Chapter 4

Results & Discussion    

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4. Results & Discussion

This part of the thesis discusses the results obtained in the

current study. This section comprises of isolation and screening of

alpha-galactosidase producing microorganisms from the soil samples

followed by characterization and identification of the isolate.

Optimization of media for alpha-galactosidase production in

submerged by using traditional and statistical techniques was

evaluated. Purification and characterization of enzyme alpha-

galactosidase followed by immobilization and finally its applications

were conferred in this chapter.

4.1 Isolation and screening of alpha-galactosidase

producing microorganisms from soil:

Soil samples were collected from dump yards of sugarcane

industry situated at Sangareddy, Andhra Pradesh and processed

within 24 hours. Isolation of microorganism was carried out by serial

dilution technique followed by plating on sucrose agar medium, pH

7.0 and incubated at 36°C for 24 hours.

20 colonies from different soil samples were selected and

inoculated into sucrose medium, pH 7.0. After 24 hours of incubation,

the cells were harvested. The supernatant obtained was assayed for

extra cellular alpha-galactosidase. The cells were incubated in lysis

buffer containing 0.3% (w/v) lysozyme, 0.1% (w/v) Triton X 100, 1mM

PMSF and incubated for 1 hour at 30°C. The cells were further

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disrupted by sonication. The supernatant obtained on centrifugation

at 10,000 g, 30 minutes, 4°C was assayed for intracellular alpha-

galactosidase enzyme activity. The extracted crude enzyme from

different isolates were screened for alpha-galactosidase activity using

both artificial (ρ-nitrophenyl-α-D-galactopyranoside) and natural

(raffinose) substrates.

Extra cellular activity was not detected in any of the isolates.

The isolate showing maximum intracellular alpha-galactosidase

activity was selected for further studies and labelled as isolate G1 at

Centre for Biotechnology, Jawaharlal Nehru Technological University

Hyderabad. Bacterial intracellular alpha-galactosidase were also

reported in Bidobacterium breve (Xaio M et al. 2000) and alkophilic

strain Micrococcus (Akiba T et al. 1976)

4.2 Characterization and identification of isolate G1:

Isolate G1 showing maximum intracellular alpha-galactosidase

activity was identified by investigating the morphological, biochemical

and cultural characteristics of the microorganism according to

Bergey’s Manual of Determinative Bacteriology, 8th edition (Buchanan

RE and Gibbons N E, 1974) and was further identified by 16s rRNA

sequencing analysis.

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4.2.1Morphological and Biochemical Characterization:

The colony morphology of the isolate G1 was observed to be

circular with entire margins and opaque. The isolate G1 is a gram-

negative bacteria with short rods (cocobacilli) of 1-2µm in size. The

isolate G1 was able to grow at different temperatures of 10°C, 25°C,

30°C, 37°C and 42°C and pH range of 6.0 to 11.0. Figure 4.1 shows

the scanning electron microscope of the isolate G1. The biochemical

tests results show that isolate G1 is catalase positive (Fig 4.2a), citrate

positive (Fig 4.2b), oxidase negative and nitrate reduction negative.

The morphological and biochemical characteristics of isolate G1 are

given in the Table 4.1 and 4.2

Figure 4.1. Scanning electron microscope of isolate G1 at

magnification of 10x.

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Table 4.1 Morphological and physiological characteristics of

isolate G1

Tests Results

Colony morphology

Configuration Circular

Margin Entire

Elevation Convex

Surface Smooth

Pigment Off-white

Opacity Opaque

Gram’s reaction negative

Cell shape Rods

Size (m) 1-2m

Spore(s) -

Motility Non-motile

Physiological characteristics

Growth at temperatures

4C -

10C +

25C +

30C +

37C +

42C +

55C +

Growth at pH

pH 4.0 -

pH 5.0 +

pH 6.0 +

pH 7.0 +

pH 8.0 +

pH 9.0 +

pH 10.0 +

pH 11.0 +

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Table 4.2 Biochemical Characteristics of isolate G1

Figure 4.2 Biochemical tests-A. citrate test (positive) B. Catalase

test (positive)

A Citrate test B. Catalase test

Substrate, Test Result

Gram Staining Negative

Citrate Positive

Catalase Positive

Gelatin liquefaction Negative

Nitrate reduction Negative

Oxidase Negative

Acid production from

Glucose

Sucrose

Mannose

Lactose

Positive

Positive

Positive

Negative

Indole Negative

Methyl red Positive

Voges-Proskauer Negative

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4.2.2 16s ribosomal RNA sequencing:

16srRNA sequencing was performed to identify isolate G1. 16s

rRNA sequence (1.47 kbp in length) of the isolate G1is given below-

GCCGAAACCGGCAGGCTTAACACATGCAAGTCGAGCGGGGAAAAGTTACTTTGGTACACTGTACCTAGCGGACGGACGGGTGAGTAATGCTTAGGAATCTGCCTATTAGTGGGGGACAACATTCCGAAAGGAATGCTAATACCGCATACGCCCTACGGGGGAAAGCAGGGGATCTTCGGACCTTGCGCTAATAGATGAGCCTAAGTCGGATTAGCTAGTTGGTGGGGTAAAGGCCTACCAAGGCGACGATCTGTAGCGGGTCTGAGAGGATGATCCGCCACACTGGGACTGAGACACGGCCCAGACTCCTACGGGAGGCAGCAGTGGGGAATATTGGACAATGGGCGGAAGCCTGATCCAGCCATGCCGCGTGTGTGAAGAAGGCCTTTTGGTTGTAAAGCACTTTAAGCGAGGAGGAGGCTCCTTTAGTTAATACCTAAAGAGAGTGGACGTTACTCGCAGAATAAGCACCGGCTAACTCTGTGCCAGCAGCCGCGGTAATACAGAGGGTGCGAGCGTTAATCGGATTTACTGGGCGTAAAGCGTGCGTAGGCGGCTTTTTAAGTCGGATGTGAAATCCCTGAGCTTAACTTAGGAATTGCATTCGATACTGGGAAAGCTAGAGTATGGGAGAGGATGGTAGAAATTCCAAGGTGTAGCGGTGAAATGCGTAGAGATCTGGgAGGAATtACCCGATGGSSGAAAGGgCAGCCATCcTGGtCCTAATACcTGACGCTGAGGTACcGAAAGCATGGGGAGCAAACcAGGGATTAGATACCCTGGTAGTCCMATGCCGTAAACGATGTCTACTAGCCGTTGGGGCCTTTGAGGCTTTAGTGGCGCAGCTAACGCGATAAGTAGACCGCCTGGGGAGTACGGTCGCAAGACTAAAACTCAAATGAATTGACGGGGGCCCGCACAAGCGGTGGAGCATGTGGTTTAATTCGATGCAACGCGAAGAACCTTACCTGGCCTTGACATACTAAGAACTTTCCAGAGATGGATTGGTGCCTTCGGGAACTTAGATACAGGTGCTGCATGGCTGTCGTCAGCTCGTGTCGTGAGATGTTGGGTTAAGTCCCGCAACGAGCGCAACCCTTTTCCTTATTTGCCAGCGGGTAATGCCGGGAACTTTAAGGATACTGCCAGTGACAAACTGGAGGAAGGCGGGGACGACGTCAAGTCATCATGGCCCTTACGGCCAGGGCTACACACGTGCTACAATGGTCGGTACAAAGGGTTGCTACCTCGCGAGAGGATGCTAATCTCAAAAAGCCGATCGTAGTCCGGATCGCAGTCTGCAACTCGACTGCGTGAAGTCGGAATCGCTAGTAATCGCGGATCAGAATGCCGCGGTGAATACGTTCCCGGGCCTTGTACACACCGCCCGTCACACCATGGGAGTTTGTTGCACCAGAAGTAGGTAGTCTAACCGCAAGGAGGACCGCTTACCCACCGGTTGCCCAAACA 

