Analysis of Suitable Signal Peptides for Designing a Secretory Thermostable Cyanide Degrading Nitrilase An in Silico Approach

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Analysis of Suitable Signal Peptides for Designing

a Secretory Thermostable Cyanide Degrading

Nitrilase: An in Silico Approach

3,4

Marzieh Asadi , Saba Gharibi , Seyyed Hossein Khatami , Zahra Shabaninejad ,

1,4*

Farzaneh Kargar , Fatemeh Yousefi , Mortaza Taheri-Anganeh *, Amir Savardashtaki

- Department of Medical Biotechnology, School of Advanced Medical Sciences and Technologies, Shiraz University of Medical

Sciences Shiraz, Iran

- Recombinant Proteins Laboratory, Department of Biochemistry, School of Medicine, Shiraz University of Medical Sciences,

Shiraz, Iran

- Department of Nanobiotechnology, School of Basic Sciences, Tarbiat Modares University, Tehran, Iran

- Pharmaceutical Sciences Research Center, Shiraz University of Medical Sciences, Shiraz, Iran

- Department of Biological Sciences, Faculty of genetics, Tarbiat Modares University, Tehran, Iran

Received: 17/05/2019

Accepted: 24/08/2019

Published: 26/08/2019

Abstract

Secretory production of recombinant proteins has many benefits such as solubility and ease of purification. The aim of this

study was to find suitable signal peptides for secretory production of nitriles in Bacillus subtilis. The signal peptides were chosen

from Signalpeptide web server. SignalP server was used to define probability of suitable signal peptides and their secretion

pathways. Physico-chemical properti es and solubility were predicted by ProtParam and Protein-sol, respectively. Bioinformatics

analysis identified SUBF_BACSU, GUB_BACSU, SACB_BACAM and AMY_BACAM that linked to nitriles are appropriate

signal peptides. Their fusion proteins could be stable, soluble and non-antigenic proteins that might have suitable secondary and

tertiary structures. The recommended signal peptides by this study are appropriate for rational designing of secretory soluble

nitriles. Thus, the results of present work can be useful for future experimental production of secretory and soluble nitriles.

Keywords: In silico, Nitrilase, Cyanide, Signal Peptide.

from cytochrome c to oxygen. As a result, it affects the

Introduction

cellular use of oxygen and oxidative metabolism (5-7). The

organs which are mainly affected by cyanide poisoning are

the central nervous system and heart (8, 9). Although

conventional methods for treatments of cyanide

wastewaters such as chemical and physical treatments

efficiently can decrease the toxicity from cyanide, they

involve high cost of construction and engineering, long-

term storage, and require reagents that can be harmfulto the

environment (10, 11). Undertaking biological processes for

cyanide degradation is the best alternatives and approaches

to overcome these problems (12, 13). Cyanides removal by

Polluted water and soil are major environmental

problems. Use of pesticides, fossil fuels, fertilizers, mining,

municipal wastes, and sewage are the main cause of this

pollution (1). Sewage from industrial wastewater and

agricultural processing can hold a considerable amount of

potential pollutants, including organisms/pathogens, trace

organic chemicals and toxic compounds, such as cyanides,

sulphates, phenols, and metals, etc.)3 ,2(. Cyanide

compounds can be highly toxic to many life forms and are

available in the form of nitriles, cyanides, and carbonitriles

(

4). Cyanide can bind to the enzyme cytochrome c

oxidase and inhibit the transportation of electrons

biodegradation

process

includes

utilizing

the

microorganisms and plants which have the various enzymes

in their systems, and able to converting cyanides to non-

toxic compounds (13, 14). Using these methods are

efficient, cost-effective, and have a faster rate compare to

conventional ones (15). Nitrilases are the enzymes related

to the group of hydrolases that convert the nitrile (R-CN)

compounds into their corresponding carboxylic acids and

ammonia using a simple hydrolytic pathway. They are

widely found in nature and can be isolated from microbes

Corresponding author: Dr. Amir Savardashtaki, (a)

Department of Medical Biotechnology, School of

Advanced Medical Sciences and Technologies, Shiraz

University of Medical Sciences Shiraz, Iran. (b)

Pharmaceutical Sciences Research Center, Shiraz

University of Medical Sciences, Shiraz, Iran. E-mail:

dashtaki63@gmail.com.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

and plants (16, 17). Nitrilases have been acknowledged as

valuable biocatalysts in the protection of the environment

due to their capability to convert cyanide to non-toxic

products (18). Bacteria are broadly used as cell factories

with the aim of recombinant proteins production. Its request

in biotechnology has increased dramatically with every

passing day, as it represents a multi-billion-dollar market

through its application such as technical bulk enzymes and

biopharmaceutical proteins (19). Most of its applications

include complex proteins are challenging to produce thus in

Methods

.1 Dataset retrieval

The amino acid sequences of 30 SPs peptides were

retrieved from database resources, “Signal Peptide

Website” (http://www.signalpeptide.de). UniProt”

http://www.uniprot.org) were employed to confirmed

(

selected signal peptides data according to the experimental

evidence. The key factors of collecting the data for the

following steps were the ones belonged to bacterial

secretory proteins and mentioned in previous studies.

