identification of deleterious mutations in myostatin gene of rohu

Research ArticleIdentification of Deleterious Mutations in MyostatinGene of Rohu Carp (Labeo rohita) Using Modeling andMolecular Dynamic Simulation Approaches

Kiran Dashrath Rasal Vemulawada Chakrapani Swagat Kumar PatraShibani D Mohapatra Swapnarani Nayak Sasmita Jena Jitendra Kumar SundarayPallipuram Jayasankar and Hirak Kumar Barman

Fish Genetics and Biotechnology Division Central Institute of Freshwater Aquaculture (ICAR) BhubaneswarOdisha 751 002 India

Correspondence should be addressed to Hirak Kumar Barman hkbarman68hotmailcom

Received 26 August 2015 Revised 13 January 2016 Accepted 27 January 2016

Academic Editor Jian-Liang Li

Copyright copy 2016 Kiran Dashrath Rasal et al This is an open access article distributed under the Creative Commons AttributionLicense which permits unrestricted use distribution and reproduction in any medium provided the original work is properlycited

The myostatin (MSTN) is a known negative growth regulator of skeletal muscle The mutated myostatin showed a double-muscular phenotype having a positive significance for the farmed animals Consequently adequate information is not availablein the teleosts including farmed rohu carp Labeo rohita In the absence of experimental evidence computational algorithmswere utilized in predicting the impact of point mutation of rohu myostatin especially its structural and functional relationshipsThe four mutations were generated at different positions (pD76A pQ204P pC312Y and pD313A) of MSTN protein of rohuThe impacts of each mutant were analyzed using SIFT I-Mutant 20 PANTHER and PROVEAN wherein two substitutions(pD76A and pQ204P) were predicted as deleterious The comparative structural analysis of each mutant protein with the nativewas explored using 3D modeling as well as molecular-dynamic simulation techniques The simulation showed altered dynamicbehaviors concerning RMSD and RMSF for either pD76A or pQ204P substitution when compared with the native counterpartInterestingly incorporated two mutations imposed a significant negative impact on protein structure and stability The presentstudy provided the first-hand information in identifying possible amino acids where mutations could be incorporated into MSTNgene of rohu carp including other carps for undertaking further in vivo studies

1 Introduction

Themyostatin belonging to themember of the TransformingGrowth Factor-120573 (TGF120573) superfamily was initially identifiedin mice by McPherron et al [1] It is a negative regulatorof skeletal muscle growth in mammalian species [2ndash5] Itinhibits myoblast specification and differentiation via down-regulating Pax3 Myf-5 and MyoD expressions in myoblasts[6] The mice carrying a targeted dominant mutation inMSTN gene resulted in the increased skeletal muscle massdue to the combined effects of muscle hypertrophy andhyperplasia [7 8] Mice lacking the biologically active regionof the MSTN C-terminal developed muscular hypertrophyin the chest and buttock [1] Cattle (Bos taurus) is the firstspecies in which a mutation in the MSTN gene showed the

double-muscling phenotype Subsequently similar growtheffects were reported in dogs cattle buffalo porcine andsheep [9ndash14] In mammals the desired effects were obtaineddue to mutation positioned in either pD76A pQ204ApC312Y or pC313Y Therefore the mutation mediatedimprovement of the skeletal muscle mass provided an avenuefor improving growth related production traits in the farmedanimals [15] A transgenic mouse containing a pD76Amutation (within the leader peptide) in the MSTN genesignificantly increased muscle mass [16] The experimentallyincorporated mutations positioned at pC313Y in cattle andpD76A in mice as well as dog led to dramatic skeletal musclegrowth

The teleosts MSTN gene is well-conserved throughoutevolution where two distinct MSTN gene isoforms such as

Hindawi Publishing CorporationBioMed Research InternationalVolume 2016 Article ID 7562368 10 pageshttpdxdoiorg10115520167562368

2 BioMed Research International

MSTNa and MSTNb were found in some fish species [17ndash20] In zebrafish (Danio rerio) the roles of MSTN gene con-cerning muscle development and growth were envisaged byinhibiting their function using its prodomain [16 21 22]TheMSTN gene characterization as well as knock-out and indelexperiments in some fish species such that of yellow catfish[23] medaka [24] and rainbow trout [25] showed double-muscling phenotype with hyperplasia and hypertrophy

Thus it would be of interest to identify each muta-tion in MSTN gene that disturbs its function leading toimproved muscle growth in commercially important aqua-culture species Commercial applications require in-depthanalyses in the diversified teleosts However limited infor-mation is available whether the fish with mutated MSTNcould display a similar phenotype or not The Indian majorcarp Labeo rohita (popularly known as rohu) is an eco-nomically important freshwater fish in India as well asother Asian countries [26ndash28] The sequence data of rohucatla (Catla catla) and zebrafish (Danio rerio) myostatingenes are available in the public domain (GenBank AccnumbersHQ850573HQ850575 andAY323521)However itsphysiological functions in Indian major carps including rohuare lacking In this study we intended to assess the impact ofpD76A pQ204A pC312Y and pC313Ymutations inMSTNgene of rohu carp

Direct experimental studies linked to incorporatingmutation in a particular gene of interests have been laboriousand time-consumingThe computational study can efficientlyproduce useful information to rationalize and guide inundertaking further experimental studies [28ndash30] Thesepoint mutations affect gene expressions and their productsby altering change in their functional activities [31]The com-putational tools such as SIFT PolyPhen I-Mutant 20 PAN-THER and PROVEAN are more useful for the pin-pointingimpact analysis of point mutation linking the gene functionor gene products [29 32ndash37] Consequently the combinationof a different algorithm will improve the accuracy of resultsor predicted effects of particular mutation [38] SNPs withamino acid (aa) substitutions can disturb protein folding andits stability leading to altered protein function and protein-protein interactions including its expression [39ndash42]

We have used several computational tools for determin-ing the impact of the selected point mutation onMSTN geneTo get insight into the atomic-level changes and the dynamicbehavior of the molecule on to the particular mutations wehave used molecular dynamic simulation approaches Wehave identified the potential mutations proposed modeledstructure of the mutant proteins and compared them withthe native protein The present in silico study is helpful forcategorizing precise mutation in MSTN gene for the purposeof selecting a definite gene targeting site

2 Materials and Methods

21 Retrieval of Data The MSTN protein sequence ofrohu Labeo rohita was retrieved from Uniprot with IDC9DE96 LABRO(httpwwwuniprotorguniprotC9DE96)SMART (a Simple Modular Architecture Research Tool) wasused for identification and annotation including architectures

of the mobile domain in the MSTN (httpsmartembl-hei-delbergde)