A comparison of G1 16s rRNA sequence with other bacterial 16s

rRNA sequences showed >98.0 similarity with Acinetobacter sp. ED

189, Acinetobacter sp. EH 65, Acinetobacter sp. V4 ME25 and

Acinetobacter schindleri EH 74. DNA-DNA distance matrix was

constructed using DNAstar Lasergene 7.0 software. 16s rRNA gene

sequence of the isolate G1 was found to have lowest distance matrix of

1.2 with Acinetobacter sp. V4 ME25 and 1.7 with Acinetobacter sp. EH

65 and Acinetobacter schindleri EH 74 (table 4.3). A neighbouring

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phylogenetic tree analysis suggest that the cluster of the isolate G1

was closely related with Acinetobacter sp. TSIW21 and Acinetobacter

sps 4-2 with 50-65% bootstrap support Figure 4.3. Bootstrap values

calculated from 1000 replications (indicated in the branch nodes). The

bar represents two substitutions per 100 nucleotides. Based on the

morphological, biochemical and molecular analysis, the isolate G1

was identified as Acinetobacter sp and strain was labelled as CBT01 at

Centre for Biotechnology, Jawaharlal Nehru Technological University

Hyderabad. Identification of Acinetobacter at species level is difficult

by using biochemical studies. Different molecular methods like AFLP,

PCR-RFLP and other sequences are required for identification at

species.

115 

 

Figure 4.3 Phylogenetic analysis of Acinetobacter sp. CBT01

116 

 

Table 4.3 DNA-DNA distance matrix of Acinetobacter sp. CBT01

and other closely related species of Acinetobacter

4.3 Optimization of fermentation medium parameters

(submerged fermentation)

Fermentation medium plays a pivotal role in enzyme

production. Several researchers reported an increase in microbial

enzyme production under optimized medium conditions (Adinaryana

et al. 2003; Prakasham et al. 2005a). Hence, medium formulation is

an important prerequisite to enhance enzyme production.

Preliminary studies were done using classical approach to

develop fermentation medium to enhance alpha-galactosidase

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production from Acinetobacter sp. CBT01 by studying various medium

parameters. Subsequently, the fermentation medium was further

optimized by using Artifical Neural Networks and Genetic Algorithm

(ANN-GA) approach.

Initially physical medium parameters such as pH, temperature,

agitation speed, time of harvest and nutritional parameters such as

carbon, nitrogen and mineral source were studied. The results are

discussed in the following sections.

4.3.1 Physical parameters:

4.3.1.1 Growth curve vs Enzyme activity:

Redmond JC, 2009 described Acinetobacter species as one of

the fast growing organism. Time of harvest varies from organism to

organism. The isolate Acinetobacter sp. was cultured at 36°C for 24

hours at agitation speed of 150 rpm in a shaking incubator. Growth

of the organism was measured at regular intervals of 2 hours at

optical density of 600nm with uninoculated broth as a blank.

Simultaneously, the samples were also assayed for alpha-

galactosidase activity. Figure 4.4 shows maximum alpha-

galactosidase activity after 12 hours of incubation and when the

bacterium was at the end of the log phase. The isolate Acinetobacter

sp. was observed to enter the stationary phase within 14 hours of

incubation. The enzyme activity declined markedly as the bacterium

entered the stationary phase.

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Figure 4.4 Growth curve vs alpha-galactosidase activity

4.3.1.2 Effect of pH: The isolate Acinetobacter sp. was cultured at varying pH range

of 5-11 at 36°C for 12 hours at 150 rpm in a shaking incubator.

Growth of organism as well as activity of the intra cellular alpha-

galactosidase enzyme were determined. From the graph (figure 4.5), it

was observed that alpha-galactosidase activity > 4 U/ml was detected

over pH range of 6 to 9 and maximum activity at optimum pH range of

7.0 to 7.5. Maximum growth of the isolate was observed at optimum

pH range of 7.0 to 8.0. In general, it was reported that bacterial alpha-

galactosidases have neutral optimum pH of 6 to 7.5 whereas yeast

and fungal alpha-galactosidases have acidic optimum of pH of 2-3

(Ulezlo IV and Zaprometova OM, 1982). Acidic alpha-galactosidases

cannot be used commercially because of their low optimum pH. For

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example, acidic enzymes when used in treatment of soya milk cause

precipitation of proteins and gives sour taste.

Figure 4.5 Effect of pH on growth of Acinetobacter sp. and alpha-

galactosidase activity

4.3.1.3 Effect of temperature: The isolate Acinetobacter sp. was grown at temperature range of

28 to 42°C. The pH of the medium was maintained at 7.0. A graphical

presentation (Figure 4.6) shows optimum temperature for alpha-

galactosidase activity and growth of the Acinetobacter sp. The isolate

has shown growth at broad temperature in the range of 32 to 42°C

whereas maximum enzyme activity was observed at 36°C. Enzyme

activity of >5 U/ml was observed upto 42°C.

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Figure 4.6 Effect of temperature on growth of Acinetobacter sp.

and alpha-galactosidase activity

4.3.1.4 Effect of agitation speed (rpm):

Agitation plays a pivotal role in growth of the microorganism

and enzyme production. Experiments were carried out by growing the

isolate CBT01 at varying rpm range of 100 to 220 at 36°C and pH 7.0.

Alpha-galactosidase activity improved with an increase in agitation

speed upto 170 rpm and further increase revealed no noticeable

improvement. Maximum alpha-galactosidase activity of 7.4U/ml was

noticed with cultures incubated at 170 rpm (figure 4.7). Growth of the

isolate Acinetobacter sps CBT01 was maximum at 170 rpm and

slightly decreased with an increase in the rpm speed.

