Farajzadeh-Dehkordi et al. BMC Molecular and Cell Biology
https://doi.org/10.1186/s12860-023-00479-0

BMC Molecular and Cell
Biology(2023) 24:17

Open Access

RESEARCH

Computational analysis of missense
variant CYP4F2*3 (V433M) in association
with human CYP4F2 dysfunction: a functionaland structural impact
Mahvash Farajzadeh-Dehkordi1, Ladan Mafakher2,3*, Fatemeh Samiee-Rad3 and Babak Rahmani1*
Abstract
Background Cytochrome P450 4F2 (CYP4F2) enzyme is a member of the CYP4 family responsible for the metabolism
of fatty acids, therapeutic drugs, and signaling molecules such as arachidonic acid, tocopherols, and vitamin K.
Several reports have demonstrated that the missense variant CYP4F2*3 (V433M) causes decreased activity of CYP4F2
and inter-individual variations in warfarin dose in different ethnic groups. However, the molecular pathogenicity
mechanism of missense V433M in CYP4F2 at the atomic level has not yet been completely elucidated.
Methods and results In the current study, we evaluated the effect of the V433M substitution on CYP4F2 using 14
different bioinformatics tools. Further molecular dynamics (MD) simulations were performed to assess the impactof the V433M mutation on the CYP4F2 protein structure, stability, and dynamics. In addition, molecular docking wasused to illustrate the effect of V433M on its interaction with vitamin K1. Based on our results, the CYP4F2*3 variant wasa damaging amino acid substitution with a destabilizing nature. The simulation results showed that missense V433M
affects the dynamics and stability of CYP4F2 by reducing its compactness and stability, which means that it tends tochange the overall structural conformation and flexibility of CYP4F2. The docking results showed that the CYP4F2*3
variant decreased the binding affinity between vitamin K1 and CYP4F2, which reduced the activity of CYP4F2*3
compared to native CYP4F2.
Conclusions This study determined the molecular pathogenicity mechanism of the CYP4F2*3 variant on the human
CYP4F2 protein and provided new information for understanding the structure-function relationship of CYP4F2 andother CYP4 enzymes. These findings will aid in the development of effective drugs and treatment options.
Keywords Damaging variant, CYP4F2*3, V433M variation, Warfarin, Vitamin K1, In silico, Molecular modeling,
Molecular dynamics simulations*Correspondence:
Ladan Mafakher
ladan.mafakher@gmail.com
Babak Rahmanib.rahmanigene@gmail.com

1

Department of Molecular Medicine, Faculty of Medical School, Qazvin
University of Medical Sciences, Qazvin, Iran2
Thalassemia & Hemoglobinopathy Research center, Health research
institute, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran3
Department of Pathology, Faculty of Medical School, Qazvin Universityof Medical Sciences, Qazvin, Iran
© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use,sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) andthe source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in thisarticle are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not includedin the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you willneed to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The
Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available
in this article, unless otherwise stated in a credit line to the data.

Farajzadeh-Dehkordi et al. BMC Molecular and Cell Biology(2023) 24:17

Introduction
Since its clinical approval, warfarin remains the mainstay of an oral vitamin K antagonist agent with verified
efficacy for preventing and treating patients with thromboembolic diseases [1]. The clinical utility of warfarin is
growing worldwide because of the increased frequency
of cardiovascular disorders and the accelerated aging of
the population. However, the considerable interindividual variation and narrow therapeutic index of warfarin
dosing make warfarin treatment more difficult [2]. The
variability in response to warfarin dose is associated withenvironmental, demographic, clinical, and mainly genetic
factors [3, 4].
Currently, genetic factors, especially single nucleotide polymorphisms (SNPs) in cytochrome P450 2C9(CYP2C9) and vitamin K epoxide reductase complex 1(VKORC1) genes are related to the both pharmacokinetics and pharmacodynamics of warfarin which could
account for 15% and 25% of warfarin dose variation,
respectively [5–7]. Furthermore, the cytochrome P450
4F2 (CYP4F2) functional missense variant, CYP4F2*3(c.1297G > A, p. V433M, rs2108622) was found to moderately contribute to the variability in warfarin doses invarious ethnic groups [8]. Individuals carrying the homozygote (TT) and heterozygote (CT) forms of CYP4F2*3
alleles require a higher dose of warfarin to get the sametargeted anticoagulant effect as individuals with the wildtype genotype (CC) [9, 10].
The gene of CYP4F2 located on chromosome 19 p13.12
encodes the CYP4F2 enzyme. This enzyme is expressedin the kidneys, liver, and human enteric microsomes [11,12] and is involved in ω- hydroxylation of long-chain fatty
acids, leukotriene-B4, arachidonic acid, tocopherols, andvitamin K [13–16]. In addition, CYP4F2 metabolizes theester prodrug gemcitabine and the antiparasitic pafuramidine [17, 18]. Moreover, CYP4F2 has an essential
biological function in activating signaling compounds
and regulating inflammation. In addition, the increased
levels of CYP4F2 protein are associated with different types of carcinomas [19]. This enzyme catalyzes thehydroxylation of vitamin K1 (VK1) to its hydroxylated
form via vitamin K oxidase activity. This process, characterized as the “siphoning” pathway, occurs when vitamin
K1 is in excess [14]. The amount of active VK1 is essential not only for the stimulation of clotting factors andmaintenance of warfarin dosing but also for many physiological processes affected by the CYP4F2 genotype [20].
Several experimental studies have attempted to predictthe molecular mechanism of the CYP4F2*3 variant that
influence the metabolic activity of the CYP4F2 enzyme[14, 15, 21, 22]. McDonald et al.(2009) reported that the
CYP4F2*3 variant induces a reduction in enzyme activity by affecting either an increased rate of protein degradation or a decreased rate of CYP4F2 translation [14].

