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.

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.

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.

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.

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.

Microbial Routes to (2R,3R)-2,3-Butanediol: Recent Advances and Future Prospects

(2R,3R)-2,3-Butanediol has many industrial applications, such as it is used as an antifreeze agent and low freezing point fuel. In addition, it is particularly important to provide chiral groups in drugs. In recent years, this valuable bio-based chemical has attracted increasing attention, and significant progress has been made in the development of microbial cell factories for (2R,3R)-2,3-butanediol production. This article reviews recent advances and challenges in microbial routes to (2R,3R)-2,3- butanediol production, and highlights the metabolic engineering and synthetic biological approaches used to improve titers, yields, productivities, and optical purities. Finally, a systematic and integrative strategy for developing high-performance microbial cell factories is proposed.

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.

Active Hydrogen Bond Network (AHBN) and Applications for Improvement of Thermal Stability and pH-Sensitivity of Pullulanase from Bacillus naganoensis

A method, so called “active hydrogen bond network” (AHBN), is proposed for site-directed mutations of hydrolytic enzymes. In an enzyme the AHBN consists of the active residues, functional residues, and conservative water molecules, which are connected by hydrogen bonds, forming a three dimensional network. In the catalysis hydrolytic reactions of hydrolytic enzymes AHBN is responsible for the transportation of protons and water molecules, and maintaining the active and dynamic structures of enzymes. The AHBN of pullulanase BNPulA324 from Bacillus naganoensis was constructed based on a homologous model structure using Swiss Model Protein-modeling Server according to the template structure of pullulanase BAPulA (2WAN). The pullulanase BNPulA324 are mutated at the mutation sites selected by means of the AHBN method. Both thermal stability and pH-sensitivity of pullulanase BNPulA324 were successfully improved. The mutations at the residues located at the out edge of AHBN may yield positive effects. On the other hand the mutations at the residues inside the AHBN may deprive the bioactivity of enzymes. The AHBN method, proposed in this study, may provide an assistant and alternate tool for protein rational design and protein engineering.

Empirical Correction to Molecular Interaction Energies in Density Functional Theory (DFT) for Methane Hydrate Simulation

A general and empirical method is proposed for correction of London dispersion and other deficiencies in density functional theory (DFT). This method is based on the existing Lennard-Jones (L-J) equation and van der Waals parameters. The benchmark of energy correction is set as the energy difference between DFT and more accurate methods, for example CCSD(T). The energy correction includes all differences between CCSD(T) and DFT, dispersion energy, configuration interaction, induction interaction, residual correlation, and other effects. The energy correction is expressed as a linear combination of van der Waals potentials of nonbonded atomic pairs. The combination coefficients are determined using a least-squares approach in a training set of molecular pairs. The coefficients then can be used for the energy corrections in DFT calculations in a molecular family. Three correction equations of molecular pair interaction energy, water-water, water-methane, and methane-methane, are derived for methane hydrate simulation. The correction equation of the water-water pair is applied in the DFT calculation of water pentamer, yielding good intermolecular potential energy surfaces (PES), very close to the results of CCSD(T) over the active interaction range from 2.1 Å to 8.0 Å.

Exploring Strong Interactions in Proteins with Quantum Chemistry and Examples of Their Applications in Drug Design

OBJECTIVES

Three strong interactions between amino acid side chains (salt bridge, cation-π, and amide bridge) are studied that are stronger than (or comparable to) the common hydrogen bond interactions, and play important roles in protein-protein interactions.

METHODS

Quantum chemical methods MP2 and CCSD(T) are used in calculations of interaction energies and structural optimizations.

RESULTS

The energies of three types of amino acid side chain interactions in gaseous phase and in aqueous solutions are calculated using high level quantum chemical methods and basis sets. Typical examples of amino acid salt bridge, cation-π, and amide bridge interactions are analyzed, including the inhibitor design targeting neuraminidase (NA) enzyme of influenza A virus, and the ligand binding interactions in the HCV p7 ion channel. The inhibition mechanism of the M2 proton channel in the influenza A virus is analyzed based on strong amino acid interactions.

