Advances in Bioinformatics

Advances in Bioinformatics / 2019 / Article

Research Article | Open Access

Volume 2019 |Article ID 1651587 |

Mujahed I. Mustafa, Tebyan A. Abdelhameed, Fatima A. Abdelrhman, Soada A. Osman, Mohamed A. Hassan, "Novel Deleterious nsSNPs within MEFV Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis", Advances in Bioinformatics, vol. 2019, Article ID 1651587, 13 pages, 2019.

Novel Deleterious nsSNPs within MEFV Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis

Academic Editor: Nurit Haspel
Received29 Sep 2018
Revised02 Jan 2019
Accepted21 Jan 2019
Published04 Jun 2019


Background. Familial Mediterranean Fever (FMF) is the most common autoinflammatory disease (AID) affecting mainly the ethnic groups originating from Mediterranean basin. We aimed to identify the pathogenic SNPs in MEFV by computational analysis software. Methods. We carried out in silico prediction of structural effect of each SNP using different bioinformatics tools to predict substitution influence on protein structure and function. Result. 23 novel mutations out of 857 nsSNPs are found to have deleterious effect on the MEFV structure and function. Conclusion. This is the first in silico analysis of MEFV gene to prioritize SNPs for further genetic mapping studies. After using multiple bioinformatics tools to compare and rely on the results predicted, we found 23 novel mutations that may cause FMF disease and it could be used as diagnostic markers for Mediterranean basin populations.

1. Introduction

Familial Mediterranean Fever is an autosomal recessive inherited inflammatory disease [13] (however, it has been observed that a substantial number of patients with clinical FMF possess only one demonstrable MEFV mutation [4, 5]) that is principally seen in different countries [610]. However, patients from different ethnicities (such as Japan) are being increasingly recognized [2, 11], and the carrier frequency for MEFV genetic variants in the population in the Mediterranean basin is about 8% [12]. Most cases of FMF usually present with acute abdominal pain and fever [1, 3, 7], both of which are also the main causes of referral in the emergency department [13]. All these factors may help in medical treatment. Colchicine is the first line therapy [14], but in resistant cases (<10% of patients) [15], it affects the responsiveness to Colchicine [16]; other anti-inflammatory drugs can be used for extra anti-inflammatory effect [17]. If FMF is not treated, it may be an etiologic factor for colonic LNH in children [18]. MEFV gene is localized on 16p13.3 of chromosome 16 at position 13.3 which consists of 10 exons with 21600 bp [3, 19]. The disease is characterized by recurrent febrile episodes and inflammation in the form of sterile polyserositis. Amyloid protein involved in inflammatory amyloidosis was named AA (amyloid‐associated) protein and its circulating precursor was named SAA (serum amyloid‐associated). Amyloidosis of the AA type is the most severe complication of the disease. The gene responsible for FMF, MEFV, encodes a protein called pyrin or marenostrin and is expressed mainly in neutrophils [3, 19].

The definition of the MEFV gene has permitted genetic diagnosis of the disease. Nevertheless, as studies have unwrapped molecular data, problems have arisen with the clinical definitions of the disease [20]. FMF is caused by mutations in the MEFV missense SNPs (we were focusing on SNPs which are located in the coding region because it is much important in disease causing potential, which are responsible for amino acid residue substitutions resulting in functional diversity of proteins in humans) [20] coding for pyrin, which is a component of inflammasome functioning in inflammatory response and production of interleukin-1β (IL-1β). Recent studies have shown that pyrin recognizes bacterial modifications in Rho GTPases, which results in inflammasome activation and increase in IL-1β. Pyrin does not directly recognize Rho modification but probably is affected by Rho effector kinase, which is a downstream event in the actin cytoskeleton pathway [19, 21, 22].

The aim of this study was to identify the pathogenic SNPs in MEFV using in silico prediction software and to determine the structure, function, and regulation of their respective proteins. This is the first in silico analysis in MEFV gene to prioritize SNPs for further genetic mapping studies. The usage of in silico approach has strong impact on the identification of candidate SNPs since they are easy and less costly and can facilitate future genetic studies [23].

2. Method

2.1. Data Mining

The data on human MEFV gene was collected from National Center for Biological Information (NCBI) website [24]. The SNP information (protein accession number and SNP ID) of the MEFV gene was retrieved from the NCBI dbSNP ( and the protein sequence was collected from Swiss Prot databases ( [25].

