INNOVATIVE FERMENTATION STRATEGIES FOR PROTEOLYTIC ENZYMES PRODUCTION

8/19/2019 INNOVATIVE FERMENTATION STRATEGIES FOR PROTEOLYTIC ENZYMES PRODUCTION

1/85


2/85

INNOVATIVE FERMENTATIONSTRATEGIES FOR PROTEOLYTIC

ENZYMES PRODUCTION

Dr. D.KEZIAAssociate Professor,

St. Martins Engineering college,Dulapally, Near Kompally, Qutubullapur, Hyderabad,

Telangana -500014, India

Dr. T. SATHISHProject-Scientist,

Andaman and Nicobar Center for Ocean Science and Technology(ANCOST), National Institute of Ocean Technology (NIOT),

(Ministry of Earth Science, Govt. of India), Industrial Estate Road,Dollygunj Port Blair - 744103, Andaman and Nicobar Islands, India

1


3/85

INNOVATIVE FERMENTATION STRATEGIES FORPROTEOLYTIC ENZYMES PRODUCTION

Authors : Dr. D. KEZIA Dr. T . SATHISH

ISBN : 978- 1-63040- 518-2

Publisher : SARA BOOK PUBLICATION 303, Maharana Pratap Complex B/H.V. S. Hospital Paldi, Ahmedabad - 380006.

Phone: +91 8866003636, 9904000288

First Edition : 2015

This book is sold subject to the condition that it shall not, by way of trade or oth-erwise, be lent, resold, hired out, or otherwise circulated without the publisher's prior writ-ten consent in any form of binding or cover other than that in which it is published andwithout a similar condition including this condition being imposed on the subsequent pur-chaser and without limiting the rights under copyright reserved above, no part of this pub-lication may be reproduced, stored in or introduced into a retrieval system, or transmittedin any form or by any means (electronic, mechanical, photocopying, recording or other-wise), without the prior written permission of both the copyright owner and the above-mentioned publisher of this book.

Copyright (c) 2015\ Sara Book Publication, Ahmedabad

Price : 300/-

2


4/85

3

PREFACE

The term fermentation is derived from the Latin word for “forever” to boil thusdescribing the appearance of the action of yeast on extracts of fruit or malted grain. The

boiling experience is due to the production of CO bubbles caused by the anaerobic2catabolism of the sugars present in the extract. Pasteur applied the term “Fermentation” tothose anaerobic reactions through which microorganisms obtained energy for growth inthe absence of oxygen. Today fermentation has much broader meaning. It applies to bothaerobic and anaerobic metabolic activities of the microorganisms where in specificchemical changes are brought about by an organic in substrate.

A variety of substances such as alcohols, organic acids, amino acids, vitamins,antibiotics, enzymes, single cell proteins, hormones etc., are produced throughfermentations by employing different microorganisms. The enzymatic yield obtained

from fermentation, cost of their production and downstream processing cost determinesthe final cost of the enzyme. To develop a viable industrial process, lowering productioncost and increasing enzyme productivity are very important. Selection of bestfermentation techniques and optimization of culture conditions contribute much inachieving enzyme productivity. Currently, enzyme production by microorganisms can beachieved using submerged fermentation or solid state fermentation.

This book is aimed at helping new comers to know about upstream anddownstream processing of enzymes and also it helps to understand the fermentationtechniques and avoid obvious mistakes and pitfalls we have all made. It has been written

for researchers, scientists, students, academicians coming from various disciplines ofChemistry, Biology, Engineering and industry personnel as well. The content in this bookis quite readable and thorough in its presentation of the subject provides the readers a

broad coverage and insight into the area. To meet the above objectives this book coversfollowing topics: chapter-1 contains introduction, mechanism of action, sources andclassification of proteolytic enzymes. This chapter also deals with properties andapplications of alkaline proteases. Chapter 2-5 contains practical aspects on isolation,screening and characterization of protease producing microorganisms, protease

production by submerged fermentation, optimization by Plackett-burman design,Response surface methodology, solid-state fermentation, enzyme recovery and

purification procedures, characterization of purified enzyme, estimation of kinetic parameters, evaluation of industrial applications. Chapter-6 deals with variousfermentation strategies to improve the yield. The present book has comprised of sixchapters presenting an overview of the research being carried out in laboratory in the fieldof fermentation technology.

Dr. D. KEZIA & Dr. T. SATHISH


5/85

4


6/85

CONTENTS

PREFACE ..................................................................................................... 03

5

SR. NO TITLEPAGENO.

CHAPTER-1 INTRODUCTION TO PROTEOLYTIC ENZYMES 07

CHAPTER-2

SCREENING OF PROTEASE PRODUCING

MICROORGANISMS 18

CHAPTER-3ENRICHMENT OF PROTEASE PRODUCTION BYPROGRESSIVE OPTIMIZATION METHODS

22

CHAPTER-4

ECONOMICALLY VIABLE PROTEASEPRODUCTION BY SOLID STATE FERMENTATIONUSING AGRO INDUSTRIAL WASTE MATERIALSAND OPTIMIZATION OF PRODUCTION

CONDITIONS

35

CHAPTER-5PURIFICATION AND EVALUATION OFINDUSTRIAL APPLICATION OF PROTEOLYTICENZYMES

53

CHAPTER-6FERMENTATION STUDIES FOR IMPROVEDPRODUCT FORMATION

67

REFERENCE 73

APPENDIX -I 83


7/85

6


8/85

1.0 INTRODUCTIONProteases are essential constituents of all forms of life on earth. Microbial proteases areamong the most important, extensively studied groups since the development ofenzymology. Alkaline proteases are so far exploited as industrial catalysts in variousindustrial sectors. Neutralophilic and alkaliphilic microbial alkaline proteases possess aconsiderable industrial potential due to their biochemical diversity and stability atextreme pH environments, respectively (Moon et al. 1994). However, the demanding

industrial conditions for technological applications and cost of alkaline proteases produc-tion resulted in continuous exercise for search of new microbial resources. Extreme envi-ronments are important sources for isolation of microorganisms for novel industrialenzymes production (Kumar & Takagi, 1999). Enzyme cost is also the most critical factorlimiting wide use of alkaline proteases for different applications. A large part of this costis accounted for the production cost of the enzyme. Therefore, reduction in the productioncost of enzymes could greatly reduce the cost of the enzyme. In submerged fermentationup to 40% of the total production cost of enzymes is due to the production of the growthsubstrate (Enshasy et al. 2008; Kirk et al. 2002). In this regard, SMF method is used tooptimize the parameters of enzyme production and SSF which uses cheap agricultural res-

idues which have enormous potential in reducing enzyme production cost. So, studies onalkaline proteases that are produced in SMF and SSF by alkaliphilic microorganisms arescarce in literature as a result, it is of great importance to pursue such studies.

1.1 MECHANISM OF ACTION OF PROTEASESThe catalytic site of proteases is flanked on one or both sides by specificity subsites, eachable to accommodate the side chain of a single amino acid residue from the substrate.These sites are numbered from the catalytic site S1 through Sn toward the N terminus ofthe structure and Sl' through Sn' toward the C terminus. The residues which they accom-modate from the substrate are numbered Pl through Pn and P1' through Pn', respectively.

The structure of the active site of the protease therefore determines which substrate resi-dues can bind to specific substrate binding sites of the protease, thereby determining sub-strate specificity of a protease (Fig. 1.1)

Fig. 1.1: Active sites of proteases. The catalytic site of proteases is indicated by * and

the scissile bond is indicated by + ; S1 through Sn and S1' through Sn' are the speci-ficity subsites on the enzyme, while P1 through Pn and P1' through Pn' are the resi-dues on the substrate accommodated by the subsites on the enzyme (Fromhttp://journals.asm. org/misc/reprints.dtl).

7

CHAPTER-1: INTRODUCTION TO PROTEOLYTICENZYMES


9/85

1.2 SOURCES OF PROTEASESSince proteases are physiologically necessary for living organisms, they are ubiquitous,found in a wide diversity of sources such as plants, animals and microorganisms (Rao,1998; Ward, 1985). Fortunately, enzymes can be separated from living cells and performcatalysis independent of their physiological environment. Commercial proteases are

derived from animal tissues, plant cells and microbial cells via fermentation.

1.2.1 Plant ProteasesThe use of plants as a source of proteases is governed by several factors such as the avail-ability of land for cultivation and the suitability of climatic conditions for growth. More-over, production of proteases from plants is a time-consuming process. (β-amylase,

papain, bromelain, urease, ficin, polyphenol oxidase (tyrosinase), lipoxygenase, etc.), rep-resent some of the well-known proteases of plant origin (Rao,1998). Plant-derived prod-ucts are perceived as safe and “natural” ingredients for use in the food applications andmay offer unique benefits and functionality.

1.2.2 Animal ProteasesThe most familiar proteases of animal origin are pancreatic trypsin, chymotrypsin, pepsinand rennin. Trypsin is the main intestinal digestive enzyme responsible for the hydrolysisof food proteins (Rao, 1998). Chymotrypsin is found in animal pancreatic extract. Purechymotrypsin is an expensive enzyme and is used only for diagnostic and analyticalapplications (Rao, 1998). Pepsin is an acidic protease that is found in the stomach ofalmost all vertebrates (Rao, 1998). Pepsin was used in laundry detergents as early as1913, but is now being replaced by a mixture of serine and metal microbial proteases thatappear to be less degradable by soaps, alkaline conditions and high temperatures

(Adinarayana et al. 2003). Rennet is a pepsin-like protease that is produced as an inactive precursor in the stomach of all nursing mammals. It is used extensively in the dairy indus-try to produce a stable curd with good flavor (Rao, 1998).

