БИООРГАНИЧЕСКАЯ ХИМИЯ, 2014, том 40, № 3, с. 275-285
ОБЗОРНАЯ СТАТЬЯ
APPLICATION OF RESPONSE SURFACE METHODOLOGY IN ENZYMATIC SYNTHESIS: A REVIEW
© 2014 Mansour Ghaffari-Moghaddam*, #, Zahra Yekke-Ghasemi**, Mostafa Khajeh*,
Mansoureh Rakhshanipour*, Yamin Y&sin***
*Department of Chemistry, University of Zabol, Zabol, Iran **Department of Chemistry, Ferdowsi University of Mashhad, Mashhad, Iran ***INTEC Education College, University Teknologi MARA, 40450 Shah Alam, Malaysia
There are very chemical reactions with very slow rates which can be catalyzed by enzymes. These biocatalysts need to moderate conditions for their catalytic activity and are stable in low temperature (between 15—50°C), average pH (5—10) and aqueous media. One of important things in enzymatic synthesis which has been recently noticed is the yield of reactions. Nowadays wide application of response surface methodology (RSM) was observed in organic chemistry. In one-variable-at-a-time technique only one parameter is changed and other parameters are kept at a constant level. It does not study the interactive effects among the variables, and does not illustrate the complete effects of the parameters on the process. Increasing the yield of product without increase in casts is carried out by modeling and optimization of reaction variables through statistical techniques such as RSM. In this paper, we reviewed some articles that used the RSM for optimization in the enzymatic synthesis.
Keywords: response surface methodology (RSM), biocatalysts, enzymatic synthesis, optimization
DOI: 10.7868/S0132342314030051
INTRODUCTION
Abundant chemical reactions are occurred any moment over our life. Many of these reactions happen at very slow rates that can be accelerated by catalysts [1]. The catalysts in biological processes with protein structure are called enzymes. They have high molecular weight and their three-dimension structure formed of a linear sequence of amino acid joined together through amide bonds [2, 3].
The existence of enzymes goes back to thousands of years ago. In the past, enzymes had been used excessively, but their structure and functions had not known [4]. The first enzyme which recognized as a protein was jack bean urease. It was crystallized in 1926 by Sumner. The enzyme catalyzes the hydrolysis of urea to CO2 and NH3. Enzymes could be produced from any living organism, either by extracting them from their cells or by recovering them from cell exudates [3]. Enzymes can accelerate the rate of reactions by choosing of shortened pathway through decrease activation energy but not consumed in reaction. They increase rate of the forward and reverse reactions equally, therefore do not change the equilibrium constant of reactions [5, 6]. Speed, selectivity and specificity are three important properties of enzymes that caused they were
#Corresponding author (phone: +98 542 2242503; e-mail: mansghafTari@uoz.ac.ir).
used by many researches. Interaction of enzymes with reactants are specific and often each enzyme accelerates only a specific reaction [3, 5, 7, 8].
Enzymes have been widely used in organic reactions because of chemo-, regio-, and stereo selectivity and accelerate reactions with remarkable rate [7, 9]. Enzymes are stable with the best catalytic activity in mild reaction conditions (low temperature and average pH) and aqueous media [8]. Because of good efficiency of enzymes in aqueous media, water has been known as a suitable solvent for enzyme catalytic activity; but many of organic compounds and substrates are not soluble in water. Herein, in the field of organic synthesis, the use of enzymes is restricted in organic media and non-aqueous solvents due to decreasing enzyme catalytic activity; but this decreasing in activity could be ignored [7]. One of the most prominent advantages of using non-aqueous media is elimination of side reactions [10]. Changing of solvent from aqueous to organic solvent is not this meaning that pH has been changed and pH maintained in memory concept. In this state pH acts with the maximum efficiency [11]. The thermo stabilization is another important case in the study of enzymatic synthesis. Most of enzymes act at room temperature but many others like lipase are remarkably stable even at 100°C for many hours [12—14].
