Assessment of Single Nucleotide Variants in hGPRC5A Gene Using in-silico Tools

doi:10.21203/rs.3.rs-3576134/v1

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Assessment of Single Nucleotide Variants in hGPRC5A Gene Using in-silico Tools

https://doi.org/10.21203/rs.3.rs-3576134/v1

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G-protein-couple receptor family C group 5 member A (GPRC5A) encodes a transmembrane protein and is induced by retinoic acid. Dysfunctional GPRC5A was reported to activate numerous signal transduction cascades and its tumor suppressor role was emphasized especially in lung cancer in recent studies. Furthermore, various cancers, including colorectal cancer, breast cancer, and pancreatic cancer, have also been associated with GPRC5A dysregulation. In current study, we analysed the functional consequences of single nucleotide variants (SNVs) in the human GPRC5A gene using in-silico tools. We classified 76 missense variants as pathogenic based on the combined scores of commonly used in-silico tools tools—MutationTaster2, Polyphen2, and MutPred2. Among these 76 variants, three (p.S336Y, p.Y347H, p.Y350H) were identified to be located in post-translational modification and regulatory protein binding sites, suggesting their potential as putative functional variants. Moreover, we obtained strong evidence for rs112948541 and rs767232322 variants being as regulatory important. Additionally, we identified 16 miRNA target sites for the two variants (rs200040454 and rs144308510) in the 3' untranslated region of the GPRC5A. The results of in-silico analyses highlight the functional importance of the GPRC5A variants that may contribute to the GPRC5A-associated cancer types and the importance of in vivo evaluation to reveal their role in human cancer.

GPRC5A

Cancer

SNV

in-silico analysis

G-protein-couple receptor family C group 5 member A (GPRC5A) encodes a 357 amino acid protein belonging to the G protein-coupling receptor (GPCR) family with seven transmembrane domains. GPRC5A was first identified as a retinoic acid (RA) sensitive gene and its most expression is observed in lung tissue, while no or absent expression in other tissues. The protein mainly localized to the plasma membrane, endoplasmic reticulum, perinuclear vesicle, extracellular vesicular exosomes and Golgi apparatus(Cheng and Lotan 1998; Gonzales et al. 2009; Gonzalez-Begne et al. 2009; Zougman et al. 2013; Prunotto et al. 2013; Zhou and Rigoutsos 2014) (see: The Human Protein Atlas, http://www.proteinatlas.org). GPRC5A, can directly or indirectly activate many signaling pathways such as NF-κB, STAT3, cAMP-Gs α, and FAK/Scr (Jiang et al. 2018). Its role in cancer has first emerged in lung cancer as a tumor suppressor; previous research has indicated that GPRC5A expression levels are much lower in lung cancer tissue compared to normal lung tissue in mammals (Fujimoto et al. 2012; Zhong et al. 2015). It has also been reported to be a tumor suppressor in oral squamous cell carcinoma (OSCC) and head & neck squamous cell carcinoma (HNSCC) (Liu et al. 2013, 2017). Conversely, GPRC5A has been reported to play a role as an oncogene in other human cancers, including colorectal, pancreatic, breast, ovarian, gastric, prostate cancer, and hepatocellular carcinoma. (Nagahata et al. 2005; Cheng et al. 2012; Zheng et al. 2014; Zhou et al. 2016; Greenhough et al. 2018a; Sawada et al. 2020; Moyano-Galceran et al. 2020).

There are some studies showing that variants in the GPRC5A may be associated with human cancers (Sokolenko et al. 2014). To the best of our knowledge, there is not any study reporting the functional consequences of variants in GPRC5A. For this reason, in this study, the functional effects of GPRC5A variants were evaluated through in-silico analyses.

Retrieving Single Nucleotide Variants Database

Data of variants corresponding to the human GPRC5A gene were retrieved from Variation Viewer (https://www.ncbi.nlm.nih.gov/variation/view) by selecting the NCBI Reference Sequence as GRCh38.p13, NC_000012.12, NM_003979.4, (ENST00000014914.6). In this study, the variants in the dbSNP database were analysed by filtering them to include missense, nonsense, frameshift, splice acceptor/donor, 5' untranslated region (5'UTR) and 3' untranslated region (3'UTR). In-silico tools/databases used in the study were demonstrated in the workflow (Fig. 1).