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Figure 4.7 Effect of agitation speed on growth of Acinetobacter

sp. and alpha-galactosidase activity

4.3.1.5 Effect of Inoculum concentration:

Initial microbial concentration does effect the growth and

metabolite production of the organism. Fermentation medium was

inoculated with varying inoculum concentration of 0.5% to 1.5% (v/v)

and incubated for 12 hours at 36°C. The results obtained indicate

that the growth of the organism and alpha-galactosidase production

increased with increase in inoculum concentration upto 1% and

further increase resulted in a decrease in both biomass and enzyme

production (Figure 4.8). Approximately 30 % variation in enzyme

production was observed with increase of inoculum concentration

from 0.5 to 1%. However, a little variation was noticed with the further

122 

 

increase of inoculum level from 1 to 1.5 % indicating that effective

enzyme production was achieved with 1 % inoculum

Figure 4.8 Effect of inoculum concentration on growth of

Acinetobacter sp. and alpha-galactosidase activity

4.3.2 Nutrition parameters

4.3.2.1 Effect of carbon sources: The effect of different carbon sources ie sugars at a varying

concentrations of 1% to 5% (w/v) on alpha-galactosidase enzyme

production was summarized in Table 4.4. Raffinose showed maximum

activity at a final concentration of 3% with slightly less activity at 2%

concentration and thereby a decrease in activity with increase in

concentration. Stachyose also showed maximum activity similar to

raffinose at a concentration of 2%. Other sugars, galactose, melibose

and sucrose showed substantial activity at different concentrations of

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3%, 2% and 5%. Presence of glucose and lactose in the medium

inhibited the enzyme production.

The results are in correlation with previous reports indicating

alpha-galactosidase production is either induced or enhanced in

presence of sugars –galactose, raffinose and stachyose (Marisa S et al.

1996; Jin F et al. 2001; kotwal SM et al. 1995) whereas lactose and

glucose completely inhibit or had a very little influence on enzyme

production. Glucose inhibition of α-galactosidase production might be

due to catabolite repression (Delente J et al. 1974).

Table 4.4 Effect of various carbon sources on alpha-galactosidase

production

Carbon source

Alpha-galactosidase activity (U/ml) at various percentages

1% 2% 3% 4% 5% Glucose 0.62 0.28 0.21 0.1 0

Galactose 4.56 6.25 6.67 5.9 5.12

Lactose 0.12 0.08 0 0 0

Sucrose 5.24 6.8 5.4 3.21 1.25

Raffinose 5.94 6.92 7.15 7.0 6.6

Melibiose 5.42 5.56 5.79 5.9 6.0

Stachyose 6.45 7.0 6.0 6.0 5.42

Glucose + galactose

0.94 0.14 0.1 0 0

Galactose + raffinose

6.84 6.72 6.0 5.26 4.32

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4.3.2.2 Effect of various nitrogen sources Both organic and inorganic compounds were chosen as nitrogen

sources at concentration levels of 0.5% and 1%. Of all tryptone

showed highest activity followed by soya meal and yeast extract at

concentration of 1%. Remaining nitrogen sources showed medium to

less activity (table 4.5). Increase of enzyme production in soya meal

may be due to the presence of galactooligosaccharides. In present

study, tryptone was choosen as nitrogen source. Many researchers

reported use of tryptone as nitrogen source in the growth medium for

alpha-galactosidase production (Delente J et al. 1974; Marisa S et al.

1996).

Table 4.5 Effect of nitrogen sources on alpha-galactosidase

production

Nitrogen source Alpha-galactosidase activity (U/ml)

0.5% 1%

Yeast extract 6.22 6.95

Peptone 5.25 6.2

Tryptone 6.4 7.32

Soya meal 6.8 7.2

Beef extract 4.9 6.0

Malt extract 4.4 5.8

Ammonium sulphate

3.9 4.6

Ammonium nitrate

4.4 4.9

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4.3.2.3 Effect of minerals sources:

Various mineral sources were chosen for enzyme production.

Maximum activity was observed in presence of K2HPO4 (1%),

MgSO4.7H2O (0.1%) and FeSO4.7H2O (0.1%) (Table 4.6). Medium

activity was observed in presence MnSO4 (0.1%) and very low activity

in presence of NaCl.

Table 4.6 Effect of minerals on alpha-galactosidase activity

Minerals Activity (U/ml)

K2HPO4 (1%) 7.4

KH2PO4 (1%) 6.2

MgSO4.7H2O (0.1%) 7.1

FeSO4.7H2O (0.1%) 7.1

MnCl2 (0.1%) 5.8

NaCl (0.1%) 3.2

Based on these results, a fermentation medium comprising

raffinose 25g/L, tryptone 10g/L, K2HPO4 10g/L, MgSO4.7H2O 1g/L

and FeSO4.7H2O 1g/L (pH 7.0) was formulated and further used in all

subsequent studies. Maximum enzyme activity was obtained when

cultures were grown at 36°C, pH 7.0 and agitation speed of 170rpm

for 12 hours. Sumeyra et al. 2010 reported the use of growth medium

comprising of K2HPO4 and MgSO4.7H2O for extracellular alpha-

galactosidase production from Aspergillus fumigates. Delente J et al.

1974 reported use of raffinose and tryptone as carbon and nitrogen

126 

 

source for alpha-galactosidase production in Bacillus

stearothermophilus.

4.4 Optimization of fermentation factors using ANN-GA

Conventional or one-at-a-time optimization process is generally

time consuming, laborious, cost-intensive and cannot reveal the

interaction between or among different factors that influence

fermentation (Sreenivas Rao et al. 2004; Prakasham et al. 2005a;

2007a; 2007b). In the present study, both physical and environmental

factors were chosen to optimize the enzyme production in shake flask.

Table 4.7 depicts the experimental design along with the observed

enzyme production by the isolated Acinetobacter sps. From the table

4.7, it was observed that the enzyme production was varied from the

3.3 to 7.5 U/ml at the various selected conditions. The observed

minimum and maximum enzyme production indicates that the

selected parameters have a greater influence on the alpha-

galactosidase production. The data was further modelled with ANN

and the conditions were optimized using the GA. The network was

constructed by using the selected parameters and alpha-galactosidase

production as an input and output neurons. The selected six variables

such as incubation temperature, pH, agitation speed, raffinose,

tryptone and K2HPO4 concentrations were chosen as an input neurons

in the input layer. Similarly the alpha-galactosidase production was

set as output neuron in the output layer. The number of neurons in

the hidden layer plays a vital role in the training time and

127 

 

S. No

Temperature (°C)

pH Agitation

(rpm) Tryptone (g/100ml)

Raffinose (g/100ml)

K2HPO4 (g/100ml)

