The Arg-Gly-Asp (RGD) cell adhesion sequence occurs in several extracellular matrix molecules known to interact with integrin cell-surface receptors. Recently published crystal structures of the extracellular regions of two integrins in complex with peptides containing or mimicking the RGD sequence have identified the Arg and Asp residues as key specificity determinants for integrin recognition, through hydrogen bonding and metal coordination interactions. The central Gly residue also appears to be in close contact with the integrin surface in these structures.When hydrogen atoms are modelled on the central Gly residue with standard stereochemistry, the interaction between this residue and a carbonyl group in the integrin surface shows all the hallmarks of Cα-H···O=C hydrogen bonding, as seen in the collagen triple helix and in many crystal structures of small organic molecules. Moreover, molecular dynamic simulations of the docking of RGD-containing fragments on integrin surfaces support the occurrence of these interactions. There appears to be an array of four weak and conventional hydrogen bonds lining up the RGD residues with main chain carbonyl groups in the integrin surface.The occurrence of weak Cα-H···O=C hydrogen bonds in the RGD-integrin interaction highlights the importance of the conserved Gly residue in the RGD motif and its contribution to integrin-ligand binding specificity. Our analysis shows how weak hydrogen bonds may also play important biological roles by contributing to the specificity of macromolecular recognition. The Arg-Gly-Asp (RGD) sequence is one of the most easily recognised motifs in molecular biology . DiscovecRGD) [As a consequence of their role in so many fundamental processes, integrin defects have been implicated in many common diseases, from cancer to pathogen invasion. An ability to block a particular integrin-ligand interaction may be a possible route to the control of certain pathological states, hence it is not surprising that some integrins have become attractive targets for drug design. Understanding the molecular bases of the interaction of integrins with their ligands is therefore essential for effective protein-based design of inhibitors or activators of their function. A milestone was reached in 2002 with the determination of the crystal structure of the extracellular segment of αVβ3 integrin in complex with a cyclic peptide containing the prototypical RGD sequence . In thatcRGD) .cRGD-αVβ3 structure and in the structures of αIIbβ3 in complex with ligand-mimetics . Both Hα atoms from the collagen Gly residues are in hydrogen bonding position (Cα–Hα···O > 90°), and their Cα-Hα···O=C hydrogen bonds adopt a three-centred and bifurcated configuration . Positional errors inevitable at that resolution may affect the precision of the fitting of the cRGD and EFB peptides and the accuracy of the hydrogen bonding geometries for both weak and strong hydrogen bonds. For example, a close look at the salt-bridge interactions between the Arg guanidinium group from the cRGD peptide and two Asp side chains on the αV integrin surface (Asp150 and Asp218), shows less than "ideal" hydrogen bonding orientation, especially for Asp150 (not shown). Yet, the accumulated knowledge of hydrogen bonding geometries in high-resolution crystal structures and their significant variability leaves no doubt about the existence of these strong hydrogen bonds and their contribution to the specificity of binding.An obvious 2 hydrogen bond from ab initio quantum calculations [cRGD-αVβ3 and EFB-αIIbβ3 structures respectively, even though the accuracy of the values shown in Table cRGD and EFB peptides and the main chain carbonyl group from Arg216 in the integrin surface bear all the characteristics of Cα-Hα···O=C hydrogen bonding. This observation is consistent with the exceptionally high frequency of intermolecular Gly-Cα-H···O=C hydrogen bonds recently reported in high resolution crystal structures of protein-ligand complexes [A similar level of confidence can be achieved for the Gly-Cα-Hα···O=C hydrogen bond by analysing the metrics of equivalent interactions in high-resolution crystal structures of small organic and organometallic molecules. Figure ulations . Figure omplexes .cRGD-αVβ3 and EFB-αIIbβ3 crystal structures), forming one or two Cα-H···O hydrogen bonds that complement the main metal-coordination and salt-bridge interactions from the Asp and Arg side chains.A simple molecular docking analysis further supports the occurrence of Gly-Cα-H···O=C hydrogen bonds between RGD-containing ligands and integrin binding sites. In a first set of calculations, an RGD tripeptide was docked into the binding sites of both αVβ3 and αIIbβ3 integrins using constrained molecular dynamics (MD). Two constraints were imposed in the docking calculations: the carboxyl group from the Asp residue had to complete the metal coordination on the β3 subunit, and the Arg side chain had to form a salt bridge with appropriate Asp residues in the αV and αIIb subunits (see Methods), as observed in the crystal structures of αVβ3 and αIIbβ3 with different ligand-mimetics. Ten