Evaluation of Pathogenicity of Missense GPRC5A Variants

The tolerance and pathogenicity of the missense mutations on GPRC5A were evaluated using three different in-silico predictive tools which have specific computational algorithms: Mutation Taster2 (MT2), Polymorphism Phenotyping v2 (Polyphen-2) and MutPred2. Polyphen-2 (PP2) is a tool predicting the possible effect of an amino acid substitution based on protein structure and function, and the predictions were categorized as benign, and possibly or probably damaging(Adzhubei et al. 2010) (http://genetics.bwh.harvard.edu/pph2). MT2 evaluates the pathogenic potential of DNA sequence alterations and classifies variants as disease causing or polymorphism(Schwarz et al. 2014) (https://www.mutationtaster.org/). MutPred2 is a new algorithm that predicts the pathogenicity of amino acid substitutions. There are two outputs of this algorithm, the general and property score. The pathogenicity of the variant is expressed by a general score ranging from 0 to 1, where a score closer to 1 indicates a greater tendency for pathogenicity (pathogenicity threshold is 0.50). The property score that ranges between 0 and 1 is cited to the effect of a substitution on different properties, the higher the property score, the more likely that disease associated molecular mechanism is related the altered property. Also, lower P-value indicate that the more likely the predicted property is related with pathogenicity (Pejaver et al. 2020).

All missense variants' functional effects on GPRC5A were classified according to MT2, PP2 and MutPred2 and combined the scores of these algorithms particularly for evaluating missense variants’ pathogenicity. Variants that had the same predictive results for these algorithms were classified as pathogenic or benign. Variants with inconsistent results attained from the algorithms were scored as VUS (Variant of Uncertain Significance).

In addition, six variants [p.Tyr350His (rs770022212), p.Tyr350Cys (rs372263202), p.Tyr347Cys (rs759993863), p.Ser336Tyr (rs745823691), p.Gly38Arg (rs767232322) and p.Cys9Phe (rs11550683)] that thought to may have functional importance in terms of pathogenicity as a result of literature review were chosen and confirmed using VarSite web server. The VarSite database aims to elucidate disease-associated variants obtained from ClinVar and UniProt and natural variants from gnomAD with structural information from the 3D structures in the Protein Data Bank (PDB)(Laskowski et al. 2020) (https://www.ebi.ac.uk/thornton-srv/databases/VarSite).

Retrieval of Allele Frequency Information of GPRC5A Variants from the GnomAD Database

GPRC5A variants' allele frequencies were retrieved from Genome Aggregation Database (GnomAD) (https://gnomad.broadinstitute.org/). Developed by an international consortium, GnomAD is a resource that contains variant information of whole genome sequence (WGS) data from large-scale sequencing projects (Karczewski et al. 2020).

Annotation of Regulatory Functions of GPRC5A Variants Using RegulomDB Database

RegulomeDB is a database that integrates data from experiments and computational predictions and grades the functional consequences of SNVs from 1 to 7; lower scores indicate strong evidence in terms of a regulatory role (Table 1) (Boyle et al. 2012).

Table 1

The scoring scheme for RegulomeDB database
Regulome-DB scores	Biological evidence
1a	eQTL + TF binding + matched TF motif + matched Footprint + chromatin accessibility peak
1b	eQTL + TF binding + any motif + Footprint + chromatin accessibility peak
1c	eQTL + TF binding + matched TF motif + chromatin accessibility peak
1d	eQTL + TF binding + any motif + chromatin accessibility peak
1e	eQTL + TF binding + matched TF motif
1f	eQTL + TF binding / chromatin accessibility peak
2a	TF binding + matched TF motif + matched Footprint + chromatin accessibility peak
2b	TF binding + any motif + Footprint + chromatin accessibility peak
2c	TF binding + matched TF motif + chromatin accessibility peak
3a	TF binding + any motif + chromatin accessibility peak
3b	TF binding + matched TF motif
4	TF binding + chromatin accessibility peak
5	TF binding or chromatin accessibility peak
6	Motif hit
7	Other

Analyses of Phosphorylation-Related GPRC5A Variants

PhosphoSitePlus (PSP) database (https://www.phosphosite.org) was used to define post-translational modification (PTM) sites in the GPRC5A protein. PSP database contains experimental data of PTMs including phosphorylation, acetylation, ubiquitylation and methylation (Hornbeck et al. 2015).

