Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

Md Mostafa Kamal; Kazi Fahmida Haque Shantanu; Shamiha Tabassum Teeya; Md Motiar Rahman; A K M Munzurul Hasan; Douglas P Chivers; Tanveer A Wani; Atekah Hazzaa Alshammari; Mahesh Rachamalla; Francisco Carlos da Silva Junior; Md Munnaf Hossen

doi:10.1371/journal.pone.0316465

. 2025 Jan 24;20(1):e0316465. doi: 10.1371/journal.pone.0316465

Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

Md Mostafa Kamal ¹, Kazi Fahmida Haque Shantanu ², Shamiha Tabassum Teeya ¹, Md Motiar Rahman ³, A K M Munzurul Hasan ^4,^*, Douglas P Chivers ⁴, Tanveer A Wani ⁵, Atekah Hazzaa Alshammari ⁶, Mahesh Rachamalla ⁴, Francisco Carlos da Silva Junior ⁷, Md Munnaf Hossen ^8,^*

Editor: Rajesh Kumar Pathak⁹

PMCID: PMC11759363 PMID: 39854591

Abstract

The cytotoxic T-lymphocyte antigen-4 (CTLA4) is essential in controlling T cell activity within the immune system. Thus, uncovering the molecular dynamics of single nucleotide polymorphisms (SNPs) within the CTLA4 gene is critical. We identified the non-synonymous SNPs (nsSNPs), examined their impact on protein stability, and identified the protein sequences associated with them in the human CTLA4 gene. There were 3134 SNPs (rsIDs) in our study. Out of these, 186 missense variants (5.93%), 1491 intron variants (47.57%), and 91 synonymous variants (2.90%), while the remaining SNPs were unspecified. We utilized SIFT, PolyPhen-2, PROVEAN, and SNAP for identifying deleterious nsSNPs, and SNPs&GO, PhD SNP, and PANTHER for verifying risk nsSNPs in the CTLA4 gene. Following SIFT analysis, six nsSNPs were identified as deleterious and reporting second and third nsSNPs as probably damaging and one as benign, respectively. From upstream analysis, rs138279736, rs201778935, rs369567630, and rs376038796 were found to be deleterious, probably damaging, and disease associated. ConSurf predicted conservation scores for four nsSNPs, and Project Hope suggested that all mutations could disrupt protein interactions. Furthermore, mCSM and DynaMut2 analyses indicated a decrease in ΔΔG stability for the mutants. GeneMANIA and STRING networks highlighted correlations with CD86 and CD80 genes. Finally, MD simulation revealed consistent fluctuation in RMSD and RMSF, consequently Rg, hydrogen bonds, and PCA in the mutant proteins compared with wild-type, which might alter the functional and structural stability of CTLA4 protein. The current comprehensive study shows how various nsSNPs in the CTLA4 gene can modify the structural and functional characteristics of the protein, potentially influencing the pathogenesis of diseases in humans. Further, experimental studies are needed to analyze the effect of these nsSNPs on the susceptibility of pathological phenotype populations.

1. Introduction

Cytotoxic T-lymphocyte antigen 4 (CTLA4), also well-known as cluster of differentiation 152 (CD152), is a key immune regulatory protein which regulates T cell activation and immunological homoeostasis [1, 2]. The CTLA4 gene can inhibit T cell activation by attaching to cluster of differentiation 80 (CD80) and cluster of differentiation 86 (CD86) on antigen-presenting cells, avoiding excessive immunological activation and maintaining immune homoeostasis [3, 4]. This pathway is critical for autoimmunity prevention, inflammation control, and immunological tolerance regulation. The CTLA4 gene, which comprises four exons and resides on chromosome 2q33, encodes the CTLA4 protein. The CTLA4 gene exhibits a variety of genetic variants, including insertions, deletions, SNPs, and microsatellite repeats [5]. These variants may modify CTLA4 protein expression, structure, or function, altering immune control and perhaps having an impact on the onset of disease [6]. Association of CTLA4 gene polymorphisms have been found in Japanese patients with rheumatoid arthritis [7], South-Moroccan patients with type 1 diabetes (T1D) [8], Han-Chinese [9] and Asian [10] patients with breast cancer, as well as Iranian patients with gastric and colorectal cancers [11]. One study also found links between the CTLA4 gene SNPs and autoimmune disorders [12]. The relationship between CTLA4 gene SNPs and autoimmune thyroid illnesses like Graves’ disease and Hashimoto’s thyroiditis highlights the potential contribution of genetic variants to the immunological dysregulation seen in these ailments. Furthermore, links between CTLA4 gene polymorphisms and rheumatoid arthritis, T1D, systemic lupus erythematosus (SLE), and multiple sclerosis have been found, pointing to a shared genetic vulnerability for autoimmune etiology [13–18].

CTLA4 gene SNPs have been studied in relation to cancer in addition to autoimmune diseases [19]. For example, SNPs such as rs231775 (CT60), have been linked to elevated cancer risk and altered immunotherapy responses [20]. These results highlighted the importance of the CTLA4 gene in tumor immunity and suggests the potential role of genetic variation in the progression and prognosis of cancer. Additionally, the relationship between CTLA4 polymorphisms and infectious disorders has been studied. Certain CTLA4 gene polymorphisms have been linked to hepatitis B and C infections, tuberculosis, and other diseases, suggesting that they may have an impact on how the body reacts to infectious agents [21]. Furthermore, it has been shown that CTLA4 polymorphisms affect the risk and severity graft-versus-host disease (GVHD) [22], a side effect of stem cell transplantation, albeit further research is needed to fully empathize the underlying mechanisms. The precise mechanisms behind how particular CTLA4 polymorphisms contribute to disease risk and development are still completely unidentified. For the complex interactions between the immune system and disease development to be fully understood, it is essential to comprehend the functional and structural implications of these genetic variants.

Within the coding region, SNPs manifest in two forms: synonymous and non-synonymous SNPs (nsSNPs), with the latter type directly impacting protein sequences [23]. Those nsSNPs could change the stability, structure, and functions of the respective protein [24, 25]. The aim of this study was to use bioinformatics tools to predict nsSNPs in the CTLA4 gene that may be deleterious. Subsequently, the CTLA4 gene underwent comprehensive analysis to assess its pathogenicity.

2. Materials and methods

The entire process used in our investigation is outlined (Fig 1).

2.1. Retrieval of protein sequence and nsSNPs in CTLA4 gene

The protein sequence (FASTA format) and nsSNP of CTLA4 gene were obtained using National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/) and dbSNP database (https://www.ncbi.nlm.nih.gov/snp/), respectively (accessed November, 2024). Since missense mutations can prevent DNA-transcription, leading to alterations in the protein expression, nsSNPs were subsequently subjected to analysis using various bioinformatic methods [26].

2.2. Identification of deleterious nsSNPs in CTLA4 gene

SIFT web-server (https://sift.bii.a-star.edu.sg/) was employed to identify deleterious non-synonymous SNPs (nsSNPs) in CTLA4 gene. SIFT assesses functional amino acids by presuming conservation of major amino acids, where changes at certain positions tend to be deleterious. SNPs with a probability score of ≤0.05 were classified as deleterious, while those with a score >0.05 were considered tolerated. The analysis involved inputting single amino acid substitutions and rsIDs of each SNP in the human CTLA4 gene [27].

