IN-SILICO SCREENING AND MODELING OF DELETERIOUS nsSNPs IN HUMAN GENE FBN1 FOR MARFAN SYNDROME

IN-SILICO SCREENING AND MODELING OF DELETERIOUS nsSNPs IN HUMAN GENE FBN1 FOR MARFAN SYNDROME

Mahendran Radha ^*, Sagolsem Mandaly Devi and Jeyabhaskar Suganya

Department of Bioinformatics, School of Life Sciences, Vels Institute of Science Technology and Advanced Studies, Chennai - 600117, Tamil Nadu, India.

ABSTRACT: Marfan syndrome (MFS) is a common dominant inherited disorder that affects connective tissue, which is associated with mutation of the gene FBN1. The protein encoded by this gene contributes to the final structure of microfibrils. Single nucleotide polymorphisms (SNPs) of this gene links to variations in gene expression phenotypically among patients. Therefore SNPs would be the main target for identification and analysis, which may help in further diagnosis of such life-threatening disorder. In this study, various computational methods have been used to analyze the genetic variations and identify non-synonymous or amino acid-changing SNPs (nsSNP). It can quicken to evaluate a considerable outcome of a mutation before literally doing the lab work. In total, 475 high-risk nsSNPs have been identified using the NCBI SNPs database. Among these nsSNPs, residues are assigned to predict deleterious or disease-related nsSNP. The conservation of functional amino acid residues and secondary and tertiary structure predictions were also reported using various tools. Swiss-Pdb Viewer allows changing amino acid side chains, causing an artificial mutation to the model. Further, HOPE and Chimera software have managed to analyze the changing structures due to the mutation and the visualization of protein 3D structure. The present article points to lay out an overview and a future direction for genetic study of this rare hereditary disorder by in silico analysis.

Marfan syndrome, in silico analysis, SNP, Prediction methods, Mutation

INTRODUCTION: Marfan syndrome [MFS] is a genetic disorder that affects connective tissue ¹, the fibers that maintain the structure of the body and provide cohesion and support internal organs ². This syndrome most commonly affects the heart ^{3, 4}, eyes ⁵, blood vessels, and skeleton ².

MFS is a rare pleiotropic disease ⁶ with three distinctive clinical criteria such as Thoracic Aortic Aneurysm and/or Dissection [TAAD] ^{7, 8}, Ectopia Lentis [EL] ^{9, 10} and Systemic Features [SFs, multisystemic manifestations] ^{2, 11, 12}.

Features of the people with Marfan syndrome are mostly thin and tall having disproportionately long arms, legs, fingers and toes ^{9, 13}. The injuries caused by this syndrome can be vague or acute. If the aorta, the large blood vessel that carries blood from our heart to the rest of our body, of the patient is affected, the condition can become life-threatening ¹⁴. Treatment usually includes medications to keep the blood pressure low to reduce the strain on the patient's aorta ¹⁵. Methodical checking for the damage progression is vital. Many people eventually require preventive surgery to repair the aorta ^{7, 16, 17, 18}. This genetic disease is associated with mutation of the gene FBN1 ^{9, 19, 20, 21}, the gene encoding fibrillin-1 ²², a structural component of the extracellular matrix (ECM) ^{4, 23}. The gene is located on chromosome 15q21.1 ^{13, 24} and also involved in the regulation of transforming growth factor β (TGF-β) bio-availability ^{25, 26, 27, 28}. The protein encoded by this gene contributes to the final structure of microfibrils, a fine fibre that bears force giving structural support in elastic and non-elastic connective tissue throughout the body ^{9, 29}.

