Introduction: NADH-cytochrome b5 reductase deficiency due to pathogenic variants in the CYB5R3 gene causes recessive congenital methemoglobinemia (RCM) type I or type II. In type I, cyanosis from birth is the only major symptom, and the enzyme deficiency is restricted only to erythrocytes. Whereas in type II, cyanosis is associated with severe neurological manifestations, and the enzyme deficiency is generalized to all tissues. Methods: In this study, several computational methods (SIFT, Polyphen-2, PROVEAN, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align, GVGD, MutPred2, I-Mutant 2.0, MUpro, Duet, ConSurf and Netsurf-2.0 tools) were used to find the most deleterious nsSNPs in the CYB5R3 gene. Furthermore, structural analysis by Swiss-PDB viewer, protein-ligand docking using FTSite, and protein-protein interaction using STRING were carried out to evaluate the impact of these nsSNPs on the protein structure and function. Results: Our in silico analysis suggested that out of 339 nsSNPs of the CYB5R3 gene, 17 (L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V), are the most deleterious. Among them, two (P96H and S128P) were reported to be associated with the severe form RCM type II, six are related to RCM type I (G104C, G144D, P145S, L149P, M177T, and I178T), and the remaining nine high-risk nsSNPs have not yet been reported in RCM patients. Discussion: This study highlighted the potential pathogenic nsSNPs of the CYB5R3 gene. To comprehend how these most harmful nsSNPs contribute to disease, it is crucial to experimentally validate their functional effects.

Recessive congenital methemoglobinemia (RCM), a rare autosomal recessive disease, is caused by nicotinamide adenine dinucleotide-cytochrome b5 reductase (NADH-CYB5R3) deficiency (EC.1.6.2.2., OMIM 250800) [Warang et al., 2015]. NADH-CYB5R3 has two distinct isoforms: a membrane-bound form mainly found in microsomes and the endoplasmic reticulum involved diverse metabolic functions, such as fatty acid desaturation and elongation, cholesterol biosynthesis, and drug metabolism [Percy and Lappin, 2008]; and a soluble form present in erythrocytes playing a role in the electron-transport system of reducing methemoglobin to functional hemoglobin [Gupta et al., 2020].

There are two clinical phenotypes of RCM. The type I is characterized by cyanosis that can potentially cause mild complaints of headaches, fatigue, and shortness of breath. In this type, NADH-CYB5R3 deficiency is restricted to the soluble form. In more severe type II RCM, cyanosis is associated with severe mental retardation, neurological impairment including mental retardation, and microcephaly, involving both soluble and membrane-bound forms of the NADH-CYB5R3 enzyme [Siendones et al., 2018].

NADH-CYB5R3 is encoded by a single cytochrome b5 reductase 3 genes (CYB5R3, initially referred to as DIA1), located on chromosome 22 q13, which covers 31 kb long region of genomic DNA. CYB5R3 contains nine exons and eight introns and encodes 301 amino acids for the membrane form and 278 amino acids for the soluble form (NC_000022.10 and NP_000389.1, NP_015565.1, respectively) [Ott et al., 2014; Warang et al., 2015]. To date, approximately 70 different mutations in CYB5R3 have been identified that give rise to both type I and type II RCM [Gupta et al., 2020]. RCM type I seems associated with missense variants causing a production of an unstable enzyme purely in the red blood cells, increasing erythrocytic methemoglobin levels, limiting oxygen fixation and delivery to all the tissues; whereas RCM type II seems to be associated with nonsense, frameshift and splice-site mutations that lead to a truncated protein with low expression of the enzyme in all tissues and disruption of all metabolic processes involving CYB5R3, such as fatty acid desaturation and elongation and cholesterol biosynthesis, which are important for normal brain development [Kobayashi et al., 1990; Maran et al., 2005; Gupta et al., 2020; Nicita et al., 2022].

In the human genome, single nucleotide polymorphisms (SNPs) are the most common genetic variations which alter one single base pair, accounting for more than 90% of all sequence variation [Collins et al., 1998]. There are various types of SNPs out of which non-synonymous SNPs (nsSNPs), also known as missense SNPs, occur in the coding region of a gene and result in a single amino acid substitution. NsSNPs are particularly interesting because they can alter protein function by reducing protein solubility and destabilizing structure and expression. However, not all nsSNPs have a functional impact on human health. Experimental characterization of the effects for each nsSNP on protein function is a difficult task, it is often too expensive and time-consuming, especially in diseases that are caused by a large and varying number of mutations.

Alternatively, several computational methods are widely used for predicting deleterious genetic mutations and determining their molecular mechanism [Thusberg and Vihinen, 2009; Leong et al., 2015; Singh and Mistry, 2016; Naveed et al., 2019; Kakar et al., 2021]. Indeed, pathogenic SNPs of various genes have been identified using these computational approaches including genes implicated in cancer, such as BRCA1, KRAS, and FGF4 [Rajasekaran et al., 2007; Wang et al., 2019; Lim et al., 2022]; in infection susceptibility, such as ACE2 and IFNAR2 [Saih et al., 2021; Akter et al., 2022], or in hereditary disease like MRE11 and ABCC2 [Tarapara and Shah, 2022; Sharma and Sharma, 2022].

To our knowledge, in silico identification of the most deleterious nsSNPs in CYB5R3 gene has not been carried out until now. Hence, the main objective of this study was to predict the nsSNP effects of CYB5R3 by using different online tools to analyze the functional and structural alterations of the protein.

Dataset Collection

Human NADH-CYB5R3 protein FASTA sequence [NP_000389.1] was collected from National Center for Biological Information protein sequence database [Sayers et al., 2010]. SNP information for our computational analysis was obtained from the database (dbSNPs) server of the National Center for Biological Information dbSNP (http://www.ncbi.nlm.nih.gov/snp/), Ensembl database (http://www.ensembl.org/index.html), and literature data. Template 3D structure [Protein Data Bank (PDB) ID: 1UMK] [Bando et al., 2004] was obtained from PDB (https://www.rcsb.org/) for structural analysis [Berman et al., 2002].

Evaluation of Missense SNPs

Various computational tools have been used to predict the functional impact of single amino acid substitution on protein function and to identify the pathogenic nsSNPs (shown in Fig. 1): Sorting Intolerant From Tolerant (SIFT) [Sim et al., 2012] (https://sift.bii.a-star.edu.sg/); Polymorphism Phenotyping v2 (Polyphen-2) [Adzhubei et al., 2013] (http://genetics.bwh.harvard.edu/pph2/); Protein variation effect analyzer (Provean) [Choi and Chan, 2015] (http://provean.jcvi.org/index.php); Mutation assessor [Reva et al., 2011] (http://mutationassessor.org/r3/); Protein Analysis Through Evolutionary Relationships (Panther) [Mi et al., 2017] (http://www.pantherdb.org/tools/); Predictor of Human Deleterious SNPs (PhD-SNP) [Capriotti et al., 2006] (http://snps.biofold.org/phd-snp/phd-snp.html); SNP database and gene ontology (SNPs and GO) [Capriotti et al., 2013] (https://snps.biofold.org/snps-and-go/snps-and-go.html); Screening of nonacceptable Polymorphism (SNAP2) [Bromberg and Rost, 2007] (https://www.rostlab.org/services/SNAP/); Align GVGD [Tavtigian et al., 2006] (http://agvgd.hci.utah.edu/agvgd_input.php); MutPred2 [Li et al., 2009] (http://mutpred.mutdb.org/#qform). All these tools use different algorithms to classify a nsSNP as deleterious or not.