1.2.3 Microbial ProteasesThe inability of the plant and animal proteases to meet current world demands has led toan increased interest in microbial proteases (Rao, 1998). Proteases of bacteria, fungi andviruses are increasingly studied due to its importance and subsequent applications inindustry and biotechnology. Commercial application of microbial proteases is attractivedue to the relative ease of large-scale production as compared to proteases from plants

and animals. Microbial proteases account for approximately 40% of the total worldwideenzyme sales. Proteases from microbial sources are preferred to the enzymes from plantand animal sources since they posses almost all the characteristics desired for their bio-technological applications (Rao, 1998). In general microbial proteases are extracellularin nature and are directly secreted into the fermentation broth by the producer, thus sim-

plifying downstream processing of the enzyme as compared to proteases obtained from plants and animals (Gupta et al. 2002a). Microbial proteases, especially from Bacillus sp.have traditionally held the predominant share of the industrial enzyme market of theworldwide enzyme sales with major application in detergent formulations and leatherindustries (Beg et al. 2003; Negi & Banerjee, 2006; Vasudeo et al. 2011).

8


10/85

1.3 CLASSIFICATION OF PROTEASESA number of microorganisms produce one or more types of protease enzymes with dif-ferent pH optima for activity. Proteases are broadly classified as endo- or exoenzymes onthe basis of their site of action on protein substrates. They are also classified based on thefunctional group at the active site, mechanism of action, pH optima, substrate specificity,

catalytic mechanism, 3-D structure and similarity in action to well characterized enzymeslike trypsin, chymotrypsin and elastase (Rawlings & Barret, 1993). Based on their aminoacid sequences, proteases are classified into different families and further classified intoclans to accommodate sets of peptidases that have diverged from a common ancestor(Rawlings & Barrett, 1993). Exopeptidases act only near the ends of polypeptide chains,further classified as amino-or carboxypeptidases based on their site of action at the N or Cterminus respectively. Aminopeptidases liberate a single amino acid residue, a dipeptide(dipeptidyl peptidase) or a tripeptide (tripeptidyl peptidase). The substrate specificities ofthe enzymes from bacteria and fungi are distinctly different and can be differentiated

based on the profiles of the products of hydrolysis (Cerny,1978). Aminopeptidases may

be classified as aminopeptidase N or aminopeptidase A, depending on their preference forneutral (uncharged) or acidic side chains, respectively. Most of the aminopeptidases fallunder metalloenzymes. Carboxypeptidases may be divided into three major groups,serine carboxypeptidases, metallocarboxypeptidases and cysteine carboxypeptidases,

based on the nature of the amino acid residues at the active site of the enzymes. Theseenzymes can also hydrolyze the peptides in which the peptidyl group is replaced by a

pteroyl moiety or by acyl groups. Other exopeptidases include dipeptidases, which cleavea dipeptide and omega peptidases which release modified residues from N- or C- termini.Endopeptidases are characterized by their preferential action at the peptide bonds in theinner regions of the polypeptide chain away from the N or C termini. Based on the pH of

their optimal activity, they are also referred to as acidic, neutral, and alkaline proteases.

More conventionally, proteases are classified into four important groups according to the Nomenclature Committee of the International Union of Biochemistry and MolecularBiology, (International Union of Biochemistry, 1992) like serine proteases (EC 3.4.21),cysteine proteases (EC 3.4.22), aspartate proteases (EC 3.4.23) and metallo proteases(EC 3.4.24) (Kalisz, 1988; Rao,1998).

1.3.1 Serine proteasesSerine proteases are characterized by the presence of a serine group in active site. This

type of proteases hydrolyzes either esters or peptide bonds utilizing mechanisms of cova-lent catalysis and preferential binding in the transition state. They play an important rolein many processes, e.g. digestion of dietary protein, blood clotting cascade, and in several

pathways of differentiation and development. Based on their structural similarities, theyare grouped into 20 families, which are further subdivided into about six clans with com-mon ancestors (Barett et al.1998). They are recognized by their irreversible inhibition by3, 4-dichloroisocoumarin, Di-isopropyl fluorophosphate (DFP), Phenyl methyl sulfonylfluoride (PMSF) and Tosyl-Llysine chloromethyl ketone (TLCK). Serine proteases aregenerally active at neutral and alkaline pH, with an optimum between pH 7 and 11. Theyhave broad substrate specificities including esterolytic and amidase activity. Their molec-

ular masses range between 18 and 35 kDa. Their isoelectric points are generally between pH 4 and 6. Serine alkaline proteases that are active at highly alkaline pH represents thelargest subgroup of serine proteases. Trypsin, chymotrypsin are the well-studied proteas-es of this sub-group.

9


11/85


12/85

Alkaline proteases being a physiologically and commercially important group ofenzymes are used primarily as detergent additives. These enzymes have broad substratespecificities and will function to some extent under extreme conditions encountered indomestic washing temperatures of 20 to 70ºC, a pH up to 11 and at high concentrations ofdetergents, polyphosphates, chelating agents such as EDTA and oxidizing agents such as

sodium perborate (Cowan, 1994; Subbarao et al. 2009). In recent years there has also been a phenomenal increase in the use of alkaline protease as industrial catalysts. InJapan, 1994 alkaline protease sales were estimated at $116 million. There is expected to

be an upward trend in the use of alkaline proteases so that by the turn of the decade thetotal value for industrial enzymes is likely to reach $700 million or more (Kumar &Takagi, 1999; Turk, 2006). Especially, alkaline proteases of microbial origin, which domi-nate the worldwide enzyme market, possess considerable industrial potential due to their

biochemical diversity and wide applications in tannery and food industries, medicinal for-mulations, detergents and processes like waste treatment, silver recovery and resolutionof amino acid mixtures (Agarawal et al. 2004; Gupta et al. 2002a; Rao, 1998).

Alkaline proteases are produced by a wide range of microorganisms including bacteria,molds, yeasts and also mammalian tissues. Despite this interest in other microbialsources, survey of the literature conclusively shows that bacteria are by far the most popu-lar source of commercial alkaline proteases to date. Bacterial alkaline proteases are char-acterized by their high activity at alkaline pH, e.g., pH 10 and their broad substrate speci-ficity. Their optimal temperature is around 60ºC. These properties of bacterial alkaline

proteases make them suitable for use in the detergent industry (Rao,1998). From all thealkaliphilic bacteria that have been screened for use in various industrial applications,members of the genus Bacillus, mainly the strains B. subtilis and B. licheniformis were

found to be predominant and a prolific source of alkaline proteases (Kumar & Takagi,1999). Some industrially important alkaline proteases produced from various Bacillus sp.are tabulated in Table 1.1.

Table 1.1: Some industrially important alkaline proteases produced from various Bacillus sp. (Anwar & Saleemuddin, 1998)

11

Bacterial Species PHoptimum/stability

Industrial applications

Bacillus stearothermophilus 9.5 Detergents and heavy duty

laundry powders Bacillus sp. Y. (BYA) 10.0-12.5 Detergent formulations

Bacillus licheniformis 8.2 Catalyst for N-protectedamino acids

Bacillus sp. (AH-101) 12.0-13.0 Dehairing/leather industry

Bacillus sp. (Savinase/Durazym) 9.0-11.0 Detergent formulations

Bacillus firmus 8.0 Detergent industry

Bacillus sp. (P-001A) 9.5 Production of biomass fromnatural waste

Bacillus licheniformis(Alcalase)

8.2 Synthesis of biologicallyactive peptides

Bacillus subtilis 8.5 Bating agent in leatherindustry


13/85

1.5 PROPERTIES OF ALKALINE PROTEASES1.5.1 Optimum Temperature are Thermostability of Alkaline ProteasesThe heat stability of enzymes is affected by at least two factors alone or in combination.The first one is the primary structure of the enzyme Secondly, specific components suchas polysaccharides and divalent cations. A wide range of microbial proteases from

thermophilic species has been extensively purified and characterized. These includeThermus sp., Desulfurococcus strain Tok12S1 and Bacillus sp. Among them alkaline pro-teases derived from alkaliphilic bacilli, are known to be active and stable in highly alka-line conditions (Rahman et al. 1994). Further studies on microbial alkaline proteases have

been done in view of their structural-function relationship and industrial applications, asthey needed stable biocatalysts capable of withstanding various conditions of operation(Rahmam et al. 1994). Generally alkaline proteases are produced from alkaliphilic

Bacillus are known to be active over a wide range of temperature. The optimum tempera-tures of alkaline proteases range from 40 to 80ºC.

1.5.2 Optimum pH of Alkaline ProteasesEnzymes are amphoteric molecules containing a large number of acid and basic groups,mainly located on their surface. The charge on these groups will vary, according to theiracid dissociation constants, with the pH of their environment. This will affect the total netcharge of the enzymes and the distribution of charges on their exterior surfaces, in addi-tion to the reactivity of the catalytically active groups. These effects are especially impor-tant in the neighborhood of the active sites, which will overall affect the activity, struc-tural stability and solubility of the enzyme (Chaplin & Bucke, 1990). The pH optima ofsome alkaline proteases produced from Bacillus sp. are given in Table 1. 2..

Table 1.2: pH optimum of various alkaline proteases produced from Bacillus sp.

12

Bacterial Species PHoptimum/stability

Industrial applications

Bacillus stearothermophilus 9.5Detergents and heavy dutylaundry powders

Bacillus sp. Y. (BYA) 10.0-12.5 Detergent formulations

Bacillus licheniformis 8.2Catalyst for N-protected aminoacids

Bacillus sp. (AH-101) 12.0-13.0 Dehairing/leather industry

Bacillus sp.(Savinase/Durazym)

9.0-11.0 Detergent formulations

Bacillus firmus 8.0 Detergent industry

Bacillus sp. (P-001A) 9.5Production of biomass fromnatural waste

Bacillus licheniformis(Alcalase)

8.2Synthesis of biologically active

peptides

Bacillus subtilis 8.5 Bating agent in leather industry


14/85

1.5.3 The Isoelectric PointThe pH referred as isoelectric point (pI) at which the net charge on the molecule is zero, isa characteristic of each enzyme, where solubility in aqueous solutions is generally mini-mum. Isoelectric pH values of various alkaline proteases produced from Bacillus sp aregiven in Table 1.3.

