Butanol production employing fed-batch fermentation by Clostridium acetobutylicum GX01 using alkali-pretreated sugarcane bagasse hydrolysed by enzymes from Thermoascus aurantiacus QS 7-2-4.

Sugarcane bagasse (SB) is a potential feedstock for butanol production. However, biological production of butanol from SB is less economically viable. In this study, evaluation of eight pretreatments on SB showed that alkali pretreatment efficiently removed lignin from SB while retaining the intact native structure of the released microfibrils. In total, 99% of cellulose and 100% of hemicellulose in alkali-pretreated SB were hydrolysed by enzymes from Thermoascus aurantiacus. The hydrolysate was used to produce butanol in a fed-batch fermentation by Clostridium acetobutylicum. At 60h, 14.17 and 21.11gL(-1) of butanol and acetone-butanol-ethanol (ABE) were produced from 68.89gL(-1) of total sugars, respectively, yielding 0.22 and 0.33gg(-1) of sugars. The maximum yield of butanol and ABE reached 15.4g and 22.9g per 100g raw SB, respectively. This established process may have potential application for butanol production from SB.

Enhanced butanol production from cassava with Clostridium acetobutylicum by genome shuffling.

To obtain strains exhibiting high levels of solvent tolerance and butanol production, wild type strains of Clostridium acetobutylicum butanol-producing strain GX01 and Lactobacillus mucosae butanol-tolerant strain M26 were subjected to mutagenesis combining N-methyl-N-nitro-N-nitrosoguanidine induction with genome shuffling. After four successive rounds of genome shuffling, the C. acetobutylicum shuffled strain GS4-3 showing greater levels of fermentation performances (such as secreting a higher level of amylase, improving the thermal stability, and possessing greater environmental robustness) compared to the wild type strains was isolated. As a result, after optimization of culture conditions, mutant GS4-3 produced 32.6 g/L of total solvent, 20.1 g/L of butanol production, and 0.35 g/L/h of butanol productivity, which were, respectively, increased by 23.5, 23.3, and 40.0 % than the wild-type strain GX01, in a 10 L bioreactor. The enhanced production of butanol and tolerance of solvent of mutant associated with GS4-3 make it promising for acetone/butanol/ethanol fermentation from cassava (Manihot esculenta).

The multiple roles of histidine in protein interactions.

BACKGROUND: Among the 20 natural amino acids histidine is the most active and versatile member that plays the multiple roles in protein interactions, often the key residue in enzyme catalytic reactions. A theoretical and comprehensive study on the structural features and interaction properties of histidine is certainly helpful. RESULTS: Four interaction types of histidine are quantitatively calculated, including: (1) Cation-π interactions, in which the histidine acts as the aromatic π-motif in neutral form (His), or plays the cation role in protonated form (His+); (2) π-π stacking interactions between histidine and other aromatic amino acids; (3) Hydrogen-π interactions between histidine and other aromatic amino acids; (4) Coordinate interactions between histidine and metallic cations. The energies of π-π stacking interactions and hydrogen-π interactions are calculated using CCSD/6-31+G(d,p). The energies of cation-π interactions and coordinate interactions are calculated using B3LYP/6-31+G(d,p) method and adjusted by empirical method for dispersion energy. CONCLUSIONS: The coordinate interactions between histidine and metallic cations are the strongest one acting in broad range, followed by the cation-π, hydrogen-π, and π-π stacking interactions. When the histidine is in neutral form, the cation-π interactions are attractive; when it is protonated (His+), the interactions turn to repulsive. The two protonation forms (and pKa values) of histidine are reversibly switched by the attractive and repulsive cation-π interactions. In proteins the π-π stacking interaction between neutral histidine and aromatic amino acids (Phe, Tyr, Trp) are in the range from -3.0 to -4.0 kcal/mol, significantly larger than the van der Waals energies.

Fermentation of xylose into ethanol by a new fungus strain Pestalotiopsis sp. XE-1.

A new fungus, Pestalotiopsis sp. XE-1, which produced ethanol from xylose with yield of 0.47 g ethanol/g of consumed xylose was isolated. It also produced ethanol from arabinose, glucose, fructose, mannose, galactose, cellobiose, maltose, and sucrose with yields of 0.38, 0.47, 0.45, 0.46, 0.31, 0.25, 0.31, and 0.34 g ethanol/g of sugar consumed, respectively. It produced maximum ethanol from xylose at pH 6.5, 30°C under a semi-aerobic condition. Acetic acid produced in xylose fermenting process inhibited ethanol production of XE-1. The ethanol yield in the pH-uncontrolled batch fermentation was about 27% lower than that in the pH-controlled one. The ethanol tolerance of XE-1 was higher than most xylose-fermenting, ethanol-producing microbes, but lower than Saccharomyces cerevisiae and Hansenula polymorpha. XE-1 showed tolerance to high concentration of xylose, and was able to grow and produce ethanol even when it was cultivated in 97.71 g/l xylose.

Multiple induced mutagenesis for improvement of ethanol production by Kluyveromyces marxianus.

Kluyveromyces marxianus GX-15 was mutated multiple times by alternately treatment with UV irradiation and NTG for two cycles. Four mutant strains with improved ethanol yield were obtained. The maximum ethanol concentration, ethanol yield coefficient and theoretical ethanol yield of the best mutant strain, GX-UN120, was 69 g/l, 0.46 g/g and 91%, respectively, when fermenting 150 g glucose/l at 40°C. The corresponding values for GX-15 were 58 g/l, 0.39 g/g and 76%, respectively. GX-UN120 grew well in 11% (v/v) of ethanol, while GX-15 could not grow when ethanol was greater than 8% (v/v).

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.

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.