Study on the lignin-derived sp2-sp3 hybrid hard carbon materials and the feasibility for industrial production.

Hard carbon has been widely used in anode of lithium/sodium ion battery, electrode of supercapacitor, and carbon molecular sieve for CO2 capture and hydrogen storage. In this study the lignin derived hard carbon products are investigated, and the conclusions are abstracted as follows. (1) The lignin derived hard carbon products consist of microcrystal units of sp2 graphene fragments, jointed by sp3 carbon atoms and forming sp2-sp3 hybrid hard carbon family. (2) From the lignin precursors to the sp2-sp3 hybrid hard carbon products, most carbon atoms retain their original electron configurations (sp2 or sp3) and keep their composition in lignin. (3) The architectures of lignin-derived hard carbon materials are closely dependent on the forms of their lignin precursors, and could be preformed by different pretreatment techniques. (4) The carbonization of lignin precursors follows the mechanism "carbonization in situ and recombination nearby". (5) Due to the high carbon ratio and abundant active functional groups in lignin, new activation techniques could be developed for control of pore size and pore volume. In general lignin is an excellent raw material for sp2-sp3 hybrid hard carbon products, a green and sustainable alternative resource for phenolic resin, and industrial production for lignin derived hard carbon products would be feasible.

Study on the microcrystal cellulose and the derived 2D graphene and relative carbon materials.

Microcrystal cellulose (MCC) is a green and sustainable resource that widely exists in various lignocellulose species in percentage 10% to 30%. The fine powder of MCC is often discarded in industrial productions that use lignocellulose as feedstock. The crystal structure of two types of MCC (sugarcane pith and bamboo pith) and their derived carbon materials are studied, and the key findings are summarized as follows. (1) In the MCC refined from sugarcane pith, there are large amount of cellulose 2D crystal, which can be converted to valuable 2D graphene crystal. (2) In the MCC refined from bamboo pith there are large amount of cluster microcrystal cellulose, which can be converted to soft and elastic graphene microcrystal (GMC). (3) The 2D cellulose in MCC of sugarcane pith has large surface area and is easily to be degraded to sugars by acid-base hydrolysis reaction, which can be carbonized to Fullerenes-like carbon spheres. (4) The crystal structures of MCC derived carbon materials are strongly impacted by the crystal structures of MCC, and the carbonization reaction of MCC follows "in situ carbonization" and "nearby recombination" mechanism. In general, the results from this study may open a new way for value-added applications of microcrystal cellulose.

Empirical formulation and parameterization of cation-π interactions for protein modeling.

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 Ca(2+)) 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.

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.

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.

Insights from investigating the interaction of oseltamivir (Tamiflu) with neuraminidase of the 2009 H1N1 swine flu virus.

The neuraminidase (NA) of influenza virus is the target of anti-flu drugs oseltamivir and zanamivir. Clinical practices showed that oseltamivir was effective to treat the 2009-H1N1 influenza but failed to the 2006-H5N1 avian influenza. To perform an in-depth analysis on such a drug-resistance problem, the 2009-H1N1-NA structure was developed. To compare it with the crystal 2006-H5N1-NA structure as well as the 1918 influenza virus H1N1-NA structure, the multiple sequential and structural alignments were performed. It has been revealed that the hydrophobic residue Try347 in H5N1-NA does not match with the hydrophilic carboxyl group of oseltamivir as in the case of H1N1-NA. This may be the reason why H5N1 avian influenza virus is drug-resistant to oseltamivir. The finding provides useful insights for how to modify the existing drugs, such as oseltamivir and zanamivir, making them not only become more effective against H1N1 virus but also effective against H5N1 virus.

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.

Energetic analysis of the two controversial drug binding sites of the M2 proton channel in influenza A virus.

