Advances in Bioinformatics
Volume 2016 (2016), Article ID 2632917, 15 pages
http://dx.doi.org/10.1155/2016/2632917

Research Article

Bioinformatics Approach for Prediction of Functional Coding/Noncoding Simple Polymorphisms (SNPs/Indels) in Human BRAF Gene

Mohamed M. Hassan,¹ Shaza E. Omer,² Rahma M. Khalf-allah,³ Razaz Y. Mustafa,⁴ Isra S. Ali,⁵ and Sofia B. Mohamed⁶

¹Faculty of Medical Laboratory Sciences, University of Medical Science and Technology, Khartoum, Sudan
²Department of Biotechnology, Faculty of Applied and Industrial Science, University of Juba, Khartoum, Sudan
³Faculty of Pharmacy, Omdurman Islamic University, Khartoum, Sudan
⁴Faculty of Science, University of Khartoum, Khartoum, Sudan
⁵Faculty of Medical Laboratory Sciences, Karary University, Khartoum, Sudan
⁶Tropical Medicine Research Institute, Khartoum, Sudan

Received 26 November 2015; Revised 10 May 2016; Accepted 12 May 2016

Academic Editor: Ming Chen

Copyright © 2016 Mohamed M. Hassan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

This study was carried out for Homo sapiens single variation (SNPs/Indels) in BRAF gene through coding/non-coding regions. Variants data was obtained from database of SNP even last update of November, 2015. Many bioinformatics tools were used to identify functional SNPs and indels in proteins functions, structures and expressions. Results shown, for coding polymorphisms, 111 SNPs predicted as highly damaging and six other were less. For UTRs, showed five SNPs and one indel were altered in micro RNAs binding sites (3′ UTR), furthermore nil SNP or indel have functional altered in transcription factor binding sites (5′ UTR). In addition for 5′/3′ splice sites, analysis showed that one SNP within 5′ splice site and one Indel in 3′ splice site showed potential alteration of splicing. In conclude these previous functional identified SNPs and indels could lead to gene alteration, which may be directly or indirectly contribute to the occurrence of many diseases.

1. Introduction

Genetic alterations (mutations) in general can be divided into two categories, inheritable (germline mutations) with 2% to 4% occurrence and sporadic (somatic mutations) [1, 2]. BRAF coding gene, member of RAF family, located on chromosome seven (7q34), region from 140,715,951 to 140,924,764 base pairs which cover approximately 190 kb, is composed of 18 exons, and its translated protein name is “B-Raf proto-oncogene serine/threonine protein kinase.” This protein belongs to raf/mil family, which plays a role in regulating the MAP kinase/ERKs signaling pathway, which affects cell division, differentiation, and secretion [3]. Several studies reported the mutation prevalence in BRAF gene through various cancers, including non-Hodgkin lymphoma, colorectal cancer, malignant melanoma, thyroid carcinoma, non-small-cell lung carcinoma, and adenocarcinoma of lung [3–5]. Mutations in this gene have also been associated with various diseases such as cardiofaciocutaneous syndrome, a disease characterized by heart defects, mental retardation, and a distinctive facial appearance, Noonan syndrome, multiple lentigines syndrome or LEOPARD syndrome, giant congenital melanocytic nevus, and Erdheim-Chester disease [6, 7].

Single nucleotide polymorphisms (SNPs) markers are single-base changes in DNA sequence, with allele frequency of 1% or greater among population; it normally occurs throughout the genome with frequency of about one in every 1000 nucleotides, which is considered the simplest and common type of the genetic markers leading to DNA variation among individuals [8]. Nonsynonymous SNPs (nsSNPs) are one of coding SNPs types, important type of SNPs leading to the diversity of encoded human proteins, whereas they affect gene regulation by altering DNA and transcriptional binding factors, maintain the structural integrity of the cell, and affect proteins function in the different signal transduction pathways [9]. About 2% of the all known single nucleotide variants associated with genetic diseases are nonsynonymous SNPs and contribute to the functional diversity of the encoded proteins in the human population [10]. SNPs may be responsible for genetic diversity, evolution process, differences in traits, drugs response, and complex and common diseases such as diabetes, hypertension, and cancers. Therefore, identification and analysis of numerous SNP variations in genes may help in understanding their effects on genes product and their association with diseases and also could help in the development of new medical testing markers and individualized medication treatment [11].

1000 Genomes Project showed that most human genetic variation is represented by SNPs. Database of SNP (dbSNP) is one of the most databases serving as a central and public store for genetic variation since its initiation in September 1998 [12]. Any laboratory or individual can use the index variation, sequence information around polymorphism, and specific experimental conditions for further research applications. As with all NCBI resources, the data within dbSNP is available for free and in a variety of forms. In November 17, 2015, SNP database contained 160508575 number of Homo sapiens variants. From total number of variants, of which 144205811 were SNPs, 16064552 were Indels (single or multi-insertion/deletion). Database of SNP contains the results of HapMap and 1000 Genomes Projects (http://www.ncbi.nlm.nih.gov/snp/).

Through noncoding regions (3′ UTR, 5′ UTR), polymorphisms such as SNPs in microRNAs (miRNAs/mRNA) binding sites which are called mirSNPs can affect miRNAs function and then gene expression, resulting in many human diseases such as cancers [13]. Identification of SNPs responsible for phenotypes change is considered a difficulty, whereas it requires multiple testing for different SNPs in candidate genes [9]. One possible way to overcome this problem was to prioritize SNPs according to their structural and functional significance using different bioinformatics prediction tools. This study was focusing on functional SNPs within coding, 5′ UTR, 3′/5′ splice sites, transcription factor, and miRNA binding sites simple polymorphisms (SNPs/Indels) in human BRAF gene.

