Key role of K+ and Ca2+ in high-yield ethanol production by S. Cerevisiae from concentrated sugarcane molasses.

BACKGROUND: Saccharomyces cerevisiae is an important microorganism in ethanol synthesis, and with sugarcane molasses as the feedstock, ethanol is being synthesized sustainably to meet growing demands. However, high-concentration ethanol fermentation based on high-concentration sugarcane molasses-which is needed for reduced energy consumption of ethanol distillation at industrial scale-is yet to be achieved. RESULTS: In the present study, to identify the main limiting factors of this process, adaptive laboratory evolution and high-throughput screening (Py-Fe3+) based on ARTP (atmospheric and room-temperature plasma) mutagenesis were applied. We identified high osmotic pressure, high temperature, high alcohol levels, and high concentrations of K+, Ca2+, K+ and Ca2+ (K+&Ca2+), and sugarcane molasses as the main limiting factors. The robust S. cerevisiae strains of NGT-F1, NGW-F1, NGC-F1, NGK+, NGCa2+ NGK+&Ca2+-F1, and NGTM-F1 exhibited high tolerance to the respective limiting factor and exhibited increased yield. Subsequently, ethanol synthesis, cell morphology, comparative genomics, and gene ontology (GO) enrichment analysis were performed in a molasses broth containing 250 g/L total fermentable sugars (TFS). Additionally, S. cerevisiae NGTM-F1 was used with 250 g/L (TFS) sugarcane molasses to synthesize ethanol in a 5-L fermenter, giving a yield of 111.65 g/L, the conversion of sugar to alcohol reached 95.53%. It is the highest level of physical mutagenesis yield at present. CONCLUSION: Our results showed that K+ and Ca2+ ions primarily limited the efficient production of ethanol. Then, subsequent comparative transcriptomic GO and pathway analyses showed that the co-presence of K+ and Ca2+ exerted the most prominent limitation on efficient ethanol production. The results of this study might prove useful by promoting the development and utilization of green fuel bio-manufactured from molasses.

Highly efficient bioconversion of icariin to icaritin by whole-cell catalysis.

BACKGROUND: Icaritin is an aglycone of flavonoid glycosides from Herba Epimedii. It has good performance in the treatment of hepatocellular carcinoma in clinical trials. However, the natural icaritin content of Herba Epimedii is very low. At present, the icaritin is mainly prepared from flavonoid glycosides by α-L-rhamnosidases and ß-glucosidases in two-step catalysis process. However, one-pot icaritin production required reported enzymes to be immobilized or bifunctional enzymes to hydrolyze substrate with long reaction time, which caused complicated operations and high costs. To improve the production efficiency and reduce costs, we explored α-L-rhamnosidase SPRHA2 and ß-glucosidase PBGL to directly hydrolyze icariin to icaritin in one-pot, and developed the whole-cell catalytic method for efficient icaritin production. RESULTS: The SPRHA2 and PBGL were expressed in Escherichia coli, respectively. One-pot production of icaritin was achieved by co-catalysis of SPRHA2 and PBGL. Moreover, whole-cell catalysis was developed for icariin hydrolysis. The mixture of SPRHA2 cells and PBGL cells transformed 200 g/L icariin into 103.69 g/L icaritin (yield 95.23%) in 4 h in whole-cell catalysis under the optimized reaction conditions. In order to further increase the production efficiency and simplify operations, we also constructed recombinant E. coli strains that co-expressed SPRHA2 and PBGL. Crude icariin extracts were also efficiently hydrolyzed by the whole-cell catalytic system. CONCLUSIONS: Compared to previous reports on icaritin production, in this study, whole-cell catalysis showed higher production efficiency of icaritin. This study provides promising approach for industrial production of icaritin in the future.

Influence of Calcium Ions on the Thermal Characteristics of α-amylase from Thermophilic Anoxybacillus sp. GXS-BL.