Retrieved sequences are listed in Table 1.

production of

a successful recombinant protein the

important consideration is the choice of a proper expression

system and also the host’s potential for the secretion of

heterologous proteins since it is critical to provide an active

and stable protein (20). High-level expression of

heterolegus proteins can lead in accumulation of insoluble

misfolded proteins in the cytoplasm, which is identified as

the inclusion bodies. These aggregated intermediates can

not have a suitable biological activity. As a result, one

important step in production of recombinant protoein is to

refolded the inclusion bodies to to get the applicable

soluble proteins (21). The gram-negative Escherichia coli

.2 Prediction of signal peptides presence, their cleavage

site location and Secretion pathway

SignalP with an accuracy of 87% (35) is an artificial neural

network (ANN) based tool and is categorized among the

most accurate and reliable tools in all proposed data mining

models for prediction of the SPs sequences and their

precise cleavage sites (36). In this study, the SignalP

(version 5) server (http://www.cbs.dtu.dk/services/SignalP)

was employed to identify the signal peptide probability and

their precise cleavage sites together with their secretion

pathway. Sequences with probability value greater than 0.5,

were introduced as competent signal peptides for next

section analysis.

(

E. coli) and the gram-positive Bacillus subtilis (B. subtilis)

are the favourite hosts for producing the vast majority of

recombinant proteins (22, 23). E. coli has several

advantages for heterologous protein expression, such as

easy growth on inexpensive media and rapid biomass

accumulation in a short fermentation period (24-26).

However, there is some limitation in using this organism, as

recombinant proteins in E. coli can express in the form of

inclusion bodies and incorrect protein folding while, B.

subtilis has many outstanding features. It is non-pathogenic

and endotoxin-free organism (27, 28) and has a great

protein secretory capability as they can secrete proteins

directly and efficiently into the cultural medium via the Sec

and Tat pathway (27). Furthermore, their outstanding

biochemical and physiological features make them easy for

experiment handling and genetic manipulation (29).

Another characteristic of B. subtilis that makes it a more

suitable host for the production of secretory heterologous

protein is its lack of outer membrane (30, 31). Therefore,

they can be applied for the efficient production of large-

scale industrial enzymes, proteins, and antibiotics. In

addition to B. subtilis potential for the secretion of

recombinant proteins, the signal peptides (SPs) that direct

the export proteins into the general secretory pathway are

important. SPs can improve the protein folding, solubility

of recombinant proteins, and reduces the purification

process. (32). SPs are normally short (15 to 30 amino acids

long) and share a common triplex structure including a

positively charged amino-terminal region (n-region), a

hydrophobic region (h-region) and a cleavable site (c-

region). N and h-region have a role in channelling the

recombinant proteins into periplasmic space. C-region as a

cleavage site location is important as it can be recognized

by signal peptidase enzyme (33, 34). This study aimed to

comparedesigning computationally the several signal

peptides with respect to their amino acid composition and

their physico-chemical properties in order to investigate

and select suitable candidates for secretory production of

nitrilase in the B. subtilis host.

.3. Evaluation of signal peptides physico-chemical

properties and solubility

ProtParam, at

online

software

(

http://web.expasy.org/protparam/) (37), was utilized for in

silico prediction of the diverse physico-chemical features of

the recorded signal peptides, containing amino acid

composition, GRAVY (grand average of hydropathicity),

aliphatic index, molecular weight (MW), theoretical

isoelectric point (pI), positively and negatively charged

residues and instability index on SPs linked to nitrilase (37,

8). Prediction of protein solubility was done by online

software, “Protein-sol”(http://protein-

sol.manchester.ac.uk). This server provides a solubility

score between 0-1 to make interpreting the results

simpler(39). For the final analysis, unstable fusion proteins

were removed and the SPs representing a solubility higher

than 0.45 selected.