2. Materials and Methods

SNPs located in target gene were obtained from the database of SNPs (dbSNP); it is a public-domain archive for a broad collection of simple genetic polymorphisms. This collection of polymorphisms includes single-base nucleotide substitutions (SNPs), small-scale multibase deletions or insertions (also called deletion-insertion polymorphisms), and retroposable element insertions and microsatellite repeat variations (short tandem repeats or STRs) (http://www.ncbi.nlm.nih.gov/snp/). The related protein sequences are obtained from UniProt database (http://www.uniprot.org/).

SNP database contains SNPs or Indels within 3′/5′ UTR, 3′/5′ splice sites, coding synonymous, intron, and nonsynonymous which represent missense, nonsense, stop gain, and frameshift. In this study Homo sapiens SNPs and Indels (single insertion or deletion) within coding (nonsynonymous), 3′/5′ UTR, and 3′/5′ splice sites had been selected and submitted to bioinformatics tools for further investigation. Distributions of single variants are shown in Table 1.

Table 1: Distributions of SNPs and Indels.

Regions
Number of SNPs
Number of Indels
Total

All over BRAF gene
9585
1111
10696

Coding
350
2
352

(i) Missense
228
—
228

(ii) Nonsense and stop gain
4
—
4

(iii) Frame shift
—
2
2

3′ UTR
331
31
362

5′ UTR
5
—
5

3′ splice site
1
1
2

5′ splice site
1
0
1

About the main diagram of SNPs analysis, for missense SNPs, analysis was done by using three tools (SIFT server, PolyPhen, and SNAP2) and SNPs predicted as functional or damaging by previous triple servers were arranged in Table 2. More information about triple predicted SNPs is shown in Table 3. For frameshift SNPs, the analysis was done using SIFT server. By the same token for 3′ UTR SNPs and Indels, PolymiRTS database was used (Table 6). After that, for 5′ UTR SNPs (in transcription factor binding sites), PROMO tool was used (Table 7). Lastly for 3′/5′ splice sites SNPs and Indels, analysis was done using HSF tool (Table 8).

Table 2: Gene, protein, and clinically significant information of triple damaging predicted SNPs.

