Ri-Bo Huang

Guangxi Academy of Sciences, Yung-ning, Guangxi Zhuangzu Zizhiqu, China

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Publications (41)112.62 Total impact

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    ABSTRACT: Abstract Although not being classified as the most fundamental protein structural elements like α-helices and β-strands, the loop segment may play considerable roles for protein stability, flexibility, and dynamic activity. Meanwhile, the protein loop is also quite elusive; i.e., its interactions with the other parts of protein as well as its own shape-maintaining forces have still remained as a puzzle or at least not quite clear yet. Here we report a molecular force, the so-called polar hydrogen-π interaction (Hp-π), which may play an important role in supporting the backbones of protein loops. By conducting the potential energy surface scanning calculations on the quasi π-plane of peptide bond unit, we have observed the following intriguing phenomena: (1) when the polar hydrogen atom of a peptide unit is perpendicularly pointing to the π-plane of other peptide bond units, a remarkable Hp-π interaction occurs; (2) the interaction is distance- and orientation-dependent, acting in a broad space, and belonging to the "point-to-plane" one. The molecular force reported here may provide useful interaction concepts and insights for better understanding the loop's unique stability and flexibility feature, as well as the driving force of the protein global folding.
    Journal of biomolecular structure & dynamics 11/2014; · 4.99 Impact Factor
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    ABSTRACT: We report the first genome sequence of Paenibacillus polymyxa DSM 365, an efficient producer of (R,R)-2,3-butanediol.•It facilitates the design of rational strategies to develop superior microbial cell factories by metabolic engineering.•It also facilitates the construction of artificial biosynthetic pathways for synthesis of chiral 2,3-butanediol or acetoin.
    Journal of Biotechnology. 11/2014;
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    ABSTRACT: Synthesis of 2,8-polysialic acid (polySia) glycans are catalyzed by two highly homologous mammalian polysialyltransferases (polySTs), ST8Sia II (STX) and ST8Sia IV (PST), which are two members of the ST8Sia gene family of sialytransferases. During polysialylation, both STX and PST catalyze the transfer of multiple Sia residues from the activated sugar nucleotide precursor, CMP-Neu5Ac (Sia), to terminal Sia residues on N- and O-linked oligosaccharide chains on acceptor glycoproteins e.g. the neural cell adhesion molecule (NCAM), which is the major carrier protein of polySia. Based on our new findings and previously published studies, this review summarizes the current knowledge regarding the molecular mechanism underlying regulation of protein-specific polysialylation of NCAM that includes the following: (1) Determination of the catalytic domains and specific regions within ST8Sia IV for recognizing and catalyzing the efficient polysialylation of NCAM; (2) Identification of key amino acid residues within the PSTD motif of ST8Sia IV that are essential for polysialylation; (3) Verification of key amino acids in the PBR domain of ST8Sia IV required for NCAM-specific polysialylation; and (4) a 3D conformational study of ST8Sia IV based on the Phyre2 server to discover the relationship between the structure and its functional domains of the polyST. Based on these results, our 3D model of ST8Sia IV was used to identify and characterize the catalytic domains and amino acid residues critical for catalyzing polysialylation, providing new structural information for revealing a detailed mechanism of polyST-NCAM interaction required for polysialylation of NCAM, findings that have not been previously reported.
    Protein and Peptide Letters 10/2014; · 1.99 Impact Factor
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    ABSTRACT: Muconic acid (MA), a high value-added bio-product with reactive dicarboxylic groups and conjugated double bonds, has garnered increasing interest owing to its potential applications in the manufacture of new functional resins, bio-plastics, food additives, agrochemicals, and pharmaceuticals. At the very least, MA can be used to produce commercially important bulk chemicals such as adipic acid, terephthalic acid and trimellitic acid. Recently, great progress has been made in the development of biotechnological routes for MA production. This present review provides a comprehensive and systematic overview of recent advances and challenges in biotechnological production of MA. Various biological methods are summarized and compared, and their constraints and possible solutions are also described. Finally, the future prospects are discussed with respect to the current state, challenges, and trends in this field, and guidelines for developing high-performance microbial cell factories for MA production by systems metabolic engineering are also proposed.