Table 1.3: Isoelectric pH values of various alkaline proteases produced from Bacillus sp.microorganism pI Reference

1.5.4 The Molecular WeightThe molecular weights of alkaline proteases generally range from 15 to 30 kDa (Kumar &

Takagi, 1999) with few reports of higher molecular weights of 32.0 kDa (Huang et al.2003), 33.5 kDa (Rahman et al. 1994). These are tabulated in Table 1.4

Table 1.4: Molecular weights of various alkaline proteases produced from Bacillus sp.

1.5.5 Metal Ion Requirement and Inhibitors of Alkaline Proteases2+ 2+ 2+

Alkaline proteases require a divalent cation like Ca , Mg and Mn or a combination ofthese cations, for maximum activity. These cations were also found to enhance the ther-mal stability of a Bacillus alkaline protease. It is believed that these cations protect theenzyme against thermal denaturation and play a vital role in maintaining the active con-formation of the enzyme at high temperatures (Kumar & Takagi, 1999). Inhibition studiesgive insight into the nature of the enzyme, its cofactor requirements, and the nature of theactive site. Alkaline proteases are completely inhibited by phenylmethylsulfonyl fluoride(PMSF) and diisopropyl fluorophosphates (DFP). In this regard, PMSF sulfonates the

essential serine residue in the active site, results in the complete loss of activity. This inhibition profile classifies these proteases as serine hydrolases. In addition, some of the alka-line proteases were found to be metal ion dependent in view of their sensitivity to metalchelating agents, such as EDTA. Thiol inhibitors have little effect on alkaline proteases of

Microorganism pI Reference

B. pumilus UN-31-C-42 9.0 Huang et al. 2003

Bacillus sp. PS719 4.8 Hutadilok-Towatana et al.1999

SourceMolecular weight

(kDa)Method Reference

B. pumilus UN-31-C-42 32 SDS-PAGE Huang et al. 2003

B. pumilusMK6-5 28 SDS-PAGE Kumar, 2002

Bacillus licheniformis MIR 29 25/40 SDS-PAGE Ferrero et al.1996

Bacillus sp. No. AH-101 30 SDS-PAGE Takami et al.1989

B. pumilusMK6-5 28 SDS-PAGE Kumar, 2002

B. pumilusUN-31-C-42 32 SDS-PAGE Huang et al. 2003

B. stearothermophilus F1 33.5 SDS Rahman et al.1994

Bacillus licheniformisMIR29 25/40 SDS-PAGE Ferrero et al.1996

Bacillus mojavensis 30 SDS-PAGE Gupta & Beg, 2003

13


15/85

Bacillus sp., although they do affect the alkaline enzymes produced by Streptomyces sp.(Kumar & Takagi, 1999).

1.5.6 Kinetic ParametersTo develop any enzyme-based process, knowledge of the kinetic parameters of the

enzyme under study is of utmost importance. To be precise, kinetic properties like Vmax,Km, Kcat, and Ea knowledge are essential for designing enzyme reactors or quantifyingthe applications of the enzyme under different conditions. This information also helps inunderstanding the catalytic behavior related to enzyme-substrate and environment speci-ficity. Various natural complex substrates like casein, azocasein, BSA, gelatin etc., andsynthetic substrates such as para nitroanilide esters are used for determining kinetic

parameters for proteases. The synthetic substrates are much more popular than complexsubstrates for defining Km and Vmax as they are convenient (Kumar, 2002; Larcher etal.1996; Kemel et al. 2011; Subbarao et al. 2009). For an alkaline protease from B.mojavensis, the Km for casein decreased with corresponding increase in Vmax, as the

reaction temperature was raised from 45 to 60°C (Beg et al. 2002). In contrast, the Kmand Vmax for an alkaline protease from Rhizopus oryzae increased with an increase intemperature from 37°C to 70°C.

1.6 APPLICATIONS OF PROTEASESIn the present era for development of environmentally friendly technologies, proteasesare believed to have extensive applications in different sectors ranging from domestic toleather processing, environmental pollution abatement to neutraceutical applications,health care products to diagnostic kits developments and value added product productionto bioremediation processes.

1.6.1 DetergentsThe detergent industry emerged as the single major consumer of alkaline protease since1913 (Kalisz, 1988). At present, approximately 25% of the total worldwide sales ofenzymes (Kalisz, 1988) used in detergent industry. Proteases are stable in a broad range of

pH and temperatures. They are compatible with surfactants, perfumes and bleaches. Fur-ther they are stable and have good shelf life. They play a key role in stain degradation andremoval (Kumar, 2002; Joo et al. 2003; Banik & Prakash, 2004; Ferid Abidi, 2008;Subbarao et al. 2009; Haddar et al. 2010; Ashis et al. 2008).

1.6.2 Leather IndustryThe enzymatic dehairing process is gaining importance as an alternative to chemicalmethods in present day concern on development of eco-friendly technologies as this pro-cess in reduction of toxicity in addition to improvement of the quality of the leather andother chemicals (soda sulfide) (Sivasubramanian et al. 2008; Ganesh et al, 2008; Vasudeoet al. 2011; Mukhter & Ikram-Ul-Haq, 2008; Ramakrishna, 2010; Arunachalam &Sarita,2009). The major building blocks of skin and hair are proteinaceous. Hence, pro-teases are used for selective hydrolysis of non-collagenous constituents of the skin andfor the removal of non-fibrin proteins such as albumins and globulins. The purpose ofsoaking is to swell the hide.

1.6.3 Food IndustryThe basic hydrolysis character of proteases was exploited to convert solid proteins frommeat, fish or legumes into liquid slurries or protein hydrolysates, to improve their flavor,

14


16/85

texture, functionality and nutritional quality (Watanabe et al. 1995; Keivan et al. 2009) inaddition to production of high-value functional ingredients from proteins, bioactive pep-tides with various functionalities, reduce allergenicity of food proteins and protect foodquality in food industry (Gautheir & Pouliot, 2003). Proteases were extensively used toimprove the palatability of reformulated low-carbohydrate/ high-protein foods.

1.6.4 Dairy IndustryMajor application of proteases in the dairy industry is in the manufacture of cheese. Worldshortage of calf rennet due to the increased demand for cheese production intensified thesearch for alternative microbial milk coagulants. Proteases produced by GRAS (geneti-cally regarded as safe) microbes such as Mucor michei, Bacillus subtilis, and Endothia

parasitica which were gradually replacing chymosin in cheese making and ripening(Schmidt ,1979).

1.6.5 Feed Industry

Proteins are essential dietary components and have a significant effect on feed quality dueto their broad substrate specificity in hydrolysis of hemoglobin, casein, egg yolk, soy, gel-atin, fish, and other proteins to lower molecular weight peptides and subsequent produc-tion of amino acids that are absorbed by the body. Many publications showed that pro-teases increase the digestibility of the proteins in soybean meal (Cheng et al.1995)reported the use of alkaline proteases from (B. subtilis B72 and B. licheniformis PWD-1)for hydrolysis of feather keratin for obtaining a protein concentrate for fodder production.

1.6.6 Pharmaceutical IndustryWide diversity and specificity character of proteases is used in developing effective thera-

peutic agents including as contact-lens enzyme cleaners and enzymic debrides.Collagenolytic protease was orally administered as a pretreatment against for osteoporo-sis, used in the preparation of isolated rat liver cells and the scission of collagen-like pep-tides in fusion proteins (Barthomeuf et al. 1989) used alkaline protease to hydrolyze col-lagen to produce low molecular weight peptides of therapeutics use while used them forfibrinolytic activity. Proteases also revealed their importance in dissolution of bloodclots, in treatment of sciatica, retained placenta (Eiler et al. 1993), adenovirus-mediatedcancer gene therapy (Kuriyama et al. 2000) and in therapy of thromboembolic diseases(Myocardial infarction, embolisms and deep vein thrombosis). Protease with elastoteraseactivity was used for the treatment of burns, purulent wounds, carbuncles, furuncles and

deep abscesses (Thangam & Rajkumar, 2002).

1.6.7 Peptide SynthesisSince the first report of (Bergman & Frankel-Conrat, 1937) on protease-catalyzed peptidesynthesis using the reverse-enzymatic reaction of hydrolysis, proteases were used for pep-tide synthesis (Clapes et al. 1997; Morihara et al. 1987). Proteases have been used suc-cessfully for the synthesis of dipeptides and tripeptide, regioselective sugar esterification(Riva et al. 1988) and dia-stereoselective hydrolysis of peptide ester used alcalase forkinetic resolution of N-protected amino acid esters in organic solvents, resolution of DL-

phenylalanine and DL-phenylglycine.

1.6.8 Photographic IndustryAlkaline proteases play a crucial role in the bioprocessing of X-ray or photographic filmsfor silver recovery. These waste films contain 1.5–2.0% silver by weight in their gelatin

15


17/85

layer, which can be used as a good source of silver for a variety of purposes. Alkaline pro-teases from B. coagulans PB-77 (Gajju et al. 1996), Bacillus sp. B18 (Fujiwara et al.1991), B. subtilis (Fujiwara et al. 1991) played a crucial role in the bioprocessing of usedX-ray or photographic films for silver recovery.

1.6.9 Textile IndustryProteases are used in the silk industry for the degumming of silk, by splitting the albumi-nous proteins. Different proteases were used for degumming the silk (Freddi et al. 2003).Proteases were used to wash down printing screens after use in order to remove the

proteinaceous glue used for thickening of printing paste (Freddi et al. 2003). They are alsoused for softening of wool fibers and to make 'shrinkproof' wool. A successful methodinvolving the partial hydrolysis of the scale tips with the protease was developed (Freddiet al. 2003).