In humans, three different genes (FBN1, FBN2, and FBN3) encode fibrillin proteins ⁹. FBN1 is a 230 kb gene, containing 66 exons, which encodes the structural protein fibrillin-1 ³⁰. Fibrillin-1 is a glycoprotein with 2871 amino acids and is a large structural macromolecule with a molecular mass of ∼320 kDa ^{9, 31}. This protein contributes to the integrity and function of all connective tissues. Fibrillins form ‘microfibrils’ with uniform diameters (∼20 nm) ³² that are irregularly cross-striated or “banded”. Fibrillinmicrofibrils have a characteristic structure consisting of light and dark or hollow areas that give the appearance of railroad tracks ³³. Fibrillin microfibrils exist as large bundles of microfibrils, as short individual microfibrils, or as the peripheral microfibril mantle around elastin in all elastic fibres ^{29, 34}. At the various types of connective tissue, fibrillin microfibrils are organized to best suit the functional integrity of the tissue ³⁵.

An enormous number of SNPs have been predicted up to date; hence it is not practicable to study all SNPs ³⁶. Many bioinformatic approaches and tools can be used to pick the most damaging SNPs and to predict their effects on protein structure, stability and function ³⁷. These approaches are based on retrieving SNPs from databases and then filtering those using different tools. These tools can broadly be classified into two categories. The first category is made up of tools that make predictions based solely on the protein sequence (e.g., SIFT, PROVEAN, PhD SNP). Meanwhile, the second one is made up of tools that integrate structural information when making predictions (e.g., PolyPhen-2, SNAP) ^{38, 39}. However, none of these methods are perfect. For instance, it is significant to get consent from several different tools before deciding which SNPs to select for further, analysis. With rapidly increasing bioinformatics tools and algorithms with better predictive power, computa-tional technologies can aid in predicting nsSNPs that are likely to have deleterious effects on FBN1 protein's structure, its expression, functions or disease susceptibility ⁴⁰. In this study, we performed a comprehensive in silico analysis of nsSNPs in coding regions in FBN1 gene using different structural bioinformatics tools.

METHODOLOGY:

Retrieval of SNPs: The SNP information of human FBN1 gene was obtained from the National Center for Biotechnology Information (NCBI) SNPs database, (dbSNP) (https://www. ncbi. nlm.nih. gov/SNP/) (accessed Nov.2018); nsSNPs in the coding regions were selected for investigation ^{41, 42}.

Predicting the Most Deleterious nsSNPs by Different Bioinformatics Tools: The effects of nsSNPs on FBN1 protein structure and function were predicted using the following bioinformatics tools: SIFT Sorting Intolerant From Tolerant (http://sift.bii.astar.edu.sg/) ^{43, 44, 45} and Poly Phen-2 Phenotyping Polymorphism v2 (http:// genetics. bwh. harvard.edu/pph2/index.shtml) ⁴⁶ were used to predict the deleterious nsSNPs ⁴⁷. To increase the accuracy of in silico techniques for prioritizing deleterious nsSNPs, the nsSNPs found to be deleterious by SIFT & PolyPhen-2 and furtherly analysed by SNAP2 Screening for Non-Acceptable Polymorphisms(https://rostlab.org/services/snap/)^{48, 49, 50}, PROVEAN Protein Variation Effect Analyzer (http://provean.jcvi.org) ^{48, 51}, PhD-SNP Predictor of human Deleterious Single Nucleotide Polymorphisms (http://snps.biofold.org/phd-snp / phd-snp. html) ^{51, 52} and SNPs & GO (http://snps-and-go.biocomp.unibo.it/snps-and-go/) ^{49, 51}.

Analysis of Protein Stability Changes: To analyse the stability changes of the target proteins, I-Mutant 3.0 (http:// gpcr. biocomp. unibo. It / cgi / predictors/I-Mutant3.0/I-Mutant3.0.cgi) was used which is an SVM based web server that predicts protein stability changes upon single point mutation starting from the protein structure or sequence.

This tool provides a free energy change value (DDG) and its direction, where positive value indicates that the mutated protein is of higher stability than the negative value. The DDG value is classified into largely unstable (DDG < −0.5 kcal/mol), largely stable (DDG>0.5kcal/mol), or neutral (-0.5≤ DDG≤0.5 kcal/mol) ^{48, 53}.