SNP ID
Ch7 location
Nucleotide change
Protein ID
Amino acid change
Clin/sig

rs150050723
140534634
T|G
ENSP00000288602
Q93H
—

rs180177034
140501336
C|G
ENSP00000288602
A246P
Path/3

rs387906660
140501350
G|A
ENSP00000288602
T241M
Path/3

rs387906660
G|C
ENSP00000288602
T241R

rs387906661
140501351
T|G
ENSP00000288602
T241P
Path/5

rs397507466
140501337
T|G
ENSP00000288602
L245F
Path/2

rs397507467
140501332
A|G
ENSP00000288602
F247S
L.Path/1

rs397509343
140501331
A|C
ENSP00000288602
F247L
L.Path/1

rs267601317
140494148
G|A
ENSP00000288602
P367L
—

rs199927105
140482930
G|T
ENSP00000288602
P402H
—

rs121913348
140481417
C|G
ENSP00000288602
G464A
Path/3

rs121913348
C|A
ENSP00000288602
G464V
L.Path/1

rs121913348
C|T
ENSP00000288602
G464E

rs121913353
140481412
C|G
ENSP00000288602
G466R
L.Path/1

rs121913357
140481403
C|T
ENSP00000288602
G469R
—

rs121913376
140481397
C|A
ENSP00000288602
V471F
Path/1

rs121913376
C|T
ENSP00000288602
V471I
L.Path/1

rs180177033
140481420
A|C
ENSP00000288602
I463S
Path/1

rs397507473
140481405
A|G
ENSP00000288602
F468S
Path/2

rs121913349
140481418
C|G
ENSP00000288602
G464R
—

rs121913371
140481478
G|A
ENSP00000288602
R444W
—

rs121913351
140481411
C|G
ENSP00000288602
G466A
Path/2

rs121913351
C|A
ENSP00000288602
G466V

rs121913351
C|T
ENSP00000288602
G466E

rs121913355
140481402
C|G
ENSP00000288602
G469A
Path/1

rs121913355
C|A
ENSP00000288602
G469V

rs121913355
C|T
ENSP00000288602
G469E

rs180177036
140477853
C|G
ENSP00000288602
L485F
Path/2

rs180177038
C|T
ENSP00000288602
E501K

rs180177039
T|C
ENSP00000288602
E501G

rs397507474
140477861
T|G
ENSP00000288602
K483Q
Path/2

rs397507475
140477854
A|G
ENSP00000288602
L485S
Path/2

rs397507476
140477811
T|G
ENSP00000288602
K499N
Path/1

rs397507477
140477795
G|A
ENSP00000288602
L505F
L.Path/1

rs375520366
140476806
G|A
ENSP00000288602
P490S
Path/1

rs180177041
140476806
C|G
ENSP00000288602
G534R
Path/1

rs397507479
140476811
C|T
ENSP00000288602
C532Y
Path/1

rs180177040
140453987
T|G
ENSP00000288602
N581H
Path/2

rs180177040
T|C
ENSP00000288602
N581D

rs397507481
140454006
G|C
ENSP00000288602
H574Q
Path/1

rs121913341
140453150
A|C
ENSP00000288602
F595L
Path/2

rs121913361
140453149
C|G
ENSP00000288602
G596R
Un.S/1

rs121913364
140453134
T|C
ENSP00000288602
K601E
—

rs121913366
140453145
A|T
ENSP00000288602
L597Q
Path/1

rs121913366
A|C
ENSP00000288602
L597R

rs121913370
140453193
T|C
ENSP00000288602
N581S
L.Path/1

rs121913375
140453139
G|A
ENSP00000288602
T599I
Path/1

rs397507483
140453148
C|A
ENSP00000288602
G596V
Path/3

rs397507484
140453133
T|A
ENSP00000288602
K601I
Path/1
L.Path/1

rs121913225
140453151
A|G
ENSP00000288602
F595S
—

rs121913337
140453153
A|T
ENSP00000288602
D594E
—

rs121913362
140453159
T|C
ENSP00000288602
I592M
—

rs121913365
140453132
T|G
ENSP00000288602
K601N
—

rs372569965
140453127
C|T
ENSP00000288602
R603Q
—

rs180177042
140449165
A|T
ENSP00000288602
D638E
Path/2

rs397507485
140439727
C|T
ENSP00000288602
R671Q
Path/1

rs397507486
140439613
T|C
ENSP00000288602
Q709R
Path/1

rs55715359
140439664
A|G
ENSP00000288602
L692S
—

rs397507487
140434543
G|A
ENSP00000288602
R719C
Path/1

rs200490285
140434452
G|T
ENSP00000288602
A749D
—

rs368528867
140434542
C|A
ENSP00000288602
R719L
—

rs180177040
140453987
T|G
ENSP00000418033
N9H
Path/2

rs121913341
140453150
A|C
ENSP00000418033
F23L
Path/1
L.Path/2

rs121913361
140453149
C|G
ENSP00000418033
G24R
Un.S

rs121913364
140453134
T|C
ENSP00000418033
K29E
Path/2
L.Path/1

rs121913366
140453145
A|T
ENSP00000418033
L25Q
Path/1

rs121913366
A|C
ENSP00000418033
L25R

rs121913375
140453139
G|A
ENSP00000418033
T27I
Path/1

rs397507483
140453148
C|A
ENSP00000418033
G24Y
Path/3

rs397507484
140453133
T|A
ENSP00000418033
K29I
Path/1
L.Path/1

rs121913225
140453151
A|G
ENSP00000418033
F23S
—

rs121913337
140453153
A|T
ENSP00000418033
D22E
—

rs121913362
140453159
T|C
ENSP00000418033
I20M
—

rs121913365
140453132
T|G
ENSP00000418033
K29N
—

rs180177042
140449165
A|T
ENSP00000418033
D66E
Path/2

rs199927105
140482930
G|T
ENSP00000419060
P10H
—

rs121913357
140481403
C|T
ENSP00000419060
G77R
Path/3
L.Path/1
Un.S/0

rs180177033
140481420
A|C
ENSP00000419060
I71S
Path/1

rs397507473
140481405
A|G
ENSP00000419060
F76S
Path/2

rs121913371
140481478
G|A
ENSP00000419060
R52W
—

rs121913351
140481411
C|A
ENSP00000419060
G74V
Path/2

rs121913351
C|T
ENSP00000419060
G74E

rs121913355
140481402
C|G
ENSP00000419060
G77A
Path/5

rs121913355
C|A
ENSP00000419060
G77V

rs121913355
C|T
ENSP00000419060
G77E

rs180177036
140477853
C|G
ENSP00000419060
L93F
Path/2

rs180177037
140477813
T|C
ENSP00000419060
K107E
Path/2

rs180177038
140477807
C|T
ENSP00000419060
E109K
Path/3
L.Path/1

rs180177039
140477806
T|C
ENSP00000419060
E109G
Path/2
L.Path/1

rs397507479
140476811
C|T
ENSP00000419060
C140Y
Path/1
Un.S/1

rs180177040
140453987
T|G
ENSP00000419060
N189H
Path/2

rs180177040
T|C
ENSP00000419060
N189D

rs397507481
140454006
G|C
ENSP00000419060
H182Q
Path/1

rs121913341
140453150
A|C
ENSP00000419060
F203L
Path/2
L.Path/1

rs121913361
140453149
C|G
ENSP00000419060
G204R
Un.S/0

rs121913364
140453134
T|C
ENSP00000419060
K209E
Path/5

rs121913366
140453145
A|T
ENSP00000419060
L205Q
Path/1

rs121913366
A|C
ENSP00000419060
L205R

rs121913370
140453193
T|C
ENSP00000419060
N189S
L.Path/1

rs397507483
140453148
C|A
ENSP00000419060
G204V
Path/3

rs121913225
140453151
A|G
ENSP00000419060
F203S
—

rs121913337
140453153
A|T
ENSP00000419060
D202E
—

rs121913362
140453159
T|C
ENSP00000419060
I200M
—

rs372569965
140453127
C|T
ENSP00000419060
R211Q
—

rs180177042
140449165
A|T
ENSP00000419060
D246E
Path/2

rs397507485
14043972
C|T
ENSP00000419060
R279Q
Path/1

rs397507486
140439613
T|C
ENSP00000419060
Q317R
Path/1

rs397507487
140434543
G|A
ENSP00000419060
R327C
Path/1

rs200490285
140434452
G|T
ENSP00000419060
A357D
—

rs397507476
140477811
T|G
ENSP00000419060
K107N
Path/1

rs150050723
140534634
T|G
ENSP00000420119
Q78H
—

rs180177032
140481423
C|A
ENSP00000419060
R70I
Path/1

rs121913378
140453137
C|T
ENSP00000288602
V600M
L.Path/1

rs121913369
140453146
G|C
ENSP00000288602
L597V
Path/4

rs121913369
G|C
ENSP00000419060
L205V

rs121913378
140453137
C|T
ENSP00000419060
V208M
L.Path/1

rs397507481
140454006
G|C
ENSP00000418033
H2Q
Path/1

SNP ID refers to dbSNP. Ch7: location within chromosome number seven (assembly GRCh37/hg19). Clin/sig: clinical significance refers to ClinVar database; significant results could be one of the following: Path: pathogenic, benign; L.Path: likely pathogenic, or/and Un.S: unsignificant. Number after significant results refers to number of diseases that are associated with this SNP.