22 Prediction of SNPEffects inMSTNProteinUsing Sequence-Based Tools For elucidation of the impact of SNPs in MSTNprotein of rohu several in silico tools comprising of SIFTPANTHER PROVEAN and I-Mutant 20 were utilizedThe SIFT (Sorting Intolerant from Tolerant) algorithm isuseful for determining the impact of single amino acidsubstitution in the resultant protein (httpsiftjcviorg)The tolerance index that is score of SIFT in the tune ofle005 determined the nsSNPs (nonsynonymous SNPs) onresultant protein [43] The higher the tolerance index theless likely the impact on AASs Subsequently PANTHER(Protein Analysis through Evolutionary Relationships) wasused so as to validate the predicted output from SIFTanalysis The PANTHER cSNP tool that estimates the likeli-hood of a particular nonsynonymous (amino acid changing)coding SNP to cause a functional impact on the protein(httpwwwpantherdborgtoolscsnp) It calculates the sub-PSEC score based on alignments of evolutionarily relatedproteins [44] The score more than minus3 is predicted as a lessdeleterious while less than minus3 is predicted as deleteriousWe have used this tool for detecting the impact of AASs atselected positions in the MSTN protein

Further PROVEAN and I-Mutant 20 algorithm wereused for assessing the impacts of SNPs in MSTN proteinof rohu PROVEAN (Protein Variation Effect Analyzer) isa tool which predicts impact of an amino acid substitutionor indel on the biological functions of a particular protein(httpproveanjcviorgindexphp) This algorithm allowsfor the best-balanced separation between the deleteriousand neutral AASs based on a threshold value A querysequence was provided in FASTA format and score forprediction was based on default threshold score minus25 Thescore lt minus25 indicates that variant is deleterious and gt minus25is considered as a neutral [45] I-Mutant 20 is a SupportVector Machine- (SVM-) based web server for the automaticprediction of protein stability changes upon single-site muta-tions (httpfoldingbiofoldorgi-mutanti-mutant20html)The predicted free energy change (DDG) is based on thedifferential unfolding Gibbs free energy change (kcalMol)between mutant and native proteins [33]

23 MSTN Protein Structure Prediction We have searchedhomologous 3D structure for MSTN protein of rohu inthe RCSB PDB data bank Due to the absence of homol-ogous 3D structure we have used I-TASSER (IterativeThreading ASSEmbly Refinement) server which is anonline platform for predicting protein structure and func-tion (httpzhanglabccmbmedumicheduI-TASSER) It isbased on multiple threading alignments and iterative struc-tural assembly simulations The prediction of the accuracyof the model depends upon a confidence score (C-score)determined by the quality of the threading alignments andstructural assembly refinement simulations In this server wehave submitted query sequence (FASTA format) of MSTNprotein for obtaining 3D model

BioMed Research International 3

The best 3D model was selected according to rank basedon C-score TM-score and RMSD value The 3D structureofMSTNwas visualized by PyMOL (httpwwwpymolorg)and UCSF-CHIMERA (httpswwwcglucsfeduchimera)The PyMoL is an open source of molecular visualizationtools for interactive visualization and analysis of molecularstructures The superimposition of both MSTN native andmutant models was carried out by using UCSF-CHIMERA(httpswwwcglucsfeduchimera) The SwissPDBViewerwas used for analyzingRMSdeviation by superimposing bothnative and mutant structures (httpspdbvvital-itch)

The structural evaluation and stereochemical qualityassessments for both native and mutant models were carriedout by using the SAVES (Structural Analysis and VerificationServer) which is an integrated server (httpnihservermbiuclaeduSAVES) The ProSA (Protein Structure Analysis)web server (httpsprosaservicescamesbgacatprosaphp)was used for refinement and validation of protein structures[46] The ProSA was used for checking model structuralquality with potential errors where program shows a plotof its residue energies and 119885-scores to determine the overallquality of the model The protein folding rate was calculatedusing PRORATE (prediction of protein folding rates) server(httpsunflowerkuicrkyoto-uacjpsimsjnfolding)

24 Investigation of SNP Effects in MSTN 3D Protein byStructure-Based Tools The mutant model was generatedusing PyMoL tool The H-bonding patterns were studiedusing the PyMoL visualization tool Both native and mutant3D structures of MSTN were used for molecular dynamic(MD) simulations

The MD simulation was performed using the program ofGROMACS 465 package (Groingen Machine for ChemicalSimulations) running on Ubuntu Linux platform 1404Respective mutant and native models were solvated indepen-dently within a dodecahedron box with SPC216 (single pointcharge 216) water molecules at a marginal radius of 10 A Theenergyminimizationwas carried out for 5000 iterationsstepswith a tolerance of 100 kJmol by steepest descent algorithm(implementing OPLS force field) Emtol convergence crite-rion was set up to 1000 kcalMol The coulomb interactionswere truncated at 10 nmThe ionization states of the residueswere set to a pH 70 Finally MD simulation for 50000 steps(10 ns) was carried out The resulted trajectory files wereanalyzed by using gromacs packages so as to differentiateRMSD and RMSF values between native and mutant struc-tures of MSTNThe graphs were constructed using XMGraceon Linux platform itself

3 Results

31 Mutation at Either pD76A or pQ204P of Rohu MSTNProtein Is Deleterious Several computational tools wereapplied to investigate the structural and functional impactsof targeted single nucleotide polymorphisms (SNPs) presentin the coding region of the MSTN gene of rohu (L rohita)The dataset of possible nsSNPs was selected based uponpreviously reported observations in the mammalian system

In this study we have used both sequence-based (SIFTPANTHER PROVEAN and I-Mutant) and structure-based(I-TASSER) algorithms We have generated mutations bypD76A pQ204P pC312Y and pD313A amino acid (aa)substitutions in the rohu MSTN protein using the PyMoLtool (Table 1) The most of the algorithms are based uponevolutionary principle Subsequently we have analyzed theeffect of each mutation using a series of computational toolsInitially SIFT PANTHER PROVEAN and I-Mutant 20analyses predicted pD76A and pQ204P substitutions asthe most deleterious with score 002 and 004 respectively(SIFT score le 005) Contrary to this pC312Y and pD313Amutations generated quite variable results when analyzedwith different algorithms of SIFT and PANTHER toolsThe resultant significant differential outputs with SIFT andPANTHER algorithms could be due to consideration ofdifferent protein sequence alignments used to characterizethe variants the same was also documented earlier [47] Thefunctional impacts of aa substitutions (AAAs) were furthervalidated by providing FASTA sequence of the protein asa query along with a change in residue(s) to PROVEANwhich is capable of classifying impacts of AASs or indels onfunctional protein [45] Except pD313A the remaining threesubstitutions were classified as deleterious To improve pre-diction accuracy I-Mutant 20 algorithm was applied This I-Mutant 20 also categorized pC312Y with increased stabilitywhile others were classified as deleterious substitutions

32 Structure-Based Analyses Also Supported the DeleteriousNature of pD76A and pQ204A Substitutions The structuralinformation could play a vital role in unraveling the molec-ular mechanism as well as being helpful for predicting theeffects of nsSNPs on overall protein structure [48] The mod-eling of rohu MSTN protein was performed by I-TASSERwhich is a structure-based automated prediction algorithmTen templates were considered for modeling MSTN proteinThe best 3D structure with a high confidence score (C-score)was selected and used for further investigations The topmodel bearing estimated C-score of 046 TM-score of 065plusmn013 and an RMSD value of 77 plusmn 43 A was finally selected(Figure 1)

