Purification of an alpha amylase from Aspergillus flavus NSH9 and molecular characterization of its nucleotide gene sequence

In this study, an alpha-amylase enzyme from a locally isolated Aspergillus flavus NSH9 was purified and characterized. The extracellular α-amylase was purified by ammonium sulfate precipitation and anion-exchange chromatography at a final yield of 2.55-fold and recovery of 11.73%. The molecular mass of the purified α-amylase was estimated to be 54 kDa using SDS-PAGE and the enzyme exhibited optimal catalytic activity at pH 5.0 and temperature of 50 °C. The enzyme was also thermally stable at 50 °C, with 87% residual activity after 60 min. As a metalloenzymes containing calcium, the purified α-amylase showed significantly increased enzyme activity in the presence of Ca2+ ions. Further gene isolation and characterization shows that the α-amylase gene of A. flavus NSH9 contained eight introns and an open reading frame that encodes for 499 amino acids with the first 21 amino acids presumed to be a signal peptide. Analysis of the deduced peptide sequence showed the presence of three conserved catalytic residues of α-amylase, two Ca2+-binding sites, seven conserved peptide sequences, and several other properties that indicates the protein belongs to glycosyl hydrolase family 13 capable of acting on α-1,4-bonds only. Based on sequence similarity, the deduced peptide sequence of A. flavus NSH9 α-amylase was also found to carry two potential surface/secondary-binding site (SBS) residues (Trp 237 and Tyr 409) that might be playing crucial roles in both the enzyme activity and also the binding of starch granules. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature

GEOMETRIC APPROACH TO MULTIPLE OBJECTIVE OPTIMIZATION WITH APPLICATION TO MULTIPLE CRITERIA DECISION-MAKING (FACES OF FINITE CONE, ALGORITHM FOR MOLP, UTILITY ANALYSIS)

A geometric approach to analyze and solve multiple objective linear programming problems is developed. Since the linear map represented by the cost matrix is not a one-to-one map then not every vertex in constraint space maps to a vertex in objective space. In addition, the dimension of the objective space is far much smaller than the dimension of the constraint space. Hence the objective space will have a simpler geometric structure. Taking advantage of this simpler geometric structure, we focus on analyzing the multiple objective optimization problem in the objective space. Some of the existing approaches to analyze and solve multiple objective linear programs in the constraint space are previewed in the first chapter. We conclude the chapter with a motivational discussion and a formulation of the multiple objective linear programming problem in objective space. From the analysis of the multiple objective simplex tableau, we have, locally at any vertex in objective space, the objective set is contained in the cone generated by the columns of the reduced cost matrix. So the problem of finding the nondominated faces in the objective space is reduced to finding the nondominated faces of a finitely generated cone. The latter problem is addressed in the second chapter. The algorithms for constructing the faces of finitely generated cone utilize the cone\u27s face lattice and structure and determine a hyperplane characterization of the face. An algorithm to construct the entire nondominated set as a union of nondominated faces is developed in the third chapter. This algorithm is based on the algorithms in the second chapter. In addition, we gave a numerical analysis of some of the X-based algorithms for comparison with our Y-based algorithm. Application of the multiple objective optimization algorithm to utility analysis in multiple criteria decision making is investigated in the fourth chapter. A technique for measuring the utility is presented. An algorithm for optimizing a given utility function over the nondominated set is developed