    Biotechnology advances 04/2014; · 8.25 Impact Factor
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    ABSTRACT: 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.
    Preparative Biochemistry &amp Biotechnology 01/2014; 44(4):342-54. · 0.41 Impact Factor
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    ABSTRACT: The recently solved solution structure of HCV (hepatitis C virus) p7 ion channel provides a solid structure basis for drug design against HCV infection. In the p7 channel the ligand amantadine (or rimantadine) was determined in a hydrophobic pocket. However the pharmocophore (-NH2) of the ligand was not assigned a specific binding site. The possible binding sites for amino group of adamantane derivatives is studied based on the NMR structure of p7 channel using QM calculation and molecular modeling. In the hydrophobic cavity and nearby three possible binding sites are proposed: His17, Phe20, and Trp21. The ligand binding energies at the three binding sites are studied using high level QM method CCSD(T)/6-311+G(d,p) and AutoDock calculations, and the interaction details are analyzed. The potential application of the binding sites for rational inhibitor design are discussed. Some useful viewpoints are concluded as follows. (1) The amino group (-NH2) of adamantane derivatives is protonated (-NH3+), and the positively charged cation may form cation-π interactions with aromatic amino acids. (2) The aromatic amino acids (His17, Phe20, and Trp21) are the possible binding sites for the protonated amino group (-NH3+) of adamantane derivatives, and the cation-π bond energies are 3 to 5 times stronger than the energies of common hydrogen bonds. (3) The higher inhibition potent of rimantadine than amantadine probably because of its higher pKa value (pKa = 10.40) and the higher positive charge in the amino group. The potential application of p7 channel structure for inhibitor design is discussed.
    PLoS ONE 01/2014; 9(4):e93613. · 3.53 Impact Factor
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    ABSTRACT: BACKGROUND: In the study of biomolecular structures and interactions the polar hydrogen-pi bonds (Hp-pi) are an extensive molecular interaction type. In proteins 11 of 20 natural amino acids and in DNA (or RNA) all four nucleic acids are involved in this type interaction. RESULTS: The Hp-pi in proteins are studied using high level QM method CCSD/6-311 + G(d,p) + H-Bq (ghost hydrogen basis functions) in vacuum and in solutions (water, acetonitrile, and cyclohexane). Three quantum chemical methods (B3LYP, CCSD, and CCSD(T)) and three basis sets (6-311 + G(d,p), TZVP, and cc-pVTZ) are compared. The Hp-pi donors include R2NH, RNH2, ROH, and C6H5OH; and the acceptors are aromatic amino acids, peptide bond unit, and small conjugate pi-groups. The Hp-pi interaction energies of four amino acid pairs (Ser-Phe, Lys-Phe, His-Phe, and Tyr-Phe) are quantitatively calculated. CONCLUSIONS: Five conclusion points are abstracted from the calculation results. (1) The common DFT method B3LYP fails in describing the Hp-pi interactions. On the other hand, CCSD/6-311 + G(d,p) plus ghost atom H-Bq can yield better results, very close to the state-of-the-art method CCSD(T)/cc-pVTZ. (2) The Hp-pi interactions are point to pi-plane interactions, possessing much more interaction conformations and broader energy range than other interaction types, such as common hydrogen bond and electrostatic interactions. (3) In proteins the Hp-pi interaction energies are in the range 10 to 30 kJ/mol, comparable or even larger than common hydrogen bond interactions. (4) The bond length of Hp-pi interactions are in the region from 2.30 to 3.00 A at the perpendicular direction to the pi-plane, much longer than the common hydrogen bonds (~1.9 A). (5) Like common hydrogen bond interactions, the Hp-pi interactions are less affected by solvation effects.