The differential Ramachandran plot is depicted in Fig-ure 2 In the plot native protein of phipsi angles for 731residues was in the additional allowed regions with 183in the most favored regions while being 86 in disallowedregionsThe comparative analysis between native andmutantstructure was carried out which revealed that 728 183and 89 residues lie in the most favored regions addition-ally allowed regions and disallowed regions respectivelyThese results indicated that incorporated mutation enforcedshifting of residues towards the disallowed region from themost favored regions in the case of pQ204A mutation Nochange was observed in pD76A and native form of MSTNprotein The lowered ERRAT score for mutant in the tune of78797 as compared to native structure (83476) also signifiedabout the overall deteriorating quality of mutant MSTN(Figure 3) The ProSA analysis showed slightly deviated 119885-score value in mutant protein (pD76A and pQ204A) ascompared to native oneThe score for native was minus441 while


Table1Listof

nsSN

Psshow

ingdeleteriousnon

deleterious

scores

aspredictedby

SIFT

PRO

VEA

NI-M

utant2and

PANTH

ER

Aminoacidsc

hange

Proteinpo

sition

subP

SEC

score

119875deleterio

usPredictio

nby

PANTH

ER-cSN

PPR

OVEA

Nscore

PROVEA

Npredictio

n(cutoff

=minus25)

DDGvalue

I-Mutant

stability

predictio

nSIFT

score

Predictio

n

D76A

minus263640

04100

9Deleterious

minus2887

Dele

terio

usminus075

Decreased

002

Deleterious

Q204P

minus403755

073838

Tolerated

minus2942

Dele

terio

usminus097

Decreased

004

Deleterious

C312Y

minus116194

099982

Dele

terio

usminus9330

Dele

terio

us087

Increased

006

Tolerated

D313A

minus172365

03933

Tolerated

minus5925

Neutral

092

Increased

009

Tolerated


Figure 1 Predicted MSTN models by I-TASSER (both native and mutant) with mutated native and mutated residues

mutants pD76A and pQ204A were minus436 and 443 respec-tively Together these results provided the clue regardingpossible conformational alterations for both mutant proteins(pD76A and pQ204A) The analysis of protein folding ratealso specified that pQ204A mutant and native possessedminus1838321 whereas folding rate varied with mutant pD76A(minus18410992) It can be inferred from the structural analysisthat the two deleterious mutations (pD76A and pQ204A)had fetched a severe change in the MSTN protein and it islikely to disturb the protein function

33 Analysis of Simulation at the Atomic Level Predictedthe Deviated Functioning of Mutated (pD76A or pQ204A)MSTN In order to gain atomic-level insights as well asunderstanding of dynamic behavioral changes in mutantproteins we performed MD simulations for both the nativeand mutant MSTNs The MD simulation has been knownto support the predictions given by sequence-based in silicotools For this purpose the RMSD of the protein back-bone as a mean for functional time was examined TheRMSD for all protein-H atoms from the initial structurewas reexamined to study the convergence of the proteinsystem (Figure 4(a)) The mutant RMSD trajectory detectedan abrupt rise of RMSD value starting from 5 ps onwardsThe RMSD graph showed higher deviation in MSTN mutant(pD76A and pQ204P) structures as compared to the nativecounterpart (Figure 4(a)) To assess the effect of mutationon the MSTN structure we also computed the RMSF valuesThe graphs show higher RMS fluctuations in MSTN mutant

(both pD76A and pQ204P) structures as compared to thenative structure (Figure 4(b)) Superimposition of native andmutant structures detected the RMS deviations in the tuneof 0049 A 0029 A 0030 A and 0024 A respectively forpD76A pQ204P pC312Y and pD313AThe varied potentialenergies between native and both forms of mutants were alsodocumented The pD76A mutant possessed minus11378342119890 +06 kJMol and pQ204P mutant contained minus11424335119890 +06 kJMol (pQ204P) whereas it possessed comparativelylesser potential energy in the tune of minus1170186119890 + 06 kJMolfor native counterpart Further the calculated total ener-gies after MD simulated production run were minus786233119890 +05 kJMol minus789900119890 + 05 kJMol and minus84884119890 + 05 kJMolrespectively for pD76A mutant pQ204P mutant and thenative structures Thus the differentially heightened energyrequirements for both the mutant structures as compared tonative one were suggestive of affecting (disturbed) the properfunctioning of mutant MSTNs

4 Discussion

Several evidences are available that the inhibition ofMSTN gene led to dramatic growth ratedouble-musclingphenotype inmammals Alongside attempts have beenmadeto attenuateMSTNactivities in fishmuscle usingmutagenesisapproach But those approaches in fishes led to conflictingoutcomes possibly due to duplicated gene events or faultyselection of mutation sites Also the MSTN protein isextremely well-conserved between fish and other vertebrates


180

180

0

0

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLN

A10 LEU

A86 GLUA65 GLU

A3 PHEA48 CYS

A266 ARG

A16 ALA

A339 CYSA103 LYS

A138 PRO

A38 PROA188 PRO

A135 LYS

A105 ALA

A106 GLY

A5 GLN

minus180

minus180

Φ

120595

(a)

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A16 ALA

A103 LYS

A29GLN

A48 CYS

A266 ARG

A86 GLUA65 GLU

A3

A10 LEUA38 PRO

A188 PRO

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(b)

Generalpre-proproline favoredGlycine favoredGeneralpre-proproline allowedGlycine allowed

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLNA188 PRO

A38 PRO

A48 CYS

A266 ARG

A3

A65

A86 GLU

A10 LEU

A103 LYS

A16 A

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(c)

Figure 2 Ramachandran plot ofMSTNmutant proteinmodels (a b and c)Themost favored regions additional allowed regions generouslyallowed regions and disallowed regions are indicated as dark blue light blue light yellow and white respectively

Nearly 90 of the amino acids (aa) are identical betweenfish and mammalian MSTN sequences The experimentalevidence in zebrafish [22] and medaka [49] demonstratedthe increased number of skeletal muscle fibers via inhibitionof MSTN Remarkably inhibition of MSTN could not leadto elevated muscle weight in both cases Therefore extensivestudies are essential so as to understand the roles beingplayed by MSTN in farmed fishes The impact analysis as a

consequence of mutation at a precise location is very impor-tant in order to gain economic benefits from farmed fishes

Computational biology has long been a guiding forcefor undertaking experimental investigations In the presentwork we have generated mutations by amino acid substi-tutions at four positions such as pD76A or pQ204P orpC312Y or pD313A SIFT PANTHER PROVEAN and I-Mutant 20 based analyses predicted pD76A and pQ204P


20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)

Residue (window centre)

Overall quality factor 83476

(a)

99

95

Erro

r val

ue (

)

Residue (window centre)20 40 60 80 100 120 140 160 180 200 220 240 260 280 300


(b)