Alpha-galactosidase activity

Observed  Predicted Error 

1  34  6.5  160  0.5  2  0.75  2.80  2.5  0.28 

2  34  6.5  160  0.5  3  1.25  4.70  4.78  ‐ 0.08

3  34  6.5  160  1.5  2  1.25  5.10  5.00  0.09 

*4  34  6.5  160  1.5  3  0.75  5.40  5.26  0.13 

5  34  6.5  180  0.5  2  1.25  5.70  5.79  ‐ 0.09

6  34  6.5  180  0.5  3  0.75  5.80  5.75  0.04 

7  34  6.5  180  1.5  2  0.75  5.90  6.12  ‐0.22 

8  34  6.5  180  1.5  3  1.25  6.60  6.69  ‐ 0.09

9  34  7.5  160  0.5  2  1.25  4.80  4.78  0.01 

10  34  7.5  160  0.5  3  0.75  4.60  4.54  0.05 

11  34  7.5  160  1.5  2  0.75  5.20  4.96  0.23 

12  34  7.5  160  1.5  3  1.25  5.80  5.88  ‐ 0.08

*13  34  7.5  180  0.5  2  0.75  3.60  3.45  0.14 

14  34  7.5  180  0.5  3  1.25  5.70  5.92  ‐0.22 

15  34  7.5  180  1.5  2  1.25  6.50  6.54  ‐ 0.04

*16  34  7.5  180  1.5  3  0.75  5.30  5.30  0 

17  38  6.5  160  0.5  2  1.25  5.30  5.26  0.03 

18  38  6.5  160  0.5  3  0.75  6.50  6.42  0.07 

19  38  6.5  160  1.5  2  0.75  5.80  5.54  0.25 

20  38  6.5  160  1.5  3  1.25  4.70  4.81  ‐ 0.11

21  38  6.5  180  0.5  2  0.75  5.40  5.28  0.11 

22  38  6.5  180  0.5  3  1.25  5.90  6.10  ‐ 0.20

*23  38  6.5  180  1.5  2  1.25  5.40  5.42  ‐ 0.02

24  38  6.5  180  1.5  3  0.75  5.60  5.58  0.01 

25  38  7.5  160  0.5  2  0.75  5.20  5.07  0.12 

26  38  7.5  160  0.5  3  1.25  6.50  6.24  0.25 

27  38  7.5  160  1.5  2  1.25  5.60  5.61  ‐ 0.01

28  38  7.5  160  1.5  3  0.75  5.70  5.57  0.12 

*29  38  7.5  180  0.5  2  1.25  4.80  4.90  ‐0.10 

30  38  7.5  180  0.5  3  0.75  4.50  4.56  ‐ 0.06

31  38  7.5  180  1.5  2  0.75  4.20  4.08  0.11 

*32  38  7.5  180  1.5  3  1.25  3.30  3.55  ‐ 0.25

33  32  7  170  1  2.5  1  5.10  5.11  ‐ 0.01

34  40  7  170  1  2.5  1  5.10  5.20  ‐0.10 

35  36  6  170  1  2.5  1  5.70  5.74  ‐ 0.04

*36  36  8  170  1  2.5  1  5.00  5.07  ‐ 0.07

37  36  7  150  1  2.5  1  5.60  6.23  ‐0.63 

38  36  7  190  1  2.5  1  7.10  6.58  0.51 

39  36  7  170  0  2.5  1  5.80  5.92  ‐0.12 

40  36  7  170  2  2.5  1  6.50  6.49  0 

*41  36  7  170  1  1.5  1  5.50  5.89  ‐ 0.39

42  36  7  170  1  3.5  1  7.00  6.72  0.27 

128 

 

*indicate data used for testing 

Table 4.7 Experimental design and alpha-galactosidase activity (Experimental and predicted) and error.

generalization property of neural networks. Lower number of neurons

in the hidden layer would increase the training time where as higher

number of neurons in the hidden layer would cause the over training

and saturation of the network which leads to the false results. There

is no general rule for selecting the number of neurons in a hidden

layer. It depends on the complexity of the system being modelled.

According to Sathish and Prakasham (2010) the best approach to

finding the optimal number of neurons in hidden layer is by trial and

error. In this study, the number of neurons in the hidden layer varied

from 3 to 18, and the optimal number chosen by the cross-validation

criterion with the number of epochs fixed at 1000 for all the

structures studied. The neural network with 10 hidden neurons was

found to have highest correlation, lowest MAPE and RMSE values. Fig

4.9 depicts the constructed neural network topology ‘6-10-1’neurons

in input, hidden and output layers.

43  36  7  170  1  2.5  0.5  5.00  5.65  ‐ 0.65

44  36  7  170  1  2.5  1.5  7.10  6.56  0.53 

45  36  7  170  1  2.5  1  7.50  7.32  0.17 

*46  36  7  170  1  2.5  1  7.20  7.32  ‐ 0.12

47  36  7  170  1  2.5  1  7.40  7.32  0.07 

48  36  7  170  1  2.5  1  7.30  7.32  ‐ 0.02

*49  36  7  170  1  2.5  1  7.40  7.32  0.07 

50  36  7  170  1  2.5  1  7.40  7.32  0.07 

129 

 

The accuracy of the neural network based prediction can be

calculated using the coefficient of R2 value based on the measured and

predicted outputs in the training and test data. The calculated R2

value was found to be 0.9994 indicating the model accuracy of the

constructed ANN. The figure 4.10 depicts good correlation between

the experimental values and ANN predicted values, suggesting the

accuracy of the ANN predictability of the nonlinear systems.

Figure 4.9. Feed forward Neural network design used for

optimization of alpha-galactosidase production. WH= weight of

connection between input and hidden layer and WO= weight of

connection between hidden and output layer

130 

 

Figure 4.10: Correlation chart for experimental and predicted

values of alpha-galactosidase activity

Further, the predictability of the neural networks was analyzed

based on the MSE, RMSE, MAE and MAPE of the training and testing

data. The overall MSE (6.1 X 10-4), RMSE (2.47 X 10-2), MAE (3.4 X 10-

3) and MAPE (4.4 X 10-4) of the training data suggested that the

constructed network is suitable for the alpha-galactosidase

production. This was further confirmed by testing data. The resultant

data values indicated a value of 2.8 X 10-4, 5.3 X 10-2, 1.4 X 10-3 and

1.8 X 10-3 for MSE, RMSE, MAE & MAPE, respectively. Such very low

magnitude values confirm that the proposed neural network model

have a good approximation for alpha-galactosidase production. Similar

magnitude values were reported by Sathish and Prakasham (2010)

and Subba Rao et al. (2008) for other microbial products.

131 

 

4.4.1 Interaction influence of selected variables on the alpha-

galactosidase production:

Analysis of the interactive influence of different selective process

parameters provides information on the concentration mediated

regulatory role of alpha-galactosidase production. Figure 4.11 shows

the interactive influence of selected variables on alpha-galactosidase

production. Fig 4.11a depicts the interaction influence of

temperature with tryptone concentration indicating that alpha-

galactosidase production increases with temperature upto 37°C.

Similarly, studies on enzyme production at different pH values

indicated that the production is better at neutral pH or pH slightly

above neutral pH (Fig 4.11b). Similar findings have been reported in

literature (temperature 34 to 38°C and pH 6.8 to 7.5) for mesophilic

bacteria. Mixing of the components in the media has a significant role

in the microbial enzyme synthesis and secretion into external

environment (Sathish and Prakasham 2010). Fig 4.11c and 4.11d

show the interaction influence of the agitation speed with tryptone and

K2HPO4 concentration. From these graphs it was observed that to

achieve higher yields of the galactosidase, higher concentration of

nutrients and higher agitation speed are needed.