2.2. SIFT

SIFT is a sequence homology-based tool [26] that sorts intolerant from tolerant amino acid substitutions and predicts whether an amino acid substitution in a protein will have a phenotypic effect. It considers the position at which the change occurred and the type of amino acid change. Given a protein sequence, SIFT chooses related proteins and obtains an alignment of these proteins with the query. Based on the amino acids appearing at each position in the alignment, SIFT calculates the probability that an amino acid at a position is tolerated conditional on the most frequent amino acid being tolerated. If this normalized value is less than a cutoff, the substitution is predicted to be deleterious. SIFT scores <0.05 are predicted by the algorithm to be intolerant or deleterious amino acid substitutions, whereas scores >0.05 are considered tolerant. It is available at (

2.3. PolyPhen-2

It is a software tool [27] to predict possible impact of an amino acid substitution on both structure and function of a human protein by analysis of multiple sequence alignment and protein 3D structure; in addition, it calculates position-specific independent count scores (PSIC) for each of the two variants and then calculates the PSIC scores difference between the two variants. The higher a PSIC score difference is, the higher the functional impact a particular amino acid substitution is likely to have. Prediction outcomes could be classified as probably damaging, possibly damaging or benign according to the value of PSIC as it ranges from (0_1); values closer to zero were considered benign while values closer to 1 were considered probably damaging and also it can be indicated by a vertical black marker inside a color gradient bar, where green is benign and red is damaging. nsSNPs that is predicted to be intolerant by SIFT has been submitted to PolyPhen as protein sequence in FASTA format obtained from UniproktB/Expasy after submitting the relevant ensemble protein (ESNP) there, and then we entered position of mutation, native amino acid, and the new substituent for both structural and functional predictions. PolyPhen version 2.2.2 is available at

2.4. Provean

Provean is a software tool [28] which predicts whether an amino acid substitution or indel has an impact on the biological function of a protein. It is useful for filtering sequence variants to identify nonsynonymous or indel variants that are predicted to be functionally important. It is available at (

2.5. SNAP2

Functional effects of mutations are predicted with SNAP2 [29]. SNAP2 is a trained classifier that is based on a machine learning device called “neural network”. It distinguishes between effect and neutral variants/nonsynonymous SNPs by taking a variety of sequence and variant features into account. The most important input signal for the prediction is the evolutionary information taken from an automatically generated multiple sequence alignment. Also structural features such as predicted secondary structure and solvent accessibility are considered. If available also annotation (i.e., known functional residues, pattern, regions) of the sequence or close homologs are pulled in. In a cross-validation over 100,000 experimentally annotated variants, SNAP2 reached sustained two-state accuracy (effect/neutral) of 82% (at an AUC of 0.9). In our hands this constitutes an important and significant improvement over other methods. It is available at (

2.6. PHD-SNP

An online Support Vector Machine (SVM) based classifier is optimized to predict if a given single point protein mutation can be classified as disease related or as a neutral polymorphism. It is available at (

2.7. SNP&Go

SNPs&GO is an algorithm developed in the Laboratory of Biocomputing at the University of Bologna directed by Prof. Rita Casadio. SNPs&GO is an accurate method that, starting from a protein sequence, can predict whether a variation is disease related or not by exploiting the corresponding protein functional annotation. SNPs&GO collects in unique framework information derived from protein sequence, evolutionary information, and function as encoded in the Gene Ontology terms and outperforms other available predictive methods [30]. It is available at (

2.8. P-Mut

P-MuT, a web-based tool [31] for the annotation of pathological variants on proteins, allows the fast and accurate prediction (approximately 80% success rate in humans) of the pathological character of single point amino acidic mutations based on the use of neural networks. It is available at (

2.9. I-Mutant 3.0

I-Mutant 3.0 is a neural network based tool [32] for the routine analysis of protein stability and alterations by taking into account the single-site mutations. The FASTA sequence of protein retrieved from UniProt is used as an input to predict the mutational effect on protein stability. It is available at (

2.10. Modeling nsSNP Locations on Protein Structure

Project hope is a new online web-server to search protein 3D structures (if available) by collecting structural information from a series of sources, including calculations on the 3D coordinates of the protein, sequence annotations from the UniProt database, and predictions by DAS services. Protein sequences were submitted to project hope server in order to analyze the structural and conformational variations that have resulted from single amino acid substitution corresponding to single nucleotide substitution. It is available at (

2.11. GeneMANIA

We submitted genes and selected from a list of data sets that they wish to query. GeneMANIA’s [33] approach is to know protein function prediction integrating multiple genomics and proteomics data sources to make inferences about the function of unknown proteins. It is available at (

3. Results and Discussion

3.1. Result

See Tables 15 and Figure 1.