PolyPhen-2 web-server (http://genetics.bwh.harvard.edu/pph2/) categorize and predict the functional effects of allele variations. It calculates the site-specific sequence conservation score of position-specific independent count (PSIC) and estimates the native and mutant variants differences. PolyPhen-2 classifies SNPs into three, 1) benign (0.00 to ≤0.45), 2) possibly damaging (>0.45 to ≤0.95), or 3) probably damaging (>0.95 to 1). The analysis entails inputting single amino acid changes and the FASTA sequence of the CTLA4 protein into PolyPhen-2 [28].

The PROVEAN web server (http://provean.jcvi.org) was employed to assess whether an amino acid alteration in protein sequences is harmful or neutral. It utilizes a delta alignment score derived from aligning homologous sequences with both the native and mutant protein sequences. Variants with a score of ≤ −2.5 are considered deleterious, while those with a score > −2.5 are deemed neutral. The input for PROVEAN includes the FASTA sequence of the CTLA4 protein and a list of SNPs [29].

SNAP, accessible at http://www.rostlab.org/services/SNAP, is a web-based tool utilized to assess the functional impact of amino acid substitutions in proteins using a neural network method. The tool rates each amino acid variation, identifying them as having either neutral or significant effects [30].

2.3. Verification of the risk nsSNPs

The SNPs&GO server (http://snps.biofold.org/snps-and-go/) was used to predict the effects of nsSNPs to evaluate the likelihood that each variant will be linked to a disease in humans. A prediction value of ≥0.5 indicates a variant’s potential involvement in disease, whereas a value <0.5 suggests a neutral impact. Additionally, this service combines its predictions with results from the PhD-SNP and PANTHER for comprehensive analysis [31].

2.4. Determination of protein structural stability using I-Mutant2.0 and MUpro server

I-mutant2.0 (http://folding.biofold.org/i-mutant/i-mutant2.0.html) was utilized to predict the potential effects of nsSNPs on protein stability. Following this analysis, Delta Delta G (ΔΔG) values were predicted. Protein stability rises with a ΔΔG score ≥0 kcal/mol and falls with a ΔΔG score <0 kcal/mol. The protein’s FASTA sequence, the location of the amino acid substitution, and the variant residue were entered for analysis [32].

The MUpro server (http://mupro.proteomics.ics.uci.edu/) is used to forecast how amino acid substitutions might lower or raise the stability of a respective protein. This server employs both neural networks and support vector machines (SVM) for its predictions. A confidence score <0 indicates a decrease in protein stability, while a score ≥0 suggests an increase [33].

2.5. Evolutionary conservation analysis by ConSurf

The ConSurf web server (http://consurf.tau.ac.il/) was employed to trace the evolutionary conservation of amino acid residues (wild type) and to pinpoint nsSNPs at particular positions. By generating a phylogenetic tree from the phylogenetic relationships among homologous sequences, this server calculates the evolutionary trajectories of amino acid sites within a protein [34].

2.6. Prediction of structural effects of CTLA4 mutants

The Project Hope web server (http://www.cmbi.ru.nl/hope/) was used to assess the structural and functional impact of point mutations. The HOPE server provides a 3D visualization of the mutated proteins, incorporating predictions from UniProt and DAS servers. Inputs included the protein sequence, wild type (WT), and mutated amino acids, with the results presented in text, graphical, and animated formats [35].

2.7. Comparative modelling of wild-type CTLA4 protein and their mutant structures

The mCSM (http://structure.bioc.cam.ac.uk/mcsm) is a machine learning method to predict the effects of missense mutations based on the structural characteristics of the corresponding protein. Here is the mCSM determination of the effects of missense variants on CTLA4 stability. The tools predict changes in protein folding free energy (ΔΔG) as a consequence of missense mutations and are classified into two categories (stabilizing and destabilizing) based on the ΔΔG [36]. The DynaMut2 web-server (https://biosig.lab.uq.edu.au/dynamut2/) was utilize to calculate the impact on the flexibility and stability of proteins. Using the normal mode analysis (NMA) approach, DynaMut2 was utilized to determine how the mutation affected protein stability and dynamics. The predicted Gibbs free energy (G) values of mutants <0 was categorized as destabilizing, and those >0 as stabilizing. Both single mutations and multiple mutations can be predicted by DynaMut2, as it utilizes a single mutation prediction feature for the corresponding protein. We inputted a list of mutations and the wild-type structure in PDB format [37].

2.8. Gene-gene, protein-protein interactions and pathway enrichment analysis

GeneMANIA (http://www.genemania.org) is a web-based tool that sifts through an extensive database to discover supplementary genes associated with a provided set of input genes. Examples of associated data include co-expression, pathways, colocalization, protein domain similarity, and protein-protein interactions. We searched GeneMANIA using CTLA4 as an input gene for Homo sapiens [38]. Protein-protein interactions are crucial for determining how proteins function and play specific roles in disease processes as proteins are a crucial component of molecular pathways. The Search Tool for the Retrieval of Interacting Genes (STRING) (https://string-db.org/) is a biological database and online tool used for interpreting the protein-protein interactions. A search conducted using the terms "CTLA4 gene" and "Homo sapiens" as input genes, using the highest confidence score of 0.999, we identified ten interacting proteins [39]. The publicly available Web-Gestalt (WEB-based Gene SeT AnaLysis Toolkit) (http://www.webgestalt.org/) was used to analyze the pathways enrichment analysis of the associated gene [40].

2.9. Molecular dynamics simulation analysis

A 100 ns MD simulation analysis were performed under the Linux framework in Schrodinger 2020–3 with “Desmond v6.3 Program” to assess the structural stability of the corresponding protein [41]. The three-site transferrable intermolecular potential (TIP3P) water model was used to analyze the MD simulation [42]. An orthorhombic box shape was used to maintain a specified volume and Na+ and Cl- were added to neutralize the whole system with a salt concentration of 0.15 M. An OPLS3e force field was applied [43]. Further the protein structure system minimized using a natural time and pressure (NPT) ensemble at a constant pressure of 101325 Pascal’s and a temperature of 300 K. The protein stability and dynamic properties were assessed using the RMSD (root means square deviation), RMSF (root means square fluctuation), Rg (radius of gyration), and hydrogen bonds. Principal Component Analysis (PCA) has been widely used to study the slow and functional movements of biomolecules [44]. Prior to performing PCA, the correlation matrix C needed to be calculated. These are the definitions of the parts C_ij in the matrix, C_ij = ⟨(x_i−⟨x_i⟩) (x_j−⟨x_j⟩)⟩ where x_i and x_j are instant coordinates of atoms and 〈x_i〉 and 〈x_j〉 are the average coordinate over the group. Principal Component Analysis (PCA) has been widely used to study the slow and functional movements of biomolecules [44]. Prior to performing PCA, the correlation matrix C needed to be calculated.

3. Results

3.1. Retrieval of protein sequence and nsSNPs in CTLA4 gene

Following dbSNP database, we have found a total of 3134 SNPs. Following these, about 186 missense variants (5.93%), 1491 intron variants (47.57%), and 91 synonymous variants (2.90%) were computed (Fig 2A). The protein sequence (UniProt ID: P16410) of CTLA4 gene has been retrieved from NCBI. In this study, missense variants were subjected to further computational analysis to explore the most deleterious nsSNPs.