Fig 4.11a, 4.11c and 4.11f depict the interaction influence of the

tryptone with other process parameters as well as other nutrients.

From this surface plots, it was observed that tryptone at 1-1.5 % is

favourable for the alpha-galactosidase production. Carbon source was

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considered as inducer for alpha-galactosidase production. Raffinose is

one of the carbon source that was reported to enhance alpha-

galactosidase production. The interaction influence of the carbon

source (raffinose) and nitrogen source (Tryptone) was depicted in the

figure 4.11f. It was observed that tryptone at 1% is suitable for the

enzyme production. Concentration ranges higher or lower than this

negatively influences the alpha-galactosidase production. Fig 4.11b

and 4.11e show the interaction influence of K2HPO4 with pH and

raffinose. From these graphs it is noticed that while increasing the

raffinose concentration K2HPO4 should also increase, this would

maintain the buffering action in the media.

4.4.2 GA Optimization and validation studies

The ANN output data was further optimized by using the GA. In

order to obtain the best suitable conditions for alpha-galactosidase

production, an objective function with weights and bais was used.

Among 500 conditions generated by GA, only 4 best suitable

conditions were chosen and at these conditions validation experiments

were performed with these conditions (Table 4.8). From table 4.8, it

could be seen that the maximum intra cellular alpha-galactosidase

production was 10.2 U/ml.

Alpha-galactosidase production titres vary for different

microorganisms and are also influenced by microbial strain, enzyme

localization, physical and nutritional factors of fermentation medium.

133 

 

To the best of our knowledge, this the first study conducted on the

optimization of intracellular alpha-galactosidase production. In the

present study, the enzyme yield was increased from 7.5 (Table 4.7) to

10.2 U/ml (Table 4.8). Similar increase in enzyme production was

reported in the case of Streptomyces griseoloalbus when optimized

using RSM (Anisha et al. 2008a). Similar trend was reported by several

researchers working with alkaline protease (Prakasham et al. 2005),

asparaginase production (Prakasham et al. 2007), L-glutaminase

production (Satish and Prakasham, 2010) and xylitol production (Rao

et al. 2006).

Table 4.8 Best possible fermentation conditions, predicted and

observed yields of enzyme alpha-galactosidase

S. No Temperature

(oC) pH

Agitation

(rpm)

Tryptone

(g/100ml)

Raffinose

(g/100ml)

K2HPO4

(g/100ml)

Alpha-galactosidase

activity

Predicted Observed

1 35.1 6.8 180 1.2 2.5 1.1 9.5 9.6

2 36 7 180 1.4 2.8 1.3 10 9.9

3 37 6.9 183 1.1 2.4 1.7 10.5 10.2

4 36.5 7 175 1.4 2.5 1.3 9.4 9.8

134 

 

Figure 4.11a: Interactive influence of tryptone concentration and temperature on

alpha-galactosidase yield

Figure 4.11b. Interactive influence of K2HPO4 concentration and pH

on alpha-galactosidase yield

135 

 

Figure 4.11C: Interactive influence of tryptone concentration and

agitation speed (rpm) on alpha-galactosidase yield

Figure 4.11d Interactive influence of raffinose concentration and

agitation speed (rpm) on alpha-galactosidase yield

136 

 

Figure 4.11e Interactive influence of K2HPO4 concentration and

raffinose on alpha-galactosidase yield

Figure 4.11f Interactive influence of raffinose concentration and

tryptone on alpha-galactosidase yield

137 

 

4.5 Purification studies of enzyme alpha-galactosidase:

The intracellular alpha-galactosidases produced by

Acinetobacter sp. were purified using various methods. Table 4.9

presents summary of results obtained at each purification step.

12 hour old culture Acinetobacter sp. was harvested. Cells were

lysed and supernatant obtained was taken as crude enzyme. The

crude enzyme was subjected to ammonium sulphate precipitation of

0-60%. The fraction of proteins obtained on precipitation has shown

alpha-galactosidase activity with specific activity of 5.1U/mg.

The protein fraction was further purified by ion exchange

chromatography. Both bound and unbound proteins were assayed for

alpha-galactosidase activity using pNPGal as substrate. The 0.3M

NaCl eluted protein has shown alpha-galactosidase activity with a

specific activity of 9.2U/mg.

The unbound active fraction of ion-exchange chromatography

was finally purified by gel-filteration chromatography on Sephacryl-S-

300. Two different alpha-galactosidase activities were identified from

two different peaks of eluted protein (Figure 4.12). The higher

molecular weight protein was labelled as Ag-I and lower molecular

weight protein as Ag-II. Purification of Ag-I resulted in 10.2% yield and

a purification fold of 7. Similar purification of Ag-II resulted in 3.8%

yield with a purification fold of 4.7. The existence of multiforms of

alpha-galactosidases have been reported in both prokaryotes and

eukaryotes. Two multiforms of alpha-galactosidases were reported in

138 

 

Bacillus stearothermophilus by Pederson DM et al. 1980. Intra cellular

α-galactosidases I and II were isolated from Bacteroides ovatus and

purified to homogeneity (Frank G et al 1984).

139 

 

Table 4.9 Summary of purification studies of alpha-galactosidases

isolated from Acinetobacter sp.

Figure 4.12 Chromatogram of gel-filtration chromatography

showing Ag-I and Ag-II peak

Purification step Total

protein

(mg)

Total

activity

(U ml-1)

Specific

activity

(U mg-1)

Recovery

(%)

Purification

Fold

Crude 186 315 1.7 100 1

(NH4)2SO4

precipitation

32 165 5.1 52.3 3

Ion exchange 7.8 71.4 9.2 22.6 5.4

Gel

Filteration

Ag-I

2.68 32.4 12.0 10.2 7.0

Ag-II

1.52 12.2 8.0 3.8 4.7

140 

 

4.6 Characterization studies of purified Alpha-

galactosidases

4.6.1 Molecular weight characterization of enzymes

Figure 4.13. SDS PAGE analysis of purified alpha-

galactosidase enzymes. Lane1-Crude, Lane2-Ammonium

sulphate precipitation, Lane3-Ion exchange chromatography,

Lane 4-Gel filtration Ag-I, Lane 5-gel filtration Ag-II and Lane

6-prestained Benchmark

Coomassie stained SDS (figure 4.13) and Native PAGE (figure

4,14) showed two bands of purified alpha-galactosidase enzyme Ag-I

and II corresponding to molecular weight of approximately 65 KDa

and 37 KDa. Both the enzymes are monomeric in nature. Three

different multiforms of alpha-galactosidases- α-Gal-I, II and III with

molecular weights of 72, 57 and 35 kDa were reported in Streptomyces

141 

 

griseoloalbus by Anisha GS et al.2009. Similar intracellular

monomeric alpha-galactosidases with molecular weight of 85 and

80kDa were reported in Bacteroides ovatus (Frank G et al 1984).

