Structural and dynamic properties of the YTH domain in complex with N6-methyladenosine RNA studied by accelerated molecular dynamics simulations

Mingwei Li, Guanglin Chen, Zhiyong Zhang

PDF(3849 KB)
PDF(3849 KB)
Quant. Biol. ›› 2023, Vol. 11 ›› Issue (1) : 72-81. DOI: 10.15302/J-QB-022-0297
RESEARCH ARTICLE
RESEARCH ARTICLE

Structural and dynamic properties of the YTH domain in complex with N6-methyladenosine RNA studied by accelerated molecular dynamics simulations

Author information +
History +

Abstract

Background: N6-methyl adenosine (m6A) modifications of mRNA and long non-coding RNA (lncRNAs) are known to play a significant role in regulation of gene expression and organismal development. Besides writer and eraser proteins of this dynamic modification, the YT521-B homology (YTH) domain can recognize the modification involved in numerous cellular processes. The function of proteins containing YTH domain and its binding mode with N6-Methyladenosine RNA has attracted considerable attention. However, the structural and dynamic characteristics of the YTH domain in complex with m6A RNA is still unknown.

Method: This work presents results of accelerated molecular dynamics (aMD) simulations at the timescale of microseconds. Principal component analysis (PCA), molecular mechanics generalized Born surface area (MM/GBSA) calculations, contact analysis and contact-based principal component analysis (conPCA) provide new insights into structure and dynamics of the YTH-RNA complex.

Results: The aMD simulations indicate that the recognition loop has a larger movement away from the binding pocket in the YTH-A3 RNA than that in the YTH-m6A3 RNA. In aMD trajectories of the apo YTH, there is a significant close-open transition of the recognition loop, that is to say, the apo YTH can take both the closed and open structure. We have found that the YTH domain binds more favorably to the methylated RNA than the non-methylated RNA. The per-residue free energy decomposition and conPCA suggest that hydrophobic residues including W380, L383-V385, W431-P434, M437, and M441-L442, may play important roles in favorable binding of the m6A RNA to the YTH domain, which is also supported by aMD simulations of a double mutated system (L383A/M437A).

Conclusion: The results are in good agreement with higher structural stability of the YTH-m6A RNA than that of the YTH-A3 RNA. The addition of a methylation group on A3 can enhance its binding to the hydrophobic pocket in the YTH domain. Our simulations support a ‘conformational selection’ mechanism between the YTH-RNA binding. This work may aid in our understanding of the structural and dynamic characteristics of the YTH protein in complex with the methylated RNA.

Author summary

N6-methyl adenosine (m6A) modifications play an important role in the regulation of RNA decay and gene expression at the post-transcriptional level. The YT521-B homology (YTH) domain-containing proteins can recognize m6A in numerous cellular processes. However, the binding mode of the YTH domain to m6A RNA is still unknown. In this work, we present an atomic description of the YTH-m6A RNA complex using computer simulations. The results have revealed the molecular mechanism of the RNA binding to the YTH domain. Our findings may be helpful for understanding the structural and dynamic characteristics of the YTH-m6A RNA complex.

Graphical abstract

Keywords

RNA methylation / YTH-m6A3 RNA / principal component analysis (PCA) / binding free energy / contact-based PCA

Cite this article

Download citation ▾
Mingwei Li, Guanglin Chen, Zhiyong Zhang. Structural and dynamic properties of the YTH domain in complex with N6-methyladenosine RNA studied by accelerated molecular dynamics simulations. Quant. Biol., 2023, 11(1): 72‒81 https://doi.org/10.15302/J-QB-022-0297