Fig 2 — (A) Pie chart representing total SNPs of *CTLA4* gene. (B) Prediction of evolutionary conserved amino acid residues by ConSurf server. Conservation score is represented as the color-coding bars.

3.2. Identification of deleterious nsSNPs in CTLA4 gene

Following the analysis of 186 nsSNPs using SIFT, 18 nsSNPs were identified, with 12 being eliminated as they were deemed tolerated by SIFT. Among the remaining 6 nsSNPs, those with a tolerance index of ≤ 0.05 were predicted as deleterious. Polyphen-2 produced two scores, HumDiv and HumVar, for the 6 nsSNPs analyzed. HumDiv identified 2 nsSNPs as probably damaging with high confidence, 1 as possibly damaging, and 1 as benign. In contrast, HumVar classified 3 nsSNPs as probably damaging with high confidence and 1 as benign. Subsequent analysis with the PROVEAN program revealed that 3 out of the 4 nsSNPs in the CTLA4 gene were deleterious. Additionally, the SNAP program was employed to evaluate the impact of 4 nsSNPs on the protein sequence, identifying 3 as having non-neutral effects.

3.3. Verification of the risk nsSNPs

We employed the SNPs&GO, PhD-SNP, and PANTHER programs to forecast disease-related nsSNPs. Out of the 4 nsSNPs examined, SNPs&GO identified 4, PhD-SNP recognized 3, and PANTHER detected 3 nsSNPs linked to disease (Table 1). Upon comparing the outcomes from analyses of the 4 nsSNPs using computational tools including SIFT, Polyphen-2, PROVEAN, SNAP, SNPs&GO, PhD-SNP, and PANTHER, we observed a strong concordance among the 7 programs regarding nsSNPs rs138279736, rs201778935, rs369567630 and rs376038796.

Table 1. Screening of deleterious single nucleotide polymorphism (SNP) predicted by SIFT, Polyphen-2, PROVEAN, SNAP, SNPs&GO, PhD-SNP, and Panther.

SNP Id	Allele	Variant	SIFT	Polyphen-2		Provean	SNAP	SNPs&GO	PhD SNP	Panther
				HumDiv	HumVar
rs138279736	G/A	R8Q	D	poss	Pro	D	E	N	Dis	N
rs201778935	C/T	R8W	D	pro	Pro	D	E	N	Dis	Dis
rs369567630	C/T	P32S	D	benign	benign	D	N	Dis	Dis	Dis
rs376038796	C/T	A86V	D	pro	Pro	N	E	Dis	N	Dis

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D: Deleterious, E: Effect N: neutral, Dis: Disease, Pro: Probably damaging (high confident), Poss: Possibly damaging (low confident).

3.4. Determination of protein structural stability I-Mutant2.0 and MUpro server

The structural stability of the protein was assessed using both I-Mutant2.0 and the MUpro server. These servers predict the structural impact of four selected nsSNPs by analyzing the change in free energy (ΔΔG) and reliability index (RI) following mutation. I-Mutant2.0 predicted the stability changes caused by the second nsSNP, while MUpro predicted those caused by the third nsSNP in the CTLA4 protein (Table 2).

Table 2. Characterization of the effect of deleterious SNPs on protein stability by I-mutant2.0 and MUpro.

SNP ID	Variant	I-Mutant2.0		MUpro
SNP ID	Variant	Stability	DDG (kcal/mol)	Prediction	Confidence score
rs138279736	R8Q	Increase	0.41	Decrease	-1.0
rs201778935	R8W	Increase	0.12	Decrease	-1.0
rs369567630	P32S	Decrease	-0.82	Decrease	-0.84
rs376038796	A86V	Decrease	-0.20	Increase	0.44

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3.5. Evolutionary conservation analysis by ConSurf

Using the ConSurf tool, a color-coded conservation score was generated for the CTLA4 protein, highlighting highly conserved functional areas. Among the four most harmful SNPs, ConSurf predicted two amino acids (R8Q and R8W) with a conservation score of 4, which are exposed residue and rest two (P32S and A86V) with a score of 5 and 9; buried and structural residue respectively (Fig 2B). Highly conserved regions typically play a critical role in the biological function of the respected protein.

3.6. Prediction of structural effects of CTLA4 mutants

The Project Hope server assesses how mutations affect the structural characteristics of a protein, considering factors (size, charge, hydrophobicity, and spatial structure) in comparison to the wild type (WT). Maintaining protein stability depends critically on the size of amino acid residues. Alterations in residue size can impair folding, structural integrity, or functional interactions, resulting in protein failure. Among the four anticipated substitutions, the mutant residues R8Q and R8W were smaller than the wild-type residues, while the mutants P32S and A86V were larger. Consequently, mutating these residues may disrupt protein interactions (Table 3).

Table 3. The effect mutation on protein using Project Hope prediction.

Rs Id	Variant	Wild Type	Mutant Type
rs138279736	R8Q	Arginine	Glutamine
rs201778935	R8W	Arginine	Tryptophan
rs369567630	P32S	Proline	Serine
rs376038796	A86V	Alanine	Valine

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3.7. Comparative modelling of WT CTLA4 protein and their mutant structures

We used mCSM to assess the impacts of missense variants on CTLA4 stability. The mCSM relies on graph-based signatures to predict the impacts of missense variants on protein stability. Here, all four variants destabilized the protein (Table 4). DynaMut2 was used to calculate the general dynamic traits of the highest deleterious nsSNPs including R8Q, R8W, P32S and A86V mutants (Table 5). DynaMut2 interprets the predictions for ΔΔG stability value among the WT and mutant CTLA4 protein. All mutants showed a decrease in the ΔΔG stability value compared to WT and were found to be responsible for destabilizing the protein. The free energy (ΔΔG) stability values for R8Q, R8W, P32S and A86V mutants were found -0.51, -0.34, -0.96 and -0.94 kcal/mol respectively.

Table 4. Alterations in stability of protein and interaction upon nsSNPs.

SL	RsID	Variants	RSA (%)	DDG Value	Prediction
01	rs138279736	R8Q	100	-0.007	Destabilizing
02	rs201778935	R8W	100	-0.369	Destabilizing
03	rs369567630	P32S	74	-0.806	Destabilizing
04	rs376038796	A86V	19.2	-0.498	Destabilizing

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Table 5. Alterations in structural stability and molecular interactions due to nsSNPs.

SL	RsID	Variant	ΔΔG	ΔΔG^Stability
01	rs138279736	R8Q	-0.51 kcal/mol	Destabilising
02	rs201778935	R8W	-0.34 kcal/mol	Destabilising
03	rs369567630	P32S	-0.96 kcal/mol	Destabilising
04	rs376038796	A86V	-0.94 kcal/mol	Destabilising

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3.8. Gene-gene, protein-protein interactions and pathway enrichment analysis

Protein interactions are fundamental in determining the resilience and flexibility of biological networks. Disruptions in these relationships may result in network malfunction, significantly impacting cellular and organismal health. The interaction network between genes associated with the CTLA4 gene demonstrated several correlations. Gene-gene interaction networks and functional analyses highlighted gene sets enriched within the CTLA4 target network. Various interactions were represented by different colors of network edges, including Physical Interactions, Co-expression, Co-localization, Predicted, Genetic Interactions, Pathway, and Shared protein domains. GeneMANIA constructed a composite gene-gene functional interaction network. It enhances our comprehension of the molecular mechanisms that elucidate observable phenotypes. The study identified an association between the CTLA4 gene and 20 other genes, with CD86, CD80, and adaptor related protein complex 2 subunit mu 1 (AP2M1) (Fig 3A and Table 6).