Evaluation of Putative Roles of 3′UTR Variants of GPRC5A on miRNA Binding Sites

The supposed roles of the 3'UTR variants of the GPRC5A on miRNA target sites were evaluated using PolymiRTS (Polymorphism in microRNAs and their Target Sites) database (https://compbio.uthsc.edu/miRSNP/). PolymiRTS database was developed to identify potential impact of variations in miRNA seed regions and miRNA target sites (Szklarczyk et al. 2021).

Prediction of Protein Interactions

The protein–protein interaction network of the GPRC5A was visualized using STRING (Search Tool for the Retrieval of Interacting Genes) database (https://string-db.org/). STRING is a database predicting protein interactions which contains information from various sources including computational prediction methods, experimental data, and public text collections (Szklarczyk et al. 2021).

The total number of variants were 2629, 2208 of them SNV, 414 of them indel, insertion or deletion (20 of which end with frameshift variant), and 7 of synonymous. From 2208 SNV’s 391 were missense, 16 were nonsense, 41 were 5'UTR, 1755 were 3'UTR, 3 were splice acceptor and 2 were splice donor variants were downloaded from the Variation Viewer. Since 55 of these variants are bi-allelic and 4 of them tri-allelic, these variants were grouped into (a), (b) and (c) respectively (e.g., rs760953581 (a), rs760953581 (b) and rs760953581 (c)). The detailed results of the list of variants of GPRC5A (missense, nonsense, and frameshift) that were analysed using in-silico tools and bioinformatics resources are shown in the Table S2. The majority of the variants (54,81%) were located in Exon 2 followed by Exon 4 (Fig. 2a).

Prediction of the consequence of missense mutations on GPRC5A protein was achieved using PP2, and MT2. Of the 391 missense variants analysed with PP2, 222 variants were classified as possibly or probably damaging and 169 as benign. According to the MT2 predictions 201 variants were classified as disease causing, while 190 variants were classified as polymorphism. Based on MutPred2 predictions, 83 variants were classified as pathogenic and 308 variants as benign. The in-silico prediction classification combining the results of PP2, MT2 and MutPred2 are as follows; 76 variants were categorized as pathogenic, 180 were VUS and 135 were categorized as benign variant (Fig. 2b).

Total 170 variants were found to have allele frequency in GnomAD database and among them only rs850932 has MAF > 0.01. Allele frequencies of variants are shown in Table S1.

The regulatory functions of all variants retrieved from the dbSNP database were also analysed using RegulomeDB, and only 6 variants (1.4%) were found to have strong evidence (RegulomeDB score < 3) for being regulatory. Of the 76 pathogenic missense variants classified based on the combined scores of PP2, MT2 and MutPred2, only one variant, rs767232322 (p.Gly38Arg), had the RegulomeDB score of2b. Minor allele frequency of this variant is 4,60E-05 according to GnomAD prediction score.

As a results of scanning PTMs sites in the GPRC5A using PSP database identified 22 PTM sites; 15 Phosphorylation and 7 Ubiquitylation (Fig. 3). In addition, 17 amino acid substitutions were identified in these PTM sites and of which 3 [p.Ser336Tyr (rs745823691), p.Tyr347His (rs757980943), p.Tyr350His (rs770022212)] were categorized as pathogenic based on combining the results of PP2, MT2 and MutPred2 (Table 2).