Table 3: Functional SNPs predicted by triple servers.

Protein ID
Amino acid change
SIFT prediction
Score
PolyPhen-2 prediction
Score
SNAP2
Score

ENSP00000288602
Q93H
Damaging
0
Probably damaging
0.974
Effect
15

ENSP00000288602
A246P
Damaging
0
Probably damaging
0.999
Effect
73

ENSP00000288602
T241M
Damaging
0
Probably damaging
1
Effect
72

ENSP00000288602
T241R
Damaging
0
Probably damaging
1
Effect
73

ENSP00000288602
T241P
Damaging
0
Probably damaging
1
Effect
86

ENSP00000288602
L245F
Damaging
0
Probably damaging
0.999
Effect
64

ENSP00000288602
F247S
Damaging
0.01
Possibly damaging
0.443
Effect
77

ENSP00000288602
F247L
Damaging
0
Probably damaging
0.987
Effect
70

ENSP00000288602
P367L
Damaging
0.01
Probably damaging
0.999
Effect
36

ENSP00000288602
P402H
Damaging
0
Probably damaging
0.948
Effect
29

ENSP00000288602
G464A
Damaging
0
Probably damaging
0.999
Effect
50

ENSP00000288602
G464V
Damaging
0
Probably damaging
1
Effect
70

ENSP00000288602
G464E
Damaging
0
Probably damaging
1
Effect
84

ENSP00000288602
G466R
Damaging
0
Probably damaging
0.996
Effect
95

ENSP00000288602
G469R
Damaging
0
Probably damaging
1
Effect
90

ENSP00000288602
V471F
Damaging
0
Probably damaging
0.954
Effect
78

ENSP00000288602
V471I
Damaging
0
Possibly damaging
0.22
Effect
37

ENSP00000288602
I463S
Damaging
0
Possibly damaging
0.714
Effect
75

ENSP00000288602
F468S
Damaging
0
Possibly damaging
0.67
Effect
82

ENSP00000288602
G464R
Damaging
0
Probably damaging
1
Effect
79

ENSP00000288602
R444W
Damaging
0
Probably damaging
0.999
Effect
67

ENSP00000288602
G466A
Damaging
0
Probably damaging
0.97
Effect
87

ENSP00000288602
G466V
Damaging
0
Probably damaging
0.983
Effect
93

ENSP00000288602
G466E
Damaging
0
Probably damaging
0.996
Effect
94

ENSP00000288602
G469A
Damaging
0
Probably damaging
0.969
Effect
73

ENSP00000288602
G469V
Damaging
0
Probably damaging
1
Effect
89

ENSP00000288602
G469E
Damaging
0
Probably damaging
1
Effect
93

ENSP00000288602
L485F
Damaging
0
Probably damaging
0.999
Effect
5

ENSP00000288602
E501K
Damaging
0.01
Probably damaging
0.997
Effect
84

ENSP00000288602
E501G
Damaging
0
Probably damaging
1
Effect
81

ENSP00000288602
K483Q
Damaging
0
Probably damaging
0.999
Effect
79

ENSP00000288602
L485S
Damaging
0
Probably damaging
0.999
Effect
42

ENSP00000288602
K499N
Damaging
0
Probably damaging
0.884
Effect
58

ENSP00000288602
L505F
Damaging
0.01
Possibly damaging
0.698
Effect
41

ENSP00000288602
P490S
Damaging
0
Possibly damaging
0.494
Effect
31

ENSP00000288602
G534R
Damaging
0
Probably damaging
1
Effect
34

ENSP00000288602
C532Y
Damaging
0
Probably damaging
1
Effect
75

ENSP00000288602
N581H
Damaging
0
Probably damaging
1
Effect
73

ENSP00000288602
N581D
Damaging
0.05
Possibly damaging
0.503
Effect
76

ENSP00000288602
H574Q
Damaging
0
Probably damaging
0.999
Effect
89

ENSP00000288602
F595L
Damaging
0
Probably damaging
1
Effect
92

ENSP00000288602
G596R
Damaging
0
Probably damaging
1
Effect
95

ENSP00000288602
K601E
Damaging
0
Probably damaging
0.997
Effect
58

ENSP00000288602
L597Q
Damaging
0
Probably damaging
1
Effect
71

ENSP00000288602
L597R
Damaging
0
Probably damaging
0.999
Effect
81

ENSP00000288602
N581S
Damaging
0.04
Possibly damaging
0.517
Effect
56

ENSP00000288602
T599I
Damaging
0
Probably damaging
0.997
Effect
62

ENSP00000288602
G596V
Damaging
0
Probably damaging
1
Effect
92

ENSP00000288602
K601I
Damaging
0
Probably damaging
0.986
Effect
18

ENSP00000288602
F595S
Damaging
0
Probably damaging
1
Effect
95

ENSP00000288602
D594E
Damaging
0
Probably damaging
0.999
Effect
90

ENSP00000288602
I592M
Damaging
0
Probably damaging
0.997
Effect
55

ENSP00000288602
K601N
Damaging
0
Probably damaging
0.939
Effect
49

ENSP00000288602
R603Q
Damaging
0.03
Probably damaging
0.971
Effect
37

ENSP00000288602
D638E
Damaging
0
Probably damaging
1
Effect
91

ENSP00000420119
Q78H
Damaging
0
Probably damaging
0.948
Effect
41

ENSP00000288602
R671Q
Damaging
0
Probably damaging
0.996
Effect
28

ENSP00000288602
Q709R
Damaging
0.01
Possibly damaging
0.776
Effect
45

ENSP00000288602
L692S
Damaging
0
Probably damaging
0.999
Effect
30

ENSP00000288602
R719C
Damaging
0
Probably damaging
0.996
Effect
34

ENSP00000288602
A749D
Damaging
0.03
Possibly damaging
0.819
Effect
47

ENSP00000288602
R719L
Damaging
0
Possibly damaging
0.551
Effect
45

ENSP00000418033
N9H
Damaging
0
Probably damaging