CONCLUSION

(1) The salt bridge interactions between acidic amino acids (Glu- and Asp-) and alkaline amino acids (Arg+, Lys+ and His+) are the strongest residue-residue interactions. However, this type of interaction may be weakened by solvation effects and broken by lower pH conditions. (2) The cation- interactions between protonated amino acids (Arg+, Lys+ and His+) and aromatic amino acids (Phe, Tyr, Trp and His) are 2.5 to 5-fold stronger than common hydrogen bond interactions and are less affected by the solvation environment. (3) The amide bridge interactions between the two amide-containing amino acids (Asn and Gln) are three times stronger than hydrogen bond interactions, which are less influenced by the pH of the solution. (4) Ten of the twenty natural amino acids are involved in salt bridge, or cation-, or amide bridge interactions that often play important roles in protein-protein, protein-peptide, protein-ligand, and protein-DNA interactions.

Insight into a molecular interaction force supporting peptide backbones and its implication to protein loops and folding

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 into better understanding the loop's unique stability and flexibility feature, as well as the driving force of the protein global folding.

Genome sequence of type strain Paenibacillus polymyxa DSM 365, a highly efficient producer of optically active (R,R)-2,3-butanediol

Paenibacillus polymyxa DSM 365, an efficient producer of (R,R)-2,3-butanediol, is known to show the highest production titer and productivity reported to date. Here, the first draft genome sequence of this promising strain may provide the genetic basis for further insights into the molecular mechanisms underlying the production of (R,R)-2,3-butanediol with high optical purity and at a high titer. It will also facilitate the design of rational strategies for further strain improvements, as well as construction of artificial biosynthetic pathways through synthetic biology for asymmetric synthesis of chiral 2,3-butanediol or acetoin in common microbial hosts.

Two-level QSAR network (2L-QSAR) for peptide inhibitor design based on amino acid properties and sequence positions

In the design of peptide inhibitors the huge possible variety of the peptide sequences is of high concern. In collaboration with the fast accumulation of the peptide experimental data and database, a statistical method is suggested for peptide inhibitor design. In the two-level peptide prediction network (2L-QSAR) one level is the physicochemical properties of amino acids and the other level is the peptide sequence position. The activity contributions of amino acids are the functions of physicochemical properties and the sequence positions. In the prediction equation two weight coefficient sets {ak} and {bl} are assigned to the physicochemical properties and to the sequence positions, respectively. After the two coefficient sets are optimized based on the experimental data of known peptide inhibitors using the iterative double least square (IDLS) procedure, the coefficients are used to evaluate the bioactivities of new designed peptide inhibitors. The two-level prediction network can be applied to the peptide inhibitor design that may aim for different target proteins, or different positions of a protein. A notable advantage of the two-level statistical algorithm is that there is no need for host protein structural information. It may also provide useful insight into the amino acid properties and the roles of sequence positions.

In depth analysis on the binding sites of adamantane derivatives in HCV (hepatitis C virus) p7 channel based on the NMR structure

BACKGROUND

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.

RESULTS

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.

CONCLUSIONS

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.

Unconventional interaction forces in protein and protein-ligand systems and their impacts to drug design

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(+), Ca(2+), and Zn(2+)) 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 orientationdependent, 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(+), Ca(2+), and Zn(2+)) 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.

Theoretical study on the polar hydrogen-π (Hp-π) interactions between protein side chains

BACKGROUND

In the study of biomolecular structures and interactions the polar hydrogen-π bonds (Hp-π) 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-π 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-π donors include R2NH, RNH2, ROH, and C6H5OH; and the acceptors are aromatic amino acids, peptide bond unit, and small conjugate π-groups. The Hp-π 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-π 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-π interactions are point to π-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-π 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-π interactions are in the region from 2.30 to 3.00 Å at the perpendicular direction to the π-plane, much longer than the common hydrogen bonds (~1.9 Å). (5) Like common hydrogen bond interactions, the Hp-π interactions are less affected by solvation effects.

