The human genome possesses various types of variation, but the amplest among these variations are single nucleotide polymorphisms (SNPs). There are roughly about 3–10 million SNPs which comprises almost 1% of the whole genome1. Non-synonymous SNP (nsSNPs) /missense SNPs residing in the coding region are very crucial and accounts for residual change which may have neutral or deleterious effect on protein2,3. These variations may account for some damaging effects i.e. protein structure destabilization, aberrant gene regulation4, alteration in protein hydrophobicity, proteins charge disturbance, change in protein geometry5, dynamics, translation, protein–protein interactions6,7 and loss of protein integrity8.

Missense mutations are responsible for almost 50% of the entire DNA mutations, associated with genetic diseases including inflammatory and autoimmune diseases either as causative or susceptibility factors9,10,11. Analysis of PTPN22 identified missense mutation (R620W), which shows association with different autoimmune diseases including diabetes type 112. Another study indicates missense mutation (Y402H) in CFH, increased the macular degeneration susceptibility13.

A significant number of studies have used insilico tools to predict the structural and functional impact of nsSNPs on different proteins. For instance, a study on ABCA1 polymorphism revealed association with familial hypoalphalipoproteinemia and tangier disease14. Another study investigated the association of CYP27B1 polymorphism with vitamin D deficiency15. Similarly, various other studies have used insilico tools to establish the role of nsSNPs in other human diseases such as mental disorders16 congenital cataracts17, rheumatoid arthritis18, steroid resistant nephrotic Syndrome19 and breast cancer20.

The computational analysis of damaging nsSNPs of CTLA4 has not been conducted before. Human CTLA4 is a receptor protein that belongs to immunoglobulin superfamily and is mainly expressed on activated T-cells. It functions as a negative regulator of T cells and competes with CD28 for binding with B7-1(CD-80) and B7-2 (CD-86) ligand present on the surface of Antigen Presenting Cells (APCs). It directly inhibits the T cells mediated immune response and blocks CD28 signaling which further leads to inhibition of T cells interaction with APCs21,22. The non-synonymous mutations in CTLA4 gene might disturb its interaction with its ligands and can lead to autoimmune diseases and cancer. Therefore, studying the effect of nsSNPs on its structure and function is crucial for establishing its role in different diseases.

The current study investigates the structural and functional influence of nsSNPs on CTLA4. CTLA4 (Cytotoxic T-lymphocyte-associated protein 4) also called CD152, encoded by CTLA4 gene in human located at chromosome 2q33.2. It is a 223 amino acids long protein which belong to immunoglobulin family, having three exons encoding V like domain, hydrophobic putative transmembrane and putative cytoplasmic domain23,24. CTLA4 act as an immune checkpoint, and downregulate T lymphocytes after CD80 or 86 attachment with it, evident from CTLA4 deficient mouse25, and can lead to elevated level of blast cells and their infiltration to heart, lung, and pancreas tissue26. Different studies proposed association of CTLA4 with rheumatoid arthritis (RA)27, autosomal dominant immune dysregulation syndrome28, juvenile idiopathic arthritis29 autoimmune Addison's disease (AAD)30 and Breast cancer31. Therefore, it is vital to analyze the potential damaging effect of nsSNPs on CTLA4. The most deleterious nsSNPs in CTLA4 and their functional consequences have been predicted in this work by means of various in silico tools. The 3D model of wild type and its mutants have also been anticipated and the comparison is carried out to explore the diversion between wild and mutants resulting from nsSNPs. This is the first computational analysis of the CTLA4 which predicts the deleterious effect of potential nsSNPs on structure, stability, protein–protein interaction and post translational modification of this protein using different publicly available computational tools. In future this study might be helpful for studying CTLA4 associated diseases.