    Chemistry Central Journal 05/2013; 7(1):92. · 1.31 Impact Factor
  • Guo-Ping Zhou, Ri-Bo Huang
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    ABSTRACT: Transmissible spongiform encephalopathies (TSEs) are prion protein misfolding diseases that involve the accumulation of an abnormal β-sheet-rich prion protein aggregated form (PrPsc) of the normal α- helix-rich prion protein (PrPc) within the central nervous system (CNS) and other organs. On account of its large size and insolubility properties, characterization of PrPsc is quite difficult. A soluble intermediate, called PrPβ or βo, exhibiting many of the same features as PrPsc, can be generated using a combination of low pH and/or mild denaturing conditions. Here, we review the current knowledge on the following five issues relevant to the conversion mechanisms of PrPc to PrPsc : (1) How is the Stability of the Helical Structures in the Native PrPc Related to the Primary Structure of the PrPc (2) Why the Low pH Solution System is a Ideal Trigger of PrPc to PrPsc Conversion (3) How are the Structural and Dynamical Characteristics of the α-helix-rich Intermediates Determined using NMR Data (4) How are the Premolten (PrPα4 and PrPαβ) and β-Oligomer (PrPβ) Intermediates Detected and Assayed, and (5) Can the Disordered N-terminal Domain be folded into the Structural Segment? Particularly, Chou's wenxiang diagram (http://en.wikipedia.org/wiki/Wenxiang_diagram) was introduced for providing an intuitive picture. This review may help to further understand the prion protein misfolding mechanism.
    Current topics in medicinal chemistry 05/2013; · 4.47 Impact Factor
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    ABSTRACT: In drug design and enzyme engineering, the information of interactions between receptors and ligands is crucially important. In many cases, the protein structures and drug-target complex structures are determined by a delicate balance of several weak molecular interaction types. Among these interaction forces several unconventional interactions play important roles, however, less familiar for researchers. The cation-π interaction is a unique noncovalent interaction only acting between aromatic amino acids and organic cations (protonated amino acids) and inorganic cations (proton and metallic). This article reports new study results in the interaction strength, the behaviors and the structural characters of cation-π interactions between aromatic amino acids (Phe, Tyr, and Trp) and organic and inorganic cations (Lys+, Arg+, H+, H3O+, Li+, Na+, K+, Ca2+, and Zn2+) in gas phase and in solutions (water, acetonitrile, and cyclohexane). Systematical research revealed that the cation-π interactions are point-to-plane (aromatic group) interactions, distance and orientation-dependent, and the interaction energies change in a broad range. In gas phase the cation-π interaction energies between aromatic amino acids (Phe, Tyr, and Trp) and metallic cations (Li+, Na+, K+, Ca2+, and Zn2+) are in the range -12 to -160 kcal/mol, and the interaction energies of protonated amino acids (Arg+ and Lys+) are in the range from -9 to -18 kcal/mol. In solutions the cation-π energies decrease with the dielectric constant ε of solvents. However, in aqueous solution the cation-π energies of H3O+ and protonated amino acids are less affected by solvation effects. The applications of unconventional interaction forces in drug design and in protein engineering are introduced.
    Current topics in medicinal chemistry 05/2013; · 4.47 Impact Factor
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    ABSTRACT: 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. Four interaction types of histidine are quantitatively calculated, including: (1) Cation-pi interactions, in which the histidine acts as the aromatic pi-motif in neutral form (His), or plays the cation role in protonated form (His+); (2) pi-pi stacking interactions between histidine and other aromatic amino acids; (3) Hydrogen-pi interactions between histidine and other aromatic amino acids; (4) Coordinate interactions between histidine and metallic cations. The energies of pi-pi stacking interactions and hydrogen-pi interactions are calculated using CCSD/6-31+G(d,p). The energies of cation-pi interactions and coordinate interactions are calculated using B3LYP/6-31+G(d,p) method and adjusted by empirical method for dispersion energy. The coordinate interactions between histidine and metallic cations are the strongest one acting in broad range, followed by the cation-pi, hydrogen-pi, and pi-pi stacking interactions. When the histidine is in neutral form, the cation-pi 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-pi interactions. In proteins the pi-pi 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.
    Chemistry Central Journal 03/2013; 7(1):44. · 1.31 Impact Factor
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    ABSTRACT: 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 {a(j)} and {b(p)} 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.