Fig. 1.

Flowchart showing the steps performed in the present study.

Fig. 1.

Flowchart showing the steps performed in the present study.

Close modal

Stability Analysis

The stability of nsSNPs associated with NADH-CYB5R3 protein was predicted by I-Mutant 2.0 (http://folding.biofold.org/i-mutant/i-mutant2.0.html), MUpro (http://mupro.proteomics.ics.uci.edu/), and DUET Server (http://bleoberis.bioc.cam.ac.uk/duet/) through comparing free energy. I-Mutant 2.0 is a support vector machine (SVM) based tool. It predicts protein stability change, upon single amino acid substitution by calculating the free energy change value (ΔΔG) between the mutated protein and native type protein [Capriotti et al., 2005]. Negative ΔΔG value indicates a decrease in protein stability and a positive ΔΔG indicates a gain in protein stability.

MUpro is a web-based tool to predict protein stability changes for single nucleotide variation in the amino acid sequence [Cheng et al., 2006]. It is based on two machine learning methods: SVM and neural networks. This tool calculates a score between −1 and 1 as the confidence of prediction. A score <0 indicates decreased stability and conversely a score >0 is an indication of increased stability.

DUET is a web server for an integrated computational approach to estimate the effects of variants on the basis of structure and protein stability [Pires et al., 2014]. This tool uses two complementary approaches (mCSM and SDM) in a consensus prediction obtained by combining the results of the separate methods in an optimized predictor using SVM. The prediction results are expressed as Gibbs Free Energy (DDG) values. A positive value corresponds to a mutation predicted as destabilizing; while a negative value corresponds to a mutation predicted as stabilizing.

Evolutionary Effect of Missense SNP

Conservation of the NADH-CYB5R3 protein sequence was analyzed using Consurf server (https://consurf.tau.ac.il/) [Ashkenazy et al., 2016]. It estimates the evolutionary conservation of amino acid positions based on the phylogenetic relations between homologous sequences. It calculates conservation scores from 1 to 9 for each amino acid of the protein, where scores 1–3 are variable, 4–6 are average conserved, and 7–9 scores are showing highly conserved regions. The input query for Consurf is FASTA sequence of the protein.

Prediction of Solvent Accessibility

NetSurfP (http://www.cbs.dtu.dk/services/NetSurfP-1.1/) predicts the solvent accessibility and secondary structure of residues in amino acid sequences. The method also simultaneously predicts the reliability of each prediction, in the form of a Z-score which is related to the surface prediction, and not to the secondary structure. Input is given in the form of FASTA sequence and the output is obtained as three subclasses about solvent accessibility, namely, buried (low accessibility), partially buried (moderate accessibility), and exposed (high accessibility) [Petersen et al., 2009].

Structural Analysis

The three-dimensional structures of mutant forms predicted to be pathogenic were modeled using the NADH-CYB5R3 crystal structure (PDB ID: 1UMK) [Bando et al., 2004] using Swiss-PDB Viewer [Kaplan and Littlejohn, 2001; Johansson et al., 2012]. The investigation of other structural properties like free energy, energy minimization, etc., were performed and visualized with the same tool. The TM-score (template modeling) and the root mean square deviation (RMSDs) provided by TM-align (https://zhanglab.ccmb.med.umich.edu/TM-align/) were used to compare wild-type and mutated protein. TM score yields a score from 0 to 1, where 1 indicates a perfect match between the two structures, while a higher RMSD indicates greater variation between wild-type and mutant structures [Zhang and Skolnick, 2005].

Hydrogen Interaction Analysis

The change in the hydrogen bonds between the amino acids of the wild-type protein and its mutant forms was assessed by using the Swiss-PDB Viewer software.

Ligand-Binding Site Prediction

The ligand-binding sites within NADH-CYB5R3 protein were predicted using FTSite server (https://ftsite.bu.edu/) [Ngan et al., 2012]. FTSite is a freely available online tool, which predicts the binding sites in over 94% of apoproteins.

Protein-Protein Interactions Analysis

Search Tool for the Retrieval of Interacting Genes/Proteins (STRING, available at https://string-db.org/) is a database dedicated to protein-protein interactions [Szklarczyk et al., 2019]. This database was utilized to identify direct (physical) and indirect (functional) interactions with NADH-CYB5R3 protein.

Distribution of SNPs

According to the literature, dbSNP, and Ensembl databases, the CYB5R3 gene (Homo sapiens) had a total of 8,944 SNPs: 339 were missense (nsSNPs), 113 were synonymous, 8 were frameshift, 698 occurred in 3′UTR, 60 in 5′UTR, 7,711 in the intronic region and the rest were other types (shown in Fig. 2). Only nsSNPs were selected for this investigation.

Fig. 2.

Percentage of SNPs in different regions of CYB5R3 gene.

Fig. 2.

Percentage of SNPs in different regions of CYB5R3 gene.

Close modal

Prediction of Deleterious nsSNPs

For initial screening, 339 nsSNPs were submitted to SIFT server, which predicted that 192 nsSNPs were deleterious (score ≤0.05), and the rest be tolerated. The 192 substitutions were subjected to nine other computational tools: Polyphen-2, Provean, Mutation Assessor, Panther, Phd-SNP, SNPs&GO, SNAP2, Align GVGD, and MutPred2.

Mutation Assessor revealed the fewest 79 nsSNPs in a total of 192 as damaging (41.14%), whereas Provean predicted the most 170 nsSNPs (88.54%) as deleterious (score ≤2.5). Polyphen-2 analysis revealed a total of 120 (62.5%) nsSNPs to be probably damaging (score >0.95). Panther predicted 147 (76.56%) nsSNPs as probably damaging. Phd-SNP results showed that 160 (83.33%) of the nsSNPs were disease-related. A total of 157 (81.77%) nsSNPs were considered associated with the disease by SNPs&GO (score >0.5). According to SNAP2, 126 (65.62%) nsSNPs were predicted having a strong effect on the protein, and Align GVGD analysis classified a total of 124 (64.58%) nsSNPs as most likely affected (classified C65).

According to all computational tools results, 46 nsSNPs were predicted as the most damaging SNPs (Table 1). These high-risk nsSNPs were then subjected to MutPred2 analysis to identify protein and molecular mechanism damaging nsSNPs. The MutPred2 g score is between 0 and 1. The g score >0.5 means the substituted amino acid is probably pathogenic, and if g score is >0.75, the substitution is more assurance pathogenic [Pejaver et al., 2020; Yazar and Ozbek, 2022]. Among the 46 nsSNPs, only three were found to be neutral and 43 nsSNPs were disease-causing (g score >0.5). A 0.75 cut-off value was applied to narrow down the number of nsSNPs to 31 (Table 2).

Table 1.