Page 2 of 13

Zhang et al.(2017) discovered a significant relationshipbetween the CYP4F2*3 variant and an increase in mRNA
expression of CYP4F2, describing more than 12% of thevariance in CYP4F2 mRNA expression [22]. However, thedetails of molecular mechanisms of the CYP4F2*3 variant that may affect protein structure and function are still
unknown.
Compared with laboratory-based experiments forevaluating the effects of SNPs, computational approaches
provide an appropriate platform for assessing genetic
mutations for their pathogenic effects and determining their underlying molecular mechanisms [23–25]. In
addition, understanding the three-dimensional structureof a protein is important to understand the mechanism
by which a protein performs its function. Over the past
few years, the molecular dynamics (MD) simulations
method has become a valuable tool for comprehending
the impact of mutations on the operation and conformation of various proteins at an atomistic level [26, 27].
Therefore, in the current study, we aimed to identify theeffect of the CYP4F2*3 variant on the function, stability,
and structure of CYP4F2 at the atomic level using bioinformatics prediction tools, MD simulations, and molecular docking calculations. Overall, the current research
provides crucial information on atomic-level alterations,which are otherwise difficult to evaluate using experimental approaches, and these results might function asa bridge to connect the in silico and clinician’s resourcesin designing effective drugs and individual treatment
options.

Materials and methods
Data collection

The FASTA sequence of the human CYP4F2 protein
was obtained from UniProt (ID: P78329) [28]. The clinical information of variant CYP4F2*3 and its relation to
warfarin dose was retrieved from the PharmGKB (ID:PA166169424) [29] and PubMed databases [8–10].
Structural modeling and verification

Because the three-dimensional structure of human
CYP4F2 does not exist, homology modeling was performed using the I-TASSER web server for both nativeand mutant form [30]. Then, the quality of the modeled
structure was evaluated using Procheck, ERRAT, andVerify3D in the SAVES web server (https://saves.mbi.
ucla.edu/). The Procheck platform checks the stereochemical quality of the model using a Ramachandranplot [31]. A protein structure with more than 80% aminoacids in the core and allowed regions indicated high quality. The ERRAT webserver statistically analyzed the nonbonded interactions between different atom types in theprotein structure [32]. Verify3D webserver has characterized the compatibility of an atomic model (3D) with its

Farajzadeh-Dehkordi et al. BMC Molecular and Cell Biology(2023) 24:17

amino acid sequence (1D) by analyzing a structural classaccording to its location and environment (alpha, beta,loop, polar, nonpolar, etc.) [33]. A protein structure witha Verify3D value greater than 80% is defined as having
good compatibility with 1D and 3D protein structuresand good quality. ProsA is a web server program that isfrequently used to check 3D models of protein structures
for possible errors. The overall quality score computed by
ProSA for a particular protein structure is represented
in a plot that indicates the scores (Z-score) of all experimentally characterized protein chains recently available
in the Protein Data Bank (PDB) [34]. The protein structure Z-score, which is located in the experimentally characterized protein structure range, indicates a high-quality
protein structure.
Protein stability prediction

