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cis,cis-Muconic acid (CCMA) is used as a platform chemical for the production of several high-value compounds. For this article, an optimization strategy has been used to optimize medium composition for CCMA production from fairly cheap benzoate by Pseudomonas sp. 1167. The effect of different concentrations of medium components on CCMA production was studied. CCMA yields obtained from Plackett–Burman design (PBD) showed wide variation (3.95–5.87 g/L), and the first-order model indicated that (NH4)2SO4 (P < 0.01) and K2HPO4 · 3H2O (P < 0.02) were the significant components for CCMA production. Then the optimization was performed by steepest ascent design (SAD) and central composite design (CCD), and a validation experiment was conducted to verify the predicted value. The optimal medium composition was: 12 g/L sodium benzoate, 2.5 g/L sodium succinate, 0.7932 g/L (NH4)2SO4, 1.5612 g/L K2HPO4 · 3H2O, 1.2 g/L MgSO4 · 7H2O, 0.4 g/L yeast extract, 0.08 g/L FeCl3 · 6H2O, and 0.08 g/L ethylenediamine tetraacetic acid (EDTA). Under these conditions, a maximum of 7.18 g/L CCMA was produced per 12 g/L benzoate with a highly efficient process within 11 hr and a molecular conversion yield of 61%. Altogether, our results provide valuable insights into nutritional supplementation of CCMA production by using statistical methods, which may benefit a cost-competitive industrial fed-batch fermentation process using a cheap substrate. [ABSTRACT FROM AUTHOR]

Cation-π interaction is comparable and as important as other main molecular interaction types, such as hydrogen bond, electrostatic interaction, van der Waals interaction, and hydrophobic interaction. Cation-π interactions frequently occur in protein structures, because six (Phe, Tyr, Trp, Arg, Lys, and His) of 20 natural amino acids and all metallic cations could be involved in cation-π interaction. Cation-π interactions arise from complex physicochemical nature and possess unique interaction behaviors, which cannot be modeled and evaluated by existing empirical equations and force field parameters that are widely used in the molecular dynamics. In this study, the authors present an empirical approach for cation-π interaction energy calculations in protein interactions. The accurate cation-π interaction energies of aromatic amino acids (Phe, Tyr, and Try) with protonated amino acids (Arg and Lys) and metallic cations (Li+, Na+, K+, and Ca2+) are calculated using B3LYP/6-311+G(d,p) method as the benchmark for the empirical formulization and parameterization. Then, the empirical equations are built and the parameters are optimized based on the benchmark calculations. The cation-π interactions are distance and orientation dependent. Correspondingly, the empirical equations of cation-π interactions are functions of two variables, the distance r and the orientation angle θ. Two types of empirical equations of cation-π interactions are proposed. One is a modified distance and orientation dependent Lennard-Jones equation. The second is a polynomial function of two variables r and θ. The amino acid-based empirical equations and parameters provide simple and useful tools for evaluations of cation-π interaction energies in protein interactions. © 2011 Wiley Periodicals, Inc. J Comput Chem , 2011 [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