High-risk nsSNPs identified by the nine in silico programs used in this study

SNP IdAA changeSIFTPolyphen-2ProveanMutation assessorPantherPhd-SNPSNP and GOSNAP2Align GVGD
rs751975938 L47H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs751975938 L47P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
S54R Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1302856929 R58W Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965007 R58P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1166650804 R61C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs774242947 R61P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs774242947 R61L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965013 L73P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965013 L73R Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1223829978 G76C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1180621698 G76D Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1454054574 R92W Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs751286514 P93H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1415200527 Y94C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1447749687 P96H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1189447865 S98C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1299251737 G104C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
K111M Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs772310694 H118Y Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs752228437 G125E Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965006 S128P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1244673706 Q129L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
L131P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1339269972 Y130C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1246960935 G137R Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs761227385 R143W Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs756047846 G144D Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs756047846 G144V Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs374075200 P145L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
P145S Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965008 L149P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1388998071 Y151H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1384467698 M177T Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
I178T Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs765326176 T182I Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs771453342 P186L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs543277216 R192C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1363670012 T212I Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1569316962 I216N Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
L218P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs772395978 G251V Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1312995565 L272P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1461544891 P276L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1055711880 P276S Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs985077497 C284Y Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
SNP IdAA changeSIFTPolyphen-2ProveanMutation assessorPantherPhd-SNPSNP and GOSNAP2Align GVGD
rs751975938 L47H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs751975938 L47P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
S54R Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1302856929 R58W Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965007 R58P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1166650804 R61C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs774242947 R61P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs774242947 R61L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965013 L73P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965013 L73R Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1223829978 G76C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1180621698 G76D Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1454054574 R92W Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs751286514 P93H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1415200527 Y94C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1447749687 P96H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1189447865 S98C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1299251737 G104C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
K111M Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs772310694 H118Y Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs752228437 G125E Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965006 S128P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1244673706 Q129L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
L131P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1339269972 Y130C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1246960935 G137R Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs761227385 R143W Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs756047846 G144D Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs756047846 G144V Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs374075200 P145L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
P145S Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs121965008 L149P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1388998071 Y151H Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1384467698 M177T Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
I178T Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs765326176 T182I Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs771453342 P186L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs543277216 R192C Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1363670012 T212I Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1569316962 I216N Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
L218P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs772395978 G251V Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1312995565 L272P Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1461544891 P276L Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs1055711880 P276S Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
rs985077497 C284Y Deleterious Probably damaging Deleterious Highly damaging Probably damaging Disease Disease Effect C65 
Table 2.