BACKGROUND: α-Amylases are starch-degrading enzymes and used widely, the study on thermostability of α-amylase is a central requirement for its application in life science and biotechnology. OBJECTIVE: In this article, our motivation is to study how the effect of Ca2+ ions on the structure and thermal characterization of α-amylase (AGXA) from thermophilic Anoxybacillus sp.GXS-BL. METHODS: α-Amylase activity was assayed with soluble starch as the substrate, and the amount of sugar released was determined by DNS method. For AGXA with calcium ions and without calcium ions, optimum temperature (Topt), half-inactivation temperature (T50) and thermal inactivation (halflife, t1/2) was evaluated. The thermal denaturation of the enzymes was determined by DSC and CD methods. 3D structure of AGXA was homology modeled with α-amylase (5A2A) as the template. RESULTS: With calcium ions, the values of Topt, T50, t1/2, Tm and ΔH in AGXA were significantly higher than those of AGXA without calcium ions, showing calcium ions had stabilizing effects on α-amylase structure with the increased temperature. Based on DSC measurements AGXA underwent thermal denaturation by adopting two-state irreversible unfolding processes. Based on the CD spectra, AGXA without calcium ions exhibited two transition states upon unfolding, including α- helical contents increasing, and the transition from α-helices to ß-sheet structures, which was obviously different in AGXA with Ca2+ ions, and up to 4 Ca2+ ions were located on the inter-domain or intra-domain regions according to the modeling structure. CONCLUSION: These results reveal that Ca2+ ions have pronounced influences on the thermostability of AGXA structure.

Inhibition of α-amylase Activity by Zn2+: Insights from Spectroscopy and Molecular Dynamics Simulations.

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 ß-sheet, ß-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.

Structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) for inhibitor design.

The inhibitions of enzymes (proteins) are determined by the binding interactions between ligands and targeting proteins. However, traditional QSAR (quantitative structure-activity relationship) is a one-side technique, only considering the structures and physicochemical properties of inhibitors. In this study, the structure-based and multiple potential three-dimensional quantitative structure-activity relationship (SB-MP-3D-QSAR) is presented, in which the structural information of host protein is involved in the QSAR calculations. The SB-MP-3D-QSAR actually is a combinational method of docking approach and QSAR technique. Multiple docking calculations are performed first between the host protein and ligand molecules in a training set. In the targeting protein, the functional residues are selected, which make the major contribution to the binding free energy. The binding free energy between ligand and targeting protein is the summation of multiple potential energies, including van der Waals energy, electrostatic energy, hydrophobic energy, and hydrogen-bond energy, and may include nonthermodynamic factors. In the foundational QSAR equation, two sets of weighting coefficients {aj} and {bp} are assigned to the potential energy terms and to the functional residues, respectively. The two coefficient sets are solved by using iterative double least-squares (IDLS) technique in the training set. Then, the two sets of weighting coefficients are used to predict the bioactivities of inquired ligands. In an application example, the new developed method obtained much better results than that of docking calculations.

Statistical energy potential: reduced representation of Dehouck-Gilis-Rooman function by selecting against decoy datasets.

Statistical effective energy function (SEEF) is derived from the statistical analysis of the database of known protein structures. Dehouck-Gilis-Rooman (DGR) group has recently created a new generation of SEEF in which the additivity of the energy terms was manifested by decomposing the total folding free energy into a sum of lower order terms. We have tried to optimize the potential function based on their work. By using decoy datasets as screening filter, and through modification of algorithms in calculation of accessible surface area and residue-residue interaction cutoff, four new combinations of the energy terms were found to be comparable to DGR potential in performance test. Most importantly, the term number was reduced from the original 30 terms to only 5 in our results, thereby substantially decreasing the computation time while the performance was not sacrificed. Our results further proved the additivity and manipulability of the DGR original energy function, and our new combination of the energy could be used in prediction of protein structures.

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.