Background: Inhibition of α-amylase activity is an important strategy in the treatment of diabetes mellitus. An important treatment for diabetes mellitus is to reduce the digestion of carbohydrates and blood glucose concentrations. Inhibiting the activity of carbohydrate-degrading enzymes such as α-amylase and glucosidase significantly decreases the blood glucose level. Most inhibitors of α-amylase have serious adverse effects, and the α-amylase inactivation mechanisms for the design of safer inhibitors are yet to be revealed., Objective: In this study, we focused on the inhibitory effect of Zn2+ on the structure and dynamic characteristics of α-amylase from Anoxybacillus sp. GXS-BL (AGXA), which shares the same catalytic residues and similar structures as human pancreatic and salivary α-amylase (HPA and HSA, respectively)., Methods: Circular dichroism (CD) spectra of the protein (AGXA) in the absence and presence of Zn2+ were recorded on a Chirascan instrument. The content of different secondary structures of AGXA in the absence and presence of Zn2+ was analyzed using the online SELCON3 program. An AGXA amino acid sequence similarity search was performed on the BLAST online server to find the most similar protein sequence to use as a template for homology modeling. The pocket volume measurer (POVME) program 3.0 was applied to calculate the active site pocket shape and volume, and molecular dynamics simulations were performed with the Amber14 software package., Results: According to circular dichroism experiments, upon Zn2+ binding, the protein secondary structure changed obviously, with the α-helix content decreasing and β-sheet, β-turn and randomcoil content increasing. The structural model of AGXA showed that His217 was near the active site pocket and that Phe178 was at the outer rim of the pocket. Based on the molecular dynamics trajectories, in the free AGXA model, the dihedral angle of C-CA-CB-CG displayed both acute and planar orientations, which corresponded to the open and closed states of the active site pocket, respectively. In the AGXA-Zn model, the dihedral angle of C-CA-CB-CG only showed the planar orientation. As Zn2+ was introduced, the metal center formed a coordination interaction with H217, a cation-π interaction with W244, a coordination interaction with E242 and a cation-π interaction with F178, which prevented F178 from easily rotating to the open state and inhibited the activity of the enzyme., Conclusion: This research may have uncovered a subtle mechanism for inhibiting the activity of α-amylase with transition metal ions, and this finding will help to design more potent and specific inhibitors of α-amylases., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

A new drug design method, the multiple field three-dimensional quantitative structure-activity relationship (MF-3D-QSAR), is proposed. It is a combination and development of classical 2D-QSAR and traditional 3D-QSAR. In addition to the electrostatic and van der Waals potentials, more potential fields (such as lipophilic potential, hydrogen bonding potential, and nonthermodynamic factors) are integrated in the MF-3D-QSAR. Meanwhile, a principal component analysis (PCA) and iterative double least square (IDLS) technique is developed for predicting the bioactivity of query drug candidates. As an example, the MF-3D-QSAR is applied to the design of neuraminidase inhibitor and to prove its predictive power, and some useful findings are obtained for developing drugs against influenza virus., ((c) 2007 Wiley Periodicals, Inc. J Comput Chem, 2008.)

Using computer-aided design of single-site mutations, three amino acid residues determined by changes in folding free energy between wild-type (wt) and mutant proteins were exchanged to enhance the stability of pyruvate formate-lyase (PFL). The mutant enzymes were tested for properties such as optimum temperature, optimum pH, kinetic parameters, and stability to temperature. There were two mutant variants, Glu336Cys and Glu400Ile, that exhibited increased thermostability as compared to the wt enzyme. The melting temperatures (T(m), the temperature at which 50% inactivation occurs after heat treatment for 20 min) of Glu336Cys and Glu400Ile increased by 3.7 and 2.2 respectively. They also showed an increase in half life of about 1.80 and 2.21-fold, whereas Ala273Cys showed a slight decrease as compared with the wt enzyme.

Based on the principle of the pathway engineering, a novel pathway of producing glycerol was built in E. coli. The gpd1 gene encoding glycerol 3-phosphate dehydrogenase and the hor2 gene encoding glycerol 3-phosphatase were cloned from Saccharomyces cerevisiae, respectively. The two genes were inserted into expression vector pSE380 together. A recombinant plasmid pSE-gpd1-hor2 containing polycistron was constructed under the control of the strong trc promoter. Then it was transformed into E. coli BL21. The result showed the recombinant microorganism GxB-gh could convert glucose to glycerol directly. And the recombinant microorganism GxB-gh was incubated to produce glycerol from D-glucose in the fermentor. The maximal concentration of glycerol was 46.67g/L at 26h. Conversion rate of glucose was 42.87%. The study is about "green" producing glycerol by recombinant microorganism and is also useful for further working in recombining microorganism of producing 1,3-propanediol.