Retrieved SNP

The dbSNP provided a total of 1835 SNPs in CTLA4 gene. Out of total SNPs, 945 were in intronic region, 111 were missense, 71 were found to be synonymous, 36 located in 5’UTR, while 294 were present in 3’UTR region and the remaining SNPs were (Nonsense = 6, Frameshift = 6, Splice acceptor = 1, Splice donor = 2, 500b downstream = 102, 2 Kb upstream = 113, Not specified = 146). Only the missense or nsSNPs were selected for further in silico analysis. The detailed information of all nsSNPs is given in Table S1 while Fig. 1 shows the graphical representation of percentage of all the SNPs.

Figure 1
figure 1

Percentage of all the SNPs in human CTLA4 gene.

Damaging nsSNPs identified

All the nsSNPs obtained were subjected to four different computational tools to investigate their effect on the structure and function of CTLA4 protein. The different tools used were SIFT, PROVEAN, PolyPhen-2, and PhD‐SNP. A TI (Tolerance Index) threshold of 0.05 was taken for SIFT and the results having values less than the threshold were considered to be affected. SIFT identified 33 nsSNPs to be affected. For PROVEAN a value of − 2.5 cut off was considered as threshold and the nsSNPs having score below this value were considered deleterious. PROVEAN filtered a total of 23 nsSNPs to be deleterious. PhD‐SNP resulted in 38 SNPs to be diseased. The nsSNPs that were found damaging by all the three tools were further submitted to PolyPhen-2. Out of total SNPs submitted to above mentioned tools, 8 were found to be damaging by all the tools and were shortlisted for further analysis. The results of all the computational tools are summarized in Table S2, and Fig. 2.

Figure 2
figure 2

Percentage of potential damaging nsSNPs predicted through online computational tools.

Structural and functional effects prediction by MutPred2

All the shortlisted 8 damaging nsSNPs were submitted to MutPred for predicting the impact of nsSNPs on CTLA4 protein structure and function. The probability scores of nsSNPs are given in the Table 1. The predictions made by MutPred2 include, loss of helix, gain of strand, Gain/loss of N-linked glycosylation and Sulfation, altered transmembrane protein, gain of Relative solvent accessibility, and altered ordered interface. The details of the above-mentioned predictions are given in the Table S3. These predictions suggest that many of the high risk nsSNPs effect the 3D structure of CTLA4 protein.

Table 1 MutPred2 P values of high risk nsSNPs identified in CTLA4.

Stability of protein

The function of a protein is associated with its stability that’s why it is important to identify the change in stability of protein due to nsSNPs. Mutant 2.0 predicted to what extent the damaging nsSNPs alter the stability of CTLA4 protein. The nsSNPs were submitted one by one and RI and DDG values were obtained. It was predicted that all of the damaging nsSNPs decrease the stability of CTLA4 protein except P137L. Three of the most damaging nsSNPs having highest RI values (P138T = 9, N145S = 9, T147A = 9) may be involved in causing greater damage to CTLA4 protein stability. The prediction of changes in stability of CTLA4 are given in Table 2.

Table 2 I-Mutant predicted CTLA4 protein stability due to deleterious nsSNPs.

Conservation of amino acids

The damaging nsSNPs located in a highly conserved region can have more effect on protein structure and function as compared to damaging nsSNPs that are present in a region that is less conserved. The conservation profiles of CTLA4 amino acids were analyzed by ConSurf. The results provided by ConSurf are given in Fig. S1. According to predictions made by ConSurf, R70W and P137L were highly conserved and exposed, P138T and T147A were found to be highly conserved and buried, G118R was found to be buried while N145S, G146L and P209R were predicted to be exposed. The amino acids and their respective conservation scores are given in Table 3.

Table 3 Consurf results showing conservation scores of deleterious nsSNPs in CTLA4.

