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In silico profiling of nonsynonymous SNPs of fat mass and obesity-associated gene: possible impacts on the treatment of non-alcoholic fatty liver disease



Nonalcoholic fatty liver, or NAFLD, is the most common chronic liver ailment. It is characterized by excessive fat deposition in hepatocytes of individuals who consume little or no alcohol and are unaffected by specific liver damaging factors. It is also associated with extrahepatic manifestations such as chronic kidney disease, cardiovascular disease, and sleep apnea. The global burden of NAFLD is increasing at an alarming rate. However, no pharmacologically approved drugs against NAFLD are available owing to their complex pathophysiology. Genome-wide association studies have uncovered SNPs in the fat mass and obesity-associated gene (FTO) that are robustly associated with obesity and higher BMI. The prevalence of NAFLD increases in parallel with the increasing prevalence of obesity. Since FTO might play a crucial role in NAFLD development, the current study identified five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools.


This study aims to identify potentially deleterious nonsynonymous SNPs (ns-SNPs) employing various in silico tools. Additionally, molecular modeling approaches further studied the structural changes caused by identified SNPs. Moreover, molecular dynamics studies finally investigated the binding potentials of the phytochemicals resveratrol, rosmarinic acid, and capsaicin with different mutant forms of FTO.


The current investigation has five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools. The present study identified five nsSNPs of the human gene FTO, Gly103Asp, Arg96Pro, Tyr295Cys, and Arg322Gln, with an apparent connection to the disease condition. Modulation of demethylation activity by phytomolecule scanning explains the hepatoprotective action of molecules. The current investigation also suggested that predicted mutations did not affect the binding ability of three polyphenols: rosamarinic acid, resveratrol, and capsaicin.


This study showed that the predicted mutations in FTO did not affect the binding of three polyphenols. Thus, these three molecules can significantly aid drug development against FTO and NAFLD.


  • • Fat Mass and Obesity Associated Gene/FTO, which codes for nucleic acid demethylase, have a robust association with obesity and higher BMI.

  • • Mutation at the Arg96 position to His results in loss of the demethylase activity of FTO, therefore reducing the catalytic activity of FTO.

  • • Molecular docking and dynamics have indicated that phytomolecules can bind to the catalytic site of FTO.

  • • Modulation of demethylation activity by phytomolecule scanning explains their hepatoprotective action.

  • • Epidemiological evidence suggests a close association of FTO with the development of NAFLD.

Nonalcoholic fatty liver disease (NAFLD) is a metabolic disorder identified by excessive fat deposition in the hepatocytes of individuals who consume little or no alcohol and are unaffected by other specific liver-damaging factors. Although the etiology of NAFLD is not entirely understood, primary risk factors include features of metabolic syndrome such as obesity, type 2 diabetes, and dyslipidemia. NAFLD development is attributed to a sedentary lifestyle, a high-calorie diet, genetic and epigenetic influences, and environmental factors such as the gut microbiome [1]. NAFLD can also lead to a progressive liver disease called nonalcoholic steatohepatitis (NASH), marked by hepatocyte ballooning and inflammation and, in severe conditions, progression to fibrosis, cirrhosis, and hepatocellular carcinoma [2,3,4]. Genome-wide association studies (GWAS) have uncovered genes robustly associated with NAFLD. These include PNPLA3, transmembrane 6 superfamily member 2 (TM6SF2), glucokinase regulator (GCKR), membrane-bound O- acyltransferase domain-containing 7 (MBOAT7), and hydroxysteroid 17β- dehydrogenase (HSD17B13) [5]. With approximately a quarter of the world’s population affected by NAFLD, it is estimated to become the foremost cause of liver-related morbidity and mortality within the next two decades [6].

The fat mass and obesity-associated (FTO) gene, discovered during a GWAS in 2007, has a robust association with obesity and higher BMI [7]. Epidemiological evidence suggesting a close association of NAFLD with obesity suggests the potential role of FTO in the development of NAFLD [8]. Upregulated liver FTO expression was observed in the NAFLD rat model [9]. In addition, increased serum FTO expression was reported in NAFLD patients, with greater prominence in patients with NASH [10]. FTO is described as the "master switch" because it influences epigenetic control over regulatory pathways in energy homeostasis [11]. FTO shares maximum sequence and structure homology with the E. coli DNA repair protein AlkB and its mammalian homologs, including ABH 2 and 3. These proteins are Fe2+− and 2-oxoglutarate (2OG)-dependent dioxygenases [12]. FTO encodes a nucleic acid demethylase in the nucleus, preferentially demethylating 3-meT in ssDNA and m6A and 3-meU in ssRNA [13]. RNA modifications affect splicing, intracellular transport, translation, and the half-life of RNA [14]. In addition to energy homeostasis, FTO shows pleiotropic effects on cell proliferation and brain function [15].

Numerous wet lab studies on FTO genes immensely contribute toward the general as well as molecular pathophysiology of the disease. However, despite those studies, a promising therapeutic intervention is far from reach, which begs the requirement of a more sophisticated approach that could use high-throughput data for advanced critical analysis of the disease. This approach is possible with evolving in silico approaches that provide statistically significant results with visually vibrant and easy-to-understand data with a very low false discovery rate. Recently, Kumar et al., (2022) [16] displayed via in silico analysis the mutational profile of the FTO gene and explained how it affects protein structure and function. In detail, they analyzed the FTO gene's mutational profile and monitored the influence of nonsynonymous (missense) mutations on the structure, dynamics, conformation, and substrate binding of the FTO protein. Through web-based bioinformatic tools, Musliji et al., Field (2021) assessed the involvement of four genes contributing to obesity [17]. The sequences of four so-called obesity genes were gathered and analyzed, including FTO (fat mass and obesity-associated protein), PPARG (peroxisome proliferator-activated receptor), ADRB3 (adrenergic receptor 3), and FABP2 (fatty acid-binding protein 2). Previously, Kumar and Mahalingam, (2018) [18], sought to identify nonsynonymous SNPs (nsSNPs) having the highest projected negative effect on neuronal growth regulator 1 (NEGR1) protein function. They employed five computational tools to forecast the deleterious and pathogenic NEGR1 nsSNPs: PolyPhen, SIFT, PROVEAN, MutPred, and M-CAP.