1.6.10 Alkaline Proteases in

Degradation of Proteinaceous Waste into Useful Biomass Recently, use of alkaline prote-ase in the management of wastes from various food processing industries and householdactivities has opened up a new era in the use of proteases in waste management. Fibrous

proteins such as horn, feather, nail and hair were converted into useful biomass, proteinconcentrate or amino acids using proteases. Alkaline proteases can also be used tosolubilize fish meat in the production of animal glue and a fodder for animals from leatherindustry waste, in hydrolysis of feather and horn (Atalo & Gashe, 1993), for the produc-tion of amino acids or peptides, for degrading waste keratinous material in household and

poultry refuse (Detoni et al. 2002; Brutt & Ichida, 1999; Mukhopadhyay & Chandra,1992) used alkaline protease from B. subtilis for management of waste feathers from poul-

try slaughterhouses. Proteases along with other enzymes were directly added in thedigester to enhance the rate of biodegradation of polymeric substances which wouldfacilitate the conversion of monomeric constituents into methane and carbon dioxide.

1.6.11 Synthesis and Resolution of D, L-Amino Acids by Alkaline ProteasesAmino acids are important as a dietary supplement for both humans and domestic ani-mals. Only the L-amino acids can be assimilated by living organisms. An extra- cellularlow molecular weight protease (6800 daltons) was purified to homogeneity from the cul-ture filtrate of C. coronatus (NCIM 1328) and used in resolving the racemic mixture ofamino acids.

1.6.12 Medical ApplicationsAlkaline proteases are also used for developing products of medical importance exploitedthe elastolytic activity of B. subtilis 316M for preparation of elastoterase, which wasapplied for the treatment of burns, purulent wounds, carbuncles furuncles and deepabscesses.

1.6.13 Other ApplicationsIt is evident from above that alkaline proteases have a wide range of industrial applica-tions. In addition to the applications already described, alkaline proteases are also used to

lesser extent in a large number of other fields, which may be technically interesting, butare not commercial success in terms of microbial enzyme sales nevertheless they are theupcoming areas of future enzyme industry. (Thangam & Rajkumar, 2002) reported thatsome proteases can replace the Proteinase which is extensively used in DNA isolation

16


18/85

(Chiplonkar et al. 1985) have reported the use of alkaline protease from Conicliobolus sp.as a substitute for trypsin in preparation of animal cell cultures. In another study, the effectof culture supernatant of M. Purpureus CCRC 31499 on the growth rate of rape and ama-ranth seedlings was investigated (Liang et al. 2006).

The subsequent chapters in this book, experimental methods and the results obtainedfrom submerged fermentation studies, solid state fermentation, purification and charac-terization of proteases and application of proteases are written in detail.

17


19/85

2.0 INTRODUCTION Bacteria of the genus Bacillus are known to produce a group of commercially importantenzymes including proteolytic enzymes. Alkaline proteases represent one of the largestgroups of industrial proteases useful in industrial process like in detergents, leather,tanning, dairy, meat tenderization, baking, brewery, photographic industry etc. (Gupta etal. 2002a). Naturally occurring and man-made alkaline inhabitants provide excellentsources of microorganisms for research programmes in innovative microbiology. The

isolation and screening of microorganisms from different origins and the nature ofalkaline sources for alkaline proteases have been reported by various investigators(Takami et al.1989). In the present investigation, a bacterial strain has been isolated fromsoil sample collected from the Andhra University, Visakhapatnam, a potentially richsource of alkalophiles, and a methodology has been standardized for the production and

partial characterization of the industrially important novel alkaline protease from bacteria.

2.1 MATERIALS AND METHODS2.1.1 Sample Collection

Samples were collected at different areas from garden soil in Visakhapatnam, A.P, Indiaoand brought to laboratory the samples were stored at 4 C till further use.

2.1.2 Enrichment of Soil Microbial Organisms1g of soil sample was suspended in 9.0 ml of sterile distilled water and mixed well for 1 hat room temperature, 1.0 ml of suspension was inoculated in 50 ml of Yeast extract-

-1Peptone-Dextrose (YPD) medium consisting (gl ) of glucose, 10.0; peptone, 7.5; yeastextract, 7.5; K HPO , 0.50; MgSO 0.05 and CaCl 0.02, pH of the medium was adjusted2 4 4, 2,

oto 9.0 using 0.1N HCl or 0.1N NaOH solution. The culture was incubated at 37 C and at200 rpm, After 24 h of incubation, the resultant culture was serially diluted using sterile

distilled water and 0.1 ml of this was spread on YPD medium agar-plates and incubated ato

37 C for isolation of pure cultures. Each developed colony was picked up and maintainedon agar based YPD medium till further use

2.1.3 Screening for Proteolytic ActivityProteolytic activity of isolated strains was screened by plating them on casein agar

-1medium consisting (gl ) of casein -10.0, MgSO - 0.05, CaCl -0.02, FeSO -0.01, yeast4 2 4

0extract -1.0 and agar- 20.0. These plates were incubated at 37 C for 24 h. Bacteriashowing clear zones of caseinolysis on casein agar plates were identified as protease

producers. Based on hydrolysis zone, few colonies were selected and maintained on the0YPD medium slants at 4 C. One of the colonies which were showing more caseionlysis

activity was designated as DKMNR and selected for further studies.

18

CHAPTER -2: SCREENING OF PROTEASEPRODUCING MICROORGANISMS


20/85

2.1.4 Biochemical and phenotypic characterization of Protease Producing IsolateMorphological studies were conducted under scanning electron microscope (model:JOEL-JSM 5600). Conventional physiological and biochemical characterization testswere carried out at Institute of Microbial Technology (IMTECH) Chandigarh, India.

2.1.5 Molecular characterization of the isolate2.1.5.1Amplification and Sequencing of 16S rRNA GeneThe 16S rRNA gene was PCR amplified according to the (Sathish & Prakasham, 2010)The DNAs program was used for connecting the sequence of fragments and the BLASTN

program was used for a gene homology search with standard defaults. The nuclearsequence of the 16S rRNA gene for the strain was deposited in the GenBank database

2.1.5.2 Phylogenetic Analysis of the StrainThe 16S rRNA gene sequence of the isolated strain was used as a query to search forhomologous sequence in the nucleotide sequence databases by running BLASTN

program. The identified sequences were aligned using CLUSTAL-W algorithm inMEGA 4.0 software. Phylogenetic trees were inferred by using the neighbor-joining

bootstrap analysis with the help of MEGA 4.0 software package based on 1000resampling.

2.1.6 Analytical MethodsProtease activity was determined using modified Auson-Hagihara method (Hagihara etal.1958). The protein content of the enzyme preparations was estimated by Lowrymethod using Bovine serum albumin as standard (Lowry,1951). The absorbance of themedium was noted at regular intervals at 600nm. From the absorbance the dry weight was

calculated by using the standard curve of absorbance vs. dry weight.

2.2 RESULTS AND DISCUSSION2.2.1 Enrichment and isolation of microorganismsSoil samples from different areas which were collected, are used for isolation of bacterialstrains producing protease enzyme. 1g of soil was suspended in sterile distilled water andmixed thoroughly for 1h at room temperature before using for isolation studies. 1 %

ocasein-agar plates were prepared and incubated at 4 C for 15 min to solidify the agarsolution. The plates were brought to room temperature and spread with 0.1 ml of soil

osolution under sterile conditions. These plates were incubated at 37 C in an incubator.

After 24h of incubation, the plates were checked for microbial colonies with clear zone ofhydrolysis.

2.2.2 Screening of protease positive cultures by casein-agar plate methodMore than 70 microbial strains from different plates were selected and grown in thecasein containing agar slants. Each isolate was further confirmed for its production

pattern using 1 % casein agar plates. Depending on the zone of clearance, 20 strains werefurther screened. Further, to evaluate the potential of these isolates for production of

protease enzyme, fermentation studies were performed using YPD medium. Thequantitative estimation of produced protease revealed that among 20 isolates, DKMNR

exhibited the highest proteolytic activity with a clear zone diameter of 25 mm (Fig. 2.1).Based on the zone diameter and broth studies DKMNR was selected for further studies.From figure 2.1 it was observed that DKMNR is grown on the casein agar plate and alsoobserved a clear hydrolysis zone on the plate.

19


21/85

Fig. 2.1: Isolated bacterial strain DKMNR growing on the casein agar plateshowing a clear hydrolyzed zone.

2.2.3 Biochemical Characterization of isolate DKMNR All the tests were performed at Institute of Microbial Technology (IMTECH)Chandigarh, India. The colonies of strain DKMNR on nutrient agar plate were round,

wavy margins rough surface and opaque density. The cell growth is aerobic, gram positive in nature and spore forming rod shaped bacteria. Figure 2.2 shows the scanningelectron microscopic pictures of the isolated bacteria DKMNR. The cells could surviveand grow in pH 5.0 to 11.0. The growth was studied in presence of different NaClconcentrations; it was observed that DKMNR would grow even at 10% NaCl. Based onthe results, it was concluded that this isolate DKMNR may belong to Bacillus subtilis andit was designated as Bacillus subtilis DKMNR.

Fig. 2.2: Scanning electron microscopic picture of the isolate DKMNR colony

20


22/85


23/85

3.0 INTRODUCTIONThe microorganisms from diverse environments were considered as an attractive sourcefor protease as they can be cultured in large quantities in fewer periods. Furthermore,microbial protease is extracellular which simplifies the downstream processing and havelonger self-time (Gupta et al. 2002a). As a rule, the wild strains usually produce limitedqualities of the desired enzyme to be useful for commercial applications (Ganesh et al.2008). Some extracellular enzymes are used in the food, dairy, detergent, pharmaceutical,

and textile industries and are produced in large amounts by microbial synthesis.

The amounts of protease produced by the microorganisms vary greatly with strain andthe media used. Thirty to forty percent of the production cost of industrial enzymes is esti-mated to be the cost of the growth medium (Joo et al. 2002). In order to obtain high andcommercial viable yields of protease it is essential to optimize fermentation media for thegrowth of biomass and production of protease (Ghorbel et al. 2003). Optimization ofmedium components was done to maintain a balance between the various medium com-

ponents, thus minimizing the amount of unutilized components at the end of fermenta-tion. Research effort is mainly to evaluate the effect of various carbon and nitrogenous

nutrients as cost-effective substrates on the yield of enzymes. So far no defined mediumhas been established for the best production of alkaline proteases from different microbialsources. Each organism has its own special conditions for maximum enzyme production.So, it is important to know the suitable nutrients and cultural conditions required toachieve higher productivity (Subbarao et al. 2008).