    Journal of Chemical Information and Modeling 04/2012; · 4.30 Impact Factor
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    ABSTRACT: The cation-π interactions occur frequently within or between proteins due to six (Phe, Tyr, Trp, Arg, Lys, and His) of the twenty natural amino acids potentially interacting with metallic cations via these interactions. In this study, quantum chemical calculations and molecular orbital (MO) theory are used to study the energies and properties of cation-π interactions in biological structures. The cation-π interactions of H⁺ and Li⁺ are similar to hydrogen bonds and lithium bonds, respectively, in which the small, naked cations H⁺ and Li⁺ are buried deep within the π-electron density of aromatic molecules, forming stable cation-π bonds that are much stronger than the cation-π interactions of other alkali metal cations. The cation-π interactions of metallic cations with atomic masses greater than that of Li⁺ arise mainly from the coordinate bond comprising empty valence atomic orbitals (AOs) of metallic cations and π-MOs of aromatic molecules, though electrostatic interactions may also contribute to the cation-π interaction. The binding strength of cation-π interactions is determined by the charge and types of AOs in the metallic cations. Cation-π interaction energies are distance- and orientation-dependent; energies decrease with the distance (r) and the orientation angle (θ). In solution, the cation-π energies decrease with the increase of the dielectric constant (ɛ) of the solvent; however, solvation has less influence on the H⁺-π and H₃O⁺-π interactions than on interactions with other cations. The conclusions from this study provide useful theoretical insights into the nature of cation-π interactions and may contribute to the development of better force field parameters for describing the molecular dynamics of cation-π interactions within and between proteins.
    Journal of molecular graphics & modelling 04/2012; 34:38-45. · 2.17 Impact Factor
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    ABSTRACT: 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.
    Journal of Computational Chemistry 01/2012; 33(2):153-62. · 3.84 Impact Factor
  • Qi-Shi Du, Ri-Bo Huang
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    ABSTRACT: For quite a long period of time in history, many intense efforts have been made to determine the 3D (three-dimensional) structure of the M2 proton channel. The reason why the M2 proton channel has attracted so many attentions is because (1) it is the key for really understanding the life cycle of influenza viruses, and (2) it is indispensable for conducting rational drug design against the flu viruses. Recently, the long-sough 3D structures of the M2 proton channels for both influenza A and B viruses were consecutively successfully determined by the high-resolution NMR spectroscopy (Schnell J.R. and Chou, J.J., Nature, 2008, 451: 591-595; Wang, J., Pielak, R.M., McClintock, M.A., and Chou, J.J., Nature Structural & Molecular Biology, 2009,16: 1267-1271). Such a milestone work has provided a solid structural basis for in-depth understanding the action mechanism of the M2 channel and rationally designing effective drugs against influenza viruses. This review is devoted to, with the focus on the M2 proton channel of influenza A, addressing a series of relevant problems, such as how to correctly understand the novel allosteric inhibition mechanism inferred from the NMR structure that is completely different from the traditional view, what the possible impacts are to the previous functional studies in this area, and what kind of new strategy can be stimulated for drug development against influenza.
    Current Protein and Peptide Science 11/2011; 13(3):205-10. · 2.33 Impact Factor
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    ABSTRACT: 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.
    Amino Acids 08/2011; 42(6):2353-61. · 3.91 Impact Factor
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    ABSTRACT: 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.
    Journal of Industrial Microbiology 08/2011; 38(8):927-33. · 1.80 Impact Factor
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    ABSTRACT: Introduction: The 2009-H1N1 influenza pandemic has prompted new global efforts to develop new drugs and drug design techniques to combat influenza viruses. While there have been a number of attempts to provide drugs to treat influenza, drug resistance has been a major problem with only four drugs currently approved by the FDA for its treatment. Areas covered: In this review, the drug-resistant problem of influenza A viruses is discussed and summarized. The article also introduces the experimental and computational structures of drug targeting proteins, neuraminidases, and of the M2 proton channel. Furthermore, the article illustrates the latest drug candidates and techniques of computer-aided drug design with examples of their application, including virtual in silico screening and scoring, AutoDock and evolutionary technique AutoGrow. Expert opinion: Structure-based drug design is the inventive process for finding new drugs based on the structural knowledge of the biological target. Computer-aided drug design strategies and techniques will make drug discovery more effective and economical. It is anticipated that the recent advances in structure-based drug design techniques will greatly help scientists to develop more powerful and specific drugs to fight the next generation of influenza viruses.