Analysis of Protein Evolutionary Conservation: Con Surf (http://consurf.tau.ac.il/) was used to predict the conservation of amino acid positions in a protein molecule ⁵⁴. This bioinformatics tool is a web-server which can predict the conservation of amino acid positions in a protein using its phylogenetic homologous sequences. The conservation scores contain nine grades ranging from grade 1 (the most variable positions, turquoise colour) to grade 9 (the most conserved positions, maroon colour), with grade 5 being the inter-mediately conserved position (white colour) ^{53, 55, 56, 57}. Those highly conserved residues which are located at the risk nsSNPs were selected for further, analysis.

Modeling of 3D Protein Structure: By homology modeling ⁵⁸, Swiss Model (https:// swissmodel. expasy. org/) was used to generate two natural 3D models of the corresponding proteins by using the FASTA sequence of the protein. The FASTA format of the protein can be acquired from Uniprot at Expasy database ^{59, 60, 61}. To construct a mutant type of each model, Swiss pdb viewer (https:// spdbv.vital-it.ch/)which is an application that provides a user-friendly interface allowing to analyze several proteins at the same time, was used. This tool helped to generate a mutant model at the specific position of the sequence ^{62, 63, 64, 65}. The resulting models were viewed using chimera version 1.8 which is an extensive program for visualization of molecular structures and analysis of protein 3D structure. Chimera packages are available from the Chimera website (http://www. cgl.ucsf.edu/chimera/) ^{65, 66, 67, 68}.

Predicting Post Mutational Changes in the Protein: The impact from the mutation on the structure of the protein is predicted by using HOPE Have (y) Our Protein Explained (https:// www 3. cmbi. umcn. nl/hope/) which is a web-based tool that analyses the effects of the mutation on 3D structure and function of the protein. It collects information from different data sources like protein’s 3D structure, Uniprot database, etc. The tool processes these data and generates a profound report that demonstrates and elucidates the effects of the mutation with figures and 3D structural visualization of mutated proteins ^{69, 70}.

RESULTS AND DISCUSSION:

Retrieval of nsSNPs from dbSNP Database: The nsSNPs of FBN1 gene investigated in this work were retrieved from dbSNP. It contained a total of 54683 SNPs: 741 SNPs found to be pathogenic by clinical significance, 2687 were nsSNPs, 849 were coding synonymous, 180 were in non-coding regions, which comprises of 142 SNPs in 5′ untranslated region (UTR) region and 623 SNPs in 3′ UTR. The rest were in the intron region. Five non-synonymous coding SNPs were selected for the investigation.

Prediction of Deleterious nsSNPs: The selected five nsSNPs were assigned to SIFT, and three of them were predicted to be damaging. The nsSNPs were submitted in FASTA format as a query sequence to the web-server. SIFT analysis predicted that the scoring of nsSNPs was damaging (score is <=0.05), and nsSNPs had tolerated (score is >0.05) effects on the FBN1 gene Table 1.  According to the Polyphen-2 results, two nsSNPs were predicted as “probably damaging” (rs137854468, rs137854462). To increase the accuracy of predictions, results of SIFT and PolyPhen-2 were joined, and SNPs with Poly Phen score> 0.90 and SIFT< 0.05 were selected. Accordingly, two nsSNPs passed both criteria and were classified as deleterious/damaging Table 1.

TABLE 1: LIST OF nsSNPs THAT PREDICTED AS DELETERIOUS BY BOTH SIFT AND POLYPHEN-2

All tools such as PhD-SNP, SNAP2 and PROVEAN accepted FASTA sequence as query except SNPs & GO, which accepts Uniprot accession id number as input.

SNPS & GO and PROVEAN confirmed the damaging effect of two nsSNPs (rs137854468 and rs137854462), whereas the nsSNPs rs137854468 found to be deleterious by only SNAP and rs137854462 by PhD-SNP Table 2. 

TABLE 2: PREDICTION OF DELETERIOUS NSSNPS BY DIFFERENT BIOINFORMATIC TOOLS

Prediction of Protein Stability: The two nsSNPs were predicted by I-Mutant as significantly decreasing the stability of the protein. It is confirmed that these nsSNPs were found to induce in decreasing the energy compared with native types Table 3.

