Team:Nanjing NFLS/Model

Introduction

Salmena and other co-researchers said competing endogenous RNA (ceRNA) hypothesis is about how messenger RNAs (mRNA), transcribed pseudogenes, and long noncoding RNAs (lncRNA) “talk” to each other using microRNA response elements (MREs) as letters of a new language¹. In other word, specific RNAs can impair microRNA (miRNA) activity through sequestration, thereby upregulating miRNA target gene expression. CeRNA is a transcript targeted by a miRNA. It sequesters the activity of the bound miRNA, effectively de-repressing other targets of that miRNA which is comparable in action to an artificially introduced miRNA sponge but distinguished by its endogenous origin. Such regulatory network plays a significant role in pathological conditions particularly in cancers.

RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Although a number of RNAs ranging from small helices to large megadalton ribonucleoprotein complexes have been solved to atomic resolution using X-ray crystallography, many have not been characterized experimentally due in part to cost and experimental limitations. This has led to the development of RNA–RNA docking algorithms, of which the top-performing methods often produce models with atomic-level accuracy. Considerable attention has been focused on predicting RNA-RNA interaction since it is a key to identifying possible targets of non-coding small RNAs that regulate gene expression post-transcriptionally. A number of computational studies have so far been devoted to predicting joint secondary structures or binding sites under a specific class of interactions. The rigid-body protein–protein docking program ZDOCK was developed in Zhiping Weng's lab (ZLAB) at the University of Massachusetts Medical School. It uses the Fast Fourier Transform algorithm to enable an efficient global docking search on a 3D grid, and utilizes a combination of shape complementarity, electrostatics and statistical potential terms for scoring. ZDOCK achieves high predictive accuracy on protein–protein docking benchmarks, and consistent success (acceptable or better predictions for 22 of the last 35 submitted targets) in the international protein–protein docking experiment, Critical Assessment of Predicted Interactions. Therefore, ZDOCK also provides a fast and effective means to produce models of RNA-RNA complexes and symmetric multimers.

In our project, ZDOCK was performed to determine the effective potentials for RNA-RNA interactions. The final result will guide our experiments.

Model

In this part, two pipelines were proposed as Figure 1 show. The first pipeline is to find out the miRNA which might interact with mRNA of Hepatitis B surface antigen (HBsAg). It is the first step of model is to find the potential binding site in mRNA of HBsAg. Based on the predicted binding site, we can find some miRNAs which might interact with HBsAg. These were implemented the online service (https://www.thermofisher.com). Then, mRNA and miRNAs were built three-dimensional structure as the input of docking. ZDOCK is a software for docking proteins and also be used in researching other biomacromolecule interaction. We used ZDOCK 3.0.2 in model to research the interaction of mRNA, miRNAs and ceRNAs². Output file of ZDOCK including Rotating angles of ligand,Position changes of ligand and ZDOCK Score which can help analyze result. According to the experiment, filtered miRNAs were verified can be well interacted with mRNA. The second pipeline is to find out ceRNAs which potentially interact with miRNA and determine what ceRNAs should be synthesized for next experiment. All the ceRNAs used in this part were designed based on the TUD methods³. Docking is also implemented by ZDOCK. We would select some ceRNAs for the further study.

Figure 1. Workflow of the model

The prediction of RNA complexes is implemented in ZDOCK through the following three detailed steps⁴:

1) Input structures and options. For running ZDOCK, files of three-dimensional structure should be constructed as the input. RNA is usually very long and linear which is too big for the software to make the grid, which is a crucial step. Some sections of structure which are not important should be removed such as those who are far away from bind site.

2) Selection of blocking/contacting residues. This step is selection of blocking (ZDOCK and M-ZDOCK) and contacting (ZDOCK only) residues for each submitted RNA, which is aided by JMol visualization of each molecule that highlights selected residues for the user.

3) Viewing results. When your docking job completes, you will obtain the results of your docking run. The top models that are available as a sets of predicted complexes can be generated according to the ZDOCK score.