Figure 3 ERRAT plots for (a) MSTN native protein and (b) MSTN mutant protein model generated by SAVES The plots for both proteinsshow overall quality factors 83476 and 78797 respectively The grey region indicates error region 95 and 99 white bars show regionof lower error rate for protein folding and black bars show misfolded region located distantly from active site The below 95 value (ierejection limit) indicates low resolution structure while more than or 95 value shows high resolution structure

025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)

Protein-H after Isq fit to protein-HRMSD

Native MSTNMutant (D76A) MSTNMutant (Q204P) MSTN

(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom


RMS fluctuation (C-120572 atoms)

(b)

Figure 4 The RMSD and RMSF of both native and mutant MSTN structures of rohu carp (red color native blue mutant MSTN (pD76A)and green mutant (pQ204P)


substitutions as the most deleterious Those computationalmethods can predict (based on evolutionary principles) theeffect of nsSNPs in the coding region of the genome [35 50ndash52]The 3D structure of a protein has been very helpful in pre-dicting the effects of nsSNPs on protein structure-function[43 48] We have generated 3D structure of MSTN andvalidated it by using Ramachandran plot Further throughMD simulation we have compared RMSD values and totalenergy values between native and mutant structures Basedon estimated RMSD values the computed energy providedthe comparative information regarding structural stabilityand the deviation between the two (native and each mutant)structures The deviated RMSD could have an impact on thefunctional activity and stability of protein [53] The rise inRMSD values in the graph indicated that there was an enor-mous deviation between the native and mutant structurespointing towards alterations in the structural and functionalstabilities We thus inferred that these nsSNPs could affectthe stability of MSTN structure of rohu Thus it is likelythat either pD76Aor pQ204P substitutionwas deleterious innature with regard to MSTN gene function Our findings arepartly in line with earlier experimental findings that a trans-genic mouse bearing pD76AMSTN gene mutation showedsignificantly improved muscle mass [16] Also mutations inthe position pQ204X producing a premature stop codonin the N-terminal domain resulted into double-musclingphenotype in Charolais and Limousine cattle breeds [54]

Thus we could find that point mutation in the MSTNgene disturbs the protein structure and affects its functionOur findings also suggested that mutations in the N-terminalmotif of TFGB chain could more effectively inhibit theMSTN gene activity Our findings will provide a clue thatimprovement in muscular mass could be experimentallypossible by incorporating pD76A andor pQ204Pmutationsin rohu MSTN gene

As the transcriptional regulation ofMSTN genes is medi-ated by different myogenic factors the previous works dem-onstrated that MSTN could able to negatively autoregulate itsexpression through a Smad7-dependent mechanism [55]

In recent times gene targeting in predetermined positionin the genome was made possible using technologies of zinc-finger nucleases (ZFNs) transcription activator-like effectornucleases (TALENs) and CRISPRCas9 in wide ranges of celllines [56ndash63] The site-specific nick by nucleases can then berepaired by error-prone nonhomologous end joining (NHEJ)resulting in animals carrying deletions or insertions at thecut site It is also possible to disrupt any gene of interests byenforcing homologous recombination (HR) mediated repairat a particular nick site Thus it is possible to generatemodel fishes by creating indels andor gene disruptions inthe myostatin geneThis in turn will lead not only to uncoverphysiological roles of the myostatin gene but also to improveskeletal muscle growth in Indian farmed carps

5 Conclusions

Our computational study identified that pD76A or pQ204Pamino acid substitutions (nsSNPs) are deleterious for thestructural and functional aspects of the rohu (L Rohita)

MSTN protein We investigated the impact of the fourvariants on MSTN in the form of energy calculation andevolutionarily conserved residues The SIFT PANTHERPROVEAN and I-Mutant algorithms predicted decrease inprotein stability The MD simulation also confirmed thatchange in the dynamic behavior of mutant protein at theatomic level to the native counterpart Based on the doc-umented differences in total energy for native and mutantmodels we speculated that the proper functioning of nativeMSTN protein would be disturbedOur results suggested thatthese mutants in TGFb domain have a potential functionalimpact on MSTN genes in teleosts The generated data fromthis study will be resourceful for undertaking future in vivoinvestigations so as to improve the growtgh rate of famedcarps

Abbreviations

MSTN MyostatinSIFT Sorting Intolerant from TolerantSNP Single nucleotide polymorphismRMSD Root means square deviationsPDB Protein data bankRMSF Root means square fluctuationsMD Molecular dynamics

Conflict of Interests

The authors have no potential conflict of interests to discloseand no competing financial interests exist

Authorsrsquo Contribution

Hirak Kumar Barman Pallipuram Jayasankar and JitendraKumar Sundaray conceived and designed experiments KiranDashrath Rasal Vemulawada Chakrapani Swagat KumarPatra Shibani DMohapatra Swapnarani Nayak and SasmitaJena performed the experiment and analyzed data KiranDashrath Rasal Vemulawada Chakrapani Swagat KumarPatra and Hirak Kumar Barman wrote the paper

Acknowledgment

The work was supported by the Indian Council of Agri-cultural Research Ministry of Agriculture Government ofIndia

References

[1] A C McPherron A M Lawler and S-J Lee ldquoRegulation ofskeletal muscle mass in mice by a new TGF-120573 superfamilymemberrdquo Nature vol 387 no 6628 pp 83ndash90 1997

[2] A Stinckens M Georges and N Buys ldquoMutations in theMyostatin gene leading to hypermuscularity in mammalsindications for a similar mechanism in fishrdquo Animal Geneticsvol 42 no 3 pp 229ndash234 2011

[3] AMHaidet L Rizo CHandy et al ldquoLong-term enhancementof skeletal muscle mass and strength by single gene admin-istration of myostatin inhibitorsrdquo Proceedings of the National


Academy of Sciences of the United States of America vol 105 no11 pp 4318ndash4322 2008

[4] S-J Lee ldquoRegulation of muscle mass by myostatinrdquo AnnualReview of Cell and Developmental Biology vol 20 pp 61ndash862004

[5] H J Xing Z Y Wang B S Zhong et al ldquoEffects of differentdietary intake on mRNA levels of MSTN IGF-I and IGF-II inthe keletal muscle of Dorper and Hu sheep hybrid F

1ramsrdquo

Genetics and Molecular Research vol 13 no 3 pp 5258ndash52682014

[6] B Langley M Thomas A Bishop M Sharma S Gilmour andR Kambadur ldquoMyostatin inhibits myoblast differentiation bydown-regulating MyoD expressionrdquo The Journal of BiologicalChemistry vol 277 no 51 pp 49831ndash49840 2002

[7] G Szabo G Dallmann G Muller L Patthy M Soller andL Varga ldquoA deletion in the myostatin gene causes the com-pact (Cmpt) hypermuscular mutation in micerdquo MammalianGenome vol 9 no 8 pp 671ndash672 1998

[8] X Zhu M Hadhazy M Wehling J G Tidball and E MMcNally ldquoDominant negative myostatin produces hypertrophywithout hyperplasia in musclerdquo FEBS Letters vol 474 no 1 pp71ndash75 2000

