# Computational Analysis of Damaging Single-Nucleotide Polymorphisms and Their Structural and Functional Impact on the Insulin Receptor

Authors: Zabed Mahmud; Syeda Umme Fahmida Malik; Jahed Ahmed; Abul Kalam Azad
Journal: BioMed Research International
 (2016)

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## Abstract

Single-nucleotide polymorphisms (SNPs) associated with complex disorders can create, destroy, or modify protein coding sites. Single amino acid substitutions in the insulin receptor (INSR) are the most common forms of genetic variations that account for various diseases like Donohue syndrome or Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance. We analyzed the deleterious nonsynonymous SNPs (nsSNPs) inINSR gene based on different computational methods. Analysis of INSR was initiated with PROVEAN followed by PolyPhen and I-Mutant servers to investigate the effects of 57 nsSNPs retrieved from database of SNP (dbSNP). A total of 18 mutations that were found to exert damaging effects on the INSR protein structure and function were chosen for further analysis. Among these mutations, our computational analysis suggested that 13 nsSNPs decreased protein stability and might have resulted in loss of function. Therefore, the probability of their involvement in disease predisposition increases. In the lack of adequate prior reports on the possible deleterious effects of nsSNPs, we have systematically analyzed and characterized the functional variants in coding region that can alter the expression and function ofINSR gene.In silico characterization of nsSNPs affectingINSR gene function can aid in better understanding of genetic differences in disease susceptibility.


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## Body

## 1. Introduction

The insulin receptor (INSR) is a tyrosine kinase-specific transmembrane receptor that is activated by insulin, insulin growth factor I, and insulin growth factor II [1]. Metabolically, the INSR plays a crucial role in the regulation of glucose homeostasis which may result in a range of clinical events including diabetes and cancer [2, 3]. The main activity of INSR is persuading uptake of glucose and because of a decrease in insulin receptor signaling leads to diabetes mellitus type 2. The cells’ inability to take glucose results in hyperglycemia and all the sequels that result in diabetes. Insulin-resistant patients may also display acanthosis nigricans. It is already proven that the presence of mutant receptors in the cell may have detrimental effects on the activity of the normal receptor. A previous study conducted with kinase-deficient INSRs transfected into cultured cells showed that such receptors suppressed the function of endogenous INSRs and functioned as dominant-negative mutations [4]. However, in most cases of insulin resistance, the mutation is expressed as a recessive form. Yamamoto-Honda et al. [5] studied the function and consequences of recessive mutation in the INSR. For example, Donohue syndrome known as Leprechaunism is a rare and severe genetic autosomal recessive disorder due to defect in theINSR gene.Single-nucleotide polymorphisms (SNPs) are the most common form of human genetic variations and nearly half a million of SNPs reside in the exons of the human genome. Among these SNPs, “nonsynonymous SNPs (nsSNPs)” can alter the amino acid residues and contribute to functional diversity in encoded proteins in the human population. The genomic distribution of SNPs is not obviously homogenous. In general, SNPs occur in noncoding regions more frequently than in coding regions [6]. Genetic recombination and mutation rate are some other factors that can also determine SNP density [7]. SNPs are usually biallelic and a single SNP may cause a Mendelian disease [8]. In case of complex diseases, SNPs do not usually function independently but rather they work as a group with other SNPs to exhibit a disease condition which has been seen in osteoporosis [9]. A wide range of human diseases, such as sickle-cell anemia, β thalassemia, and cystic fibrosis, result from SNPs [10–12]. For drug discovery, diseases with different SNPs may become crucial pharmacogenomic targets; some SNPs are also associated with the metabolism of different drugs [13–15]. For genome-wide association studies, SNPs can serve as a useful genetic marker [16]. The consequences or deleterious effects of SNPs are generally attributed to their impact on the protein structure and function. However, very few studies have been done to predict the SNPs and their impacts on INSR.In this study, we identified the nsSNPs’ deleterious mutationsin silico which may have an impact on the structural integrity of human INSR protein and are involved in several genetic diseases. Knowledge ofin silico analysis of SNPs will play a major role in the understanding of the genetic basis of several complex genetic human diseases. Furthermore, the genetics of human phenotypic diversity could also be implied by establishing the functions of these SNPs. Using laboratory techniques, it is still a major obstacle to identify the functional SNPs in a disease-related gene. However, with recent advancements in the“in silico” technique and procedures, it is now possible to carry out research investigations without the need for extensive lab work. The main focus of this work is to investigate the SNPs genetic variations in the humanINSR gene and their possible effects on structure and functions of INSR using bioinformatics and computational algorithms.


## 2. Materials and Methods

### 2.1. Datasets

The data of humanINSR gene was collected from Online Mendelian Inheritance in Man (OMIM) and Entrez Gene on National Center for Biological Information (NCBI) web sites. The SNPs information (protein accession number and SNP ID) of theINSR gene was retrieved from the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/).


### 2.2. Analysis of Protein Variation Effects

A sequence based predictor estimates the effect of protein sequence variation on protein function. Many web servers are available to predict the effect of single amino acid variations on protein stability and protein binding efficiency. PROVEAN (http://provean.jcvi.org/index.php), I-Mutant 3.0 (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi), and PolyPhen (http://genetics.bwh.harvard.edu/pph2/) were used in this study.In PROVEAN, protein sequences of BLAST hits with more than 75% global sequence identity were clustered together and top clusters formed a supporting sequence set. A delta alignment scoring system was used, where the scores of each supporting sequence were averaged within and across clusters to generate the final PROVEAN score. A protein variant is said to be “deleterious” if the final score is below a certain threshold (default is −2.5) or is predicted to be “neutral” if the score is above the threshold [17].PolyPhen version 2 predicts the influence of amino acid substitution on the structure and function of proteins by using the specific empirical rules. Protein sequence, database ID/accession number, amino acid position, and amino acid variant details are the input options for PolyPhen [18]. The tool estimates the position-specific independent count (PSIC) score for every variant and calculates the score difference between variants.I-Mutant 2.0 and I-Mutant 3.0 are based on Support Vector Machine (SVM) algorithm to predict the stability of the protein due to single amino acid variations. It can predict protein stability changes by using protein sequence or structure. It has an overall accuracy of 77% when prediction is based on protein sequence. I-Mutant 2.0 and I-Mutant 3.0 predict the DDG values as a regression estimator and the sign of the stability change. I-Mutant 3.0 furthermore classifies mutations into three categories: neutral mutation (−0.5 ≤ DDG ≤ 0.5), large decrease (≤ −0.5), and large increase (>0.5) [19, 20].


### 2.3. 3D Modeling and Analysis of Protein Structure

The EMBL-EBI web-based tool PDBsum (http://www.ebi.ac.uk/pdbsum/) was used to find the proteins related to the INSR. PDBsum provides an at-a-glance overview of every macromolecular structure deposited in the Protein Data Bank (PDB). It performs a FASTA search against all sequences in the PDB to obtain a list of the closest matches [21]. LS-SNP/PDB [22] annotates all human SNPs that produce an amino acid change in a protein structure in PDB [23], using features of their local structural environment, putative binding interactions, and evolutionary conservation. The presence of an nsSNP in a highly conserved surface patch or a charged surface patch suggests possible biological importance. These annotations allow users to quickly scan a large number of nsSNPs of interest and prioritize those with higher likelihood of impacting normal protein activities. LS-SNP server is also useful to analyze human nsSNPs onto protein homology models [24].PYMOL was used to generate the mutant models of each of the selected PDB entries for the corresponding amino acid substitutions. PYMOL allows browsing through a rotamer library to change amino acids. A “Mutagenesis Wizard” was used to replace the native amino acid with new one. The mutation tool facilitates the replacement of the native amino acid by the “best” rotamer of the new amino acid. The “.pdb” files were saved for all the models.