Amino Acid Change SIFTPolyphenPROVEANSNAP2
predictionScorePredictionscoreScorePrediction (cutoff= -2.5)predictionscore

S749YDAMAGING0PROBABLY DAMAGING1.000-3.116Deleteriouseffect64
F743SDAMAGING0PROBABLY DAMAGING1.000-5.563Deleteriouseffect68
Y741CDAMAGING0PROBABLY DAMAGING1.000-6.035Deleteriouseffect77
F731VDAMAGING0PROBABLY DAMAGING1.000-5.159Deleteriouseffect81
I720TDAMAGING0PROBABLY DAMAGING1.000-3.639Deleteriouseffect58
L709RDAMAGING0PROBABLY DAMAGING1.000-4.311Deleteriouseffect77
V691GDAMAGING0PROBABLY DAMAGING1.000-4.667Deleteriouseffect66
W689RDAMAGING0PROBABLY DAMAGING1.000-10.132Deleteriouseffect89
G668RDAMAGING0PROBABLY DAMAGING1.000-6.287Deleteriouseffect92
V659FDAMAGING0PROBABLY DAMAGING1.000-3.811Deleteriouseffect64
F636CDAMAGING0PROBABLY DAMAGING1.000-6.49Deleteriouseffect79
R461WDAMAGING0PROBABLY DAMAGING1.000-5.456Deleteriouseffect68
H407QDAMAGING0PROBABLY DAMAGING1.000-7.335Deleteriouseffect41
H407RDAMAGING0PROBABLY DAMAGING1.000-7.332Deleteriouseffect51
H404RDAMAGING0PROBABLY DAMAGING1.000-7.349Deleteriouseffect75
C398YDAMAGING0PROBABLY DAMAGING1.000-10.314Deleteriouseffect51
C395YDAMAGING0PROBABLY DAMAGING1.000-10.262Deleteriouseffect19
C395FDAMAGING0PROBABLY DAMAGING1.000-10.315Deleteriouseffect27
C395RDAMAGING0PROBABLY DAMAGING1.000-11.074Deleteriouseffect27
H378QDAMAGING0PROBABLY DAMAGING1.000-5.886Deleteriouseffect38
H378YDAMAGING0PROBABLY DAMAGING1.000-4.884Deleteriouseffect45
C375RDAMAGING0PROBABLY DAMAGING1.000-8.429Deleteriouseffect66
L86PDAMAGING0PROBABLY DAMAGING1.000-4.1Deleteriouseffect19

Amino Acid ChangeSNP&GOPHD-SNP P-Mut

S749YDisease10.573Disease30.6490.67 (85%)Disease
F743SDisease20.617Disease40.6960.82 (90%)Disease
Y741CDisease60.797Disease70.8690.61 (83%)Disease
F731VDisease60.79Disease80.8990.93 (94%)Disease
I720TDisease60.811Disease50.7690.81 (89%)Disease
L709RDisease30.672Disease40.6950.66 (85%)Disease
V691GDisease10.55Disease30.6750.92 (93%)Disease
W689RDisease70.841Disease80.9240.93 (94%)Disease
G668RDisease60.778Disease70.840.93 (94%)Disease
V659FDisease60.805Disease70.840.82 (90%)Disease
F636CDisease60.809Disease70.860.60 (82%)Disease
R461WDisease30.644Disease10.5720.63 (84%)Disease
H407QDisease60.788Disease40.7050.79 (89%)Disease
H407RDisease50.769Disease30.6730.86 (91%)Disease
H404RDisease50.744Disease50.7340.80 (89%)Disease
C398YDisease70.864Disease80.9120.86 (91%)Disease
C395YDisease70.864Disease80.9120.91 (93%)Disease
C395FDisease70.859Disease80.9140.92 (94%)Disease
C395RDisease70.842Disease80.8920.92 (94%)Disease
H378QDisease40.714Disease40.6980.88 (92%)Disease
H378YDisease50.732Disease50.7280.80 (89%)Disease
C375RDisease60.784Disease60.8220.92 (94%)Disease
L86PDisease50.729Disease60.8010.51 (79%)Disease