3D modelling of CTLA4 and its mutants

The 3D structures of wild type CTLA4 and 8 of its mutants were predicted by Phyre2. The Phyre2 results were incomplete as it predicted structure for only 118 amino acids (53% Coverage) out of 223 total amino acids. The wild type CTLA4 and the mutants were then submitted to I-TASSER which is a more advance and reliable modeling tool. It predicted 5 models for CTLA4 protein and each of its mutant. The models with the highest C value were selected for further analysis. The protein models were also subjected to three different protein structure validation tools (MolProbity, ERRAT, and ProSA Web). For wild type CTLA4 the ERRAT calculated the overall quality factor as 57.672 whereas ProSA Web and MolProbity predicted − 3.49 Z-Score and 3.816 MolProbity score respectively which suggests that the protein structure was of good quality. The finalized 3 mutant models were also validated with the above-mentioned tools and the results showed Z-Score, overall quality factor and MolProbity Score for P137L (− 3.32, 58.13 and 3.815), G146L (− 3.48, 56.74 and 3.91) and P209R (− 3.4, 56.74 and 3.814) respectively. The protein structures were further refined with Galaxy Web. The predicted protein models were then compared using TM-Align to obtain TM-scores and RMSD values. The TM-score gives information about topological similarities between two proteins and RMSD values shows the average distance between backbone atoms of wild type and mutant proteins. The mutant with high RMSD value indicates greater deviation from its wild type. The mutant model for P137L (rs1553657429), P209R (rs778534474), G146L (rs1559591863), and P138T (rs1553657430) showed highest variation with RMSD values of 4.18, 3.73, 3.65, and 3.61 respectively. N145S and T147A showed RMSD values of 3.33 and 3.32 while R70W and G118R showed the lowest values of 3.0 and 2.49. Table 4 shows the TM-scores and RMSD values for all the models. The mutants having highest RMSD values (P137L, P209R, G146L) were selected and superimposed over wild type for further analysis using Chimera 1.14 shown in Fig. 3. The I-TASSER predicted structures were selected based on Confidence score (C-score). As per the reviewer/s comment the protein models were subjected to three different protein structure validation tools (MolProbity, ERRAT, and ProSA Web).

Table 4 TM-Align results showing TM-score and RMSD values of 8 mutants of CTLA4 protein.
Figure 3
figure 3

(a) Structure of Wild type CTLA4 protein. (b) Superimposed wild type CTLA4 protein and its mutant having Proline to Leucine mutation at position 137. (c) Superimposed wild type CTLA4 protein and mutant having Glycine to Leucine mutation at position 146. (d) Superimposed structure of wild type CTLA4 protein and mutant having mutation from Proline to Arginine at position 209.

PTM predictions

The results of Post translational modifications (PTMs) sites predicted by using different tools are discussed below.


For the prediction of potential methylated sites in CTLA4, GPS‐MSP 3.0 was used, and no methylation sites were predicted.


The phosphorylated sites in CTLA4 predicted by ModPred and NetPhos 3.1 are mentioned in Table S4. NetPhos predicted 20 residues and ModPred predicted 7 residues having phosphorylation potential. The amino acid residues that were found to be phosphorylated by both NetPhos and ModPred are Serine at position 62 and 194, Threonine at 197 and 207, and Tyrosine at position 60 and 201. The NetPhos 3.1 predicted that mutant P137L and P138T showed a loss of phosphorylation site at position 140, while T147A showed a loss of phosphorylation at 147 and gain at position 150.


Potential glycosylated sites were found by N-Glyde and NetOGlyc4.0. N-Glyde predicted 2 sites 113 and 145 with scores 0.732072 and 0.9271713 respectively, to be N glycosylated while NetOGlyc4.0 predicted no site to be glycosylated. N-Glyde also predicted that mutant N145S and T147A showed loss of N-glycosylation at position 145. The results are given in Table S5.


UbPred predicted 3 residues in CTLA4 capable of ubiquitination while BDM-PUB predicted 5 residues to get ubiquitinated and mutant R70W showed loss of ubiquitination at position 65. None of these ubiquitylation sites predicted were at deleterious SNPs regions. The results obtained from BDM-PUB and UbPred are given in Table S6.