Despite identifying various risk factors and associated genes, no pharmacologically approved medications are available to treat NAFLD, owing to the complex and multifactorial pathophysiology of the disease [1]. Although promising drugs are in the pipeline for treating NAFLD/NASH, lifestyle intervention remains the front-line treatment [19, 20]. It has been shown that the Mediterranean diet can decrease liver fat content and decrease hepatic steatosis [21, 22]. This is primarily attributed to the antioxidant properties of polyphenolic phytochemicals in the Mediterranean diet [23]. Resveratrol in grapes improves hepatic damage in NAFLD patients [24]. Rosamarinic acid obtained from various plants of the Lamiaceae family has shown potency against NAFLD through an antioxidative mechanism in HepG2 cells [4]. Dietary intake of capsaicin has also been reported to prevent lipid deposition in HepG2 cells and liver tissues through TRPV1 activation-mediated lipolysis (Fig. 1).

Fig. 1
figure 1

Structure of (A) rosamarinic acid, (B) resveratrol and (C) capsaicin

In hepatic disorders, several factors are involved, including the creation of reactive oxygen species (ROS), lipid peroxidation, peroxynitrite formation, complement factors, and proinflammatory agents such as chemokines and cytokines [25]. Several biological activities have been reported for rosamarinic acid, capsaicin, and resveratrol. Rosamarinic acid is an ROS scavenger and lipid peroxidation inhibitor [26]. It also possesses anti-inflammatory, angiogenic, and neuroprotective effects. Surprisingly, decreasing the synthesis of IL-6, TNF-alpha, monocyte chemoattractant protein 1, and cyclooxygenase can reduce fat-induced inflammation in adipose tissue. Adiponectin expression is also increased by capsaicin. Resveratrol may also have hepatoprotective effects in NAFLD [27]. It improves lipid and carbohydrate metabolism by reducing hepatic lipid buildup. In addition, resveratrol effectively enhances insulin sensitivity. Several phytochemicals benefit obesity and NAFLD; however, the mechanisms are still unknown. Some compounds bind to FTO protein and thus inhibit the demethylase activity of FTO [28, 29]. Quercetin binds strongly and inhibits FTO activity [30]. These findings open a new avenue toward developing therapeutic interventions through alterations in FTO activity. Based on these findings, it is rational to hypothesize that the selected phytochemicals could be a beneficial therapeutic intervention targeting metabolic abnormalities in NAFLD. The FTO gene is associated with NAFLD patients and animal models [9, 10, 31]. Polymorphism of the FTO gene is also associated with hepatic fat content [11]; therefore, the current study investigated potentially deleterious nonsynonymous SNPs (ns-SNPs) employing various in silico tools. The structural changes caused by the identified SNPs were studied using molecular modeling approaches. Furthermore, molecular docking and dynamics studies examined the binding potential of the phytochemicals resveratrol, rosmarinic acid, and capsaicin with different mutant forms of FTO.

Several studies have identified phytochemicals that inhibit the demethylation activity of FTO and their impact on the wild-type FTO structure [32]. However, the effects of the inhibitors on the mutant FTO structure have not been investigated. Therefore, molecular docking was used to study the affinity of the polyphenols with native and wild-type FTO to identify any possible variation in the binding affinity of the selected phytochemicals and mutant protein.

Materials and methods

SNP retrieval

The SNPs were retrieved from the SNP database of the National Center for Biotechnology Information (NCBI) ( with various limits (Homosapiens, missense) [33]. An overview of the methodology is shown in Fig. 2.

Fig. 2
figure 2

Overview of the methodologies used

Predictions of deleterious effects of FTO

In silico tools were employed to distinguish detrimental SNPs from the retrieved SNPs for further analysis. The tool SIFT (Sorting intolerant from tolerant) uses sequence homology to predict whether an amino acid substitution will alter the protein function [20]. The substitution was considered deleterious if the normalized value was less than the cutoff value of 0.05. Polymorphism phenotyping (PolyPhen 2.0) is a tool that recognizes the possible result of amino acid substitutions based on structure and function. The mutations are categorized as ‘probably damaging,’ ‘possibly damaging,’ and ‘benign’ based on the false-positive rate (FPR) threshold [34]. Single-site mutations can lead to changes in protein stability. The protein stability change can be predicted by I-Mutant [35]. The output provides the difference in Gibbs free energy values (DDG/DDG) between mutated and native types (kcal/mol). Protein analysis through evolutionary relationships (Panther) classifies proteins according to family, subfamily, and molecular functions [36]. In addition, functional analysis was performed using Hidden Markov Models v2.3 (FATHMM). FATHMM, a high-throughput web server, was used. This can predict the functional consequences of both coding variants, i.e., nonsynonymous single nucleotide variants (ns-SNVs), and noncoding variants in the human genome, classifying the substitutions as tolerated or damaging [37].