Understanding the mechanism of the M2 proton channel of influenza A is crucially important to both basic research and drug discovery. Recently, the structure was determined independently by high-resolution NMR and X-ray crystallography. However, the two studies lead to completely different drug-binding mechanisms: the X-ray structure shows the drug blocking the pore from inside; whereas the NMR structure shows the drug inhibiting the channel from outside by an allosteric mechanism. Which one of the two is correct? To address this problem, we conducted an in-depth computational analysis. The conclusions drawn from various aspects, such as energetics, the channel-gating dynamic process, the pK(a) shift and its impact on the channel, and the consistency with the previous functional studies, among others, are all in favour to the allosteric mechanism revealed by the NMR structure. The findings reported here may stimulate and encourage new strategies for developing effective drugs against influenza A, particularly in dealing with the drug-resistant problems.

Recent advances in QSAR and their applications in predicting the activities of chemical molecules, peptides and proteins for drug design.

This review is to summarize three new QSAR (quantitative structure-activity relationship) methods recently developed in our group and their applications for drug design. Based on more solid theoretical models and advanced mathematical techniques, the conventional QSAR technique has been recast in the following three aspects. (1) In the fragment-based two dimensional QSAR, or abbreviated as FB-QSAR, the molecular structures in a family of drug candidates are divided into several fragments according to the substitutes being investigated. The bioactivities of drug candidates are correlated with physicochemical properties of the molecular fragments through two sets of coefficients: one is for the physicochemical properties and the other for the molecular fragments. (2) In the multiple field three dimensional QSAR, or MF-3D-QSAR, more molecular potential fields are integrated into the comparative molecular field analysis (CoMFA) through two sets of coefficients: one is for the potential fields and the other for the Cartesian three dimensional grid points. (3) In the AABPP (amino acid-based peptide prediction), the bioactivities of peptides or proteins are correlated with the physicochemical properties of all or partial residues of the sequence through two sets of coefficients: one is for the physicochemical properties of amino acids and the other for the weight factors of the residues. Meanwhile, an iterative double least square (IDLS) technique is developed for solving the two sets of coefficients in a training dataset alternately and iteratively. Using the two sets of coefficients, one can predict the bioactivity of a query peptide, protein, or drug candidate. Compared with the old methods, the new QSAR approaches as summarized in this review possess machine learning ability, can remarkably enhance the prediction power, and provide more structural information. Meanwhile, the future challenge and possible development in this area have been briefly addressed as well.

An in-depth analysis of the biological functional studies based on the NMR M2 channel structure of influenza A virus.

The long-sought three-dimensional structure of the M2 proton channel of influenza A virus was successfully determined recently by the high-resolution NMR [J.R. Schnell, J.J. Chou, Structure and mechanism of the M2 proton channel of influenza A virus, Nature 451 (2008) 591-595]. Such a milestone work has provided a solid structural basis for studying drug-resistance problems. However, the action mechanism revealed from the NMR structure is completely different from the traditional view and hence prone to be misinterpreted as "conflicting" with some previous biological functional studies. To clarify this kind of confusion, an in-depth analysis was performed for these functional studies, particularly for the mutations D44N, D44A and N44D on position 44, and the mutations on positions 27-38. The analyzed results have provided not only compelling evidences to further validate the NMR structure but also very useful clues for dealing with the drug-resistance problems and developing new effective drugs against H5N1 avian influenza virus, an impending threat to human beings.

Localization and visualization of excess chemical potential in statistical mechanical integral equation theory 3D-HNC-RISM.

In this study the excess chemical potential of the integral equation theory, 3D-RISM-HNC [Q. Du, Q. Wei, J. Phys. Chem. B 107 (2003) 13463-13470], is visualized in three-dimensional form and localized at interaction sites of solute molecule. Taking the advantage of reference interaction site model (RISM), the calculation equations of chemical excess potential are reformulized according to the solute interaction sites s in molecular space. Consequently the solvation free energy is localized at every interaction site of solute molecule. For visualization of the 3D-RISM-HNC calculation results, the excess chemical potentials are described using radial and three-dimensional diagrams. It is found that the radial diagrams of the excess chemical potentials are more sensitive to the bridge functions than the radial diagrams of solvent site density distributions. The diagrams of average excess chemical potential provide useful information of solute-solvent electrostatic and van der Waals interactions. The local description of solvation free energy at active sites of solute in 3D-RISM-HNC may broaden the application scope of statistical mechanical integral equation theory in solution chemistry and life science.