slightly different RGD models were obtained from the NMR structures of the adhesion domain of fibronectin , and wercRGD-αVβ3 and EFB-αIIbβ3 complexes? A quantitative analysis of C-H···O hydrogen bonding at protein-protein interfaces has shown that they have an important contribution to the association and stability of protein complexes, accounting for about one third of the total hydrogen bonding interaction energy [How important are these weak C-H···O hydrogen bonds in stabilising the n energy . In factn energy .N-terminal to the conventional N-H···O=C hydrogen bond in β-sheets [N-terminal to the one carrying the N-H donor group, and both share the same C = O group as acceptor, an arrangement sometimes referred as "bifurcated" hydrogen bond [N-terminal to the one carrying the N-H donor group is Gly, with one or two Hα from Gly being in hydrogen bonding position. The bifurcated hydrogen bond scenario also occurs in the cRGD-αVβ3 and EFB-αIIbβ3 structures, where the N-H group from the Asp residue in the RGD peptide donates a hydrogen bond to the main chain carbonyl group from Arg216 of β3. This hydrogen bond has very bad geometry in the cRGD-αVβ3 structure , but looks better in the EFB-αIIbβ3 structure . These deviations from ideal hydrogen bonding geometry might be consequence of the resolution of the crystal structures, but all the MD docking simulations described above result in N-H···O=C hydrogen bonds that are slightly longer  and slightly less linear  than the average hydrogen bonds between peptide groups in protein secondary structures. Automatic computational docking calculations of known integrin ligands on structural models of αVβ3 and αVβ5 consistently predict this N-H···O=C hydrogen bond to occur whenever an N-H group is present in the proximity of the carboxylate moiety [The recurrent appearance of some weak hydrogen bonding topologies in many structures of proteins and at protein-protein interfaces also reinforces the notion that they have a significant contribution to macromolecular stability. The most common occurrence of C-H···O hydrogen bonds in protein structures is a widespread Cα-H···O=C hydrogen bond β-sheets ,17 and iβ-sheets . In thisgen bond ,27. Thise moiety ,36. Had cRGD and EFB peptides and the αVβ3 and αIIbβ3 surfaces suggests an additional candidate for classification as C-H···O=C hydrogen bond, between the Cβ-Hβ group from the Asp residue of the cRGD and EFB peptides and the main chain C = O group from Asn215 in the β3 subunit. This weak hydrogen bond is adjacent to the stronger, conventional hydrogen bond between the main chain N-H group from Asn215 and Oδ2 from the Asp residue in the RGD motif. Thus, a total of four hydrogen bonds, weak and conventional, aligns the bottom of the cRGD and EFB peptides against the integrin surfaces , PDB accession code 1L5G [cRGD or EFB were also maintained. Hydrogen atoms were built with standard stereochemistry for the cRGD and EFB peptides and for the integrin RGD-binding sites as defined above, using the program REDUCE [The following crystal structure coordinates were downloaded from the Protein Data Bank : αVβ3 inode 1L5G ; αIIbβ3 ode 1L5G ; αIIbβ3 ode 1L5G ; αIIbβ3 m REDUCE . For thecRGD-αVβ3 and EFB-αIIbβ3 structures for guidance. Then each docked model was pulled away to about 10 Å from the integrin surfaces, and was docked back into the integrin binding site via molecular dynamics (MD) simulations using the program CNS [cRGD-αVβ3 and EFB-αIIbβ3 structures. The side chain of the Asp residue group was restrained to coordinate the bound metal ion in the RGD-binding sites and to receive a hydrogen bond from the amide group of Asn215, in the β3 subunit. The side chain of the Arg residue was restrained to form hydrogen bonds with residues Asp150 and Asp218 on the αV subunit or residue Asp224 on the αIIb subunit. Additional restraints were imposed in the MD simulations with the VTGRGDSPAS peptide: the ring of Pro172 was restrained to hydrophobic contact with the side chain of Lys125, in the β3 subunit, and the Cα atoms of the N- and C-terminal residues in the peptide model were restrained not to separate more than 5 Å from each other. The coordinates of the integrin binding sites were kept fixed in all the simulations, and only the peptides were allowed to refine by restrained MD and energy minimisation. All molecular models were analysed with the program CHAIN [For the molecular docking calculations, conformational models for RGD and VTGRGDSPAS peptides were obtained from the NMR structures of the adhesion domain in fibronectin . Ten congram CNS . Five siam CHAIN  in a SilA survey in the Cambridge Structural Database  July 20.cRGD, cyclic pentapeptide with sequence Arg-Gly-Asp-D-Phe-N(Me)-Val; EFB, eptifibatide; PDB, Protein Data Bank; CSD, Cambridge Structural Database; MD, molecular dynamics.ECM, extracellular matrix; RGD, Arg-Gly-Asp sequence; J.B. conceived the study and carried out the analysis of the structural data and molecular docking calculations. Both authors participated in the design, coordination and writing of the manuscript. Both authors read and approved the final manuscript.