0.98
Effect
66

ENSP00000418033
F23L
Damaging
0
Probably damaging
0.993
Effect
69

ENSP00000418033
G24R
Damaging
0
Probably damaging
1
Effect
90

ENSP00000418033
K29E
Damaging
0.04
Probably damaging
0.909
Effect
41

ENSP00000418033
L25Q
Damaging
0
Probably damaging
0.996
Effect
42

ENSP00000418033
L25R
Damaging
0
Probably damaging
0.996
Effect
61

ENSP00000418033
T27I
Damaging
0.01
Probably damaging
0.996
Effect
64

ENSP00000418033
G24Y
Damaging
0
Probably damaging
1
Effect
79

ENSP00000418033
K29I
Damaging
0
Probably damaging
0.907
Effect
8

ENSP00000418033
F23S
Damaging
0
Probably damaging
0.996
Effect
87

ENSP00000418033
D22E
Damaging
0
Probably damaging
0.997
Effect
80

ENSP00000418033
I20M
Damaging
0
Probably damaging
0.945
Effect
28

ENSP00000418033
K29N
Damaging
0
Probably damaging
0.949
Effect
35

ENSP00000418033
D66E
Damaging
0
Probably damaging
0.997
Effect
93

ENSP00000419060
P10H
Damaging
0
Probably damaging
0.976
Effect
6

ENSP00000419060
G77R
Damaging
0
Probably damaging
0.962
Effect
89

ENSP00000419060
I71S
Damaging
0
Probably damaging
0.999
Effect
86

ENSP00000419060
F76S
Damaging
0
Probably damaging
0.999
Effect
64

ENSP00000419060
R52W
Damaging
0
Probably damaging
0.976
Effect
55

ENSP00000419060
G74V
Damaging
0
Possibly damaging
0.351
Effect
81

ENSP00000419060
G74E
Damaging
0
Possibly damaging
0.51
Effect
87

ENSP00000419060
G77A
Damaging
0
Possibly damaging
0.344
Effect
52

ENSP00000419060
G77V
Damaging
0
Possibly damaging
0.276
Effect
82

ENSP00000419060
G77E
Damaging
0
Probably damaging
0.883
Effect
89

ENSP00000419060
L93F
Damaging
0
Probably damaging
0.919
Effect
6

ENSP00000419060
K107E
Damaging
0
Probably damaging
0.989
Effect
50

ENSP00000419060
E109K
Damaging
0.01
Probably damaging
0.992
Effect
91

ENSP00000419060
E109G
Damaging
0
Probably damaging
0.998
Effect
90

ENSP00000419060
C140Y
Damaging
0
Probably damaging
0.932
Effect
81

ENSP00000419060
N189H
Damaging
0
Probably damaging
0.996
Effect
66

ENSP00000419060
N189D
Damaging
0.03
Probably damaging
0.998
Effect
66

ENSP00000419060
H182Q
Damaging
0
Probably damaging
0.999
Effect
72

ENSP00000419060
F203L
Damaging
0
Probably damaging
1
Effect
89

ENSP00000419060
G204R
Damaging
0
Probably damaging
0.999
Effect
90

ENSP00000419060
K209E
Damaging
0
Possibly damaging
0.477
Effect
51

ENSP00000419060
L205Q
Damaging
0
Probably damaging
1
Effect
56

ENSP00000419060
L205R
Damaging
0
Probably damaging
1
Effect
75

ENSP00000419060
N189S
Damaging
0.03
Probably damaging
0.989
Effect
43

ENSP00000419060
G204V
Damaging
0
Possibly damaging
0.242
Effect
87

ENSP00000419060
F203S
Damaging
0
Probably damaging
1
Effect
94

ENSP00000419060
D202E
Damaging
0
Probably damaging
1
Effect
87

ENSP00000419060
I200M
Damaging
0
Probably damaging
1
Effect
32

ENSP00000419060
R211Q
Damaging
0.05
Probably damaging
0.979
Effect
8

ENSP00000419060
D246E
Damaging
0
Probably damaging
0.998
Effect
96

ENSP00000419060
R279Q
Damaging
0.02
Probably damaging
1
Effect
56

ENSP00000419060
Q317R
Damaging
0.03
Probably damaging
0.975
Effect
2

ENSP00000419060
R327C
Damaging
0
Possibly damaging
0.291
Effect
43

ENSP00000419060
A357D
Damaging
0.03
Possibly damaging
0.507
Effect
22

ENSP00000419060
K107N
Damaging
0
Probably damaging
0.994
Effect
32

2.1. SIFT (Sorting Intolerant from Tolerant) Server

SIFT server is an online bioinformatics server that is used to predict the damaging effect of nucleotide substitution and frame shift (insertion/deletion) on protein function based on the maintenance degree of the amino acid residues in sequence alignments derived from closely related sequences with the main assumption; that is, evolutionarily conserved regions tend to be less tolerant to mutations, and so mutations in these regions mainly affect its function [14]. SIFT server has different input data order as follows: dbSNP reference number (rs ID number), protein sequence, and chromosome location. For this tool coding SNPs and Indels were separated from total and submitted as rs ID numbers for (missense, nonsense, and stop gain) SNPs and as chromosome location for frame shift Indels. SIFT server assigns score for each residue from 0 to 1, where ≤0.05 score is considered by the algorithm to be damaging amino acid substitutions and >0.05 score is predicting tolerance [15]. SIFT version 5.2.2 is available at http://sift.bii.a-star.edu.sg/index.html.