References

[1]
Meyer, K. D. Jaffrey, S. (2014). The dynamic epitranscriptome: N6-methyladenosine and gene expression control. Nat. Rev. Mol. Cell Biol., 15: 313–326
CrossRef Google scholar
[2]
Fu, Y., Dominissini, D., Rechavi, G. (2014). Gene expression regulation mediated through reversible m6A RNA methylation. Nat. Rev. Genet., 15: 293–306
CrossRef Google scholar
[3]
Meyer, K. D., Saletore, Y., Zumbo, P., Elemento, O., Mason, C. E. Jaffrey, S. (2012). Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell, 149: 1635–1646
CrossRef Google scholar
[4]
Dominissini, D., Moshitch-Moshkovitz, S., Schwartz, S., Salmon-Divon, M., Ungar, L., Osenberg, S., Cesarkas, K., Jacob-Hirsch, J., Amariglio, N., Kupiec, M. . (2012). Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature, 485: 201–206
CrossRef Google scholar
[5]
Liu, N., Zhou, K. I., Parisien, M., Dai, Q., Diatchenko, L. (2017). N6-methyladenosine alters RNA structure to regulate binding of a low-complexity protein. Nucleic Acids Res., 45: 6051–6063
CrossRef Google scholar
[6]
Wang, X., Lu, Z., Gomez, A., Hon, G. C., Yue, Y., Han, D., Fu, Y., Parisien, M., Dai, Q., Jia, G. . (2014). N6-methyladenosine-dependent regulation of messenger RNA stability. Nature, 505: 117–120
CrossRef Google scholar
[7]
Chandola, U., Das, R. (2015). Role of the N6-methyladenosine RNA mark in gene regulation and its implications on development and disease. Brief. Funct. Genomics, 14: 169–179
CrossRef Google scholar
[8]
Liu, J., Yue, Y., Han, D., Wang, X., Fu, Y., Zhang, L., Jia, G., Yu, M., Lu, Z., Deng, X. . (2014). A METTL3-METTL14 complex mediates mammalian nuclear RNA N6-adenosine methylation. Nat. Chem. Biol., 10: 93–95
CrossRef Google scholar
[9]
Lee, M., Kim, B. Kim, V. (2014). Emerging roles of RNA modification: m6A and U-tail. Cell, 158: 980–987
CrossRef Google scholar
[10]
Jia, G., Fu, Y., Zhao, X., Dai, Q., Zheng, G., Yang, Y., Yi, C., Lindahl, T., Pan, T., Yang, Y. G. . (2011). N6-methyladenosine in nuclear RNA is a major substrate of the obesity-associated FTO. Nat. Chem. Biol., 7: 885–887
CrossRef Google scholar
[11]
Aik, W., Scotti, J. S., Choi, H., Gong, L., Demetriades, M., Schofield, C. J. McDonough, M. (2014). Structure of human RNA N6-methyladenine demethylase ALKBH5 provides insights into its mechanisms of nucleic acid recognition and demethylation. Nucleic Acids Res., 42: 4741–4754
CrossRef Google scholar
[12]
Wang, X., Zhao, B. S., Roundtree, I. A., Lu, Z., Han, D., Ma, H., Weng, X., Chen, K., Shi, H. (2015). N6-methyladenosine modulates messenger RNA translation efficiency. Cell, 161: 1388–1399
CrossRef Google scholar
[13]
Zhou, J., Wan, J., Gao, X., Zhang, X., Jaffrey, S. R. Qian, S. (2015). Dynamic m6A mRNA methylation directs translational control of heat shock response. Nature, 526: 591–594
CrossRef Google scholar
[14]
Zhang, Z., Theler, D., Kaminska, K. H., Hiller, M., de la Grange, P., Pudimat, R., Rafalska, I., Heinrich, B., Bujnicki, J. M., Allain, F. H. T. . (2010). The YTH domain is a novel RNA binding domain. J. Biol. Chem., 285: 14701–14710
CrossRef Google scholar
[15]
Hsu, P. J., Zhu, Y., Ma, H., Guo, Y., Shi, X., Liu, Y., Qi, M., Lu, Z., Shi, H., Wang, J. . (2017). YTHDC2 is an N6-methyladenosine binding protein that regulates mammalian spermatogenesis. Cell Res., 27: 1115–1127
CrossRef Google scholar
[16]
Imai, Y., Matsuo, N., Ogawa, S., Tohyama, M. (1998). Cloning of a gene, YT521, for a novel RNA splicing-related protein induced by hypoxia/reoxygenation. Brain Res. Mol. Brain Res., 53: 33–40
CrossRef Google scholar
[17]
Hartmann, A. M., Nayler, O., Schwaiger, F. W., Obermeier, A. (1999). The interaction and colocalization of Sam68 with the splicing-associated factor YT521-B in nuclear dots is regulated by the Src family kinase p59fyn. Mol. Biol. Cell, 10: 3909–3926