.4. In silico prediction of signal peptides secretion

pathway and sub-cellular localization

Searches for Sub-cellular localization of different signal

peptide infusion with nitrilase and their final destination

were performed using an online tool “ProtCompB”

(

http://www.softberry.com). The Softberry prediction is based

on neural networks and reports between 86–100% correct

prediction (40-42).

Result

.1 Selection of signal peptides and sequence definition

A total number of 30 prokaryotic signal peptides was

selected from several different Gram-positive and Gram-

negative bacteria and their primary structure was retrieved

from online server which are listed in Table 1.

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

Table 1: Amino acid sequences of the signal peptides.

Accession No. Signal Peptide

Protein Name

Source

Amino Acid Sequence

Bacillus licheniformis

Bacillus subtilis

MKQQKRLYAR LLTLLFALIFLLPHSAAAA

MKLWFSTLKLKK AAAVLLFSCVALAG

MRKKTKNRL ISSVLSTVVISSLLFPGAAGA

MKSCK QLIVCSLAAILLLIPSVSFA

Beta-lactamase

Bacillopeptidase F

Amidase enhancer

MVKSFRMKALIAGAAVAAAVSAGAVSDVP

AAKVLQPTAAYA

P42111

YXAL_BACSU

Uncharacterized protein yxaL

Uncharacterized protein ylqB

Alpha-amylase

Bacillus subtilis

MKK IGLLFMLCLAALFTIGFPAQQADA

MFAKR FKTSLLPLFAGFLLLFHLVLAG

MKK VMLATALFLGLTPAGANA

Pectate lyase

Endopeptidase lytF

MKKK LAAGLTASAIVGTTLVVTPAEA

MAYDSRFDEWVQKLKEESFQNNTFDRRKFIQ

GAGKIAGLSLGLTIAQSVGAFEVNA

P42251

PPBD_BACSU

Alkaline phosphatase D

Quinol oxidase subunit 2

Beta-glucanase

Bacillus subtilis

M VIFLFRALKPLLVLALLTVVFVLGG

MPYLK RVLLLLVTGLFMSLFAVTATASA

MMKMEGIALKKRLSWISVCLLVLVSAAGM

Bacillus amyloliquefaciens

Bacillus amyloliquefaciens

MNIKK IVKQATVLTFTTALLAGGATQAFA

MIQKRKRTVSFRLVLMCTLLFVSLPITKTSA

MKR VLLILVTGLFMSLCGITSSVSA

D-alanyl-D-alanine

carboxypeptidase dacA

Bacillus stearothermophilus

MRRRK QNWLFWLLSICLCLTFGPFQQTVKA

MRRWLSLVLSMSFVFSAIFIVSDTQKVTVEA

P06874

Q5ZF24

P23550

THER_BACST

Q5ZF24_LACSK

GUNB_PAELA

Thermolysin

n/a

Bacillus stearothermophilus

Lactobacillus sakei

MNKR AMLGAIGLAFGLLAAPIGASA

MQNTKELSVVELQQILGG

Endoglucanase B

Paenibacillus lautus

MKKRRSSKVILSLAIVVALLAAVEPNAALA

Q93MG8

Q93MG8_THIFE

n/a

Thiobacillus ferrodoxin

MFKRLANAAIPFALVGMLFGLSVSVASA

MSEKDKMITRRDALRNIAVVVGSVATTTMM

GVGVADA

MYTQNTMKKNWYVTVGAAAALAATVGMG

TAMA

MTTYLSQDRLRNKENDTMTYQHSKMYQSRT

FLLFSALLLVAGQASAA

P50500

P24930

IRO_THIFE

Iron oxidase

Rusticyanin

Thiobacillus ferrodoxin

RUS2_THIFE

P74917

P45741

P21543

CY552_THIFE

THI1_PANTH

AMYB_PAEPO

Cytochrome c-552

Thiaminase-1

Paenibacillus thiaminolyticus MSKVKGFIYKPLMVMLALLLVVVSPAGAG

MTLYRSLWKKGCMLLLSLVLSLTAFIGSPSNT

ASA

Beta/alpha-amylase

Paenibacillus polymyxa

Arabinoxylan

P45796

P31835

P04830

XYND_PAEPO

CDGT2_PAEMA

CDGT1_PAEMA

Paenibacillus polymyxa

Paenibacillus macerans

MIRKCLVLFLSFALLLSVFPMLNVDA

MKKQVKWLTSVSMSVGIALGAALPVWA

MKSRYKRLTSLALSLSMALGISLPAWA

arabinofuranohydrolase

Cyclomaltodextrin

glucanotransferase

Cyclomaltodextrin

glucanotransferase

The amino acids in the n-region are shown in boldface and the underlined amino acids represent the c-region.

