In-Silico Investigation of SNP rs9637231 effect on LINC02892 lncRNA secondary structure

doi:10.21203/rs.3.rs-3102259/v1

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In-Silico Investigation of SNP rs9637231 effect on LINC02892 lncRNA secondary structure

https://doi.org/10.21203/rs.3.rs-3102259/v1

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Long noncoding RNAs include more than 200 nucleotides and do not translate to protein. they have a multidimensional role in the cell cycle, cell differentiation, cell proliferation, and stress response via their secondary and tertiary structures. They have an important role in gene expression at multiple levels such as chromatin modification, transcription, splicing, post-transcriptional regulation, translation, protein modification, and RNA-protein interaction. The secondary structure of RNA involves gene regulation at several stages, including RNA localization, stability, splicing, and translation efficiency. DNA variations such as single nucleotide polymorphisms (SNPs) and mutations may impact the structure, expression, and function of lncRNAs. Here We investigated the effects of C to T alteration of SNP rs9637231 on lncRNA secondary and tertiary structures by in-silico study and we enquired about SNP rs9637231 alteration effect on lncRNA-protein interaction statically.

Biological sciences/Biotechnology

Biological sciences/Cancer

lncRNA

in-silico study

rs9637231

secondary structure

tertiary structure

Long noncoding RNAs (lncRNA) are now identified as critical molecules that regulate various cell processes. They are comprised of over 200 nucleotides and do not translate into proteins, can be spliced, capped, polyadenylated, and localized either in the nucleus or the cytoplasm of the cell[1, 2]. LncRNAs perform various functions such as the cell cycle, cell differentiation, cell proliferation, and stress response via their secondary and tertiary structures. They may be involved in gene expression regulation at multiple levels, including chromatin modification, transcription, splicing, post-transcriptional regulation, translation, protein modification, and RNA-protein interaction[3, 4]. Since lncRNAs are not only located in dedicated noncoding gene loci but also can be part of protein-coding genes, it showed that understanding lncRNA function and modes of action is a matter for RNA biologists. While a rapidly growing number of lncRNAs have been functionally determined, studies unveiling modes of action of lncRNAs are still sparse. Amore likely conservation of ncR-NAs includes conservation on (a)secondary or tertiary structure,(b) functional features, or (c) syntenic transcription [5, 6]. Some lncRNAs, for example, alter chromatin conformation via interactions with the chromatin remodeling complex. They may also have a heterochromatin composition that reduces gene expression[4]. Because of their diverse roles in various regulatory processes, lncRNAs are becoming increasingly important in a variety of diseases, particularly cancer[4, 7, 8]. Furthermore, lncRNAs may serve as tumor suppressors or oncogenic agents in certain cancers[9]. Some oncogenic lncRNAs, including CACS1, CCAT family, and PVT1, might cause colorectal cancer (CRC) by interacting with Myc, stimulating proteins, or other genes involved in the Wnt pathway post-transcriptional level[10]. The structure of lncRNAs may influence these interactions.

The secondary structure of RNA can influence gene regulation at several stages, including RNA localization, stability, splicing, and translation efficiency[11]. Many unresolved issues concerning how these changes in lncRNA structure impact biological activities and the actual underlying mechanisms are unknown. Variations in DNA sequence have been shown to influence changes in RNA secondary structure[12]. Indeed, DNA variations such as single nucleotide polymorphisms (SNPs) and mutations may impact the structure, expression, and function of lncRNAs[13]. fifteen percent of all the transcribed single nucleotide variants (SNVs) in a trio family (father, mother, and kid) have been found to modify the RNA structure hence this dubbed riboSNitches (SNVs that could change the lncRNA structure)[14].

These studies are yielding fresh insights, such as the cooperative folding of functional RNAs or the investigation of new functional elements. Methods that can probe the structure of RNA under complex invivo-like conditions will enhance our understanding of in vivo RNA structural motifs. Overcoming the challenges of purification of invivo or invivo-like RNAs will allow the field to accomplish the ultimate goal, to understand how RNA folds in the cell, and there by might help for the discovery of yet undiscovered mechanisms of lncRNAs.

