Biomedical

Communication

Biosci. Biotech. Res. Comm. 9(3): 503-511 (2016)

Computational analysis of polymorphisms of ubiquitin

carboxyl–terminal esterase L1 (UCHL1) gene involved

in Parkinson’s disease

Sowmya Dhawan

* and Usha Chouhan

Department of Bioinformatics, Maulana Azad National Institute of Technology, Bhopal, M.P. 452051, India

ABSTRACT

Presence of genetic variations is a key player among many others which affect susceptibility and progression of the

disease. Single nucleotide polymorphisms are the most frequent variations in human genome. Ubiquitin carboxyl-

terminal esterase L1 (UCHL1) located on chromosome 4p14 is one of the potential candidate neuropathogenic pro-

tein involved in Parkinson’s Disease. The aim of this study was to investigate the functional consequences of UCHL

1 single nucleotide polymorphisms (SNPs) to understand the biological basis of complex traits and diseases as the

Genetics of human phenotypic variation could be understood by knowing the functions of SNPs derived from the

data available in dbsSNP data base and different computer applications are used. Nonsynymous SNPs are relevant

in many of the human inherited disease since they change the aminoacid sequence of the protein. Few common

single –nucleotide polymorphisms (SNPs) of the UCHL1 genes were analyzed by using different bioinformatics tools

based on evolutionary analysis- sequence homology based, structure based approach. Protein structural analysis was

also performed by using I- Mutant. It was recognized that rs6063 and rs74315205 SNPs of UCHL1 gene were found

to be more damaging in PD and is responsible for the alteration in the levels of expression. Conclusion: It has been

concluded that among the entire SNPs of UCHL1 gene, the mutation in rs6063 and rs74315205 have the most sig-

ni cant effect on functional variation. The study suggested that G191R, G199 R, G88R and R231G variants of UCHL1

could directly or indirectly destabilize the amino acid interactions and hydrogen bond networks thus explaining the

functional deviations of protein to some extent. These results may further form the basis of large- scale population

based association studies.

KEY WORDS: PARKINSON’S DISEASE, SINGLE NUCLEOTIDE POLYMORHISMS, SNP, UCHL 1 GENE

503

ARTICLE INFORMATION:

*Corresponding Author: sowmyadhawan@gmail.com

Received 30

July, 2016

Accepted after revision 14

Sep, 2016

BBRC Print ISSN: 0974-6455

Online ISSN: 2321-4007

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reserved.

Online Contents Available at: http//www.bbrc.in/

504 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Sowmya Dhawan and Usha Chouhan

INTRODUCTION

Parkinson’s disease (PD) is the second most common

neurodegenerative disorder after Alzheimer’s affecting

approximately 1–2% of the population over the age of

65 and reaching a prevalence of almost 4% in those aged

above 85. Resting tremor, bradykinesia, rigidity, and

postural instability are the main clinical symptoms of

the disease often accompanied by nonmotor symptoms

including autonomic insuf ciency, cognitive impair-

ment, and sleep disorders (Gómezet al.2015).

There are two forms of the disease, the sporadic and

familial forms. The patients with familial PD are dis-

tinguished from the ones who suffer from sporadic PD

because of the early onset, greater consanguinity rate,

and greater frequency of similar disease in their parents

Familial PD cases are of 10% of the total no of cases

and are based on the genetic component of the disease

(Christine and Ana 2012). There is a life risk of 1.3% for

women and 2% for men as per the study of Olmstead

country. The disease is going to increase in the future

to come due to the medical expenses and other reasons

(Prasad et al.2016).

UCHL1 /Park 5 gene is a compelling candidate gene

for PD (Maraganore et al.2004) on biological grounds

because the protein it encodes plays a pivotal role in

the ubiquitin proteasome system (UPS), displays neuron-

speci c expression and is found in Lewy bodies, the neu-

ropathologic hallmark of PD .The ubiquitin proteasome

system regulates the degradation of key regulatory pro-

teins as well as misfolded and damaged proteins (Aaron

& Yong 2014). Ubiquitin carboxy-terminal hydrolase L1

(UCHL1) is a 223-a.a. protein which is a component of

the UPS, which cleaves the carboxy-terminal peptide

bond of polyubiquitine chains, working as a deubiqui-

tinating enzyme (Liu et al.2002). It encodes for one of

the most abundant proteins in the brain. Mutations in

this target were found to be responsible for a genetic

form of PD. It is thought a mutation at amino acid posi-

tion 93 for methionine may decrease UCHL1 hydrolase

activity, leading to accumulation of proteins that should

have been degraded, and subsequently the progression

of PD (Contu et al.2014).