Fragment-based quantitative structure-activity relationship (FB-QSAR) for fragment-based drug design.

In cooperation with the fragment-based design a new drug design method, the so-called "fragment-based quantitative structure-activity relationship" (FB-QSAR) is proposed. The essence of the new method is that the molecular framework in a family of drug candidates are divided into several fragments according to their substitutes being investigated. The bioactivities of molecules are correlated with the physicochemical properties of the molecular fragments through two sets of coefficients in the linear free energy equations. One coefficient set is for the physicochemical properties and the other for the weight factors of the molecular fragments. Meanwhile, an iterative double least square (IDLS) technique is developed to solve the two sets of coefficients in a training data set alternately and iteratively. The IDLS technique is a feedback procedure with machine learning ability. The standard Two-dimensional quantitative structure-activity relationship (2D-QSAR) is a special case, in the FB-QSAR, when the whole molecule is treated as one entity. The FB-QSAR approach can remarkably enhance the predictive power and provide more structural insights into rational drug design. As an example, the FB-QSAR is applied to build a predictive model of neuraminidase inhibitors for drug development against H5N1 influenza virus.

Multiple field three dimensional quantitative structure-activity relationship (MF-3D-QSAR).

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.

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.

Peptide reagent design based on physical and chemical properties of amino acid residues.

It has tremendous values for both drug discovery and basic research to develop a solid bioinformatical tool for guiding peptide reagent design. Based on the physical and chemical properties of amino acids, a new strategy for peptide reagent design, the so-called AABPD (amino acid based-peptide design), is proposed. The peptide samples in a training dataset are described by a series of HMLP (heuristic molecular lipophilicity potential) parameters and other physicochemical properties of amino acid residues that form a three-dimensional data matrix where each component is defined by three indexes: the first index refers to the peptide samples, the second to the amino acid positions, and the third to the amino acid parameters. The binding free energy between a peptide ligand and its protein receptor is calculated by a linear free energy equation through the physicochemical parameters, resulting in a set of simultaneous linear equations between the bioactivity of the peptides and the physicochemical properties of amino acids. An iterative double least square technique is developed for the solution of the three-dimensional simultaneous linear equation set to determine the amino acid position coefficients of peptide sequence and the physicochemical parameter coefficients of amino acid residues alternately. The two sets of coefficients thus obtained are used for predicting the bioactivity of other query peptide reagents. Two calculation examples, the peptide substrate specificity of the SARS coronavirus 3C-like proteinase and the affinity prediction for epitope-peptides with Class I MHC molecules are studied by using the peptide reagent design strategy.

Computer-aided design of the stability of pyruvate formate-lyase from Escherichia coli by site-directed mutagenesis.

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.

[Co-expression of gpd1 and hor2 from Saccharomyces cerevisiae in Escherichia coli].

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.

Cloning, expression and identification of a new trehalose synthase gene from Thermobifida fusca genome.

A new open reading frame in Thermobifida fusca sequenced genome was identified to encode a new trehalose synthase, annotated as "glycosidase" in the GenBank database, by bioinformatics searching and experimental validation. The gene had a length of 1830 bp with about 65% GC content and encoded for a new trehalose synthase with 610 amino acids and deduced molecular weight of 66 kD. The high GC content seemed not to affect its good expression in E. coli BL21 in which the target protein could account for as high as 15% of the total cell proteins. The recombinant enzyme showed its optimal activities at 25 degrees and pH 6.5 when it converted substrate maltose into trehalose. However it would divert a high proportion of its substrate into glucose when the temperature was increased to 37 degrees, or when the enzyme concentration was high Its activity was not inhibited by 5 mM heavy metals such as Cu2+, Mn2+, and Zn2+ but affected by high concentration of glucose. Blasting against the database indicated that amino acid sequence of this protein had maximal 69% homology with the known trehalose synthases, and two highly conserved segments of the protein sequence were identified and their possible linkage with functions was discussed.