Energies and physicochemical properties of cation-π interactions in biological structures

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.

Structural position correlation analysis (SPCA) for protein family

Background

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.

Methodology

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.

Conclusions

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.

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.

A possible CO2 conducting and concentrating mechanism in plant stomata SLAC1 channel

BACKGROUND

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.

METHODOLOGY

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.

CONCLUSIONS

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.

Progress in structure-based drug design against influenza A virus

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.

Correlation analysis for protein evolutionary family based on amino acid position mutations and application in PDZ domain

BACKGROUND

It has been widely recognized that the mutations at specific directions are caused by the functional constraints in protein family and the directional mutations at certain positions control the evolutionary direction of the protein family. The mutations at different positions, even distantly separated, are mutually coupled and form an evolutionary network. Finding the controlling mutative positions and the mutative network among residues are firstly important for protein rational design and enzyme engineering.

METHODOLOGY

A computational approach, namely amino acid position conservation-mutation correlation analysis (CMCA), is developed to predict mutually mutative positions and find the evolutionary network in protein family. The amino acid position mutative function, which is the foundational equation of CMCA measuring the mutation of a residue at a position, is derived from the MSA (multiple structure alignment) database of protein evolutionary family. Then the position conservation correlation matrix and position mutation correlation matrix is constructed from the amino acid position mutative equation. Unlike traditional SCA (statistical coupling analysis) approach, which is based on the statistical analysis of position conservations, the CMCA focuses on the correlation analysis of position mutations.

CONCLUSIONS

As an example the CMCA approach is used to study the PDZ domain of protein family, and the results well illustrate the distantly allosteric mechanism in PDZ protein family, and find the functional mutative network among residues. We expect that the CMCA approach may find applications in protein engineering study, and suggest new strategy to improve bioactivities and physicochemical properties of enzymes.

Empirical and accurate method for the three-dimensional electrostatic potential (EM-ESP) of biomolecules

The electrostatic potential (ESP) is an important property of interactions within and between macromolecules, including those of importance in the life sciences. Semiempirical quantum chemical methods and classical Coulomb calculations fail to provide even qualitative ESP for many of these biomolecules. A new empirical ESP calculation method, namely, EM-ESP, is developed in this study, in which the traditional approach of point atomic charges and the classical Coulomb equation is discarded. In its place, the EM-ESP generates a three-dimensional electrostatic potential V(EM)(r) in molecular space that is the sum of contributions from all component atoms. The contribution of an atom k is formulated as a Gaussian function g(r(k);alpha(k),beta(k)) = alpha(k)/r(k)(betak) with two parameters (alpha(k) and beta(k)). The benchmark for the parameter optimization is the ESP obtained by using higher-level quantum chemical approaches (e.g., CCSD/TZVP). A set of atom-based parameters is optimized in a training set of common organic molecules. Calculated examples demonstrate that the EM-ESP approach is a vast improvement over the Coulombic approach in producing the molecular ESP contours that are comparable to the results obtained with higher-level quantum chemical methods. The atom-based parameters are shown to be transferrable between one part of closely related aromatic molecules. The atom-based ESP formulization and parametrization strategy can be extended to biological macromolecules, such as proteins, DNA, and RNA molecules. Since ESP is frequently used to rationalize and predict intermolecular interactions, we expect that the EM-ESP method will have important applications for studies of protein-ligand and protein-protein interactions in numerous areas of chemistry, molecular biology, and other life sciences.

Designing inhibitors of M2 proton channel against H1N1 swine influenza virus

Background

M2 proton channel of H1N1 influenza A virus is the target protein of anti-flu drugs amantadine and rimantadine. However, the two once powerful adamantane-based drugs lost their 90% bioactivity because of mutations of virus in recent twenty years. The NMR structure of the M2 channel protein determined by Schnell and Chou (Nature, 2008, 451, 591–595) may help people to solve the drug-resistant problem and develop more powerful new drugs against H1N1 influenza virus.