It was planned to formulate a suitable production medium for alkaline protease produc-tion from isolated Bacillus subtilis DKMNR by optimizing the effect of various culturaland environmental factors on alkaline protease production with the help of advanced sta-tistical methods.

3.1 MATERIALS AND METHODS3.1.1 MicroorganismA strain of Bacillus subtilis DKMNR was used in the present study. This culture was

0maintained on agar based YPD medium slants at 4 C and sub cultured at monthly interval.

3.1.2 Medium for protease production by submerged fermentation-1

Yeast extract-Peptone-Dextrose (YPD) medium consisting (gl ) of glucose, 10.0; peptone, 7.5; yeast extract, 7.5; K HPO , 0.50; MgSO , 0.05 and CaCl , 0.02. pH of the2 4 4 2medium was adjusted to 8.0 using 0.1N HCl or 0.1N NaOH solution.

3.1.3 Shake flask fermentationA loop full culture from the ( Bacillus subtilis DKMNR) slant was transferred asepticallyinto 250 ml Erlenmeyer flasks containing 50 ml of sterile production medium the produc-

22

CHAPTER-3: ENRICHMENT OF PROTEASEPRODUCTION BY PROGRESSIVE OPTIMIZATION

METHODS


24/85

tion media was composed of (YPD) yeast extract-peptone-dextrose medium consisting-1

(gl ) of glucose 10.0; peptone, 7.5; yeast extract, 7.5; K HPO , 0.50; MgSo 0.05 and2 4 40

CaCl 0.02 and pH 8 after inoculation with 1% (v/v) culture media, incubated at 30 C on2rotary shaker at 120 Rpm. Initially the fermentation was carried out for 24 hrs, the culturemedia was separated by centrifugation and the supernatant was used for assaying enzyme

activity

3.1.4 Estimation of protease ActivityThe protease was assayed according to the method of modified Auson-Hagihara method(Hagihara et al.1958). One unit of alkaline protease activity was defined as 1 ug of tyro-

-1sine liberated ml under the assay conditions.

3.1.5 Screening of nutrients for the production of alkaline protease from Bacillus SubtilisDKMNR using Plackett-Burman designIn order to select the carbon and nitrogen sources for enhancement of the protease produc-

tion was carried out by employing the PB design. Table 3.1 indicates the selected vari-ables and their levels. The experimental plan was shown in the Table 3. 2. Analysis of theexperimental results was performed based on the effect of each variable. The effect ofeach selected variable on protease production was determined using the following equa-tion 3.1.

Where; E (x ) = the concentration effect of the tested variable.i

+ -Yi and Y = the protease production from the trials where the variable (x ) was measuredi iat high and low concentrations, respectively and N = the number of trials.

Table 3.1: Selected variables for Planckett-Burman design.

S. No Compound levels

Real Coded -1 (Low) 0 (medium) 1(High)

1 Starch X1 0.4 0.2 0.1

2 Maltose X2 0.4 0.2 0.1

3 Sucrose X3 0.4 0.2 0.14 Glucose X4 0.4 0.2 0.1

5 Fructose X5 0.4 0.2 0.1

6 Xylose X6 0.4 0.2 0.1

7 Galactose X7 0.4 0.2 0.1

8 Peptone X8 0.5 0.2 0.1

9 Casein X9 0.5 0.2 0.1

10 Yeast extract X10 0.5 0.2 0.1

11 Beef extract X11 0.5 0.2 0.1

12 Malt extract X12 0.5 0.2 0.1

13 Urea X13 0.5 0.2 0.1

14 Ammonium sulphate X14 0.5 0.2 0.1

15 Ammonium nitrate X15 0.5 0.2 0.1

23


25/85

The contribution of an ingredient towards the growth of the organism or yield of theenzyme is determined based on the t-value (main effect) calculated from the experimentalresult (Ramana Murthy, 1999). The nutrients are ranked based on their t-values. Thenutrient with highest t- value is considered to be the best and ranked one. The sign of theeffect indicates the level at which it is considered for further improvement. For example,

if a variable have the negative sign means the compound gives the best yield at the lowlevel and experiments should be carried out using further decreased concentration of thecompound. All experiments were carried out in triplicate and the average of protease pro-ductivity was taken as response (Y). The variables whose confidence levels were higherthan 90% were considered to significantly influence on enzyme production.

Table 3.2: Planckett-Burman experimental design along with observed and pre-dicted protease yield

S. No X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15

Protease

Activity(U/mL)

1 -1 -1 -1 -1 1 1 1 1 1 1 -1 -1 -1 -1 1 248

2 1 -1 -1 -1 -1 -1 -1 1 1 1 1 1 1 -1 -1 387

3 -1 1 -1 -1 -1 1 1 -1 -1 1 1 1 -1 1 -1 646

4 1 1 -1 -1 1 -1 -1 -1 -1 1 -1 -1 1 1 1 247

5 -1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 1 -1 9476 1 -1 1 -1 -1 1 -1 -1 1 -1 -1 1 -1 1 1 1055

7 -1 1 1 -1 -1 -1 1 1 -1 -1 -1 1 1 -1 1 267

8 1 1 1 -1 1 1 -1 1 -1 -1 1 -1 -1 -1 -1 450

9 -1 -1 -1 1 1 1 -1 1 -1 -1 -1 1 1 1 -1 555

10 1 -1 -1 1 -1 -1 1 1 -1 -1 1 -1 -1 1 1 610

11 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 850

12 1 1 -1 1 1 -1 1 -1 1 -1 -1 1 -1 -1 -1 954

13 -1 -1 1 1 1 -1 -1 -1 -1 1 1 1 -1 -1 1 1104

14 1 -1 1 1 -1 1 1 -1 -1 1 -1 -1 1 -1 -1 1046

15 -1 1 1 1 -1 -1 -1 1 1 1 -1 -1 -1 1 -1 971

16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 557

17 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 754

18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 697

19 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 734

20 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 764

24


26/85

Response surface methodology consists of a group of empirical techniques devoted to theevaluation of relations existing between a cluster of controlled experimental factors andthe measured responses, according to one or more selected criteria. A prior knowledgeand understanding of the process and the process variables are necessary for achieving arealistic model. The factors such as pH, incubation temperature, agitation speed, size of

inoculum and concentrations of carbon (glucose) and nitrogen (peptone) supplementswere the major variables effecting the protease production. Thus, these variables wereselected to find the optimized conditions for higher protease production using CentralComposite Design (CCD).

The range and the levels of the experimental variables investigated in this study are givenin the Table 3.3. The central values (zero level) chosen for experiment design were glu-

-1 -1cose 15 gl , peptone 10 gl , pH 9.0, temperature 33°C, agitation 200 rpm and inoculumsize 2%, were selected and each of the variables were coded at five levels –2, –1, 0, 1, and 2 by using Equation 3.2.

x = (X – X )/DX ------------------------------------------- (3.2)i i 0 i

th thWhere x is the coded value of the i independent variable, X is the natural value of the ii i

thindependent variable, X is the natural value of the i independent variable at the center0

point and X is the step change value.i

---------------------------- (3.3)

Where Y is the predicted response, β is intercept term, β is linear effect, β is the squared0 i ii

effect, and βij the interaction effect. The full quadratic equation for 6 factors is given bymodel 3.4.

Y = β + β x + β x + β x + β x + β x + β x + β x *x + β x *x + β x *x + β x *x0 1 1 2 2 4 3 4 4 5 5 6 6 11 1 1 12 1 2 13 1 3 14 1 4+ β x *x + β x *x + β x *x + β x *x + β x *x + β x *x + β x *x + β x *x +15 1 5 16 1 6 22 2 2 23 2 3 24 2 4 25 2 5 26 2 6 33 3 3β x *x + β x *x + β x *x + β x *x + β x *x + β x *x + β x *x + β x *x + β 34 3 4 35 3 5 36 3 6 44 4 4 45 4 5 46 4 6 55 5 5 56 5 6 66

x *x ----------------------------------------(3.4)6 6

Table 3.3: Experimental range and levels of the independent variables

6-1For this study, 2 fractional factorial design with 12 star points and 6 replicates at the cen-tral points was employed to fit the second order polynomial model, which indicated that50 experiments were required for this procedure.

25

S. No Variable Levels step change

Real Coded -2 -1 0 1 2 ∆

1 Temperature (°C) X1 31 32 33 34 35 1

2 pH X2 8.0 8.5 9.0 9.5 10 0.5

3 Agitation (rpm) X3 160 180 200 220 240 20

4 Size of inoculum (ml) X4 1.0 1.5 2.0 2.5 3.0 0.5

5 Glucose (%w/v) X5 0.5 1.0 1.5 2.0 2.5 0.5

6 Peptone (%w/v) X6 0.5 0.75 1.0 1.25 1.5 0.25


27/85

The design and results of FFCCD experiments for studying the effect of six independentvariables were presented along with the mean predicted and observed responses in Table3.5. The regression equations obtained after the analysis of variance (ANOVA) gave thelevel of protease production as a function of the initial values of glucose, peptone, pH,temperature, agitation and volume of the inoculum.

3.2 RESULTS AND DISCUSSION3.2.1 Screening of nutrients using Plackett-Burman designIn the present investigation, the significance of 15 different carbon and nitrogen com-

pounds on production of protease was screened at two levels (high and low values) byapplying the 20 experimental Plackett–Burman design. Table 3.2 gives the experimental

plan along with the results. It was observed that the enzyme production was varied-1

between 247 - 1104 Uml . It indicates that the selected nutritional compounds show a sig-nificant effect on production of the protease.