Fig 3 — (A) GeneMANIA gene-gene interaction for *CTLA4* Gene Different colors of the network edge indicate: co-expression, website prediction, pathway, physical interactions and co-localization. (B) CTLA4 interacts with a total of 10 different proteins. Colored nodes: query proteins and first shell of interactors. Empty nodes: proteins of unknown 3D structure. Filled nodes: 3D structure is known/predicted. Edges represent protein-protein associations.

Table 6. Gene functionally linked to CTLA4 identified using GeneMANIA.

Gene	Description
CTLA4	cytotoxic T-lymphocyte associated protein 4
CD86	CD86 molecule
CD80	CD80 molecule
AP2M1	adaptor related protein complex 2 subunit mu 1
FOXP3	forkhead box P3
LRBA	LPS responsive beige-like anchor protein
FYN	FYN proto-oncogene, Src family tyrosine kinase
PTPN11	protein tyrosine phosphatase non-receptor type 11
STAT5B	signal transducer and activator of transcription 5B
CD28	CD28 molecule
YES1	YES proto-oncogene 1, Src family tyrosine kinase
JAK2	Janus kinase 2
NFIA	nuclear factor I A
STAT5A	signal transducer and activator of transcription 5A
LCK	LCK proto-oncogene, Src family tyrosine kinase
LYN	LYN proto-oncogene, Src family tyrosine kinase
NFATC2	nuclear factor of activated T cells 2
PTPN7	protein tyrosine phosphatase non-receptor type 7
BATF	basic leucine zipper ATF-like transcription factor
LAX1	lymphocyte transmembrane adaptor 1
CD5	CD5 molecule

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Proteins collaborate and interact with others to facilitate cell signaling and various cellular functions. Consequently, variations in amino acids within a protein can impact other proteins within the network. The STRING tool predicted the functional partners of CTLA4 gene. CTLA4, also known as Cytotoxic T-lymphocyte protein 4, serves as a significant negative regulator of T-cell responses. It exhibits notably stronger affinity towards its natural B7 family ligands, CD80 and CD86, compared to the stimulatory coreceptor cluster of differentiation 28 (CD28). LTF emerged as the primary interacting partner of the T-lymphocyte activation antigen CD80 (Fig 3B).

The KEGG pathway enrichment analysis, based on the highest enrichment ratio and FDR value revealed that those gene regulate several metabolic pathways (S1 Table). According to this analysis, the most enriched pathways were PD-L1 expression and PD-1 checkpoint pathway in cancer, T cell receptor signaling pathway, Th17 cell differentiation, Cell adhesion molecules and Autoimmune thyroid disease (S1 Table).

3.9. Molecular dynamics simulation analysis

The structural stability of native and four mutant proteins were calculated (RMSD and RMSF) and compared to evaluate structural and functional change due to the mutation of the corresponding protein. Here, 100 ns MD simulation analysis were conducted to observe this deviation in an artificial environment. Mutants R8Q, R8W, P32S and A86V showed considerable fluctuations compare with the native protein CTLA4. The RMSD values for the native protein were observed ranging from 0.325 nm to 4.038 nm while 0.398 nm to 3.980 nm for the R8Q mutant, 0.364 nm to 3.628 nm for the R8W mutant, 0.323 nm to 3.968 nm for the P32S mutant, 0.317 nm to 4.436 nm for the A86V mutant. The average RMSD values of native CTLA4, and mutant R8Q, R8W, P32S and A86V are 3.103 nm, 2.987 nm, 2.759 nm, 3.164 nm, and 3.680 nm, respectively (Fig 4). Overall, the RMSD plot showed that the native protein was stable along the simulation run time and also demonstrated that the mutant protein has a major impact on the structural confirmation of the CTLA4 protein. The RMSF of protein residue was counted to observe how these changes impact the local flexibility. In addition, RMSF analysis demonstrated considerable fluctuations between native and mutant structures throughout the simulation run. The average RMSF values are 1.102 nm, 0.907 nm, 0.886 nm, 0.87 nm, 0.891 nm, and 1.060 nm correspondingly (Fig 5). The Rg measures how the atoms are distributed around the axis of a protein. It is an important indicator for predicting the structural activity of macromolecules and for assessing changes in the compactness of the protein structure. The native protein and four mutants R8Q, R8W, P32S and A86V revealed Rg ranges of 2.189 nm to 5.025 nm, 2.651 nm to 4.877 nm, 2.246 nm to 5.249 nm, 2.181 nm to 5.060 nm, and 1.982 nm to 5.189 nm, respectively. Average fluctuation of these mutants was 3.108 nm, 3.297 nm, 3.406 nm, 2.856 nm, and 2.583 nm, respectively (Fig 6). The mutated protein structures were unstable in 100 ns simulations with greater fluctuation differences from lowest to highest, suggesting that the mutated proteins significantly alter the conformation of the respective protein’s active site. The number of hydrogen bonds can help to characterize a protein. Therefore, hydrogen bond numbers were calculated from initial to final times during the 100 ns simulation run to observe each hydrogen bond. All the proteins formed several hydrogen bonds ranging between 128 to 184 occur simultaneously until 100 ns simulation time (Fig 7 and S2 Table). These results indicated that the native protein has a higher specificity while the mutated protein has a higher fluctuating range of hydrogen bonds denoted the structural instability. The PCA plots provide insights into the genetic variance and clustering patterns for four mutations compared to the wild-type. Each mutant SNP variant exhibits unique clustering patterns compared to the wild-type. The difference patterns in the PCA plots show that each mutation (R8W, R8Q, P32S, and A86V) might change structure or function in a way that is different from the wild-type (Fig 8).

Fig 4 — The color scheme is as follows: native (blue), R8Q mutant (red), R8W mutant (green), P32S mutant (violet), and A86V mutant (orange). RMSF analysis shows Cα atom fluctuations over 100 ns simulation.

Fig 5 — The color scheme is as follows: native (blue), R8Q mutant (red), R8W mutant (green), P32S mutant (violet), and A86V mutant (orange).

Fig 6 — It is represented as a time-dependent change during the simulation. The color scheme is as follows: native (blue), R8Q mutant (red), R8W mutant (green), P32S mutant (violet), and A86V mutant (orange).

Fig 7 — The color scheme is as follows: native (blue), R8Q mutant (red), R8W mutant (green), P32S mutant (violet), and A86V mutant (orange).

4. Discussion

The human Cytotoxic T-lymphocyte antigen 4 (hg-CTLA4) serves as a crucial negative regulator in the immune system, particularly for regulatory T cells (Tregs), which play a role in suppressing T cell proliferation and differentiation. Constitutively expressed by Tregs, hg-CTLA4 is also induced in activated conventional T cells [1, 2, 45, 46]. Mutations in the CTLA4 gene have been associated with a range of clinical manifestations, encompassing different autoimmune conditions affecting specific organs, hypo-gammaglobulinemia, recurrent infections, and cancer [47]. Missense or non-synonymous mutations contribute to protein destabilization and can influence susceptibility to diseases as well as responses to drug treatments [48, 49]. Numerous SNPs within the CTLA4 gene have been identified and documented in the dbSNP database. Consequently, we conducted systematic and comprehensive bioinformatic analyses to uncover the functional and structural impact of nsSNPs within the human CTLA4 gene. Our aim was to understand how these mutations affect the gene’s functionality and its role in disease pathogenesis. Through in silico structural and functional analyses, we identified potential nsSNPs within the CTLA4 gene.