TABLE 3: I-MUTANT 3.0 STABILITY PREDICTIONS FOR nsSNPs

Prediction of Conservation of the Substituent Residues: Analysis of the two deleterious nsSNPs with Con Surf revealed that all of them were located in highly conserved regions and predicted to have functional and structural impacts on FBN1 protein Table 4.

TABLE 4: CONSURF PREDICTIONS OF AMINO ACID CONSERVATION ACCOUNT FOR nsSNPs

Analysis of 3D Structure:

Modeling of Native 3D Structure: By retrieving the FASTA sequence of the protein from Uniprot at Expasy database, a three-dimensional model of the gene FBN1 was to be created by homology modelling using the Swiss model platform.

Constructing the Mutant Models by Swiss pdb Viewer: To construct two mutant models, Swiss pdb viewer, an application that provides a user-friendly interface allowing analysis of several proteins at the same time, was used. Swiss model repository is integrated with several external resources, such as Uniprot, etc.

FIG. 3: nsSNP rs137854462 MUTATES THE AMINO ACID ASPARAGINE (GREEN) INTO ISOLEUCINE (RED) AT POSITION 548

FIG. 4: nsSNP rs137854468 MUTATES THE AMINO ACID GLYCINE (GREEN) INTO SERINE (RED) AT POSITION 1127

Visualizing the 3D Models of Nature and Mutant Protein Structure: Chimera was used to visualize changes in the protein 3D structure due to deleterious nsSNPs.

The following figures show the different 3D structures of the protein before and after mutation. The wild types were represented by the figures on the left while the mutants were on the right.

FIG. 5: THE 3D STRUCTURAL VISUALIZATION OF WILD TYPE ASPARAGINE (PINK COLOUR) AND MUTANT TYPE ISOLEUCINE (BLUE COLOUR) AT POSITION 548

FIG. 6: THE 3D STRUCTURAL VISUALISATION OF WILD TYPE GLYCINE (GREEN COLOUR) AND MUTANT TYPE SERINE (RED COLOUR) AT POSITION 1127

Prediction of Post Translational Mutational Changes in the Protein Structure by Project HOPE:  The 3D analysis of the wild-type and mutant protein structures was performed by project HOPE.

Mutation (rs137854462) of Asparagine into Isoleucine at Position 548 (N548I): For this variant, the mutant residue is smaller; this may cause the loss of external interactions. The wild-type residue was uncharged. The mutant residue is more hydrophobic than the wild-type residue. This mutant residue is located near a highly conserved position. The mutant residue is situated in a domain that is important for the binding of other molecules. The mutant residue is connected with residues in another domain. It is possible that the mutation might disturb the interaction between these two domains and as such, affect the function of the protein and thereby affect signal transfer from the binding domain to the activity domain.

FIG. 7: THE MUTATION OF AN ASPARAGINE INTO AN ISOLEUCINE AT POSITION 548. The figure shows the schematic structures of the wild type (left) and the mutant type (right) amino acid; each amino acid is coloured red, except the side chain, unique for each amino acid, is coloured black.

Mutation (rs137854468) of Glycine into Serine at Position 1127(G1127S): The mutant residue is bigger than the wild-type residue. The protein has a new residue that has a different property from wild type due to the mutation, which can disturb this domain and abolish its function. The most flexible wild-type residue glycine is mutated into a less flexible mutant serine; this flexibility might be necessary for the protein's function. Mutation of this glycine can abolish this function. Mutation of a 100% conserved residue is usually damaging for the protein. The mutant residue is situated near a highly conserved position. The torsion angles for this residue are unusual. The only glycine is flexible enough to make these torsion angles, mutation into another residue will force the local backbone into an incorrect confirmation and will disturb the local structure.

FIG. 8: THE MUTATION OF A GLYCINE INTO A SERINE AT POSITION 1127. The figure shows the schematic structures of the wild type (left) and the mutant type(right) amino acid, each amino acid is coloured red, except the side chain, unique for each amino acid, is coloured black.