Five different prediction tools, I-mutant2, MUpro, DUET,
SDM, and mCSM were utilized to identify the structural
impact of the CYP4F2*3 (V433M) variant on the CYP4F2
protein. The server of I-Mutant 2.0 (http://folding.biofold.org/i-mutant/i-mutant2.0.html) estimates the impactof amino acid substitution on protein stability via a support vector machine-based method. This program wastrained on ProTherm, the most comprehensive dataset ofexperimental information on protein mutations [35]. The
MUpro tool (https://mupro.proteomics.ics.uci.edu/) uses
neural networks and support vector machine methods toassess any alteration in the stability of the protein withapproximately 84% accuracy [36]. Three structure-basedalgorithms, DUET, SDM, and mCSM (http://biosig.
unimelb.edu.au/duet/) are closely intertwined algorithms
and their input can be supplied together in the form of anative protein structure, while separately bringing up the
position of relevant SNP [37].
Protein property and evolutionary conservation analysis

GETAREA is a structure-based tool (https://curie.utmb.
edu/getarea.html) used to determine the effect of theV433M variant on the solvent accessibility surface area
(SASA) of the CYP4F2 protein. The percentage of sidechain surface area to random coil value per residue (%SASA) estimates whether the residue is buried in the solvent or exposed [38]. The server of Project HOPE (http://
www.cmbi.ru.nl/hope/input/) by utilizing the integrating
information from the UniProt database and Distributed
Annotation System (DAS) servers, predicts the effect ofamino acid substitution on the protein structure [39]. To
visualize the impact of the V433M variant at the structural level, Chimera 1.13 software was used. This software
is an extended system for the interactive visualizationand evaluation of molecular structures and related data[40]. The ConSurf web server (http://consurf.tau.ac.il)
was used to define the conservation sites in the protein

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compared to its homologs during evolution through conservation scores ranging from 1 to 9, which are classified
as variable, intermediate, and conserved [41].
Functional impact prediction

To determine the functional consequences of the V433M
variant on the CYP4F2 protein, five different tools
including PolyPhen-2, FATHMM-MKl, PANTHERPSEP, CADD, and PhD-SNPg were employed. The serverof PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/)determines the possible effect of amino acid substitution
on protein properties, based on position-specific independent count (PSIC) scores [42]. The difference between
the scores categorizes the variants as probably damaging,possibly damaging, or benign. FATHMM-MKL (https://fathmm.biocompute.org.uk/) is a web-based tool inwhich a prediction score (p-value) above 0.5 identifies thevariant that has a harmful effect on protein function [43].
Based on the substitution position-specific evolutionary conservation (subPSEC) score, PANTHER (http://pantherdb.org/) predicts the probability of a variant generating a harmful effect on the protein. If the score of
subPSEC is ≥ 0.5, the variant is deemed deleterious [44].
CADD (http://cadd.gs.washington.edu/) as a linear kernel support vector machine-based, predicts functionally
important variants using a Phred-scale C score. CADD
considers amino acid substitutions as the most deleterious when they have a C-score of 20 or greater (≥ C20)[45]. PhD-SNPg (http://snps.biofold.org/phd-snpg./) is amachine learning-based server that identifies the impact
of variants in both non-coding and coding regions using
evolutionary data [46].
Molecular dynamic (MD) simulation analysis

Gromacs 2019 was applied to run an MD simulation with
an optimized potential for liquid simulations (OPLS)
force field. Simple point charge (SPC) water was added
to the simulation box cubic with a 10 A° [47, 48]. The
simulation systems’ charges were neutralized using Na+
and Cl− ions. To minimize the simulation system, a steepest descent minimization integrator with a maximum
force of less than 100 KJ.mol− 1. nm− 1 and 5000 minimization steps were used. The simulation system was thenequilibrated by a constant number of particles, volume,and temperature (NVT) and a constant number of particles, pressure, and temperature (NPT) ensemble. For
NVT equilibration through 100 picoseconds, the leapfrog integrator and linear constraint solver (LINCS) algorithm were used [49]. The PME (particle mesh Ewald)algorithm with a grid spacing of 0.16 nm and a radiuscutoff of 1.0 nm was considered for calculating electrostatic interaction. The equilibrated system was put into
an MD simulation with a time step of 2 femtoseconds(fs) for 200 nanoseconds (ns) of simulation. The output