Seven web servers, namely SIFT, PolyPhen-2, PROVEAN, SNAP, SNPs&GO, PhD-SNP, and PANTHER, were utilized to identify the most detrimental nsSNPs. By comparing the scores from all servers, we determined that four nsSNPs R8Q, R8W, P32S, and A86V were considered deleterious, likely causing damage, affecting protein function, and associated with diseases [50]. These nsSNPs were then selected for further analysis using additional in silico tools. Stability is a vital property of corresponding protein that affects the function, activity, and regulation of protein. Stability changes occurred due to mutation of proteins which are involved in diseases [51]. Using the I-Mutant2.0 and MUpro programs, protein stability changes were observed. In P32S and A86V nsSNPs, and R8Q, R8W, and P32S nsSNPs were decreased protein stability using I-Mutant2.0 and MUpro programs respectively. Subsequently, the ConSurf tool generated a color-coded conservation score for the CTLA4 protein, highlighting regions of high conservation. Within the four most harmful nsSNPs, ConSurf identified two amino acids with a conservation score of 4, one with a score of 5, and one with a score of 9. These conserved residues typically play critical roles in biological function. When a highly conserved residue undergoes mutation, it tends to have more detrimental effects compared to a less conserved one [24].

Once more, HOPE offers 3D structural depictions of both mutated and wild-type (WT) proteins. Predicted substitutions from the HOPE server indicated that mutant residues R8Q and R8W were smaller than the wild residues, while P32S and A86V mutants were larger. This disparity could result in an unoccupied area within the protein’s core, potentially leading to the loss of hydrophobic interactions. Such vacancies might affect the protein’s function, properties, or reactivity [52]. Those variants were analyzed following mCSM, where ΔΔG < −0 kcal/mol were considered to reduce CTLA4 stability (destabilizing). Here, all four variants resulted in reduced protein stability (Table 4). The WT and mutant CTLA4 protein ΔΔG stability value for each protein structure was predicted using DynaMut2. The free energy (ΔΔG) stability values for R8Q, R8W, P32S and A86V mutants were found -0.51, -0.34, -0.96 and -0.94 kcal/mol respectively (Table 5). All mutants showed a decrease in the ΔΔG stability value compared to WT and were found to be responsible for destabilizing the protein. This difference in the free energy landscape explains how the mutation influences the protein’s stability [37].

GeneMANIA analysis showed that CTLA4 interacts significantly with 20 different proteins, which are particularly crucial in neurogenesis. Among these, CD86, CD80, and AP2M1 exhibit particularly significant interactions with CTLA4. CD86 is involved in regulating B-cell IgG1 production levels and signaling for self-regulation and cell-cell association or disassociation. Similarly, CD80, also known as the CD80 molecule, plays a role in T-cell activation and in regulating the activity of both normal and malignant B cells [53, 54]. Together with CD80, CD86 delivers costimulatory signals crucial for T cell activation and survival. AP2M1 facilitates autophagy-induced degradation of CLDN2 through endocytosis and interaction with LC3, consequently reducing intestinal epithelial tight junction permeability. The String tool was utilized to anticipate the close interactor proteins of CTLA4 (Fig 3B). Proteins operate in a concerted manner and interact with others to execute cell signaling and various cellular functions. CTLA4 exhibits notably stronger affinity towards its natural B7 family ligands, CD80 and CD86, compared to the stimulatory coreceptor CD28. LTF emerged as the primary interacting partner of the T-lymphocyte activation antigen CD80. The pathway enrichment analysis revealed that genes interacting with the CTLA4 gene were associated with disease and therefore, might play a significant regulatory network in the progression of disease pathogenesis [55]. In order to investigate the impact of alterations on the protein structure, 100 ns MD simulations were utilized for the above-mentioned variations. This analysis was conducted to observe this deviation in an artificial environment. Mutants R8Q, R8W, P32S and A86V showed considerable fluctuations compare with the native protein CTLA4. The RMSD of protein residue was counted to observe how these changes impact the local flexibility (Fig 4). In addition, RMSF analysis demonstrated considerable fluctuations between native and mutant structures throughout the simulation run (Fig 5). The protein structure is stabilized when the RMSD and RMSF values of a protein are within 0.1–0.3 nm [56]. The Rg plot displays against time for all mutant proteins as well as the wild type protein. It was discovered that the average Rg of the wild type protein was 3.108 nm, whereas the mutant was 3.297 nm, 3.406 nm, 2.856 nm, and 2.583 nm, respectively (Fig 6). Similarly, the increased number of hydrogen bonds causes the structural unsteadiness of the corresponding protein (Fig 7). The PCA analysis provide insights into the genetic variance and clustering patterns. The difference patterns in the PCA plots show that each mutation (R8W, R8Q, P32S, and A86V) might change structure or function in a way that is different from the wild-type. The PCA study reveals genetic differences between wild-type and each SNP mutation add discrete genetic diversity (Fig 8). These can direct next research on the possible consequences of any mutation on protein stability, function, or phenotype.

In Chinese Han population and Egyptian, rs231775 of CTLA4 gene has found involvement in hepatocellular carcinoma and a direct association with cancer [57, 58]. In Pakistan, these rs231775 are also associated with HCC [59]. In CTLA4 gene, rs221775 the variant is associated with multiple sclerosis susceptibility [60]. Similarly, rs11571317 and rs3087243 variants play role in breast cancer progression [61]. Genetic variants rs3087243 and rs231775 have an association with Graves’ disease in Chinese Han population [62]. Interestingly, three major high-risk SNPs, rs1553657429, rs1559591863, rs778534474 also have been found within CTLA4 gene [63]. Also, association of CTLA-4 gene polymorphisms were found in Japanese patients with rheumatoid arthritis [7], South Moroccan with type 1 diabetes [64], Chinese Han [9] and Asian populations [10], and Iranian patients with gastric and colorectal cancers [11].

The non-synonymous SNPs (nsSNPs) R8Q, R8W, P32S, and A86V (rs138279736, rs201778935, rs369567630, and rs376038796) have not yet been examined, underscoring the need for research that accounts for the ethnic variability in disease susceptibility across diverse populations. This study has identified SNPs within the CTLA4 gene that may potentially interfere with ligand-receptor interactions, suggesting a significant risk. Nevertheless, additional in-vitro and in-vivo experimentation, as well as genetic association studies in human population are needed to validate the impact of these nsSNPs on the pathological phenotypes and drive future progress about genetic polymorphism research.