CONCLUSION: It is not mandatory to focus on the study of nsSNPs to treat Marfan syndrome, but a mutation or an SNP can alter both the structure and function of a protein. This study shows multiple damaging effects which is possibly caused due to nsSNPs, with the help of various structural bioinformatics tools. The two main mutations shown are: Asparagine into Isoleucine at position 548(rs137854462) and Glycine into Serine at position 1127(rs137854468). These nsSNPs predicted on screening of Marfan related with FBN1 gene occur in a functional domain of the protein may be useful for further analysis using next-generation gene sequencing. Therefore, it may be helpful in advancing the diagnosis by surveying or scanning the disease-related nsSNPs beforehand.

ACKNOWLEDGEMENT: We are thankful to the Vels Institute of Science, Technology and Advanced Studies (VISTAS) for providing us with the required infrastructure and support with the systems we needed.

CONFLICTS OF INTEREST: We declare that we have no competing interests with anybody.

REFERENCES:

1. Wilcox WR: Connective tissue and its heritable disorders: molecular, genetic and medical aspects. Am J Hum Genet 2003; 72(2): 503-04.
2. Dietz H: Marfan syndrome. In: Adam MP, Ardinger HH, Pagon RA. Editors Gene Reviews® Internet 2017. Seattle (WA): University of Washington, Seattle; 1993-2020.https://www.ncbi.nlm.nih.gov/books/NBK1335/
3. Groth KA, Stochholm K and Hove H: Aortic events in a nationwide Marfan syndrome cohort. Clin Res Cardiol 2017; 106: 105-12.
4. Levine RA, Hagége AA and Judge DP: Mitral valve disease--morphology and mechanisms. Nat Rev Cardiol 2015; 12(12): 689-10.
5. Gehle P, Goergen B, Pilger D, Ruokonen P, Robinson PN and Salchow DJ: Biometric and structural ocular manifestations of Marfan syndrome. PLoS ONE 2017; 12(9): 0183370.
6. Pyeritz RE and Wappel MA: Mitral valve dysfunction in the Marfan syndrome. Clinical and echocardiographic study of prevalence and natural history. Am J Med 1983; 74(5): 797-07.
7. De-Beaufort HWL, Trimarchi S and Korach A: Aortic dissection in patients with Marfan syndrome based on the IRAD data. Ann Cardiothorac Surg 2017; 6(6): 633-41.
8. Iakoubova OA, Tong CH and Rowland CM: Genetic variants in FBN-1 and risk for thoracic aortic aneurysm and dissection. PLoS One 2014; 9(4): 91437.
9. Sakai LY, Keene DR, Renard M and De Backer J: FBN1: The disease-causing gene for Marfan syndrome and other genetic disorders. Gene 2016; 591(1): 279-91.
10. Esfandiari H, Ansari S, Mohammad-Rabei H and Mets MB: Management Strategies of Ocular Abnormalities in Patients with Marfan syndrome: Current Perspective. J Ophthalmic Vis Res 2019; 14(1): 71-77.
11. Beighton P, de Paepe A and Danks D: International Nosology of Heritable Disorders of Connective Tissue, Berlin, 1986. Am J Med Genet 1988; 29(3): 581-94.
12. Child AH: Non-cardiac manifestations of Marfan syndrome. Ann Cardiothorac Surg 2017; 6(6): 599-09.
13. Meester JAN, Verstraeten A, Schepers D, Alaerts M, Van Laer L and Loeys BL: Differences in manifestations of Marfan syndrome, Ehlers-Danlos syndrome and Loeys-Dietz syndrome. Ann Cardiothorac Surg 2017; 6(6): 582-94.
14. Silverman DI, Burton KJ and Gray J: Life expectancy in the Marfan syndrome. Am J Cardiol 1995; 75(2): 157-60.
15. Isekame Y, Gati S, Aragon-Martin JA, Bastiaenen R, Kondapally Seshasai SR and Child A: Cardiovascular Management of Adults with Marfan Syndrome. Eur Cardiol 2016; 11(2): 102-10.