2.2. PolyPhen-2 (Polymorphism Phenotyping) Server

An online bioinformatics server automatically predicts the nsSNPs that affect with amino acid substitution structure and function of protein, using a comparative method. PolyPhen searches for protein 3D structures and make multiple alignments of homologous sequences and amino acid contact in several protein databases and calculate position-specific independent count scores (PSIC) for each of two variants and then computes the PSIC scores difference between two variants, where the higher PSIC score difference indicates that the functional impact of amino acid substitution is likely to occur [16]. PolyPhen-2 outcome can be one of the following: probably damaging, possibly damaging, or benign, with score range from 0 to 1 [9]. PolyPhen server is available at http://genetics.bwh.harvard.edu/pph2/index.shtml.

2.3. SNAP2 Server

SNAP2 is a trained classifier that is based on a machine learning device called “neural network.” It distinguishes between effect and neutral variants/nonsynonymous SNPs by taking a variety of sequence and variant features into account. The most important input signal for the prediction is the evolutionary information taken from an automatically generated multiple sequence alignment. Also structural features such as predicted secondary structure and solvent accessibility are considered. If available, also annotation (i.e., known functional residues, pattern, and regions) of the sequence or close homologs are pulled in. Predicting a score (ranges from −100 strong neutral prediction to +100 strong effect prediction), analysis suggests that the prediction score is to some extent correlated to the severity of effect [17] (https://rostlab.org/services/snap/).

From the total functional nsSNPs predicted by the three previous tools (SIFT server, PolyPhen, and SNAP2), the higher 15 functional nsSNPs (got higher predicted score) were selected for next analysis.

2.4. I-Mutant Suite

I-Mutant version 3.0 is a suite of support vector machine, based predictors integrated in a unique web server. It offers the opportunity to predict the protein stability changes upon single-site variations from the protein structure or sequence. I-Mutant result is designed as follows: DDG < 0: decrease stability, DDG > 0: increase stability, or DDG = 0: neutral [18]. I-Mutant 3.0 is available at http://gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-Mutant3.0.cgi.

2.5. CPH Models

A protein homology modeling prediction server, used to predict the 3D structure of proteins with an unknown 3D structure model, in CPH models the template recognition based on profile-profile alignment guided by secondary structure and exposure predictions [19]. Protein sequences requirements were submitted to CPH server to get the model as PDB file (for the structure that could not be predicted by automated Project HOPE server). The resultant PDB files were opened using Chimera program which was used to visualize the PDB structure (http://www.cbs.dtu.dk/services/CPHmodels/).

2.6. UCSF Chimera Model Software

Chimera is a high-quality extensible molecular graphics program designed to maximize interactive visualization, analysis system, and related data [20]. This software was produced by University of California, San Francisco [9]. Chimera outcome was used to get high-quality images of, first, whole protein 3D structure that needed protein IDs ENSP00000288602, ENSP00000418033 and ENSP00000419060 (Figure 1) and, second, determined native and mutant residues for mutations that could not be detected by next automated Project HOPE server (Figure 2) (http://www.cgl.ucsf.edu/chimera/).

Figure 1: Showing proteins tertiary structure backbone and their protein secondary structures (alpha helix, beta sheet, and random coil) of higher deleterious nsSNP related proteins using CPH models 3.2 server and Chimera software. ID number below figures related to protein sequences records in UniProt database.

Figure 2: Close-up of the mutation. The protein is colored grey, and the side chain of both wild type (in green color) and mutant (in red color) residue shows in their target positions.

2.7. Automatic Protein Structural Analysis and Information Using HOPE Server

Automatic mutant analysis server can provide insight into the structural effects of a mutation. HOPE collects information from a wide range of information sources including calculations on the 3D coordinates of the protein by using WHAT IF Web services, sequence annotations from the UniProt database, and predictions by DAS services. Homology models are built with YASARA. Data is stored in a database and used in a decision scheme to identify the effects of a mutation on the protein’s 3D structure and function. HOPE builds a report with text, figures, and animations that is easy to use and understandable for (bio)medical researchers [21] (http://www.cmbi.ru.nl/hope/method) (Figure 2).

2.8. PolymiRTS Database (3′ UTR)

It is an integrated platform for analyzing the functional impact of genetic polymorphisms (SNPs and Indels) within microRNAs binding sites [13]. Single variants within 3′ UTR were selected from total variants and submitted to PolymiRTS server, to check if these variants could disrupt or create new miRNA binding sites or have no impact at all. PolymiRTS is available at http://compbio.uthsc.edu/miRSNP/ (Table 6).

2.9. Effect of SNPs within 5′ UTR on Transcription Factor Binding Sites

PROMO is a virtual laboratory for the identification of putative transcription factor binding sites (TFBS) in DNA sequences from a species or groups of species of interest. TFBS defined in the TRANSFAC database are used to construct specific binding site weight matrices for TFBS prediction. The user can inspect the result of the search through a graphical interface and downloadable text files [22]. Input data was two sequences for each SNP within 5′ UTR: first sequence contained a wide nucleotide allele and the second contained a new substitution nucleotide as in Table 7 (http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3).