Based on experimental data, the Pareto chart effects was plotted for identifying the fac-tors that are important in enzyme production in this bacterial strain. This chart shows thefactors main effect estimates on the horizontal axis. The selected factors main effects arerank ordered according to their significance. The chart also show a vertical line to indicatethe statistical significance (P=0.05). If selected variable is significant in the process, thevariable-bar crosses the vertical line or vice versa.

From the Pareto chart (Fig.3.1) carbon sources (glucose, sucrose, maltose and fructose)and nitrogen sources (peptone, urea, casein and ammonium nitrate) are significant. Table3.4 indicates the ANOVA data from this Table it is observed that the peptone has the high-

est effect (-350.5) and followed by the glucose (300).

Fig. 3.1: Pareto chart showing the effect of medium components on proteaseyield.

26


28/85


29/85

Fig. 3.2: The probability plot of effects on protease production.

Based on the above inference for further studies the peptone and glucose concentrationswere selected to optimize the higher production of protease. Nitrogen source plays amajor role on protease secretion. Peptone and yeast extract enhances the protease produc-tion by Bacillus sp (Subbarao et al. 2008). Similarly yeast extract and peptone combina-tion enhanced the enzyme production in the fungus (Subbarao et al. 2008). However therole of urea on production of protease by B. Subtilis DKMNR differs from the usage of

Peptone and yeast extract. The authors observed the inhibition of protease with additionof urea.

3.2.2 Optimization by Response surface Methodology (RSM):The RSM is an effective and sequential and stepwise procedure the lead objective of theRSM was to run rapidly and efficiently along the path of improvement towards the gen-eral vicinity of the optimum. It is appropriate when the optimal region for running the pro-cess was identified before performing RSM experiments. The influence of different fer-mentation parameters such as carbon source (glucose), nitrogen source (peptone),medium pH, incubation temperature, agitation speed and size of inoculum were opti-

mized by central composite design. Table 3.5 depicts the FFCCD experimental design lay-out and experimental results. A predicted value for each performed experiment was cal-culated and the correlation between experimental and predicted values is shown inFig.3.3.

28


30/85

Table 3.5: Experimental design along with observed and predicted proteaseactivity.

S.No

Temp.(°C)

pHAgitation

(rpm)

Size ofinoculum

(ml)

Glucose(% w/v)

Peptone(% w/v)

Protease activity (U/mL)

Observed Predicted Error

1 32 8.5 180 1.5 1.0 0.8 1007 1011.445 -4.445

2 32 8.5 180 1.5 2.0 1.3 1129 1113.045 15.955

3 32 8.5 180 2.5 1.0 1.3 687 928.345 -241.34

4 32 8.5 180 2.5 2.0 0.8 1262 1216.745 45.255

5 32 8.5 220 1.5 1.0 1.3 1373 1341.945 31.055

6 32 8.5 220 1.5 2.0 0.8 1497 1530.345 -33.345

7 32 8.5 220 2.5 1.0 0.8 1350 1319.645 30.355

8 32 8.5 220 2.5 2.0 1.3 1353 1328.745 24.255

9 32 9.5 180 1.5 1.0 1.3 997 966.545 30.455

10 32 9.5 180 1.5 2.0 0.8 1035 1067.445 -32.445

11 32 9.5 180 2.5 1.0 0.8 887 855.245 31.755

12 32 9.5 180 2.5 2.0 1.3 1079 1051.345 27.655

13 32 9.5 220 1.5 1.0 0.8 1221 1217.345 3.655

14 32 9.5 220 1.5 2.0 1.3 1705 1756.945 -51.945

15 32 9.5 220 2.5 1.0 1.3 995 983.245 11.755

16 32 9.5 220 2.5 2.0 0.8 876 929.145 -53.145

17 34 8.5 180 1.5 1.0 1.3 699 656.845 42.155

18 34 8.5 180 1.5 2.0 0.8 694 716.745 -22.745

19 34 8.5 180 2.5 1.0 0.8 1396 1355.045 40.955

20 34 8.5 180 2.5 2.0 1.3 1107 1121.645 -14.645

21 34 8.5 220 1.5 1.0 0.8 1037 1075.645 -38.645

22 34 8.5 220 1.5 2.0 1.3 1143 1185.745 -42.74523 34 8.5 220 2.5 1.0 1.3 1284 1262.545 21.455

24 34 8.5 220 2.5 2.0 0.8 1126 1167.445 -41.445

25 34 9.5 180 1.5 1.0 0.8 674 709.245 -35.245

26 34 9.5 180 1.5 2.0 1.3 965 1006.345 -41.345

27 34 9.5 180 2.5 1.0 1.3 1104 1081.645 22.355

28 34 9.5 180 2.5 2.0 0.8 857 899.045 -42.045

29 34 9.5 220 1.5 1.0 1.3 1138 1194.245 -56.245

30 34 9.5 220 1.5 2.0 0.8 1142 911.645 230.35531 34 9.5 220 2.5 1.0 0.8 934 960.945 -26.945

32 34 9.5 220 2.5 2.0 1.3 1159 1165.545 -6.545

33 31 9.0 200 2.0 1.5 1.0 1656 1595.720 60.280

29


31/85

The above results were analyzed using statistical programme and the coefficient of deter-2mination ( R ) was calculated as 0.9575 for alkaline protease production by this bacterial

strain indicating that the statistical model can explain 95.75% of variability in the2

response and only 4.25% of the total variations were not explained by the model. The R 2

value should always be between 0 and 1. If the R is closer to 1.0, the stronger the model2 2

and the better it predicts the response. The adjusted R value corrects the R value for thesample size and for the number of terms in the model. The value of adjusted determina-

2tion coefficient (Adj R = 0.9053) was also very high suggesting a higher significance ofthe model used for analyzing the data (Cochran & Cox, 1957). In this enzyme production

2 2study the adjusted R value (0.9053) was lesser than the R value (0.9575). This is

because, if there are many terms in the model and the sample size is not very large, the2 2adjusted R may be noticeably smaller than the R . At the same time, a relatively lower

value of the coefficient of variation (CV= 7.4 %) indicated a better precision and reliabil-ity of the experiments carried out by (Cochran & Cox, 1957)

30

34 35 9.0 200 2.0 1.5 1.0 1311 1327.320 -16.320

35 33 8.0 200 2.0 1.5 1.0 1483 1411.020 71.980

36 33 10.0 200 2.0 1.5 1.0 1186 1214.020 -28.020

37 33 9.0 160 2.0 1.5 1.0 1145 1078.120 66.880

38 33 9.0 240 2.0 1.5 1.0 1502 1524.920 -22.920

39 33 9.0 200 1.0 1.5 1.0 1276 1295.220 -19.22040 33 9.0 200 3.0 1.5 1.0 1379 1315.820 63.180

41 33 9.0 200 2.0 0.5 1.0 1205 1158.520 46.480

42 33 9.0 200 2.0 3.0 1.0 1312 1314.520 -2.520

43 33 9.0 200 2.0 1.5 0.3 1120 1167.920 -47.920

44 33 9.0 200 2.0 1.5 1.5 1410 1318.120 91.880

45 33 9.0 200 2.0 1.5 1.0 1712 1737.320 -25.320

46 33 9.0 200 2.0 1.5 1.0 1708 1737.320 -29.320

47 33 9.0 200 2.0 1.5 1.0 1745 1737.320 7.680

48 33 9.0 200 2.0 1.5 1.0 1680 1737.320 -57.320

49 33 9.0 200 2.0 1.5 1.0 1754 1737.320 16.680

50 33 9.0 200 2.0 1.5 1.0 1737 1737.320 -0.320

S.No

Temp.(°C)

pHAgitation (rpm)

Size ofinoculum

(ml)

Glucose(% w/v)

Peptone(% w/v)

Protease activity (U/mL)

Observed Predicted Error


32/85

Fig. 3.3: Correlation between the observed and predicted values.

The protease experimental data was analyzed by applying multiple regression and theresults of the FFCCD design were fitted with a second order full polynomial equation.

The empirical relationship between protease production (Y) and the 6 test variables incoded units obtained by the application of RSM is given by Equation 3.5.

Y = 1737.32 - 67.1 x - 49.25 x + 111.7 x + 5.15 x + 39 x + 37.55 x - 68.95 x1*x +1 2 3 4 5 6 110.94 x *x - 25.62 x *x + 92.19 x *x - 46.62 x *x + 17.37 x *x - 106.2 x *x - 19.061 2 1 3 1 4 1 5 1 6 2 2x *x - 61.62 x *x + 12.19 x *x + 65.94 x *x - 108.95 x *x - 73.69 x *x - 0.25 x *x2 3 2 4 2 5 2 6 3 3 3 4 3 5

+ 31.62 x *x - 107.95 x *x - 30.69 x *x - 23.81 x *x - 125.5 x *x + 43.12 x *x -3 6 4 4 4 5 4 6 5 5 5 6123.57 x *x --------------------------------------- (3.5)6 6

-1Where Y is the alkaline protease production in U ml was response and x - x were the1 6coded values of the test variables as per the Table 3.3.

The ANOVA was conducted for the second order response surface model. The signifi-cance of each coefficient was determined by Student's t-test and p-values, which werelisted in Table 3.6. The larger the magnitude of the t-value and smaller the p-value, themore significant is the corresponding coefficient. (Cochran & Cox, 1957).