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Fig. 1 A) Tertiary structure of modeled CYP4F2 protein by I-TASSER web server in cartoon view. B) ProSA plot of quality assessment of CYP4F2 structurecompared to proteins characterized experimentally. Data showed that CYP4F2 (shown in a black dot in the plot) is located in the region of the protein
structure identified in the lab. C)Tertiary structure alignment of V433M (blue) and native (gray) form. A red surface color characterizes the position of theV433M variant. D) Close up view of structural alignment of native and mutant form. Data shows that methionine (red stick) has larger more buried side
chain than valine (yellow stick)

Table 1 Physicochemical quality assessment of CYP4F2 protein structure
Model
CYP4F2

Procheck
Core
76.9%

Allowed18.1%

Generally allowed
2.6%

trajectories were analyzed using root-mean-square deviation (RMSD), radius of gyration (Rg), solvent accesssurface area (SASA), total number of intramolecularhydrogen bonds, root-mean-square fluctuation (RMSF),
secondary structure by DSSP, principal component analysis (PCA) and free energy landscapes (FELs) to determine the stability and protein structure changes during
simulation.
Molecular docking analysis

As VK1 is a substrate for the CYP4F2 protein and its
effective interaction is essential for the proper function of
CYP4F2, molecular docking calculations were performed
using the HADDOCK web server to determine the effectof the CYP4F2*3 variant on the native protein structure[50]. This web server uses a flexible docking approach todefine the best protein-ligand interaction mode. In addition, the PRODIGY web server was applied to compute

Disallowed
2.4%

Verify3D

ERRAT

ProsA
Z-score87.12%

91.01

-8.66

the binding affinity of the protein-small molecule [51]and the bond-type analyses were done by Discovery Studio 4.5 [52].

Results
Structural modeling validation and quality estimation

Owing to the lack of tertiary structure of CYP4F2, homology modeling was applied using the I-TASSER webserver(Fig. 1A). The quality of the predicted structure was analyzed using different web servers (Table 1). Checking
the quality of a model by Ramachandran plot with Procheck web server illustrated that more than 80% of theprotein structures were located in the core and allowed
regions, indicating the high physicochemical quality ofthe protein structure. A Verify3D value of more than 80%
showed high compatibility between the primary and tertiary structures of CYP4F2. The ERRAT rate of 91.01 isinterpreted good overall quality of protein structure. The

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Table 2 Stability prediction of V433M variant on CYP4F2 protein by using five prediction tools
dbSNP ID

Amino acid change

I-mutant2
ΔΔG (kcal/mol)

MUpro
ΔΔG (kcal/mol)

DUET
ΔΔG (kcal/mol)

CYP4F2*3 (rs2108622)

V433M

-2.18

-1.129-0.605Z-score of -8.66 was located in regions of proteins thatwere identified experimentally, indicating the high quality of the predicted model of CYP4F2 (Fig. 1B). The position of wild-type residue (V433) and mutant-type residue(M433) of V433M variant in CYP4F2 protein was shownin Fig. 1C and D.
Protein stability prediction

I-mutant2, MUpro, DUET, SDM and mCSM tools predict protein stability changes in the CYP4F2*3 (V433M)
variant based on the ΔΔG value (change in Gibbs freeenergy). The ΔΔG value is a metric used to calculate theimpact of a single nucleotide variant on protein stability. A ΔΔG score less than zero indicated lower stability. From the results illustrated in Table 2, the CYP4F2*3(V433M) variant exhibited reduced protein stability.
Destabilization of a protein structure can change its biological function and disrupt the signaling cascades andnormal pathways of the protein [53].
Protein property and evolutionary conservation analysis

GETAREA calculates the SASA of individual residues. The threshold is described as %SASA > 50 and%SASA < 20, indicating exposed and buried residues
to the solvent, respectively [38]. Based on the results
obtained from the GETAREA online for the V433M
variant, both wild-type (V433) and mutant-type (M433)
amino acids were buried or located in the interior of
the CYP4F2 protein. However, the mutated residue hada lower % SASA value (% 5.9) than the wild-type V433(% 9). This result was compatible with the deep view ofthe locations of the wild-type (V433) and mutant-type(M433) amino acids (Fig. 1D), which showed that M433
residue had more buried side chain than V433 residue.
The Project HOPE server investigated the effect of theV433M variant on the physiochemical characteristics,
intermolecular interactions, and functional and structural properties. It was predicted that the wild-type residue (V433) would be replaced by a mutant-type residue(M433), which has a larger side chain than the V433
moiety and may not fit into the new position. For physical and structural reasons, if a substituted amino aciddoes not fit into a protein, it leads to structural changes
that are harmful [54]. Furthermore, the Project HOPE
server predicted that the V433 residue is located near ahighly conserved region and in contact with residues in adomain that is important for protein activity.