CrossRef Google scholar
[18]
Theler, D., Dominguez, C., Blatter, M., Boudet, J. Allain, F. H. (2014). Solution structure of the YTH domain in complex with N6-methyladenosine RNA: a reader of methylated RNA. Nucleic Acids Res., 42: 13911–13919
CrossRef Google scholar
[19]
Luo, S. (2014). Molecular basis for the recognition of methylated adenines in RNA by the eukaryotic YTH domain. Proc. Natl. Acad. Sci. USA, 111: 13834–13839
CrossRef Google scholar
[20]
Xu, C., Wang, X., Liu, K., Roundtree, I. A., Tempel, W., Li, Y., Lu, Z., He, C. (2014). Structural basis for selective binding of m6A RNA by the YTHDC1 YTH domain. Nat. Chem. Biol., 10: 927–929
CrossRef Google scholar
[21]
Govindaraju, G., Kadumuri, R. V., Sethumadhavan, D. V., Jabeena, C. A., Chavali, S. (2020). N6-adenosine methylation on mRNA is recognized by YTH2 domain protein of human malaria parasite Plasmodium falciparum. Epigenet. Chromatin, 13: 33
CrossRef Google scholar
[22]
Li, F., Zhao, D., Wu, J. (2014). Structure of the YTH domain of human YTHDF2 in complex with an m6A mononucleotide reveals an aromatic cage for m6A recognition. Cell Res., 24: 1490–1492
CrossRef Google scholar
[23]
Xu, C., Liu, K., Ahmed, H., Loppnau, P., Schapira, M. (2015). Structural basis for the discriminative recognition of N6-methyladenosine RNA by the human YT521-B homology domain family of proteins. J. Biol. Chem., 290: 24902–24913
CrossRef Google scholar
[24]
Li, Y., Bedi, R. K., Wiedmer, L., Sun, X., Huang, D. (2021). Atomistic and thermodynamic analysis of N6-methyladenosine (m6A) recognition by the reader domain of YTHDC1. J. Chem. Theory Comput., 17: 1240–1249
CrossRef Google scholar
[25]
Li, Y., Bedi, R. K., Moroz-Omori, E. V. (2020). Structural and dynamic insights into redundant function of YTHDF proteins. J. Chem. Inf. Model., 60: 5932–5935
CrossRef Google scholar
[26]
Amadei, A., Linssen, A. B. M. Berendsen, H. J. (1993). Essential dynamics of proteins. Proteins, 17: 412–425
CrossRef Google scholar
[27]
Berendsen, H. J. C. (2000). Collective protein dynamics in relation to function. Curr. Opin. Struct. Biol., 10: 165–169
CrossRef Google scholar
[28]
Miller, B. R. McGee, T. D. Swails, J. M., Homeyer, N., Gohlke, H. Roitberg, A. (2012). MMPBSA. py: An efficient program for end-state free energy calculations. J. Chem. Theory Comput., 8: 3314–3321
CrossRef Google scholar
[29]
Ernst, M., Sittel, F. (2015). Contact- and distance-based principal component analysis of protein dynamics. J. Chem. Phys., 143: 244114
CrossRef Google scholar
[30]
Koshland, D. E. (1958). Application of a theory of enzyme specificity to protein synthesis. Proc. Natl. Acad. Sci. USA, 44: 98–104
CrossRef Google scholar
[31]
Williamson, J. (2000). Induced fit in RNA-protein recognition. Nat. Struct. Biol., 7: 834–837
CrossRef Google scholar
[32]
Liao, Q. (2020). Enhanced sampling and free energy calculations for protein simulations. In: Progress in Molecular Biology and Translational Science, 177
[33]
Pearlman, D. A., Case, D. A., Caldwell, J. W., Ross, W. S., Cheatham, T. E. Debolt, S., Ferguson, D., Seibel, G. (1995). Amber, a package of computer-programs for applying molecular mechanics, normal-mode analysis, molecular-dynamics and free-energy calculations to simulate the structural and energetic properties of molecules. Computer Physics Communications. Comput. Phys. Commun., 91: 1–41
CrossRef Google scholar
[34]
Case, D. A., Cheatham, T. E. Darden, T., Gohlke, H., Luo, R., Merz, K. M. Onufriev, A., Simmerling, C., Wang, B. Woods, R. (2005). The Amber biomolecular simulation programs. J. Comput. Chem., 26: 1668–1688
CrossRef Google scholar
[35]
Maier, J. A., Martinez, C., Kasavajhala, K., Wickstrom, L., Hauser, K. E. (2015). ff14SB: Improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput., 11: 3696–3713
