This article analyzes the parasporin (anti-cancer) proteins of Bacillus thuringiensis through in silico methods. Physico-chemical properties, secondary structure, 3D structure, motifs, and phylogenetic relationships were analyzed for 19 parasporin proteins. The findings help characterize the proteins and predict their cancer cell killing mechanisms. Secondary structure analysis showed predominance of random coil. 3D structure prediction identified domains and structural quality. Motif analysis found endotoxin motifs common to most proteins. Understanding these proteins could help develop them as anti-cancer agents.

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ARTICLE INFO ABSTRACT
Keywords:
Cancer
B. thuringiensis
Parasporin
Protein
In silico.
Introduction
Bacillus thuringiensis, Gram positive, endospore forming
bacterium that produces large crystalline parasporal inclusion
proteins during sporulation and these inclusions often contain δ-
endotoxin proteins having specific cytoxicity against
agriculturally and medically important insect pests of several
orders, including Lepidoptera, Diptera, and Coleoptera [1]. Earlier
studies have reported that non-insecticidal B. thuringiensis
strainsaremorewidelydistributedthantheinsecticidalones[2,3,
4, 5, 6, 7, and 8]. Recent studies have shown that cytocidal activity
against human cancer cells is associated with non-insecticidal
parasporal inclusions of certain B. thuringiensis strains [9]. The
abundance of these non-hemolytic parasporal inclusion proteins
in nature and their selective heterogeneous cytotoxicity spectra
and cytocidal activity levels to human cancer cells [9, 10] made
thempotentialcandidatesforcancertreatment[11].
Cancer is the leading cause of death in worldwide, whose
commonly used treatments are costly and has different physical
and emotional side effects (World Health Organization).
Considering these factors parsporal inclusion proteins from B.
thuringiensis, having specific cytotoxic activity against specific
cancer cells can be used for cancer treatment which might have
relativelylowersideeffectsandmightbecosteffective.
Our study is to analyze the parasporin proteins of B. thuringiensis,
which will help to understand their nature, which will help to
revealtheirmechanismofkillingdifferenttypesofcancercells.We
have analyzed all the proteins in the 6 groups (19 parasporin
proteins) [12] and found different important investigations such
as their physico-chemical characteristics, functional motifs, 3D
structure, binding sites, binding ligands and phylogenetic
relationshipamongtheseproteins.Allof thesefindings would
decipher an effective way to understand different types of
cancer cell killing mechanism of the parasporin proteins of B.
thuringiesis and which could be used as an agent to treat different
typesofcancer.
MATERIALSANDMETHODS
Retrievalofparasporinproteinsequences
Retrieval of Bacillus thuringiensis paasporin protein
sequences: Protein sequences of Bacillus thuringiensis were
retrieved in FASTA format from NCBI [http://www
.ncbi.nlm.nih.gov/]forinsilicoanalysis.
Physico-chemicalcharacterization
Parasporin proteins exhibit its cancer cell-killing activity only
when digested with proteases. Proteolytic processing is essential
for activation of the insecticidal Cry proteins [13]. Different
properties of parasporin protein such as molecular weight,
theoreticalisoelectric point [pI], number of amino acids,amino
acid composition [%], number of positively [Arg+Lys] and
negatively charged [Asp+Glu] residues, extinction co-efficient,
instability index, aliphatic index and Grand Average of
Hydropathicity [GRAVY] were calculated using ExPASy's
ProtParam tool [http://expasy.org/tools/protparam.html] which
uses protein sequences in FASTA format [14].The crystallization
tendency of the proteins was determined by using the CRYSTALP2
WEBSERVER,[http://biominews.ece.ualberta.ca/CRYSTALP2.ht
ml], a kernel-based method that uses the composition and
collocation of amino acids, hydrophobicity and isoelectric point of
the given sequences to estimate the crystallization tendency of the
proteinsandacceptsproteinsequencesinFASTAformat [15].
ABSTRACT- Cancer is the leading cause of death in worldwide whose treatments are costly and
has different physical and emotional side effects. Parsporal inclusion proteins from B.
thuringiensis having specific cytotoxic activity against specific cancer cells can be used for
cancer treatment which might have relatively lower side effects and might be cost effective. We
havereportedinsilicoanalysisthatisphysico-chemicalcharacteristics,secondarystructure,3-
D structure, motifs and phylogenetic relationship among these 19 parasporin proteins to
characterizethemandpredicttheircancercellkillingmechanism.