5. Conclusion

Currently, in silico methodologies are gaining prominence as a crucial strategy for identifying SNPs associated with diseases. In the present work, a thorough investigation of the CTLA4 gene was conducted using various computational tools to explore how non-synonymous SNPs (nsSNPs) impact the protein’s structure and functionality. The study identified 3134 SNPs within the CTLA4 gene, encompassing 186 missense variants (5.93%), 1,491 intron variants (47.57%), and 91 synonymous variants (2.90%). Four specific nsSNPs within the CTLA4 gene rs138279736, rs201778935, rs369567630, and rs376038796 were highlighted as potentially harmful, likely to be damaging, and associated with diseases. Further analysis using I-Mutant2.0 and MUpro suggested that the stability of the CTLA4 protein could be altered by these nsSNPs. Additionally, ConSurf analysis indicated high conservation across most regions of the protein, suggesting that these mutations may significantly alter the protein’s physicochemical characteristics, such as size, charge, and hydrophobicity. These changes, in turn, could impact the protein’s stability and function, potentially leading to disease.

The results of this study suggest that the R8Q, R8W, P32S, and A86V mutations may contribute to the destabilization of the protein, which has also been confirmed by MD simulation. Finally, PCA study also reveals the genetic differences between wild-type and four SNP due to mutation. However, it is important to acknowledge that these conclusions are based on in silico analysis, and the potential link between these nsSNPs and disease susceptibility across diverse populations requires further experimental validation.

Supporting information

S1 Table. KEGG pathway enrichment analysis of CTLA4 gene and its associate gene.

(DOCX)

pone.0316465.s001.docx^{(14.2KB, docx)}

S2 Table. Number of hydrogen bonds analysis of wild type and R8Q, R8W, P32S and A86V mutant type protein over 100 ns simulation.

(XLSX)

pone.0316465.s002.xlsx^{(209.9KB, xlsx)}

Acknowledgments

AKM Munzurul Hasan is the recipient of University Graduate Scholarships (UGS) and Graduate Teaching Fellowships (GTF) at the University of Saskatchewan.

Data Availability

All relevant data are within the paper and its Supporting Information files.

Funding Statement

This work was funded by the NSERC Discovery Grant awarded to D.P.C. and a grant (Project Number: RSP2024R357) from King Saud University, Riyadh, Saudi Arabia, for molecular dynamics simulations.

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PLoS One. doi: 10.1371/journal.pone.0316465.r001

Decision Letter 0

Rajesh Kumar Pathak

23 Sep 2024

PONE-D-24-35176Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico studyPLOS ONE

Dear Dr. Hasan,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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PLOS ONE

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Additional Editor Comments:

The manuscript has been reviewed and found to be interesting. Comprehensive feedback addressing critical areas has been provided. Key revisions include updating dbSNP data, improving the molecular dynamics simulation results, and conducting secondary structure and PCA analyses. The authors should correct grammatical errors and vague phrasing, and ensure consistency in terminology. Expanding the discussion on the biological and clinical relevance of the findings will strengthen the impact of the study.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Yes

Reviewer #3: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: N/A

Reviewer #3: No

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: No

Reviewer #2: Yes

Reviewer #3: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Manuscript titled “Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study’ presents good information about the nsSNPs in CTLA4. However, in my opinion following queris needs to be answered before accepting manuscript.

Major revision

1. dbSNP database (https://www.ncbi.nlm.nih.gov/snp/), respectively (accessed December, 2022) this is 1.5 years old data. What is the current status in this databse for the CTLA4?

Authors need to give current status also.

2. A 100 ns MD simulation analysis is not sufficient for the prediction, at least triplicate analysis should be done for better predicted results.

3. Line 280“The structural stability of native and four mutant proteins were calculated RMSD and 281 RMSF and compare to evaluate structural and functional change due to the mutation of the 282 corresponding protein” correct english

4. Manuscript needs to be checked with reference to english properly. There are many grammatical errors.

5. RMSD, RMSF, Rg values should be given in nm instead of A

6. Secondary structure analysis should be done for wild type and mutant structures.

7. PCA analysis should also be done to understand the structural impact of mutations.

Reviewer #2: The manuscript Titled: “Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study” presents an in-silico analysis of the effects of non-synonymous single nucleotide polymorphisms (nsSNPs) in the cytotoxic T-lymphocyte antigen-4 (CTLA4) gene. The authors used various bioinformatics tools, including SIFT, PolyPhen-2, PROVEAN, and SNAP, to identify deleterious nsSNPs. Additionally, they evaluated protein stability, structural changes, and interactions using tools like ConSurf, I-Mutant, and molecular dynamics simulations.

The manuscript is timely and covers an important topic, as SNPs in the CTLA4 gene have been implicated in immune regulation and several diseases, including cancer and autoimmune disorders. Using computational tools for analyzing genetic variants is appropriate for this study, and the results may serve as a foundation for further experimental work. However, there are several areas where the manuscript can be strengthened in terms of clarity, depth of analysis, and presentation of results.

Major Comments:

The study presents a comprehensive computational analysis of nsSNPs in the CTLA4 gene. The significance of this work lies in its potential to shed light on the pathogenicity of certain SNPs that might contribute to disease susceptibility. However, the manuscript could benefit from stronger contextualization of its results with reference to previous experimental findings. While the authors mention autoimmune diseases and cancer, a more detailed discussion on the clinical relevance of the identified nsSNPs would enhance the paper’s impact.

The manuscript employs a wide array of computational tools, which is commendable. However, some methodological details are either missing or could be clarified:

Selection of SNPs: The criteria for selecting the 165 missense SNPs for further analysis are not fully explained. Were all missense variants analyzed, or was there a filtering process? Clarifying this would improve the transparency of the study.

Protein Modeling: The molecular dynamics simulation section is interesting, but further details are needed regarding the parameters used in these simulations. Specifically, what were the time steps and temperature conditions? Were multiple simulations conducted for each variant to ensure reproducibility?

The results provide a solid foundation for understanding how specific nsSNPs affect the structure and function of the CTLA4 protein. However:

The protein-protein interaction analysis using GeneMANIA and STRING is intriguing but lacks depth. A more detailed discussion of the relevance of interactions with proteins such as CD80 and CD86 in the context of immunological function would be beneficial.

A visual representation of the molecular dynamics simulation results would significantly enhance the clarity of the findings.

The discussion section should place more emphasis on how the findings can inform future in vitro or in vivo studies. The authors briefly mention the need for experimental validation, but they do not elaborate on how their results could be applied in a practical setting, for example, by guiding targeted mutagenesis experiments or developing therapeutic interventions.

Minor Comments:

The manuscript is well-written overall, but there are several areas where the clarity of writing could be improved:

The abstract is dense and could benefit from more concise wording, particularly in the results section. Summarizing the key findings in a few clear sentences would make it more accessible to a broader audience.

In the results section, the use of terms such as “deleterious,” “probably damaging,” and “benign” are used without providing sufficient context for how these terms were determined by each computational tool. A short explanation in the methodology or results section about how these terms were defined would aid comprehension.

The manuscript contains a number of useful figures and tables. However, it would be helpful to:

Add more detailed captions for each figure and table. For example, in Table 4, providing more information on how the ΔΔG stability values were calculated and what these values mean in terms of biological relevance would enhance understanding.

Include a schematic that summarizes the workflow of the computational analyses performed. This would provide readers with a quick overview of the methodologies used.

The references are appropriate and up to date. However, the authors could strengthen the manuscript by citing more recent experimental studies that have validated nsSNPs in the CTLA4 gene, particularly in relation to cancer and autoimmune diseases.

Recommendations:

Clarify the methodology, particularly the selection process for the SNPs analyzed and the details of the molecular dynamics simulations.