CrossRef Google scholar
[36]
Cornell, W. D., Cieplak, P., Bayly, C. I., Gould, I. R., Merz, K. M., Ferguson, D. M., Spellmeyer, D. C., Fox, T., Caldwell, J. W. Kollman, P. (1995). A 2nd generation force-field for the simulation of proteins, nucleic-acids, and organic-molecules. J. Am. Chem. Soc., 117: 5179–5197
CrossRef Google scholar
[37]
Otyepka, M., Sponer, J., dek, A., Cheatham, T. E. (2011). Refinement of the Cornell et al. nucleic acids force field based on reference quantum chemical calculations of glycosidic torsion profiles. J. Chem. Theory Comput., 7: 2886–2902
CrossRef Google scholar
[38]
Aduri, R., Psciuk, B. T., Saro, P., Taniga, H., Schlegel, H. B. (2007). AMBER force field parameters for the naturally occurring modified nucleosides in RNA. J. Chem. Theory Comput., 3: 1464–1475
CrossRef Google scholar
[39]
Mark, P. (2001). Structure and dynamics of the TIP3P, SPC, and SPC/E water models at 298 K. J. Phys. Chem. A, 105: 9954–9960
CrossRef Google scholar
[40]
Pastor, R. W., Brooks, B. R. (2006). An analysis of the accuracy of Langevin and molecular dynamics algorithms. Mol. Phys., 65: 1409–1419
CrossRef Google scholar
[41]
Feller, S. E., Zhang, Y. H., Pastor, R. W. Brooks, B. (1995). Constant-pressure molecular-dynamics simulation―the Langevin piston method. J. Chem. Phys., 103: 4613–4621
CrossRef Google scholar
[42]
Forester, T. R. (2000). SHAKE, rattle, and roll: Efficient constraint algorithms for linked rigid bodies. J. Comput. Chem., 21: 157–157
CrossRef Google scholar
[43]
Darden, T., York, D. (1993). Particle mesh Eewald: an N. Log(N) method for Ewald sums in large systems. J. Chem. Phys., 98: 10089–10092
CrossRef Google scholar
[44]
Hamelberg, D., Mongan, J. McCammon, J. (2004). Accelerated molecular dynamics: a promising and efficient simulation method for biomolecules. J. Chem. Phys., 120: 11919–11929
CrossRef Google scholar
[45]
Kollman, P. A., Massova, I., Reyes, C., Kuhn, B., Huo, S., Chong, L., Lee, M., Lee, T., Duan, Y., Wang, W. . (2000). Calculating structures and free energies of complex molecules: combining molecular mechanics and continuum models. Acc. Chem. Res., 33: 889–897
CrossRef Google scholar
[46]
Massova, I. Kollman, P. (2000). Combined molecular mechanical and continuum solvent approach (MM-PBSA/GBSA) to predict ligand binding. Perspect. Drug Discov. Des., 18: 113–135
CrossRef Google scholar
[47]
Pronk, S., ll, S., Schulz, R., Larsson, P., Bjelkmar, P., Apostolov, R., Shirts, M. R., Smith, J. C., Kasson, P. M., van der Spoel, D. . (2013). GROMACS 4. 5: a high-throughput and highly parallel open source molecular simulation toolkit. Bioinformatics, 29: 845–854
CrossRef Google scholar

SUPPLEMENTARY MATERIALS

The supplementary materials can be found online with this article at https://doi.org/10.15302/ J-QB-022-0297.

ACKNOWLEDGEMENTS

This work is supported by the National Natural Science Foundation of China (No. 91953101), and the Strategic Priority Research Program of the Chinese Academy of Science (No. XDB37040202). The Supercomputing Center of USTC provides computer resources for this project, and we are grateful to Mr. Yundong Zhang for his technical supports.

COMPLIANCE WITH ETHICS GUIDELINES

The authors Mingwei Li, Guanglin Chen and Zhiyong Zhang declare that they have no conflict of interests.
This article does not contain any studies with human or animal subjects performed by any of the authors.

OPEN ACCESS

This article is licensed by the CC By under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

RIGHTS & PERMISSIONS

2022 The Author(s) 2022. Published by Higher Education Press.
AI Summary AI Mindmap
PDF(3849 KB)

Accesses

Citations

Detail

Sections
Recommended

/