The SNP rs9637231 C > T is located at chromosomal position 21: 46670717 (GRCh38). This study was conducted follow on kerachian and et al. That showed the C allele of rs9637231 was highly demethylated in CRC patients however it was methylated in normal individuals. In addition, it is located in the region of the second exon of the proposed LNCRNA(LINC02892), previously reported by Kerachian et al. (NCBI: National Center for Biotechnology Information (nih.gov)). This LNCRNA has three transcripts; SNP rs9637231 is available in all three structures. Since the SNP rs9637231 is located in the second exon of LINC02892, it may affect on LNCRNA structure and follow on protein interaction with LNCRNA. We investigated the effects of C to T alteration of SNP rs9637231 on lncRNA secondary and tertiary structures by in-silico study and we enquired about SNP rS9637231 alteration effect on lncRNA-protein interaction statically and dynamically.

Functional in silico analysis of SNP

To predict the functional effects of rs9637231, HaploReg v4.1(https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) [16]. and regulomeDB 2.0 (https://regulomedb.org/regulome-search/) were used [17]. In addition, eQTLs of rs9637231 were obtained from GTEx database V8 (https://gtexportal.org/home/)[18].

Genotyping was performed twice by two different individuals. Structure Prediction and Base Pair Probability Analysis

The effect of rs9637231 on RNA secondary structure was predicted by the RNAfold web server (http://rna.tbi.univie.ac.at/cgi-bin/RNAWebSuite/RNAfold.cgi). Tertiary structural changes were predicted by 3DRNA v2.0 (http://biophy.hust.edu.cn/3dRNA) (task type: 3d RNA with optimization)[19]. Changes in the accessibility of the molecule, its intra-molecular base-pairing potential, and the base-pairing probabilities were calculated by MutaRNA (https://rna.informatik.uni-freiburg.de/MutaRNA/Input.jsp) (Maximal base pair span and Window size = 2000)[20]. RNAsnp )https://rth.dk/resources/rnasnp/( with mode 1 (based on global folding) and mode 2 (based on local folding) on default settings was used to calculate the p-value of the change in structures[21]. P-values less than 0.2 were considered significant structural changes. RNAsnp focuses on the local areas with the most significant structural change between wild-type and mutant, and the mutation effects are assessed using empirical p-values. Furthermore, RNAsnp utilizes pre-computed tables that show the distribution of SNP effects as a function of sequence length, GC content, and SNP location.

LncRNA-protein interactions

catRAPID omics (http://s.tartaglialab.com/page/catrapid_omics_group), lncpro (http://bioinfo.bjmu.edu.cn/lncpro/), and RBPDB (http://rbpdb.ccbr.utoronto.ca/) were used to predict the potential interacting proteins. Protein sequences were downloaded from Uniprot (https://www.uniprot.org). RPISeq (http://pridb.gdcb.iastate.edu/RPISeq/) was used to predict the interaction probability between the lncRNA transcripts and the predicted proteins[22–26].

LncRNA-Protein Docking

To predict potential binding sites between the transcripts and the proteins, catRAPID fragment module available on catRAPID omics server was used[22]. Protein structures were retrieved from RCSB Protein Data Bank. The 3D structures were retrieved from alphfold ( https://alphafold.ebi.ac.uk/).

Subcellular localization prediction

LncLocator and iLoc-LncRNA were used to predict the subcellular localization of the transcripts[28, 29].

RNA-RNA interaction prediction

LncRNASNP2 (http://bioinfo.life.hust.edu.cn/lncRNASNP) and mirDB (http://mirdb.org) were used to predict putative miRNA targets for each transcript. IntaRNA (https://rna.informatik.uni-freiburg.de/IntaRNA/Input.jsp) was used to investigate the potential interactions between the transcripts and the predicted miRNAs[30, 31].