High-risk nsSNPs predicted with MutPred2

SNP IdnsSNPMutPred2 scorePredicted molecular mechanism (p value)
rs751975938 L47H 0.872 Gain of strand (0.04) 
Altered stability (0.01) 
rs751975938 L47P 0.924 Altered ordered interface (0.04) 
Altered stability (0.01) 
rs121965007 R58P 0.821 Altered metal binding p = 6.2e-03 
Altered transmembrane protein p = 0.02 
Altered stability p = 0.03 
rs774242947 R61P 0.868 Altered metal binding (0.02) 
Loss of relative solvent accessibility (0.05) 
Altered stability (0.01) 
Altered transmembrane protein (0.01) 
rs121965013 L73P 0.948 Altered ordered interface p = 9.9e-03 
Altered metal binding p = 6.2e-03 
Gain of loop p = 0.04 
Gain of allosteric site at q77 p = 0.04 
Gain of catalytic site at h78 p = 0.03 
Altered stability p = 0.04 
rs121965013 L73R 0.932 Altered ordered interface (0.02) 
Altered metal binding (0.01) 
Gain of allosteric site at h78 (0.03) 
Altered DNA binding (0.05) 
Gain of catalytic site at h78 (0.03) 
rs1180621698 G76D 0.895 Altered metal binding p = 1.4e-03 
Gain of allosteric site at h78 p = 0.04 
Gain of catalytic site at h78 p = 0.03 
rs1223829978 G76C 0.908 Altered metal binding (8.1e-04) 
Gain of allosteric site at h78 (0.03) 
Altered DNA binding (0.05) 
Loss of catalytic site at h78 (0.03) 
rs751286514 P93H 0.782 Altered ordered interface p = 7.2e-03 
Altered DNA binding p = 4.1e-03 
Gain of strand p = 0.01 
Gain of allosteric site at y94 p = 7.8e-03 
Altered metal binding p = 0.04 
Loss of catalytic site at y94 p = 0.01 
Altered transmembrane protein p = 5.4e-03 
rs1415200527 Y94C 0.917 Loss of allosteric site at y94 p = 3.0e-03 
Altered DNA binding p = 3.1e-03 
Altered ordered interface p = 7.1e-03 
Altered disordered interface p = 0.03 
Loss of strand p = 0.02 
Loss of loop p = 0.03 
Loss of catalytic site at y94 p = 4.9e-03 
Gain of relative solvent accessibility p = 0.04 
Altered metal binding p = 0.04 
Altered transmembrane protein p = 8.5e-03 
rs1447749687 P96H 0.863 Altered ordered interface p = 0.01 
Loss of allosteric site at y94 p = 4.1e-03 
Gain of strand p = 0.01 
Gain of loop p = 0.02 
Altered DNA binding p = 4.6e-03 
Altered metal binding p = 8.1e-03 
Gain of relative solvent accessibility p = 0.03 
Loss of catalytic site at y94 p = 7.7e-03 
Altered transmembrane protein p = 9.2e-03 
rs1299251737 G104C 0.904 Loss of relative solvent accessibility p = 8.8e-05 
Altered metal binding p = 8.1e-03 
Altered ordered interface loss of b-factor p = 7.7e-03 
Loss of b-factor p = 0.02 
Loss of methylation at k103 p = 0.02 
Altered transmembrane protein p = 0.03 
K111M 0.795 Altered metal binding p = 1.6e-03 
Altered ordered interface p = 0.03 
Loss of acetylation at k115 p = 0.02 
rs752228437 G125E 0.872 Altered ordered interface p = 0.04 
Gain of helix p = 0.04 
rs121965006 S128P 0.911 Altered ordered interface p = 0.04 
L131P 0.938 Gain of intrinsic disorder p = 0.03 
Loss of helix p = 0.02 
Altered stability p = 0.05 
rs756047846 G144D 0.935 Altered DNA binding p = 0.01 
Altered transmembrane protein p = 0.03 
rs756047846 G144V 0.940 Altered DNA binding p = 7.2e-03 
rs374075200 P145L 0.927 Altered DNA binding p = 0.02 
P145S 0.892 Gain of strand p = 0.04 
Gain of strand p = 9.8e-03 
rs121965008 L149P 0.960 Gain of acetylation at k154 p = 0.02 
Altered DNA binding p = 0.01 
Altered stability p = 0.01 
rs1388998071 Y151H 0.907 Loss of acetylation at k154 p = 0.01 
Altered DNA binding p = 0.03 
Altered stability p = 0.04 
rs1384467698 M177T 0.945 Altered ordered interface p = 8.6e-03 
Altered stability p = 6.0e-03 
Altered metal binding p = 5.0e-03 
Loss of allosteric site at m177 p = 7.3e-03 
Altered DNA binding p = 5.4e-03 
Loss of strand p = 0.04 
Loss of catalytic site at m177 p = 0.03 
Gain of methylation at k173 p = 0.03 
I178T 0.905 Altered DNA binding p = 1.7e-03 
Altered ordered interface p = 0.03 
Gain of allosteric site at g181 p = 3.9e-03 
Altered metal binding p = 7.2e-03 
Gain of catalytic site at m177 p = 0.01 
Gain of acetylation at k173 p = 0.04 
Altered stability p = 0.03 
Gain of methylation at k173 p = 0.03 
rs765326176 T182I 0.876 Altered metal binding p = 1.1e-03 
Loss of allosteric site at g183 p = 1.6e-03 
Altered DNA binding p = 1.3e-03 
Altered ordered interface p = 0.03 
Gain of catalytic site at t185 p = 2.2e-03 
rs771453342 P186L 0.856 Loss of allosteric site at i184 p = 5.7e-04 
Loss of catalytic site at t185 p = 7.4e-04 
Altered ordered interface p = 8.9e-03 
Altered DNA binding p = 2.9e-03 
Altered metal binding p = 9.3e-03 
rs1569316962 I216N 0.908 Altered metal binding p = 3.4e-03 
Gain of relative solvent accessibility p = 0.03 
L218P 0.902 Gain of intrinsic disorder p = 0.03 
Loss of helix p = 7.3e-03 
Altered ordered interface p = 0.03 
Loss of relative solvent accessibility p = 0.01 
Altered metal binding p = 6.2e-03 
Altered stability p = 0.04 
rs772395978 G251V 0.899 Altered metal binding p = 7.3e-04 
Altered transmembrane protein p = 4.8e-04 
Loss of relative solvent accessibility p = 0.02 
Altered DNA binding p = 5.4e-03 
Gain of allosteric site at w246 p = 0.01 
Gain of catalytic site at w246 p = 0.04 
rs1312995565 L272P 0.928 Gain of intrinsic disorder p = 6.1e-03 
Altered stability p = 3.7e-03 
Gain of loop p = 0.04 
Loss of allosteric site at p276 p = 0.03 
Altered metal binding p = 0.03 
rs985077497 C284Y 0.790 Altered metal binding p = 0.02 
Gain of allosteric site at m279 p = 0.03 
SNP IdnsSNPMutPred2 scorePredicted molecular mechanism (p value)
rs751975938 L47H 0.872 Gain of strand (0.04) 
Altered stability (0.01) 
rs751975938 L47P 0.924 Altered ordered interface (0.04) 
Altered stability (0.01) 
rs121965007 R58P 0.821 Altered metal binding p = 6.2e-03 
Altered transmembrane protein p = 0.02 
Altered stability p = 0.03 
rs774242947 R61P 0.868 Altered metal binding (0.02) 
Loss of relative solvent accessibility (0.05) 
Altered stability (0.01) 
Altered transmembrane protein (0.01) 
rs121965013 L73P 0.948 Altered ordered interface p = 9.9e-03 
Altered metal binding p = 6.2e-03 
Gain of loop p = 0.04 
Gain of allosteric site at q77 p = 0.04 
Gain of catalytic site at h78 p = 0.03 
Altered stability p = 0.04 
rs121965013 L73R 0.932 Altered ordered interface (0.02) 
Altered metal binding (0.01) 
Gain of allosteric site at h78 (0.03) 
Altered DNA binding (0.05) 
Gain of catalytic site at h78 (0.03) 
rs1180621698 G76D 0.895 Altered metal binding p = 1.4e-03 
Gain of allosteric site at h78 p = 0.04 
Gain of catalytic site at h78 p = 0.03 
rs1223829978 G76C 0.908 Altered metal binding (8.1e-04) 
Gain of allosteric site at h78 (0.03) 
Altered DNA binding (0.05) 
Loss of catalytic site at h78 (0.03) 
rs751286514 P93H 0.782 Altered ordered interface p = 7.2e-03 
Altered DNA binding p = 4.1e-03 
Gain of strand p = 0.01 
Gain of allosteric site at y94 p = 7.8e-03 
Altered metal binding p = 0.04 
Loss of catalytic site at y94 p = 0.01 
Altered transmembrane protein p = 5.4e-03 
rs1415200527 Y94C 0.917 Loss of allosteric site at y94 p = 3.0e-03 
Altered DNA binding p = 3.1e-03 
Altered ordered interface p = 7.1e-03 
Altered disordered interface p = 0.03 
Loss of strand p = 0.02 
Loss of loop p = 0.03 
Loss of catalytic site at y94 p = 4.9e-03 
Gain of relative solvent accessibility p = 0.04 
Altered metal binding p = 0.04 
Altered transmembrane protein p = 8.5e-03 
rs1447749687 P96H 0.863 Altered ordered interface p = 0.01 
Loss of allosteric site at y94 p = 4.1e-03 
Gain of strand p = 0.01 
Gain of loop p = 0.02 
Altered DNA binding p = 4.6e-03 
Altered metal binding p = 8.1e-03 
Gain of relative solvent accessibility p = 0.03 
Loss of catalytic site at y94 p = 7.7e-03 
Altered transmembrane protein p = 9.2e-03 
rs1299251737 G104C 0.904 Loss of relative solvent accessibility p = 8.8e-05 
Altered metal binding p = 8.1e-03 
Altered ordered interface loss of b-factor p = 7.7e-03 
Loss of b-factor p = 0.02 
Loss of methylation at k103 p = 0.02 
Altered transmembrane protein p = 0.03 
K111M 0.795 Altered metal binding p = 1.6e-03 
Altered ordered interface p = 0.03 
Loss of acetylation at k115 p = 0.02 
rs752228437 G125E 0.872 Altered ordered interface p = 0.04 
Gain of helix p = 0.04 
rs121965006 S128P 0.911 Altered ordered interface p = 0.04 
L131P 0.938 Gain of intrinsic disorder p = 0.03 
Loss of helix p = 0.02 
Altered stability p = 0.05 
rs756047846 G144D 0.935 Altered DNA binding p = 0.01 
Altered transmembrane protein p = 0.03 
rs756047846 G144V 0.940 Altered DNA binding p = 7.2e-03 
rs374075200 P145L 0.927 Altered DNA binding p = 0.02 
P145S 0.892 Gain of strand p = 0.04 
Gain of strand p = 9.8e-03 
rs121965008 L149P 0.960 Gain of acetylation at k154 p = 0.02 
Altered DNA binding p = 0.01 
Altered stability p = 0.01 
rs1388998071 Y151H 0.907 Loss of acetylation at k154 p = 0.01 
Altered DNA binding p = 0.03 
Altered stability p = 0.04 
rs1384467698 M177T 0.945 Altered ordered interface p = 8.6e-03 
Altered stability p = 6.0e-03 
Altered metal binding p = 5.0e-03 
Loss of allosteric site at m177 p = 7.3e-03 
Altered DNA binding p = 5.4e-03 
Loss of strand p = 0.04 
Loss of catalytic site at m177 p = 0.03 
Gain of methylation at k173 p = 0.03 
I178T 0.905 Altered DNA binding p = 1.7e-03 
Altered ordered interface p = 0.03 
Gain of allosteric site at g181 p = 3.9e-03 
Altered metal binding p = 7.2e-03 
Gain of catalytic site at m177 p = 0.01 
Gain of acetylation at k173 p = 0.04 
Altered stability p = 0.03 
Gain of methylation at k173 p = 0.03 
rs765326176 T182I 0.876 Altered metal binding p = 1.1e-03 
Loss of allosteric site at g183 p = 1.6e-03 
Altered DNA binding p = 1.3e-03 
Altered ordered interface p = 0.03 
Gain of catalytic site at t185 p = 2.2e-03 
rs771453342 P186L 0.856 Loss of allosteric site at i184 p = 5.7e-04 
Loss of catalytic site at t185 p = 7.4e-04 
Altered ordered interface p = 8.9e-03 
Altered DNA binding p = 2.9e-03 
Altered metal binding p = 9.3e-03 
rs1569316962 I216N 0.908 Altered metal binding p = 3.4e-03 
Gain of relative solvent accessibility p = 0.03 
L218P 0.902 Gain of intrinsic disorder p = 0.03 
Loss of helix p = 7.3e-03 
Altered ordered interface p = 0.03 
Loss of relative solvent accessibility p = 0.01 
Altered metal binding p = 6.2e-03 
Altered stability p = 0.04 
rs772395978 G251V 0.899 Altered metal binding p = 7.3e-04 
Altered transmembrane protein p = 4.8e-04 
Loss of relative solvent accessibility p = 0.02 
Altered DNA binding p = 5.4e-03 
Gain of allosteric site at w246 p = 0.01 
Gain of catalytic site at w246 p = 0.04 
rs1312995565 L272P 0.928 Gain of intrinsic disorder p = 6.1e-03 
Altered stability p = 3.7e-03 
Gain of loop p = 0.04 
Loss of allosteric site at p276 p = 0.03 
Altered metal binding p = 0.03 
rs985077497 C284Y 0.790 Altered metal binding p = 0.02 
Gain of allosteric site at m279 p = 0.03 

Threshold score >0.75.