.2 Signal Peptide prediction by SignalP-5.0 Server

Based on the SignalP result, SPs with probability below

.5 were excluded from this analysis since signal peptidase

QOX2_BACSU, RNBR_BACAM and Q5ZF24_LACSK

were under the cut-off value.

might not identify their cleavage site. The probability

results are shown in Table 2. BLAC_BACLI,

3.3 Physico-chemical properties and solubility

ProtParam results as shown in Table 3. The length of

YXAL_BACSU,

AMY_BACSU,

PPBD_BACSU,

all signal peptides were in the range of 21 to 47 amino

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

acids and net positive charge in the n-region was between 1

the high expression levels of recombinant proteins, protein

secretion can be a solution and for secretion through above

mentioned pathways, an appropriate SP selection is an

essential step. Therefore, the SPs have a key role in

directing the protein into the periplasmic space and out of

cell (43).

Computational tools are being used in the large variety

of eras in medical and biological fields. Using

bioinformatics tools helps to reduce the cost of experiments

and provide more accurate and validate outcomes (44, 45).

In this study, several different signal peptides were

evaluated and a total number of prokaryotic SPs from

different organisms with the acceptable efficiency in the

previous studies were selected. For a successful secretion,

various features should be balanced during the secretory

pathway. The physicochemical and structural features of a

signal peptide are important properties that should be

carefully considered. Therefore, different computational

methods have been applied to predict the physicochemical

properties of the SPs.

to 6.

The highest aliphatic index belonged to

CWBA_BACSU (160.00), XYND_PAEPO (161.15) and

GUNB_PAELA (153.00). XYND_PAEPO (1.669),

CWBA_BACSU (1.596) and GUB_BACAM (1.508) had

the highest GRAVY. Based on instability and solubility

results, nitrilase fused with all SPs were predicted to be

stable and soluble.

.4 In silico prediction of signal peptides sub-cellular

localization

Based on ProtCompB results, SUBF_BACSU,

GUB_BACSU, SACB_BACAM and AMY_BACAM can

translocate nitrilase into extracellular space. So other SPs

were excluded in this study (Table 4).

Discussion

Nitrilase, as a monomeric protein and lacks disulfide

bonds, looks to be a suitable candidate for secretory

production in prokaryote hosts like Bacillus subtilis. Sec,

SRP, and TAT are pathways that are recruited by

prokaryotes. Considering the problem of creating high

amounts of inclusion bodies, aggregation and misfolding in

Table 2: Signal Peptide Probability, Cleavage Site and Secretion pathway

No.

Signal peptide

Probability

0.58

0.15

0.51

0.70

0.26

0.76

Cleavage site

AAA-AM (28,29)

Secretion pathway

(Sec/SPI)

AGA-MV (30,31)

SFA-MV (25,26)

(Sec/SPI)

ADA-MV (27,28)

(Sec/SPI)

0.80

0.91

0.44

ANA-MV (21,22)

AEA-MV (26,27)

(Sec/SPI)

Q5ZF24_LACSK

GUNB_PAELA

Q93MG8_THIFE

IRO_THIFE

0.69

0.07

0.78

0.75

0.56

0.89

0.64

0.76

ASA-MV (28,29)

(Sec/SPI)

AFA-MV (29,30)

TSA-MV (31,32)

VSA-MV (25,26)

VKA-MV (30,31)

VEA-MV (31,32)

ASA-MV (25,26)

(Sec/SPI)

0.79

0.69

0.73

0.63

0.55

0.60

0.82

0.86

0.73

0.84

ALA-MV (30,31)

ASA-MV (28,29)

ADA-MV (37,38)

AMA-MV (32,33)

ASA-AM (46,47)

AGA-GM (28,29)

ASA-MV (35,36)

VDA-MV (26,27)

VWA-MV (27,28)

AWA-MV (27,28)

(Sec/SPI)

(Tat/SPI)

(Sec/SPI)

RUS2_THIFE

CY552_THIFE

THI1_PANTH

AMYB_PAEPO

XYND_PAEPO

CDGT2_PAEMA

CDGT1_PAEMA

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

Table 3: Physico-chemical properties and solubility and Final prediction site of signal peptides

Amino

Aliphatic

Instability Index

Without protein

unstable

Instability index

with protein

stable

Solubility

(Probability)

No.