Figure 4.14 Native analysis of purified alpha-galactosidase

enzymes. L1-Crude, L2-Ion exchange chromatography, L3-Gel

filtration, Ag-I and L4-Gel filtration, Ag-II

4.6.2 Biochemical characterization of alpha-galactosidase

enzymes

4.6.2.1 Optimum pH and stability

The alpha-galactosidases, Ag-I and Ag-II isolated from

Acinetobacter sp. was found to be more stable at neutral pH. Both the

multiforms showed activity at broad pH range of 5.0 to 11.0. Ag-I

142 

 

showed maximum activity at pH 7.0 with more than 60% activity up

to pH 9.0 and mild activity towards acidic pH. After 24 hours of

incubation at 36°C, Ag-I retained 85% enzyme activity at pH 7.0

whereas Ag-II retained 52% activity. Ag-II was found to be more

unstable compared to Ag-II (figure 4.15). These results are consistent

with the findings of Pederson and Goodman (1980) who reported a

neutral pH of 7.0 for the alpha-galactosidase isoenzymes isolated from

Bacillus stearothermophilus. A broad pH range of 3.0 to 9.0 was

reported in Geobacillus stearothermophilus (NCIM-5146) (Gote MM et al.

2006) and pH range of 5.0 to 7.0 in Streptomyces griseoloalbus (Anisha

GS et al. 2009).

Figure 4.15 Effect of pH on purified enzymes Ag-I (A) and Ag-II (B)

after 2 hours and 24 hours

4.6.2.2 Optimum temperature:

The temperature activity profile of purified alpha-galactosidase

Ag-I and Ag-II was shown in figure 4.16. The purified Ag-I enzyme

showed an optimum temperature at 50-60°C and was able to retain >

143 

 

80% activity at 40°C and upto 70°C and markedly decreased at

temperatures >70°C. The other enzyme Ag-II displayed an optimum

temperature at 40°C but markedly decrease at higher temperatures

>60°C. Alpha-galactosidases from Bifidobacterium breve (Xiao M et al.

2000) and Lactobacillus fermentum (Carrera-Silva, EA et al. 2006)

demonstrated an optimum temperature of 50°C similar to purified

enzyme Ag-I and optimum temperature range of 50-65°C was reported

in Streptomyces olivaceous (Oishi K and Aida K, 1975). The thermal

stability of the enzyme was discussed in thermal denaturation studies

section.

Figure 4.16 Effect of temperature of purified enzymes Ag-I and

Ag-II

144 

 

4.6.2.3 Effects of metal ions:

The effect of various metal ions on enzyme activity of Ag-I and Ag-

II was shown in figure 4.17. Both the enzymes Ag-I and Ag-II were not

inhibited in presence of EDTA indicating these enzymes isolated and

purified from Acinetobacter sp. are not metalloenzymes. The study has

shown that Ag-I was positively regulated in presence of metal ions

Mg2+ and Co2+ whereas it was completely inhibited in presence of Ag2+

and Hg+. Greater than or equal to 90% activity was observed for

enzyme Ag-I in presence of other metal ions K+, Ca2+ and Zn2+. A

marginal decrease of activity was observed for enzyme Ag-II in

presence of Mg2+ and 40% to 50% decrease in activity for metal ions-

K+, Ca2 and Cu2 while no activity was observed for Ag2+ and Hg+ ions.

Alpha-galactosidases isolated from Bacillus stearothermophilus NUB

3621 (Fridjonsson O et al. 1999), Bifidobacterium breve (Zhao H et al.

2008) and Bacillus sps (Akiba T and Horikoshi, K, 1976) were reported

to be strongly inhibited by Ag2+ and Hg+ ions. Chinen et al. 1981

suggested that the inhibition may be due to reaction with amino,

carboxyl and imidazolium group of histidine present at the active site.

Unlike alpha-galactosidase of Escherichia coli (Burstein C and Kepes

A, 1971), the enzyme Ag-I was neither not strongly stimulated by

metal ions nor was inhibited by EDTA indicating that it does not

require any cofactor for enzyme activity.

145 

 

Figure 4.17. Effect of metal ions on purified enzymes Ag-I & Ag-II

4.6.2.4 Effect of inhibitors and Surfactants:

The effect of various sugars and non-sugars at a final

concentration of 5mM on enzyme activity of Ag-I and Ag-II were

studied (Table 4.10 ). Both the enzymes Ag-I and Ag-II were inhibited

by sugars-glucose, galactose, lactose and stachyose. Similar reports of

substrate/product inhibition by various sugars was reported in other

alpha-galactosidases isolated from Bacillus stearothermophilus (Gote,

MM et al. 2006) and Pencillium griseoroseum (Falkoski DL et al. 2006).

According to Zapater et al. 1990 most of the inhibitions by sugars are

competitive inhibitions.

Enzymes Ag-I and Ag-II were strongly inhibited by pCMB (para-

cholromercuricbenzoate) and decrease in activity was observed in

146 

 

presence of Beta-mercaptoethanol. Inhibition by pCMB, a thiol

modifier may be due to reaction with thiol groups at active site.

Similar reports of alpha-galactosidases isolated from Bacillus

stearothermophilus (NCIM-5146) (Gote MM et al. 2006) and

Streptococcus pneumoniae (Dey PM and Pridham JB, 1972) were

reported to strongly inhibited by pCMB.

No activity was detected in presence of surfactant SDS for

enzymes Ag-I and Ag-II whereas increase in activity was observed in

presence of Triton X-100 and Tween-20 for Ag-I. SDS was reported to

strongly inhibit the enzyme activity of alpha-galactosidase isolated

from Pencillium griseoroseum (Falkoski DL et al. 2006). The inhibition

by SDS may be due to strong affinity to proteins and its denaturing

property.

Table 4.10 Effect of inhibitors on purified enzymes, Ag-I and Ag-II

Inhibitors Relative activity (%)

Ag-I Ag-II Control 100 100 Sugar inhibitors (5mM) Glucose 82 75 Galactose 92 72 Lactose 74 70 Stachyose 71 63 Non-sugars (5mM) PCMB 0 0 Urea 100 92 Beta-mercaptoethanol 88 74 EDTA 100 100 Surfactants (1%) SDS 0 0 Triton X-100 110 100 Tween-20 128 100

147 

 

4.7 Enzyme kinetics:

4.7.1 Determination of Kinetic constants for soluble enzyme Ag-I

& II:

The enzymes Ag-I and Ag-II were further characterized by

determining the Km, Vmax, Kcat and Kcat/Km values from Lineweaver-

burk plots (Figure 4.18). Table 4.11 gives the values of the kinetic

parameters determined at 50°C for substrates- ρ-nitrophenyl-α-D-

galactopyranoside (ρNPGal) and raffinose.

Kinetic studies of alpha-galactosidase shows that substrate

dependent Km and Vmax are unrelated to each other. Data from table

4.10 indicates that both enzymes Ag-I and Ag-II have a higher affinity

to synthetic substrate ρNPGal (low Km value) in comparison with

natural substrate raffinose. The rate of hydrolysis is high for raffinose

(Ag-I) although the Km value is high compared to synthetic substrate.