### 2.4. Structure Validation and Energy Minimization

Structural Analysis and Verification Server (SAVES) was implemented for evaluating the quality and validation of the refined 3D structural models. The SAVES integrates PROCHECK, PROVE, and ERRAT software programs to check overall quality of the 3D models obtained from the PYMOL mutagenesis tool. Structure refinement was carried out using KoBaMIN which is based on knowledge based potential refinement for proteins protocol [25].


### 2.5. Protein Stability Validation for Mutant Structure

The approach called Mutation Cutoff Scanning Matrix (mCSM) uses the concept of graph-based structural signatures to study and predict the impact of single-point mutations on protein stability and protein-protein and protein-nucleic acid affinity. The mCSM encodes distance patterns between atoms to represent protein residue environments [26].


### 2.6. Structural Analysis

The predicted structures were viewed in University of California San Francisco (UCSF) Chimera. It is a computationally intensive program for visualization of molecular models and it provides an interactive interface for the user for analyzing the models and model related data. It provides a platform for analyzing sequence alignments, generating homology models, molecular docking, viewing various density models, and also comparing different models by superimposition [27]. The mutant and wild type structures were superimposed and the effect of the nonsynonymous variation was observed in terms of steric hindrance due to the changes of the side chains and charge of the amino acid. Then, the degree of change in the hydrophobicity or hydrophilicity of the substituted amino acid and its effect on the interacting intrachain and interchain molecules was analyzed. A summary ofin silico approaches used in this study is shown in Figure 1.Figure 1
Workflow ofin silico approaches used in this study.


## 2.1. Datasets

The data of humanINSR gene was collected from Online Mendelian Inheritance in Man (OMIM) and Entrez Gene on National Center for Biological Information (NCBI) web sites. The SNPs information (protein accession number and SNP ID) of theINSR gene was retrieved from the NCBI dbSNP (http://www.ncbi.nlm.nih.gov/snp/).


## 2.2. Analysis of Protein Variation Effects

A sequence based predictor estimates the effect of protein sequence variation on protein function. Many web servers are available to predict the effect of single amino acid variations on protein stability and protein binding efficiency. PROVEAN (http://provean.jcvi.org/index.php), I-Mutant 3.0 (http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi), and PolyPhen (http://genetics.bwh.harvard.edu/pph2/) were used in this study.In PROVEAN, protein sequences of BLAST hits with more than 75% global sequence identity were clustered together and top clusters formed a supporting sequence set. A delta alignment scoring system was used, where the scores of each supporting sequence were averaged within and across clusters to generate the final PROVEAN score. A protein variant is said to be “deleterious” if the final score is below a certain threshold (default is −2.5) or is predicted to be “neutral” if the score is above the threshold [17].PolyPhen version 2 predicts the influence of amino acid substitution on the structure and function of proteins by using the specific empirical rules. Protein sequence, database ID/accession number, amino acid position, and amino acid variant details are the input options for PolyPhen [18]. The tool estimates the position-specific independent count (PSIC) score for every variant and calculates the score difference between variants.I-Mutant 2.0 and I-Mutant 3.0 are based on Support Vector Machine (SVM) algorithm to predict the stability of the protein due to single amino acid variations. It can predict protein stability changes by using protein sequence or structure. It has an overall accuracy of 77% when prediction is based on protein sequence. I-Mutant 2.0 and I-Mutant 3.0 predict the DDG values as a regression estimator and the sign of the stability change. I-Mutant 3.0 furthermore classifies mutations into three categories: neutral mutation (−0.5 ≤ DDG ≤ 0.5), large decrease (≤ −0.5), and large increase (>0.5) [19, 20].


## 2.3. 3D Modeling and Analysis of Protein Structure

The EMBL-EBI web-based tool PDBsum (http://www.ebi.ac.uk/pdbsum/) was used to find the proteins related to the INSR. PDBsum provides an at-a-glance overview of every macromolecular structure deposited in the Protein Data Bank (PDB). It performs a FASTA search against all sequences in the PDB to obtain a list of the closest matches [21]. LS-SNP/PDB [22] annotates all human SNPs that produce an amino acid change in a protein structure in PDB [23], using features of their local structural environment, putative binding interactions, and evolutionary conservation. The presence of an nsSNP in a highly conserved surface patch or a charged surface patch suggests possible biological importance. These annotations allow users to quickly scan a large number of nsSNPs of interest and prioritize those with higher likelihood of impacting normal protein activities. LS-SNP server is also useful to analyze human nsSNPs onto protein homology models [24].PYMOL was used to generate the mutant models of each of the selected PDB entries for the corresponding amino acid substitutions. PYMOL allows browsing through a rotamer library to change amino acids. A “Mutagenesis Wizard” was used to replace the native amino acid with new one. The mutation tool facilitates the replacement of the native amino acid by the “best” rotamer of the new amino acid. The “.pdb” files were saved for all the models.


## 2.4. Structure Validation and Energy Minimization

Structural Analysis and Verification Server (SAVES) was implemented for evaluating the quality and validation of the refined 3D structural models. The SAVES integrates PROCHECK, PROVE, and ERRAT software programs to check overall quality of the 3D models obtained from the PYMOL mutagenesis tool. Structure refinement was carried out using KoBaMIN which is based on knowledge based potential refinement for proteins protocol [25].


## 2.5. Protein Stability Validation for Mutant Structure

The approach called Mutation Cutoff Scanning Matrix (mCSM) uses the concept of graph-based structural signatures to study and predict the impact of single-point mutations on protein stability and protein-protein and protein-nucleic acid affinity. The mCSM encodes distance patterns between atoms to represent protein residue environments [26].


## 2.6. Structural Analysis

The predicted structures were viewed in University of California San Francisco (UCSF) Chimera. It is a computationally intensive program for visualization of molecular models and it provides an interactive interface for the user for analyzing the models and model related data. It provides a platform for analyzing sequence alignments, generating homology models, molecular docking, viewing various density models, and also comparing different models by superimposition [27]. The mutant and wild type structures were superimposed and the effect of the nonsynonymous variation was observed in terms of steric hindrance due to the changes of the side chains and charge of the amino acid. Then, the degree of change in the hydrophobicity or hydrophilicity of the substituted amino acid and its effect on the interacting intrachain and interchain molecules was analyzed. A summary ofin silico approaches used in this study is shown in Figure 1.Figure 1
Workflow ofin silico approaches used in this study.