Amino Acid ChangeSVM2 Prediction EffectRIDDG Value Prediction

C375RIncrease 2 -0.01

FunctionFDRGenes in networkGenes in genome

nucleotide-binding domain, leucine rich repeat containing receptor signaling pathway1.42E-07647
regulation of interleukin-1 beta production0.000129426
interleukin-1 beta production0.000129430
regulation of interleukin-1 production0.000129430
interleukin-1 production0.000196435
intracellular receptor signaling pathway0.0002016207
positive regulation of cysteine-type endopeptidase activity0.0104384101
positive regulation of endopeptidase activity0.0106634105
positive regulation of peptidase activity0.0114109
inflammatory response0.0182465283
regulation of chemokine production0.018246339
chemokine production0.022338344
regulation of cysteine-type endopeptidase activity0.0332384160
tumor necrosis factor production0.033238354
regulation of tumor necrosis factor production0.033238354
tumor necrosis factor superfamily cytokine production0.0407359
regulation of I-kappaB kinase/NF-kappaB signaling0.0469024185
I-kappaB kinase/NF-kappaB signaling0.0577224198
positive regulation of cytokine production0.0650044207
positive regulation of cysteine-type endopeptidase activity involved in apoptotic process0.099763393
positive regulation of interleukin-1 beta secretion0.099763215
defense response to Gram-negative bacterium0.099763216
cysteine-type endopeptidase activator activity involved in apoptotic process0.099763217
regulation of endopeptidase activity0.0997634251
glycosaminoglycan binding0.099763388
regulation of extrinsic apoptotic signaling pathway0.099763392
regulation of peptidase activity0.0997634258
positive regulation of interleukin-1 secretion0.099763216
regulation of interleukin-1 beta secretion0.099763217

FDR: false discovery rate is greater than or equal to the probability that this is a false positive.

Gene 1Gene 2WeightNetwork group

CASP1MEFV0.469715Physical Interactions
NLRP3PYCARD0.570819Physical Interactions
PYCARDMEFV0.03673Physical Interactions
PYCARDPSTPIP10.028273Physical Interactions
CASP1PYCARD0.017772Physical Interactions
CASP1CEBPB0.010941Physical Interactions
RELACEBPB0.00247Physical Interactions
COG5MEFV0.211887Physical Interactions
NLRP3MEFV0.111467Physical Interactions
MAP1LC3CMEFV0.104412Physical Interactions
PYCARDMEFV0.292858Physical Interactions
NLRP3PYCARD0.189095Physical Interactions
PSTPIP1MEFV0.260595Physical Interactions
PYCARDMEFV0.204673Physical Interactions
CASP1PYCARD0.042335Physical Interactions
RELACEBPB0.007591Physical Interactions
COG5MEFV0.387501Physical Interactions
NLRP3PYCARD0.304828Physical Interactions
PYCARDMEFV0.00952Shared protein domains
CASP1PYCARD0.013543Shared protein domains
NLRP3MEFV0.009339Shared protein domains
NLRP3PYCARD0.018527Shared protein domains
NLRP14MEFV0.009512Shared protein domains
NLRP14PYCARD0.018871Shared protein domains
NLRP14NLRP30.036989Shared protein domains
ZNF528ZNF7470.002699Shared protein domains
PYCARDMEFV0.011528Shared protein domains
CASP1PYCARD0.031451Shared protein domains
NLRP3MEFV0.009427Shared protein domains
NLRP3PYCARD0.015448Shared protein domains
NLRP14MEFV0.009815Shared protein domains
NLRP14PYCARD0.016085Shared protein domains
NLRP14NLRP30.019774Shared protein domains
ZNF528ZNF7470.002759Shared protein domains

4. Discussion

23 novel mutations have been found (see Table 3) which affected the stability and function of the MEFV gene using bioinformatics tools. The methods used were based on different aspects and parameters describing the pathogenicity and provided clues on the molecular level about the effect of mutations. It was not easy to predict the pathogenic effect of SNPs using single method. Therefore, multiple methods were used to compare and rely on the results predicted. In this study we used different in silico prediction algorithms: SIFT, PolyPhen-2, Provean, SNAP2, SNP&GO, PHD-SNP, P-MuT, and I-Mutant 3.0 (see Figure 1).

