39]
Krokhotin A, Houlihan K, Dokholyan N V 2015 Bioinformatics 31 2891
[40]
He J, Wang J, Tao H, Xiao Y, Huang S Y 2019 Nucleic Acids Res. 47 W35
[41]
Bao L, Zhang X, Jin L, Tan Z J 2015 Chin. Phys. B 25 018703
[42]
Siegfried N A, Busan S, Rice G M, Nelson J A, Weeks K M 2014 Nat. Methods 11 959
[43]
Harris M E, Christian E L 2009 Methods Enzymol. 468 127
[44]
Hafner M, Landthaler M, Burger L, Khorshid M, Hausser J, Berninger P, Rothballer A, Ascano M, Jungkamp A C, Munschauer M, Ulrich A, Wardle G S, Dewell S, Zavolan M, Tuschl T 2010 Cell 141 129
[45]
Zhao J, Ohsumi T K, Kung J T, Ogawa Y, Grau D J, Sarma K, Song J J, Kingston R E, Borowsky M, Lee J T 2010 Mol. Cell 40 939
[46]
Nilsen T W 2014 Cold Spring Harb. Protoc. 2014 683
[47]
Stork C, Zheng S 2018 Reporter Gene Assays Reporter Gene Assays New York Humana Press 1755 65 74
[48]
Shi Y Z, Wu Y Y, Wang F H, Tan Z J 2014 Chin. Phys. B 23 078701
[49]
Yang Y, Gu Q, Zhang B G, Shi Y Z, Shao Z G 2018 Chin. Phys. B 27 038701
[50]
Mueller F, Döring T, Erdemir T, Greuer B, Jünke N, Osswald M, Rinke-Appel J, Stade K, Thamm S, Brimacombe R 1995 Biochem. Cell Biol. 73 767
[51]
Massire C, Westhof E 1998 J. Mol. Graph. Model. 16 197
[52]
Jossinet F, Westhof E 2005 Bioinformatics 21 3320
[53]
Martinez H M, Maizel J V Jr, Shapiro B A 2008 J. Biomol. Struct. Dyn. 25 669
[54]
Jossinet F, Ludwig T E, Westhof E 2010 Bioinformatics 26 2057
[55]
Cao S, Chen S J 2005 RNA 11 1884
[56]
Parisien M, Major F 2008 Nature 452 51
[57]
Flores S C, Altman R B 2010 RNA 16 1769
[58]
Rother M, Rother K, Puton T, Bujnicki J M 2011 Nucleic Acids Res. 39 4007
[59]
Biesiada M, Purzycka K J, Szachniuk M, Blazewicz J, Adamiak R W 2016 RNA Structure Determination New York Humana Press 1490 199 215
[60]
Das R, Baker D 2007 Proc. Natl. Acad. Sci. USA 104 14664
[61]
Jonikas M A, Radmer R J, Laederach A, Das R, Pearlman S, Herschlag D, Altman R B 2009 RNA 15 189
[62]
Das R, Karanicolas J, Baker D 2010 Nat. Methods 7 291
[63]
Boniecki M J, Lach G, Dawson W K, Tomala K, Lukasz P, Soltysinski T, Rother K M, Bujnicki J M 2016 Nucleic Acids Res. 44 e63
[64]
Gutell R R, Power A, Hertz G Z, Putz E J, Stormo G D 1992 Nucleic Acids Res. 20 5785
[65]
Freyhult E, Moulton V, Gardner P 2005 Appl. Bioinformatics 4 53
[66]
Dunn S D, Wahl L M, Gloor G B 2008 Bioinformatics 24 333
[67]
Edgar R C 2004 Nucleic Acids Res. 32 1792
[68]
Chenna R, Sugawara H, Koike T, Lopez R, Gibson T J, Higgins D G, Thompson J D 2003 Nucleic Acids Res. 31 3497
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