Expand the discussion to include more implications for future experimental work and clinical relevance.

Enhance the figures and tables by adding more detailed captions and including a workflow schematic.

Improve the clarity of the writing, particularly in the abstract and results sections.

The manuscript presents valuable findings but requires minor revisions to improve clarity, detail, and depth of analysis. Once these revisions are made, the manuscript will strongly contribute to the field.

Reviewer #3: Title: Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

1. In section 3.1, Accession number of the sequence is missing, please provide it, in order to verify the result?

2. In line 219, You mention 5 nsSNPs for SNAP and 4 nsSNPs for PROVEAN, but it might be useful to explain why different numbers of nsSNPs were used for these analyses?

3. In line 227, Avoid Vague Phrasing: Instead of “notable consistency,” I used “strong concordance” to make the statement more specific.

4. Use the correct symbol for free energy change (ΔΔG instead of DDG) to maintain consistency with standard scientific terminology.

5. In Line 238 and 239, specify what a conservation score of 4, 5, and 9 indicates in terms of evolutionary conservation, as readers might not be familiar with the ConSurf scoring system.

6. Please correct the caption of Table 3.

7. In Line 246, can you please focus that why the size of amino acid residues is responsible for protein’s function disruption?

8. Alanine residue is generally an accepted single residue first choice for mutation, can you try to mutate the key residues to Alanine here and study the result?

9. Section 3.8 should be improved by explaining of how protein interactions can impact network function.

10. In Section 3.9, since you simulated all the systems for 100 ns, could you incorporate principal component analysis (PCA) to examine the atomic movements, particularly at the mutated residue sites?

11. In the Materials and Methods section, while you mention that molecular dynamics simulations were performed using the Desmond v6.3 program, there is no information provided regarding the number of steps for energy minimization, position restraints, or equilibration. Please include these details.

12. Since you simulated the systems in a water medium, you should also include solvent accessible surface area (SASA) analysis to examine the role of water around the mutated regions and compare it with the wild type. This is particularly relevant since you previously mentioned that the mutations were based on the shape of the amino acid residues.

13. How many simulation repeats did you performed?

14. Please correct the typographical and grammatical errors throughout the manuscript to improve its readability and clarity.

15. The plot in Figure 7 is unclear. Please add the average plot of hydrogen bonds for each system to make the data for each system more distinguishable.

**********

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Reviewer #1: Yes: Monika Jain

Reviewer #2: No

Reviewer #3: Yes: Navaneet Chaturvedi, Amity Institute of Biotechnology, Amity University, Noida, 201313, Uttar Pradesh, India

**********

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Attachment

Submitted filename: PONE-D-24-35176_review_report_SN.pdf

pone.0316465.s003.pdf^{(45.4KB, pdf)}

PLoS One. 2025 Jan 24;20(1):e0316465. doi: 10.1371/journal.pone.0316465.r002

Author response to Decision Letter 0

18 Nov 2024

Dear Reviewers,

Thank you so much for your comments. We have addressed all of your comments and revised our manuscript accordingly. All revisions are done under track change option. Responses of reviewer 1 are green highlighted, responses of reviewer 2 are yellow highlighted and responses of reviewer 3 are cyan coloured highlighted in the revised manuscript.

Reviewer #1

Manuscript titled “Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study’ presents good information about the nsSNPs in CTLA4. However, in my opinion following queries needs to be Responseed before accepting manuscript.

Major revision

1. dbSNP database (https://www.ncbi.nlm.nih.gov/snp/), respectively (accessed December, 2022) this is 1.5 years old data. What is the current status in this database for the CTLA4? Authors need to give current status also.

Response: Thank you for your comment. We have modified data and mentioned the current status of nsSNP of CTLA4 gene (accessed November, 2024).

2. A 100 ns MD simulation analysis is not sufficient for the prediction; at least triplicate analysis should be done for better predicted results.

Response: There has been done numerous functional and structural analysis along-side MD simulation.

Response: We have revised accordingly.

4. Manuscript needs to be checked with reference to english properly. There are many grammatical errors.

Response: We have revised accordingly.

5. RMSD, RMSF, Rg values should be given in nm instead of A

Response: We have revised accordingly.

6. Secondary structure analysis should be done for wild type and mutant structures.

Response: We have already analyzed comparative modelling of wild type CTLA4 protein and their mutant structures.

7. PCA analysis should also be done to understand the structural impact of mutations.

Response: Thank you for your comment. We have added PCA analysis in the revised manuscript.

Reviewer #2

The manuscript Titled: “Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study” presents an in-silico analysis of the effects of non-synonymous single nucleotide polymorphisms (nsSNPs) in the cytotoxic T-lymphocyte antigen-4 (CTLA4) gene. The authors used various bioinformatics tools, including SIFT, PolyPhen-2, PROVEAN, and SNAP, to identify deleterious nsSNPs. Additionally, they evaluated protein stability, structural changes, and interactions using tools like ConSurf, I-Mutant, and molecular dynamics simulations.

Major Comments:

Response: Thank you for your comment. We have retrieved the SNPs data from the dbSNP database which already mentioned in the methodology section. We have used missense variants as they are the disease-causing mutation (Zhang et al., 2012).

Response: All details regarding the parameters used in these simulations has already mentioned in this study, please see the methodology section in the revised manuscript.

The results provide a solid foundation for understanding how specific nsSNPs affect the structure and function of the CTLA4 protein. However:

The explanation of molecular dynamics simulation results could be expanded. The authors report RMSD and RMSF values but do not provide a thorough interpretation of what these fluctuations imply in terms of biological function. The protein-protein interaction analysis using GeneMANIA and STRING is intriguing but lacks depth. A more detailed discussion of the relevance of interactions with proteins such as CD80 and CD86 in the context of immunological function would be beneficial. A visual representation of the molecular dynamics simulation results would significantly enhance the clarity of the findings.

Response: --

Minor Comments:

The manuscript is well-written overall, but there are several areas where the clarity of writing could be improved:

Response: Thank you for your comment. We have revised accordingly.

Response: Identification of deleterious nsSNPs in CTLA4 gene, we have found the corresponding result from PolyPhen2 tools. We have already mentioned in the methodology section about this prediction and scoring system. Please see the revised manuscript.

The manuscript contains a number of useful figures and tables. However, it would be helpful to:

Include a schematic that summarizes the workflow of the computational analyses performed. This would provide readers with a quick overview of the methodologies used.

Response: Thank you for your comment. Please see the Figure 1, we have already mentioned the workflow. We have revised the manuscript accordingly.

Recommendations:

Clarify the methodology, particularly the selection process for the SNPs analyzed and the details of the molecular dynamic’s simulations.

Response: Thanks. Done.

Expand the discussion to include more implications for future experimental work and clinical relevance.

Response: Thanks. Done

Enhance the figures and tables by adding more detailed captions and including a workflow schematic.

Response: We have revised the manuscript accordingly.

Improve the clarity of the writing, particularly in the abstract and results sections.

Response: We have revised the manuscript accordingly.

Response: Thank you.

Reviewer #3

Title: Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

1. In section 3.1, Accession number of the sequence is missing, please provide it, in order to verify the result?

Response: Thanks for your comments. We have revised accordingly and added Id. Please see page line in the revised manuscript.