## 3. Results

### 3.1. SNP Dataset from dbSNP

The dbSNP contains both validated and nonvalidated polymorphisms. In spite of this drawback, we opted to avail the dbSNP because allelic frequency of most of nsSNPs of INSR has been recorded there and that is the most extensive SNP database. In our data search, some previously reported SNPs in dbSNP have been identified as invalid because of wrong sequencing and alignment. These erroneous SNPs have expired or have merged with other SNPs. SomeINSR genes have been renamed. We carefully cross-examined the databases and removed those old and invalid SNPs. At dbSNP,INSR gene contains data for 4967 SNPs. Out of 4967 SNPs, only 57 were nsSNPs in the coding region (Table 1). Our investigation accounted for the nsSNPs in the coding region only.Table 1
List of nsSNPs that were predicted by PROVEAN to have functional significance.
SNP_ID
Mutation
PROVEAN results
PROVEAN score
rs1799816
V1012M
Neutral
−2.418
rs52836744
G58R
Deleterious
−6.762
rs121913144
R1027∗
rs121913145
H236R
Deleterious
−5.616
rs121913156
R1201Q
Deleterious
−3.701
rs891087
D261E
Neutral
−0.116
rs2162771
P830L
Neutral
−1.576
rs13306449
Y1361C
Deleterious
−4.157
rs35045353
G811S
Neutral
−2.401
rs1051691
I448T
Deleterious
−3.774
rs1051692
Y171H
Neutral
−1.304
rs2229429
D546E
Neutral
−2.474
rs7508518
A2G
Neutral
0.465
rs52800171
W1220L
Deleterious
−11.648
rs55816055
S353P
Deleterious
−2.761
rs56395521
L1065V
Neutral
−1.133
rs72549237
V362I
Neutral
−0.211
rs76077021
R889W
Deleterious
−4.254
rs76673783
E664G
Deleterious
−5.068
rs78433961
R796S
Neutral
−0.984
rs78827745
M65K
Deleterious
−3.808
rs79312957
R413C
Deleterious
−5.623
rs113527718
S1297G
Deleterious
−2.539
rs138528064
T320M
Neutral
−1.959
rs140762552
T107M
Deleterious
−2.922
rs140852238
E51K
Deleterious
−2.545
rs141484557
G262S
Deleterious
−3.037
rs142391704
A706D
Neutral
0.622
rs142910337
D75G
Neutral
1.672
rs143523271
S748L
Neutral
−0.793
rs143919163
G192D
Neutral
−1.884
rs144029037
V900I
Neutral
−0.698
rs146588336
D946E
Neutral
−0.743
rs147671523
E517G
Deleterious
−4.293
rs148838377
P755S
Neutral
−0.076
rs149536206
H8598∗
rs150114699
L991I
Neutral
−1.71
rs181150880
R410Q
Neutral
−2.011
rs182552223
T858A
Deleterious
−2.612
rs183360558
D893N
Neutral
−1.752
rs185736681
R1053C
Deleterious
−5.933
rs187282966
R889Q
Neutral
−1.492
rs199580495
S1033F
Deleterious
−5.642
rs199599404
M1319I
Neutral
−1.196
rs199659271
C219R
Deleterious
−9.831
rs200059069
K411Q
Neutral
−1.21
rs200110540
V866I
Neutral
−0.134
rs200199169
P271L
Neutral
−2.315
rs200400127
A1340V
Neutral
−1.519
rs200921389
G1048D
Neutral
−1.585
rs201147780
K294R
Neutral
−0.904
rs201466857
T858M
Deleterious
−3.384
rs201506342
P1312T
Deleterious
−3.008
rs201978448
A537V
Deleterious
−3.252
rs201979105
S1221A
Deleterious
−2.698
rs202160383
R1128H
Neutral
−2.063
∗Premature stop codon.


### 3.2. Effects of nsSNPs on INSR Predicted by Different Tools

The PROVEAN algorithm works mainly with primary sequence for prediction while other tools perform similar task with the structure. Since PROVEAN can predict a large number of substitutions and does not require structures, it is advantageous over other tools. PROVEAN predicts the effect of the variant on the biological function of the protein based on sequence homology. The scores of PROVEAN are classified as “deleterious” below a certain threshold (here −2.5) and “neutral” above it. A  .txt file containing “db SNP rsIDs” of all 57 nsSNPs was submitted to the “dbSNP rsIDs” page to calculate the PROVEAN score. Out of 57 nsSNPs, PROVEAN predicted 24 as deleterious and 33 as neutral (Table1). Among the 24 deleterious nsSNPs mutations, W1220L and C219R were predicted as highly deleterious with PROVEAN scores of −11.648 and −9.831, respectively.PolyPhen identifies homologues of the input sequences via BLAST and calculates PSIC scores for every variant and estimates the difference between the variant scores; the difference of 0.339 is detrimental. There are certain empirical rules applied to the sequences and the accuracy is approximately 82% with a chance of 8% false-positive prediction. The protein accession number of INSR (P06213) and the amino acid substitutions corresponding to each of the 57 nsSNPs were submitted separately. Table2 summarizes the results obtained from the PolyPhen server. A PSIC score difference was assigned to categorize SNPs as benign and damaging. “PolyPhen-2: scores are evaluated as 0.000 (most probably benign) to 0.999 (most probably damaging).” Twenty-one of the 57 nsSNPs were predicted as “damaging,” and the PSIC scores fell into the range of 1.51 to 3.41. 18 nsSNPs predicted to be deleterious by the SIFT (Sorting Intolerant from Tolerant) program were also predicted to be damaging by the PolyPhen server.Table 2
Potential effect of amino acid substitution for nsSNPs in human INSR predicted by the PolyPhen algorithm.
SNP_ID
Mutation
PolyPhen results
Score
Sensitivity
Specificity
rs1799816
V1012M
Probably damaging
0.992
0.7
0.97
rs52836744
G58R
Probably damaging
1
0
1
rs121913144
R1027∗
rs121913145
H236R
Probably damaging
1
0
1
rs121913156
R1201Q
Probably damaging
1
0
1
rs891087
D261E
Benign
0
1
0
rs2162771
P830L
Benign
0
1
0
rs13306449
Y1361C
Probably damaging
1
0
1
rs35045353
G811S
Benign
0.441
0.89
0.9
rs1051691
I448T
Probably damaging
0.996
0.55
0.98
rs1051692
Y171H
Benign
0.024
0.95
0.81
rs2229429
D546E
Benign
0.032
0.95
0.82
rs7508518
A2G
Benign
0
1
0
rs52800171
W1220L
Probably damaging
1
0
1
rs55816055
S353P
Possibly damaging
0.528
0.88
0.9
rs56395521
L1065V
Benign
0
1
0
rs72549237
V362I
Benign
0.003
0.98
0.44
rs76077021
R889W
Benign
0.111
0.93
0.86
rs76673783
E664G
Possibly damaging
0.592
0.87
0.91
rs78433961
R796S
Benign
0.001
0.99
0.15
rs78827745
M65K
Possibly damaging
0.934
0.8
0.94
rs79312957
R413C
Probably damaging
0.999
0.14
0.99
rs113527718
S1297G
Benign
0.004
0.97
0.59
rs138528064
T320M
Benign
0.199
0.92
0.88
rs140762552
T107M
Probably damaging
1
0
1
rs140852238
E51K
Benign
0.003
0.98
0.44
rs141484557
G262S
Possibly damaging
0.939
0.8
0.94
rs142391704
A706D
Benign
0
1
0
rs142910337
D75G
Benign
0
1
0
rs143523271
S748L
Benign
0
1
0
rs143919163
G192D
Benign
0.005
0.97
0.74
rs144029037
V900I
Benign
0.048
0.94
0.83
rs146588336
D946E
rs147671523
E517G
Possibly damaging
0.726
0.86
0.92
rs148838377
P755S
Benign
0
1
0
rs149536206
H8598∗
rs150114699
L991I
Benign
0.442
0.89
0.9
rs181150880
R410Q
Possibly damaging
0.935
0.8
0.94
rs182552223
T858A
Benign
0.007
0.96
0.75
rs183360558
D893N
Benign
0
1
0
rs185736681
R1053C
Benign
0.223
0.91
0.88
rs187282966
R889Q
Benign
0.252
0.91
0.88
rs199580495
S1033F
Probably damaging
0.968
0.77
0.95
rs199599404
M1319I
Benign
0.007
0.96
0.75
rs199659271
C219R
Probably damaging
1
0
1
rs200059069
K411Q
Possibly damaging
0.75
0.85
0.92
rs200110540
V866I
Benign
0
1
0
rs200199169
P271L
Benign
0
1
0
rs200400127
A1340V
Benign
0.021
0.95
0.8
rs200921389
G1048D
Benign
0.009
0.96
0.77
rs201147780
K294R
Benign
0.008
0.96
0.76
rs201466857
T858M
Probably damaging
0.994
0.69
0.97
rs201506342
P1312T
Possibly damaging
0.616
0.87
0.91
rs201978448
A537V
Benign
0.143
0.92
0.86
rs201979105
S1221A
Probably damaging
0.997
0.41
0.98
rs202160383
R1128H
Benign
0.031
0.95
0.82
∗
Premature stop codon.I-Mutant is a neural network based routine tool used in the analysis of protein stability alterations by considering the single-site mutation. I-Mutant also provides the scores for free energy alterations, calculated with the FOLD-X energy based web server. By assimilating the FOLD-X estimations with those of I-Mutant, about 93% precision can be achieved. We have considered a threshold of −1.5 Kcal/mol to predict a SNP to be destabilized. Forty-six nsSNPs were considered as destabilized with DDG values by I-Mutant (Table3). Finally, we selected 18 significant nsSNPs because they were predicted to be deleterious by PROVEAN, PolyPhen, and SIFT programs and showed decreased structural stability following analysis by I-Mutant (Table 4).Table 3
List of nsSNPs’ stability predicted by I-MUTANT.
SNP_ID
Mutation
Stability
SNP_ID
Mutation
Stability
rs1799816
V1012M
Decrease
rs141484557
G262S
Decrease
rs52836744
G58R
Decrease
rs142391704
A706D
Decrease
rs121913144
R1027∗
rs142910337
D75G
Decrease
rs121913145
H236R
Decrease
rs143523271
S748L
Increase
rs121913156
R1201Q
Decrease
rs143919163
G192D
Decrease
rs891087
D261E
Increase
rs144029037
V900I
Decrease
rs2162771
P830L
Decrease
rs146588336
D946E
Increase
rs13306449
Y1361C
Increase
rs147671523
E517G
Increase
rs35045353
G811S
Decrease
rs148838377
P755S
Decrease
rs1051691
I448T
Decrease
rs149536206
H8598∗
rs1051692
Y171H
Decrease
rs150114699
L991I
Decrease
rs2229429
D546E
Increase
rs181150880
R410Q
Decrease
rs7508518
A2G
Decrease
rs182552223
T858A
Decrease
rs52800171
W1220L
Decrease
rs183360558
D893N
Decrease
rs55816055
S353P
Increase
rs185736681
R1053C
Decrease
rs56395521
L1065V
Decrease
rs187282966
R889Q
Decrease
rs72549237
V362I
Decrease
rs199580495
S1033F
Increase
rs76077021
R889W
Decrease
rs199599404
M1319I
Decrease
rs76673783
E664G
Decrease
rs199659271
C219R
Decrease
rs78433961
R796S
Decrease
rs200059069
K411Q
Increase
rs78827745
M65K
Decrease
rs200110540
V866I
Decrease
rs79312957
R413C
Decrease
rs200199169
P271L
Decrease
rs113527718
S1297G
Decrease
rs200400127
A1340V
Decrease
rs138528064
T320M
Decrease
rs200921389
G1048D
Decrease
rs140762552
T107M
Decrease
rs201147780
K294R
Increase
rs140852238
E51K
Decrease
rs201466857
T858M
Decrease
rs201506342
P1312T
Decrease
rs201979105
S1221A
Decrease
rs201978448
A537V
Increase
rs202160383
R1128H
Decrease
∗Premature stop codon.Table 4
Common amino acid change due to deleterious nsSNPs in human INSR predicted by PROVEAN and PolyPhen algorithms.
SNP_ID
Mutation
PROVEAN results
PROVEAN score
PolyPhen results
PolyPhen score
rs52836744
G58R
Deleterious
−6.762
Probably damaging
1
rs121913145
H236R
Deleterious
−5.616
Probably damaging
1
rs121913156
R1201Q
Deleterious
−3.701
Probably damaging
1
rs13306449
Y1361C
Deleterious
−4.157
Probably damaging
1
rs1051691
I448T
Deleterious
−3.774
Probably damaging
0.996
rs52800171
W1220L
Deleterious
−11.648
Probably damaging
1
rs55816055
S353P
Deleterious
−2.761
Possibly damaging
0.528
rs76673783
E664G
Deleterious
−5.068
Possibly damaging
0.592
rs78827745
M65K
Deleterious
−3.808
Possibly damaging
0.934
rs79312957
R413C
Deleterious
−5.623
Probably damaging
0.999
rs140762552
T107M
Deleterious
−2.922
Probably damaging
1
rs141484557
G262S
Deleterious
−3.037
Possibly damaging
0.939
rs147671523
E517G
Deleterious
−4.293
Possibly damaging
0.726
rs199580495
S1033F
Deleterious
−5.642
Probably damaging
0.968
rs199659271
C219R
Deleterious
−9.831
Probably damaging
1
rs201466857
T858M
Deleterious
−3.384
Probably damaging
0.994
rs201506342
P1312T
Deleterious
−3.008
Possibly damaging
0.616
rs201979105
S1221A
Deleterious
−2.698
Probably damaging
0.997