Investigating the effect of rs9637231 on the secondary structure of LINC02892

We used the RNAfold web server to predict the secondary structure alteration of the LINC02892 by rs9637231 (Fold algorithms and basic options: minimum free energy (MFE) and partition function algorithms, avoid isolated base pairs). A slight increase in stability was observed upon C to U alteration in all transcripts and also the stability of lncRNA transcriptions are transcript1 > transcript2 > transcript3 (Fig. 1, Fig. 2, Fig. 3 -figtranscript1,2 and 3. pdf(A)). Furthermore, we predicted the changes in the accessibility of the molecules (Fig. 1, Fig. 2, -Fig. 3 figtranscript1,2 and 3.pdf (B)), its intra-molecular base pairings potential (Fig. 1, Fig. 2, Fig. 3 -figtranscript1,2 and 3.pdf (C)) and the base-pairing probabilities for the SNP using MutaRNA (Fig. 1, Fig. 2, Fig. 3 -figtranscript1,2 and 3.pdf (D)). To calculate the p-value of the change in the structures of different LINC02892 transcripts, we used RNAsnp mode one and mode two (Table 1). RNAsnp results showed that other than mode 2 for transcript 1, the structural changes based on C344T were significant in both methods for all transcripts. Furthermore, we used 3dRNA to predict the tertiary structure of the transcripts with C and T's alleles based on the secondary structure retrieved from RNAfold. The structures with the least free energy were selected. (Fig. 4 -3dstructures.pdf shows) the aligned transcripts with T and C.

Investigating the interaction of LINC02892 Transcripts with proteins

Prediction of putative RNA binding proteins

To predict the potential RNA binding proteins, we used catRAPID omics, Lncpro and RDPDB to retrieve a list of putative RNA binding proteins (Fig. 5- 01fig A and B). Then, we retrieved the common protein sequences from the uniport and used RPIseq to calculate binding probability. RPIseq uses the random forest and support vector machine algorithms to calculate the probability of binding. Interactions with more than 0.5 probability are considered positive and the RNA and protein are likely to interact (Fig. 5- 01fig C).

LncRNA-Protein Docking

To investigate the effect of the SNP on protein binding, we selected the intersection between the prediction of Lncpro, catRAPID omics, and RBPDB (Fig. 5–0fig1). Also, we searched the literature to find whether each of the other proteins affected CRC. SRSF2, ELAV1, ELAV2, SRSF9, SRSF1, and ZFP36 were selected for the molecular docking study after we modeled the computational secondary and tertiary structure of the lncRNA. RNAfold predicted the most thermodynamically stable secondary structures of LINC02892 for each transcript. The 3d structures of these transcripts were then modeled via 3dRNA (Fig. 4–3dsturctures.pdf). Based on the sequence, we used CatRAPID fragments to predict the binding sites between the lncRNA transcripts and the selected proteins. We then used I-TASSER to model the complete protein structures of the selected proteins. Molecular docking was performed using Haddock 2.4 with default nucleic acid and protein settings using the predicted binding sites. The ligand interface-RMSD values were determined according to the interacting contacts. The graphs in Fig. 6 demonstrate the clusters in different colors and the Individual energy, total energy score, and negative Z-score of the modeled complex. Moreover, structural analysis via HADDOCK was carried out with the lowest HADDOCK score of 10Å cut off, and the intramolecular residues such as CA, C, N, O, and P atoms with N > 1 were assessed as interface-RMSD (i-RMSD) and the inter-molecular contacts with 5Å cut-off. Table 1 represents the best Haddock scores for the docking of each transcript with the selected proteins and their binding sites. (Fig. 7 -Fig-doc) shows the haddock scores for the best binding sites of each protein. The wild type and variant forms of transcript 2 exhibited the most differential energy scores when binding to Elav1 in comparison to two other transcripts. In addition, simultaneously transcript3 and ELAV1 have the high affinity and differential energy score (C = -29.1 +/- 55.8, T = 35.5 +/- 11.2), (Table 1).

Subcellular localization

To see how rs9637231 affects the subcellular localization of LINC02892, we used i-locLncRNA and lncLocator algorithms. The first algorithm predicted that rs9637231 could not alter the localization of LINC02892 in cells. Transcripts 1 and 2 were predicted to be located in the cytoplasm, while transcript 3 was predicted to be located in exosomes. LncLocator algorithm, on the other hand, predicted that transcript 1 was predominantly located in the cytoplasm while the other two transcripts were predicted to be predominantly located in the nucleus. After C to T substitution, the score of cytoplasm increased for both transcripts 2 and 3 while a decrease in nucleus localization probability was predicted (Fig. 8).

