In-Silico Analysis of nsSNPs Associated with CYP11B2 Gene

CYP11B2 gene is located over the upper layer of the kidney. It produces aldosterone synthase enzyme and thereby has an essential role to balance salt and mineral level in the body. A mutation in this gene can deregulate the production of aldosterone hormone in the body which may lead to many diseases including hypertension and cardiac diseases. To control the excess production of this aldosterone an inhibitor “Fadrozole” is being used which is associated with an active site cavity of CYP11B2. This study has been divided into two parts. In the first part, the four computational tools (SIFT, Polyphen-2, I-Mutant, ConSurf) were used to identify 29 deleterious SNPs out of 1600 CYP11B2 SNPs. In the second part, five residues (R448G, R141P, W260R, F130S, and F445S) were identified in the active site cavity (out of 29 deleterious CYP11B2 SNPs) at the distance of 5A°. Binding free energy calculation as well as Dynamics simulation techniques were applied to determine the effect of these mutations on the CYP11B2-Fadrozole compound. The results showed that Fadrozole binding with CYP11B2 became stronger which proved the efficiency of this drug inhibitor with these highly damaging mutations. Our study will be useful for selecting the high priority CYP11B2 mutations, which could be further, investigated in this gene-associated study, for better understanding of the structural and functional aspects of the observed (CYP11B2) protein.

During a study, 175 cardiac patients of European Continental Ancestry Population were 62 diagonosed and it was identified that C allele (CT, CC) at -344T/C SNP position in "aldosterone 63 synthase" gene does not considerably effect clinical prognosis of cardiac heart-failure [7].

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Similarly the excess of aldosterone has been reported for hypertension [8] which is one of the most 65 prevalent diseases these days around the world and affects more than 1 billion people 66 worldwide [12]. Regular hypertension control medicines work fine but still a large number of 67 patients don't get benefit of them and their blood pressure remains high even after treatment [13].

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One of the possible solutions used by scientists is the use of an inhibitor (Fadrozole) to limit the 69 biosynthesis of aldosterone hormone [14]. 70 Jia et.al [9] studied CYP11B2 gene for 52 SNPs and found four SNPs as deleterious. The novelty 71 of this study was that we filtered out 29 deleterious SNPs from a large number of non-risk alleles 72 and studied the effect of Fadrozole drug over some pathogenic SNPs. There are more than 1600 73 identified SNPs in CYP11B2 gene but only one mutation (-344T/C rs1799998) got primary focus 74 for disease association studies. In this study, we performed computational analysis of CYP11B2 75 gene to identify the pathogenic mutations. The second objective of this study was to identify the 76 effect of missense mutations over receptor-ligand (CYP11B2-Fadrozole) binding; whether these 77 mutations strengthen or weaken the inhibitor affinity with CYP11B2. Fadrozole is an inhibitor 78 used to limit the excess production of aldosterone from CYP11B2 to control diseases e.g.  binding) regions were used as active sites for docking. Figure 1 shows the workflow for in-silico 96 analysis of functional SNPs in the human CYP11B2 gene.

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In order to evaluate potential impact of the selected missense SNPs, we utilized four 103 prediction tools and filtered all missense SNPs that were classified as deleterious by all of them.  can be accessed at (http://sift.bii.a-star.edu.sg/index.html) [11]. This tool was used to predict 7 107 whether the protein will tolerate the newly introduced amino acid caused by SNP mutation or it 108 will be proved as a damaging mutation [16]. Genome tool "Sift non-synonymous single nucleotide 109 variant (human build 37)" was used in our study. The SIFT input query string includes  in proteins using Empirical Bayesian inference (protein structure and sequence respectively).

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Amino acid analysis was performed for PDBID (4FDH) and identifier 'A'. The conservation 131 scores were calculated among protein and its homology whose color scale ranges from 1 to 9, 132 where the scores in the range 7 to 9 indicate conserved region, probability in ascending order.  an octahedron box of TIP3P water molecules to achieve water molecules up-to minimum 10°A 164 distance between protein and edge of box. Extra water molecules were replaced by Cl -/ Na + 165 counter-ions by using LEaP protocol.

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AMBER ff99SB force field, for protein, and General AMBER force field (GAFF together with 167 RESP charges), for ligand, was used to assign force field parameters for each protein-ligand

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The in-silico analysis for 275 missense SNPs of CYP11B2 (These were total nsSNPs when 185 we started work; now there is more than 600 nsSNPs) was performed ( 117 nsSNPs (62.9%) show a DDG < -0.5 hence they are largely unstable (Table S4).

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The combined results of these three computational tools screened down the CYP11B2 nsSNPs 193 from 275 to 57 (20.72%). Thus 57 mutations (Given in supporting information S5 Table) were 194 predicted by all these three tools that had higher probability of damaging protein structure and 195 cause disease. Figure 2 shows the distribution of benign and deleterious and nsSNPs found using

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It is a known fact that mutation in highly conserved protein regions keeps more chances to 203 cause disease against any mutation. The 4FDH.pdb protein structure was analyzed using Consurf  (Table S6). When compared with 57 nsSNP (predicted as damaging by Polyphen, I-Mutant and 206 SIFT), only 29 nsSNPs were found in conserved area (Table 1).

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The docking identified 18 residues at the distance of 5Ȧ from the active site of CYP11B2-

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As docking scores cannot be considered as 100% reliable to distinguish ligands on their selectivity 233 basis. Moreover, in molecular docking approach physiological conditions are not considered.   In order to identify the driving forces for selective bindings of ligand on inhibitor, total binding 272 free energy was decomposed into independent (binding free energy) components. (Fig 34-  The calculated values of individual binding free energy components for four systems (Fig. 5 A- Analysis of free energy components showed that increase in selectivity was directly associated 299 with reduction of binding free energy of amino acids.  showed that binding energy of complex increased even after these deleterious mutations.

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The literature on CYP11B2 SNPs with disease causing probability is scarce. One mutation (-

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This study identified 29 deleterious CYP11B2 mutations using four computational tools including SIFT,