16. Bitterman AD and Sponseller PD: Marfan syndrome: A Clinical Update. J Am Acad Orthop Surg 2017; 25(9): 03-609.
17. Hetzer R, Siegel G and Delmo Walter EM: Cardio-myopathy in Marfan syndrome. European Journal of Cardio Toracic Surgery 2016; 49(2): 561-68.
18. Flynn CD, Tian DH and Wilson-Smith A: Systematic review and meta-analysis of surgical outcomes in Marfan patients undergoing aortic root surgery by composite-valve graft or valve sparing root replacement. Ann Cardiothorac Surg 2017; 6(6): 570-81.
19. Liu W, Schrijver I and Brenn T: Multi-exon deletions of the FBN1gene in Marfan syndrome. BMC Med Genet 2001; 2: 11.
20. Dietz HC, Cutting GR and Pyeritz RE: Marfan syndrome caused by a recurrent de novo missense mutation in the fibrillin gene. Nature 1991; 352(6333): 337-39.
21. Wang J, Yan Y and Chen J: Novel FBN1 mutations are responsible for cardiovascular manifestations of Marfan syndrome. Mol Biol Rep 2016; 43(11): 1227-32.
22. Schrenk S, Cenzi C, Bertalot T, Conconi MT and Di Liddo R: Structural and functional failure of fibrillin‑1 in human diseases (Review). Int J Mol Med 2018; 41: 1213-23.
23. Parwani P, Avierinos JF, Levine RA and Delling FN: Mitral Valve Prolapse: Multimodality Imaging and Genetic Insights. ProgCardiovasc Dis 2017; 60(3): 361-69.
24. Aragon-Martin JA and Child AH: MarfanSyndrome (MFS): Inherited Microfibrillar Disorder Caused by Mutations in the Fibrillin-1 Gene. In: Child A. (eds) Diagnosis and Management of Marfan Syndrome. Springer 2016. https://doi.org/10.1007/978-1-4471-5442-6_22
25. Wagner AH, Zaradzki M, Arif R, Remes A, Müller OJ and Kallenbach K: Marfan syndrome: A therapeutic challenge for long-term care. Biochem Pharmacol 2019; 164: 53-63.
26. Ramirez F, Caescu C, Wondimu E and Galatioto J: Marfan syndrome; A connective tissue disease at the crossroads of mechanotransduction, TGFβ signaling and cell stemness. Matrix Biol 2018; 71(72): 82-89.
27. Cook JR, Carta L, Galatioto J and Ramirez F: Cardiovascular manifestations in Marfan syndrome and related diseases; multiple genes causing similar phenotypes. Clin Genet 2015; 87(1): 11-20.
28. Robertson IB and Rifkin DB: Regulation of the Bioavailability of TGF-β and TGF-β-Related Proteins. Cold Spring Harb Perspect Biol 2016; 8(6): 021907.
29. Thomson J, Singh M, Eckersley A, Cain SA, Sherratt MJ and Baldock C: Fibrillin microfibrils and elastic fibre proteins: Functional interactions and extracellular regulation of growth factors. Semin Cell Dev Biol 2019; 89: 109-17.
30. Nijbroek G, Sood S and McIntosh I: Fifteen novel FBN1 mutations causing Marfan syndrome detected by heteroduplex analysis of genomic amplicons. Am J Hum Genet 1995; 57(1): 8-21.
31. Kielty CM: Fell-Muir Lecture: Fibrillinmicrofibrils: structural tensometers of elastic tissues. Int J Exp Pathol 2017; 98(4): 172-90.
32. Godwin ARF, Singh M, Lockhart-Cairns MP, Alanazi YF, Cain SA and Baldock C: The role of fibrillin and microfibril binding proteins in elastin and elastic fibre assembly. Matrix Biol 2019; 84: 17-30.
33. Ramirez F, Sakai LY, Dietz HC and Rifkin DB: Fibrillinmicrofibrils: multipurpose extracellular networks in organismal physiology. Physiol Genomics 2004; 19(2): 151-54.
34. Reyes-Hernández OD, Palacios-Reyes C and Chávez-Ocaña S: Skeletal manifestations of Marfan syndrome associated to heterozygous R2726 W FBN1 variant: sibling case report and literature review. BMC Musculoskelet Disord 2016; 17(1). https:// doi. Org / 10. 1186 /s12891-016-0935-9.