One of the important gene speci c mutations

described for the familial forms of PD, include autoso-

mal dominant mutations of UCHL1 (PARK5). However,

the pathogenic mechanisms underlying mitochondrial

dysfunction in familial PD require further detailed inves-

tigation at the molecular level. The loss of dopaminergic

neurons in PD is preceded by the formation of Lewy

Bodies, insoluble proteinaceous inclusions enriched

with ubiquitinated aggregates, as well as displaying

extensive protein oxidative modi cation, (Hyo and Sun

2015).

The structure of UCH-L1 contains a central -sheet

that is  anked on either side by -helices as shown in

 gure 1. In the crystal structure, UCH-L1 is an asymmet-

ric dimer; however, equilibrium sedimentation analysis

showed that the protein is monomeric in solution. The

catalytic triad comprises Cys90, His161, and Asp176; in

the crystal structure, the side chains of these residues are

not close enough for catalytic activity, suggesting that

in the absence of substrate, UCH-L1 is in an inactive

form. In addition, the active site is covered by a loop (L8)

that has been suggested to restrict the size of substrates

that can access the active-site cleft ( Bishop et al.2016).

A single nucleotide polymorphism (SNP) is a source

of variance in a genome. A SNP is a single base muta-

tion in DNA. SNPs are the most simple form and most

common source of individual genetic polymorphism

in the human genome (90% of human DNA polymor-

phisms). A SNP in a coding region may have two dif-

ferent effects on the resulting protein: Synonymous, the

substitution causes no amino acid change to the protein

it produces; non synonymous, the substitution results

in an alteration of the encoded amino acid. One half of

all coding sequence SNPs result in non- synonymous

codon changes (Smith 2002). A non- synonymous single

nucleotide polymorphism (nsSNP) occurring in a cod-

ing gene may cause an amino acid substitution in the

corresponding protein product, thus affecting the phe-

notype of the host organism .Non synonymous variants

constitute more than 50% of the mutations known to be

involved in human inherited diseases Single nucleotide

polymorphisms (SNPs) (Kumar 2009). Computational

methods are suf ciently fast and  exible to provide reli-

able predictions of functionally signi cant SNPs with a

high accuracy of 80–85%when combined with sequence,

structure, and phylogenetic relationships (Minyue et al.,

2014). Here we are trying to consider computationally

a suitable protocol for missense mutation (point muta-

tion/single amino acid polymorphism) analysis before

wet lab experimentation and provided an optimal path

for further clinical and experimental studies.

MATERIAL AND METHODS

The data on protein sequence and variants (single

amino acid polymorphisms/missense mutations/point

mutations) for UCHL1 gene were collected from NCBI

database (http://www.ncbi.nlm.nih.gov/snp/)of SNP by

applying appropriate limits like homo-sapiens, Chromo-

some 4, cited in Pubmed etc. to detect the detrimental

point mutants.

Further deleterious SNP analysis were performed

using the computational tools sorting intolerant from

tolerant (SIFT) and Polyphen 2 for nsSNPs and FASTSNP

and UTRscan for UTR SNPs.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS 505

Sowmya Dhawan and Usha Chouhan

SEQUENCE HOMOLOGY BASED METHOD (SIFT)

We have used the program SIFT (http://sift.bii.a-star.edu.

sg/index.html) to detect deleterious coding nonsynony-

mous SNPs. SIFT is a sequence homology-based tool to

predict whether an amino acid substitution in a protein

would be tolerated or damaging (Pauline et al., 2003). We

performed SIFT by submitting the query in the form of

SNP IDs or chromosome positions and alleles in nsSNVs

tool. Variants at the position with tolerance index score

#0.05 were considered as deleterious. A lower tolerance

index indicates that the particular amino acid substitution

likely has a more functional impact (Pauline et al., 2001).

STRUCTURE HOMOLOGY BASED METHOD

(POLYPHEN)

Analyzing the damaged coding nonsynonymous SNPs

at the structural level is considered to be very important

to understand the functional activity of the protein of

concern. We have used PolyPhen server (http://genet-

ics.bwh.harvard.edu/pph2/) for this purpose. This is an

automatic tool that predicts the possible impact of an

amino acid substitution on a number of features, includ-

ing the sequence, phylogenetic, and structural informa-

tion. The query was submitted in the form of protein

sequence with mutational position and substitution. The

PolyPhen output comprises a score that ranges from 0

to 1, with zero indicating a neutral effect of amino acid

substitutions on protein function. Conversely, a high

score represents a variant that is more likely to be dam-

aging (Ramensky et al., 2002).