It suggests that a substrate with low Km value may not be rapidly

hydrolysed. Similar Km values were reported for ρNPGal for different

isoenzymes isolated from Bacillus stearothermophilus (Pederson DM

and Goodman, 1980).

148 

 

Table 4.11 Kinetic constants of purified soluble enzymes Ag-I and

Ag-II

Figure 4.18 Lineweaver-burk plots of purified enzyme Ag-I and

Ag-II for two substrates- ρNPGal and raffinose. A- ρNPGal (Ag-I),

B-Raffinose (Ag-I), C- ρNPGal (Ag-II), D-Raffinose (Ag-II),

Substrates Maximum

velocity

(Vmax)

µM min-1 ml-

1

Michaelis

constant

(Km)

mM ml-1

Turnover

number

(Kcat)

min-1

Specificity

constant

(Kcat/Km)

mM-1 min-1

Alpha-galactosidase I (Ag-I)

ρNPGal 14.51 0.33 4.45 × 102 13.48 × 102

Raffinose 27.24 3.26 1.1 × 102 0.33 × 102

Alpha-galactosidase II (Ag-II)

ρNPGal 6.73 0.68 1.52 × 102 2.23 × 102

Raffinose 5.44 0.74 0.6 × 102 0.81 × 102

149 

 

4.7.2 Enzyme hydrolysis studies of soluble enzyme Ag-I:

Activation energy (Ea) for rate of hydrolysis at 50°C was

determined from Arrhenius plot for substrates ρNPGal and raffinose

(Figure 4.19). Other thermodynamic properties like enthalpy ΔH,

Gibb’s energy ΔG and entrophy ΔS were also calculated using the

formula mentioned in section 3.8.3.2. The data (Table 4.12) shows

that reaction is endothermic and non-spontaneous. Enthalpy is >0

suggesting large amount of stretching, squeezing or breaking chemical

bonds for the formation of the transition state is necessary. Large and

negative ΔS indicates that the formation of transition state requires

the reacting molecules to adopt precise conformations and approach

one another at a precise angle.

Table 4.12 Enzyme hydrolysis kinetics of purified enzyme, Ag-I at

50°C

�Substrates Activation

energy (Ea)

kJ mol−1

Enthalpy

(ΔH)

kJ mol−1

Gibb’s

energy

(ΔG)

kJ mol−1

Entrophy

(ΔS)

J mol−1

K−1

ρNPGal 8.48 5.79 73.93 -210.95

Raffinose 10.90 8.214 62.17 -167.09

150 

 

Figure 4.19 Determination of activation energy (Ea) of

hydrolysis for Ag-I. Arrhenius plot of substrates- ρNPGal (A)

and raffinose(B)

A B

4.7.3 Thermal inactivation studies of soluble enzyme Ag-I:

Thermal inactivation studies were carried from 70 to 90°C by Ln

Et/E0 verses pre-incubation time (upto 5 hours) after fitting the data

in linear regression (Fig 4.20). Ea, activation energy for denaturation

was calculated to be 80 kJ mol−1 obtained from the slope of Arrhenius

graph (Figure 4.21). Table 4.13 presents data of various

thermodynamic properties for deactivation at different temperatures.

To date only α-galactosidase of hyperthermophilic bacteria Thermotoga

maritima (t1/2 6 h at 85 ºC) (Miller ES et al, 2001), Thermotoga

neapolitana (t1/2 9 h at 85 ºC) (Duffaud et al. 1997) and (4 h at 85 ºC,

retains 75 % activity) and Thermus brockianus (t1/2 17 h 80ºC )

(Giuseppin et al. 1993)have demonstrated activity and prolonged

151 

 

stability above 75 ºC. However the potential applications of these

enzyme preparations in high temperature processes were either not

demonstrated or briefly demonstrated (Giuseppin et al. 1993).

Figure 4.20 Temperature dependence of the thermal inactivation

of purified enzyme, Ag-I

Figure 4.21 Arrhenius type of plot of thermal inactivation of

purified enzyme Ag-I

2.74 2.76 2.78 2.80 2.82 2.84 2.86 2.88 2.90 2.92-2.6

-2.4

-2.2

-2.0

-1.8

-1.6

-1.4

-1.2

-1.0

-0.8

Ln

Kd

1/T * 10 -3 K

B Linear Fit of Data1_B

152 

 

Table 4.13 Thermodynamic properties of deactivation at various

temperature

4.8 Immobilization of the purified enzyme, Ag-I:

The purified alpha-galactosidase Ag-I was immobilized in four

different matrices. A comparative analysis of four different matrices

was given table 4.14. Agarose showed lowest activity yield of 41%. The

cubes of agarose were fragile and cannot be repeatedly used for

conversion of substrate to product. Although sodium alginate beads

were white, transparent, spherical and uniform in size, leakage of

enzyme into substrate solution or reaction mixture was detected. K-

carrageenan showed highest activity yield of 68% followed by gelatin

57%. The storage stability and reusability of both matrixs- K-

carrageenan and gelatin was almost similar. K-carrageenan

immobilized enzyme was able to retain > 20% even after 10 repeated

batch reactions. The stability of gelatin and K-carrageenan may be

Temperature (OC)

K d Half-life

(t1/2)

hrs

Enthalpy (ΔH)

kJ mol−1

Gibb’s energy

(ΔG)

kJ mol−1

Entrophy

(ΔS)

J mol−1 K−1

70  0.09009 7.69 77.14 102.94 ‐75.21 

75  0.09546 7.26 77.10 104.31 ‐78.18 

80  0.17209 4.02 77.06 104.12 ‐76.65 

85  0.26377 2.62 77.02 104.37 ‐76.39 

90  0.37283 1.85 76.98 104.83 ‐76.72 

153 

 

attributed to cross linking with glutaraldehyde solution. Alpha-

galactosidase purified from Aspergillus oryzae was also reported to be

less stable in calcium alginate compared to other matrixs-gelatin and

gelatin-alginate fibres (Naganagouda. K and Mulimani VH, 2006). Figure

4.22 shows the reusability of four different matrices in repeated batch

reaction. Gelatine immobilization sometimes may lead to denaturation

of enzyme due to freezing and thawing procedure. Thus K-carrageenan

which was more stable than other matrices was chosen for further

experiments.

Table 4.14 Comparison of immobilized alpha-galactosidase

activity in four different matrices

Matrix Activity of

soluble enzyme U/g of beads

Activity of immobilized enzyme U/g of beads

Activity yield (%)

No. of cycles

Storage stability (days)

Sodium alginate (3%)

12.16 6.0 50 7 >25

Gelatin (10%) 13.6 7.74 57 11 30

Agarose (2%) 12.16 4.96 41 5 10

k-

Carrageenan

(3%)

12.92 8.84 68 11 30

154 

 

Figure 4.22 Reusability studies on four matrices-gelatin, agarose,

sodium alginate and K-carrageenan

4.8.1 Effect of temperature on K-carrageenan immobilized

enzyme:

Higher relative activity for immobilized alpha-galactosidase Ag-I

was observed at optimum temperature of 60°C whereas soluble

enzyme has an optimum temperature of 50°C (Figure 4.23).