### 3.3. Effects of nsSNPs on Protein Structure

By using the EMBL-EBI web-based tool PDBsum, the INSR protein structures were searched. Two related protein structures, namely, 2HR7 and 4IBM, were found to share 100% amino acid sequence similarity. The single amino acid polymorphism (SAAP) database server (http://www.bioinf.org.uk/saap/db/) is offline due to essential maintenance. Thus, we were unable to map the deleterious nsSNPs into protein structure through SAAP. Mapping the deleterious nsSNPs into protein structure information was performed through the LS-SNP/PDB server.According to this resource, 2HR7 accounted for 9 nsSNPs and 4IBM had 4 nsSNPs. Apart from the SNP scanning, LS-SNP/PDB server also predicts solvent accessibility and conservation ratio of given protein structures. An overview of mapping of mutant structures and their solvent accessibility and conservation ratios is given in Table5.Table 5
Mapping of nsSNPs in 2HR7 and 4IBM 3D structures.
SNP_ID
Mutation
PDB residue number
Solvent accessibility
Conservation
2HR7
rs52836744
G58R
31
Intermediate 10%
5%
rs121913145
H236R
209
Intermediate 23%
10%
rs1051691
I448T
421
Buried 1%
1%
rs55816055
S353P
326
Buried 5%
3%
rs78827745
M65K
38
Buried 0%
8%
rs79312957
R413C
386
Exposed 58%
3%
rs140762552
T107M
80
Buried 2%
5%
rs141484557
G262S
235
Exposed 46%
25%
rs199659271
C219R
192
Buried 5%
11%
4IBM
rs121913156
R1201Q
1174
Intermediate 10%
0%
rs52800171
W1220L
1193
Buried 1%
0%
rs199580495
S1033F
1006
Intermediate 30%
0%
rs201979105
S1221A
1194
Buried 5%
0%Out of 18 nsSNPs predicted to be deleterious by PROVEAN or PolyPhen, a total of 13 were mapped to the PDB ID 2HR7 and 4IBM native structures. All the functional nsSNPs predicted using the PROVEAN and PolyPhen tools were subjected to the PYMOL mutation tool. A model for each functional nsSNP was made by PYMOL mutagenesis tool and visualized using UCSF Chimera tool for comparison with the native structures (Figure2, only mutants rs1051691 (I421T) and rs121913156 (R1174Q) are shown).Figure 2
A comparison of amino acid substitutions due to nsSNPs. Two mutant structures of deleterious nsSNPs rs1051691 (I421T) (c) and rs121913156 (R1174Q) (d) were compared to their native structures 2HR7 (a) and 4IBM (b), respectively. Models were generated by using PYMOL and visualized by UCSF Chimera.
(a)
(b)
(c)
(d)Energy minimization is performed for the native structures (2HR7 and 4IBM) and the mutant modeled structures. The KoBaMIN web server uses a force field for energy minimization. The total energy for all the mutant and native models after minimization is listed in Table6. The total energies for the native structures of 2HR7 and 4IBM are −22087.6969 kJ/mol and −13041.4646 kJ/mol, respectively. Change in total energy due to mutation is noticeable in the both 2HR7 and 4IBM mutant models. RMSD is the measure of the deviation of the mutant structures from their native configurations. The higher the RMSD value, the more the deviation between the two structures. Structural changes, in turn, affect functional activity. RMSDs for all the mutant structures are listed in Table 6. The mutants rs79312957 and rs121913156 have higher RMSD value of 6.025 and 0.436 compared to native structures RMSD value 6.019 and 0.404, respectively. These two nsSNPs could be believed to affect the structure of the proteins. These two nsSNPs were also shown to be deleterious according to the PROVEAN and PolyPhen server. The 3D structure of the native INSR protein crystal structures 2HR7 and 4IBM and the predicted mutant structures were superimposed over chain A. The superimposed structures revealed that the mutants might have considerably affected the protein structure and thus its function (Figure 3; only rs79312957 is shown). Substituted amino acid residues in the mutants might have altered the conformation of the INSR or networking among neighboring amino acids or interaction between the substrate and receptor [28, 29].Table 6
RMSD and total energy after energy minimization of native structures and their mutant 3D models.
Molecules
RMSD (Å)
Total energy after energy minimization (kJ/mol)
2HR7 native-type structure
6.019
−22087.6969
2HR7 mutant (rs52836744)
6.007
−21968.7347
2HR7 mutant (rs121913145)
5.985
−21815.0585
2HR7 mutant (rs1051691)
5.997
−22160.7304
2HR7 mutant (rs55816055)
5.957
−21903.7516
2HR7 mutant (rs78827745)
5.991
−21816.4353
2HR7 mutant (rs79312957)
6.025
−21962.4576
2HR7 mutant (rs140762552)
5.992
−21823.0448
2HR7 mutant (rs141484557)
5.995
−22076.7362
2HR7 mutant (rs199659271)
5.98
−21736.5417
4IBM native-type structure
0.404
−13041.4646
4IBM mutant (rs121913156)
0.436
−13091.3512
4IBM mutant (rs52800171)
0.39
−12830.5659
4IBM mutant (rs199580495)
0.402
−11940.1628
4IBM mutant (rs201979105)
0.376
−13076.6808Figure 3
Superimposition of native and mutant structures. Native structure (2HR7) shows arginine at position 386 (a) and mutant modeled structure rs79312957 (R386C) shows cysteine residue at the corresponding position (b). (c) shows the superimposition of the native structure (red) with mutant modeled structure (green) in position 386.
(a)
(b)
(c)