RNA-RNA

We used LncRNASNP2 and mirDB (custom prediction tool) to predict miRNA targets based on the lncRNA sequence. LncRNASNP2 predicted hsa-miR-141-3p and hsa-miR-200a-3p to interact only with the variant transcripts. Then, we utilized IntaRNA to examine how the polymorphism impacts the anticipated miRNAs' binding. (Fig. 9, Table 2).

Translation and folding of polypeptides are influenced by folding energy and mRNA secondary structure stability. To avoid ribosomal jamming and ensure the correct folding of newly translated peptides, stable RNA structures control the speed of translation[32]. LncRNAs could bind to several different molecules and the change in structure could be an important factor for lncRNA interactions. The secondary structure of RNA may be determined using Selective 2′ Hydroxyl Acylation assessed by Primer Extension (SHAPE), however little study has been done on how the SNPs influence structural alteration[33]. Studies have also shown that; ribosnitchs (SNP that alter the structure of lncRNAs) play an important role in regulating gene expression and subsequently in cancer; In fact, ribosnitchs can alter the secondary structure of lncRNAs; As a result, affect intermolecular interactions, such as interactions with proteins, miRNAs, and the binding site of miRNAs on the structure of mRNA; The latter case led to a change in the expression of the relevant gene; Finally, it can be involved in the development and progression of cancer[34]. In addition, other studies show that LncRNA PRNCR1 causes prostate cancer; moreover, the SNPs in this lncRNA change their secondary structure; then interfered with LncRNA-partners interactions; and finally, increase the risk of cancer[35–37]. We predicted that rs9637231 could change the structure of LINC02892. Similar to us, AlMutairi et al. demonstrated that the rs1456315 mutant's secondary structure differs from that of the wild-type[38]. rs114020893 was predicted to change the secondary structure of NEXN-AS1 at 1p31.1, increasing the risk of lung cancer[39]. According to Fu et al., the C > T mutation rs12982687 might change the secondary structure of UCA1[40].

Protein binding affinity might be affected by structural changes. Wang et al. discovered that the rs55829688 variation in the GAS5 promoter was linked to an increased risk of CRC and reduced GAS5 production via altering the binding affinity of the transcription factors YY1 to GAS5(41).in addition, The rs7763881 and rs1328867 In the HULC promoter have led to a change in its expression And is associated with the risk of colorectal cancer[42]. Also, in another study by Hua et al.; It was shown that SNP rs11672691 within the LncRNA PCAT19 promoter, which reduces the expression levels of both long and short transcripts; Increases the risk of prostate cancer[43]. according to Ming-li Yang et al., the SNP rs26332159 within the promoter region of PCAT1 lncRNA affects its expression; and subsequently has a positive association with colorectal cancer; In fact, this study shows that; This polymorphism increases the risk of cancer by 1.37 and 2.19 times in dominant and recessive models, respectively; Quantitative trait locus (eQTL) expression analysis showed that; This polymorphism alters the binding of transcription factors EBF, LUN-1, and TCF12 to the promoter region of the lncRNA gene, and affects its expression[44]. This prompted us to investigate if the alteration in LINC02892 transcripts could change the binding affinity of proteins. We utilized catRAPID, lncpro, and RBPDB to determine which proteins bind to LINC02892. We discovered that various proteins, including SRSF2, SRSF9, and ELAV1, may bind to LINC02892. We confirmed the bind of these proteins to LINC02892 via docking and found that the alteration in rs9637231 could change the affinity of the protein:lncRNA complex. The results of molecular dynamics also confirmed this.

To improve our understanding of the possible protein binding mechanisms of LINC02892, we planned an in silico study based on molecular dynamics simulation. In this study, the interaction between LINC02892 and ELAVL1 was selected as a well-known lncRNA-protein interaction. Overall, the simulation results indicated that the LINC02892 with the C allele has a higher affinity for protein than the T allele.

A change in the structure of lncRNA could alter the localization of these molecules. Therefore, we predicted the effect of rs9637231 on changing the localization of LINC02892 and found no significant alteration.