Immobilized enzyme was able to retain >80% compared to soluble

enzyme at 70°C. Girigowda K and Mulimani VH, 2005 have reported

an optimum temperature of 53°C for K-Carrageenan immobilized

alpha-galactosidase enzyme and 50 °C for soluble enzyme isolated

from Aspergillus oryzae. Prashanth & Mulimani (2005) have also

reported an increase in optimum temperature stability of immobilized

155 

 

alpha-galactosidase (57°C) compared to soluble enzyme (50°C). An

increase in optimum temperature of immobilized enzyme compared to

soluble enzyme can be reflected to increase in enzyme rigidity (Abdel-

Naby, 1993) and some changes in physical properties of the enzyme

(Farag & Hassan, 2004).

Figure 4.23 Effect of temperature of immobilized and soluble

enzyme, Ag-I

4.8.2 Effect of pH on K-carrageenan immobilized enzyme:

Optimum pH of immobilized enzyme was similar to soluble

enzyme. The immobilized enzyme was able to retain 95% activity at pH

7.0 on incubation for 2 hours (Figure 4.24). Similar results were

reported by Girigowda and V.H.Mulimani, 2005 where pH of

immobilized enzyme was similar to soluble enzyme.

156 

 

Figure 4.24 Effect of pH of immobilized and soluble enzyme, Ag-I

after 2 hours

4.8.3 Determination of kinetic constants of the immobilized

enzyme:

The kinetic constants Km and Vmax values (0.69 mM ml-1 and 9.6

µM min-1 ml-1) were calculated for substrate- ρNPGal from Lineweaver-

Burk plot at varying range of 0.4mM to 2.8mM (Figure 4.25). The Km

value of the immobilized enzyme is higher than the soluble enzyme.

Bodalo, et al. (1991) reported that Km value increased and Vmax

decreased upon immobilisation of alpha-galactosidase in alginate. The

increase in Km after immobilisation may be due to mass transfer

resistance of the substrate into the K-carrageenan beads.

157 

 

Figure 4.25 Lineweaver-Burk plot of immobilized enzyme Ag-I for

substrate ρNPGal

4.9 Evaluating Industrial and Medical Applications of

alpha-galactosidase

4.9.1 Enzymatic batch hydrolysis of cowpeas:

Cowpeas were subjected to enzymatic hydrolysis of

oligosaccharides by both soluble and k-Carrageenan immobilized

alpha-galactosidase enzyme (Ag-I). Figure 4.26A and B shows the

batch reaction hydrolysis of raffinose and stachyose by soluble and

immobilized enzyme at time intervals of 2, 4, 6, 8 and 10 hours. After

10 hours of incubation, oligosaccharide hydrolysis by soluble enzyme

led to decrease of 84% in raffinose and 76% in stachyose

concentration whereas immobilized enzyme led to decrease in 72%

and 54% of raffinose and stachyose concentrations. Soluble enzyme

158 

 

showed higher efficiency of hydrolysis compared to immobilized

enzyme. This may be attributed to the mass transfer resistance or

limitation of immobilized enzyme to the substrate. It was also

observed that as the time of incubation increased, rate of hydrolysis

decreased. This may be due to substrate depletion or product

inhibition. Richard IS and Esther B, 1993 previously reported the

effect of soaking, cooking and crude alpha-galactosidase enzyme on

oligosaccharide content of cowpeas. Soaking for 16 h resulted in an

average reduction of 26·2% for stachyose and 28·0% for raffinose,

while cooking for 50 min resulted in a reduction of 28·6% for

stachyose and 44·0% for raffinose. On the other hand, treatment of

cowpea flours at 50°C with crude α-galactosidase isolated from

fungus , brought about a mean decrease of 82·3% for stachyose and

93·3% for raffinose. Similar reports of hydrolysis of oligosaccharides

in soya milk with soluble and k-Carrageenan immobilized alpha-

galactosidase enzyme were reported by kotiguda et al.2006.

Figure 4.26 Enzymatic batch degradation of Raffinose (A) and Stachyose (B) by both soluble and k-carrageenan immobilized

enzyme

159 

 

4.9.1.1 TLC AND HPLC ANALYSIS:

TLC and HPLC analysis were carried out using galactose,

raffinose and stachyose as standards (Figures 4.27 and 4.28). On

HPLC analysis, peaks for standards galactose, raffinose and stachyose

were observed at retention time of 5.8, 10.5 and 17.1. Similar peaks

corresponding to retention time of standards were observed before and

after enzyme treatment. A decrease in the oligosaccharides (raffinose

and stachyose) concentration after enzyme treatment was observed

whereas there was no reduction in galactose concentration.

Figure 4.27 TLC Analysis of enzymatic galactooligosaccharide

hydrolysis of cowpeas. Lane 1-Raffinose standard, Lane 2-

Stachyose standard, Lane 3- Galactose standard, Lane 4-

Unhydrolysed cowpeas sample and Lane 5-Enzymatically

hydrolysed cowpeas sample

160 

 

Figure 4.28: HPLC Analysis of enzymatic galactooligosaccharide

hydrolysis of cowpeas. A- Raffinose, B-Galactose, C-Stachyose, D-

Unhydrolysed sample and E- Enzymatically hydrolysed sample

161 

 

4.9.2 Conversion of ‘B’ to ‘O’ group:

Enzymatically treated ‘B’ erythrocytes was taken and examined

by adding anti-B antibodies every 15 minutes. Fig (4.29) shows the

conversion of ‘B’ to ‘O’ erythrocytes at 30 and 60 minutes.

Agglutination was observed at 0 hour. Mild agglutination was

observed after 30 minutes incubation when anti-B antibodies were

added. No agglutination was observed after one hour incubation. In

case of ‘A’ group agglutination was observed from 0 hour upto 2 hours

when anti-A antibodies were added. ‘A’ group differs from ‘O’ group by

terminal N-acetyl galactosamine and cannot be removed by alpha-

galactosidase. Similar report of conversion of ‘B’ group to ‘O’ group red

blood cells by using recombinant alpha-galactosidase enzyme isolated

from Coffee beans was reported by Zhang YP et al. 2007.

Figure 4.29 Conversion of B to O group by purified enzyme alpha-

galactosidase, Ag-I

162 

 

4.8.2.1 Microscopic examination of ECORBC

Stability and integrity of ECORBC SURFACE was visualised

microscopically. Figure 4.30 shows microscopic examination of ‘B’

group erythrocytes with anti-B antibodies at 0 hour and one hour.

On microscopic examination at 0 hour agglutination was observed

whereas after one hour incubation, no agglutination was observed

and cell surface of ECORBC ie enzymatically converted O red blood

cells was stable and no cell lysis was observed.

Figure 4.30 Microscopic examination of ECORBC cells at 0 and 60

minutes.