Methodology

Docking calculation is performed to build the complex structure between receptor M2 proton channel and ligands, including existing drugs amantadine and rimantadine, and two newly designed inhibitors. The computer-aided drug design methods are used to calculate the binding free energies, with the computational biology techniques to analyze the interactions between M2 proton channel and adamantine-based inhibitors.

Conclusions

1) The NMR structure of M2 proton channel provides a reliable structural basis for rational drug design against influenza virus. 2) The channel gating mechanism and the inhibiting mechanism of M2 proton channel, revealed by the NMR structure of M2 proton channel, provides the new ideas for channel inhibitor design. 3) The newly designed adamantane-based inhibitors based on the modeled structure of H1N1-M2 proton channel have two pharmacophore groups, which act like a “barrel hoop”, holding two adjacent helices of the H1N1-M2 tetramer through the two pharmacophore groups outside the channel. 4) The inhibitors with such binding mechanism may overcome the drug resistance problem of influenza A virus to the adamantane-based drugs.

A fast and accurate method for predicting pKa of residues in proteins

Predicting the pH-activities of residues in proteins is an important problem in enzyme engineering and protein design. A novel predictor called 'Pred-pK(a)' was developed based on the physicochemical properties of amino acids and protein 3D structure. The Pred-pK(a) approach considers the influence of all other residues of the protein to predict the pK(a) value of an ionizable residue. An empirical equation was formulated, in which the pK(a) value was a distance-dependent function of physicochemical parameters of 20 amino acid types, describing their electrostatic and van der Waals interaction, as well as the effects of hydrogen bonds and solvation. Two sets of coefficients, {a(alpha)} and {b(l)}, were used in the predictor: {a(alpha)} is the weight factors of 20 amino acid types and {b(l)} is the weight factors of physicochemical properties of amino acids. An iterative double least square procedure was proposed to solve the two sets of weight factors alternately and iteratively in a training set. The two coefficient sets {a(alpha)} and {b(l)} thus obtained were used to predict the pK(a) values of residues in a protein. The average predictive error is +/-0.6 pH in less than a minute in common personal computer.

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.

Molecular potential energies in dodecahedron cell of methane hydrate and dispersion correction for DFT

The interaction potential energies of water-water and water-methane in structure-I unit cell of methane hydrate are calculated from 2.1 to 8.0A using density functional theory (DFT) B3LYP/TZVP. The curves of potential energies are corrected for basis set superposition error (BSSE) and dispersion interaction using a 4-term L-J (4,6-8,12) correction equation, which is derived from CCSD(T)/cc-pVTZ calculations of water-water and water-methane molecular pairs, using least squares curve-fitting. The methane hydrate unit cell is a regular water dodecahedron cell consisting of 20 water molecules with a methane molecule in the center. The geometries of water and methane are optimized at CCSD(T)/cc-pVTZ level. The BSSE-corrections are calculated for water-water and water-methane interaction energies as functions of the side length, l, of the dodecahedron cell at B3LYP/TZVP level in the range from 2.1 to 8.0A. The BSSE CP-corrected and dispersion-corrected potential energy surfaces (PES) of water-water and water-methane are useful for molecular dynamics simulation of gas clathrate-hydrates.

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.