31


33/85

Table 3.6: ANOVA & Regression coefficients

SS = Sum of squires; df = degree of freedom; MS = Mean squire

32

Coefficients Effect SS df MS F-value p-value

Mean/Intercept

1737.32 1737.32 0

X1 -67.1 -134.2 180096 1 180096 21.6821 0.00012

X1*X1 -68.95 -137.9 152131 1 152131 18.3153 0.00031

X2 -49.25 -98.5 97023 1 97022.5 11.6807 0.00247

X2*X2 -106.2 -212.4 360910 1 360910 43.4505 1E-06

X3 111.7 223.4 499076 1 499076 60.0844 0

X3*X3 -108.95 -217.9 379843 1 379843 45.7299 1E-06

X4 5.15 10.3 1061 1 1060.9 0.12772 0.72421

X4*X4 -107.95 -215.9 372902 1 372903 44.8942 1E-06

X5 39 78 60840 1 60840 7.32461 0.01289

X5*X5 -125.2 -250.4 501601 1 501601 60.3885 0

X6 37.55 75.1 56400 1 56400.1 6.79009 0.01614

X6*X6 -123.58 -247.15 488665 1 488665 58.8311 0

X1*X2 10.938 21.875 3828 1 3828.1 0.46087 0.5043

X1*X3 -25.625 -51.25 21013 1 21012.5 2.52972 0.12599

X1*X4 92.188 184.375 271953 1 271953 32.7408 9E-06

X1*X5 -46.625 -93.25 69565 1 69564.5 8.37497 0.00842

X1*X6 17.375 34.75 9660 1 9660.5 1.16304 0.29252

X2*X3 -19.063 -38.125 11628 1 11628.1 1.39993 0.24937

X2*X4 -61.625 -123.25 121525 1 121525 14.6305 0.00092

X2*X5 12.188 24.375 4753 1 4753.1 0.57224 0.4574

X2*X6 65.938 131.875 139128 1 139128 16.7498 0.00048

X3*X4 -73.688 -147.38 173755 1 173755 20.9186 0.00015

X3*X5 -0.25 -0.5 2 1 2 0.00024 0.98776

X3*X6 31.625 63.25 32005 1 32004.5 3.85307 0.06242

X4*X5 -30.688 -61.375 30135 1 30135.1 3.62801 0.06997

X4*X6 -23.813 -47.625 18145 1 18145.1 2.18452 0.15358

X5*X6 43.125 86.25 59513 1 59512.5 7.16479 0.01378

Error 182737 22 8306.2Total SS 4299893 49


34/85


35/85

duction data revealed 1885.7 Uml-1 under optimized conditions. The experimental valueof the protease production was almost equal if we consider 95% of the confidence limitsfor the prediction of Y value at optimized conditions with shake flask results.

Fig. 3.4 (a-f): Interaction influence of selected parameters

34


36/85

4.0 INTRODUCTIONMost of the microbial products at industrial scale are generally produced using sub-merged fermentation due to its apparent advantages in consistent enzyme production char-acteristics with defined medium and process conditions. Further, it has advantages indownstream processing in spite of the cost-intensiveness for medium components(Subbarao et al. 2009; Sathish & Prakasham, 2010; Mahalaxmi et al. 2009). However,solid-state fermentation has gained renewed interest and fresh attention from researchers

because of its edge in biomass energy conservation, solid waste treatment and its applica-tion to produce secondary metabolites over submerged fermentation (Mahalakshmi et al.2010; Hymavathi et al. 2009). Production of biocatalysts using agro-biotech substratesunder solid-state fermentation conditions provide several advantages in productivity,cost-effectiveness in labour, time and medium components further the effluent produc-tion is less and thus it is eco-friendly (Mahalakshmi et al. 2010; Hymavathi et al. 2009).However, these production characteristics have to offer a competitive advantage overexisting products.

There are a large number of techniques available to design culture media. They can varyfrom the traditional one-variable at- a-time method to more complex statistical and math-ematical techniques (Subbarao et al. 2009), involving experimental designs such as fulland partial factorials, Plackett-Burman design, followed by optimization techniques suchas response surface methodology (RSM) (Hymavathi et al. 2009), artificial neural net-works (ANNs) (Subbarao et al. 2008), fuzzy logic and genetic algorithms (GA)(Subbarao et al. 2008), among others. Regrettably, no multipurpose technique is knownto be applicable to all situations. Mixture designs were found to be effective tool for

screening and evaluation of the various solid substrates. In a mixture experiment, theindependent factors are proportions of different components of a blend. The interpreta-tion of data in mixture experiments where the components represent proportionateamounts of the factors differs from classical factorial experiments where the response var-ies depending on the amounts of each input variable. The key to mixture experiments isthat the mixture components are subject to a constraint requiring that the proportions sumto one. In mixture experiments, the measured response is assumed to depend only on therelative proportions of the ingredients or components in the mixture, and not on theamount of the mixture. However, one can overcome this limitation by adding the amountof mixture as an additional factor in the experiment, thereby allowing mixture and pro-

cess variables to being treated together. The advantage of mixture experiments over fac-torial design is that one can more efficiently study the interaction influence amongst fac-tors on the production, and subsequently eliminate both neutral and negative factors.Mixture experiments have been the subject of many studies and have enjoyed extensiveapplication in pharmaceuticals, geology, petroleum, food, and tobacco industries.

35

CHAPTER-4: ECONOMICALLY VIABLE PROTEASEPRODUCTION BY SOLID STATE FERMENTATIONUSING AGRO INDUSTRIAL WASTE MATERIALS

AND OPTIMIZATION OF PRODUCTIONCONDITIONS


37/85

The present study was aimed to exploit the locally available, inexpensive agro-substratefor alkaline protease production using B. subtilis DKMNR under solid-state fermentationand optimization of the various parameters for improvement of the yield.

4.1MATERIALS AND METHODS

4.1.1 Collection and processing of the substratesVarious agro industrial materials such as rice bran, wheat bran, coconut oil cake, Zuzubioil cake, peanut press cake, rice husk, green gram husk, red gram husk, bengal gram husk,

black gram husk, soya bean meal, corn cobs, corn stover, corn leaves, sweet sorghum pulp, sugarcane baggase and sugarcane leaves are obtained from the local agriculturalmarket. Whereas apple pomac, pineapple waste, orange waste, banana peels and orange

peels were obtained from the local fruit market and juice shops. The potato peels, mashed potatoes, processed tea powder and processed coffee waste were collected from the

0home. All the materials were dried at 80 C for 2 hours and the leaf materials and fruit pulpwere powdered. All the materials were sieved to avoid the fine powder, which causes the

clumps formation upon addition of water, and decrease the substrate availability to themicroorganism.

4.1.2 Solid-state fermentationSelected substrate materials were used as a solid medium for protease production. Fivegrams each of the substrate was taken separately in 250 ml Erlenmeyer flasks and mois-turized with 5 ml of distilled water. These flasks were sterilized and inoculated with 2 ml

0of inoculum solution. The flasks were mixed thoroughly and incubated at 32 C in an incu-

bator for 48 hours.

4.1.3 Mixture DesignMixture design was chosen to study the effect of mixed substrates on protease production.The best three substrates, which could yield highest protease at individual level were cho-sen. A simplex lattice design was employed to optimize the substrate mixture. All theexperiments were conducted according to the (Sathish et al. 2008).

In a mixture design, two models have to be taken into account, one for each mixture beingconsidered. In this case, a product model can be used with the two groups of componentsx and z. This model is represented by Eq. (4.1):

y = f(x)*g(z) + e ……. (4.1)

In Eq. (4.1) y is the response, the functions f(x) and g (z) are separated polynomial modelsthat represent the two mixtures, and e is a zero-mean random variable with variance inde-

pendent of x and z.

The polynomial models used in Eq. (4.1) was modified some terms from the complete polynomial expression in order to eliminate the constraint originated in the correlatedvariables. Eq. (4.2) shows the canonical form of the quadratic model:

……. (4.2)

36


38/85

37

Geometrically, in Eq. (4.2) the parameter βi represents the expected response to the puremixture x=1, x =0, j≠i. The first term in the Eq. (4.2) represents the response when blend-i jing is strictly additive and there are no interactions between the components of the mix-ture. The quadratic term β x x represents the excess response over the linear model due toij i jthe interaction between two components, and this effect is often called synergism or

antagonism.

4.1.4 Enzyme extractionThe enzyme was extracted according to the method described by (Prakasham , 2006). Fer-mented medium was mixed thoroughly with 50 mM glycine–NaOH buffer, pH 11 for 30min and the extract was separated by squeezing through a cloth. This process wasrepeated three times and extracts were pooled together and then centrifuged. Thesupernatant was used as enzyme source for protease assay.

4.1.5 Optimization of the various process parameters

In order to increase the protease production in the solid-state fermentation various pro- –1cess parameters such as pH (5 to 12), moisture content (20 to 200 % w/w ), inoculum size

0(0.5 to 3.5) and temperature (26 to 40 C) were optimized.

4.1.5.1 Effect of carbon and nitrogen additives on protease productionThe optimum SSF medium was supplemented with different carbon and nitrogen sourcesinitially at 0.5g. Further the best suitable additional carbon and nitrogen source was stud-ied at various concentrations in order to improve the protease production.

4.2 RESULTS AND DISCUSSION

4.2.1Evaluation of Different Agro-Industrial Material for Alkaline Protease Pro-ductionThe selection of an ideal agro-biotech waste for economic enzyme production in solid-state fermentation process depends upon several factors, mainly related with cost andavailability of the substrate material. Thus it involves screening of several agro-industrialresidues. Agro industrial materials such as rice bran (RB), wheat bran (WB), coconut oilcake (COC), zuzubi oil cake (ZOC), peanut press cake (PPC), rice husk (RH), green gramhusk (GH), red gram husk (RH), bengal gram husk (BGH), black gram husk (BLH), soya

bean meal (SM), corn cobs (CC), corn stover (CS), corn leaves (CL), sweet sorghum pulp(SSP), sugarcane baggase (SB) and sugarcane leaves (SL) (Fig.4.1), household waste

materials like apple pomac (AP), pine apple waste (PW), orange waste (OW), banana peels (BP), orange peels (OP), potato peels (PP), mashed potatoes (MP), processed tea powder (PTP) and processed coffee waste (PCW) Figure.4.1 were used as solid support/substrate matrices for production of enzyme by B. subtilis DKMNR. The data indi-cated that protease production pattern varied with the type of agro-waste. This phenome-non might be attributed to the dual role of solid materials on supply of nutrients to thegrowing microbial culture and providing anchorage for the growing cells.