Original article
Analysis of Parasporin (Anti-cancer) proteins of Bacillus thuringiensis
a b b b
Nasima Aktar* , Shakila Nargis Khan , Anowara Begum , Md. Mozammel Hoq
a
Biotechnology, Basic and Applied Research on Jute
b
Department of Microbiology, University of Dhaka
* Corresponding Author :
Department of Microbiology University of Dhaka, Dhaka-1000,
Bangladesh nasima_du@yahoo.com
Nasima Aktar
Copyright 2011. CurrentSciDirect Publications. IJBMR - All rights reserved.
c

2. Morphology, Target cancer cell and cancer cell killing
mechanism
The parasporins differ in morphology, molecular weight and
composition. Yet, a more important point is that they have no
identical rule in target cell specificity. Although no generalities for
cell-specificity of the toxins are found, it is interesting that some
cancer cells seem more toxin-sensitive than normal cells. Another
point of view, each toxin-specific receptor could be on the target
cells because each parasporin can recognize a different class of
cancer cell lines [16]. However, none of the receptors are known
for certain. Identification of the receptors will greatly enhance not
only scientific knowledge but also encourage their application
especiallyinthemedicalfield.
Characterizationofsecondarystructure
Protein sequences (FASTA format) of parasporin proteins
were used to predict the secondary structure of parasporin
proteins using SOPMA (Self-Optimized Prediction Method with
Alignment)(http://npsapbil.ibcp.fr/cgibin/npsa_automat.pl?pag
e=/NPSA/npsa_sopma.html) [17]. The number of conformational
stateswasadjustedtofourinordertopredictHelix,Sheet,Coiland
Turn while other parameters were set as default. By using this
software, Alpha helix [Hh], 310 helix, [Gg], Pi helix [Ii], Beta bridge
[Bb], Extended strand [Ee], Beta turn [Tt], Bend region [Ss],
Random coil [Cc], Ambiguous states, and other states were
predicted.
Protein3Dstructureprediction
The3Dstructuresofparasporinproteinswerepredictedusing
t h e R a p t o r X m o d e l i n g s e r v e r
[http://raptorx.uchicago.edu/StructurePrediction/predict/]
[18], which accepts sequences in FASTA format and predicts 3D
structures for protein sequences without close homologs in the
Protein Data Bank (PDB). RaptorX predicts its secondary and
tertiary structures as well as solvent accessibility and disordered
regions. To indicate the quality of a predicted 3D model certain
confidence score such as P-value for the relative global quality,
GDT (global distance test) and uGDT (un-normalized GDT) for the
absoluteglobalqualityarealsoassignedbythisserver.
Detectionofmotif
Motifs and their locations in the sequence of Parasporin
protein was identified using motif search tool
[www.genome.jp/tools/motif/]. These protein sequences were
given in FASTA format in PROSITE pattern and scanned against
pfamdatabase.Locations,e-valueofthemotifswereanalyzed.
PredictionofEvolutionaryrelationship
Evolutionary relationship among these proteins was
determined by multiple sequence alignment through Clustal
Omega (http://www.ebi.ac.uk/Tools/msa/clustalo/) [19] and
followed by construction of evolutionary tree. Input data was
parasporin protein sequences in FASTA format and phylogenetic
treewasconstructedusingNeighborJoining[NJ]method.
RESULTSANDDISCUSSION
Retrievalofparasporinproteinsequences
Parasporin protein can be divided mainly in two categories-
the three domain type protein and the non- three domain type
protein. Parasporin 1, 3 and 6 are in the three domain type protein
thatcontainsallthreedomainsandparapsorin2,4and5areinthe
non-three domain type protein. There are 19 parasporin proteins
inthese6groups[12].
Physico-ChemicalCharacterization
N-terminal and C-terminal protease digestion is occurred for
all three-domain and all non-three domain parasporin protein
respectively. Whereas parasorin 2 (Non-three domain) and
parasporin 3 (Three domain) protein requires both N- and C-
terminaldigestion(Table-1)[20].
Isoelectric point [pI] is a pH in which net charge of protein is
zero. pI of parasporin proteins lie in the acidic range indicates
their bearing of acidic side chain that introduces extra negative
charge which results in neutralization of protein in acidic
conditions[21]. From the study of instability index, it was found
that all parsporin protein except PS1Ad1 are stable, as instability
index value less than 40 indicates stability of a protein.