[9] A Stinckens T Luyten J Bijttebier et al ldquoCharacterization ofthe complete porcine MSTN gene and expression levels in pigbreeds differing in muscularityrdquo Animal Genetics vol 39 no 6pp 586ndash596 2008

[10] D SMosher PQuignonCD Bustamante et al ldquoAmutation inthe myostatin gene increases muscle mass and enhances racingperformance in heterozygote dogsrdquo PLoS Genetics vol 3 no 5article e79 2007

[11] I A Boman G Klemetsdal O Nafstad T Blichfeldt and D IVage ldquoImpact of two myostatin (MSTN) mutations on weightgain and lamb carcass classification in Norwegian White Sheep(Ovis aries)rdquoGenetics Selection Evolution vol 42 article 4 2010

[12] Y Chen J Ye L Cao Y Zhang W Xia and D Zhu ldquoMyostatinregulates glucose metabolism via the AMP-activated proteinkinase pathway in skeletal muscle cellsrdquo The InternationalJournal of Biochemistry amp Cell Biology vol 42 no 12 pp 2072ndash2081 2010

[13] S HuW Ni W Sai et al ldquoKnockdown of myostatin expressionby RNAi enhances muscle growth in transgenic sheeprdquo PLoSONE vol 8 no 3 Article ID e58521 2013

[14] D-S Han C-S Yu C-K Chiang et al ldquoSerum myostatin isreduced in individuals with metabolic syndromerdquo PLoS ONEvol 9 no 9 Article ID e108230 2014

[15] C Rehfeldt M F W Te Pas K Wimmers et al ldquoAdvancesin research on the prenatal development of skeletal musclein animals in relation to the quality of muscle-based food IRegulation of myogenesis and environmental impactrdquo Animalvol 5 no 5 pp 703ndash717 2011

[16] S-J Lee ldquoGenetic analysis of the role of proteolysis in theactivation of latent myostatinrdquo PLoS ONE vol 3 no 2 ArticleID e1628 2008

[17] P R Biga S B Roberts D B Iliev et al ldquoThe isolationcharacterization and expression of a novel GDF11 gene and asecond myostatin form in zebrafish Danio reriordquo ComparativeBiochemistry andPhysiology Part B Biochemistry andMolecularBiology vol 141 no 2 pp 218ndash230 2005

[18] T Kerr EH Roalson andBD Rodgers ldquoPhylogenetic analysisof themyostatin gene sub-family and the differential expressionof a novel member in zebrafishrdquo Evolution amp Development vol7 no 5 pp 390ndash400 2005

[19] S B Roberts and F W Goetz ldquoDifferential skeletal muscleexpression of myostatin across teleost species and the isolationofmultiplemyostatin isoformsrdquo FEBS Letters vol 491 no 3 pp212ndash216 2001

[20] B Funkenstein V Balas Y Rebhan and A Pliatner ldquoCharac-terization and functional analysis of the 51015840 flanking region ofSparus auratamyostatin-1 generdquoComparative Biochemistry andPhysiologymdashA Molecular amp Integrative Physiology vol 153 no1 pp 55ndash62 2009

[21] B M Meyer J M Froehlich N J Galt and P R Biga ldquoInbredstrains of zebrafish exhibit variation in growth performance andmyostatin expression following fastingrdquo Comparative Biochem-istry and Physiology Part A Molecular amp Integrative Physiologyvol 164 no 1 pp 1ndash9 2013

[22] C Xu GWu Y Zohar and S-J Du ldquoAnalysis ofmyostatin genestructure expression and function in zebrafishrdquo The Journal ofExperimental Biology vol 206 no 22 pp 4067ndash4079 2003

[23] Z Dong J Ge K Li et al ldquoHeritable targeted inactivation ofmyostatin gene in yellow catfish (Pelteobagrus fulvidraco) usingengineered zinc finger nucleasesrdquo PLoS ONE vol 6 no 12Article ID e28897 2011

[24] S-I Chisada H Okamoto Y Taniguchi et al ldquoMyostatin-deficient medaka exhibit a double-muscling phenotype withhyperplasia and hypertrophy which occur sequentially duringpost-hatch developmentrdquoDevelopmental Biology vol 359 no 1pp 82ndash94 2011

[25] M P Phelps I M Jaffe and T M Bradley ldquoMuscle growthin teleost fish is regulated by factors utilizing the activin II Breceptorrdquo The Journal of Experimental Biology vol 216 no 19pp 3742ndash3750 2013

[26] H K Barman V Das R Mohanta C Mohapatra R P Pandaand P Jayasankar ldquoExpression analysis of 120573-actin promoter ofrohu (Labeo rohita) by direct injection into musclerdquo CurrentScience vol 99 no 8 pp 1030ndash1032 2010

[27] H K Barman S K Patra V Das et al ldquoIdentification andcharacterization of differentially expressed transcripts in thegills of freshwater prawn (Macrobrachium rosenbergii) undersalt stressrdquo The Scientific World Journal vol 2012 Article ID149361 11 pages 2012

[28] C Mohapatra H K Barman R P Panda et al ldquoCloning ofcDNA and prediction of peptide structure of Plzf expressed inthe spermatogonial cells of Labeo rohitardquoMarine Genomics vol3 no 3-4 pp 157ndash163 2010

[29] C G P Doss and B Rajith ldquoComputational refinement offunctional single nucleotide polymorphisms associated withATM generdquo PLoS ONE vol 7 no 4 Article ID e34573 2012

[30] K D Rasal V Chakrapani S K Patra et al ldquoIdentificationand prediction of the consequences of nonsynonymous SNPsin glyceraldehyde 3-phosphate dehydrogenase (GAPDH) geneof zebrafish Danio reriordquo Turkish Journal of Biology vol 40 pp43ndash54 2016

[31] P D Thomas and A Kejariwal ldquoCoding single-nucleotidepolymorphisms associated with complex vsMendelian diseaseevolutionary evidence for differences in molecular effectsrdquoProceedings of the National Academy of Sciences of the UnitedStates of America vol 101 no 43 pp 15398ndash15403 2004

[32] M M Johnson J Houck and C Chen ldquoScreening for dele-terious nonsynonymous single-nucleotide polymorphisms ingenes involved in steroid hormone metabolism and responserdquoCancer Epidemiology Biomarkers and Prevention vol 14 no 5pp 1326ndash1329 2005


[33] E Capriotti P Fariselli and R Casadio ldquoI-Mutant20 predict-ing stability changes upon mutation from the protein sequenceor structurerdquo Nucleic Acids Research vol 33 no 2 pp W306ndashW310 2005

[34] C George Priya Doss R Rajasekaran C Sudandiradoss KRamanathan R Purohit and R Sethumadhavan ldquoA novelcomputational and structural analysis of nsSNPs inCFTRgenerdquoGenomic Medicine vol 2 no 1-2 pp 23ndash32 2008