    Expert Opinion on Drug Discovery 06/2011; 6(6):619-31. · 2.30 Impact Factor
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    ABSTRACT: The proteins in a family, which perform the similar biological functions, may have very different amino acid composition, but they must share the similar 3D structures, and keep a stable central region. In the conservative structure region similar biological functions are performed by two or three catalytic residues with the collaboration of several functional residues at key positions. Communication signals are conducted in a position network, adjusting the biological functions in the protein family. A computational approach, namely structural position correlation analysis (SPCA), is developed to analyze the correlation relationship between structural segments (or positions). The basic hypothesis of SPCA is that in a protein family the structural conservation is more important than the sequence conservation, and the local structural changes may contain information of biology functional evolution. A standard protein P(0) is defined in a protein family, which consists of the most-frequent amino acids and takes the average structure of the protein family. The foundational variables of SPCA is the structural position displacements between the standard protein P(0) and individual proteins P(i) of the family. The structural positions are organized as segments, which are the stable units in structural displacements of the protein family. The biological function differences of protein members are determined by the position structural displacements of individual protein P(i) to the standard protein P(0). Correlation analysis is used to analyze the communication network among segments. The structural position correlation analysis (SPCA) is able to find the correlation relationship among the structural segments (or positions) in a protein family, which cannot be detected by the amino acid sequence and frequency-based methods. The functional communication network among the structural segments (or positions) in protein family, revealed by SPCA approach, well illustrate the distantly allosteric interactions, and contains valuable information for protein engineering study.
    PLoS ONE 01/2011; 6(12):e28206. · 3.53 Impact Factor
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    ABSTRACT: The plant SLAC1 is a slow anion channel in the membrane of stomatal guard cells, which controls the turgor pressure in the aperture-defining guard cells, thereby regulating the exchange of water vapour and photosynthetic gases in response to environmental signals such as drought, high levels of carbon dioxide, and bacterial invasion. Recent study demonstrated that bicarbonate is a small-molecule activator of SLAC1. Higher CO(2) and HCO(3)(-) concentration activates S-type anion channel currents in wild-type Arabidopsis guard cells. Based on the SLAC1 structure a theoretical model is derived to illustrate the activation of bicarbonate to SLAC1 channel. Meanwhile a possible CO(2) conducting and concentrating mechanism of the SLAC1 is proposed. The homology structure of Arabidopsis thaliana SLAC1 (AtSLAC1) provides the structural basis for study of the conducting and concentrating mechanism of carbon dioxide in SLAC1 channels. The pK(a) values of ionizable amino acid side chains in AtSLAC1 are calculated using software PROPKA3.0, and the concentration of CO(2) and anion HCO(3)(-) are computed based on the chemical equilibrium theory. The AtSLAC1 is modeled as a five-region channel with different pH values. The top and bottom layers of channel are the alkaline residue-dominated regions, and in the middle of channel there is the acidic region surrounding acidic residues His332. The CO(2) concentration is enhanced around 10(4) times by the pH difference between these regions, and CO(2) is stored in the hydrophobic region, which is a CO(2) pool. The pH driven CO(2) conduction from outside to inside balances the back electromotive force and maintain the influx of anions (e.g. Cl(-) and NO(3)(-)) from inside to outside. SLAC1 may be a pathway providing CO(2) for photosynthesis in the guard cells.
    PLoS ONE 01/2011; 6(9):e24264. · 3.53 Impact Factor
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    ABSTRACT: 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).
    Biotechnology Letters 12/2010; 32(12):1847-51. · 1.85 Impact Factor

Publication Stats

369 Citations
112.62 Total Impact Points

Institutions

  • 2007–2013
    • Guangxi Academy of Sciences
      Yung-ning, Guangxi Zhuangzu Zizhiqu, China
  • 2004–2013
    • Guangxi University
      • • College of Life Science and Technology
      • • Guangxi Key Laboratory of Subtropical Bioresource Conservation and Utilization
      Yung-ning, Guangxi Zhuangzu Zizhiqu, China
  • 2008
    • Tianjin Normal University
      T’ien-ching-shih, Tianjin Shi, China