Structure modeling and root mean square deviation (RMSD) calculations

Mutations can significantly change the structure and stability of proteins. Three-dimensional structure analyses were performed between the native and mutant proteins to evaluate the changes. SWISS-MODEL, an automated homology modeling software, was used to model the 3D structure of the protein. PROCHECK evaluates the stereochemical quality of a protein structure by generating a series of PostScript graphs that analyze its overall and residue-by-residue geometry. Then, the mutants were generated through Swiss PDB Viewer [38]. The energy of both native and mutant proteins was minimized using NOMAD-Ref, a GROMACS-based default tool for minimization that performs calculations of functionally relevant movements in biological macromolecules [39]. Finally, the differences in total energy were computed using the minimized structures. Finally, Swiss PDB Viewer was used to compute the root mean square value deviation (RMSD) between the Natives and mutants.

Trajectory analysis

The stabilizing residues are defined by SRide based on their long-range interactions, hydrophobicity, and amino acid residue conservation [40]. Further investigation was performed using PSIPRED, a simple and precise secondary structure prediction method. It analyses the output obtained from PSI-BLAST by including two feed-forward neural networks [41]. The functional regions in proteins were identified by using ConSurf [42]. Apart from these, the Missense-3D predicts structural changes introduced by an amino acid substitution and is applicable to analyzing both PDB coordinates and homolog position-Specifictures [43].

ADMET analysis

Biochemical absorption, distribution, metabolism, excretion, and toxicity (ADMET) are all critical factors of organic or inorganic compounds that could be used for therapeutic purposes. Therefore, a high-quality drug candidate should be effective against the therapeutic target and have adequate ADMET qualities at therapeutic dosages. Therefore, various bioinformatics tools are used to analyze any compound's ADMET properties. Thus, in the current investigation, SWISS ADME software of the Swiss Institute of Bioinformatics and the pkCSM server were used to estimate individual ADME scores of resveratrol, capsaicin, and rosamarinic acid [44, 45]. The toxicity of the phytocompounds was evaluated by using ProTox-II. ProTox-II predicted various toxicity endpoints, such as the compounds' hepatotoxicity, acute toxicity, carcinogenicity, mutagenicity, cytotoxicity, and immunotoxicity. The similarity between the query molecule’s functional group and those reported and found in the software database forms the basis of prediction.

Molecular docking

FTO’s crystal structure (PDB ID:4QHO) was retrieved from RCSB for all 5 SNPs and was modeled, in addition to energy minimization, using the SWISS-PDB viewer [46]. The drug molecules capsaicin, rosamarinic acid, and resveratrol were acquired from PubChem (Compound CID: 1,548,943, 445,154, and 5,281,792, respectively) in SDF format converted to PDBQT after the addition of hydrogen using Open Babel. The drug molecules were used as ligands, and the energy-minimized native FTO structure and five mutants harboring the solitary SNPs were used as receptors for the docking studies. Protein preparation for docking was performed using AutoDock Tools (version 1.5.6) (ADT). The PDB structure was converted to PDBQT format after removing crystal water molecules and native ligands, assigning Kollman charges, and adding polar hydrogen. The ligand charges were merged, and nonpolar hydrogens were removed in ADT. The search space was defined according to the binding site of FTO, constituted by the residues Arg95, Tyr108, Asn205, His231, Asp233, Tyr295, His307, Thr320, and Arg322 [13]. Docking was performed using AutoDock Vina (version 1.1.2) [47] with exhaustiveness. The interaction of the drug molecules with the active site was analyzed using Schrödinger Maestro. The binding pose with the best binding affinity and maximum interactions with the active site residues was selected for MD simulations.

Molecular dynamics simulations

The docked complexes of five FTO mutants and the native protein with the three selected drug molecules capsaicin, resveratrol, and rosamarinic acid were subjected to molecular dynamics simulation using the GROMACS (version 2018.7) software suite by previously described methods [48]. The GROMOS 54a7 force field was used to build the protein topology, while ligand topologies were generated using the PRODRG server [49]. A cubic box was generated extending 10 Å from the protein atoms in all directions and solvated using the SPC three-point water model. The system was neutralized by adding an appropriate number of sodium atoms. Using the steepest descent algorithm with a 1000 kJ/mol/nm convergence criterion, the solvated electroneutral system was energy minimized to eliminate steric clashes. A cutoff of 1.2 nm was used for computing both van der Waals and Coulombic interactions, while long-range electrostatic interactions were handled using particle‒mesh Ewald (PME). The complex's equilibrium was performed first in a canonical ensemble (NVT) by restraining solvent and ions at 300 K for 100 ps. This was followed by NPT equilibration for 100 ps, where the restraint weight on the protein‒ligand complex was gradually reduced. The LINC algorithm was used as the bond constraint algorithm, while constant temperature and pressure were simulated using Berendsen’s thermostat and Parrinello-Rahman pressure coupling [50, 51]. A production simulation was performed for 100 ns with a step size of 2 fs using a leapfrog dynamics integrator. Postsimulation analysis was performed using GROMACS modules and in-house python scripts.


Data mining

A total of 383 SNPs were obtained from the NCBI database, with functions limited to Homo sapiens retrieved.

Prediction of deleterious ns-SNPs

Twenty-seven deleterious ns-SNPs were identified by PolyPhen 2.0, SIFT, Panther, FATHMM, and I-Mutant 3.0. Previous studies have indicated that predicting deleterious ns-SNPs by combined tool analysis leads to more accurate results. It was ascertained that only 27 ns-SNPs are expected to be ‘‘possibly deleterious’’ by all tools named in Table S1. Variation in alleles showed 10 ns-SNPs for Adenine → Guanine; 6 for Cytosine → Thymine; 1 for Adenine → Cytosine; 3 for Guanine → Thymine; 5 for Cytosine → Guanine; and 2 for Adenine → Thymine. The Adenine → Guanine nucleotide variation was seen maximum times with a SIFT score of zero, followed by Cytosine → Thymine. This verified the results obtained with the FTO gene analyzed in this study and confirmed that point mutations might be deleterious. The results are shown in Supplementary Table S1.