Structural changes might influence miRNA binding in lncRNA. Wu et al. discovered that the rs664589 G allele changed miR-194-5p binding in the nucleus, resulting in higher MALAT1 expression and colorectal cancer development[45]. Structural changes in lncRNA might change miRNA binding sites on these molecules. The rs2366152C SNP on HOTAIR, for example, affected the secondary structure, resulting in the removal of the binding site for miR-22. Furthermore, Wu et al. showed that the rs664589 G allele changed binding to miR-194-5p in the nucleus, leading to increased MALAT1 expression and colorectal cancer development[45]. Zhuo. Et al. Showed that rs619586 could; Connect to mir-214, And directly suppress MALAT1 expression. Several studies have shown that increased MALAT1 expression leads to an increased risk of cancer(46, 47). Fu et al .; Showed that the SNPrs12982687 in the UCA1 lncRNA may affect the interaction of lncRNA with miR-873; Therefore affecting the performance of the HIF-1 signaling pathway; And thus increasing the risk and progression of colorectal cancer;101, in addition, Shen et al. Showed that the T > C SNPrs1317082 variant in the CCSlnc362 lncRNA enhances the binding of the lncRNA to miR-4658, reduces the expression of this lncRNA, and ultimately reduces the susceptibility to CRC.102 We discovered that changing C to T resulted in the addition of binding sites for hsa-miR-141-3p and hsa-miR-200a-3p on LINC02892 and changes to the binding sites of numerous other miRNAs (Table 2).

The significant information gap between SNP association and the molecular mechanism leading to disease risk provides both a question and an opportunity to discover lncRNAs with essential roles in cancer. A regulatory element polymorphism may affect the quantity of a gene transcript. However, many SNPs are located in intergenic or intronic areas, and their interaction with lncRNA activity in the cell environment may be far more complicated. As of now, the most important consequences of lncRNA SNP research are cancer risk assessment and susceptibility. However, with the advancement of next-generation sequencing and the introduction of single-cell sequencing, probably, a slew of new findings on the role of lncRNAs in cancer is finally on the way(51). The in silico study carried out showed that alteration of SNP rs9637231 from C to U led to the change in the secondary and tertiary structure of the LINC02892 in all 3 transcripts and also the stability of the LINC02892 increase for C to U alteration in all 3 transcripts and also the stability of lncRNA transcriptions are transcript1 > transcript2 > transcript3. (Fig. 1, Fig. 2, Fig. 3 -figtranscript1,2 and 3. pdf(A)) In addition in slico study determined the proteins that interacted with 3 transcripts. To simulate static and dynamic lens-pr interactions, docking and grow macks were performed. Table 1 showed that alteration for c to u leads to the difference in the haddock score of all 3 lncRNA-protein, which shows the amount of interaction and stability changed for c to u alteration. And also transcript2-elav1 has high affinity and differential interaction for C to U alteration because of the negative haddock score and the high differential had dock score respectively( C = 54.9 +/- 12.5, T=-36.5 +/- 5.2),.

We found that changes in rs9637231 alleles affect the secondary and tertiary structure of the novel lncRNA follow on that, we discovered lncRNA-cell environment interactions. Cell environments such as proteins, miRNAs, and mRNAs are important to involve in cancer progression. We analyzed the putative RNA-binding motifs and interacting residues using a silico approach. finally, we determined some proteins that interact with lncRNA. Also, we assess the affinity between proteins and lncRNA statically and dynamically. Binding free energy from docking showed that the binding of LINC02892 with C allele to ELAVL1 protein was stronger than that of LINC02892 with T allele We determine the stability of lncRNA transcripts by in silico study.