Stability Modification Prediction

The 31 selected missense substitutions predicted as deleterious from the previous steps were analyzed with the I-Mutant2.0, MUpro, and Duet tools. According to I-Mutant’s threshold, DDG values showed that 25 nsSNPs decreased stability (DDG <0), whereas six nsSNPs increased stability (DDG >0). The MuPro server reported 27 SNPs as decreasing protein stability, and 26 nsSNPs were predicted to decrease protein stability with DUET analysis (Table 3). By combining results from these three tools, 23 variants (L47H, L47P, R58P, R61P, L73R, G76D, G76C, P93H, P96H, G104C, S128P, L131P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, L218P, G251V, L272P, and C284Y) were selected for further studies.

Table 3.

Effect of missense SNPs on protein stability through I-Mutant, MUpro, and Duet Servers

SNP IdnsSNPI-MutantMUproDuet
DDGstabilityscorestabilityDDGstability
rs751975938 L47H −1.12 Decrease −1.6417678 Decrease −2.752 Decrease 
rs751975938 L47P −0.56 Decrease −1.6000283 Decrease −2.468 Decrease 
rs121965007 R58P −1.76 Decrease −1.2224429 Decrease −2.573 Decrease 
rs774242947 R61P −1.60 Decrease −1.5727689 Decrease −1.668 Decrease 
rs121965013 L73P 0.06 Increase −1.8438673 Decrease −2.131 Decrease 
rs121965013 L73R −1.22 Decrease −1.5979775 Decrease −1.423 Decrease 
rs1180621698 G76D −0.10 Decrease −0.37392351 Decrease −2.764 Decrease 
rs1223829978 G76C −0.56 Decrease −0.37541617 Decrease −1.303 Decrease 
rs751286514 P93H −2.19 Decrease −1.4548025 Decrease −0.699 Decrease 
rs1415200527 Y94C 0.49 Increase −0.61011965 Decrease −1.753 Decrease 
rs1447749687 P96H −2.27 Decrease −1.2346031 Decrease −1.929 Decrease 
rs1299251737 G104C −1.99 Decrease −1.4410294 Decrease −1.625 Decrease 
K111M 0.19 Increase 0.082543988 Increase 0.334 Increase 
rs752228437 G125E 0.49 Increase −0.57521385 Decrease −0.675 Decrease 
rs121965006 S128P −1.07 Decrease −0.38717314 Decrease −0.118 Decrease 
- L131P −0.96 Decrease −1.7230808 Decrease −1.489 Decrease 
rs756047846 G144D −0.67 Decrease −0.55552657 Decrease −1.738 Decrease 
rs756047846 G144V 0.51 Increase −0.39113215 Decrease 0.355 Increase 
rs374075200 P145L −1.56 Decrease 0.2685051 Increase 0.263 Increase 
- P145S −1.54 Decrease −0.56189852 Decrease −1.208 Decrease 
rs121965008 L149P −1.99 Decrease −1.5498909 Decrease −1.361 Decrease 
rs1388998071 Y151H −1.07 Decrease −0.83244883 Decrease −1.113 Decrease 
rs1384467698 M177T −0.17 Decrease −1.8936565 Decrease −2.453 Decrease 
- I178T −0.76 Decrease −1.5562535 Decrease −3.566 Decrease 
rs765326176 T182I −0.60 Decrease 0.48294908 Increase 0.153 Increase 
rs771453342 P186L 0.25 Increase 0.18148662 Increase 0.328 Increase 
rs1569316962 I216N −0.81 Decrease −1.5550898 Decrease −2.54 Decrease 
- L218P −1.55 Decrease −2.1376682 Decrease −1.777 Decrease 
rs772395978 G251V −1.40 Decrease −0.20375648 Decrease −0.712 Decrease 
rs1312995565 L272P −0.45 Decrease −1.615,536 Decrease −2.417 Decrease 
rs985077497 C284Y −1.10 Decrease −1.2305994 Decrease −1.795 Decrease 
SNP IdnsSNPI-MutantMUproDuet
DDGstabilityscorestabilityDDGstability
rs751975938 L47H −1.12 Decrease −1.6417678 Decrease −2.752 Decrease 
rs751975938 L47P −0.56 Decrease −1.6000283 Decrease −2.468 Decrease 
rs121965007 R58P −1.76 Decrease −1.2224429 Decrease −2.573 Decrease 
rs774242947 R61P −1.60 Decrease −1.5727689 Decrease −1.668 Decrease 
rs121965013 L73P 0.06 Increase −1.8438673 Decrease −2.131 Decrease 
rs121965013 L73R −1.22 Decrease −1.5979775 Decrease −1.423 Decrease 
rs1180621698 G76D −0.10 Decrease −0.37392351 Decrease −2.764 Decrease 
rs1223829978 G76C −0.56 Decrease −0.37541617 Decrease −1.303 Decrease 
rs751286514 P93H −2.19 Decrease −1.4548025 Decrease −0.699 Decrease 
rs1415200527 Y94C 0.49 Increase −0.61011965 Decrease −1.753 Decrease 
rs1447749687 P96H −2.27 Decrease −1.2346031 Decrease −1.929 Decrease 
rs1299251737 G104C −1.99 Decrease −1.4410294 Decrease −1.625 Decrease 
K111M 0.19 Increase 0.082543988 Increase 0.334 Increase 
rs752228437 G125E 0.49 Increase −0.57521385 Decrease −0.675 Decrease 
rs121965006 S128P −1.07 Decrease −0.38717314 Decrease −0.118 Decrease 
- L131P −0.96 Decrease −1.7230808 Decrease −1.489 Decrease 
rs756047846 G144D −0.67 Decrease −0.55552657 Decrease −1.738 Decrease 
rs756047846 G144V 0.51 Increase −0.39113215 Decrease 0.355 Increase 
rs374075200 P145L −1.56 Decrease 0.2685051 Increase 0.263 Increase 
- P145S −1.54 Decrease −0.56189852 Decrease −1.208 Decrease 
rs121965008 L149P −1.99 Decrease −1.5498909 Decrease −1.361 Decrease 
rs1388998071 Y151H −1.07 Decrease −0.83244883 Decrease −1.113 Decrease 
rs1384467698 M177T −0.17 Decrease −1.8936565 Decrease −2.453 Decrease 
- I178T −0.76 Decrease −1.5562535 Decrease −3.566 Decrease 
rs765326176 T182I −0.60 Decrease 0.48294908 Increase 0.153 Increase 
rs771453342 P186L 0.25 Increase 0.18148662 Increase 0.328 Increase 
rs1569316962 I216N −0.81 Decrease −1.5550898 Decrease −2.54 Decrease 
- L218P −1.55 Decrease −2.1376682 Decrease −1.777 Decrease 
rs772395978 G251V −1.40 Decrease −0.20375648 Decrease −0.712 Decrease 
rs1312995565 L272P −0.45 Decrease −1.615,536 Decrease −2.417 Decrease 
rs985077497 C284Y −1.10 Decrease −1.2305994 Decrease −1.795 Decrease 

SNPs indicated in bold are predicted to decrease the stability of the protein structure and are selected for further evaluation.