### 3.4. Effects of nsSNP on Protein Stability

The effects of the nsSNPs on protein stability were computed with FOLD-X by mCSM server which uses an empirical energy equation to calculate the Gibbs free energy DDG. The empirical energy terms consider the location and type of a substituted residue. The mCSM is a structure based prediction tool. Two different analysis protocols were utilized to obtain maximum information over the effect of the single amino acid substitutions: (1) all the nsSNPs were considered singularly and their effect on the protein stability and interaction potential was determined; (2) the nsSNPs were considered according to the allelic sequences. Initially, all the structures were minimized and obtained a stable protein stability value. Then the structures for each single amino acid variation were generated using the Build Model feature of FOLD-X 3.0. Finally, the effect of each single amino acid variation on the protein stability of INSR was determined using the analyzed complex features. The mutation was considered as destabilizing and stabilizing when the DDG was >0 and <0, respectively. In this prediction method, all the mutant structures ultimately derived from the PROVEAN, PolyPhen, and I-Mutant programs were finally submitted to the mCSM server to predict mutant structure’s protein stability upon mutation. The mCSM predicted all structures as “Destabilizing” including two as “Highly Destabilizing” (Table7).Table 7
Protein stability upon mutation.
Molecules
Mutation
PDB residue number
RSA (%)
Predicted ΔΔG
Outcome
2HR7 native-type structure
2HR7 mutant (rs52836744)
G58R
31
33.2
−1.11
Destabilizing
2HR7 mutant (rs121913145)
H236R
209
30.5
−0.248
Destabilizing
2HR7 mutant (rs1051691)
I448T
421
0
−2.403
Highly Destabilizing
2HR7 mutant (rs55816055)
S353P
326
15.5
−0.339
Destabilizing
2HR7 mutant (rs78827745)
M65K
38
0
−1.568
Destabilizing
2HR7 mutant (rs79312957)
R413C
386
70.2
−0.931
Destabilizing
2HR7 mutant (rs140762552)
T107M
80
2.3
−0.387
Destabilizing
2HR7 mutant (rs141484557)
G262S
235
70.3
−0.775
Destabilizing
2HR7 mutant (rs199659271)
C219R
192
3.2
−0.039
Destabilizing
4IBM native-type structure
4IBM mutant (rs121913156)
R1201Q
1174
11.5
−1.347
Destabilizing
4IBM mutant (rs52800171)
W1220L
1193
2.1
−3.223
Highly Destabilizing
4IBM mutant (rs199580495)
S1033F
1006
16.7
−0.888
Destabilizing
4IBM mutant (rs201979105)
S1221A
1194
9.5
−1.858
Destabilizing


## 3.1. SNP Dataset from dbSNP

The dbSNP contains both validated and nonvalidated polymorphisms. In spite of this drawback, we opted to avail the dbSNP because allelic frequency of most of nsSNPs of INSR has been recorded there and that is the most extensive SNP database. In our data search, some previously reported SNPs in dbSNP have been identified as invalid because of wrong sequencing and alignment. These erroneous SNPs have expired or have merged with other SNPs. SomeINSR genes have been renamed. We carefully cross-examined the databases and removed those old and invalid SNPs. At dbSNP,INSR gene contains data for 4967 SNPs. Out of 4967 SNPs, only 57 were nsSNPs in the coding region (Table 1). Our investigation accounted for the nsSNPs in the coding region only.Table 1
List of nsSNPs that were predicted by PROVEAN to have functional significance.
SNP_ID
Mutation
PROVEAN results
PROVEAN score
rs1799816
V1012M
Neutral
−2.418
rs52836744
G58R
Deleterious
−6.762
rs121913144
R1027∗
rs121913145
H236R
Deleterious
−5.616
rs121913156
R1201Q
Deleterious
−3.701
rs891087
D261E
Neutral
−0.116
rs2162771
P830L
Neutral
−1.576
rs13306449
Y1361C
Deleterious
−4.157
rs35045353
G811S
Neutral
−2.401
rs1051691
I448T
Deleterious
−3.774
rs1051692
Y171H
Neutral
−1.304
rs2229429
D546E
Neutral
−2.474
rs7508518
A2G
Neutral
0.465
rs52800171
W1220L
Deleterious
−11.648
rs55816055
S353P
Deleterious
−2.761
rs56395521
L1065V
Neutral
−1.133
rs72549237
V362I
Neutral
−0.211
rs76077021
R889W
Deleterious
−4.254
rs76673783
E664G
Deleterious
−5.068
rs78433961
R796S
Neutral
−0.984
rs78827745
M65K
Deleterious
−3.808
rs79312957
R413C
Deleterious
−5.623
rs113527718
S1297G
Deleterious
−2.539
rs138528064
T320M
Neutral
−1.959
rs140762552
T107M
Deleterious
−2.922
rs140852238
E51K
Deleterious
−2.545
rs141484557
G262S
Deleterious
−3.037
rs142391704
A706D
Neutral
0.622
rs142910337
D75G
Neutral
1.672
rs143523271
S748L
Neutral
−0.793
rs143919163
G192D
Neutral
−1.884
rs144029037
V900I
Neutral
−0.698
rs146588336
D946E
Neutral
−0.743
rs147671523
E517G
Deleterious
−4.293
rs148838377
P755S
Neutral
−0.076
rs149536206
H8598∗
rs150114699
L991I
Neutral
−1.71
rs181150880
R410Q
Neutral
−2.011
rs182552223
T858A
Deleterious
−2.612
rs183360558
D893N
Neutral
−1.752
rs185736681
R1053C
Deleterious
−5.933
rs187282966
R889Q
Neutral
−1.492
rs199580495
S1033F
Deleterious
−5.642
rs199599404
M1319I
Neutral
−1.196
rs199659271
C219R
Deleterious
−9.831
rs200059069
K411Q
Neutral
−1.21
rs200110540
V866I
Neutral
−0.134
rs200199169
P271L
Neutral
−2.315
rs200400127
A1340V
Neutral
−1.519
rs200921389
G1048D
Neutral
−1.585
rs201147780
K294R
Neutral
−0.904
rs201466857
T858M
Deleterious
−3.384
rs201506342
P1312T
Deleterious
−3.008
rs201978448
A537V
Deleterious
−3.252
rs201979105
S1221A
Deleterious
−2.698
rs202160383
R1128H
Neutral
−2.063
∗Premature stop codon.