35. Eckersley A, Mellody KT and Pilkington S: Structural and compositional diversity of fibrillin microfibrils in human tissues. J Biol Chem 2018; 293(14): 5117-33.
36. Johnson AD: Single-nucleotide polymorphism bio-informatics: a comprehensive review of resources. Circ Cardiovasc Genet 2009; 2(5): 530-36.
37. Arshad M, Bhatti A and John P: Identification and in-silico analysis of functional SNPs of human TAGAP protein: A comprehensive study. PLoS One 2018; 13(1): 0188143.
38. Brown DK and Bishop ÖT: Role of structural bioinformatics in drug discovery by computational SNP analysis: analyzing variation at the protein level. Global Heart 2017; 12(2): 151-61.
39. Wu J and Jiang R: Prediction of deleterious non-synonymous single-nucleotide polymorphism for human diseases. Scientific World Journal 2013; 2013: 675851.
40. Melzer AM and Palanisamy N: Deleterious single nucleotide polymorphisms of protein kinase R identified by the computational approach. Mol Immunol 2018; 101: 65-73.
41. Sayers EW, Agarwala R and Bolton EE: Database resources of the National Center for Biotechnology Information. Nucleic Acids Res 2019; 47(1): 23-28.
42. Dakal TC, Kala D, Dhiman G, Yadav V, Krokhotin A and Dokholyan NV: Predicting the functional consequences of non-synonymous single nucleotide polymorphisms in IL8 gene. Sci Rep 2017; 7(1): 6525.
43. Vaser R, Adusumalli S, Leng SN, Sikic M and Ng PC: SIFT missense predictions for genomes. Nature Protocols 2015; 11(1): 1-9.
44. Hosen SMZ, Dash R, Junaid M, Mitra S and Absar N: Identification and structural characterization of deleterious non-synonymous single nucleotide polymorphisms in the human SKP2 gene. Comput Biol Chem 2019; 79: 127-36.
45. Saleh MA, Solayman M, Paul S, Saha M, Khalil MI and Gan SH: Impacts of Nonsynonymous Single Nucleotide Polymorphisms of Adiponectin Receptor 1 Gene on Corresponding Protein Stability: A Computational Approach. Biomed Res Int 2016; 9142190.
46. Takeshita H, Utsunomiya N, Chino T, Oyama N, Hasegawa M, and Yasuda T: Survey of single-nucleotide polymorphisms in the gene encoding human deoxyribonuclease I-like 2 producing loss of function potentially implicated in the pathogenesis of parakeratosis. PLOS ONE 2017; 12(4): 0175083.
47. Samadian E, Gharaei R, Colagar HA and Sohrabi H: Computational study of putative functional variants in human kisspeptin. J Genet Eng Biotechnol 2017; 15(2): 419-22.
48. Nailwal M and Chauhan JB: Analysis of consequences of non-synonymous SNPs of USP9Y gene in human using bioinformatics tools. Meta Gene 2017; 12: 13-17.
49. Mustafa MI, Abdelhameed TA, Abdelrhman FA, Osman SA and Hassan MA: Novel Deleterious nsSNPs within MEFV Gene that Could Be Used as Diagnostic Markers to Predict Hereditary Familial Mediterranean Fever: Using Bioinformatics Analysis. Adv Bioinformatics 2019; 1651587.
50. Solayman M, Saleh MA, Paul S, Khalil MI and Gan SH: In silico analysis of nonsynonymous single nucleotide polymorphisms of the human adiponectin receptor 2 (ADIPOR2) gene. Computational Biology and Chemistry 2017; 68: 175-85.
51. Hussien A and Osman AA: In silico Screening and Analysis of SNPs in Human ABCB1 (MDR1) Gene. Bio Rxiv 2019; DOI: 10.1101/505859.
52. Emidio C and Piero F: PhD-SNPg: a webserver and lightweight tool for scoring single nucleotide variants. Nucleic Acids Research 2017; 45: 247-52.