Analogue inhibitors by modifying oseltamivir based on the crystal neuraminidase structure for treating drug-resistant H5N1 virus

The worldwide spread of H5N1 avian influenza and the increasing reports about its resistance to the existing drugs have made a priority for the development of the new anti-influenza molecules. The crystal structure of H5N1 avian influenza neuraminidase reported recently by Russell et al. [R.J. Russell, L.F. Haire, D.J. Stevens, P.J. Collins, Y. P. Lin, G.M. Blackburn, A.J. Hay, S.J. Gamblin, J.J. Skehel, The structure of H5N1 avian influenza neuraminidase suggests new opportunities for drug design, Nature 443 (2006) 45-49] have provided new opportunities for drug design in this regard. It is revealed through the structure that the active sites of the group-1 neuraminidases, which contain the N1 subtype, have a very different three-dimensional structure from those of group-2 neuraminidases. The key difference is in the 150-loop cavity adjacent to the conserved active site in neuraminidase. Based on these findings and by modifying oseltamivir, six analog inhibitors were proposed as candidates for developing inhibitors against H5N1 virus, particularly against the oseltamivir-resistant H5N1 virus strain.

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.

Study of drug resistance of chicken influenza A virus (H5N1) from homology-modeled 3D structures of neuraminidases

The spread of highly pathogenic H5N1 influenza virus in many Asian and European countries as well as its drug-resistance have raised serious worldwide concerns. In this paper, the structure-activity relationship between NA (neuraminidase) and its three inhibitors (DANA, zanamivir, and oseltamivir) was investigated. A homology model of H5N1-NA (BAE46950), which is the first reported oseltamivir-resistance virus strain, and the 108 homology-modeled 3D structures of chicken influenza H5N1 NAs downloaded from the website at , formed the molecular structural basis for the drug-resistance study. The multiple sequence and structure alignment for these NAs indicated that 11 functional residues were highly conserved except for AAF02313 with the mutated virus strain. However, the framework residues have remarkable mutations from N9-NA to H5N1-NA, and a few mutated residues were observed in different H5N1-NAs. A partially hydrophobic site S5 (formed by Ala246 and Thr247) in N9-NA is changed to a hydrophilic site (formed by Ala227 and Asn228) in H5N1-NA, while a hydrophilic site S6 (formed by Asn346 and Asn347) in N9-NA was replaced by a hydrophobic site (formed by Ala323 and Tyr324). All these mutations might be the reason for the oseltamivir-resistance by some H5N1 viruses. In order to find the possible drug-resistant H5N1 virus, similarity analysis was performed using the BAE46950 sequence as the benchmark template, and 21 sequences were found from the database of the 108 H5N1 NAs that had over 95% sequence similarity with BAE46950.

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.

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.

Molecular modeling studies of peptide drug candidates against SARS

Flexible alignment and docking studies were conducted for the three octapeptides, ATLQANEV, AVLQSGFR, and ATLQAIAS, that were cleavable by SARS-CoV Mpro. It has been observed that all pharmacophores of the three peptides overlap very well, and that ATLQANEV binds best with the receptor, followed by AVLQSGFR, and then ATLQAIAS. During the process of docking the octapeptides to the SARS enzyme, the residues of the catalytic dyad, i.e., His-41 and Cys-145 are actively involved in forming the hydrogen bonds, so is the center residue (Gln) of all the three octapeptides. The findings are fully consistent with experimental observations. The present studies suggest that the octapeptides ATLQANEV and ATLQAIAS, like AVLQSGFR, might also be the good starting points for designing potential drugs against SARS.

Progress in Computational Approach to Drug Development Against SARS

Since the outbreak of SARS (severe acute respiratory syndrome) in November 2002 in Southern China's Guangdong Province, considerable progress has been made in the development of drugs for SARS therapy. The present mini review is focused on the area of computer-aided drug discovery, i.e., the advances achieved mainly from the approaches of structural bioinformatics, pharmacophore modeling, molecular docking, peptide-cleavage site prediction, and other computational means. It is highlighted that the compounds C(28)H(34)O(4)N(7)Cl, C(21)H(36(O)5)N(6) and C(21)H(36)O(5)N(6) (Wei et al., Amino Acids, 2006, 31: 73-80), as well as KZ7088 (Chou et al. Biochem. Biophys. Res. Commun., 2003, 308: 148-151), a derivative of AG7088, might be the promising candidates for further investigation, and that the octapeptides ATLQAIAS and ATLQAENV, as well as AVLQSGFR, might be converted to effective inhibitors against the SARS enzyme. Meanwhile, how to modify these octapeptides based on the "distorted key" theory to make them become potent inhibitors is explicitly elucidated. Finally, a brief introduction is given for how to use computer-generated graphs to rapidly diagnose SARS coronavirus.