Gene–gene interaction

For the prediction of interaction of CTLA4 with other genes inside the cell GeneMANIA and STRING were used. Results obtained from STRING are given in Table S7. The GeneMANIA predicted physical interaction of CTLA4 with CD80, CD86, AP2M1, JAK2, STAT5A, STAT5B, PTPN11 and FYN. The genes that were predicted to be co-expressed with CTLA4 are CD5, CXCL9, GPR132, CD200, CTSZ, JAK2 and FYN. In pathways it showed relation with CD86, PTPN11, CD80, FOXP3, CD28, PTPN7, PTPN6, and NFATC2. Co-localization was found with STAT5B, CD86, FYN, STAT5A, PLA2G2D, CD28, GPR132, CD4 CXCL9, CTSZ, PTPN6 and NFATC2. The proteins that were predicted to share domain with CTLA4 are CD28, CD80 and CD86. Predictions made by GeneMania and STRING are given in Figs. 4, and 5 respectively.

Figure 4
figure 4

Gene–Gene Interaction of CTLA4 predicted by GeneMANIA. The CTLA4 shows main physical interaction with CD80, CD86, and AP2M1.

Figure 5
figure 5

Gene–Gene interaction of CTLA4 predicted by STRING showing major interaction with CD80, CD86, and FOXP3.


The function of a protein is determined by the tertiary structure and therefore any modification in the amino acid sequence of that protein can have the potential to change the structure of protein and lead to disease. Bioinformatics analysis gives us the opportunity to predict the structural and functional effects of single nucleotide polymorphisms on a protein using different tools and algorithms. However, the sophistication of these algorithms is completely dependent on raw experimental data. The inauthentic and inaccurate raw data can lead to incorrect downstream structural and functional analysis. Therefore, it is suggested to use multiple tools and draw a consensus by comparing the results obtained from these tools. Furthermore, the bioinformatics results should be validated in the laboratory through different in-vitro and in-vivo experiments.

Different studies have investigated the role of CTLA4 polymorphism with various diseases. The association of CTLA4 polymorphism has been established with various autoimmune diseases like rheumatoid arthritis30,32,33, type 1 diabetes34,35, and multiple sclerosis36,37 and also different cancers such as breast cancer38,39, colorectal cancer40,41, lung cancer42,43, and cervical cancer44,45.

In the present study a total of 1835 SNPs were obtained from dbSNP out of which 111 non-synonymous or missense SNPs were subjected to different in silico tools including SIFT, PROVEAN, Polyphen2, and PhD‐SNP. These in silico tools predicted 8 SNPs to be damaging while other were found to be neutral. The damaging SNPs were subjected to further computational analysis to investigate their effect on protein structure and function. All these SNPs decreased protein stability as predicted by Mutant 22.0 except P137L. The amino acids that are directly involved in biological processes tend to be more conserved and thus changes in these amino acids will significantly affect the function of protein (Miller and Kumar, 2001). The conservation analysis for CTLA4 revealed that rs606231422 at position R70W and rs1553657429 at position P137L were found highly conserved and exposed while rs1553657430 and rs1466152724 at positions P138T and T147A respectively were found to be highly conserved and buried. The rest of the SNPs were only found buried or exposed and not very conserved. The mutation in buried resides of protein can affect the structural integrity of the protein whereas the polymorphism in exposed resides may alter the protein function46. The structure of CTLA4 and its mutants were predicted via I-TASSER. The nsSNPs directly influence the structure and hence function of a protein therefore, the effect of nsSNPs on structure of CTLA4 was assessed47. It was observed that the predicted mutant structures of CTLA4 have significantly distinguished RMSD values than the wild type and may compromise the structural integrity of the protein47.

For PTMs predictions of our protein different in silico tools were used. Phosphorylation is an important PTM which can activate or deactivate a protein by changing its structural conformation. The NetPhos result showed that mutant P137L and P138T have lost a phosphorylation site at position 40 and T147A lost a phosphorylation site at position 147 and gained phosphorylation site at position 150. As T147 is one of the most damaging nsSNP predicted in this study and was also found to be highly conserved and buried, that’s why a loss of phosphorylation at this site can be very significant for protein structure and function. The mutations that leads to the abolishing of a phosphorylation site can cause a direct deleterious effect on protein48. Similarly, N-Glyde showed that mutant N145S and T147A resulted in loss of N-glycosylation at position 145 which is the site of another most damaging SNP that’s why a loss of glycosylation at position 145 is important.