Additionally, Aliphatic index (AI) refers to the relative volume of a
protein occupied by its aliphatic side chains (alanine, valine,
isoleucine, and leucine) [22]. The higher the Aliphatic index of
proteins, the more thermally stable the protein is. Aliphatic index
of parasporin PS6Aa1 (94.79) classifies them as most
thermostable, closely followed by other most of the proteins in
parasporin 1 group followed by parasporin 3 group. Parasporin 2
is the least thermally stable protein group with lower aliphatic
index PS2Aa1 (63.11), PS2Aa2 (62.54), PS2Aa3 (75.0). Grand
average of hydropathicity index [GRAVY] indicates the interaction
ofproteinsinwater.GRAVYvaluesoftheparasporinproteinswere
observed within a wide range of -0.171 to -0.508 meaning all of
them are hydrophilic [23]. Most hydrophilic protein is PS3Ab1 (-
0.508)andleasthydrophilicisPS4Aa1(-0.171)(Table-2).
Some properties of the proteins (isoelectric point,
hydrophobicity,andthefrequencyofcertaincollocateddi-andtri-
peptides)arepivotalindicatorsofcrystallization.The CRYSTALP2
accounts all such characteristics of given sequences for
estimating the confidence of crystallization. The higher the
confidence, the more probable that protein is crystallizable
and vice versa.CRYSTALP2resultrevealedthathalfofparasporin
1; all of class 3 and 4 class is crystallizable, whereas members of
class 2, 5 and 6 are non-crystallizable (Table-3). Crystallization
tendency of protein is important to identify the structure-function
relationshipoftheproteinsisimportantindrugdesign[24].
The amino acid composition of each parasporin protein
sequence calculated showed variation in richness of amino acid
among these proteins. Parasporin 1 is rich in Serine and Leucine.
Whereas, Parasporin 2 and 5 are enriched by Threonine and
Serine, however parasporin 3 is dominated by Asparagine and
Leucine. Threonine and Glycine are predominant in parasporin 4.
Moreover, parasporin 6 is dominated by Asparagine and
Isoleucine (Table-4). Threonine and Serine are polar amino acid
that is small in size and hence reasonably common to occur within
the tight turns on the protein surface or within the interior of a
protein.Side-chainhydroxyloxygen ofSerinecan forma hydrogen
bond with the protein backbone [25]. Also, Threonine is quite
common in protein functional centers as their hydroxyl group is
fairly reactive to form hydrogen bonds with a variety of polar
substrates. A common role for Threonines and Serines within
intracellular proteins is phosphorylation in order to facilitate the
signal transduction process. Leucine and isoleucine are
hydrophobic amino acid as it has a branched hydrocarbon side
chains usually buried in folded proteins. The hydrophobic effect
accounts for stabilization of water-soluble proteins [26].
Isoleucine is rarely directly involved in protein function due to its
non-reactive side chain but may play role in substrate recognition.
In particular, hydrophobic amino acids can be involved in
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3. binding/recognitionofhydrophobicligandssuchaslipids.Glycine
is often found at the surface of proteins, often within loops,
providing high flexibility to these regions. High percentage of
glycine may be responsible for the stability of triple helical
structure, since large amino acids incorporation can cause steric
hindrance [27]. Being polar, Asparagine prefers generally to be on
the surface of proteins, exposed to an aqueous environment.
Asparagines are quite frequently involved in protein active or
binding sites. The polar side-chain is good for interactions with
otherpolarorchargedatoms.
Morphology, Target cancer cell and cancer cell killing
mechanism
Morphology of some parasporin proteins was determined and
some are yet to be determined. Same goes for the toxic-specific
receptors and determination of these receptors will help to
decipher target cell specificity which will eventually encourage
the use of these proteins as anticancer drug. Morphology,
receptors, target cell specificity is given in the Table-5 along with
cellkillingmechanism[16][28][29][30].
Characterizationofsecondarystructure
Secondary structure analysis revealed predominance of
random coil, followed by alpha helix, extended strand, and beta
sheet respectively in parasporin 1, 3 and 6 (Except PS1Aa6 and
PS1Ab1) (Table-6). Predominance of random coil, followed by
extended strand, alpha helix and beta turn were observed in
parasporin 2, 4 and 5. Secondary structure analysis showed
predominance of random coil responsible for the flexibility and
bond forming ability with adjacent units in protein monomers.
Information on packaging of secondary structural elements may
assisttoderivepotentialtertiaryproteinstructures[24].