Modeling protein structures and RMSD calculation

Point mutations significantly alter the stability of the protein structures. Therefore, it is essential to study the relationship between the structure and function of proteins using three-dimensional structures [52]. The total energy of the native type was -30, 249 kJ/mol. Considerable variations were found in the total energy of 25 mutants compared to the native type. The structure of the modeled mutant proteins is shown in Supplementary Figure S1.

An in silico study on the FTO gene predicted that ns-SNPs, with RMSD > 0.3, play a substantial role in the commencement of disease. Swiss PDB Viewer was used to computing RMSD values. The present study found 12 mutant models with deviations greater than 0.3A. A higher RMSD value indicates more deviation between the native and mutant structures. This variation is damaging, as it leads to divergence in the 3-D space of the binding site of FTO genes. This, in turn, leads to a change in the critical efficiencies of the inhibitors. Hence, these 12 ns-SNPs (Table S2) can be expected to be ‘‘deleterious’’ and were selected for further analysis.

Trajectory analysis

Stabilizing residues for deleterious ns-SNPs were identified and compared with native structures. Panther was used to identifying destabilizing residues in the ns-SNPs. Destabilizing residues were present in five out of the twelve ns-SNPs. The added residues with negative free energy (DDG) values were categorized as “destabilized.” For the five ‘‘highly deleterious ns-SNPs,’’ with RMSD > 0.3 and destabilizing residues in proteins, solvent accessibility and secondary structure predictions were carried out through SRide, PSIPRED, ConSurf, and Missense 3D (Table 1).

Table 1 Trajectory analysis and stabilizing residues of the deleterious SNPs. Destabilizing residues are shown in bold. (E- Extracellular, C-coil, S-strands)

ADMET Analysis

The results of the ADMET analysis are tabulated in Table 2 and Table 3. All three molecules are not substrates for P-gp. The GI absorption is high for capsaicin and resveratrol, whereas it is low for rosamarinic acid (Table 3). Capsaicin and resveratrol are capable of crossing the brain blood barrier (BBB). CYP isoforms (CYP1A2, CYP2C19, CYP2D6, and CYP3A4) and P-gp can synergistically process small molecules [42]. This, in turn, improves tissue and organism protection. Inhibition of these isoenzymes may lead to harmful effects such as drug accumulation and lower clearance of its metabolites. Capsaicin and resveratrol inhibit CYP1A2. Resveratrol also inhibits CYP2C9, whereas capsaicin inhibits CYP2D6. None of the CYP isoforms were inhibited by rosmarinic acid. Capsaicin and resveratrol are CYP3A4 inhibitors. The KP or skin permeability coefficient correlates with molecular size and lipophilicity (Table 3) [43]. The skin permeant of a molecule will be greater if the log KP value is less negative. Resveratrol with a log KP value of -5.47 (cm/s) will have high skin permeability, whereas rosamarinic acid will have the least (-6.82 cm/s). The ProTox-II results showed that resveratrol did not have any possible toxic effects.

Table 2 ADME profile of the three phytomolecules
Table 3 Pharmacokinetic properties of the selected molecules capsaicin, rosamarinic acid and resveratrol

Molecular docking

Molecular docking of the three selected phytochemicals with FTO was performed using AutoDock Vina to predict their binding poses. Rosamarinic acid showed the best binding affinity among all three drugs against native and predicted deleterious mutants of FTO. MD simulations selected the binding pose with the best affinity for further refinement. The predicted binding relationship of drug molecules with FTO native and mutant forms obtained from AutoDock Vina is represented in Table 4. Detailed interactions of the phytomolecules with different mutant forms of FTO are shown in Fig. 3.

Table 4 Predicted binding affinity of drug molecules with FTO native and mutant forms obtained from AutoDock Vina
Fig. 3
figure 3

Drug-ligand interaction predicted for FTO and its mutants: (A) native FTO with capsaicin, (B) native FTO with resveratrol, (C) native FTO with rosmarinic acid, (D) R96P with capsaicin, (E) R96P with resveratrol, (F) R96P with rosmarinic acid, (G) G103D with capsaicin, (H) G103D with resveratrol, (I) G103D with rosmarinic acid, (J) Y295C with capsaicin, (K) Y295C with resveratrol, (L) Y295C with rosmarinic acid, (M) R316Q with capsaicin, (N) R316Q with resveratrol, (O) R316Q with rosmarinic acid, (P) R322Q with rosmarinic acid, (Q) R322Q with resveratrol, and (R) R322Q with rosmarinic acid

Molecular dynamics simulation studies

Molecular dynamics simulations were performed to refine the binding pose and assess the stability of the protein‒ligand complex under aqueous conditions. In addition, the structural perturbations of drug binding to native and mutant forms of FTO were examined using various parameters obtained from the analysis of the 100 ns trajectory.

The root mean square deviation (RMSD) was calculated to quantify the conformational changes in the protein structure. After stabilization, the average protein backbone RMSD was less than 3 nm. The well-equilibrated backbone RMSD profile (Fig. 4) indicates no significant structural perturbations in the presence of all five mutations and drug binding. The structural integrity of FTO mutants and native protein was also evidenced by the radius of the gyration plot (Fig. S2), which indicates the compactness of the protein. The compactness of the protein was marginally higher in the case of rosmarinic acid binding, displaying lower Rg values, followed by resveratrol and then capsaicin.

Fig. 4
figure 4

Structure modeling and root mean square deviation (RMSD) profile of backbone coordinates of FTO native and mutant forms with the three molecules for the 100 ns simulation

Similarly, more minor changes in SASA with time reflect the global structural stability of the protein since conformational changes are often accompanied by changes in solvent accessibility of the protein. The differences in the SASA profile for different protein complexes resulted from differences in solvent substitution upon drug binding (Fig. S3). The plot of RMSF of Cα atoms throughout the simulations is shown in Fig. 5.