2. In line 219, You mention 5 nsSNPs for SNAP and 4 nsSNPs for PROVEAN, but it might be useful to explain why different numbers of nsSNPs were used for these analyses?

Response: We have corrected this number in the revised manuscript.

3. In line 227, Avoid Vague Phrasing: Instead of “notable consistency,” I used “strong concordance” to make the statement more specific.

Response: Done.

4. Use the correct symbol for free energy change (ΔΔG instead of DDG) to maintain consistency with standard scientific terminology.

Response: We have revised accordingly.

5. In Line 238 and 239, specify what a conservation score of 4, 5, and 9 indicates in terms of evolutionary conservation, as readers might not be familiar with the ConSurf scoring system.

Response: Thank you for your comment. We have added adequate data in the revised manuscript and It has already been mentioned in figure 2B.

6. Please correct the caption of Table 3.

Response: We have revised accordingly.

7. In Line 246, can you please focus that why the size of amino acid residues is responsible for protein’s function disruption?

Response: We have revised accordingly. Please see page line in the revised manuscript.

8. Alanine residue is generally an accepted single residue first choice for mutation, can you try to mutate the key residues to Alanine here and study the result?

Response: Thanks for your comments.

9. Section 3.8 should be improved by explaining how protein interactions can impact network function.

Response: Thanks for your comment. We have revised accordingly.

Response: We have added PC analysis in the revised manuscript.

Response: We have already added all information regarding molecular dynamics simulation. (Protein structure system minimized using a natural time and pressure (NPT) ensemble at a constant pressure of 101325 Pascal’s and a temperature of 300 K).

Response: We have added PC analysis in the revised manuscript as SASA is not possible for this corresponding study for limitation of facilities.

13. How many simulation repeats did you performed?

Response: Thanks for your comments.

14. Please correct the typographical and grammatical errors throughout the manuscript to improve its readability and clarity.

Response: Thanks for your comments.

15. The plot in Figure 7 is unclear. Please add the average plot of hydrogen bonds for each system to make the data for each system more distinguishable.

Response: We have provided the supplementary data of figure 7 in the revised manuscript. Please see the supplementary table S2.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0316465.s004.docx^{(23.9KB, docx)}

PLoS One. doi: 10.1371/journal.pone.0316465.r003

Decision Letter 1

Rajesh Kumar Pathak

12 Dec 2024

Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

PONE-D-24-35176R1

Dear Dr. Hasan,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Rajesh Kumar Pathak, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

The manuscript can be accepted for publication.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #3: (No Response)

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #3: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #3: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #3: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #3: Yes

**********

6. Review Comments to the Author

Reviewer #1: Authors have answered almost all queries, except triplicate simulation, Manuscript can be accepted now

Reviewer #3: All responses have been thoroughly reviewed and are presented in a clear and coherent manner. I believe that the content meets the necessary criteria for academic rigor and relevance. Therefore, I recommend that the work be accepted for publication.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Monika Jain

Reviewer #3: No

**********

PLoS One. doi: 10.1371/journal.pone.0316465.r004

Acceptance letter

Rajesh Kumar Pathak

14 Jan 2025

PONE-D-24-35176R1

PLOS ONE

Dear Dr. Hasan,

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on behalf of

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PLOS ONE

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Table. KEGG pathway enrichment analysis of CTLA4 gene and its associate gene.

(DOCX)

pone.0316465.s001.docx^{(14.2KB, docx)}

S2 Table. Number of hydrogen bonds analysis of wild type and R8Q, R8W, P32S and A86V mutant type protein over 100 ns simulation.

(XLSX)

pone.0316465.s002.xlsx^{(209.9KB, xlsx)}

Attachment

Submitted filename: PONE-D-24-35176_review_report_SN.pdf

pone.0316465.s003.pdf^{(45.4KB, pdf)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0316465.s004.docx^{(23.9KB, docx)}

Data Availability Statement

All relevant data are within the paper and its Supporting Information files.

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PERMALINK

Investigating the functional and structural effect of non-synonymous single nucleotide polymorphisms in the cytotoxic T-lymphocyte antigen-4 gene: An in-silico study

Md Mostafa Kamal

Kazi Fahmida Haque Shantanu

Shamiha Tabassum Teeya

Md Motiar Rahman

A K M Munzurul Hasan

Douglas P Chivers

Tanveer A Wani

Atekah Hazzaa Alshammari

Mahesh Rachamalla

Francisco Carlos da Silva Junior

Md Munnaf Hossen

Roles

Abstract

1. Introduction

2. Materials and methods

Fig 1. The complete workflow employed in this study.

2.1. Retrieval of protein sequence and nsSNPs in CTLA4 gene

2.2. Identification of deleterious nsSNPs in CTLA4 gene

2.3. Verification of the risk nsSNPs

2.4. Determination of protein structural stability using I-Mutant2.0 and MUpro server

2.5. Evolutionary conservation analysis by ConSurf

2.6. Prediction of structural effects of CTLA4 mutants

2.7. Comparative modelling of wild-type CTLA4 protein and their mutant structures

2.8. Gene-gene, protein-protein interactions and pathway enrichment analysis

2.9. Molecular dynamics simulation analysis

3. Results

3.1. Retrieval of protein sequence and nsSNPs in CTLA4 gene

Fig 2. Retrieved SNPs of CTLA4 gene and evolutionary conservation analysis.

3.2. Identification of deleterious nsSNPs in CTLA4 gene

3.3. Verification of the risk nsSNPs

Table 1. Screening of deleterious single nucleotide polymorphism (SNP) predicted by SIFT, Polyphen-2, PROVEAN, SNAP, SNPs&GO, PhD-SNP, and Panther.

3.4. Determination of protein structural stability I-Mutant2.0 and MUpro server

Table 2. Characterization of the effect of deleterious SNPs on protein stability by I-mutant2.0 and MUpro.

3.5. Evolutionary conservation analysis by ConSurf

3.6. Prediction of structural effects of CTLA4 mutants

Table 3. The effect mutation on protein using Project Hope prediction.

3.7. Comparative modelling of WT CTLA4 protein and their mutant structures

Table 4. Alterations in stability of protein and interaction upon nsSNPs.

Table 5. Alterations in structural stability and molecular interactions due to nsSNPs.

3.8. Gene-gene, protein-protein interactions and pathway enrichment analysis

Fig 3. Gene-gene and protein-protein interactions analysis.

Table 6. Gene functionally linked to CTLA4 identified using GeneMANIA.

3.9. Molecular dynamics simulation analysis

Fig 4. RMSF analysis shows Cα atom fluctuations over 100 ns simulation.

Fig 5. Root mean square fluctuation (RMSF) analysis reveals variations in Cα atom dynamics over 100 ns.

Fig 6. Radius of gyration (Rg) analysis shows structural stability differences in wild-type and mutant proteins.

Fig 7. Analysis of hydrogen bonds in wild type and mutant proteins over 100 ns simulation conducted.

Fig 8. Principal component analysis (PCA) of wild type and mutant proteins for molecular dynamics simulation trajectories.

4. Discussion

5. Conclusion

Supporting information

Acknowledgments

Data Availability

Funding Statement

References

Decision Letter 0

Rajesh Kumar Pathak

Roles

Author response to Decision Letter 0

Decision Letter 1

Rajesh Kumar Pathak

Roles

Acceptance letter

Rajesh Kumar Pathak

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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