Simulated Protein Thermal Detection (SPTD) for Enzyme Thermostability Study and an Application Example for Pullulanase from Bacillus deramificans.

BACKGROUND: The relationship between protein structure and its bioactivity is one of the fundamental problems for protein engineering and pharmaceutical design. METHOD: A new method, called SPTD (Simulated Protein Thermal Detection), was proposed for studying and improving the thermal stability of enzymes. The method was based on the evidence observed by conducting the MD (Molecular Dynamics) simulation for all the atoms of an enzyme vibrating from the velocity at a room temperature (e.g., 25°C) to the desired working temperature (e.g., 65°C). According to the recorded MD trajectories and the coordinate deviations of the constituent residues under the two different temperatures, some new strategies have been found that are useful for both drug delivery and starch industry. CONCLUSION: The SPTD technique presented in this paper may become a very useful tool for pharmaceutical design and protein engineering.

Fabrication and Characterization of Graphene Microcrystal Prepared from Lignin Refined from Sugarcane Bagasse.

Graphene microcrystal (GMC) is a type of glassy carbon fabricated from lignin, in which the microcrystals of graphene are chemically bonded by sp³ carbon atoms, forming a glass-like microcrystal structure. The lignin is refined from sugarcane bagasse using an ethanol-based organosolv technique which is used for the fabrication of GMC by two technical schemes: The pyrolysis reaction of lignin in a tubular furnace at atmospheric pressure; and the hydrothermal carbonization (HTC) of lignin at lower temperature, followed by pyrolysis at higher temperature. The existence of graphene nanofragments in GMC is proven by Raman spectra and XRD patterns; the ratio of sp² carbon atoms to sp³ carbon atoms is demonstrated by XPS spectra; and the microcrystal structure is observed in the high-resolution transmission electron microscope (HRTEM) images. Temperature and pressure have an important impact on the quality of GMC samples. With the elevation of temperature, the fraction of carbon increases, while the fraction of oxygen decreases, and the ratio of sp² to sp³ carbon atoms increases. In contrast to the pyrolysis techniques, the HTC technique needs lower temperatures because of the high vapor pressure of water. In general, with the help of biorefinery, the biomass material, lignin, is found to be qualified and sustainable material for the manufacture of GMC. Lignin acts as a renewable substitute for the traditional raw materials of glassy carbon, copolymer resins of phenol formaldehyde, and furfuryl alcohol-phenol.

A comparative study for the organic byproducts from hydrothermal carbonizations of sugarcane bagasse and its bio-refined components cellulose and lignin.

Sugarcane bagasse was refined into cellulose, hemicellulose, and lignin using an ethanol-based organosolv technique. The hydrothermal carbonization (HTC) reactions were applied for bagasse and its two components cellulose and lignin. Based on GC-MS analysis, 32 (13+19) organic byproducts were derived from cellulose and lignin, more than the 22 byproducts from bagasse. Particularly, more valuable catechol products were obtained from lignin with 56.8% share in the total GC-MS integral area, much higher than the 2.263% share in the GC-MS integral areas of bagasse. The organic byproducts from lignin make up more than half of the total mass of lignin, indicating that lignin is a chemical treasure storage. In general, bio-refinery and HTC are two effective techniques for the valorization of bagasse and other biomass materials from agriculture and forest industry. HTC could convert the inferior biomass to superior biofuel with higher energy quantity of combustion, at the same time many valuable organic byproducts are produced. Bio-refinery could promote the HTC reaction of biomass more effective. With the help of bio-refinery and HTC, bagasse and other biomass materials are not only the sustainable energy resource, but also the renewable and environment friendly chemical materials, the best alternatives for petroleum, coal and natural gas.