Fig. 5
figure 5

RMSF profile of the mutant and native forms of FTO bound to these molecules

The binding potential of FTO with the three selected molecules shown in docking studies was substantiated in MD simulation studies. The relative stability of the protein‒ligand complexes was assessed from the ligand RMSD profiles (Fig. 6). The complexes of drugs rosamarinic acid and resveratrol with different forms of FTO were stable with well-equilibrated ligand RMSD values throughout the 100 ns. In the case of capsaicin, the complex with Arg96Pro, Gly103Asp, Arg316Gln, and Arg322Gln mutants of FTO showed stable binding through interactions with the residues in the jelly roll motif, while native and Tyr295Cys mutant FTO had comparatively fewer stable complexes with capsaicin.

Fig. 6
figure 6

Ligand RMSD profile for the 100 ns simulation


Despite the elusive molecular mechanism underlying the biology of the FTO gene, it remains a crucial modulator of various diseases, specifically NAFLD. FTO genetic variants with potential disease association in the present study using an in silico approach (Table 5). Of the 383 ns-SNPs obtained from dbSNP for the human FTO gene, 27 were predicted to be deleterious using SIFT, PolyPhen 2.0, I-Mutant, Panther, and FATHMM. Furthermore, 12 out of 27 were shortlisted after RMSD calculations. Five final ns-SNPs of the human FTO gene were identified after trajectory analysis using SRide vis-à-vis Arg96Pro, Gly103Asp, Arg316Gln, Tyr295Cys, and Arg322Gln. The clinical significance of some of these mutations is reported based on ACMG classification obtained from Var Some and ClinVar, Table 5 [53, 54].

Table 5 Genetic variants of FTO with potential disease association

The FTO structure comprises two domains: an N-terminal domain with β-helices in a jelly roll motif that harbors the catalytic core and an α-helical C-terminal domain with unknown structural homology [13]. The residues at positions 96, 103, 295, 316, and 322 are well conserved among all FTO homologs [55]. Tyr295, Arg316, and Arg322 are known to form crucial interactions with the carboxylate of the cosubstrate 2OG, including salt bridges and hydrogen bonding. Arg96 is located on the substrate recognition lid and is responsible for specific substrate base recognition through hydrogen bond interactions. The residue Gly103 is present at the interface of the N-terminal and C-terminal domains. Despite having the catalytic core, the N-terminal domain (NTD) is nonfunctional when expressed in the absence of the C-terminal domain (CTD), or the interface between NTD and CTD is perturbed [56].

The mutation Arg316Gln is known to cause the inactivation of FTO enzymatic activity and cause the autosomal-recessive lethal disorder. Studies using fibroblasts harboring the FTO protein with the Arg316Gln mutation showed enlarged cell size, altered cell morphology, increased senescence, and decreased cell viability [57]. Since the Arg316Gln SNP has been classified as pathogenic, it endorses the pipeline for identifying potentially deleterious ns-SNPs. Similarly, the mutation Arg322Gln also renders FTO inactive and is known to be associated with malformation syndrome, an autosomal recessive disorder. Mutation at the Arg96 position to His results in loss of demethylase activity and a similar effect is thus expected for the conversion to proline [56]. Gly103Asp mutations have shown reduced demethylase activity in AlkB and ABH homologs of FTO. Since aspartic acid is a charged and large amino acid compared to glycine, the structural changes accompanying the mutation can drastically alter the interactions at the NTD and CTD of the FTO interface. The effect of the Tyr295Cys mutation in FTO has not been studied in detail. The change from tyrosine to cysteine at position 295 hampers the hydrogen bond between Tyr295 and 2-OG, reducing FTO activity.

Molecular docking and dynamics studies have shown that rosamarinic acid, resveratrol, and capsaicin show promising effects against NAFLD, as they can potentially bind to the catalytic site of FTO. In detail, the predicted binding affinity was calculated in kcal/mol by docking. Rosamarinic acid is bound to the target native FTO protein with the highest binding affinity of approximately -9.2 kcal/mol. The higher binding affinity was maintained in all the mutated states of the proteins, such as for R96P, G103D, Y295C, R316Q, and R322Q, and the respective binding affinities were -8.7, -8.9, -9.3, -9.2 and -9.7 kcal/mol. Similar results were also observed for resveratrol and capsaicin, as presented in Table 4, suggesting that in addition to native FTO, the mutated forms of the proteins could also be inhibited effectively through natural phytocompounds. Molecular dynamics, on the other hand, resulted in quantifying the conformational changes in the protein structure after the interaction of the compounds rosamarinic acid, resveratrol, and capsaicin with the native form and the mutated versions of the FTO proteins. This suggested that the compounds altered the structural confirmation of both native FTO protein and its mutated forms visualized in the figures. Furthermore, other polyphenolic compounds, such as flavonoids, could interact with FTO. Modulating the demethylation activity by these phytochemicals can explain the hepatoprotective action of these molecules, as the molecules' binding was not affected by the mutations in the FTO gene caused by the predicted deleterious ns-SNPs.

Study strengths and limitations

The global burden of NAFLD is increasing at an alarming rate. However, no pharmacologically approved drugs against NAFLD are available owing to their complex pathophysiology. Since FTO might play a crucial role in NAFLD development, the current study identified five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools. The structural effect of the mutations was studied using molecular modeling. The mutations Arg316 and Arg322 have previously been validated through in vitro assays; further validation is required for Arg96Pro, Gly103Asp, and Tyr295Cys.