Protein3Dstructure
The 3D structure of protein is very crucial to comprehend
protein functions, their sub- cellular localization as well as their
receptor identification and protein-protein interactions. Tertiary
structure of parasporin proteins were determined by RaptorX
(Fig-1). Also Number of domains as well as solvent accessibility
alongwithsomeconfidencescoresthatdeterminesthequalityofa
predicted 3D model (P-value, GDT (global distance test), uGDT
(un-normalized GDT), and RMSD for the absolute local quality of
each residue in the model) was also determined (Table-8). Four of
Eleven Parasporin 1 proteins only contained 2 domains (PS1Aa3,
PS1Aa4, PS1Ac1 and PS1Ac2). Moreover PS3Aa1 and PS3Ab1
contained 3 and 2 domains respectively. All the other proteins
containedonlyonedomain(Table-7).
MotifIdentification
Motifs for parasporin protein were identified using Motif scan
tool and number of motifs, their location and e-value is varied
amongtheparasporinmembers(Table-8).
Motif analysis revealed that apart from three, one and one
parasporin protein from parasporin class 2, 4 and 5 respectively
all of the proteins contained Endotoxin N motif. And all the protein
(Other than these five and parasporin class 6) also contains
Endotoxin-C motif. This is important as they contribute in protein
structure. All three-domain protein contains Endotoxin-N and
Endotoxin-C motif. However, all the non-three domain type
proteinscontainsETX-MTX2andAerolysinmotif.
Evolutionaryrelationship
The evolutionary history was inferred using the Neighbor-
Joining method [31]. The optimal tree with the sum of branch
length = 10.80307799 is shown. The percentage of replicate trees
in which the associated taxa clustered together in the bootstrap
test (1000 replicates) are shown next to the branches [32]. The
tree is drawn to scale, with branch lengths in the same units as
those of the evolutionary distances used to infer the phylogenetic
tree.TheevolutionarydistanceswerecomputedusingthePoisson
correction method [33] and Evolutionary analyses were
conductedinMEGA6[34].
There are two major clades on the tree. Most of three-domain
(Parasporin 1 and parasporin 3) protein resides in a clade while
non-three domain (parasporin 2, 4, 5) proteins reside in another
clade. Some three-domain (PS1Aa6, PS1Ad1 and PS6Aa1) resides
protein resides in a closely related tree of non-three domain
proteinclade.
Table-1: Types and Characteristics of parasporins:
AnticancerParasporinofB.thuringiensis.
Table-2: Physicochemical parameters of different parasporin
proteins
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4. Table-3:CrystallizationtendencyofParasporinProtein
Table-4: Presence of Amino acid in different parasporin
proteininpercentage(%)
Table-5:Relationshipbetweeninclusionmorphologyandcell
deathmechanism.
Table-6: Secondary structure prediction of parasporin
protein(%)
Table-8: Motif detection of Parasporin protein
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5. Table-3:CrystallizationtendencyofParasporinProtein
Table-4: Presence of Amino acid in different parasporin
proteininpercentage(%)
Table-5:Relationshipbetweeninclusionmorphologyandcell
deathmechanism.
Evolutionaryrelationship
The evolutionary history was inferred using the Neighbor-
Joining method [31]. The optimal tree with the sum of branch
length = 10.80307799 is shown. The percentage of replicate trees
in which the associated taxa clustered together in the bootstrap
test (1000 replicates) are shown next to the branches [32]. The
tree is drawn to scale, with branch lengths in the same units as
those of the evolutionary distances used to infer the phylogenetic
tree.TheevolutionarydistanceswerecomputedusingthePoisson
correction method [33] and Evolutionary analyses were
conductedinMEGA6[34].
There are two major clades on the tree. Most of three-domain
(Parasporin 1 and parasporin 3) protein resides in a clade while
non-three domain (parasporin 2, 4, 5) proteins reside in another
clade. Some three-domain (PS1Aa6, PS1Ad1 and PS6Aa1) resides
protein resides in a closely related tree of non-three domain
proteinclade.
Table-1: Types and Characteristics of parasporins:
AnticancerParasporinofB.thuringiensis.
Table-2: Physicochemical parameters of different parasporin
proteins
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6. Figure 1: 3D structure of parasporin protein
Figure 2: Phylogenetic relationship among parasporin
proteins.
CONCLUSION
Comparative studies of physico-chemical, secondary
structural, functional and phylogenetic relationship analysis gave
extensive information of protein's structure, function and its
relationshipwith othermembersofthe family. Characterizationof
parasporin protein yielded new insights. We have found that they
share some common features such as they are all thermostabe,
hydrophilic in nature and they also contain acidic side chains.