Another important subject in enzymatic synthesis is amount of reaction yield. For increasing the yield of
product without increase in casts, the reaction is carried out by modeling and optimization of reaction variables through statistical techniques. In the traditional optimization method only one parameter is changed and others are kept at a constant level which is called the one-variable-at-a-time technique [15]. This technique does not study the interactive effects among the variables and does not illustrate the complete effects of the parameters on the process. Also, there are so many necessary experiments to conduct the research which lead to an increase the time and expenses as well as an increase in the consumption of reagents and materials [16].
In order to overcome this problem, response surface methodology (RSM) can be used for optimization studies that were presented by Box and Wilson [17] which developed the way that engineers, scientists, and statisticians approached industrial experimentation [18]. Later, Myers and Montgomery [19] provided a detailed discussion of RSM and its application. According to Montgomery's definition, "collection of mathematical and statistical techniques useful for the modeling and analysis of problems in which a response of interest is influenced by several independent variables and the objective is to optimize this response" is called RSM which is an ideal approach to optimize a product or process [20]. Nowadays, RSM is used in wide range of chemistry sciences such analytical chemistry [21—44], organic chemistry [45—60], biochemistry [61—83] and nanochemistry [84]. The aim of this review is to discuss the use of RSM for optimization in enzymatic synthesis.
RESPONSE SURFACE METHODOLOGY (RSM)
It is important to improve the performance of the process and to increase the yield of reactions without increasing chemicals. The method used for this purpose is called optimization. In this field, the one-vari-able-at-a-time method can be useful. The major disadvantage of this method is that it does not describe interactive the complete effects of the parameters and variables among the process. In order to overcome this problem, optimization studies can be carried out using RSM [85]. The RSM is a collection of mathematical and statistical techniques useful for the modeling, optimization and analysis of problems in which a response is influenced by several variables [86]. RSM contains a group of mathematical and statistical techniques that can be used to explain the effect of the independent alone or in combination variables on the processes. Moreover, to analyze the effects of the independent variables and determine the relationships between the response and the independent variables, the RSM generates a mathematical model which describes the chemical or biochemical processes. The graphical perspective of the mathematical model has led to the term response surface methodology [85, 87, 88].
It is possible to divide an optimization study using RSM into six stages as follows: (i) selection of independent variables of major effects on the system through screening studies and the delimitation of the experimental region, according to the objective of the study and the experience of the researcher, (ii) choice of the experimental design and carrying out the experiments according to the selected experimental matrix, (iii) mathematic-statistical treatment of the obtained experimental data through the fit of a polynomial function, (iv) evaluation of the model's fitness, (v) verification of the necessity and possibility of performing a displacement in direction to the optimal region, and (vi) obtaining the optimum values for each studied variable [89].
Experimental design is a specific set of experiments from a matrix composed by the different level combinations of the variables considered [89]. There are several symmetrical second-order experimental designs which can be used for RSM including (i) Full three-level factorial design: the number of experiments required for this design can be calculated by N = 3k, where N is experiment number and k is factor number. It has limited application in RSM when the factor number is more than 2 because the number of experiments required for this design will be very large, (ii) Box—Behnken design: the number of experiments (N) is obtained according to N = 2k(k — 1) + cp, where k is the number of factors and cp is the number of the central points. All levels must adjust only at three levels (—1, 0, +1) with equally spaced intervals between these levels. This design is more efficient and economical than Full three-level factorial design, mainly for a large number of variables, (iii) Central composite designs: this design consists of a full factorial or fractional factorial design, an additional design (often a star design that the experimental points are at a distance a from its center) and a central point. The number of experiments is calculated according to N = k2 + 2k + cp, where k is the factor number and cp is the replicate number of the central point. The a-values depend on the number ofvariables which can be obtained according to a = 2(k - p)/4. All factors are in five levels (—a, —1, 0, +1, +a), (iv) Doehlert design: this design has some advantages such as less experimental points and high efficiency. The number of experiment number can be calculated by N = k2 + k + cp, where k is the factor number and cp is the replicate number of the central point [89].
APPLICATIONS OF RSM IN ENZYMATIC SYNTHESIS
Yuksel et al. [90] carried out the enzymatic production of human milk fat analogues containing steari-donic acid
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