Signal peptide

Net Charge

GRAVY

0.752

acids

Index

41.72

17.00

60.00

19.63

11.90

16.54

42.86

07.93

19.35

48.00

1.00

Soluble(0.540)

Soluble(0.558)

Soluble(0.533)

Soluble(0.548)

Soluble(0.575)

Soluble(0.582)

Soluble(0.567)

Soluble(0.564)

Soluble(0.533)

Soluble(0.545)

Soluble(0.476)

Soluble(0.528)

Soluble(0.576)

Soluble(0.585)

(

50.51)

stable

20.75)

unstable

42.97)

stable

31.87)

stable

10.61)

stable

0.70)

unstable

40.54)

stable

26.18)

unstable

47.44)

unstable

45.44)

unstable

86.84)

unstable

46.86)

stable

33.02)

unstable

67.87)

stable

18.74)

unstable

61.56)

stable

-3.23)

unstable

51.76)

stable

34.22)

unstable

45.90)

stable

15.75)

stable

34.65)

stable

36.17)

(30.42)

stable

(27.43)

stable

(29.48)

stable

(28.54)

stable

(26.89)

stable

(25.71)

stable

(29.39)

stable

(28.00)

stable

(30.23)

stable

(29.70)

stable

(34.22)

0.497

1.596

1.122

0.948

0.769

1.443

0.710

0.606

1.508

0.117

0.887

1.100

0.920

(

GUNB_PAELA

(

16.13

21.60

53.00

stable

(30.17)

stable

(28.62)

stable

(

(32.27)

22.14

1.379

stable

(27.28)

Q93MG8_THIFE

Soluble(0.572)

(

2.16

0.276

0.372

-0.355

1.359

0.751

1.669

0.785

0.467

stable

(32.33)

stable

(24.78)

stable

(31.78)

stable

(28.80)

stable

(30.28)

stable

(27.07)

stable

(28.80)

stable

IRO_THIFE

Soluble(0.549)

Soluble(0.541)

Soluble(0.488)

Soluble(0.575)

Soluble(0.500)

Soluble(0.549)

Soluble(0.536)

Soluble(0.541)

(

4.38

RUS2_THIFE

(

4.89

CY552_THIFE

THI1_PANTH

AMYB_PAEPO

XYND_PAEPO

CDGT2_PAEMA

CDGT1_PAEMA

(

41.03

17.14

61.15

15.56

15.93

(

(28.94)

A critical step in designing constructs for secretory

production of the recombinant proteins is the exact

prediction of the cleavage sites located in the signal

peptide. For this purpose, the SignalP server was employed

and the probability was considered as the most important

parameter to identify SPs. According to the default cutoff

value of 0.5, SPs with cut-off value > 0.5 was identified as

potential SPs for nitrilase (46).

SignalP can distinguish appropriate and non-

appropriate sequences for secretory protein production.

Based on the results of SignalP analysis, the cleavage sites

of BLAC_BACLI, YXAL_BACSU, AMY_BACSU,

PPBD_BACSU, QOX2_BACSU, RNBR_BACAM, and

Q5ZF24_LACSK signal peptides infusion with nitrilase

cannot properly recognize by signal peptidase.

Consequently, they are not recognized as potential choices

for nitrilase secretion and were disregarded from further

analysis. According to SignalP (Table 2), 23 out of 30

collected SPs were identified as proper SPs for nitrilase.

However, more characteristics were required to select

appropriate SPs.

Data aslo were compared according to the key

physicochemical features of the signal peptides including

net positive charge, GRAVY, instability and aliphatic

indexes by ProtParam server (Table 3). GRAVY and

Aliphatic index are the two factors which are directly

associated with hydrophobicity (22) and improving the

level of this parameter and length of the h-region, leads to

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

improve the rate of the protein secretion. (47). As shown in

Table 3, all 23 listed SPs in this step, had suitable aliphatic

indexes and hydrophobicity for secretion.

subcellular localization of SPs infusion with nitliase. It was

elucidated that among 23 stable and soluble SPs, 15 of

them can translocate nitrilas protein to cytoplasmic

compartment. four SPs could guide nitrilase to extracellular

space while 11 of them could not translocate nitrilas into

growth medium. Therefore, it seems only these four signal

It is also suggested that the higher net positive charge

could be an advantage for increasing the rate of

translocation desired protein from the phospholipid

membrane (48). According to Table 3, the net positive

charge of all evaluated SPs were between 1 and 6 based on

ProtParam results and they were suitable for the next step

analysis. Protein instability was indicated by Instability

index above 40. It means proteins with instability index

sequences

(GUB_BACSU,

SACB_BACAM,

AMY_BACAM, and SUBF_BACSU) can be introduced as

applicable SPs. The secretion of heterologous proteins into

the extracellular space can considerably reduce the

purification process since cell disruption and the

subsequent purification would be skipped. Moreovere,

intracellular expression of recombinannat protein can

lessen the toxic impacte of target proteins on the production

of host microorganism and As a result, intracellular

expression strategies can significantly reduce the cost of

recombinanat protein production (50).