Table 2

Posttranslational modifications-related variants identified in the *GPRC5A* gene.
PTM sites	Flanking sequence	PTMs	Phosphorylation-related variant	CS
K285	PVEDAFCkPQLVkks	Ubiquitylation	Lys285Thr	B
K290	FCkPQLVkksyGVEN	Ubiquitylation	Lys290Arg	VUS
K291	CkPQLVkksyGVENR	Ubiquitylation	NA	-
S292	kPQLVkksyGVENRA	Phosphorylation	NA	-
Y293	PQLVkksyGVENRAy	Phosphorylation	Tyr293Cys	VUS
Y300	yGVENRAysQEEItQ	Phosphorylation	Tyr300His	VUS
S301	GVENRAysQEEItQG	Phosphorylation	Ser301Phe	VUS
T306	AysQEEItQGFEEtG	Phosphorylation	Thr306Pro	B
T312	ItQGFEEtGDtLyAP	Phosphorylation	Thr312Arg	B
T315	GFEEtGDtLyAPyst	Phosphorylation	Thr315Ala	B
Y317	EEtGDtLyAPystHF	Phosphorylation	Tyr317Cys	VUS
Y320	GDtLyAPystHFQLQ	Phosphorylation	NA	-
S321	DtLyAPystHFQLQN	Phosphorylation	Ser321Cys	VUS
T322	tLyAPystHFQLQNQ	Ubiquitylation	Thr322Ala	B
K333	LQNQPPQkEFsIPRA	Ubiquitylation	NA	-
S336	QPPQkEFsIPRAHAW	Phosphorylation	Ser336Tyr	P
S345	PRAHAWPsPykDyEV	Phosphorylation	Ser345Arg	VUS
Y347	AHAWPsPykDyEVkK	Phosphorylation	Tyr347His	VUS
K348	HAWPsPykDyEVkKE	Ubiquitylation	NA	-
Y350	WPsPykDyEVkKEGs	Phosphorylation	Tyr350His	P
K353	PykDyEVkKEGs___	Ubiquitylation	Lys353Arg	B
S357	yEVkKEGs_______	Phosphorylation	Ser357Asn	B

The potential roles of the 3'UTR variants of the GPRC5A on miRNA target sites were evaluated using PolymiRTS database, and 16 miRNA target sites were identified for two variants in the 3'UTR region of the GPRC5A gene (Table 3).

Table 3

Potential impacts of the 3'UTR variants in *GPRC5A* on miRNA target sites.
dbSNP ID	Allele	miRNA ID	Function	Contex + score change∗
rs200040454	G	hsa-miR-4632-3p	D	-355
rs144308510	A	hsa-miR-3617-3p	D	-107
		hsa-miR-4690-3p	D	-197
		hsa-miR-5685	D	-197
	C	hsa-miR-15a-5p	C	-128
		hsa-miR-15b-5p	C	-125
		hsa-miR-16-5p	C	-132
		hsa-miR-195-5p	C	-132
		hsa-miR-424-5p	C	-132
		hsa-miR-4524a-5p	C	-149
		hsa-miR-4524b-5p	C	-149
		hsa-miR-497-5p	C	-125
		hsa-miR-503-5p	C	-166
		hsa-miR-562	C	-149
		hsa-miR-646	C	-132
		hsa-miR-6838-5p	C	-125

∗Differences in context + scores of between the reference and derived alleles for each SNP or INDEL in putative miRNA target sites; a more negative value indicates an increased possibility that the variation in miRNA target site causes functional change. D; distrups target site, C; creates target site.

The protein-protein interaction network was identified using the STRING database. The proteins GRM2, EIF4A1, GTF2F2, SMYD5, FXYD3, SLC25A3, RARS, KRTCAP3, TSPEAR and NOLC1 are among the proteins that most closely interact with GPRC5A. (Fig. 4).

GPRC5A is a family member of the GPCRs, which is one of the largest and most various group of membrane protein family in the human genome. Previous studies have been reported that transcription of the GPRC5A can be induced through retinoic acid response element in the 5' UTR region (Allenby et al. 1993; Ye et al. 2009). In addition, GPRC5A transcription has been reported to be regulated by cAMP (cyclic adenosine monophosphate), TP53 (Tumor Protein P53), BRCA1 (Breast Cancer Gene 1), and hypoxia-inducible factors (HIFs) (Wu et al. 2005; Hirano et al. 2006; Sokolenko et al. 2014; Greenhough et al. 2018b). Expression of GPRC5A differs in various human cancers, its dual role in tumorigenesis makes it intriguing. GPRC5A has tumor suppressor activity in lung cancer. It has been reported by Tao et al. that knockout of the Gprc5a results in development of lung cancers in mice (Tao et al. 2007). Deng et al. have demonstrated that knockout of Gprc5a leads to activation of NF-κB (Nuclear Factor kappa B) in lung epithelial cells in mice, resulting in increased inflammatory microenvironment, and have suggested that this may promote tumorigenesis (Deng et al. 2010). Moreover, deletion of Gprc5a leads to activation of signal transducer and activator of transcription 3 (Stat3) that was important for lung cancer cell survival and transformation (Chen et al. 2010). Furthermore, it has also been reported that GPRC5A has a potential role in inhibition of epidermal growth factor receptor (EGFR) expression and activation. It is thought that GPRC5A deficiency may contribute to the initiation of lung tumorigenesis by leading to dysregulated EGFR (Zhong et al. 2015). However, GPRC5A display oncogenic properties in other cancer types such as breast, stomach, pancreatic, liver, and colon cancers. For example, Greenhough et al. showed that upregulation of GPRC5A during hypoxia activates the Hippo pathway effector YAP and protects colorectal tumor cells from apoptosis; HIFs directly induce the GPRC5A expression (Greenhough et al. 2018b). In gastric cancer, GPRC5A expression levels are high compared with normal tissues (Cheng et al. 2012). Knockdown of GPRC5A has been reported that lead to decreased proliferation, migration, and cell growth in pancreatic cancer cell lines via STAT3 inactivation (Jahny et al. 2017). rs1640875 in GPRC5A is thought to have a pleiotropic effect on both nevus and melanoma (Duffy et al. 2018). In a study by Sokolenko et al. it was revealed that GPRC5A c.183delG (p.Arg61fs ) allele affects the disease susceptibility in carriers of BRCA1 mutation in breast cancer. They also found that this allele was marginally overrepresented in lung cancer patients who smoke relative to healthy inividuals (Sokolenko et al. 2014). This information indicates that variants in GPRC5A gene may play important roles in many different types of cancer in human.