[35] M R M Hussain N A Shaik J Y Al-Aama et al ldquoIn silicoanalysis of Single Nucleotide Polymorphisms (SNPs) in humanBRAF generdquo Gene vol 508 no 2 pp 188ndash196 2012

[36] H S H A Mohamoud M R Hussain A A El-Harouni etal ldquoFirst comprehensive in silico analysis of the functional andstructural consequences of SNPs in human GalNAc-T1 generdquoComputational and Mathematical Methods in Medicine vol2014 Article ID 904052 15 pages 2014

[37] K D Rasal T M Shah M Vaidya S J Jakhesara and C GJoshi ldquoAnalysis of consequences of non-synonymous SNP infeed conversion ratio associated TGF-120573 receptor type 3 gene inchickenrdquoMeta Gene vol 4 pp 107ndash117 2015

[38] L G Martelotto C K Ng M R De Filippo et al ldquoBenchmark-ing mutation effect prediction algorithms using functionallyvalidated cancer-related missense mutationsrdquo Genome Biologyvol 15 article 484 2014

[39] R De Cristofaro A Carotti S Akhavan et al ldquoThe naturalmutation by deletion of Lys9 in the thrombinA-chain affects thepKa value of catalytic residues the overall enzymersquos stability andconformational transitions linked to Na+ bindingrdquo The FEBSJournal vol 273 no 1 pp 159ndash169 2006

[40] MHardt andRA Laine ldquoMutation of active site residues in thechitin-binding domain ChBDChiA1 from chitinase A1 of Bacilluscirculans alters substrate specificity use of a green fluorescentprotein binding assayrdquo Archives of Biochemistry and Biophysicsvol 426 no 2 pp 286ndash297 2004

[41] R Jones M Ruas F Gregory et al ldquoA CDKN2A mutationin familial melanoma that abrogates binding of p16INK4a toCDK4 but not CDK6rdquo Cancer Research vol 67 pp 9134ndash91412007

[42] H Ode S Matsuyama M Hata et al ldquoComputational charac-terization of structural role of the non-active sitemutationM36Iof human immunodeficiency virus type 1 proteaserdquo Journal ofMolecular Biology vol 370 no 3 pp 598ndash607 2007

[43] P C Ng and S Henikoff ldquoPredicting the effects of amino acidsubstitutions on protein functionrdquo Annual Review of Genomicsand Human Genetics vol 7 pp 61ndash80 2006

[44] P DThomas M J Campbell A Kejariwal et al ldquoPANTHER alibrary of protein families and subfamilies indexed by functionrdquoGenome Research vol 13 no 9 pp 2129ndash2141 2003

[45] Y Choi G E Sims S Murphy J R Miller and A P ChanldquoPredicting the functional effect of amino acid substitutions andindelsrdquo PLoS ONE vol 7 no 10 Article ID e46688 2012

[46] M Wiederstein and M J Sippl ldquoProSA-web interactive webservice for the recognition of errors in three-dimensionalstructures of proteinsrdquoNucleic Acids Research vol 35 no 2 ppW407ndashW410 2007

[47] S A De Alencar and J C D Lopes ldquoA comprehensive in silicoanalysis of the functional and structural impact of SNPs inthe IGF1R generdquo Journal of Biomedicine and Biotechnology vol2010 Article ID 715139 8 pages 2010

[48] P Kumar S Henikoff and P C Ng ldquoPredicting the effects ofcoding non-synonymous variants on protein function using the

SIFT algorithmrdquo Nature Protocols vol 4 no 7 pp 1073ndash10822009

[49] E Sawatari R Seki T Adachi et al ldquoOverexpression of thedominant-negative form of myostatin results in doubling ofmuscle-fiber number in transgenic medaka (Oryzias latipes)rdquoComparative Biochemistry and Physiology PartA Molecular ampIntegrative Physiology vol 155 no 2 pp 183ndash189 2010

[50] M Alanazi Z Abduljaleel W Khan et al ldquoIn silico analysisof single nucleotide polymorphism (snps) in human 120573-globingenerdquo PLoS ONE vol 6 no 10 Article ID e25876 2011

[51] S Khan and M Vihinen ldquoPerformance of protein stabilitypredictorsrdquo Human Mutation vol 31 no 6 pp 675ndash684 2010

[52] B Rajith and C G P Doss ldquoPath to facilitate the predic-tion of functional amino acid substitutions in red blood celldisordersmdasha computational approachrdquo PLoS ONE vol 6 no9 Article ID e24607 2011

[53] J-H Han N Kerrison C Chothia and S A TeichmannldquoDivergence of interdomain geometry in two-domain proteinsrdquoStructure vol 14 no 5 pp 935ndash945 2006

[54] R Kambadur M Sharma T P L Smith and J J BassldquoMutations in myostatin (GDF8) in double-muscled BelgianBlue and Piedmontese cattlerdquoGenome Research vol 7 no 9 pp910ndash916 1997

[55] C McFarlane A Vajjala H Arigela et al ldquoNegative auto-regulation of myostatin expression is mediated by Smad3 andmicroRNA-27rdquo PLoS ONE vol 9 no 1 Article ID e87687 2014

[56] A R Bassett and J-L Liu ldquoCRISPRCas9 and genome editingin Drosophilardquo Journal of Genetics and Genomics vol 41 no 1pp 7ndash19 2014

[57] T Gaj C A Gersbach and C F Barbas III ldquoZFN TALEN andCRISPRCas-based methods for genome engineeringrdquo Trendsin Biotechnology vol 31 no 7 pp 397ndash405 2013

[58] D Hockemeyer H Wang S Kiani et al ldquoGenetic engineeringof human pluripotent cells using TALE nucleasesrdquo NatureBiotechnology vol 29 no 8 pp 731ndash734 2011

[59] W Y Hwang Y Fu D Reyon et al ldquoEfficient genome editingin zebrafish using a CRISPR-Cas systemrdquoNature Biotechnologyvol 31 no 3 pp 227ndash229 2013

[60] M Kanatsu-Shinohara M Kato-Itoh M Ikawa et al ldquoHomol-ogous recombination in rat germline stem cellsrdquo Biology ofReproduction vol 85 no 1 pp 208ndash217 2011

[61] A Klug ldquoThe discovery of zinc fingers and their developmentfor practical applications in gene regulation and genomemanip-ulationrdquo Quarterly Reviews of Biophysics vol 43 no 1 pp 1ndash212010

[62] P Mali J Aach P B Stranges et al ldquoCAS9 transcriptionalactivators for target specificity screening and paired nickases forcooperative genome engineeringrdquoNature Biotechnology vol 31no 9 pp 833ndash838 2013

[63] N E Sanjana L Cong Y Zhou M M Cunniff G Feng andF Zhang ldquoA transcription activator-like effector toolbox forgenome engineeringrdquoNature Protocols vol 7 no 1 pp 171ndash1922012

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Anatomy Research International