Evolutionary Effect of Missense SNPs

The evolutionary conservation of the 23 nsSNPs in NADH-CYB5R3 protein was estimated using the ConSurf web server based on the phylogenetic relations between homologous sequences. The nsSNPs located in highly conserved regions are more deleterious than SNPs in nonconserved sites.

The results showed that the selected nsSNPs are located in highly conserved regions with conservation scores ranging between 7 and 9. Out of the 23 selected high-risk SNPs in NADH-CYB5R3 protein, 17 nsSNPs (L47H, L47P, R61P, L73R, G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V) were located in regions with 9 as a conservation score. Among them, 13 variants were predicted as structural residues, while the rest were predicted as functional residues (shown in Fig. 3). Only the ones with a score of 9 were selected for further investigation.

Fig. 3.

Analysis of evolutionary conserved amino acids in human NADH-CYB5R3 protein predicted by ConSurf. Colors of ConSurf results showing the level of confidence for the sequence conservation where sky-blue color indicates variables and dark purple color indicates highly conserved residues.

Fig. 3.

Analysis of evolutionary conserved amino acids in human NADH-CYB5R3 protein predicted by ConSurf. Colors of ConSurf results showing the level of confidence for the sequence conservation where sky-blue color indicates variables and dark purple color indicates highly conserved residues.

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Surface Accessibility

Solvent accessibility was assessed for the 17 selected variants by NetsurfP. The class assignment does not change for any nsSNPs. Among them, one variant (G104C) and its respective wild variant were exposed (E) on the surface, whereas the remaining 16 variants and their respective wild variants were buried (B) (Table 4).

Table 4.

Surface accessibility of native and mutants through Netsurf for NADH-CYB5R3 protein

Amino acid changeClass assignmentRelative surface accessibility (RSA)Absolute surface accessibilityZ-fit score for RSA prediction
L47H 0.023 4.285 1.001 
0.026 4.820 0.840 
L47P 0.023 4.285 1.001 
0.025 3.547 0.934 
R61P 0.112 25.625 1.361 
0.118 16.758 1.369 
L73R 0.059 10.821 0.554 
0.066 15.000 0.615 
G76D 0.048 3.770 −1.187 
0.033 4.770 −1.978 
G76C 0.048 3.770 −1.187 
0.038 5.405 −2.509 
P96H 0.037 5.236 −0.257 
0.039 7.076 −0.199 
G104C 0.346 27.214 −0.019 
0.311 43.594 0.034 
S128P 0.050 5.860 0.613 
0.055 7.762 0.243 
G144D 0.197 15.488 −0.514 
0.175 25.203 −0.615 
P145S 0.171 24.222 −1.273 
0.161 18.834 −1.125 
L149P 0.043 7.782 0.773 
0.040 5.648 0.484 
Y151H 0.084 18.058 0.040 
0.083 15.007 −0.375 
M177T 0.016 3.142 1.521 
0.016 2.178 1.488 
I178T 0.017 3.071 1.376 
0.017 2.344 1.328 
I216N 0.133 24.605 1.018 
0.135 19.823 1.045 
G251V 0.084 6.611 −1.878 
0.078 12.019 −1.752 
Amino acid changeClass assignmentRelative surface accessibility (RSA)Absolute surface accessibilityZ-fit score for RSA prediction
L47H 0.023 4.285 1.001 
0.026 4.820 0.840 
L47P 0.023 4.285 1.001 
0.025 3.547 0.934 
R61P 0.112 25.625 1.361 
0.118 16.758 1.369 
L73R 0.059 10.821 0.554 
0.066 15.000 0.615 
G76D 0.048 3.770 −1.187 
0.033 4.770 −1.978 
G76C 0.048 3.770 −1.187 
0.038 5.405 −2.509 
P96H 0.037 5.236 −0.257 
0.039 7.076 −0.199 
G104C 0.346 27.214 −0.019 
0.311 43.594 0.034 
S128P 0.050 5.860 0.613 
0.055 7.762 0.243 
G144D 0.197 15.488 −0.514 
0.175 25.203 −0.615 
P145S 0.171 24.222 −1.273 
0.161 18.834 −1.125 
L149P 0.043 7.782 0.773 
0.040 5.648 0.484 
Y151H 0.084 18.058 0.040 
0.083 15.007 −0.375 
M177T 0.016 3.142 1.521 
0.016 2.178 1.488 
I178T 0.017 3.071 1.376 
0.017 2.344 1.328 
I216N 0.133 24.605 1.018 
0.135 19.823 1.045 
G251V 0.084 6.611 −1.878 
0.078 12.019 −1.752 

Structural Analysis

Every single nsSNP was mapped to the 1UMK native structure. Mutation at a specified position was performed by the SWISS-PDB Viewer to obtain 17 mutant modeled structures. Then, the total energy before and after energy minimization of the native and mutant structures was done.

Our results showed deviations for the native and all mutant models (Table 5). In this investigation, the total energy of the native protein structure was determined to be −10,895.431 kJ/mol before the energy minimization and −13,552.636 kJ/mol after the energy minimization. 13 out of 17 mutant modeled structures (L73R, G76D, L47P, R61P, L149P, G144D, Y151H, G76C, P96H, M177T, S128P, I178T, and L47H) showed an increase in energy after minimization in comparison with the native structure, suggesting that the mutant models were less stable than the wild-type model. The mutant model L73R revealed the highest increase in energy, which may be explained by the energetically unfavorable substitution of a nonpolar leucine amino acid residue with a positively charged arginine amino acid residue at the surface of the protein structure.

Table 5.

Energy minimization, RMSD, and TM-score results of mutant NADH-CYB5R3 protein modeled structures

Amino acid variantEnergy before minimization, kJ/molEnergy after minimization, kJ/molRMSDTM-score
Native −10,895.431 −13,552.636 
L47H −10,784.809 −13,542.553 0.00 1.00 
L47P 31,855.756 −13,182.993 0.00 1.00 
R61P −9,789.690 −13,202.318 0.00 1.00 
L73R 345,886.125 −12,822.132 0.00 1.00 
G76D 13,786,670.000 −13,100.775 0.00 1.00 
G76C −10,148.595 −13,443.055 0.00 1.00 
P96H −7,064.764 −13,448.717 0.00 1.00 
G104C −10,788.622 −13,595.547 0.00 1.00 
S128P −10,852.946 −13,523.195 0.00 1.00 
G144D −9,674.539 −13,429.377 0.00 1.00 
P145S −10,938.629 −13,597.192 0.00 1.00 
L149P −10,370.926 −13,378.395 0.00 1.00 
Y151H −10,746.410 −13,433.419 0.00 1.00 
M177T −10,852.662 −13,521.392 0.00 1.00 
I178T −10,880.456 −13,539.243 0.00 1.00 
I216N −11,038.818 −13,701.750 0.00 1.00 
G251V −10,795.777 −13,594.912 0.00 1.00 
Amino acid variantEnergy before minimization, kJ/molEnergy after minimization, kJ/molRMSDTM-score
Native −10,895.431 −13,552.636 
L47H −10,784.809 −13,542.553 0.00 1.00 
L47P 31,855.756 −13,182.993 0.00 1.00 
R61P −9,789.690 −13,202.318 0.00 1.00 
L73R 345,886.125 −12,822.132 0.00 1.00 
G76D 13,786,670.000 −13,100.775 0.00 1.00 
G76C −10,148.595 −13,443.055 0.00 1.00 
P96H −7,064.764 −13,448.717 0.00 1.00 
G104C −10,788.622 −13,595.547 0.00 1.00 
S128P −10,852.946 −13,523.195 0.00 1.00 
G144D −9,674.539 −13,429.377 0.00 1.00 
P145S −10,938.629 −13,597.192 0.00 1.00 
L149P −10,370.926 −13,378.395 0.00 1.00 
Y151H −10,746.410 −13,433.419 0.00 1.00 
M177T −10,852.662 −13,521.392 0.00 1.00 
I178T −10,880.456 −13,539.243 0.00 1.00 
I216N −11,038.818 −13,701.750 0.00 1.00 
G251V −10,795.777 −13,594.912 0.00 1.00 