FUNCTIONAL SIGNIFICANCE OF NONCODING

SNPS IN REGULATORY UNTRANSLATED

REGIONS

The Web server FastSNP (http://fastsnp.ibms.sinica.

edu.tw) was used for predicting the functional signi -

cance of the 5’ and 3’UTRs of the UCHL 1 gene (Hsiang

et al., 2006). The FastSNP server follows the decision

tree principle with external Web service access to TF

Search, which predicts whether a noncoding SNP alters

the transcription factor-binding site of a gene. The score

was given by this server on the basis of levels of risk

with a ranking of 0, 1, 2, 3, 4, or 5. This signi es the

levels of no, very low, low, medium, high, and very high

effect, respectively.

SCANNING OF UTR SNPS IN UTR SITE

The 5’ and 3’ UTRs are involved in various biologi-

cal processes such as posttranscriptional regulatory

pathways, stability, and translational ef ciency. We

used the program UTRscan (http://itbtools.ba.itb.cnr.it/

utrscan) which allows one to search the user-submitted

sequences for any of the patterns collected in the UTR

site (Graziano and Sabino 1999). UTRsite is a collection

of functional sequence patterns located in 5’ or 3’UTR

sequences. Brie y, two or three sequences of each UTR

SNP that have a different nucleotide at an SNP position

are analyzed by UTRscan, which looks for UTR func-

tional elements by searching through user-submitted

sequence data for the patterns de ned in the UTRsite

and UTR databases. If different sequences for each UTR

SNP are found to have different functional patterns, this

UTR SNP is predicted to have functional signi cance.

The Internet resources for UTR analysis are UTRdb and

UTRsite. UTRdb contains experimentally proven biologi-

cal activity of functional patterns of UTR sequence from

eukaryotic mRNAs(Graziano et al., 2002). The UTRsite

has the data collected from UTRdb and also is continu-

ously enriched with new functional patterns.

SUPPORT VECTOR MACHINE (I-MUTANT 3.0

AND FOLD- X)

The analyses were also conducted by using I-Mutant

Suite is a suite of support vector machine (SVM)- based

predictors of protein stability changes according to

Gibbs free energy change, enthalpy change, heat capac-

FIGURE 1. Schematic of UCH-L1 structure, Sche-

matic illustrating the -helical and -strand

structure of UCH-L1. The residues 1–11 at the

N-terminus, 220–223 at the C-terminus and residues

Ile

and Cys

152

are highlighted. It has been proposed

that modi cation at these points can affect the

hydrophobic core of -strands that are otherwise

protected from solution.

506 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Sowmya Dhawan and Usha Chouhan

FIGURE 2. A graphical representation of distribution of nonsynonymous, SNPs for UCHL 1

(based on the dbSNP database).

ity change, and transition temperature (Capriotti et al.,

2005).The analysis was performed based on protein

sequence combined with mutational position and corre-

lated new residue. And the output result of the predicted

free energy change (DDG) classi es the prediction into

one of three classes: largely unstable (DDG, 20.5 kcal/

mol), largely stable (DDG.0.5 kcal/mol), or neutral (-0.5#

DDG#0.5 kcal/mol). IMutant Suite is available at (http://

gpcr2.biocomp.unibo.it/cgi/predictors/I-Mutant3.0/I-

Mutant3.0.cgi).

The FASTA sequence of protein retrieved from Uni-

Prot was used as an input to predict the mutational

effect on protein stability. I-Mutant also provides the

scores for free energy alterations, calculated with the

FOLD-X energy based web server (Schymkowitz et al.,

2005). FOLD-X is a computer algorithm for quantita-

tive estimation of interactions facilitating the stability

of proteins. The FOLD-X tool was used to provide the

comparison between wild type and mutant models in the

form of van der Waals clashes, which greatly in uence

the energy decomposition.

RESULTS AND DISCUSSION

SINGLE AMINO ACID POLYMORPHISM

DATASET FROM NCBI DBSNP DATABASE

The dbSNP database contains both validated and non-

validated polymorphisms. In spite of this drawback, we

opted to avail the dbSNP because the allelic frequency of

most of nsSNPs of UCHL 1 has been recorded there and

that is the most extensive SNP database. We selected 15

SNPs, out of which 2 were nsSNPs, as shown in Fig. 2.