Result and Discussion

Three binding sites were predicted respectively at the 70, 95, 125 nucleobases of the mRNA of HBsAg which are consisted of 21 nucleobase sequences. Thus, the binding sites were named HBsAg-70, HBsAg-95 and HBsAg-125 in following sections. Three miRNAs were thought interacting with the mRNA of HBsAg named miRNA-HBsAg-70, miRNA-HBsAg-95 and miRNA-HBsAg-125. These RNAs are built tovery linear 3D structures which are too long for ZDOCK to run. Therefore, we captured some nucleobase fragments around the binding site for docking. The result can be found in table 1. Binding mode diagram are shown in Figure 2. According these, three miRNAs are potential interaction with mRNA, the result of experiment also testified miRNAs can interact with mRNA.

Table 1. Docking output file between mRNA of HBsAg and miRNA

Binding Sites	Rotating angles of ligand			Position changes of ligand			ZDOCK Score
HBsAg-70	0	1.504	0.637	356	12	355	4170.524
HBsAg-95	0	1.504	0.637	355	12	355	4292.382
HBsAg-125	0	1.504	0.637	355	12	355	4275.115

Figure 2. Interaction between mRNA and miRNA predicted by ZDOCK. A: mRNA - miRNA-HBsAg-70. B: mRNA - miRNA-HBsAg-95. C: mRNA - miRNA-HBsAg-125.

According to these three miRNAs, twelve ceRNAs were designed that each miRNA has four candidates. Same treatments were taken that using fragments around the binding site for docking. The result is shown in Table 2 and binding mode diagram are shown in Figure 3 - 5.

Table 2. Docking output file between miRNA and ceRNA

miRNA	ceRNA	Rotating angles of ligand			Position changes of ligand			ZDOCK Score
miRNA -HBsAg-70	1	0	1.504	0.637	355	12	355	4324.168
	2	0	1.504	0.637	355	12	355	4351.381
	3	0	1.504	0.637	355	12	355	4319.166
	4	0	1.504	0.637	355	12	355	4284.459
miRNA -HBsAg-95	1	0	1.569	-3.004	5	352	1	4278.329
	2	0	1.569	-3.004	5	352	1	4318.346
	3	0	1.569	-3.004	5	352	1	4243.843
	4	0	1.569	-3.004	5	352	1	4237.402
miRNA -HBsAg-125	1	0	1.504	0.637	355	12	355	4353.774
	2	0	1.504	0.637	355	12	355	4339.801
	3	0	1.504	0.637	355	12	355	4292.518
	4	0	1.504	0.637	355	12	355	4306.491

Figure 3. Interaction between four ceRNAs and miRNA-HBsAg-70 predicted by ZDOCK.

Figure 4. Interaction between four ceRNAs and miRNA-HBsAg-95 predicted by ZDOCK

Figure 5. Interaction between four ceRNAs and miRNA-HBsAg-125 predicted by ZDOCK.

By comparing Table 1 and 2, almost all ZDOCK Scores of ceRNAs are greater than mRNA. Such situation might illustrate that interaction between ceRNAs and miRNA is stronger than mRNA. In Table 2, rotating angles of ligand and position changes of ligand are the same. There is a little difference in ZDOCK Score. We selected the top two ceRNA of each miRNA to synthesize for experiment. Final result proved the feasibility of the model that can be instructive in experiment.

References

1. Salmena L, Poliseno L, Tay Y, Kats L, Pandolfi PP. A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language? Cell 2011; 146: 353-8.

2. Pierce BG, Hourai Y, Weng Z. Accelerating Protein Docking in ZDOCK Using an Advanced 3D Convolution Library. PloS One 2011 6(9): e24657.

3. Hooykaas MJ, Soppe JA, De Buhr H, et al. RNA accessibility impacts potency of Tough Decoy microRNA inhibitors[J]. RNA Biology, 2018, 15(11): 1410-1419.

4. Brian G. Pierce, Kevin Wiehe, et al. ZDOCK server: interactive docking prediction of protein–protein complexes and symmetric multimers. Bioinformatics. 2014 Jun 15; 30(12): 1771–1773.