mCSM
ΔΔG
(kcal/mol)-0.391SDM
ΔΔG (kcal/mol)-1.1Structural analysis of neighboring amino acids aroundthe V433 residue in less than 5-angstrom regions using
the Chimera software revealed that amino acids including H103, P104, T427, N430, P431, A432, W434, P435,
R453, S454 and A457 were located close to the V433
residue. For the V-to-M conversion at position 433, themutated residue with a larger side chain than the wildtype residue caused a clash score with neighboring residues, especially with the P104 residue when compared towild-type (Fig. 2A and B).
The ConSurf web server determined that V433 residue
is positioned in the variable site (conservation score of 2)
in the protein, while it is located close to residues that arehighly conserved in protein when compared to homologous proteins of CYP4F2 during evolution (Fig. 2C).
Functional impact prediction

Five tools have been utilized to identify the functional
effect of the CYP4F2*3 (V433M) variant on the CYP4F2
protein (Table 3). PolyPhen-2, FATHMM-MKl, PANTHER-PSEP, CADD, and PhD-SNPg tools have identified
the CYP4F2*3 (V433M) as a deleterious variant.
MD simulation findings

200 ns MD simulations were conducted to analyze thedeviation between native and CYP4F2*3 (V433M) variant
proteins in physiological environments (Fig. 3). RMSD
deviations produced during MD simulations were used
to estimate the stability of the protein [55]. As presented
in the RMSD plot (Fig. 3A), the native protein attains a
relatively stable conformation after 20 ns. In comparison, the V433M variant was stable after approximately
70 ns of simulations suggesting that the V433M variant
experienced more significant structural rearrangement
before reaching a stable structure. In addition, the RMSD
average values verified that the mutant structure hada slightly higher RMSD value (0.49 ± 0.05 nm) than thenative structure (0.46 ± 0.04 nm), which means that the
mutant form caused instability in the protein structure of
CYP4F2.
Rg is an essential parameter for evaluating the dynamic
adaptability of proteins and other biopolymers [56]. The
Rg plot showed that the V433M mutant fluctuated morethan that of the native form (Fig. 3B). In addition, theaverage Rg value analysis showed that the V433M mutation had a higher average Rg value (2.45 ± 0.02 nm) than
the native protein (2.51 ± 0.01 nm) during the simulation

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Fig. 2 Structural and conservation analysis of CYP4F2 in native and mutant form. A) Structure analysis of native form of CYP4F2 with its neighboring residues. B) Structure analysis of CYP4F2*3 (V433M) variant with its neighboring residues. C) The conservation analysis of CYP4F2 illustrates that V433 residue(characterized by blue arrow) is located near conserve region of CYP4F2 protein which shows with red rectangular

Table 3 Pathogenicity evaluation of V433M variant on CYP4F2 protein by using bioinformatics tools
dbSNP ID

Amino
acid
changeCYP4F2*3(rs2108622)

V433M

PolyPhen-2

Prediction
Possiblydamaging

FATHMM-MKl

Score
1.0

Prediction
AEFBHCI

Score
0.754

time. This result indicates that the V433M mutation isless compact and more flexible than native CYP4F2.
SASA denotes the portion of the macromolecule’s surface accessible to the water solvent [57]. As shown in
Fig. 3C, SASA analysis revealed that the V433M mutation
had a higher average SASA value (250.74 ± 6.13 nm) thannative CYP4F2 (245.17 ± 5.48 nm), suggesting that structural rearrangements in the V433M mutation resulted inincreased protein expansion.
The total number of intramolecular hydrogen bonds
was estimated during the 200 ns MD simulation as shownin Fig. 3D. From the analysis, it can be observed thatnative CYP4F2 forms a greater number of H-bonds, withan average of 371 ± 12.67, while the V433M variant exhibits fewer H-bonds, with an average of 350 ± 12.74. The
lowest number of hydrogen bonds indicated an increasein the overall flexibility of CYP4F2 upon V433M variant.
RMSF based on residue displacement explains the thermal stability, local flexibility, and heterogeneity of macromolecules over MD simulation time [58]. The RMSF
plot indicated that residues in the N terminal region
had a major difference in fluctuation between the native
CYP4F2 structure and the V433M variant after 200 ns