In this study, the putative effects of GPRC5A variants on protein function, gene regulation and post-translational mechanisms were investigated. In recent years, several prediction tools which have specific computational algorithms were developed for pathogenicity assessment of variants of the genome (Cline and Karchin 2011). Since different scoring algorithms were used, the are not always consistent, it is very important to carefully evaluate the estimated results (Ghosh et al. 2017). In the current study, the functional consequences of GPRC5A missense variants were investigated. For this reason, the PP2, MT2 and MutPred2 tools were used. These tools are the most common predictors for amino acid substitutions in in-silico analyses. 76 missense variants were categorized as pathogenic based on combined scores of these three algorithms from 391 missense variants, 210 have the same score using three different algorithms. The results of this study show that the three in-silico tools frequently give the same results even though they used different algorithms.

Proteins are vital to life, and understanding their structure is important to understanding their functions (Lee et al. 2007). AlphaFold is an AI system that predicts the 3D protein structure, developed by DeepMind(Varadi et al. 2022) (https://alphafold.ebi.ac.uk). The AlphaFold model of the GPRC5A protein and the position of six variants (p.Tyr350His, p.Tyr350Cys, p.Tyr347Cys, p.Ser336Tyr, p.Gly38Arg, and p.Cys9Phe) on GPRC5A that have the potential of being functional importance in terms of pathogenicity are shown in Fig. 5a. Additionally, VarSite predictions of these variants, as a schematic diagram of the variant and as histogram of the disease propensity are shown in Fig. 5b. According to MutPred2 p.Tyr347Cys variant have a g-score of 0.576 and was predicted to alter the transmembrane protein with a p-value of 3.1e-04. Two different variants on the same location (p.Tyr350Cys and p.Tyr350His (g = 0,65; p = 1.6e-03)) were predicted to the loss of phosphorylation on this amino acid.There is a disulfide bond between Cys9 and Cys21; p.Cys9Phe in GPRC5A has been predicted tocause loss of disulfide linkage at C9 (g = 0,810; p = 8.3e-05). p.Cys9Phe has also been predicted to cause alterations in the metal binding ability (p = 3.8e-03). p.Gly38Arg was the only variant with a RegulomeDB score < 3, which may have a regulatory effect among the variants that was classified as pathogenic. p.Ser336Tyr is the only PTM-related pathogenic variant as well as predicted to alter transmembrane protein (g = 0,578; p = 3.5e-04) by MutPred2.

Due to the effects of PTM-associated SNVs on human diseases, particularly cancer, the PTM sites of GPRC5A were analysed using PhosphositePlus. Lin et al. have shown that GPRC5A undergoes EGFR-mediated phosphorylation at double tyrosine motifs (Y317/Y320 and Y347/Y350). Importantly, they showed by immunohistochemical analysis that GPRC5A, which is non-phosphorylated in normal lung tissues, was highly tyrosine-phosphorylated in NSCLC tissues (Lin et al. 2014). In the current study, among these 4 tyrosine motif containing locations, three variants (p.Tyr347Cys (rs759993863), p.Tyr350His (rs770022212) and p.Tyr350Cys (rs372263202)) were classified as pathogenic and two other (p.Tyr347His (rs757980943) and p,Tyr317Cys (rs146929355) variants were classified as VUS according to PP2, MT2 and MutPred results.