Enhanced production of optical (S)-acetoin by a recombinant Escherichia coli whole-cell biocatalyst with NADH regeneration.

Acetoin is an important platform chemical with a variety of applications in foods, cosmetics, chemical synthesis, and especially in the asymmetric synthesis of optically active pharmaceuticals. It is also a useful breath biomarker for early lung cancer diagnosis. In order to enhance production of optical (S)-acetoin and facilitate this building block for a series of chiral pharmaceuticals derivatives, we have developed a systematic approach using in situ-NADH regeneration systems and promising diacetyl reductase. Under optimal conditions, we have obtained 52.9 g L-1 of (S)-acetoin with an enantiomeric purity of 99.5% and a productivity of 6.2 g (L h)-1. The results reported in this study demonstrated that the production of (S)-acetoin could be effectively improved through the engineering of cofactor regeneration with promising diacetyl reductase. The systematic approach developed in this study could also be applied to synthesize other optically active α-hydroxy ketones, which may provide valuable benefits for the study of drug development.

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.

Inhibitor design for SARS coronavirus main protease based on "distorted key theory".

In order to find effective peptide inhibitors against SARS CoV M(pro), an analysis was performed for 11 oligo-peptides that can be cleaved by the SARS coronavirus main protease (CoV M(pro), or 3CL(pro)). Flexible molecular alignments of the 11 cleavable peptides have provided useful insights into the chemical properties of their amino acid residues close to the cleavage site. Moreover, it was found through the ligand-receptor docking studies that of the 11 cleavable peptides, NH2-ATLQ / AIAS-COOH and NH2-ATLQ / AENV-COOH had the highest affinity with SARS CoV M(pro). The two octapeptides were selected as initial templates for further chemical modification to make them become effective inhibitors against the SARS enzyme according to the "distorted key" theory [K. C. Chou, Analytical Biochemistry 233 (1996) 1-14]. The possible chemical modification methods are proposed and examined. The approach developed in this study and the findings thus obtained might stimulate new strategies and provide useful information for drug design against SARS.

Exploring the possibility to store the mixed oxygen-hydrogen cluster in clathrate hydrate in molar ratio 1:2 (O2+2H2).

An interesting possibility is explored: storing the mixture of oxygen and hydrogen in clathrate hydrate in molar ratio 1:2. The interaction energies between oxygen, hydrogen, and clathrate hydrate are calculated using high level quantum chemical methods. The useful conclusion points from this study are summarized as follows. (1) The interaction energies of oxygen-hydrogen mixed cluster are larger than the energies of pure hydrogen molecular cluster. (2) The affinity of oxygen molecules with water molecules is larger than that of the hydrogen molecules with water molecules. (3) The dimension of O2-2H2 interaction structure is smaller than the dimension of CO2-2H2 interaction structure. (4) The escaping energy of oxygen molecules from the hydrate cell is larger than that of the hydrogen molecules. (5) The high affinity of the oxygen molecules with both the water molecules and the hydrogen molecules may promote the stability of oxygen-hydrogen mixture in the clathrate hydrate. Therefore it is possible to store the mixed (O2+2H2) cluster in clathrate hydrate.

2L-PCA: a two-level principal component analyzer for quantitative drug design and its applications.

A two-level principal component predictor (2L-PCA) was proposed based on the principal component analysis (PCA) approach. It can be used to quantitatively analyze various compounds and peptides about their functions or potentials to become useful drugs. One level is for dealing with the physicochemical properties of drug molecules, while the other level is for dealing with their structural fragments. The predictor has the self-learning and feedback features to automatically improve its accuracy. It is anticipated that 2L-PCA will become a very useful tool for timely providing various useful clues during the process of drug development.