PeptidesInternational Journal of


Hindawi Publishing Corporation httpwwwhindawicom

International Journal of

Volume 2014

Zoology


Molecular Biology International

GenomicsInternational Journal of


The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014


BioinformaticsAdvances in

Marine BiologyJournal of



Signal TransductionJournal of


BioMed Research International

Evolutionary BiologyInternational Journal of



Biochemistry Research International

ArchaeaHindawi Publishing Corporationhttpwwwhindawicom Volume 2014


Genetics Research International


Advances in

Virolog y

Hindawi Publishing Corporationhttpwwwhindawicom

Nucleic AcidsJournal of

Volume 2014

Stem CellsInternational



Enzyme Research



Microbiology


MSTNa and MSTNb were found in some fish species [17ndash20] In zebrafish (Danio rerio) the roles of MSTN gene con-cerning muscle development and growth were envisaged byinhibiting their function using its prodomain [16 21 22]TheMSTN gene characterization as well as knock-out and indelexperiments in some fish species such that of yellow catfish[23] medaka [24] and rainbow trout [25] showed double-muscling phenotype with hyperplasia and hypertrophy

Thus it would be of interest to identify each muta-tion in MSTN gene that disturbs its function leading toimproved muscle growth in commercially important aqua-culture species Commercial applications require in-depthanalyses in the diversified teleosts However limited infor-mation is available whether the fish with mutated MSTNcould display a similar phenotype or not The Indian majorcarp Labeo rohita (popularly known as rohu) is an eco-nomically important freshwater fish in India as well asother Asian countries [26ndash28] The sequence data of rohucatla (Catla catla) and zebrafish (Danio rerio) myostatingenes are available in the public domain (GenBank AccnumbersHQ850573HQ850575 andAY323521)However itsphysiological functions in Indian major carps including rohuare lacking In this study we intended to assess the impact ofpD76A pQ204A pC312Y and pC313Ymutations inMSTNgene of rohu carp

Direct experimental studies linked to incorporatingmutation in a particular gene of interests have been laboriousand time-consumingThe computational study can efficientlyproduce useful information to rationalize and guide inundertaking further experimental studies [28ndash30] Thesepoint mutations affect gene expressions and their productsby altering change in their functional activities [31]The com-putational tools such as SIFT PolyPhen I-Mutant 20 PAN-THER and PROVEAN are more useful for the pin-pointingimpact analysis of point mutation linking the gene functionor gene products [29 32ndash37] Consequently the combinationof a different algorithm will improve the accuracy of resultsor predicted effects of particular mutation [38] SNPs withamino acid (aa) substitutions can disturb protein folding andits stability leading to altered protein function and protein-protein interactions including its expression [39ndash42]

We have used several computational tools for determin-ing the impact of the selected point mutation onMSTN geneTo get insight into the atomic-level changes and the dynamicbehavior of the molecule on to the particular mutations wehave used molecular dynamic simulation approaches Wehave identified the potential mutations proposed modeledstructure of the mutant proteins and compared them withthe native protein The present in silico study is helpful forcategorizing precise mutation in MSTN gene for the purposeof selecting a definite gene targeting site

2 Materials and Methods

21 Retrieval of Data The MSTN protein sequence ofrohu Labeo rohita was retrieved from Uniprot with IDC9DE96 LABRO(httpwwwuniprotorguniprotC9DE96)SMART (a Simple Modular Architecture Research Tool) wasused for identification and annotation including architectures

of the mobile domain in the MSTN (httpsmartembl-hei-delbergde)

22 Prediction of SNPEffects inMSTNProteinUsing Sequence-Based Tools For elucidation of the impact of SNPs in MSTNprotein of rohu several in silico tools comprising of SIFTPANTHER PROVEAN and I-Mutant 20 were utilizedThe SIFT (Sorting Intolerant from Tolerant) algorithm isuseful for determining the impact of single amino acidsubstitution in the resultant protein (httpsiftjcviorg)The tolerance index that is score of SIFT in the tune ofle005 determined the nsSNPs (nonsynonymous SNPs) onresultant protein [43] The higher the tolerance index theless likely the impact on AASs Subsequently PANTHER(Protein Analysis through Evolutionary Relationships) wasused so as to validate the predicted output from SIFTanalysis The PANTHER cSNP tool that estimates the likeli-hood of a particular nonsynonymous (amino acid changing)coding SNP to cause a functional impact on the protein(httpwwwpantherdborgtoolscsnp) It calculates the sub-PSEC score based on alignments of evolutionarily relatedproteins [44] The score more than minus3 is predicted as a lessdeleterious while less than minus3 is predicted as deleteriousWe have used this tool for detecting the impact of AASs atselected positions in the MSTN protein

Further PROVEAN and I-Mutant 20 algorithm wereused for assessing the impacts of SNPs in MSTN proteinof rohu PROVEAN (Protein Variation Effect Analyzer) isa tool which predicts impact of an amino acid substitutionor indel on the biological functions of a particular protein(httpproveanjcviorgindexphp) This algorithm allowsfor the best-balanced separation between the deleteriousand neutral AASs based on a threshold value A querysequence was provided in FASTA format and score forprediction was based on default threshold score minus25 Thescore lt minus25 indicates that variant is deleterious and gt minus25is considered as a neutral [45] I-Mutant 20 is a SupportVector Machine- (SVM-) based web server for the automaticprediction of protein stability changes upon single-site muta-tions (httpfoldingbiofoldorgi-mutanti-mutant20html)The predicted free energy change (DDG) is based on thedifferential unfolding Gibbs free energy change (kcalMol)between mutant and native proteins [33]

23 MSTN Protein Structure Prediction We have searchedhomologous 3D structure for MSTN protein of rohu inthe RCSB PDB data bank Due to the absence of homol-ogous 3D structure we have used I-TASSER (IterativeThreading ASSEmbly Refinement) server which is anonline platform for predicting protein structure and func-tion (httpzhanglabccmbmedumicheduI-TASSER) It isbased on multiple threading alignments and iterative struc-tural assembly simulations The prediction of the accuracyof the model depends upon a confidence score (C-score)determined by the quality of the threading alignments andstructural assembly refinement simulations In this server wehave submitted query sequence (FASTA format) of MSTNprotein for obtaining 3D model






3 Results






Table1Listof

nsSN

Psshow

ingdeleteriousnon

deleterious

scores

aspredictedby

SIFT

PRO

VEA

NI-M

utant2and

PANTH

ER

Aminoacidsc

hange

Proteinpo

sition

subP

SEC

score

119875deleterio

usPredictio

nby

PANTH

ER-cSN

PPR

OVEA

Nscore

PROVEA

Npredictio

n(cutoff

=minus25)

DDGvalue

I-Mutant

stability

predictio

nSIFT

score

Predictio

n

D76A

minus263640

04100

9Deleterious

minus2887

Dele

terio

usminus075

Decreased

002

Deleterious

Q204P

minus403755

073838

Tolerated

minus2942

Dele

terio

usminus097

Decreased

004

Deleterious

C312Y

minus116194

099982

Dele

terio

usminus9330

Dele

terio

us087

Increased

006

Tolerated

D313A

minus172365

03933

Tolerated

minus5925

Neutral

092

Increased

009

Tolerated






4 Discussion



180

180

0

0

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLN

A10 LEU

A86 GLUA65 GLU

A3 PHEA48 CYS

A266 ARG

A16 ALA

A339 CYSA103 LYS

A138 PRO

A38 PROA188 PRO

A135 LYS

A105 ALA

A106 GLY

A5 GLN

minus180

minus180

Φ

120595

(a)