## 3.2. Effects of nsSNPs on INSR Predicted by Different Tools

The PROVEAN algorithm works mainly with primary sequence for prediction while other tools perform similar task with the structure. Since PROVEAN can predict a large number of substitutions and does not require structures, it is advantageous over other tools. PROVEAN predicts the effect of the variant on the biological function of the protein based on sequence homology. The scores of PROVEAN are classified as “deleterious” below a certain threshold (here −2.5) and “neutral” above it. A  .txt file containing “db SNP rsIDs” of all 57 nsSNPs was submitted to the “dbSNP rsIDs” page to calculate the PROVEAN score. Out of 57 nsSNPs, PROVEAN predicted 24 as deleterious and 33 as neutral (Table1). Among the 24 deleterious nsSNPs mutations, W1220L and C219R were predicted as highly deleterious with PROVEAN scores of −11.648 and −9.831, respectively.PolyPhen identifies homologues of the input sequences via BLAST and calculates PSIC scores for every variant and estimates the difference between the variant scores; the difference of 0.339 is detrimental. There are certain empirical rules applied to the sequences and the accuracy is approximately 82% with a chance of 8% false-positive prediction. The protein accession number of INSR (P06213) and the amino acid substitutions corresponding to each of the 57 nsSNPs were submitted separately. Table2 summarizes the results obtained from the PolyPhen server. A PSIC score difference was assigned to categorize SNPs as benign and damaging. “PolyPhen-2: scores are evaluated as 0.000 (most probably benign) to 0.999 (most probably damaging).” Twenty-one of the 57 nsSNPs were predicted as “damaging,” and the PSIC scores fell into the range of 1.51 to 3.41. 18 nsSNPs predicted to be deleterious by the SIFT (Sorting Intolerant from Tolerant) program were also predicted to be damaging by the PolyPhen server.Table 2
Potential effect of amino acid substitution for nsSNPs in human INSR predicted by the PolyPhen algorithm.
SNP_ID
Mutation
PolyPhen results
Score
Sensitivity
Specificity
rs1799816
V1012M
Probably damaging
0.992
0.7
0.97
rs52836744
G58R
Probably damaging
1
0
1
rs121913144
R1027∗
rs121913145
H236R
Probably damaging
1
0
1
rs121913156
R1201Q
Probably damaging
1
0
1
rs891087
D261E
Benign
0
1
0
rs2162771
P830L
Benign
0
1
0
rs13306449
Y1361C
Probably damaging
1
0
1
rs35045353
G811S
Benign
0.441
0.89
0.9
rs1051691
I448T
Probably damaging
0.996
0.55
0.98
rs1051692
Y171H
Benign
0.024
0.95
0.81
rs2229429
D546E
Benign
0.032
0.95
0.82
rs7508518
A2G
Benign
0
1
0
rs52800171
W1220L
Probably damaging
1
0
1
rs55816055
S353P
Possibly damaging
0.528
0.88
0.9
rs56395521
L1065V
Benign
0
1
0
rs72549237
V362I
Benign
0.003
0.98
0.44
rs76077021
R889W
Benign
0.111
0.93
0.86
rs76673783
E664G
Possibly damaging
0.592
0.87
0.91
rs78433961
R796S
Benign
0.001
0.99
0.15
rs78827745
M65K
Possibly damaging
0.934
0.8
0.94
rs79312957
R413C
Probably damaging
0.999
0.14
0.99
rs113527718
S1297G
Benign
0.004
0.97
0.59
rs138528064
T320M
Benign
0.199
0.92
0.88
rs140762552
T107M
Probably damaging
1
0
1
rs140852238
E51K
Benign
0.003
0.98
0.44
rs141484557
G262S
Possibly damaging
0.939
0.8
0.94
rs142391704
A706D
Benign
0
1
0
rs142910337
D75G
Benign
0
1
0
rs143523271
S748L
Benign
0
1
0
rs143919163
G192D
Benign
0.005
0.97
0.74
rs144029037
V900I
Benign
0.048
0.94
0.83
rs146588336
D946E
rs147671523
E517G
Possibly damaging
0.726
0.86
0.92
rs148838377
P755S
Benign
0
1
0
rs149536206
H8598∗
rs150114699
L991I
Benign
0.442
0.89
0.9
rs181150880
R410Q
Possibly damaging
0.935
0.8
0.94
rs182552223
T858A
Benign
0.007
0.96
0.75
rs183360558
D893N
Benign
0
1
0
rs185736681
R1053C
Benign
0.223
0.91
0.88
rs187282966
R889Q
Benign
0.252
0.91
0.88
rs199580495
S1033F
Probably damaging
0.968
0.77
0.95
rs199599404
M1319I
Benign
0.007
0.96
0.75
rs199659271
C219R
Probably damaging
1
0
1
rs200059069
K411Q
Possibly damaging
0.75
0.85
0.92
rs200110540
V866I
Benign
0
1
0
rs200199169
P271L
Benign
0
1
0
rs200400127
A1340V
Benign
0.021
0.95
0.8
rs200921389
G1048D
Benign
0.009
0.96
0.77
rs201147780
K294R
Benign
0.008
0.96
0.76
rs201466857
T858M
Probably damaging
0.994
0.69
0.97
rs201506342
P1312T
Possibly damaging
0.616
0.87
0.91
rs201978448
A537V
Benign
0.143
0.92
0.86
rs201979105
S1221A
Probably damaging
0.997
0.41
0.98
rs202160383
R1128H
Benign
0.031
0.95
0.82
∗
Premature stop codon.I-Mutant is a neural network based routine tool used in the analysis of protein stability alterations by considering the single-site mutation. I-Mutant also provides the scores for free energy alterations, calculated with the FOLD-X energy based web server. By assimilating the FOLD-X estimations with those of I-Mutant, about 93% precision can be achieved. We have considered a threshold of −1.5 Kcal/mol to predict a SNP to be destabilized. Forty-six nsSNPs were considered as destabilized with DDG values by I-Mutant (Table3). Finally, we selected 18 significant nsSNPs because they were predicted to be deleterious by PROVEAN, PolyPhen, and SIFT programs and showed decreased structural stability following analysis by I-Mutant (Table 4).Table 3
List of nsSNPs’ stability predicted by I-MUTANT.
SNP_ID
Mutation
Stability
SNP_ID
Mutation
Stability
rs1799816
V1012M
Decrease
rs141484557
G262S
Decrease
rs52836744
G58R
Decrease
rs142391704
A706D
Decrease
rs121913144
R1027∗
rs142910337
D75G
Decrease
rs121913145
H236R
Decrease
rs143523271
S748L
Increase
rs121913156
R1201Q
Decrease
rs143919163
G192D
Decrease
rs891087
D261E
Increase
rs144029037
V900I
Decrease
rs2162771
P830L
Decrease
rs146588336
D946E
Increase
rs13306449
Y1361C
Increase
rs147671523
E517G
Increase
rs35045353
G811S
Decrease
rs148838377
P755S
Decrease
rs1051691
I448T
Decrease
rs149536206
H8598∗
rs1051692
Y171H
Decrease
rs150114699
L991I
Decrease
rs2229429
D546E
Increase
rs181150880
R410Q
Decrease
rs7508518
A2G
Decrease
rs182552223
T858A
Decrease
rs52800171
W1220L
Decrease
rs183360558
D893N
Decrease
rs55816055
S353P
Increase
rs185736681
R1053C
Decrease
rs56395521
L1065V
Decrease
rs187282966
R889Q
Decrease
rs72549237
V362I
Decrease
rs199580495
S1033F
Increase
rs76077021
R889W
Decrease
rs199599404
M1319I
Decrease
rs76673783
E664G
Decrease
rs199659271
C219R
Decrease
rs78433961
R796S
Decrease
rs200059069
K411Q
Increase
rs78827745
M65K
Decrease
rs200110540
V866I
Decrease
rs79312957
R413C
Decrease
rs200199169
P271L
Decrease
rs113527718
S1297G
Decrease
rs200400127
A1340V
Decrease
rs138528064
T320M
Decrease
rs200921389
G1048D
Decrease
rs140762552
T107M
Decrease
rs201147780
K294R
Increase
rs140852238
E51K
Decrease
rs201466857
T858M
Decrease
rs201506342
P1312T
Decrease
rs201979105
S1221A
Decrease
rs201978448
A537V
Increase
rs202160383
R1128H
Decrease
∗Premature stop codon.Table 4
Common amino acid change due to deleterious nsSNPs in human INSR predicted by PROVEAN and PolyPhen algorithms.
SNP_ID
Mutation
PROVEAN results
PROVEAN score
PolyPhen results
PolyPhen score
rs52836744
G58R
Deleterious
−6.762
Probably damaging
1
rs121913145
H236R
Deleterious
−5.616
Probably damaging
1
rs121913156
R1201Q
Deleterious
−3.701
Probably damaging
1
rs13306449
Y1361C
Deleterious
−4.157
Probably damaging
1
rs1051691
I448T
Deleterious
−3.774
Probably damaging
0.996
rs52800171
W1220L
Deleterious
−11.648
Probably damaging
1
rs55816055
S353P
Deleterious
−2.761
Possibly damaging
0.528
rs76673783
E664G
Deleterious
−5.068
Possibly damaging
0.592
rs78827745
M65K
Deleterious
−3.808
Possibly damaging
0.934
rs79312957
R413C
Deleterious
−5.623
Probably damaging
0.999
rs140762552
T107M
Deleterious
−2.922
Probably damaging
1
rs141484557
G262S
Deleterious
−3.037
Possibly damaging
0.939
rs147671523
E517G
Deleterious
−4.293
Possibly damaging
0.726
rs199580495
S1033F
Deleterious
−5.642
Probably damaging
0.968
rs199659271
C219R
Deleterious
−9.831
Probably damaging
1
rs201466857
T858M
Deleterious
−3.384
Probably damaging
0.994
rs201506342
P1312T
Deleterious
−3.008
Possibly damaging
0.616
rs201979105
S1221A
Deleterious
−2.698
Probably damaging
0.997