This study identified the total number of nsSNP in Homo sapiens located in coding region of MEFV gene, which were investigated in dbSNP/NCBI Database [24]. Out of 2369, there are 856 nsSNPs (missense mutations) submitted to SIFT server, PolyPhen-2 server, Provean sever, and SNAP2, respectively, and 392 SNPs were predicted to be deleterious in SIFT server. In PolyPhen-2 server, the result showed that 453 were found to be damaging (147 possibly damaging and 306 probably damaging showing deleterious). In Provean server our result showed that 244 SNPs were predicted to be deleterious. While in SNAP2 server the result showed that 566 SNPs were predicted to have effect. The differences in prediction capabilities refer to the fact that every prediction algorithm uses different sets of sequences and alignments. In Table 2 we submitted four positive results from SIFT, PolyPhen-2, Provean, and SNAP2 (see Table 1) to observe the disease causing one by SNP&GO, PHD-SNP, and P-Mut servers.

In SNP&GO, PHD-SNP and P-Mut softwares were used to predict the association of SNPs with disease. According to SNP&GO, PHD-SNP and P-Mut (70, 91 and 58 SNPs respectively) were found to be disease-related SNPs. We selected the triple disease-related SNPs only in 3 softwares for further analysis by I-Mutant 3.0, Table 3. I-Mutant result revealed that the protein stability decreased which destabilizes the amino acid interaction (S749Y, F743S, Y741C, F731V, I720T, L709R, V691G, W689R, G668R, V659F, F636C, H407Q, H407R, H404R, C398Y, H378Q, H378Y, and L86P). C375R, C395F, C395R, C395Y, and R461W were found to increase the protein stability (see Table 3).

BioEdit software was used to align 10 amino acid sequences of MEFV demonstrating that the residues predicted to be mutated in our band (indicated by red arrow) are evolutionarily conserved across species (see Figure 2). While Project HOPE software was used to submit the 23 most deleterious and damaging nsSNPs (see Figures 325), L86P: Proline (the mutant residue) is smaller than Leucine (the wild-type residue); this might lead to loss of interactions. The wild-type and mutant amino acids differ in size. The mutation is located within a domain, annotated in UniProt as Pyrin. The mutation introduces an amino acid with different properties, which can disturb this domain and abolish its function. The wild-type residue is located in a region annotated in UniProt to form an α-helix. Proline disrupts an α-helix when not located at one of the first 3 positions of that helix. In case of the mutation at hand, the helix will be disturbed and this can have severe effects on the structure of the protein.

GeneMANIA revealed that MEFV has many vital functions: chemokine production, inflammatory response, interleukin-1 beta production, interleukin-1 production, intracellular receptor signaling pathway, nucleotide-binding domain, Leucine rich repeat containing receptor signaling pathway, positive regulation of cysteine-type endopeptidase activity, positive regulation of endopeptidase activity, positive regulation of peptidase activity, regulation of chemokine production, regulation of cysteine-type endopeptidase activity, regulation of endopeptidase activity, regulation of interleukin-1 beta production, regulation of interleukin-1 production, and regulation of peptidase activity. The genes coexpressed with, sharing similar protein domain, or participated to achieve similar function were shown in (see Figure 26) Tables 4 and 5.

In this study we also retrieved all these SNPs as untested (V659F, L709R, F743S, S749Y). We found it to be all damaging. Our study is the first in silico analysis of MEFV gene which was based on functional analysis while all previous studies [34, 35] were based on frequency. This study revealed that 23 novel pathological mutations have a potential functional impact and may thus be used as diagnostic markers for Mediterranean basin populations.

5. Conclusion

In this work the influence of functional SNPs in the MEFV gene was investigated through various computational methods, which determined that S749Y, F743S, Y741C, F731V, I720T, L709R, V691G, W689R, G668R, V659F, F636C, R461W, H407Q,, H407R, H404R, C398Y, C395Y, C395F, C395R, H378Q, H378Y, C375R, and L86P are new SNPs having a potential functional impact and can thus be used as diagnostic markers. They constitute possible candidates for further genetic epidemiological studies with a special consideration of the large heterogeneity of MEFV SNPs among the different populations.

Data Availability

The data which support our findings in this study are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Authors’ Contributions

Mujahed I. Mustafa wrote Abstract, Methodology, and Result & Discussion. Fatima A. Abdelrhman did Introduction. Conclusion was written by Soada A. Osman. Writing the original draft was carried out by Mujahed I. Mustafa.


The authors wish to acknowledge the enthusiastic cooperation of Africa City of Technology, Sudan.


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Copyright © 2019 Mujahed I. Mustafa et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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