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A16 ALA

A103 LYS

A29GLN

A48 CYS

A266 ARG

A86 GLUA65 GLU

A3

A10 LEUA38 PRO

A188 PRO

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(b)


A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLNA188 PRO

A38 PRO

A48 CYS

A266 ARG

A3

A65

A86 GLU

A10 LEU

A103 LYS

A16 A

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(c)






20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)



(a)

99

95

Erro

r val

ue (

)



(b)


025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)



(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom



(b)







5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






3 Results






Table1Listof

nsSN

Psshow

ingdeleteriousnon

deleterious

scores

aspredictedby

SIFT

PRO

VEA

NI-M

utant2and

PANTH

ER

Aminoacidsc

hange

Proteinpo

sition

subP

SEC

score

119875deleterio

usPredictio

nby

PANTH

ER-cSN

PPR

OVEA

Nscore

PROVEA

Npredictio

n(cutoff

=minus25)

DDGvalue

I-Mutant

stability

predictio

nSIFT

score

Predictio

n

D76A

minus263640

04100

9Deleterious

minus2887

Dele

terio

usminus075

Decreased

002

Deleterious

Q204P

minus403755

073838

Tolerated

minus2942

Dele

terio

usminus097

Decreased

004

Deleterious

C312Y

minus116194

099982

Dele

terio

usminus9330

Dele

terio

us087

Increased

006

Tolerated

D313A

minus172365

03933

Tolerated

minus5925

Neutral

092

Increased

009

Tolerated






4 Discussion



180

180

0

0

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLN

A10 LEU

A86 GLUA65 GLU

A3 PHEA48 CYS

A266 ARG

A16 ALA

A339 CYSA103 LYS

A138 PRO

A38 PROA188 PRO

A135 LYS

A105 ALA

A106 GLY

A5 GLN

minus180

minus180

Φ

120595

(a)

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A16 ALA

A103 LYS

A29GLN

A48 CYS

A266 ARG

A86 GLUA65 GLU

A3

A10 LEUA38 PRO

A188 PRO

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(b)


A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLNA188 PRO

A38 PRO

A48 CYS

A266 ARG

A3

A65

A86 GLU

A10 LEU

A103 LYS

A16 A

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(c)






20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)



(a)

99

95

Erro

r val

ue (

)



(b)


025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)



(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom



(b)







5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


Table1Listof

nsSN

Psshow

ingdeleteriousnon

deleterious

scores

aspredictedby

SIFT

PRO

VEA

NI-M

utant2and

PANTH

ER

Aminoacidsc

hange

Proteinpo

sition

subP

SEC

score

119875deleterio

usPredictio

nby

PANTH

ER-cSN

PPR

OVEA

Nscore

PROVEA

Npredictio

n(cutoff

=minus25)

DDGvalue

I-Mutant

stability

predictio

nSIFT

score

Predictio

n

D76A

minus263640

04100

9Deleterious

minus2887

Dele

terio

usminus075

Decreased

002

Deleterious

Q204P

minus403755

073838

Tolerated

minus2942

Dele

terio

usminus097

Decreased

004

Deleterious

C312Y

minus116194

099982

Dele

terio

usminus9330

Dele

terio

us087

Increased

006

Tolerated

D313A

minus172365

03933

Tolerated

minus5925

Neutral

092

Increased

009

Tolerated






4 Discussion



180

180

0

0

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLN

A10 LEU

A86 GLUA65 GLU

A3 PHEA48 CYS

A266 ARG

A16 ALA

A339 CYSA103 LYS

A138 PRO

A38 PROA188 PRO

A135 LYS

A105 ALA

A106 GLY

A5 GLN

minus180

minus180

Φ

120595

(a)

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A16 ALA

A103 LYS

A29GLN

A48 CYS

A266 ARG

A86 GLUA65 GLU

A3

A10 LEUA38 PRO

A188 PRO

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(b)


A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLNA188 PRO

A38 PRO

A48 CYS

A266 ARG

A3

A65

A86 GLU

A10 LEU

A103 LYS

A16 A

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(c)






20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)



(a)

99

95

Erro

r val

ue (

)



(b)


025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)



(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom



(b)







5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






4 Discussion



180

180

0

0

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLN

A10 LEU

A86 GLUA65 GLU

A3 PHEA48 CYS

A266 ARG

A16 ALA

A339 CYSA103 LYS

A138 PRO

A38 PROA188 PRO

A135 LYS

A105 ALA

A106 GLY

A5 GLN

minus180

minus180

Φ

120595

(a)

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A16 ALA

A103 LYS

A29GLN

A48 CYS

A266 ARG

A86 GLUA65 GLU

A3

A10 LEUA38 PRO

A188 PRO

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(b)


A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLNA188 PRO

A38 PRO

A48 CYS

A266 ARG

A3

A65

A86 GLU

A10 LEU

A103 LYS

A16 A

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(c)






20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)



(a)

99

95

Erro

r val

ue (

)



(b)


025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)



(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom



(b)







5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


180

180

0

0

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLN

A10 LEU

A86 GLUA65 GLU

A3 PHEA48 CYS

A266 ARG

A16 ALA

A339 CYSA103 LYS

A138 PRO

A38 PROA188 PRO

A135 LYS

A105 ALA

A106 GLY

A5 GLN

minus180

minus180

Φ

120595

(a)

A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A16 ALA

A103 LYS

A29GLN

A48 CYS

A266 ARG

A86 GLUA65 GLU

A3

A10 LEUA38 PRO

A188 PRO

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(b)


A138 PRO

A108 ALA

A183 GLY

A266 LYSA238 ALA

A260 PRO

A29GLNA188 PRO

A38 PRO

A48 CYS

A266 ARG

A3

A65

A86 GLU

A10 LEU

A103 LYS

A16 A

A105 ALA

A106 GLY

A5 GLN

LYS

180

180

0

0minus180

minus180

Φ

120595

(c)






20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)



(a)

99

95

Erro

r val

ue (

)



(b)


025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)



(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom



(b)







5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology


20 40 60 80 100 120 140 160 180 200 220 240 260 280 300

99

95

Erro

r val

ue (

)



(a)

99

95

Erro

r val

ue (

)



(b)


025

02

015

01

005

05 10 15 200

RMSD

(nm

)

Time (ps)



(a)

025

02

015

01

005

01000 2000 3000 4000 5000 60000

(nm

)

Atom



(b)







5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology






5 Conclusions



Abbreviations






Acknowledgment


References








1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology





1ramsrdquo





































































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology









































Volume 2014

Zoology






















Advances in

Virolog y



Volume 2014




Enzyme Research



Microbiology

identification of deleterious mutations in myostatin gene of rohu

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