The gene–gene interaction was performed to identify the interacting partners of CTLA4 protein. The mutation analysis performed in the present study is important in this regard as mutation especially in ligand binding domains and motifs can disrupt the interaction of CTLA4 with its interacting proteins such as CD80, and CD86 which can lead to various disease conditions.

The domain analysis of the CTLA4 protein was performed to check the location of the predicted mutations in different domains of CTLA4. It was found that two of the predicted mutations (P137L, and G146L) were found to be in Immunoglobulin V-set domain, while the third mutation site (P209R) was in cytoplasmic domain. The mutations in the Immunoglobulin V-set domain can alter the binding affinity of CTLA4 with CD80 and CD80 that are involve in the negative regulation of T-cells whereas, the polymorphism lied in the cytoplasmic domain may affect the binding of multiple proteins i.e., PI3K, lipid kinase phosphatidylinositol 3-kinase (PI3K), the phosphatases SH2 domain containing protein tyrosine phosphatases (SHP-2), the serine threonine phosphatase PP2A and clathrin adaptor proteins activator protein1 (AP-1) and AP-2 may results in cancer development49,50.

The three nsSNPs in CTLA4 identified in the current study are unique and their association with human diseases have not been assessed in wet lab experiments. It is evident from this insilico analysis that these nsSNPs have resulted in lower stability of CTLA4 protein in comparison with their wild type protein. Moreover, the mutant proteins deviated in structure and showed loss of potential PTMs sites. It has been established that CTLA4 is a negative regulator of T cells and inhibit immune responses by interacting with CD80 and CD8622. The proper interaction of CTLA4 with its ligand is very crucial for its immune inhibitory function. Analysis of mutations especially in the ligand binding domain of CTLA4 protein can disrupt its interaction with ligands which can lead to various autoimmune diseases and also cancer.

The current study predicted high-risk SNPs in CTLA4 which can potentially disrupt ligand-receptor interactions. However, further in-vitro and in-vivo studies are required to investigate and establish the role of these nsSNPs in different diseases. Moreover, Molecular Dynamics (MD) simulation analysis of the proteins predicted is required to study the stability and structural flexibility of predicted wild-type and mutant proteins in dynamic environment.


A schematic flowchart of complete methodology is given in Fig. 6.

Figure 6
figure 6

Flowchart representing the methodology of the study.

SNP data mining

The NCBI dbSNP ( (accessed: 20 April,2020) database was used to retrieve all the SNPs of CTLA4 gene. The identification number (rsIDs) of nsSNPs were obtained from NCBI and protein sequence of CTLA4 in FASTA format was retrieved from UniProt ( Only the missense or non-synonymous SNPs (nsSNPs) were selected for further in silico study.

Identification of high risk nsSNPs

After retrieving the nsSNPs and protein sequence, the functionally damaging nsSNPs were predicted using different in silico tools including, SIFT (Sorting Intolerant From Tolerant)51, PROVEAN (Protein Variation Effect Analyzer)52, and PhD‐SNP (Predictor of human Deleterious SNP)53. The damaging nsSNPs found by these in silico tools were then submitted to PolyPhen2 (Polymorphism Phenotyping 2)54. The protein sequence in FASTA format and details of amino acids substitutions were used as input data for PolyPhen2.

Prediction of nsSNPs effects on structure and function of CTLA4 protein

To analyze the structural and functional effect of nsSNPs on CTLA4 protein, MutPred2 was used55. It is a web application which predicts the pathogenicity of amino acid change in a protein. The sequence of CTLA4 protein was submitted in FASTA format to MutPred 2 along with the information of amino acid substitutions. The p- value less than 0.05 (p < 0.05) was taken as “Confident” and less than 0.01 (p < 0.01) as “Very Confident”.