The current study explored diverse computational techniques (molecular docking and simulation) to establish natural compound-based therapeutic strategies for NAFLD. If the work had been carried out in wet lab experimentation, this would require more sophisticated instrumentation, time, and manpower. Despite the in silico efficacy of these phytocompounds, in vitro and in vivo analyses are still required for definitive conclusions. The binding of the three molecules was not affected by the predicted mutations in FTO. Thus, these molecules can significantly aid drug development against FTO and NAFLD. The mentioned compounds have been studied in silico, providing evidence of their beneficial effects on liver diseases, such as NAFLD. Although lifestyle modifications involving diet and exercise currently remain the first line of treatment for NAFLD, the compounds reviewed here could also improve NAFLD treatment.


The global burden of NAFLD is increasing at an alarming rate. However, no pharmacologically approved drugs against NAFLD are available due to its complex pathophysiology. Since FTO might play a crucial role in NAFLD development, the present study identified five potentially deleterious mutations from 383 ns-SNPs in the human FTO gene using various in silico tools. The structural effect of the mutations was studied using molecular modeling. Arg316 and Arg322 were previously validated through in vitro assays; further validation is required for Arg96Pro, Gly103Asp, and Tyr295Cys.

Additionally, molecular docking and simulations were used to analyze the potential binding ability of three natural polyphenols, namely, rosamarinic acid, resveratrol, and capsaicin. The binding of the three molecules was not affected by the predicted mutations. Thus, these molecules can significantly aid in drug development against FTO and NAFLD. However, wet lab research should be used to support the study further for higher clinical relevance.

Availability of data and materials

The datasets and analysis of the current study can be availed upon request.


  1. Hardy T, Oakley F, Anstee QM, Day CP. Non-alcoholic fatty liver disease: pathogenesis and disease spectrum. Annu Rev Pathol: Mech Dis. 2016;11:451–96.

    Article  CAS  Google Scholar 

  2. Mitra S, De A, Chowdhury A. Epidemiology of non-alcoholic and alcoholic fatty liver diseases. Transl Gastroenterol Hepatol. 2020;5:16.

    Article  Google Scholar 

  3. Diedrich T, Kummer S, Galante A, Drolz A, Schlicker V, Lohse AW, Kluwe J, Eberhard JM, Schulze Zur Wiesch J. Characterisation of the immune cell landscape of patients with NAFLD. PLoS One. 2020;15(3):e0230307.

    Article  CAS  Google Scholar 

  4. Chakravarthy MV, Neuschwander-Tetri BA. The metabolic basis of non-alcoholic steatohepatitis. Endocrinol Diabetes Metab. 2020;3(4): e00112.

    Article  Google Scholar 

  5. Eslam M, George J. Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology. Nat Rev Gastroenterol Hepatol. 2020;17(1):40–52.

    Article  Google Scholar 

  6. Muthiah MD, Sanyal AJ. Burden of disease due to nonalcoholic fatty liver disease. Gastroenterol Clin. 2020;49(1):1–23.

    Article  Google Scholar 

  7. Hinney A, Nguyen TT, Scherag A, Friedel S, Brönner G, Müller TD, Grallert H, Illig T, Wichmann HE, Rief W, Schäfer H. Genome wide association (GWA) study for early onset extreme obesity supports the role of fat mass and obesity associated gene (FTO) variants. PLoS ONE. 2007;2(12): e1361.

    Article  Google Scholar 

  8. Reeves HL, Zaki MY, Day CP. Hepatocellular carcinoma in obesity, type 2 diabetes, and NAFLD. Dig Dis Sci. 2016;61(5):1234–45.

    Article  CAS  Google Scholar 

  9. Guo J, Ren W, Li A, Ding Y, Guo W, Su D, Hu C, Xu K, Chen H, Xu X, Yang T. Fat mass and obesity-associated gene enhances oxidative stress and lipogenesis in nonalcoholic fatty liver disease. Dig Dis Sci. 2013;58(4):1004–9.

    Article  CAS  Google Scholar 

  10. Guo J, Ren W, Li X, Xi G, Li Y, Gao L, Liu J, Su D. Altering of FTO in the serum and livers of NAFLD patients: a correlation analysis. Int J Clin Exp Med. 2018;11(6):6046–53.

    Google Scholar 

  11. Zhou Y, Hambly BD, McLachlan CS. FTO associations with obesity and telomere length. J Biomed Sci. 2017;24(1):1–7.

    Article  Google Scholar 

  12. Yeo GS, O’Rahilly S. Uncovering the biology of FTO. Mol Metab. 2012;1(1–2):32–6.

    Article  CAS  Google Scholar 

  13. Han Z, Niu T, Chang J, Lei X, Zhao M, Wang Q, Cheng W, Wang J, Feng Y, Chai J. Crystal structure of the FTO protein reveals basis for its substrate specificity. Nature. 2010;464(7292):1205–9.

    Article  CAS  Google Scholar 

  14. Maity A, Das B. N6-methyladenosine modification in mRNA: machinery, function and implications for health and diseases. FEBS J. 2016;283(9):1607–30.

    Article  CAS  Google Scholar 

  15. Ng PC, Henikoff S. SIFT: Predicting amino acid changes that affect protein function. Nucleic Acids Res. 2003;31(13):3812–4.

    Article  CAS  Google Scholar 

  16. Kumar R, Ningombam SS, Kumar R, Goel H, Gogia A, Khurana S, Deo SVS, Mathur S, Tanwar P. Comprehensive mutations analyses of FTO (fat mass and obesity-associated gene) and their effects on FTO’s substrate binding implicated in obesity. Front Nutr. 2022;9: 852944.

    Article  Google Scholar 

  17. Musliji ZS, Pollozhani AK, Lisichkov K, Deligios M, Popovski ZT. Comparative analysis of genes associated with obesity in humans using bioinformatic data and tools. Balkan J Med Genet. 2021;24(1):35–40.