Moreover, protease digestion is required for the activation of
protein toxin. We have also found the richness of amino acids like
Serine, Threonine, Asparagine, Glycine, Arginine, Isoleucine and
Leucine which play vital role both structurally and functionally.
Moreover, secondary structure revealed that all three domain
protein group followed the dominance pattern of secondary
structureunitandallthenon-threedomainproteinfollowedsame
pattern of secondary structure unit. 3-D structure of the proteins
was also predicted. From which we can assume that they will
follow similar mechanism for recognition and exertion of toxicity
effects towards different types of cancer cells. Study of
evolutionary relationship reveals that all of the proteins shared a
common ancestor. All of these findings help us to understand the
characteristics of these proteins which will help to use parsporin
proteinsaspotentialanticancerdrug.
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7. 1) C.C. Beegle, and T. Yamamoto. 1992. History of Bacillus thuringiensis
Berlinerresearchanddevelopment.Can.Entomol.124:587–616.
2) S. Hastowo, B. W. Lay, and M. Ohba. 1992. Naturally occurring Bacillus
thuringiensisinIndonesia.J.Appl.Bacteriol.73:108–113.
3) M.P. Meadows, D.J. Ellar, J. Butt et al (1992) Distribution, frequency and
diversity of Bacillus thruringiensis in an animal feed mill. Appl. Environ.
Microbiol.58,1344-1350.
4) E. Mizuki, T. Ichimatsu, S.-H. Hwang, Y. S. Park, H. Saitoh, K. Higuchi, and M.
Ohba. 1999. Ubiquity of Bacillus thuringiensis on phylloplanes of arboreous
andherbaceousplantsinJapan.J.Appl.Microbiol.86:979–984.
5) M. Ohba, 1996. Bacillus thuringiensis populations naturally occurring on
mulberry leaves: a possible source of the populations associated with silk-
worm-rearinginsectaries.J.Appl.Bacteriol.80:56–64.
6) M. Ohba, 1997. Three Bacillus thuringiensis flagellar serovars widely
occurringinnaturalenvironmentsofJapan.J.BasicMicrobiol.37:71–76.
7) M. Ohba,and K. Aizawa. 1986. Insect toxicity of Bacillus thuringiensis
isolatedfromsoilsofJapan.J.Invertebr.Pathol.47:12–20.
8) J.Y. Roh, H. W. Park, B. R. Jin, H. S. Kim, Y. M. Yu, and S. K. Kang. 1996.
Characterization of novel non-toxic Bacillus thuringiensis isolated from
Korea.Lett.Appl.Microbiol.23:249–252.
9) E. Mizuki, M. Ohba, T. Ichimatsu, S. H. wang, K. Higuchi, H. Saitoh, and T. Akao,
1998. “Unique Appendages Associated with Spores of Bacillus cereus
Isolates,”JournalofBasicMicrobiology.Vol.38,pp.33-39.
10) E. Mizuki, T. Ichimatsu, S. H. wang, Y.S. Park, H. Saitoh, K. Higuchi & M. Ohba.
1999 Ubiquity of Bacillus thuringiensis on phylloplanes of arboreous and
herbaceousplantsinJapan.JournalofAppliedMicrobiology86,979–984.
11) S.Y.R.Wong,ReviewArticle,IeJSME2010:4(2):3-9.
12) Committee of Parasporin Classification and Nomenclature 2006
http://parasporin.fitc.pref.fukuoka.jp/index.html.
13) E. Schnepf, N. Crickmore, J. Van-Rie, D. Lereclus, J. Baum, J. Feitelson, D.R.
Zeigler, and D.H. Dean. 1998. “Bacillus thuringiensis and Its Insecticidal
Proteins,” Microbiology and Molecular Biology Reviews. Vol. 62, pp. 774-
806.
14) Gasteiger E, Hoogland C, Gattiker A, Duvaud S, Wilkins MR, Appel RD,
Bairoch A. Protein Identification and Analysis Tools on the ExPASy Server:
TheProteomicsProtocolsHandbook,HumanaPress2005;571-607.
15) K. Lukasz, A.A. Razib, S. Aghakhani, S. Dick, M. Mizianty, S. Jahandideh.
CRYSTALP2: sequence-based protein crystallization propensity prediction.
BMCStructuralBiology2009;9:50.