Prediction of protein stability

To study the influence of all the damaging nsSNPs on the stability of CTLA4 protein, I‐Mutant 2.0 was used. It is an online tool based on support vector machines (SVM) which predicts the extent to which a mutation affects a protein stability. The protein sequence of CTLA4 gene was submitted at 25 °C with pH = 7.0. It gives result in the form of RI (Reliability index) with the values ranging from 0 to 10 (0 showing lowest and 10 showing highest reliability)56.

Prediction of evolutionary conservation of CTLA4

ConSurf server was used for the prediction of the effect of nsSNPs on amino acids that are evolutionary conserved in CTLA4. ConSurf predicts the conserved amino acids in each protein by analyzing phylogenetic relation among homologous sequences using an empirical Bayesian inference and gives conservation scores ranging from 1 to 957. FASTA sequence of CTLA4 was submitted as input option. The nsNSPs that were identified as highly conserved were further analyzed.

Protein 3D structure prediction

The 3D models for native and mutant (R70W, G118R, P137L, P138T, N145S, G146L, T147A, and P209R) CTLA4 gene were predicted using Phyre2 . It is an online tool which predicts 3D models for protein based on principles of homology modeling58. The wild type CTLA4 and selected mutant proteins were then submitted to I-TASSER59 for remodeling. The I-TASSER predicted top five protein structures for wild type and all the mutants using fold recognition or threading approach. Among the top 5 predicted models the best models were selected for further study. After that the wild type and all the mutant models were compared using an online structure alignment tool called TM-Align which provides TM scores (Template Modelling score) and RMSD (root‐mean‐square deviation) values. The values of TM score ranges from 0 to 1, and 1 means the two proteins are perfectly matching. The higher RMSD means high structure variations between mutant and wild‐type and vice versa60. Three mutants with high RMSD values were selected and further analyzed using Chimera V1.1461.

Prediction of potential PTM sites

Post translational modifications (PTMs) are very important for the structure, folding and proper function of proteins. Potential PTMs sites in CTLA4 protein and the gain/loss of PTMs sites in all the mutants due to nsSNPS were identified using several in silico tools. The sites where methylation occur in CTLA4 protein were predicted using GPS‐MSP62. For the prediction of phosphorylation at serine, threonine and tyrosine sites in CTLA4, ModPred63 and NetPhos3.1 was used. For NetPhos 3.1 the threshold was set to 0.5 and the amino acids having values higher than the threshold were predicted to be phosphorylated64. The potential glycosylation sites in CTLA4 were predicted by NetOglyc4.065 and N-Glyde. For N-Glyde the residues with prediction score higher than 0.6 were predicted to have glycosylation potential66. BDM‐PUB, and UbPred were used for predicting Ubiquitylation sites in CTLA4. UbPred showed lysine residues having ubiquitylation potential with score equal to or higher than the threshold (0.62)67. For BDM-PUB balanced cut-off was selected68.

Interaction of CTLA4 with other proteins

A protein interacts with many other proteins inside the cell and this interaction is important for the function and regulation of protein. The functional interaction of CTLA4 with other proteins inside the cells was predicted using GeneMANIA and STRING (accessed: 16 June 2020). The GeneMANIA use different parameters including genetic and protein interaction, co-expression, co-localization, pathways and protein domain similarities to predicts the interaction of input gene with many other genes69. STRING uses its database of 24′584′628 proteins from 5′090 organisms and predicts protein protein–protein interaction networks either through direct or indirect association among proteins70. The terms CTLA4 and Homo sapiens were searched as input options for both the tools.


This study identified 3 major high risk nsSNPs, rs1553657429 (P137L), rs1559591863 (G146L), rs778534474 (P209R) within the coding region of CTLA4 gene. They may have a major role in diseases associated with CTLA4 gene as they are involved in decreasing the stability of protein and loss of potential phosphorylation site. The mutants possessing these nsSNPs showed deviation in structure from wild type CTLA4 protein. These nsSNPs can be significance for therapeutic strategies and personalized medicine and can be used for further experimental investigations to study the role of these nsSNPs in pathogenesis of related diseases.