Furthermore, RMSD and TM-scores were calculated for each mutant model. RMSD measured the average distance between the alpha carbon backbones of the wild-type and mutant proteins. TM-score was used to evaluate the topological similarity between the two protein structures. High RMSD values and low TM-scores indicate structural dissimilarity. Results showed that there are no structural differences between the native and mutant modeled structures (Table 5).

Impact of Deleterious SNPs on Hydrogen Interactions of NADH-CYB5R3

To extend our structural analysis, we used also Swiss-PDB Viewer to evaluate changes in hydrogen bonds between the native and mutants. The results showed a significant alteration in hydrogen bonding interactions of amino acids in ten mutant structures (L47H, R61P, L73R, G76D, G76C, S128P, P145S, Y151H, M177T, and I216N) compared to native (Table 6). Swiss-PDB Viewer results for these ten variants are shown in Figure 4.

Table 6.

Effect of nsSNPs of CYB5R3 gene on hydrogenic interactions

Amino acid variantsVariation impact on the hydrogen bond
L47H Gain of hydrogen bond with asp 49 
L47P No change 
R61P Loss of two hydrogen bonds with asp 107 
L73R Gain of hydrogen bond with tyr 94 
Gain of hydrogen bond with gln 77 
G76D Gain of hydrogen bond with val 150 
G76C Gain of hydrogen bond with tyr 151 
P96H No change 
G104C No change 
S128P Loss of hydrogen bonds with the ligand fad and tyr 113 
G144D No change 
P145S Gain of hydrogen bond with gln 77 
L149P No change 
Y151H Gain of hydrogen bond with gly 153 
Loss of hydrogen bonds with lys 154, gly 155 et asp 200 
M177T Gain of hydrogen bond with his 205 
I178T No change 
I216N Gain of hydrogen bond with glu 213 
G251V No change 
Amino acid variantsVariation impact on the hydrogen bond
L47H Gain of hydrogen bond with asp 49 
L47P No change 
R61P Loss of two hydrogen bonds with asp 107 
L73R Gain of hydrogen bond with tyr 94 
Gain of hydrogen bond with gln 77 
G76D Gain of hydrogen bond with val 150 
G76C Gain of hydrogen bond with tyr 151 
P96H No change 
G104C No change 
S128P Loss of hydrogen bonds with the ligand fad and tyr 113 
G144D No change 
P145S Gain of hydrogen bond with gln 77 
L149P No change 
Y151H Gain of hydrogen bond with gly 153 
Loss of hydrogen bonds with lys 154, gly 155 et asp 200 
M177T Gain of hydrogen bond with his 205 
I178T No change 
I216N Gain of hydrogen bond with glu 213 
G251V No change 
Fig. 4.

Hydrogen bond changes for native and mutants in NADH-CYB5R3 protein. The wild-type residues are shown in cyan and the mutant residues are indicated in red. Hydrogen bonding is marked by green dashed lines.

Fig. 4.

Hydrogen bond changes for native and mutants in NADH-CYB5R3 protein. The wild-type residues are shown in cyan and the mutant residues are indicated in red. Hydrogen bonding is marked by green dashed lines.

Close modal

Ligand-Binding Site Prediction

To check whether the predicted deleterious nsSNPs are located in the NADH-CYB5R3 ligand-binding region, the FTSite tool was used. Three ligand-binding sites were identified: the first binding site consisted of 15 residues, the second site consisted of 14, and the third site consisted of 9 residues (Table 7).

Table 7.

Analysis of ligand-binding sites of the NADH-CYB5R3 protein with FTSite

Ligand-binding site 1Ligand-binding site 2Ligand-binding site 3
H78 R92 K111 
P93 P93 Y113 
Y94 Y94 G180 
T95 V109 G181 
P96 I110 T182 
L108 K111 G183 
V109 Y113 C274 
I110 G125 G275 
K111 K126 P276 
T182 M127  
G183 S128  
T185 Q129  
P186 L131  
C274 T182  
F301   
Ligand-binding site 1Ligand-binding site 2Ligand-binding site 3
H78 R92 K111 
P93 P93 Y113 
Y94 Y94 G180 
T95 V109 G181 
P96 I110 T182 
L108 K111 G183 
V109 Y113 C274 
I110 G125 G275 
K111 K126 P276 
T182 M127  
G183 S128  
T185 Q129  
P186 L131  
C274 T182  
F301   

Out of the 17 selected variants, P96 is involved in the first ligand-binding site, whereas S128 is found to be involved in the second site. These nsSNPs have been visualized using PyMol (shown in Fig. 5).

Fig. 5.

Binding site prediction using FTSite showing Pro 96 at site 1 and Ser 128 at site 2. a Pink, green, and purple colored mesh are 1st, 2nd, and 3rd ligand-binding site, respectively, of human NADH-CYB5R3 protein predicted using FTSite server. b Zoom in on interaction at Pro 96. c Zoom in on interaction at Ser 128.

Fig. 5.

Binding site prediction using FTSite showing Pro 96 at site 1 and Ser 128 at site 2. a Pink, green, and purple colored mesh are 1st, 2nd, and 3rd ligand-binding site, respectively, of human NADH-CYB5R3 protein predicted using FTSite server. b Zoom in on interaction at Pro 96. c Zoom in on interaction at Ser 128.

Close modal

Protein-Protein Interactions Using STRING

STRING interaction analysis showed that CYB5R3 protein is involved in many molecular and biological processes and has functional interactions with 10 proteins including cytochrome b5 type A, cytochrome b5 type B, hemoglobin subunit alpha 1, hemoglobin subunit alpha 2, hemoglobin subunit beta, mitochondrial amidoxime reducing component 1, mitochondrial amidoxime reducing component 2, protein diaphanous homolog 3, NADPH-cytochrome P450 reductase (POR), and alpha-hemoglobin-stabilizing protein (shown in Fig. 6).

Fig. 6.

Protein-protein interaction network of CYB5R3 protein shown by STRING.

Fig. 6.

Protein-protein interaction network of CYB5R3 protein shown by STRING.

Close modal

Amino acid substitution may alter the structure, stability, and function of proteins, ultimately leading to various human genetic diseases. However, not all nsSNPs cause a damage to the protein function, some of them are neutral. For that reason, studying the effects of missense mutations on a protein is a crucial step to understand the relationship between genetic variants and clinical manifestations [Nzabonimpa et al., 2016]. Computational studies play an important role in identifying and predicting the pathogenic SNPs which affect protein function. This approach is easier and more reliable to carry out biological experiments [Kumar and Mahalingam, 2018].