A single variant had MAF > 0.01 according to GnomAD results. This information indicates that variants of this gene are rare in the human populations.

miRNAs are molecules with key regulatory roles in the cell in both normal and pathological conditions. Polymorphisms in miRNA target sites have been associated with a wide range of diseases including cancers (Ryan et al. 2010). There are various algorithms that could predict possible miRNA-binding sites. PolymiRTS is a database including that naturally occurring DNA variations in putative miRNA target sites (Bhattacharya et al. 2014). The 16 miRNA target sites identified for two variants (rs144308510 and rs144308510) were found in the 3'UTR region of the GPRC5A gene using the PolymiRTS database (Table 4). Some of these miRNA target sites have been associated with various cancers (Darvishi et al. 2020; Ghanbari et al. 2021; Li et al. 2021; Dastmalchi et al. 2022). miR-195 inhibits gastric cancer cell proliferation and metastasis by targeting GPRC5A (Liang et al. 2019). DNA methyltransferase 1 (DNMT1) mediates the miR-497/GPRC5A axis in breast cancer, leading to increased chemotherapy resistance and metastasis (Liu et al. 2022).

Cellular function depends on functional interactions among the proteins. STRING database anaylsis showed that GPRC5A strongly interacts with proteins of GRM2 (Glutamate metabotropic receptor 2) and EIF4A1 (Eukaryotic initiation factor 4A-I). GRM2 - a GPCR family member protein- is activated by glutamate and modulates the activity of down-stream effectors, such as adenylate cyclase via G proteins and is widely expressed in brain (González-Maeso et al. 2008). It may mediate suppression of neurotransmission or participate in the modulation of synaptic transmission and neuronal excitability (Joo et al. 2001; Niswender and Conn 2010). Fukuda et al., suggested that in humans the GRM2 gene product may regulate the metastatic phenotype of some colorectal cancers (Fukuda et al. 1997). Bidkhori et al., in their study used a "genome-scale co-expression network" approach, and identified a number of genes, including GMR2, that play a role in lung adenocarcinoma (Bidkhori et al. 2013). EIF4A1, an ATP-dependent RNA helicase, is required for mRNA to attach to the ribosome (Jeong et al. 2009). EIF4A1 has been found to be overexpressed in various malignancies such as hepatocellular carcinoma, NSCLC, endometrial cancer, cervical cancer, and breast cancer (Raza et al. 2015). Furthermore, high expression level of EIF4A1 has been associated with poor prognosis in breast and cervical cancer patients and metastasis in NSCLC patients. In addition to all this, EIF4A1 is known to play a role in many cancers (Xue et al. 2021).

In conclusion, the potential pathogenic consequences of GPRC5A coding variants resulting amino acid substitutions have been investigated comprehensively and their putative roles in gene regulation and PTMs evaluated by in-silico analysis. The results highlight the functional consequences of variants in GPRC5A. The results of this study may have important impact to future studies for further understanding the pathogenecity of GPRC5A-associated cancer types by unraveling the roles of putative functional variants.

Acknowledgements: This study was supported by Bursa Uludag University, BAP Section (Project nr: BUAP(F)-2020/1). MDK is a PhD student financed by the Council of Higher Education (YÖK) 100/2000 PhD Scholarship and Technology Program and supported by TUBITAK 2211-A General National PhD Scholarship Program.

Funding This research did not receive any specifc grant from funding agencies in the public, commercial, or not-for-proft sectors.

Confict of interest The authors declare that they have no competing interests

Author Contributions All authors contributed to the study conception and design. Data collection and analysis were performed by Merve DEMİRBAĞ KARAALİ, Elif UZ Yıldırım and Serap ÇELİKLER. The first draft of the manuscript was written by Merve DEMİRBAĞ KARAALİ and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Assessment of Single Nucleotide Variants in hGPRC5A Gene Using in-silico Tools

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Retrieving Single Nucleotide Variants Database

Prediction of Protein Interactions

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