PANTHER-PSEP

CADD

Prediction
Probablydamaging

Prediction
Deleterious

Score
0.74

PhD-SNPg

Score
22.3

Prediction
Pathogenic

Score
0.865

MD simulation. In addition, the V433M variant exhibited a higher fluctuation region in the residues 244–249
of the Gˊ helix and residues 251–260 of the G helix,
followed by increased flexibility in the residues 400–470
when compared to the native CYP4F2 structure (Fig. 3E).
It was also noticed that the average of RMSF wasincreased from 0.08 ± 0.03 in native CYP4F2 to 0.11 ± 0.07
in V433M variant, suggested that the V433M mutation
altered structural flexibility. These data are compatible with the structural alignment of native and mutant
forms after molecular dynamics simulation, indicatingthat mutant form had a more open structure than thenative form especially in region 400–470 which near tosite of mutation V433M and showed significant structurechanges (Fig. 4).
Changes in the secondary structure provide insight
into the folding mechanism and conformational behavior of a protein. The DSSP plot (Fig. 5) showed that thenative protein (Fig. 5A), and V433M variant (Fig. 5B)
exhibited almost the same secondary structural arrangement. The data illustrated that native CYP4F2 contains
an overall 60% secondary structure, including β-bridges,
β-sheets, α-helices, and turns (Table 4). However, theFarajzadeh-Dehkordi et al. BMC Molecular and Cell Biology(2023) 24:17

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Fig. 3 Result of structural dynamics of native CYP4F2 (black color) and variant V433M (red color) as a function of time obtained by 200 ns of the MD simulations. A) RMSD values, B) Rg values, C) SASA calculations, D) total number of H-bond count and E) Residual fluctuations of the Cα atoms as a functionof residue number. The native form of CYP4F2 is shown in black and the V433M variant is in red. The gray and blue blocks show α-helices (indicate withletters) and β-strands (identify with numbers), respectively

V433M variant showed a slight decrease in the secondary structure profile compared that with of the native
protein. Significant changes in the secondary structure
content of the V433M variant can be seen due to bend
and turn formation and loss of the A-Helix and 3-Helix
without any change in β-sheets and coils.

PCA uses covariance matrices of Cα atoms to calculate the significant motions of atom pairs associated withvital biological functions. The first two principal components (PC1 and PC2) of the native and V433M variant
proteins were generated by projecting the trajectories
onto the respective eigenvectors. As seen in Fig. 5C,

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Fig. 4 Superimposed structures of native CYP4F2 (black color) and CYP4F2*3 (V433M) variant (red color), the position of wild-type residue (V433) andmutant-type residue (M433) was characterized. The close-up view of V433 location illustrates that mutation to M433 caused changes from helix structure
to turn which resulted in more flexibility in 445–460 region in mutant form compared to native. V433 shows in green stick and M433 shows in blue stickform. The 445–470 region is colored in yellow and cyan in mutant and native form, respectively

the V433M variant covered the slightly higher subspacespanned along the two eigenvectors (PC1: -3.56 nm to+ 14.12 nm and PC2: -11.27 nm to + 8.76 nm) comparedto the native (PC1: -2.31 nm to + 15.65 nm, and PC2: -5.2
to + 15.12 nm). It was also noticed that the trace of the
diagonalized covariance matrix of the native form andV433M variant was calculated to be 64.13 and 69.99 nm,
respectively. The rmsf eigenvector plot (Fig. 5D), basedon atom number analysis, illustrated a higher V433M
motion than the native form. In addition, the Gibbs freeenergy landscapes (FELs) of the first and second PCA
showed that the V433M variant (Fig. 5E) had wider global
energy minima (18.5 KJ/mol) than the native form (17.9
KJ/mol) (Fig. 5F), which indicates more conformationalchanges of the protein in the defined space. These results
confirmed an increase in the overall flexibility of theV433M variant, which is in agreement with the RMSD,
Rg, H-bonds, and RMSF values, as well as the SASA andDSSP analyses.
Molecular docking