As far as we know, this is the first study with the aim of

investigating the suitable SPs connected with nitrilase by

evaluating their potential impact on the secretion pathway

of this protein. This study evaluated 30 different signal

peptides to introduce the most reliable ones for secreting

the recombinant nitrilase protein out of Bacillus subtilis

host. The results of this study showed that GUB_BACSU,

SACB_BACAM, AMY_BACAM, and SUBF_BACSU

could be considered as appropriate candidates for the

nitrilase secretion. However, additional experimental

studies should be carried out to confirm these results.

lower than 40 are stable (49). Some of SPs are unstable

alone but after fusing to nitrilase they would be stable.

These SPs were chosen for solubility predictions. In silico

methods for evaluation of the Protein solubility in the

experimental studies is a very crucial parameter because it

can cause the formation of inclusion bodies. By

implementing the SOLpro server, all 23 stable fusion

proteins were soluble. Hence, according to the stability and

solubility analysis in Table 3, all signal peptides connected

to nitrilase were predicted to be stable and none of them

could be omitted based on these analysis.

Secretory proteins can be directed to the subcellular

locations by fusing suitable signal peptides to their N-

terminus. To more efficient purification and production of

the heterologous proteins, the ProtCompB server was

implemented to predict the ultimate destination and

Table 4: Evaluation of secretion pathways and sub-cellular location of SPs

Cytoplasmic Cytoplasmic-Membrane Cell wall Extracellular Final prediction site

0.00 8.80 0.22

0.00 0.09

No. Signal peptides

0.98

9.73

0.01

2.50

0.73

3.33

9.72

9.73

9.72

0.73

0.00

2.92

2.50

0.01

0.73

2.50

0.01

2.50

0.98

0.01

3.33

2.92

Cytoplasmic-Membrane

Extracellular

0.18

0.12

2.50

0.62

3.33

0.18

0.62

0.00

2.48

2.50

0.12

0.62

2.50

0.12

2.50

0.22

0.06

3.33

2.48

0.32

02.50

7.50

0.00

0.01

0.00

0.01

7.50

0.00

2.50

0.00

0.32

7.50

2.50

0.32

2.50

0.00

1.78

0.00

9.55

2.50

1.15

3.33

0.09

1.15

10.00

4.60

2.50

9.87

9.55

1.15

2.50

9.55

2.50

8.80

8.16

3.33

4.60

Cytoplasmic-Membrane

Unknown

Cytoplasmic

GUNB_PAELA

Q93MG8_THIFE

IRO_THIFE

Unknown

Extracellular

Cytoplasmic

Cytoplasmic-Membrane

Unknown

Cytoplasmic-Membrane

Cytoplasmic

RUS2_THIFE

Unknown

CY552_THIFE

THI1_PANTH

AMYB_PAEPO

XYND_PAEPO

CDGT2_PAEMA

CDGT1_PAEMA

Cytoplasmic-Membrane

Unknown

Cytoplasmic-Membrane

Unknown

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

synthesis. Molecular Biology Research Communications.

Reference

019;8(1):17-26.

. Kabata-Pendias A, Pendias H. Trace elements in soil and

plants. 1984.

1.Kaur J, Kumar A, Kaur J. Strategies for optimization of

heterologous protein expression in E. coli: Roadblocks and

. Gijzen HJ, Bernal E, Ferrer H. Cyanide toxicity and cyanide

degradation in anaerobic wastewater treatment. Water

Research. 2000;34(9):2447-54.

reinforcements.

International

Journal

Biological

Macromolecules. 2018;106:803-22.

2.Low KO, Mahadi NM, Illias RM. Optimisation of signal

peptide for recombinant protein secretion in bacterial hosts.

Applied microbiology and biotechnology. 2013;97(9):3811-26.

3.Schumann W. Production of recombinant proteins in Bacillus

subtilis. Advances in applied microbiology. 2007;62:137-89.

4.Arora D, Khanna N. Method for increasing the yield of properly

folded recombinant human gamma interferon from inclusion

bodies. Journal of biotechnology. 1996;52(2):127-33.

. Wild SR, Rudd T, Neller A. Fate and effects of cyanide during

wastewater treatment processes. Science of the total

environment. 1994;156(2):93-107.