In this study, various computational tools were used to study the impact of the 339 reported nsSNPs in the human CYB5R3 gene, their disease associations, their impact on the protein stability, their phylogenetic conservations, and their surface accessibility. A total of 17 missense mutations were found to be the most deleterious and were defined as “high-risk nsSNPs” – L47H, L47P, R61P, L73R G76D, G76C, P96H, G104C, S128P, G144D, P145S, L149P, Y151H, M177T, I178T, I216N, and G251V.

According to the literature and HGMD database, eight out of the seventeen predicted deleterious nsSNPs were reported to be associated with RCM: G104C, G144D, P145S, L149P, M177T, and I178T were associated with RCM type I, whereas P96H and S128P were associated with the severe form RCM type II with a neurological disorder [Gupta et al., 2020]. The remaining nine high-risk nsSNPs have not yet been reported in RCM patients. The low number of nsSNPs associated with RCM type II can be explained by the fact that only 7 missense mutations have been reported in this type [Gupta et al., 2020]. Among them, only the S128P was described at homozygous state, the others missenses were either found in the compound heterozygous state with other mutations, suggesting that the clinical manifestations in these patients are determined by the combination of the two variants; or data are missing, calling into question the association of the mutation with RCM type II [Toelle et al., 2004; Maran et al., 2005].

NADH-CYB5R3 function is determined by its tertiary structure which consists of two functional domains: the N-terminal or FAD-binding domain (Thr31-Ser146) and the C-terminal or NADH-binding domain (Ser174-Phe301). The FAD-binding domain consists of three prominent structural features: a six-stranded antiparallel β-barrel (Fβ1-Fβ6) with a Greek-key motif, an α-helix Fα1 (Gly125-Met134), and a long loop (Val112-Gly124). The NADH-binding domain consists of three β1-α-β2 motifs: Nβ1-Nα1-Nβ2 (Ser174Asn210), Νβ2−Να3−Νβ3 (Val203-Leu239), and Νβ5−Να6−Νβ6 (Leu270-Phe301). These two domains are linked by a small domain (Gly147-Lys173) consisting of a triple-stranded antiparallel β-sheet, which is important for maintaining of the critical protein architecture required for enzyme activity [Bando et al., 2004]. The main function of NADH-CYB5R3 is to transfer two electrons from NADH to two cytochrome b5 molecules through an enzyme-bound FAD. Electrons from reduced cytochrome b5 are then transferred to various electron acceptors, participating in the reduction of methemoglobin, where hemoglobin is returned to its functional form [Shirabe et al., 1994; Nicolas-Jilwan, 2019]. Among the 17 high-risk nsSNPs, eleven mutations were found in the FAD-binding domain (L47H, L47P, R61P, L73R, G76D, G76C, P96H, G104C, S128P, G144D, and P145S), 4 in NADH-binding domain (M177T, I178T, I216N, and G251V), and 2 in the interdomain hinge region (L149P and Y151H). Furthermore, the 17 nsSNPs were mapped to the native NADH-CYB5R3 (1UMK) structure using SWISS-PDB Viewer software and tested for different properties. In this study, the total energy after minimization of 13 mutant models was higher than the native protein, suggesting that these 13 nsSNPs can cause structural damage to the protein by affecting stability and function [Chandrasekaran et al., 2017]. Moreover, ten mutant models (L47H, R61P, L73R, G76D, G76C, S128P, P145S, Y151H, M177T, and I216N) showed a significant variation in hydrogen bonds. This disruption can be explained by the difference in size and hydrophobicity between wild-type and mutant residues. Hydrogen bonds and other nonbonding interactions play an important role in stabilizing the secondary structure of proteins. Hence, the loss or gain of hydrogen bonds due to deleterious SNPs can affect the protein’s structure and function [Ippolito et al., 1990].

Furthermore, the FTSite server showed that RCM type II mutations P96 and S128 substitutions are involved in the ligand-binding site’s interactions and form the catalytic coordination sphere. This active site, composed by two regions, the flavin binding Arg-Pro-Tyr-Thr-Pro-Ile-Ser (92–98) motif and the FAD/FMN specificity motif Gly-Lys-Met-Ser (125–128), is crucial in providing structural integrity for electron transfer in the NADH-CYB5R3 system and flavin cofactor binding and, hence, conserved within the members of flavoprotein family [Kimura et al., 2001]. Thus, replacement of a hydrophobic amino acid (Pro) with a basic amino acid (His) in residue 96, leads to the FAD-binding domain disruption, causing a significant reduction in the NADH-CYB5R3 catalytic activity. Similarly, substitution of the buried S128 by a bigger and more hydrophobic residue (Pro) causes loss of hydrogen bonds and as a result disturbs correct folding.

Among the 6 nsSNPs related to RCM type I, three (G104C, G144D, P145S) were present in FAD-binding domain, two (M177T and I178T) in the NADH-binding domain and one (L149P) in the hinge region. None of them are located in the enzyme’s active site, and therefore they do not affect the protein function. Instead, they lead to an unstable enzyme [Davis et al., 2004; Percy et al., 2004; Kedar et al., 2008; Warang et al., 2015].

Based on our results, we can deduce that different mechanisms determine the pathogenic severity of nsSNPs but we suggest that RCM type II mutations are located in the active site of the protein, which can lead to a significant reduction or loss of the catalytic activity of the enzyme. In contrast, RCM type I mutations are found in the nonfunctional domain, which reduced the stability of the enzyme without catalytic function affect.

Deleterious nsSNPs can also disrupt the NADH-CYB5R3 interacting partners. The STRING tool showed that NADH-CYB5R3 is related to other proteins implicated in hemoglobin complex (hemoglobin subunit alpha 1, hemoglobin subunit alpha 2, hemoglobin subunit beta, and alpha-hemoglobin-stabilizing protein) and methemoglobin (cytochrome b5 type A, cytochrome b5 type B, mitochondrial amidoxime reducing component 1, mitochondrial amidoxime reducing component 2, and POR). This suggests that the deleterious nsSNPs in the CYB5R3 may disrupt the normal function of other interacting proteins. Therefore, any changes in protein function may have an impact on different pathways involved in the disease. Our study highlights the importance of investigating of the whole protein interaction network when a mutation is identified in the CYB5R3 gene.

Discriminating between pathogenic and neutral variants provides information that help understand the clinical phenotype, develop diagnosis and treatment strategies for the disease-associated genes. Computational analysis has now become a roadmap for the analysis and evolution of genetic mutations and their pathological consequences. In this study, several in silico tools were used to identify 17 high-risk nsSNPs in the CYB5R3 gene, eight of which have been reported as RCM-causative mutations. Our results can help understand the impact of these variants on NADH-CYB5R3 protein and their association with the different severity of RCM. These nsSNPs should be further experimentally investigated to study their biological impact on the pathogenesis of the disease.

We would like to thank Dr. Kais Ghedira for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us improve the quality of the manuscript.

An ethics statement was not required for this study type, no human or animal subjects or materials were used.

The authors have no conflicts of interest to declare.

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

Sonia Nouira and Houyem Ouragini designed the study. Emna Bouatrous performed all the in silico analyses and wrote the original draft preparation. Samia Menif and Houyem Ouragini reviewed and edited the manuscript. Houyem Ouragini supervised the study. All authors read and approved the final manuscript.

All data generated or analyzed during this study are included in this article. Further inquiries can be directed to the corresponding author.

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