## 3.3. Effects of nsSNPs on Protein Structure

By using the EMBL-EBI web-based tool PDBsum, the INSR protein structures were searched. Two related protein structures, namely, 2HR7 and 4IBM, were found to share 100% amino acid sequence similarity. The single amino acid polymorphism (SAAP) database server (http://www.bioinf.org.uk/saap/db/) is offline due to essential maintenance. Thus, we were unable to map the deleterious nsSNPs into protein structure through SAAP. Mapping the deleterious nsSNPs into protein structure information was performed through the LS-SNP/PDB server.According to this resource, 2HR7 accounted for 9 nsSNPs and 4IBM had 4 nsSNPs. Apart from the SNP scanning, LS-SNP/PDB server also predicts solvent accessibility and conservation ratio of given protein structures. An overview of mapping of mutant structures and their solvent accessibility and conservation ratios is given in Table5.Table 5
Mapping of nsSNPs in 2HR7 and 4IBM 3D structures.
SNP_ID
Mutation
PDB residue number
Solvent accessibility
Conservation
2HR7
rs52836744
G58R
31
Intermediate 10%
5%
rs121913145
H236R
209
Intermediate 23%
10%
rs1051691
I448T
421
Buried 1%
1%
rs55816055
S353P
326
Buried 5%
3%
rs78827745
M65K
38
Buried 0%
8%
rs79312957
R413C
386
Exposed 58%
3%
rs140762552
T107M
80
Buried 2%
5%
rs141484557
G262S
235
Exposed 46%
25%
rs199659271
C219R
192
Buried 5%
11%
4IBM
rs121913156
R1201Q
1174
Intermediate 10%
0%
rs52800171
W1220L
1193
Buried 1%
0%
rs199580495
S1033F
1006
Intermediate 30%
0%
rs201979105
S1221A
1194
Buried 5%
0%Out of 18 nsSNPs predicted to be deleterious by PROVEAN or PolyPhen, a total of 13 were mapped to the PDB ID 2HR7 and 4IBM native structures. All the functional nsSNPs predicted using the PROVEAN and PolyPhen tools were subjected to the PYMOL mutation tool. A model for each functional nsSNP was made by PYMOL mutagenesis tool and visualized using UCSF Chimera tool for comparison with the native structures (Figure2, only mutants rs1051691 (I421T) and rs121913156 (R1174Q) are shown).Figure 2
A comparison of amino acid substitutions due to nsSNPs. Two mutant structures of deleterious nsSNPs rs1051691 (I421T) (c) and rs121913156 (R1174Q) (d) were compared to their native structures 2HR7 (a) and 4IBM (b), respectively. Models were generated by using PYMOL and visualized by UCSF Chimera.
(a)
(b)
(c)
(d)Energy minimization is performed for the native structures (2HR7 and 4IBM) and the mutant modeled structures. The KoBaMIN web server uses a force field for energy minimization. The total energy for all the mutant and native models after minimization is listed in Table6. The total energies for the native structures of 2HR7 and 4IBM are −22087.6969 kJ/mol and −13041.4646 kJ/mol, respectively. Change in total energy due to mutation is noticeable in the both 2HR7 and 4IBM mutant models. RMSD is the measure of the deviation of the mutant structures from their native configurations. The higher the RMSD value, the more the deviation between the two structures. Structural changes, in turn, affect functional activity. RMSDs for all the mutant structures are listed in Table 6. The mutants rs79312957 and rs121913156 have higher RMSD value of 6.025 and 0.436 compared to native structures RMSD value 6.019 and 0.404, respectively. These two nsSNPs could be believed to affect the structure of the proteins. These two nsSNPs were also shown to be deleterious according to the PROVEAN and PolyPhen server. The 3D structure of the native INSR protein crystal structures 2HR7 and 4IBM and the predicted mutant structures were superimposed over chain A. The superimposed structures revealed that the mutants might have considerably affected the protein structure and thus its function (Figure 3; only rs79312957 is shown). Substituted amino acid residues in the mutants might have altered the conformation of the INSR or networking among neighboring amino acids or interaction between the substrate and receptor [28, 29].Table 6
RMSD and total energy after energy minimization of native structures and their mutant 3D models.
Molecules
RMSD (Å)
Total energy after energy minimization (kJ/mol)
2HR7 native-type structure
6.019
−22087.6969
2HR7 mutant (rs52836744)
6.007
−21968.7347
2HR7 mutant (rs121913145)
5.985
−21815.0585
2HR7 mutant (rs1051691)
5.997
−22160.7304
2HR7 mutant (rs55816055)
5.957
−21903.7516
2HR7 mutant (rs78827745)
5.991
−21816.4353
2HR7 mutant (rs79312957)
6.025
−21962.4576
2HR7 mutant (rs140762552)
5.992
−21823.0448
2HR7 mutant (rs141484557)
5.995
−22076.7362
2HR7 mutant (rs199659271)
5.98
−21736.5417
4IBM native-type structure
0.404
−13041.4646
4IBM mutant (rs121913156)
0.436
−13091.3512
4IBM mutant (rs52800171)
0.39
−12830.5659
4IBM mutant (rs199580495)
0.402
−11940.1628
4IBM mutant (rs201979105)
0.376
−13076.6808Figure 3
Superimposition of native and mutant structures. Native structure (2HR7) shows arginine at position 386 (a) and mutant modeled structure rs79312957 (R386C) shows cysteine residue at the corresponding position (b). (c) shows the superimposition of the native structure (red) with mutant modeled structure (green) in position 386.
(a)
(b)
(c)