Amino Acid Principal Component Analysis (AAPCA) and its applications in protein structural class prediction

The extremely complicated nature of many biological problems makes them bear the features of fuzzy sets, such as with vague, imprecise, noisy, ambiguous, or input-missing information For instance, the current data in classifying protein structural classes are typically a fuzzy set To deal with this kind of problem, the AAPCA (Amino Acid Principal Component Analysis) approach was introduced. In the AAPCA approach the 20-dimensional amino acid composition space is reduced to an orthogonal space with fewer dimensions, and the original base functions are converted into a set of orthogonal and normalized base functions The advantage of such an approach is that it can minimize the random errors and redundant information in protein dataset through a principal component selection, remarkably improving the success rates in predicting protein structural classes It is anticipated that the AAPCA approach can be used to deal with many other classification problems in proteins as well.

Insights from modeling the 3D structure of H5N1 influenza virus neuraminidase and its binding interactions with ligands

The highly pathogenic H5N1 influenza virus, which is rapidly mutating and becoming increasingly drug-resistant, was investigated by means of structure-activity relationship between NA (neuraminidase) and three inhibitors, i.e., DANA (2,3-didehydro-2-deoxy-N-acetylneuraminic acid), zanamivir, and oseltamivir. A homology model of the H5N1-NA from the highly pathogenic chicken H5N1 A viruses isolated during the 2003-2004 influenza outbreaks in Japan was built based on the crystal structure of N9-NA complexed with DANA (PDB code: 1F8B). It was found that the traditional constituent residues around the active site of NA family are highly conserved in the H5N1-NA. However, a partially lipophilic pocket composed by Ala248 and Thr249 in N9-NA becomes a hydrophilic pocket because the two residues in the H5N1-NA are replaced by hydrophilic residues Ser227 and Asn228, respectively. On the other hand, two hydrophilic residues Asn347 and Asn348 in the N9-NA are replaced by two lipophilic residues Ala323 and Tyr324 in the H5N1-NA, respectively, leading to the formation of a new lipophilic pocket. This kind of subtle variation not only destroys the original lipophilic environment but also changes the complement interaction between the H5N1-NA and DANA. Such a finding might provide insights into the secret why some of H5N1 strains bear high resistance for existing NA inhibitors, and stimulate new strategies for designing new drugs against these viruses.

Heuristic molecular lipophilicity potential (HMLP): Lipophilicity and hydrophilicity of amino acid side chains

Heuristic molecular lipophilicity potential (HMLP) is applied in the study of lipophilicity and hydrophilicity of 20 natural amino acids side chains. The HMLP parameters, surface area S(i), lipophilic indices L(i), and hydrophilic indices H(i) of amino acid side chains are derived from lipophilicity potential L(r). The parameters are correlated with the experimental data of phase-transferring free energies of vapor-to-water, vapor-to-cyclohexane, vapor-to-octanol, cyclohexane-to-water, octanol-to-water, and cyclohexane-to-octanol through a linear free energy equation DeltaG(0)(tr,i) = b(0) + b(1)S(i) (+) + b(2)S(i) (-) + b(3)L(i) + b(4)H(i). For all above six phase-transfer free energies, the HMLP parameters of 20 amino acid side chains give good calculation results using linear free energy equation. HMLP is an ab initio quantum chemical approach and a structure-based technique. Except for atomic van der Waals radii, there are no other empirical parameters used. The HMLP has clear physical and chemical meaning and provides useful lipophilic and hydrophilic parameters for the studies of proteins and peptides.