Effects of different expression systems on characterization of adenylate deaminase from Aspergillus oryzae.

Adenylate deaminase (AMPD) is an amino hydrolase that catalyzes the irreversible hydrolysis of adenosine monophosphate (AMP) to inosine monophosphate (IMP) and ammonia. In this study, the effect of different hosts on the enzymatic properties of AMPD from Aspergillus oryzae GX-08 was investigated and showed that Bacillus subtilis WB600 was more suitable for producing AMPD with a higher activity of 2540 U/mL. After purification, the optimal temperature and pH of recombinant AMPD were 55 °C and pH 6.0, respectively, and its activity was significantly enhanced by 10 mM Fe3+ with an increase of 236%. More importantly, the recombinant AMPD specifically and effectively catalyzed the conversion between AMP and IMP, in which 10 mL of crude AMPD achieved a conversion ratio of 76.4% after 40 min. Therefore, B. subtilis WB600 provides a potential platform for producing AMPD with excellent catalytic ability and catalytic specificity.

Isolation, purification and characterization of 5'-phosphodiesterase from Aspergillus fumigatus.

5'-Phosphodiesterase (5'-PDE) catalyzes the hydrolysis of ribonucleic acid to obtain a mixture of ribonucleotides, such as 5'-guanosine monophosphate and 5'-adenosine monophosphate. In this study, a 5'-PDE was newly isolated and purified from Aspergillus fumigatus. Following purification, this enzyme exhibited a specific activity of 1036.76 U/mg protein, a molecular weight of 9.5 kDa, and an optimal temperature and pH for enzyme activity of 60°C and 5.0, respectively. However, its activity was partially inhibited by Fe3+, Cu2+, and Zn2+, but slightly improved by the presence of K+ and Na+. Additionally, chemical-modification experiments were also applied to investigate the structural information of 5'-PDE, in which the residues containing carboxyl and imidazole groups were essential for enzyme activity based on their localization in the 5'-PDE active site. Furthermore, purified 5'-PDE could specifically catalyze the synthesis of ribonucleotides with a Vmax 0.71 mmol/mg·min and a KM of 13.60 mg/mL.

Enzymatic production of 5'-inosinic acid by AMP deaminase from a newly isolated Aspergillus oryzae.

5'-adenylic acid deaminase (AMP deaminase), an important enzyme for the food industry, can catalyze the irreversible hydrolysis of adenosine monophosphate (AMP) to inosine monophosphate (IMP) and ammonia. In this study, a new strain was screened that efficiently produces 3191.6U/g of AMP deaminase at 32°C. After purification, the optimal temperature and pH of the AMP deaminase were found to be 40°C and 6.0, respectively, but it was partially inhibited by Fe(3+), Cu(2+), Al(3+), and Zn(2+). With amplification of the AMP deaminase production system, 6mL of crude enzyme could produce 2.00mg/g of IMP from 2.04mg/g of dried yeast with an 84.8% molar yield after 40min. These results provide a new insight into AMP deaminase production and offer a potential platform for producing 5'-IMP.

Overproduction, Purification and Characterization of Adenylate Deaminase from Aspergillus oryzae.

Adenylate deaminase (AMPD, EC 3.5.4.6) is an aminohydrolase that widely used in the food and medicine industries. In this study, the gene encoding Aspergillus oryzae AMPD was cloned and expressed in Escherichia coli. Induction with 0.75 mM isopropyl ß-D-l-thiogalactopyranoside resulted in an enzyme activity of 1773.9 U/mL. Recombinant AMPD was purified to electrophoretic homogeneity using nickel affinity chromatography, and its molecular weight was calculated as 78.6 kDa. Purified AMPD exhibited maximal activity at 35 °C, pH 6.0 and 30 mM K+, with apparent K m and V max values of 2.7 × 10-4 M and 77.5 µmol/mg/min under these conditions. HPLC revealed that recombinant AMPD could effectively catalyse the synthesis of inosine-5'-monophosphate (IMP) with minimal by-products, indicating high specificity and suggesting that it could prove useful for IMP production.

Physics and chemistry-driven artificial neural network for predicting bioactivity of peptides and proteins and their design.

Predicting the bioactivity of peptides and proteins is an important challenge in drug development and protein engineering. In this study we introduce a novel approach, the so-called "physics and chemistry-driven artificial neural network (Phys-Chem ANN)", to deal with such a problem. Unlike the existing ANN approaches, which were designed under the inspiration of biological neural system, the Phys-Chem ANN approach is based on the physical and chemical principles, as well as the structural features of proteins. In the Phys-Chem ANN model the "hidden layers" are no longer virtual "neurons", but real structural units of proteins and peptides. It is a hybridization approach, which combines the linear free energy concept of quantitative structure-activity relationship (QSAR) with the advanced mathematical technique of ANN. The Phys-Chem ANN approach has adopted an iterative and feedback procedure, incorporating both machine-learning and artificial intelligence capabilities. In addition to making more accurate predictions for the bioactivities of proteins and peptides than is possible with the traditional QSAR approach, the Phys-Chem ANN approach can also provide more insights about the relationship between bioactivities and the structures involved than the ANN approach does. As an example of the application of the Phys-Chem ANN approach, a predictive model for the conformational stability of human lysozyme is presented.

Predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201: an application of amino acid-based peptide prediction.

A new peptide design strategy, the amino acid-based peptide prediction (AABPP) approach, is applied for predicting the affinity of epitope-peptides with class I MHC molecule HLA-A*0201. The AABPP approach consists of two sets of predictive coefficients. The former is the coefficients for the physicochemical properties of amino acids and the latter is the weight factors for the residue positions in a peptide sequence. An iterative double least square technique is introduced to determine the two sets of coefficients alternately through a benchmark dataset. The coefficients converged through such an iterative process are further used to predict the bioactivities of query peptides. In the AABPP algorithm, the following eight physicochemical properties are used as the descriptors of amino acids: (i) lipophilic indices, (ii) hydrophilic indices, (iii) lipophilic surface area, (iv) hydrophilic surface area, (v) alpha-potency indices, (vi) beta-potency indices, (vii) coil-potency indices and (viii) volume of amino acid side chains. In comparison with the existing methods in this area, a remakable advantage of the current approach is that there is no need to know the exact conformation of a query peptide and its alignment with a template. The two steps are indispensable but cannot always be successfully realized otherwise. It is anticipated that the AABPP approach will become a powerful tool for peptide drug design, or at least play a complemetary role to the existing methods.