The enzymatic activity of the CYP4F2 depends on its
interaction with VK1. For this reason, molecular docking using the HADDOCK web server was performed tofigure out the effect of the V433M variant on the interaction of the CYP4F2 protein with VK1 (Fig. 6). The binding site of VK1 on CYP4F2 protein was characterizedaccording to a previous work of Li et al. (2018), that predicted binding site of VK1 in this protein. Based on thisresearch, the amino acids including Trp59, Trp61, Met92
Phe124, His236, Phe327, Glu328, Val397, and Leu504
are responsible for interacting with VK1 [59]. The docking result (Table 5) showed that although the mutatedform could still interact with VK1, it had a lower binding affinity (-8.5 ± 0.31 kcal/mol) than the native form(-10.0 ± 0.07 kcal/mol), which caused less activity of themutated state compared to the native form. Bond-type
analyses illustrated that in native form, VK1 formed
strong forces such as hydrogen bonding, Pi-Pi stacking, and Pi-sulfur interactions with Val397, Met92 andPhe124 amino acids, whereas, in the mutant form, only
Phe60 residue formed a Pi-Pi stacking with VK1. This
caused VK1 to have more strong interaction with thenative form than the mutant (Fig. 6).

Discussion
Several in vitro experiments have revealed that the
CYP4F2*3 (V433M) variant is associated with decreased
CYP4F2 activity and VK1 metabolism [14, 15, 21, 22].
McDonald et al.(2009) assessed that carriers of the
CYP4F2*3 (V433M) variant allele have a reduced capacity for ω-hydroxylation VK1 [14]. Edson et al. (2013)
also reported that the catabolism of menaquinone (vitamin K2) is reduced by the V433M mutation in CYP4F2[15]. Furthermore, Stec DE et al. (2007) exploited the
CYP4F2*3 (V433M) variant allele, which is linked todecreased biosynthesis of 20-hydroxyeicosatetraenoic
acid (20-HETE) and increased hypertension risk [21]. A
study by Zhang et al.(2017) suggested that the CYP4F2*3(V433M) variant also affects mRNA expression levels of
CYP4F2 [22].
The results of this computational study agree with previous experimental findings. According to these results,
the CYP4F2*3 (V433M) variant had a significant damaging effect on the CYP4F2 protein structure.

Farajzadeh-Dehkordi et al. BMC Molecular and Cell Biology(2023) 24:17

Page 9 of 13

Fig. 5 Secondary structure content in native CYP4F2 (A) and V433M variant (B) over 200 ns of the simulation period. PCA analysis plot of CYP4F2 in native (black) and V433M variant form (red) (C). The rmsf plot of eigenvector reveals V433M had higher fluctuation compared to native form (D). The Gibbs
energy landscape plot during 200ns of simulation of native (E) and V433M variant form (F)Table 4 Secondary structure percentage of native CYP4F2 and V433M variant. * Structure = β-sheet + β-bridge + Turn + A-helix
Structure
NativeV433M

Structure*
60%
59%

Coil
17%
17%

B-Sheet8%8%B-Bridge0%0%

In this study, five different stability prediction tools(I-mutant2, MUpro, DUET, SDM and mCSM) revealed asignificant destabilizing structural effect of the CYP4F2*3(V433M) variant on the CYP4F2 protein.
In the case of the V433M variant, the buried structural
methionine residue was larger than the valine residue,
which could disturb the interaction with other residues
in the protein, as predicted by the Project HOPE. This

Bend
16%
18%

Turn
15%
16%

A-Helix
37%35%5-Helix0%0%3-Helix
7%
6%

finding is compatible with the structural analysis of the
CYP4F2*3 (V433M) variant compared to the wild typeusing Chimera software, which showed that a larger side
chain of methionine caused a clash score with neighboring residues, especially with the P104 residue. These dataagree with the study of Cai et al. (2016) regarding thepathogenicity impact of the V139M variant on the PSEN2
protein, who discovered that both valine and methionine

Farajzadeh-Dehkordi et al. BMC Molecular and Cell Biology(2023) 24:17

Page 10 of 13

Fig. 6 Interaction mode of VK1 with native CYP4F2 (A) and V433M variant form (B) in tertiary mode. Bond types analyses in native (C) and V433M variant
form (D) using Discovery studio

Table 5 the docking result of CYP4F2 in both native and mutant
form by HADDOCK webserver. Amino acids in bold are aminoacids of CYP4F2 which are identified as important in interaction
with VK1.
Complex structure
NativeV433M

Amino acids in interaction
with Vitamin K
Val397, Phe327, Glu328,
Leu128, Leu504, Ser399,
Tyr125, His236, Phe127,
Phe124, Ile398
His236, Val397, Pro396,
Ser323, Met66, Leu504,
Pro502, Phe60, His327, Val67,
Trp59