. Sharma M, Akhter Y, Chatterjee S. A review on remediation of

cyanide containing industrial wastes using biological systems

with special reference to enzymatic degradation. World Journal

of Microbiology and Biotechnology. 2019;35(5):70.

. Cooper CE, Brown GC. The inhibition of mitochondrial

cytochrome oxidase by the gases carbon monoxide, nitric oxide,

hydrogen cyanide and hydrogen sulfide: chemical mechanism

and physiological significance. Journal of bioenergetics and

biomembranes. 2008;40(5):533.

5.Baneyx F, Mujacic M. Recombinant protein folding and

misfolding in Escherichia coli. Nature biotechnology.

004;22(11):1399.

6.Savardashtaki A, Sharifi Z, Hamzehlou S, Farajollahi MM.

Analysis of immumoreactivity of heterologously expressed

non-structural protein 4B (NS4B) from hepatitis C virus (HCV)

genotype 1a. Iranian journal of biotechnology. 2015;13(4):32.

7.Tjalsma H, Antelmann H, Jongbloed JD, Braun PG, Darmon E,

Dorenbos R, et al. Proteomics of protein secretion by Bacillus

subtilis: separating the “secrets” of the secretome. Microbiol

Mol Biol Rev. 2004;68(2):207-33.

. Geller RJ, Barthold C, Saiers JA, Hall AH. Pediatric cyanide

poisoning: causes, manifestations, management, and unmet

needs. Pediatrics. 2006;118(5):2146-58.

. Isom GE, Burrows GE, Way JL. Effect of oxygen on the

antagonism of cyanide intoxication-cytochrome oxidase, in

vivo. Toxicology and applied pharmacology. 1982;65(2):250-6.

. Pham JC, Huang DT, McGeorge FT, Rivers EP. Clarification of

cyanide’s effect on oxygen transport characteristics in a canine

model. Emergency Medicine Journal. 2007;24(3):152-6.

. Tshala-Katumbay DD, Ngombe NN, Okitundu D, David L,

Westaway SK, Boivin MJ, et al. Cyanide and the human brain:

perspectives from a model of food (cassava) poisoning. Annals

of the New York Academy of Sciences. 2016;1378(1):50.

0.Akcil A. Destruction of cyanide in gold mill effluents:

biological versus chemical treatments. Biotechnology

Advances. 2003;21(6):501-11.

1.Mekuto L, Ntwampe SK, Akcil A. An integrated biological

approach for treatment of cyanidation wastewater. Science of

the Total Environment. 2016;571:711-20.

2.Dash RR, Gaur A, Balomajumder C. Cyanide in industrial

wastewaters and its removal: a review on biotreatment. Journal

of hazardous materials. 2009;163(1):1-11.

8.Westers L, Westers H, Quax WJ. Bacillus subtilis as cell

factory for pharmaceutical proteins:

approach to optimize the host organism. Biochimica et

Biophysica Acta (BBA)-Molecular Cell Research.

004;1694(1-3):299-310.

biotechnological

9.Schallmey M, Singh A, Ward OP. Developments in the use of

Bacillus species for industrial production. Canadian journal of

microbiology. 2004;50(1):1-17.

0.Simonen M, Palva I. Protein secretion in Bacillus species.

Microbiology

and

Molecular

Biology

Reviews.

993;57(1):109-37.

1.Song Y, Nikoloff JM, Zhang D. Improving protein production

on the level of regulation of both expression and secretion

pathways in Bacillus subtilis.

015;25(7):963-77.

J Microbiol Biotechnol.

2.Kane JF, Hartley DL. Formation of recombinant protein

inclusion bodies in Escherichia coli. Trends in Biotechnology.

3.Gupta N, Balomajumder C, Agarwal V. Enzymatic mechanism

and biochemistry for cyanide degradation: a review. Journal of

Hazardous Materials. 2010;176(1-3):1-13.

4.Trapp S, Larsen M, Pirandello A, Danquah-Boakye J.

Feasibility of cyanide elimination using plants. ejmp & ep

988;6(5):95-101.

3.Emanuelsson O, Brunak S, Von Heijne G, Nielsen H. Locating

proteins in the cell using TargetP, SignalP and related tools.

Nature protocols. 2007;2(4):953.

4.Zimmermann R, Eyrisch S, Ahmad M, Helms V. Protein

translocation across the ER membrane. Biochimica et

Biophysica Acta (BBA)-Biomembranes. 2011;1808(3):912-24.