    Article  CAS  Google Scholar 

  18. Kumar P, Mahalingam K. In silico approach to identify non-synonymous SNPs with highest predicted deleterious effect on protein function in human obesity related gene, neuronal growth regulator 1 (NEGR1). 3 Biotech. 2018;8(11):466.

    Article  Google Scholar 

  19. Raza S, Rajak S, Upadhyay A, Tewari A, Sinha RA. Current treatment paradigms and emerging therapies for NAFLD/NASH. Front Biosci. 2021;26:206.

    Article  CAS  Google Scholar 

  20. Sarwar R, Pierce N, Koppe S. Obesity and nonalcoholic fatty liver disease: current perspectives. Diabetes Metab Syndr Obes. 2018;11:533.

    Article  CAS  Google Scholar 

  21. Chuang YF, Tanaka T, Beason-Held LL, An Y, Terracciano A, Sutin AR, Kraut M, Singleton AB, Resnick SM, Thambisetty M. FTO genotype and aging: pleiotropic longitudinal effects on adiposity, brain function, impulsivity and diet. Mol Psychiatry. 2015;20(1):133–9.

    Article  Google Scholar 

  22. Ryan MC, Itsiopoulos C, Thodis T, Ward G, Trost N, Hofferberth S, O’Dea K, Desmond PV, Johnson NA, Wilson AM. The Mediterranean diet improves hepatic steatosis and insulin sensitivity in individuals with non-alcoholic fatty liver disease. J Hepatol. 2013;59(1):138–43.

    Article  CAS  Google Scholar 

  23. Briguglio G, Costa C, Pollicino M, Giambo F, Catania S, Fenga C. Polyphenols in cancer prevention: new insights. Int j funct nutr. 2020;1(2):1.

    Article  Google Scholar 

  24. Theodotou M, Fokianos K, Moniatis D, et al. Effect of Resveratrol on non-alcoholic fatty liver disease. Exp Ther Med. 2019;18(1):559–65.

    CAS  Google Scholar 

  25. Cichoż-Lach H, Michalak A. Oxidative stress as a crucial factor in liver diseases. World J Gastroenterol. 2014;20(25):8082.

    Article  Google Scholar 

  26. Popov AM, Krivoshapko ON, Osipov AN, Korepanova EA. Vopr Pitan. 2014;83(3):25–31.

    CAS  Google Scholar 

  27. Tejada S, Capó X, Mascaró CM, Monserrat-Mesquida M, Quetglas-Llabrés MM, Pons A, Tur JA, Sureda A. Hepatoprotective effects of resveratrol in non-alcoholic fatty live disease. Curr Pharm Des. 2021;27(22):2558–70.

    Article  CAS  Google Scholar 

  28. Chen B, Ye F, Yu L, Jia G, Huang X, Zhang X, Peng S, Chen K, Wang M, Gong S, Zhang R. Development of cell-active N 6-methyladenosine RNA demethylase FTO inhibitor. J Am Chem Soc. 2012;134(43):17963–71.

    Article  CAS  Google Scholar 

  29. Li Q, Huang Y, Liu X, Gan J, Chen H, Yang CG. Rhein inhibits AlkB repair enzymes and sensitizes cells to methylated DNA damage. J Biol Chem. 2016;291(21):11083–93.

    Article  CAS  Google Scholar 

  30. Mohammed A, Al-Numair KS, Balakrishnan A. Docking studies on the interaction of flavonoids with fat mass and obesity associated protein. Pak J Pharm Sci. 2015;28(5):1647–53.

    CAS  Google Scholar 

  31. Lim A, Zhou J, Sinha RA, Singh BK, Ghosh S, Lim KH, Chow PK, Woon EC, Yen PM. Hepatic FTO expression is increased in NASH and its silencing attenuates palmitic acid-induced lipotoxicity. Biochem Biophys Res Commun. 2016;479(3):476–81.

    Article  CAS  Google Scholar 

  32. Huang L, Zhang J, Zhu X, Mi X, Li Q, Gao J, Zhou J, Zhou J, Liu XM. The phytochemical rhein mediates M6A-independent suppression of adipocyte differentiation. Front nutr. 2021;8: 756803.

    Article  Google Scholar 

  33. Sherry ST, Ward MH, Kholodov M, Baker J, Phan L, Smigielski EM, Sirotkin K. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 2001;29(1):308–11.

    Article  CAS  Google Scholar 

  34. Adzhubei IA, Schmidt S, Peshkin L, Ramensky VE, Gerasimova A, Bork P, Kondrashov AS, Sunyaev SR. A method and server for predicting damaging missense mutations. Nat Methods. 2010;7(4):248–9.

    Article  CAS  Google Scholar 

  35. Capriotti E, Fariselli P, Casadio R. I-Mutant2. 0: predicting stability changes upon mutation from the protein sequence or structure. Nucleic Acids Res. 2005;33(suppl_2):W306-10.

    Article  CAS  Google Scholar 

  36. Mi H, Ebert D, Muruganujan A, Mills C, Albou LP, Mushayamaha T, Thomas PD. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 2021;49(D1):D394-403.

    Article  CAS  Google Scholar 

  37. Shihab HA, Gough J, Mort M, Cooper DN, Day IN, Gaunt TR. Ranking non-synonymous single nucleotide polymorphisms based on disease concepts. Hum Genomics. 2014;8(1):1–6.

    Article  Google Scholar 

  38. Laskowski RA, MacArthur MW, Moss DS, Thornton JM. PROCHECK: a program to check the stereochemical quality of protein structures. J Appl Crystallogr. 1993;26(2):283–91.

    Article  CAS  Google Scholar 

  39. Lindahl E, Azuara C, Koehl P, Delarue M. NOMAD-Ref: visualization, deformation and refinement of macromolecular structures based on all-atom normal mode analysis. Nucleic Acids Res. 2006;34(supp_2):W52-6.