Data availability

Data are available upon request from the corresponding author

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Table1 the had dock score

Protein	Transcript	Allele	protein binding site	RNA binding site	Haddock Score
ELAV1	1	C	176-227,251-302	338-389	-28.5 +/- 18.6
	1	T	126-202,251-302	338-389	8.7 +/- 10.9
	2	C	151-202	351-402	-54.9 +/- 12.5, -50.5 +/- 15.0, -46.7 +/- 7.0
	2	T	176-227	403-454	-36.5 +/- 5.2, -39.0 +/- 5.7, -40.4 +/- 4.9
	3	C	251-302	276-352	18.4 +/- 7.0
	3	T	251-302	276-352	-39.6 +/- 4.7
SRSF1	1	C	76-224	76-127	-19.4 +/- 24.2,
	1	T	76-224	351-402	-0.4 +/- 18.3
	2	C	73-127	76-127	36.6 +/- 7.2, 13.7 +/- 12.5, 13.7 +/- 12.5
	2	T	73-127	76-127	-39.1 +/- 17.2, -56.3 +/- 6.1, -39.5 +/- 16.6
	3	C	76-224	76-128	-52.5 +/- 8.1
	3	T	76-224	77-128	-5.1 +/- 22.0
SRSF2	1	C	77-177	338-389	-39.2 +/- 10.4
	1	T	77-177	338-389	-92.4 +/- 17.8
	2	C	26-127	501-552	-26.3 +/- 15.4, -6.2 +/- 11.3, -16.2 +/- 9.9
	2	T	26-127	501-552	-41.9 +/- 10.5, -55.9 +/- 13.0, -49.1 +/- 6.6
	3	C	26-127	77-128	-49.2 +/- 22.8
	3	T	26-127	77-128	-58.7 +/- 7.1
SRSF9	1	C	45-197	63-114	9.0 +/- 24.6
	1	T	45-197	226-277	-63.0 +/- 37.4
	2	C	49-197	53-104	99.0 +/- 21.6, 112.3 +/- 34.7, 109.6 +/- 6.6
	2	T	101-197	53-104	120.4 +/- 18.7, 124.9 +/- 14.5, 100.3 +/- 33.0
	3	C	26-175	52-103	20.8 +/- 42.4
	3	T	26-175	52-103	-7.3 +/- 17.3
ZFP36	1	C	126-177,226-277	613-664	56.3 +/- 15.0
	1	T	126-177,226-277	613-664	26.0 +/- 14.6
	2	C	126-177	351-454	18.8 +/- 18.9
	2	T	226-277	351-454	214.8 +/- 32.2, 195.2 +/- 22.9, 232.7 +/- 52.0
	3	C	126-177,226-277	177-278	213.3 +/- 6.2
	3	T	126-177,226-277	327-402	101.0 +/- 22.8

Table 2 the binding site alteration of several miRNAs to LINC02892 for C to U conversion 11