## 3.4. Effects of nsSNP on Protein Stability

The effects of the nsSNPs on protein stability were computed with FOLD-X by mCSM server which uses an empirical energy equation to calculate the Gibbs free energy DDG. The empirical energy terms consider the location and type of a substituted residue. The mCSM is a structure based prediction tool. Two different analysis protocols were utilized to obtain maximum information over the effect of the single amino acid substitutions: (1) all the nsSNPs were considered singularly and their effect on the protein stability and interaction potential was determined; (2) the nsSNPs were considered according to the allelic sequences. Initially, all the structures were minimized and obtained a stable protein stability value. Then the structures for each single amino acid variation were generated using the Build Model feature of FOLD-X 3.0. Finally, the effect of each single amino acid variation on the protein stability of INSR was determined using the analyzed complex features. The mutation was considered as destabilizing and stabilizing when the DDG was >0 and <0, respectively. In this prediction method, all the mutant structures ultimately derived from the PROVEAN, PolyPhen, and I-Mutant programs were finally submitted to the mCSM server to predict mutant structure’s protein stability upon mutation. The mCSM predicted all structures as “Destabilizing” including two as “Highly Destabilizing” (Table7).Table 7
Protein stability upon mutation.
Molecules
Mutation
PDB residue number
RSA (%)
Predicted ΔΔG
Outcome
2HR7 native-type structure
2HR7 mutant (rs52836744)
G58R
31
33.2
−1.11
Destabilizing
2HR7 mutant (rs121913145)
H236R
209
30.5
−0.248
Destabilizing
2HR7 mutant (rs1051691)
I448T
421
0
−2.403
Highly Destabilizing
2HR7 mutant (rs55816055)
S353P
326
15.5
−0.339
Destabilizing
2HR7 mutant (rs78827745)
M65K
38
0
−1.568
Destabilizing
2HR7 mutant (rs79312957)
R413C
386
70.2
−0.931
Destabilizing
2HR7 mutant (rs140762552)
T107M
80
2.3
−0.387
Destabilizing
2HR7 mutant (rs141484557)
G262S
235
70.3
−0.775
Destabilizing
2HR7 mutant (rs199659271)
C219R
192
3.2
−0.039
Destabilizing
4IBM native-type structure
4IBM mutant (rs121913156)
R1201Q
1174
11.5
−1.347
Destabilizing
4IBM mutant (rs52800171)
W1220L
1193
2.1
−3.223
Highly Destabilizing
4IBM mutant (rs199580495)
S1033F
1006
16.7
−0.888
Destabilizing
4IBM mutant (rs201979105)
S1221A
1194
9.5
−1.858
Destabilizing


## 4. Discussion

The SNP inINSR can manifest several insulin-resistant syndromes like Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance [30, 31]. Diagnostic measures have already been established on clinical examination as well as laboratory diagnostic tests with elevated insulin levels as a constant feature. Functional and DNA analysis can be used for absolute confirmation, but certain mutations do not contribute to insulin binding and DNA analysis is still not able to identify all the putative mutations. Although there is no direct genotype-phenotype correlation, but mutations in the alpha subunit of the insulin receptor are associated with a more severe phenotype compared to the mutations affecting the beta subunit [32]. Numerous studies have been conducted usingin silico analysis approaches to predict the functional effects of nsSNPs on genes such asG6PD,BARF, andPTEN [33, 34]. Therefore, for addressing this issue, we selectedin silico strategy to analyze and predict the functional effects of SNPs on INSR.We used differentin silico methods based on the combination of two distinctive approaches which are sequence and structural based approaches. In comparison with the structure based methods, sequence based prediction methods are one step ahead because they can be applied to any proteins with known relatives, whereas structure based approaches are not feasible to implement for proteins with unknown 3D structures. Software programs and servers that integrate both sequence and structure resources have advantage of being able to assess the authenticity of the predicted results by cross-referencing the results from both methods. Most computational methods utilize this information for the prediction and analyses of deleterious nsSNPs, among which PROVEAN and PolyPhen algorithms are the main representatives. Considering normalized probability score below −2.5 in PROVEAN and a PSIC score 1.5 in PolyPhen as deleterious, 24 and 21 of amino acid substitutions were predicted to have functional impact on INSR gene. The variation in prediction score of PROVEAN and PolyPhen is mainly because of the difference in sequence alignment and the values used to classify the variants. Significant similarity was observed between the results obtained by PROVEAN and PolyPhen. PROVEAN and PolyPhen in predicting the effect of nsSNPs on protein function might be suitablein silico approach [35].In order to predict the impact of nsSNPs on protein structure, I-Mutant 3.0 was used which evaluated the stability change upon single-site mutation. I-Mutant 3.0 was ranked as one of the most reliable predictors based on the work performed by Khan and Vihinen [36]. Based on the difference in Gibbs free energy value of mutant and wild type protein, 45 nsSNPs were found to largely destabilize the protein. Structures of the several human INSRs are available in PDB and have been used to analyze the effect of polymorphisms. A 3D structure is essential for analyzing the impact of the SNPs in structural level. Therefore, we predicted the 3D structure most similar to human INSR through the EMBL PDBsum program. Depending on the highest sequence similarity and alignment, we selected 2HR7 and 4IBM from the PDB. Already predicted deleterious and disease-related nsSNPs predicted by PROVEAN and PolyPhen were further subjected to LS-SNP/PDB server for mapping SNPs in 2HR7 and 4IBM crystal structures. The mutant structures served as valuable tool to compare and predict protein stability, RMSD, and energy calculation between wild type and mutant type structures.Each mutation was considered individually to study the inherent effect of the SNP. In addition, the allelic sequences were analyzed to investigate if the polymorphisms neutralized each other by occurring simultaneously as an act of preservation of function by nature. The mCSM was used to analyze the effects of single amino acid variations on the structure and stability of the protein.Our results indicated that all of the 13 mutant structures of 2HR7 and 4IBM were predicted as “Destabilizing” which signified our results found by PROVEAN and PolyPhen. Among all the destabilized mutant structures, two mutants were labelled as “Highly Destabilizing” which were rs1051691 and rs52800171 in their I448T and W1220L positions, respectively, which suggested that these polymorphisms should be considered as a potential target for future experiments. If a single amino acid variation shows a change in protein stability or protein-protein interaction, it should give comparable values with the sign reversal for the reverse mutation. This would indicate that the prediction of the effect of the single amino acid variation on the protein structure or protein-protein interaction might be substantial.


## 5. Conclusions

This study shows a correlation between SNPs in theINSR gene and several diseases like insulin-resistant syndromes such as Leprechaunism, Rabson-Mendenhall syndrome, and type A insulin resistance. The present study concludes that 13 nsSNPs especially rs1051691 and rs52800171 decreases protein stability and are not tolerated or may result in loss of function. Their presence in the INSR increases the possibility of altered transcriptional and cell cycle regulation and INSR mediated diseases. Therefore, the probability of their involvement in disease predisposition increases. Thus, for further analysis, these mutations should be given priority to obtain detailed information on their effects. In order to confirm the structures modeled in this study, the actual structures should be determined by X-ray crystallography or nuclear magnetic resonance spectroscopy. We anticipate that the results obtained from our analysis would pave the way for providing useful information to the researchers and can play an important role in bridging the gap between biologists and bioinformaticians.



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*Source: 2023803-2016-10-20.xml*