    Article  CAS  Google Scholar 

  40. Magyar C, Gromiha MM, Pujadas G, Tusnady GE, Simon I. SRide: a server for identifying stabilizing residues in proteins. Nucleic Acids Res. 2005;33(supp_2):W303-5.

    Article  CAS  Google Scholar 

  41. Buchan DW, Jones DT. The PSIPRED protein analysis workbench: 20 years on. Nucleic Acids Res. 2019;47(W1):W402–7.

    Article  CAS  Google Scholar 

  42. Glaser F, Pupko T, Paz I, Bell RE, Bechor-Shental D, Martz E, Ben-Tal N. ConSurf: identification of functional regions in proteins by surface-mapping of phylogenetic information. Bioinformatics. 2003;19(1):163–4.

    Article  CAS  Google Scholar 

  43. Ittisoponpisan S, Islam SA, Khanna T, Alhuzimi E, David A, Sternberg MJ. Can predicted protein 3D structures provide reliable insights into whether missense variants are disease associated? J Mol Biol. 2019;431(11):2197–212.

    Article  CAS  Google Scholar 

  44. Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017;7(1):1–3.

    Article  Google Scholar 

  45. Pires DE, Blundell TL, Ascher DB. pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J Med Chem. 2015;58(9):4066–72.

    Article  CAS  Google Scholar 

  46. Guex N, Peitsch MC. SWISS‐MODEL and the Swiss‐Pdb Viewer: an environment for comparative protein modeling. Electrophoresis. 1997;18(15):2714–23.

    Article  CAS  Google Scholar 

  47. Trott O, Olson AJ. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem. 2010;31(2):455–61.

    CAS  Google Scholar 

  48. Abraham MJ, Murtola T, Schulz R, Páll S, Smith JC, Hess B, Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX. 2015;1:19–25.

    Article  Google Scholar 

  49. Schüttelkopf AW, Van Aalten DM. PRODRG: a tool for high-throughput crystallography of protein–ligand complexes. Acta Crystallogr D ACTA CRYSTALLOGR D. 2004;60(8):1355–63.

    Article  Google Scholar 

  50. Hess B. P-LINCS: A parallel linear constraint solver for molecular simulation. J Chem Theory Comput. 2008;4(1):116–22.

    Article  CAS  Google Scholar 

  51. Berendsen HJ, Postma JV, Van Gunsteren WF, DiNola AR, Haak JR. Molecular dynamics with coupling to an external bath. J Chem Phys. 1984;81(8):3684–90.

    Article  CAS  Google Scholar 

  52. Chen J, Shen B. Computational analysis of amino acid mutation: a proteome wide perspective. Curr Proteom. 2009;6(4):228–34.

    Article  CAS  Google Scholar 

  53. Landrum MJ, Lee JM, Benson M, Brown GR, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Jang W, Karapetyan K. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 2018;46(D1):D1062–7.

    Article  CAS  Google Scholar 

  54. Kopanos C, Tsiolkas V, Kouris A, Chapple CE, Aguilera MA, Meyer R, Massouras A. VarSome: the human genomic variant search engine. Bioinformatics. 2019;35(11):1978.

    Article  CAS  Google Scholar 

  55. Sanchez-Pulido L, Andrade-Navarro MA. The FTO (fat mass and obesity-associated) gene codes for a novel member of the non-heme dioxygenase superfamily. BMC Biochem. 2007;8(1):1–6.

    Article  Google Scholar 

  56. Zhang X, Wei LH, Wang Y, Xiao Y, Liu J, Zhang W, Yan N, Amu G, Tang X, Zhang L, Jia G. Structural insights into FTO’s catalytic mechanism for the demethylation of multiple RNA substrates. Proc Natl Acad Sci. 2019;116(8):2919–24.

    Article  Google Scholar 

  57. Boissel S, Reish O, Proulx K, Kawagoe-Takaki H, Sedgwick B, Yeo GS, Meyre D, Golzio C, Molinari F, Kadhom N, Etchevers HC. Loss-of-function mutation in the dioxygenase-encoding FTO gene causes severe growth retardation and multiple malformations. Am J Hum Genet. 2009;85(1):106–11.

    Article  CAS  Google Scholar 

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Authors and Affiliations



DP and AKDR participated in the design of the project. DP, RJK, and ABJ participated in data extraction and analysis, prepared figures, and wrote the manuscript. All the authors read and approved the final manuscript. The authors declare that all data were generated in-house, and no paper mill was used.

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Correspondence to Asim K. Duttaroy.

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Supplementary Information

Additional file 1:

Table S1. List of ‘‘possible deleterious ns-SNPs’’by combined prediction. 12 ns-SNPs predicted to be deleterious are in bold. Table S2. RMSD Values of Deleterious SNPs. Figure S1. 3-Dimensional structure of deleterious ns-SNPs (A) R96P (B) G103D (C) Y295C (D) R322Q (E) R316Q. Figure S2. Radius of Gyration plots for mutant andnative forms of FTO bound to the drug molecules. Figure S3. Solvent Accessible Surface Area of theprotein-ligand complex throughout the simulation length.

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Patnaik, D., Jena, A.B., Kerry, R.G. et al. In silico profiling of nonsynonymous SNPs of fat mass and obesity-associated gene: possible impacts on the treatment of non-alcoholic fatty liver disease. Lipids Health Dis 22, 17 (2023).

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  • Fat mass and obesity-associated gene (FTO)
  • Nonalcoholic fatty liver disease (NAFLD)
  • Lipogenesis
  • Genetic polymorphism
  • FTO ns-SNPs
  • Nonalcoholic steatohepatitis (NASH)