53. Akhtar M, Jamal T and Jamal H: Identification of most damaging nsSNPs in human CCR6 gene: In silico Int J Immunogenet 2019; 46(6): 459-71.
54. Goswami AM: Structural modeling and in silico analysis of non-synonymous single nucleotide polymorphisms of human 3β-hydroxysteroid dehydrogenase type 2. Meta Gene 2015; 5: 162-72.
55. Al Mehdi K, Fouad B and Zouhair E: Molecular Modelling and Dynamics Study of nsSNP in STXBP1 Gene in Early Infantile Epileptic Encephalopathy Disease. Biomed Res Int 2019; 4872101.
56. Liu G, Zhang S, Fan X, Xia H and Liang H: Phenotype Prediction of Pathogenic Nonsynonymous Single Nucleotide Polymorphisms in Insulin with Bioinformatics Tools. 5^th International Conference on Information Science and Control Enginee (ICISCE) Zhengzhou 2018; 380-84.
57. Ashkenazy H, Abadi S and Martz E: Con Surf an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res 2016; 44(1):344-50.
58. Patel B, Singh V and Patel D: Structural Bioinformatics. Essentials of Bioinformatics 2019; 1: 169-99.
59. Waterhouse A, Bertoni M and Bienert S: SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res 2018; 46(1): 296-303. Bienert S, Waterhouse A and de Beer TA: The Swiss-Model Repository-new features and functionality. Nucleic Acids Res 2017; 45(1): 313-19.
60. Arnold K, Bordoli L, Kopp J and Schwede T: The Swiss-Model workspace: a web-based environment for protein structure homology modelling. Bioinformatics 2006; 22(2): 195-201.
61. Li B, Xu L, Hong N, Chen S and Xu R: In silico Analyses Reveal the Relationship between SIX1/EYA1 Mutations and Conotruncal Heart Defects. Pediatric Cardiology 2018; 39(1): 176-82.
62. Guex N and Peitsch MC: Swiss-Model and the Swiss-Pdb Viewer: an environment for comparative protein modeling. Electrophoresis 1997; 18(15): 2714-23.
63. Kaplan W and Littlejohn TG: Swiss-PDB Viewer. Brief Bioinform 2001; 2(2): 195-97.
64. Chen J, Li X, Niu Y, Wu Z and Qiu G: Functional and in silico Assessment of GDF3 Gene Variants in a Chinese Congenital Scoliosis Population. Med Sci Monit 2018; 24: 2992-3001.
65. Conrad C Huang, Elaine C Meng, John H Morris, Eric F Pettersen and Thomas E Ferrin: Enhancing UCSF Chimera through web services. Nucleic Acid Res 2014; 42: 478-84.
66. Dovramadjiev T: Interaction of 3D models from Protein Data Bank base with UCSF Chimera and work in Blender software. XIII International Congress Machines, Technologies Materials Scientific-technical Union of Mechanical Engineering Bulgaria 2016; 1: 67-68.
67. Meng EC, Pettersen EF, Couch GS, Huang CC and Ferrin TE: Tools for integrated sequence-structure analysis with UCSF Chimera. BMC Bioinformatics 2006; 7: 339.
68. Liza R, Tanima S, Tahsin N and Reaz MM: Effect of non-synonymous SNP on JAK1 protein structure and subsequent function. Bioinformatio 2019; 15(10): 723-29.
69. Peterson MW: Project HOPE archive. Bull Med Libr Assoc 1990; 78(4): 406-7.
    

    ---

    How to cite this article:

    Radha M, Devi SM and Suganya J: In silico screening and modeling of deleterious nsSNPs in human gene FBN1 for marfan syndrome. Int J Pharm Sci & Res 2021; 12(4): 2257-64. doi: 10.13040/IJPSR.0975-8232.12(4).2257-64.

    ---

    All © 2013 are reserved by the International Journal of Pharmaceutical Sciences and Research. This Journal licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License.

    ---