DELETERIOUS SINGLE POINT MUTANTS

IDENTIFIED BY THE SIFT PROGRAM

The conservation level of a particular position in a protein

was determined by using a sequence homology-based

tool, SIFT. The protein sequences of 64 variants were sub-

mitted independently to the SIFT program to determine

the tolerance index. The higher the tolerance index, the

less functional impact a particular amino acid substitu-

tion is likely to have, and vice versa. Among the 64vari-

ants, 24 were found to be deleterious, having a tolerance

index score of ≤0.05. The results are shown in Table 2.

UTRSCAN ANALYSIS

Functional SNPs in UTR found by the UTRscan server

Polymorphisms in the 3’ UTR affect gene expression by

Table 1: SIFT classi cation

Ranking Risk Division

0 No effect

1 Very low

2 Low

3 Medium

4 High

5 Very high

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS 507

Sowmya Dhawan and Usha Chouhan

Table 2: SIFT analysis of SNPs, Variants with tolerance

index ≤0.05 score are considered as deleterious while

others are taken to be tolerant.

SNPs Amino acid

Change

Score Prediction

rs6063 G191R 0.002 DELETERIOUS

 G191R 0.002 DELETERIOUS

 G199R 0.002 DELETERIOUS

 G88R 0.003 DELETERIOUS

rs1799895 R231G 0.017 DELETERIOUS

rs45454496 E3931K 0.037 DELETERIOUS

 E3898K 0.037 DELETERIOUS

 E941K 0.055 TOLERATED

 E22K 0.057 TOLERATED

 E1022K 0.147 DELETERIOUS

 E1837K 0.186 TOLERATED

 E1846K 0.188 TOLERATED

 E529K 0.233 TOLERATED

 E878K 0.368 TOLERATED

rs62625014 S389F 0.054 TOLERATED

 S320F 0.101 TOLERATED

rs63749888 E47Q 0.102 TOLERATED

 E37Q 0.248 TOLERATED

rs66785829 V3601D 0.011 DELETERIOUS

 V3634D 0.012 DELETERIOUS

 V644D 0.063 TOLERATED

 V201D 0.067 TOLERATED

 V725D 0.193 TOLERATED

 V550D 0.209 TOLERATED

 V1549D 0.28 TOLERATED

 V1540D 0.282 TOLERATED

rs74315205 E864K 0 DELETERIOUS

rs75353611 D25V 0.003 DELETERIOUS

 D25V 0.003 DELETERIOUS

 D27V 0.003 DELETERIOUS

 D25V 0.003 DELETERIOUS

 D25V 0.027 DELETERIOUS

rs112534524 G261A 0.27 TOLERATED

 G261A 0.274 TOLERATED

 G261A 0.277 TOLERATED

 G261D 0.114 TOLERATED

 G261D 0.119 TOLERATED

 G261D 0.126 TOLERATED

rs121912705 T754N 0.039 DELETERIOUS

 T3744N 0.053 TOLERATED

 T3711N 0.055 TOLERATED

 T311N 0.302 TOLERATED

 T660N 0.386 TOLERATED

 T835N 0.45 TOLERATED

 T1659N 0.535 TOLERATED

 T1650N 0.556 TOLERATED

rs121912706 R3873W 0.001 DELETERIOUS

 R3906W 0.001 DELETERIOUS

 R916W 0.002 DELETERIOUS

 R1821W 0.003 DELETERIOUS

 R997W 0.003 DELETERIOUS

 R1812W 0.004 DELETERIOUS

 R853W 0.049 DELETERIOUS

 R504W 0.06 TOLERATED

rs180843436 E137K 0.014 DELETERIOUS

 E486K 0.015 DELETERIOUS

 E3537K 0.021 DELETERIOUS

 E3570K 0.021 DELETERIOUS

 E580K 0.06 TOLERATED

 E661K 0.061 TOLERATED

 E1485K 0.062 TOLERATED

 E1476K 0.063 TOLERATED

rs199473343 L1622M 0.168 TOLERATED

 L1655M 0.169 TOLERATED

 T854N 0.105 TOLERATED

 T3844N 0.147 TOLERATED

 T3811N 0.148 TOLERATED

 T411N 0.267 TOLERATED

 T935N 0.356 TOLERATED

 T1759N 0.432 TOLERATED

 T1750N 0.434 TOLERATED

 T760N 0.476 TOLERATED

rs386833750 CC2D2A 0 DELETERIOUS

rs386833752 T1065M 0.001 DELETERIOUS

 T1114M 0.001 DELETERIOUS

affecting the ribosomal translation of mRNA or by in u-

encing the RNA half-life. Table 3 shows the list of SNPs

in the 3 that are predicted to be damaging because of the