5.Bendtsen JD, Nielsen H, von Heijne G, Brunak S. Improved

prediction of signal peptides: SignalP 3.0. Journal of molecular

biology. 2004;340(4):783-95.

6.Choo KH, Tan TW, Ranganathan S, editors. A comprehensive

assessment of N-terminal signal peptides prediction methods.

Bmc Bioinformatics; 2009: BioMed Central.

7.Gasteiger E, Hoogland C, Gattiker A, Wilkins MR, Appel RD,

Bairoch A. Protein identification and analysis tools on the

ExPASy server. The proteomics protocols handbook: Springer;

(European Journal of Mineral Processing and Environmental

Protection). 2003;3(1):128-37.

5.Desai J, Ramakrishna C. Microbial degradation of cyanides and

its commercial applications. Journal of scientific & industrial

research. 1998;57(8):441-53.

6.Howden AJ, Preston GM. Nitrilase enzymes and their role in

plant–microbe

009;2(4):441-51.

interactions.

Microbial

biotechnology.

7.Kobayashi M, Goda M, Shimizu S. Nitrilase catalyzes amide

hydrolysis as well as nitrile hydrolysis. Biochemical and

Biophysical Research Communications. 1999;2(255):549.

8.Park JM, Sewell BT, Benedik MJ. Cyanide bioremediation: the

potential of engineered nitrilases. Applied microbiology and

biotechnology. 2017;101(8):3029-42.

9.Savardashtaki A, Sarkari B, Arianfar F, Mostafavi-Pour Z.

Immunodiagnostic value of Echinococcus granulosus

recombinant B8/1 subunit of antigen B. Iranian journal of

immunology. 2017;14(2):111-22.

005. p. 571-607.

8.Walker JM. The proteomics protocols handbook: Springer;

005.

9.Hebditch M, Carballo-Amador MA, Charonis S, Curtis R,

Warwicker J. Protein–Sol: a web tool for predicting protein

solubility from sequence. Bioinformatics. 2017;33(19):3098-

0.Taheri-Anganeh M, Khatami SH, Jamali Z, Savardashtaki A,

Ghasemi Y, Mostafavipour Z. In silico analysis of suitable

00.

0.Klee EW, Ellis LB. Evaluating eukaryotic secreted protein

prediction. BMC bioinformatics. 2005;6(1):256.

signal peptides for secretion of a recombinant alcohol

dehydrogenase with a key role in atorvastatin enzymatic

Journal of Environmental Treatment Techniques

2019, Volume 7, Issue 3, Pages: 506-513

1.Mousavi P, Mostafavi-Pour Z, Morowvat MH, Nezafat N,

Zamani M, Berenjian A, et al. In silico analysis of several

signal peptides for the excretory production of reteplase in

Escherichia coli. Current Proteomics. 2017;14(4):326-35.

2.Zeng R, Gao S, Xu L, Liu X, Dai F. Prediction of pathogenesis-

related secreted proteins from Stemphylium lycopersici. BMC

microbiology. 2018;18(1):191.

3.Baradaran A, Sieo CC, Foo HL, Illias RM, Yusoff K, Rahim

RA. Cloning and in silico characterization of two signal

peptides from Pediococcus pentosaceus and their function for

the secretion of heterologous protein in Lactococcus lactis.

Biotechnology letters. 2013;35(2):233-8.

4.Diniz W, Canduri F. Bioinformatics: an overview and its

applications. Genet Mol Res. 2017;16.

5.Zamani M, Nezafat N, Negahdaripour M, Dabbagh F, Ghasemi

Y. In silico evaluation of different signal peptides for the

secretory production of human growth hormone in E. coli.

International Journal of Peptide Research and Therapeutics.

015;21(3):261-8.

6.Nielsen H. Predicting secretory proteins with SignalP. Protein

function prediction: Springer; 2017. p. 59-73.

7.Chen H, Kim J, Kendall DA. Competition between functional

signal peptides demonstrates variation in affinity for the

secretion pathway. Journal of bacteriology. 1996;178(23):6658-

8.Owji H, Nezafat N, Negahdaripour M, Hajiebrahimi A,

Ghasemi Y. A comprehensive review of signal peptides:

Structure, roles, and applications. European journal of cell

biology. 2018;97(6):422-41.

9.Guruprasad K, Reddy BB, Pandit MW. Correlation between

stability of a protein and its dipeptide composition: a novel

approach for predicting in vivo stability of a protein from its

primary sequence. Protein Engineering, Design and Selection.

990;4(2):155-61.

0.Freudl R. Signal peptides for recombinant protein secretion in

bacterial expression systems. Microbial cell factories.

018;17(1):52.