2.10. Effect of 3′/5′ Splice Sites SNPs/Indels (HSF Tool)

Human Splicing Finder (HSF) is a tool to predict the effects of mutations on splicing signals or to identify splicing motifs in any human sequence. It contains all available matrices for auxiliary sequence prediction as well as new ones for binding sites of the 9G8 and Tra2-β serine-arginine proteins and the hnRNP A1 ribonucleoprotein. It also developed new position weight matrices to assess the strength of 5′ and 3′ splice sites and branch points [23]. In this study HSF was used to detect the functional SNPs and Indels within 3′/5′ splice sites. Input data was nucleotide sequence containing the single substitution as SNP or insertion/deletion as Indel as in Table 8 (http://www.umd.be/HSF3/index.html).

3. Results and Discussion

Some information about total single variants and functional nsSNPs predicted with triple or double tools is obtained from many databases (dbSNP, UniProt, HapMap, 1000 Genomes Project, gene bank, and ClinVar) (Tables 1 and 2). In addition there was no functional SNP presented within HapMap or 1000 Genomes Project databases.

3.1. Predicted Results by SIFT, PolyPhen, and SNAP2 Servers

For 232 nsSNPs of BRAF gene, 111 variants were predicted to be damaging or effect by triple (SIFT, PolyPhen, and SNAP2) servers (Table 3). In addition one SNP (rs180177032, R70I) was predicted to be functional by double (SIFT and SNAP2) tools only. Furthermore five SNPs (V600M, L597V, L205V, V208M, and H2Q) were predicted as functional by double (SIFT and PolyPhen) servers only (Table 4). On the other hand, two Indels, frame shift (rs35546910, ch7:140834611; rs777474487, ch7:140783126-), showed no effect on protein at all.

Table 4: Functional SNPs predicted by double tools.

Protein ID
Amino acid change
SIFT prediction
Score
PolyPhen-2 prediction
Score
SNAP2 prediction
Score

ENSP00000419060
R70I
Damaging
0
Benign
0.311
Effect
34

ENSP00000288602
V600M
Damaging
0
Probably damaging
0.99
Neutral

ENSP00000288602
L597V
Damaging
0
Probably damaging
0.862
Neutral
0

ENSP00000419060
L205V
Damaging
0
Probably damaging
0.995
Neutral

ENSP00000419060
V208M
Damaging
0.03
Probably damaging
0.996
Neutral

ENSP00000418033
H2Q
Damaging
0
Probably damaging
0.998
Neutral

From the previous results (Table 3), 15 nsSNPs with the maximum predicted score through triple servers were selected to predict their stability index (Table 5) and visualize wide and mutant residues in their protein 3D structure (Figure 2).

Table 5: Predicted results of I-Mutant software.

Protein ID
Amino acid position
WT
MT
PH
Temperature (°C)
SVM2 prediction effect
DDG value prediction kcal/mol
RI

ENSP00000288602
466
G
R
7.0
25
Increase

4

ENSP00000288602
469
G
R
7.0
25
Increase

4

ENSP00000288602
466
G
V
7.0
25
Increase

2

ENSP00000288602
466
G
E
7.0
25
Decrease

6

ENSP00000288602
469
G
E
7.0
25
Increase

5

ENSP00000288602
595
F
L
7.0
25
Decrease

8

ENSP00000288602
596
G
R
7.0
25
Decrease

1

ENSP00000288602
596
G
V
7.0
25
Decrease

2

ENSP00000288602
595
F
S
7.0
25
Decrease

8

ENSP00000288602
638
D
E
7.0
25
Decrease

7

ENSP00000418033
24
G
R
7.0
25
Decrease

8

ENSP00000418033
66
D
E
7.0
25
Increase

3

ENSP00000419060
109
E
K
7.0
25
Decrease

8

ENSP00000419060
203
F
S
7.0
25
Decrease

8

ENSP00000419060
246
D
E
7.0
25
Decrease

7

WT: wild type amino acid. MT: mutant type amino acid. DDG: delta DG (units of free energy) (DDG < 0: decreased stability, DDG > 0: increased stability). RI: reliability index.

Table 6: Predicted results of functional 3′ UTR SNPs/Indels.

dbSNP ID
Variant type
Ancestral allele
Allele
miR ID
Conservation
miRSite
Function class
Context + score change