∆∆G
(Kcal/mol)
10.00 ± 0.07

-8.5 ± 0.31

are nonpolar and hydrophobic residues, whereas methionine is larger in size. They suggested that the difference inthe size of methionine and valine alters the interactions
with neighboring amino acids and could result in extra
stress inside the protein [60]. In addition, the results of

the Project HOPE server determined that the V433 residue is positioned close to a highly conserved region andin contact with residues in a domain that is important for
the activity of the protein. These data are in accordancewith the ConSurf web server results, which illustrated
that V433 is located near the conserved residues.
Based on the functional analysis of five different web
servers, the CYP4F2*3 (V433M) variant was predicted
to be a damaging amino acid variant compatible withthe data of Naushad et al. (2021) using three tools (SIFT,
PROVEAN, and PSEP), demonstrating the harmful
impact of the CYP4F2*3 variant [61].
To further investigate the harmful effects of the
CYP4F2*3 (V433M) variant, MD simulations and molecular docking analyses were performed. The apparent lossof stability and gain of flexibility were mainly observed in
the RMSD and Rg plots, which were accompanied by asignificant increase in SASA and a decrease in the number of hydrogen bonds for the V433M variant comparedFarajzadeh-Dehkordi et al. BMC Molecular and Cell Biology(2023) 24:17

to native CYP4F2. We also found a relative increase inflexibility of residues 244–249 of the Gˊ helix, residues
251–260 of the G helix and residues 400–470 that was
accompanied by the rise in bend and turn formation anddecrease of A-Helix and 3-Helix conformation in the
DSSP results of the V433M variant. PCA and FELs analyses also confirmed that the V433M variant showed a significant difference in overall flexibility compared to thenative form, affecting protein folding, thereby decreasing
the stability of the protein. As the proper structure of theprotein is required for its activity, altering its conformational geometry could cause a loss of catalytic activity[62–64]. This result agrees with the molecular docking
finding that the V433M variant showed lower binding
affinity than the native form, which could influence theactivity of the protein and impact the response to thewarfarin dose.

Conclusion
CYP4F2 is one of the critical CYP4 enzymes responsible
for the metabolism of fatty acids, therapeutic drugs, andsignaling molecules such as arachidonic acid, tocopherols, and vitamin K. Numerous studies have revealed
that the missense variant CYP4F2*3 (V433M) causes
decreased activity of CYP4F2 and inter-individual variations in warfarin dose in various ethnic groups. In thecurrent study, we used various bioinformatics and computational tools to analyze the impact of the CYP4F2*3(V433M) variant on the structure and function of the
CYP4F2. Based on our findings, the CYP4F2*3 (V433M)
variant is a damaging amino acid substitution with adestabilizing protein effect. Molecular dynamics simulation results showed that the V433M variant affected
the dynamics and stability of CYP4F2 by decreasing thecompactness and stability of the protein structure, which
resulted in overall structural conformational changes
and increased flexibility. The docking results showedthat the CYP4F2*3 variant had a lower binding affinity
between VK1 and CYP4F2, which caused lower activity of CYP4F2*3 compared to native CYP4F2. This study
determined the molecular pathogenicity mechanism of
the CYP4F2*3 variant on the human CYP4F2 protein andprovided new information for understanding the structure-function relationship of CYP4F2 and other CYP4
enzymes. These findings will facilitate the developmentof effective drugs and individual treatment options.
List of abbreviationsCYP2C9	Cytochrome P450 2C9
CYP4F2	Cytochrome P450 4F2
FEL	Free energy landscapes
fs	Femtoseconds
MD	Molecular dynamic
NPT	Constant number of particles, pressure, and temperature
NVT	Constant number of particles, volume, and temperaturenm	Nanometer

Page 11 of 13

ns	Nanoseconds
OPLS	Optimized potential for liquid simulations
PDB	Protein Data Bank
PCA	Principal component analysis
RMSD	Root means square deviation
RMSF	Root mean square fluctuation
Rg	Radius of gyration
SASA	Solvent accessible surface area
SPC	Simple point charge
VIF	Variance inflation factor
VKORC1	Vitamin K epoxide reductase complex 1
Acknowledgements
Not applicable.
Authors’ contributions
Conceptualization, Investigation, Methodology, Writing - Original Draft andWriting – review & editing: M.F.D, F.S, and L.M. Review & Editing: B.R and L.M.
The authors read and approved the final manuscript.
Funding
Not applicable.
Data Availability
The datasets generated during and/or analyzed during the current study areavailable from the corresponding author on reasonable request.

Declarations
Competing interests
The authors state that they have no interest conflict.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Received: 11 October 2022 / Accepted: 2 May 2023

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