presence of regulatory elements and are of functional

signi cance. We used the UTRscan server for this pur-

pose. We analyzed the same 64 variants in UTRscan that

were analyzed by the SIFT. The UTRscan server  nds

patterns of regulatory region motifs from the UTR data-

Sowmya Dhawan and Usha Chouhan

508 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Table 3: UTRScan analysis of the SNPs where Uorf - Upstream open reading frame.IRES- Internal

ribosome entry site, MBE-Mushashi Binding site

SL.No SNPs No of signal Matches Regulatory Elements

1 rs6063 4 uORF MBE GY-BOX ARE2

2 rs6533526 4 uORF MBE IRES K-BOX

3 rs62625014 4 uORF MBE IRES BRD-BOX

4 rs35530544 1 uORF    

5 rs36210415 1 uORF    

6 rs45570339 2 uORF MBE   

7 rs63749888 4 uORF MBE IRES PAS 

8 rs66785829 4 uORF MBE IRES SXL

9 rs72544141 3 uORF MBE PAS 

10 rs72556370 2 uORF  PAS 

11 rs74315205 2 uORF  IRES  

12 rs74821926 4 uORF MBE IRES PAS 

13 rs75353611 4 uORF MBE IRES PAS 

14 rs77335374 3 uORF MBE IRES  

15 rs77408163 4 uORF MBE IRES PAS 

16 rs77449454 4 uORF MBE IRES GY-BOX 

17 rs79228041 5 uORF MBE ADH_DRE SXL_BS GY-BOX

18 rs112534524 2 uORF MBE   

19 rs121912705 1 uORF    

20 rs121912706 2 uORF IRES  

21 rs121913101 3 uORF IRES DMRT1_RE 

22 rs121913103 2 uORF  DMRT1_RE 

23 rs121913105 1 uORF    

24 rs121918124 1 uORF    

25 rs121918125 1 uORF    

26 rs121965070 1 uORF    

27 rs140126678 2 uORF MBE PAS 

28 rs143228029 4 uORF MBE IRES SXL_BS 

29 rs148654834 3 uORF MBE PAS 

30 rs148654834 3 uORF MBE PAS 

31 rs199473643 1  MBE   

32 rs202247811 1  IRES  

33 rs386833750 1 uORF    

34 rs386833751 1 IRES  

35 rs386833752 1 IRES 

36 rs386833757 2 TOP IRES  

37 rs386833760 2 MBE IRES  

38 rs386833761 2 MBE IRES  

39 rs587778769 1  IRES  

40 rs587778773 1  IRES  

41 rs587778775 2 TOP IRES  

42 rs587778776 1  IRES  

43 rs587778801 1  IRES  

44 rs587778809 3 uORF IRES PAS 

45 rs587778811 1  IRES  

46 rs796051882 1 BRD-BOX   

Sowmya Dhawan and Usha Chouhan

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS 509

base and gives information about whether the matched

pattern is damaged. Various studies have shown that the

transcriptional regulation is biologically important and

the alteration in the transcriptional compo-nents leads

to disease.

DAMAGING SINGLE POINT MUTATIONS

IDENTIFIED BY THE POLYPHEN SERVER

The structural levels of alteration were determined by

applying the PolyPhen program.64 protein sequences

of nsSNPs investigated in this work were submitted as

input to the PolyPhen server and the results are shown

in Table 4. A PSIC score difference of 0.5 and above

was considered to be damaging. we could infer that the

results obtained on the basis of sequence details (SIFT)

were in good correlation with the results obtained for

structural details (PolyPhen), as can be seen from Tables

2 and 4. Interestingly, some of the deleterious variants

identi ed by SIFT also were seen to be less stable by the

Polyphen server. It is predicted that the rs6063 mutation

effect is the damaging one among the SNPs identi ed.

Hence the mutations occurring with this nsSNP would

be of prime importance in the identi cation of UCHL 1

induced Parkinson’s disease according to SIFT and Poly-

Phen results.