–1 Maximum protease production (914 Ugds ) was observed with green gram husk while

–1minimum protease production (145 Ugds ) was noticed with rice husk as sub-

strate/support material. Soya bean meal and bengal gram husk were found to be best sub-strates for protease production next to the green gram husk. This data further supportedthat, the composition of the substrate was one of the important parameters for evaluationof extracellular microbial enzymes production. The results were in accordance with the


39/85

observations made with alkalophilic and thermophilic Bacillus species JB-99 (Johnveslyet al. 2002), and others reported about bacterial strains (Prakasham, 2006; Gessesse,1997). However, the results varied in the production values suggesting that the presentinvestigated bacterial strain was different in its metabolic and biochemical aspects to thatof Bacillus species JB-99 (Johnvesly et al. 2002). Evaluation of protease production val-

ues of the strain with different organisms reported in the literature did not indicate that,this strain could be a potential organism after optimization and scale up studies (Table4.1). To obtain the maximum protease production the fermentation media is modified bymixing the different agro-industrial wastes. Three different wastes such as soybean meal,

bengal gram husk and green gram husk were selected as they exhibited the maximum pro-tease activity when used as individual substrates.

Fig. 4.1: Protease production by isolated Bacillus subtilis DKMNR using market

and house hold waste.

Table 4.1: Protease production by different microorganisms

Microorganism Solid substrate usedProtease production

–1(Ugds material)

References

Bacillus sp ecies Green gram husk 35,000 Prakasham ,2006

Bacillus species JB99 Pigeon pea 12,430 Johnvesly et al. 2002

Bacillus species Wheat bran 429 Ramesh & Lonsane, 1990

Bacillus species Lentil husk 168 Ramesh & Lonsane, 1990

Rhizopus oryzae Wheat bran 358 Tunga et al. 2001

Rhizopus oryzae Wheat bran 58.7 Aikat & Bhattacharyya, 2002

38


40/85

39

4.2.2 Mixture designs for substrate optimizationAn augmented simplex lattice design was employed for the present investigation. Inorder to observe the mixed substrate effect on protease production, three solid substratessoybean meal (SM), green gram husk (GH) and bengal gram husk (BGH) were used in

these designs, the total proportions of the different substrates made to 100% i.e., 5g mate-rial. The design consists of total 10 runs where three with pure mixtures (one for each component), other three with binary blends for each possible two-component blend, anotherthree with complete blends (all three components are included but not in equal propor-tions) and last one as centroid (where equal proportions of all three components areincluded in this blend). Table 4.2 shows all the experimental runs with the composition ofsubstrate as per mixture design model and the protease output values. Assuming that, themeasured response of protease production was dependent on the relative proportions ofthe components in the mixture, linear through to cubic models were used for analysis ofthe mixture design using following equations.

-------Linear model (Eq. 4.3)

----- Quadratic model (Eq. 4.4)

------Special cubic (Eq. 4.5)

---Full cubic (Eq. 4.6)

Where Y is a response, β is a linear, β is a quadratic and β cubic coefficients, β is ai ij ijk ij parameter of the model. The βx represents linear blending portion and the parametersβ i i ijrepresents either synergic or antagonistic blending.

Table 4.2 presents the varying concentrations of solid substrate used during fermentation process and the protease production data for each experiment. The protease production

–1values varied from 685 to 1045 Ugds . This variation of protease yield under similar fer-mentation conditions but with different substrates suggested the importance of substratecomposition on fermentative protease production. Analysis of individual substrateimpact on protease production pattern indicated that green gram husk is the best substratewith 53% higher yield compared to other two selected materials. Further analysis of thedata Table 4.2 revealed that mixed substrate improved protease yield. This can be evi-denced based on higher protease yield from experiment 5 compared to 1 and 2 (Table 4.2).

However, presence of GH in mixed substrate fermentations, supported better productioncompared to SM and BGH which can be evidenced from experiments 4,6, 7 and 9 where ahigher protease production was observed than individual or in other combination sub-strates as sole substratum. These results suggest that green gram husk is playing the vital


41/85


42/85

41

MS= Mean square; SS= Sum of square2

In spite of the greater R values than the quadratic model they have smaller F-value (F spe-cial cubic = 0.0366 & F cubic = 0.7702) and higher p-values (P special cubic =0.8605 & Pcubic = 0.6274). Such data suggests that the quadratic model is the most significant.Therefore further data analysis was performed using only quadratic model. The noticed

R2 value of the quadratic model is 0.9588 indicating that 3 substrate components alto-gether would explain about 95.88% of the variability in the response leaving only 4.12%of the variability remaining unexplained. In the present study, a good co-relation was iden-tified between predicted and experimental protease production with a variation of 5.8 %.The empirical relationship between protease production (Y) and substrate variables incoded units is obtained by the application of second order model as per Eq.4.7.

Y = 685.75 ´ SM + 910.38 ´ GH + 808.84 ´ BGH + 684.4 ´ SM ´ GH + 809.32 ´

SM ´ BGH + 506.6 ´ GH ´ BGH ----------------- (Eq. 4.7)

–1

Where Y is the response of protease production in Ugds and GH, SM and BGH were thesubstrates with the respective coded experimental values testing the experiments as perthe Table 4.2. The significance of each coefficient in Eq. 4.7 was determined by Student'st-test and p-values and listed in Table 4.4. The larger magnitude of the t-value and smaller

p-value denote the corresponding coefficient significance. The observed lower p-value(


43/85

Further, a numerical method given by (Myers and Montgomery, 1995) was used to solvethe regression Eq. 4.7 to optimize the substrate mixture ratio. The results indicated thatthe optimum mixture for higher protease production is 44.4% of GH, 25% SM and 30.6%BGH by dry weight. For this substrate combination the predicted protease yield was

–11029.864 Ugds . Our results validated these fermentation conditions with a protease pro-

–1

duction rate of 1045 Ugds . A similar optimization approach was performed by Sathish etal. (2008), for L-glutaminase production in solid state fermentation in cutinase produc-tion in submerged fermentation.

Fig. 4.2: Triaxial diagrams of protease production as a function of substrate con-centration.

The role of each substrate material was optimized for the production of protease in mix-ture design fermentation using natural agro-wastes such as SM, BGH and GH. Among allmaterials, GH presence is essential and SM is the least important component for maxi-mizing protease production among selected substrates. An optimum protease production

–1of 1045 Ugds could be obtained with a 44: 31: 25 ratio of GH: BGH: SM respectivelywithout any pretreatment of the material. Further work was preceded with respect to opti-

mization of the fermentation parameters to increase the protease production. The parame-ters such as pH, moisture content, inoculum concentration, incubation time, and incuba-tion temperature, concentration of the optimized carbon and nitrogen sources from theselected sources were investigated. Keeping the potentiality of this microbial strain in pro-tease production further evaluation was continued using the mixture of soybean meal,

bengal gram husk and green gram husk as solid support/substrate for solid state fermenta-tion.

4.2.3 Role of pH on protease productionEnzyme production by microbial strains strongly depends on the extracellular pH

because culture pH strongly influences many enzymatic processes and transport of vari-ous components across the cell membranes which in turn support the cell growth and prod-uct production (Subbarao et al. 2008), pH dependent alkaline protease production studies

by B. subtilis DKMNR in solid state fermentation using the mixture of SM, BGH and GH

42


44/85

43

suggested that, the enzyme production was influenced by the pH of the medium. Maxi- –1

mum protease production (1246 Ugds ) was observed at pH 9.0 (Fig. 4.3). The synthesisof the enzyme increased with increase of the pH of the medium towards alkaline rangefrom neutrality up to 9.0 and was less constant in the pH range 9.0–12.0 by B. subtilisDKMNR. The enzyme production pattern suggested that, the isolated bacterial strain was

alkalophilic in nature and produces maximum quantity of enzyme at alkaline pH condi-tions. Further evaluation of enzyme data in the studied pH range indicated a linear increase inthe biocatalyst production up to pH 9.0. The observed variation in protease productionunder solid state fermentation with mixed substrate attributed to higher enzyme produc-tion in the pH range of 9.0 to 10.0. In the literature the authors reported that protease fromsolid state cultures of Aspergillus parasiticus showed optimum activity at pH 8.0 and 80% less activity at pH 5.0. (Johnvesly et al. 2002) working on alkaline thermo stable prote-ase found that, the enzyme produced by thermo alkalophilic Bacillus species JB-99

showed catalytic activity in a broad pH range (6.0 to 12.0) with 11.0 as optimum pH. Thedata generated in the present investigation suggested that, the influence of pH on alkaline

protease produced by isolated B. subtilis DKMNR might be related with synthesis level because, the extraction of the enzyme after solid state fermentation was performed usingalkaline pH buffer. The adapted extraction procedure eliminated the inhibition of prote-ase at cellular transport and at activity level and the observed growth associated nature ofthis enzyme production in this bacterial strain.

Fig. 4.3: Effect of pH on the production of protease by isolated B. subtilis DKMNR.


45/85

4.2.4 Role of moisture content on protease productionAmong the several factors that are important for microbial growth and enzyme produc-tion under solid-state fermentation using a particular substrate, moisture content andwater activity was one of the most critical factors (Sathish et al. 2008). Solid-state fer-mentation processes are different from submerged fermentation culturing, since micro-

bial growth and product formation occurs at or near the surface of the solid substrate parti-cle having low moisture contents (Pandey et al. 2000). Thus, it is crucial to provide opti-mized water level that controls the water activity (a ) of the fermenting substrate forwachieving maximum product production. Reports on enzyme production by microbialspecies under solid-state fermentation indicated that the availability of water in lower orhigher concentrations affected microbial activity adversely (Ramesh & Lonsane, 1990).Moreover, water is known to have profo

Documents

INNOVATIVE FERMENTATION STRATEGIES FOR PROTEOLYTIC ENZYMES PRODUCTION