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.

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.

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.

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.

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.

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.

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.

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.