FUNCTIONAL SNPS IN UTR FOUND BY THE

FASTSNP SERVER

By the use of Fast SNP server functionally signi cant

variants were predicted as shown in table 5. Accord-

ing to this server, the functional information derived

about rs6063 predicted it as damaging with a score of

0.741. Studies show that SNPs have functional effects

on protein structure by a single change in the amino

acid (Cargill et al., 1999 & Sunyaev et al., 2000) and

on transcriptional regulation (Prokunina et al., 2002 &

Prokunina et al., 2004).

STRUCTURAL ANALYSIS OF MUTANT

STRUCTURES

Out of all the above methods the SNPs predicted to be

deleterious i.e., rs6063 and rs74315205 were mapped to

the native structure by I mutant 2.0 server to understand

its structural stability.

PREDICTION OF PROTEIN STRUCTURAL

STABILITY

I-Mutant is a neural network based routine tool used in

the analysis of protein stability alterations by consider-

Table 4: PolyPhen analysis

SNP Mutation effect Scoring

rs6063 Probably Damaging 1

rs1799895 Bengin 0.067

Table 5: Fast SNP analysis

Functional Category Prediction Tool Prediction Result Prediction Detail

protein coding PolyPhen probably damaging rs6063.html

SIFT damaging rs6063.html

SNPeffect deleterious rs6063.1.html

LS-SNP deleterious destabilizing.html

destabilizing.html

SNPs3D deleterious SNPs3D.html

Ensembl-NS nonsynonymous rs6063.html

splicing_regulation ESE nder changed rs6063.A.html

rs6063.G.html

ESRSearch changed rs6063.A.html

Table 6: Protein structural stability based on standard free energy

change Where, “WT” is the amino acid in native protein, “New” is

mutant amino acid and DDG is the stability (DDG b 0: decrease stability,

DDG N 0: increase stability).

Mutation Position WT New PH Temperature Stability DDG

G191R 191 G R 7.0 25 Decrease -0.25

G199R 199 G R 7.0 25 Decrease -0.83

G88R 88 G R 7.0 25 Increase 0.38

R231 G 231 R G 7.0 25 Increase 0.48

Sowmya Dhawan and Usha Chouhan

510 IDENTIFICATION OF NOVEL MICRO RNAS AND THEIR TARGETS BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

ing 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, the 93%

precision can achieved. The mutations of UCHL 1 gene

have been selected on the basis of prediction scores of

Poly Phen. These variants were given to I-Mutant web

server to predict the DDG stability and reliability index

(RI) upon mutation. Out of the 4 variants 2 were found

to be less stable as shown in Table 6.

RATIONAL CONSIDERATION OF SIFT,

UTR SCAN, POLYPHEN-2, FAST SNP AND

I-MUTANT 3.0

We considered the 64 most potential hindering point

changes for further course of examinations in light of

the fact that they were generally discovered to be less

steady, injurious, and harming by the I-Mutant 3.0, SIFT

and Poly Phen-2 servers individually. The most com-

monly affected among the 6 computational tools has

been taken for further studies i.e. 2 variants as shown

in Figure 3.

CONCLUSION

Hence the combined approach using SIFT, UTRscan and

Polyphen 2 predicts the mutation rs6063 and rs74315205

are most deleterious among the mutations for UCHL1

gene causing Parkinson’s disease characterized. The

recognition of these SNPs as deleterious ones provides

insight into PD biology and presents as anti Parkin-

son’s disease therapeutic targets and diagnostic markers

.Since missense mutations are nucleotide substitutions

that change an amino acid in a protein, the deleterious

effects of these mutations are commonly attributed to

their impact on primary amino acid sequence and pro-

tein structure. Structural analysis results showed that the

amino acid residue substitutions which had the great-

est impact on the stability of the UCHL 1 protein were

mutations in rs6063 and rs74315205 and the variants

like G191R, G199 R, G88R and R231G. Based on our

results, we conclude that these SNPs should be consid-

ered important candidates in UCHL1 related P.D. Based

on our results we conclude that these SNPs should be

considered as important candidates in causing Parkin-

son’s disease.

ACKNOWLEDGEMENT

The authors are highly thankful to the Department of

Biotechnology, Delhi, India for providing support in the

form of Bioinformatics infrastructure facility to carry

out this work.

DISCLOSURE STATEMENT

No competing  nancial interests exist.

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