rs114105685
SNP
G
A
hsa-miR-30a-5p
3
gTGTTTACAggtg
C

hsa-miR-30b-5p
3
gTGTTTACAggtg
C

hsa-miR-30c-5p
3
gTGTTTACAggtg
C

hsa-miR-30d-5p
3
gTGTTTACAggtg
C

hsa-miR-30e-5p
3
gTGTTTACAggtg
C

hsa-miR-3607-3p
4
gtgTTTACAGgtg
C
No change

rs184804021
SNP
T
T
hsa-miR-3908
4
tagaCATTGCTAa
D

hsa-miR-3942-5p
5
tagacATTGCTAa
D

hsa-miR-4703-5p
5
tagacATTGCTAa
D

hsa-miR-4766-3p
5
tagacATTGCTAA
D

C
hsa-miR-374b-3p
5
tagacaCTGCTAA
C

hsa-miR-4274
5
tagacACTGCTAa
C

hsa-miR-4519
4
tagaCACTGCTAa
C

hsa-miR-4524a-5p
5
tagacaCTGCTAA
C

hsa-miR-4524b-5p
5
tagacaCTGCTAA
C

hsa-miR-6499-3p
7
tagACACTGCtaa
C

hsa-miR-6733-3p
5
taGACACTGctaa
C

rs140083479
SNP
A
A
hsa-miR-3908
4
atagaCATTGCTA
D

hsa-miR-3942-5p
5
atagacATTGCTA
D

hsa-miR-4703-5p
5
atagacATTGCTA
D

hsa-miR-4766-3p
5
atagacATTGCTA
D

hsa-miR-4796-5p
3
ATAGACAttgcta
D

T
hsa-miR-548p
5
atagacTTTGCTA
C

rs200393520
Indel
—
—
hsa-miR-4311
2
acTCTCTTTtttt
O
0.004

T

rs143647707
SNP
A
A
hsa-miR-5580-3p
3
gaaCATATGTttg
D

T
hsa-miR-7-1-3p
4
gaacATTTGTTtg
C

hsa-miR-7-2-3p
4
gaacATTTGTTtg
C

rs202148822
SNP
G
G
hsa-miR-1976
3
tCAGGAGAgtagc
D

hsa-miR-6845-3p
3
tcAGGAGAGtagc
D

Conservation: occurrence of the miRNA site in other vertebrate genomes in addition to the query genome. miRSite: sequence context of the miRNA site: bases complementary to the seed region are in capital letters and SNPs are highlighted in bold font. Function class: D: the derived allele disrupts a conserved miRNA site (ancestral allele with support > 2); C: the derived allele creates a new miRNA site; O: the ancestral allele can not be determined. Context score: negative increase = increase in SNP functionality.

Table 7: 5′ UTR SNPs within TF binding sites.

SNP ID
Transcription factor predicted
Prediction

rs71645935
Pax-5, p53
No effect

rs71645936
FOXP3
No effect

rs397507453
None
—

rs762432076
None
—

rs769116177
None
—

Table 8: Polymorphisms and impact on splice sites.

Polymorphism ID
Type of splice site
Prediction

rs199910929 (SNP)
5′ splice site
(i) Alteration of an exonic ESE site
(ii) Potential alteration of splicing

rs776683449 (SNP)
3′ splice site
No significant splicing motif alteration detected
This mutation has probably no impact on splicing

rs775598011 (Indel)
3′ splice site
(i) Alteration of an exonic ESE site
(ii) Potential alteration of splicing

3.2. UTRs and Splice Sites

Results in untranslated regions showed lower number of functional SNPs and Indels than coding nsSNPs. 3′ UTR SNPs and Indels showed that five SNPs and one Indel were altered in microRNAs binding sites, which lead to disturbing or creating new binding sites (Table 6). Furthermore miRNAs associated with these functional SNPs/Indel are associated with many genes, and defect in these miRNAs could lead to effect on all associated genes expressions.

On the other hand, for 5′ UTR SNPs (five SNPs obtained), results showed that two SNPs were found in transcription factor binding sites with none being altered, and the remaining three were not located within any TF binding sites, meaning that none of five SNPs showed an effect on TF binding sites (Table 7). In addition, about the three single variants (two SNPs and one Indel) within 5′/3′ splice sites, analysis showed that one SNP within 5′ splice site and one Indel in 3′ splice site showed potential alteration of splicing (Table 8).

To date the complete mechanisms by which a nucleotide variant may result in a phenotypic change are for the most part unknown. In silico analysis using powerful software tools can facilitate predicting the phenotypic effect of nonsynonymous coding SNPs on the physicochemical properties of the concerned proteins. Such information is critical for genotype-phenotype correlations and also to understand disease biology. Given the fact that nsSNPs in critical cellular genes such as BRAF modify the normal programs of cell proliferation, differentiation, and death, they are believed to play an important role in disease predisposition. Therefore, efforts were made to identify SNPs that can modify the structure, function, and expression of the BRAF gene.

Through one of the most significant BRAF mutations, when thymine is substituted with adenine at nucleotide 1799, it results in an amino acid substitution at position 600 from valine (V) to a glutamic acid (E), which is called V600E, located in the activation segment that has been found in many human cancers. For example, it was reported as the most common genetic mutation related to papillary thyroid cancer and occurs in approximately 45% of patients [24, 25]. In silico investigation also presented this mutation as highly damaging substitution that could cause a disease using SIFT and PolyPhen online tools. Furthermore Project HOPE server results showed that the wide type residue (V) is smaller in size (Figure 3), neutral in charge, and more hydrophobic. On the other hand mutant residue (E) is bigger in size (Figure 3), negatively charged, and less hydrophobic. In addition the mutated residue is located in a domain that is important for the activity of the protein and in contact with another domain that is also important for the activity. The interaction between these domains could be disturbed by the mutation, which might affect the function of the protein.

Figure 3: Showing the schematic structures of the original (a) and the mutant (b) amino acid. The backbone, which is the same for each amino acid, is colored red. The side chain, unique for each amino acid, is colored black.

4. Conclusion

The current study shows the in silico analysis of genetic single variants within the coding region, 3′/5′ UTR and 3′/5′ splice sites of BRAF gene. These polymorphisms could directly or indirectly influence the intermolecular and intramolecular interactions of amino acid residues and protein expression and can culminate into disease risks. By analyzing the conformational changes and interactions of amino acid residues within BRAF proteins, we have identified significant structural and functional changes that can explain the activity deviations, caused by several mutations. Furthermore significant pathology or likely pathology showed association of many detected SNPs with many diseases through clinical variation database (http://www.ncbi.nlm.nih.gov/clinvar/). They include the following diseases: cardiofaciocutaneous syndrome, Noonan syndrome, LEOPARD syndrome, RASopathy, non-small-cell lung cancer, carcinoma of colon, adenocarcinoma of lung, thyroid cancer, malignant lymphoma, non-Hodgkin lymphoma. Screening for BRAF variants may be useful for molecular diagnosis and development of vital molecular inhibitors of genes pathways. This study demonstrates the significance of different bioinformatics tools to figure out the phenotypic changes and protein function, associated with the structure-function relationship of BRAF gene. More evidence is required for the involvement of deregulated miRNA networks in cancer development. Resultant SNPs can be applied for further investigation and diagnosis of many associated diseases.

Competing Interests

The authors declare that there are no competing interests regarding the publication of this paper.

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