16) S. Kitada, Y. Abe, A. Ito, O. Kuge, T. Akao, E. Mizuki, and M. Ohba. Molecular
Identification and Cytocidal Action of Parasporin, a Protein Group of Novel
Crystal Toxins Targeting Human Cancer Cells. 6th Pacific Rim Conference on
the Biotechnology of Bacillus thuringiensis and its Environmental Impact,
B.C.Victoria,2005.J.-C.Côté,I.S.Otvos,J.–L.Schwartz,andC.Vincent.(eds).
17) C. Geourjon, G. Deleage. SOPMA: significant improvements in protein
secondary structure prediction by consensus prediction from multiple
alignments. ComputApplBiosci1995Dec;11[6]:681-84.
18) M. Källberg, H. Wang, S. Wang, J. Peng, Z. Wang, H. Lu & J. Xu. Template-based
protein structure modeling using the RaptorX web server. Nature Protocols
7,1511–1522,2012.
19) M.A. Larkin, G. Blackshields, N.P. Brown, R. Chenna, P.A. McGettigan, H.
McWilliam, F. Valentin, I.M. Wallace, A. Wilm, R. Lopez, J.D. Thompson, T. J.
Gibson, D.G. Higgins. ClustalW and ClustalX version 2. Bioinformatics 2007;
23[21]:2947-48.
20) M. Ohba, E. Mizuki and A. Uemori. Review paper: Parasporin, a New
Anticancer Protein Group from Bacillus thuringiensis. Anticancer Research
29:427-434(2009).
21) http://www.mhhe.com/physsci/chemistry/carey5e/Ch27/ch27-1-
4.html.
22) A. IKAI. Thermostability and Aliphatic Index of Globular Proteins. J.
Biochem. 88,1895-1898(1980).
23) Bioinformatics for Beginners: Genes, Genomes, Molecular Evolution,
DatabasesandAnalyticalTools:SupratimChoudhuri.
24) K. Lukasz, A.A. Razib, S. Aghakhani, S. Dick, M. Mizianty, S. Jahandideh.
CRYSTALP2: sequence-based protein crystallization propensityprediction.
BMCStructuralBiology2009;9:50.
25) A. Butler, C. Franklin, N. Jayme. The role of vanadium bromo peroxidase in
the biosynthesis of halogenated marine natural products. Natural Product
Reports2004;21[1]:180–8.
26) H.A. Wagenknecht, W.D. Woggon. Identification of intermediates in the
catalyticcycleofchloroperoxidase.ChemBiol1997;4:367-72.
27) E. Gasteiger, C. Hoogland, A. Gattiker, S. Duvaud, M.R. Wilkins, R.D. Appel, A.
Bairoch. ProteinIdentificationandAnalysisToolsontheExPASyServer:The
ProteomicsProtocolsHandbook,HumanaPress2005;571-607.
28) K. Ekino, S. Okumura, T. Ishikawa, S. Kitada, H. Saitoh, T. Akao, T. Oka, Y.
Nomura, M. Ohba, T. Shin and E. Mizuki. Cloning and Characterization of a
Unique Cytotoxic Protein Parasporin-5 Produced by Bacillus thuringiensis
A1100Strain.Toxins2014,6,1882-1895;doi:10.3390/toxins6061882.
29) C. Xu, Bi-Cheng Wang, Z.Yu and M. Sun. Structural Insights into Bacillus
thuringiensis Cry, Cyt and Parasporin Toxins. Toxins 2014, 6, 2732-2770;
doi:10.3390/toxins6092732.
30) S. Okumura, H. Saitoh, T. Ishikawa, K. Inouye, E. Mizuki. Mode of action of
parasporin-4, a cytocidal protein from Bacillus thuringiensis. Biochim.
Biophys.Acta2011,1808,1476–1482.
31) N. Saitou and M. Nei (1987). The neighbor-joining method: A new method
for reconstructing phylogenetic trees. Molecular Biology and Evolution
4:406-425.
32) J. Felsenstein (1985). Confidence limits on phylogenies: An approach using
thebootstrap.Evolution39:783-791.
33) E. Zuckerkandl and L. Pauling (1965). Evolutionary divergence and
convergence in proteins. Edited in Evolving Genes and Proteins by V. Bryson
andH.J.Vogel,pp.97-166.AcademicPress,NewYork.
34) K. Tamura, G. Stecher, D. Peterson, A. Filipski, and S. Kumar. (2013). MEGA6:
Molecular Evolutionary Genetics Analysis version 6.0. Molecular Biology
andEvolution30:2725-2729.
REFERENCES
7516
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