miRNA	Transcript	Type	Binding site on LncRNA	Energy (kcal/mol)
				Sum	Hybridization	Unfolding
				Sum	Hybridization	Target	Query
hsa-miR-2682-3p	Transcript 1	Wild type	340-353	-17.84	-23.5	3.98	1.68
hsa-miR-2682-3p	Transcript 1	Variant	798-818	-17.42	-19.77	0.67	1.68
hsa-miR-6793-3p	Transcript 1	Wild type	341-352	-12.23	-16.58	3.89	0.46
hsa-miR-6793-3p	Transcript 1	Variant	270-276	-12.08	-15.89	3.36	0.45
hsa-miR-3193	Transcript 1	Wild type	383-390	-6.52	-13.59	2.95	4.12
	Transcript 1	Variant	372-390	-6.14	-19.01	8.64	4.23
	Transcript 2	Wild type	383-390	-6.59	-13.6	2.89	4.12
	Transcript 2	Variant	372-390	-6.15	-19.03	8.65	4.23
	Transcript 3	Wild type	372-390	-6.65	-19.02	8.14	4.23
	Transcript 3	Variant	383-390	-7.1	-13.71	2.49	4.12
hsa-miR-3691-5p	Transcript 1	Wild type	333-339	-8.01	-10.96	2.48	0.47
hsa-miR-3691-5p	Transcript 1	Variant	216-237	-7.71	-15.74	7.01	1.02
hsa-miR-4520-5p	Transcript 1	Wild type	382-389	-12.04	-15.24	3.05	0.15
	Transcript 1	Variant	270-288	-10.67	-16.42	5.59	0.16
	Transcript 2	Wild type	382-389	-12.1	-15.23	2.98	0.15
	Transcript 2	Variant	270-285	-11.01	-16.24	5.07	0.16
hsa-miR-4733-3p	Transcript 1	Wild type	366-381	-7.85	-20.26	8.87	3.54
	Transcript 1	Variant	147-157	-7.75	-15.72	4.34	3.63
	Transcript 3	Wild type	147-157	-7.75	-15.72	4.34	3.63
	Transcript 3	Variant	366-381	-7.88	-20.32	8.9	3.54
hsa-miR-5006-5p	Transcript 1	Wild type	424-440	-14.28	-22.85	5.96	2.61
		Variant	372-383	-14.23	-23.13	6.29	2.61
	Transcript 2	Wild type	364-377	-13.23	-22.82	6.98	2.61
		Variant	372-383	-14.09	-23.29	6.59	2.61
	Transcript 3	Wild type	364-377	-13.59	-22.81	6.61	2.61
		Variant	372-383	-14.54	-23.26	6.11	2.61
hsa-miR-6793-3p	Transcript 1	Wild type	341-352	-12.23	-16.58	3.89	0.46
hsa-miR-6793-3p		Variant	270-276	-12.08	-15.89	3.36	0.45
hsa-miR-200a-3p	Transcript 1	Variant	336-347	-8.08	-13.38	3.96	1.34
	Transcript 2	Variant	336-347	-8	-13.36	4.02	1.34
	Transcript 3	Variant	336-347	-8.16	-13.36	3.86	1.34
hsa-miR-141-3p	Transcript 1	Variant	336-347	-5.36	-13.38	3.96	4.06
	Transcript 2	Variant	336-347	-5.28	-13.36	4.02	4.06
	Transcript 3	Variant	336-347	-5.44	-13.36	3.86	4.06
hsa-miR-382-3p	Transcript 1	Wild type	207-214	-6.55	-8.4	1.72	0.13
	Transcript 1	Variant	342-359	-7.9	-15.86	7.83	0.13
	Transcript 2	Wild type	342-359	-6.78	-12.02	5.11	0.13
	Transcript 2	Variant	342-359	-8.42	-15.91	7.36	0.13
	Transcript 3	Wild type	207-214	-6.55	-8.4	1.72	0.13
	Transcript 3	Variant	342-359	-8.09	-15.87	7.65	0.13
hsa-miR-30d-5p	Transcript 2	Wild type	335-362	-9.69	-17.39	5.89	1.81
hsa-miR-30d-5p	Transcript 2	Variant	202-211	-9.47	-16.08	4.81	1.8
hsa-miR-4324	Transcript 2	Wild type	342-351	-6.9	-11.41	3.41	1.1
hsa-miR-4324	Transcript 2	Variant	84-104	-5.69	-19.34	12.54	1.11
hsa-miR-4731-3p	Transcript 2	Wild type	92-100	-10.15	-17.26	3.74	3.37
hsa-miR-4731-3p	Transcript 2	Variant	342-351	-11.15	-18.61	5.65	1.81
hsa-miR-629-3p	Transcript 2	Wild type	344-355	-11.42	-15.22	3.52	0.28
	Transcript 2	Variant	349-355	-8.88	-10.56	1.44	0.24
	Transcript 3	Wild type	344-355	-11.23	-15.22	3.71	0.28
	Transcript 3	Variant	273-279	-8.6	-10.4	1.56	0.24
hsa-miR-1183	Transcript 2	Wild type	430-460	-8.16	-23.51	11.55	3.8
hsa-miR-1183	Transcript 2	Variant	452-460	-8.18	-15.08	3.45	3.45

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In-Silico Investigation of SNP rs9637231 effect on LINC02892 lncRNA secondary structure

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Version 1

Abstract

Figures

Introduction

Methods

Functional in silico analysis of SNP

LncRNA-protein interactions

LncRNA-Protein Docking

Subcellular localization prediction

RNA-RNA interaction prediction

Results

Investigating the effect of rs9637231 on the secondary structure of LINC02892

Investigating the interaction of LINC02892 Transcripts with proteins

Prediction of putative RNA binding proteins

LncRNA-Protein Docking

Subcellular localization

RNA-RNA

Discussion

Conclusion

Declarations

Data availability

References

Tables

Additional Declarations

Status:

Version 1