The structure involves hydrogen bonded layers with apertures stacked along the  Hydrogen bonded organic structures have emerged to become an important area of research because of the wide variety of fascinating structural features as well as properties exhibited by them. Of the many interesting hydrogen bonded systems, those formed by cyanuric acid and melamine are specially interesting. Thus, cyanuric acid forms hydrogen bonded adducts with melamine,  4,4'-bipRod-shaped crystals of the 1:1 molecular complex of melamine and uracil were obtained by the slow evaporation of an equimolar solution of the two compounds in methanol at room temperature. The crystal data was collected on a Bruker-Nonius diffractometer with Kappa geometry attached with an APEX-II CCD detector. The intensity data were processed using SAINT  softwarea and b axes. Such an arrangement of the chains gives rise to an aperture structure as shown in a and b directions. We show one perspective in a-direction. It is interesting to note that melamine engages in hydrogen bonding utilizing all the donor-acceptor sites, much the same way it does in the cyanuric acid adduct. [The crystal structure determination gave the asymmetric unit as shown in  adduct.  However,In conclusion, we have illustrated the molecular recognition process of melamine with nucleobase uracil. We have also established the presence of channels, in the three-dimensional supramolecularly hydrogen-bonded structure of this adduct.File 1The data provided represent the structural and refinement details of the melamine-uracil crystal.


Trimethoprim  is an antifolate drug. It selectively inhibits the bacterial dihydrofolate reductase (DHFR) enzyme.1) and trimethoprim-hydrogen adipate (2), one of the N atoms of the pyrimidine ring is protonated and it interacts with the deprotonated carboxylate oxygens through a pair of nearly parallel N-H...O hydrogen bonds to form a fork-like interaction. In the compound 1, the pyrimidine moieties of the TMP cations are centrosymmetrically paired through a pair of N-H...N hydrogen bonds involving 4-amino group and the N (N3) atom of the pyrimidine rings to form a 8-membered hydrogen bonded ring [R22(8)]. The 4-amino group of one TMP moiety and 2-amino group of another TMP moiety (both moieties are members of a base pair) are bridged by the carbonyl oxygen of the phthalate moiety through N-H...O hydrogen bonds forming 8-membered hydrogen-bonded ring [R22(8)]. The characteristic hydrogen-bonded rings observed in the structure aggregate into a supramolecular ladder consisting of a pair of chains, each of which is built up of alternate TMP and hydrogen phthalate ions. In the compound 2, two TMP cations and two hydrogen adipate anions are arranged about an inversion center so that the complementary DDAA  arrays of quadruple hydrogen-bonding patterns are formed. The head-to-tail arrangement of the hydrogen adipate ions leads to a hydrogen-bonded supramolecular chain. From crystal engineering point of view, it is interesting to note that the compound 1 has a hydrogen-bonded network remarkably identical with its aliphatic analogue, trimethoprim hydrogen maleate. Similarly the compound 2, resembles its homolog trimethoprim hydrogen glutarate.In the crystal structures of trimethoprim (TMP)-hydrogen phthalate (In the crystal structure of trimethoprim hydrogen phthalate, the hydrogen-bonded network is remarkably identical with its aliphatic analogue, trimethoprim hydrogen maleate. Similarly in the crystal structure of trimethoprim hydrogen adipate the hydrogen bonded network resembles its homolog trimethoprim hydrogen glutarate. Non-covalent interactions are the essential tool for both crystal engineering and supramolecular chemistry –4. SupraThe hydrogen-bonding networks in the crystal structures of TMP/pyrimethamine salts of various dicarboxylic acids have been investigated in our laboratory ,21. Rece1 & 2 is shown in 1  and 2 (trimethoprim hydrogen adipate)see  one of tpate)see  hydrogen1 (22(8)] [21(4)]  . The ato (22(8)] , trimeth (22(8)] , pyrimet (22(8)]  etc,. Th (22(8)] . The one [21(4)] .2 ° and 119.6(2)° respectively, the corresponding angle in the neutral trimethoprim(TMP) molecule [1 and 2 the dihedral angles between the plane of the pyrimidine and phenyl rings are 74.0(7)° and 88.8° respectively. These values are closer to the crystal structures of TMP-sulfate trihydrate [The internal angles at N1 (C2-N1-C6) in the protonated pyrimidine ring of the compounds molecule  being 11ihydrate  (75.8(9)ihydrate  (89.1(1)gauche-gauche-trans (ggt) and the gauche-trans-trans (gtt) forms, respectively. Of the 46 adipic acid fragments present in the Cambridge Crystallographic Data Base [ggt conformation [gtt form [syn orientation so that they can fasten the 2- and 4-amino groups of the TMP molecule. The disorder, on the other hand, might result from incompatible dimensions between the adipic acid and the two amino groups of the TMP molecule. Much better fit between the 2- and 4-amino groups of the TMP molecule on one side, and aliphatic dicarboxylic acid on the other side is achieved in the case of glutaric acid [The major (77%) and minor (23%) components in the disordered hydrogen adipate molecule adopt quite unusual bent carbon chain conformations: the ata Base  there isormation  and two gtt form –35. The ric acid . This is1 and 2 the corresponding torsion angles adopt the values -161.4(1) and 63.5°, and 69.1(2) and 36.5(3)°, respectively. Hence the observed TMP conformations match the two most densely populated conformations observed in other crystal structures containing the TMP moieties.The TMP molecule can be regarded as having a rigid frame, built on the methyl group, on which the substituted phenyl and pyrimidine six-membered rings are free to rotate. An arbitrary conformation of this molecule can be described by the torsion angles of the two rings to the frame. We define these torsion angles as C4-C5-C7-C8 and C5-C7-C8-C9, i.e. with respect to one of the rings the other can rotate around the C5-C7 or around the C7-C8. 1, the ionized and non-ionized carboxyl groups are inclined at an angles of 6.0(1)° and 9.0(1)° respectively to the plane of the phenyl ring. The bond angles of C19-C17-O4, C19-C17-O5 in the carboxyl group are 119.1(2)° and 120.7(17)° respectively. The similar angles at the carboxylate group C20-C18-O6 and C20-C18-O7 are 120.4(2)° and 117.4(2)° respectively. These values are comparable with the crystal structure of pyrimethamine hydrogen phthalate [2, the bond angles at carboxylate group, C18-C17-O5 and C18-C17-O4 are 120.1(2)° and 116.5(2)° respectively, whereas the angle at the carboxyl group C21-C22-O6, C21-C22-O7 are 123.1(2)° and 113.1(2)° respectively.In the compound hthalate . In the 2 is further stabilized by two C-H...π interactions [The crystal structure of compound ractions  [C16-H16File 1the CIF informationFile 2experimental details


The limited fossil fuel prompts the prospecting of various unconventional energy sources to take over the traditional fossil fuel energy source. In this respect the use of hydrogen gas is an attractive alternate source. Attributed by its numerous advantages including those of environmentally clean, efficiency and renew ability, hydrogen gas is considered to be one of the most desired alternate. Cyanobacteria are highly promising microorganism for hydrogen production. In comparison to the traditional ways of hydrogen production , Cyanobacterial hydrogen production is commercially viable. This review highlights the basic biology of cynobacterial hydrogen production, strains involved, large-scale hydrogen production and its future prospects. While integrating the existing knowledge and technology, much future improvement and progress is to be done before hydrogen is accepted as a commercial primary energy source. Its advantages as fuel are numerous: it is eco-friendly, efficient, renewable, and during its production and utilization no COenerated . By the Gloeocapsa alpicola under sulphur starvation shows increased hydrogen production [Arthrospira (Spirulina platensis) can produce hydrogen (1 μmole H2/12 hr/mg cell dry weight) in complete anaerobic and dark condition [Anabaena cylindrica, produces hydrogen and oxygen gas simultaneously in an argon atmosphere for 30 days in light limited condition [Cycas revoluta  and Zamia furfuracea showed a significant in vivo hydrogen uptake [Anabaena sp. is able to produce significant amount of hydrogen. Among them nitrogen-starved cells of Anabaena cylindrica produces highest amount of hydrogen (30 ml of H2/lit culture/hour). Hydrogenase-deficient cyanobacteria Nostoc punctiforme NHM5 when incubated under high light for a long time, until the culture was depleted of CO2 shows increase in hydrogen production [Cyanobacteria form a large and diverse group of oxygenic photoautotrophic prokaryotes, many of which have the ability to produce hydrogen Table . Hydrogeoduction . Arthrosondition . Anotherondition . Symbiotn uptake . Anabaenoduction .Nitrogenase and it is found in the heterocysts of filamentous cyanobacteria when they grow under nitrogen limiting conditions. Hydrogen is produced as a byproduct of fixation of nitrogen into ammonia. The reaction consumes ATP and has the general form:Cyanobacteria are photoautotrophic microorganisms -23 that nifD and nifK, α and β respectively) and the other is dinitrogenase reductase . Dinitrogenase is a α2β2 heterotetramer, having molecular weight of about 220 to 240 kDa respectively, breaks apart the atoms of nitrogen. Dinitrogenase reductase is a homodimer of about 60 to 70 kDa and plays the specific role of mediating the transfer of electrons from the external electron donor (a ferredoxin or a flavodoxin) to the dinitrogenase [A Nitrogenase enzyme consists of two parts:one is dinitrogenase  [hoxFUYH) and it can either take up or produce hydrogen. Uptake hydrogenase enzymes are found in the thylakoid membrane of heterocysts from filamentous cyanobacteria, where it transfers the electrons from hydrogen for the reduction of oxygen via the respiratory chain in a reaction known as oxyhydrogenation or Knallgas reaction. The enzyme consists of two subunits. The hupL-coded protein is responsible for the up taking hydrogen and the smaller subunit that is coded by hupS looks after the reduction affair. The hydrogen formed is usually re oxidized by an uptake hydrogenase via a Knallgas reaction and hence there is no net H2 production in strains with uptake hydrogenases under ambient conditions. So it is counterproductive when the goal is to produce hydrogen on a commercial scale. The reaction catalyzed by the uptake hydrogenase takes the following form:The other hydrogen-metabolizing/producing enzymes in cyanobacteria are y hupSL) , has thebidirectional or reversible hydrogenase, is poorly understood and thought to control ion levels in the organism. Reversible hydrogenase is associated with the cytoplasmic membrane and likely functions as an electron acceptor from both NADH and H2 [+ reducing type consisting of a hydrogenase dimmer coded by hoxYH gene. Maturation of reversible hydrogenases requires the action of several auxillary proteins collectively termed as hyp  [The biological role of H and H2 . The revH and H2 ,35. Molend hypB) . Unlike Hydrogen photo evolution catalyzed by nitrogenases or hydrogenases can only function under anaerobic conditions due to their extreme sensitivity to oxygen. Since oxygen is a byproduct of photosynthesis, organisms have developed the following spatial and temporal strategies to protect the enzyme from inactivation by oxygen, these are:(a) Heterocyst-containing cyanobacteria physically separate oxygen evolution from nitrogenase activity by segregating oxygenic photosynthetic activity in vegetative cells and nitrogenase activity in heterocystis with reduced oxygen-permeability  and(b) Non-heterocystous cyanobacteria separate oxygen-evolution from nitrogenase activity by performing these functions during light and dark periods respectively .Important parameters for hydrogen production: Efficiency of hydrogen production depends on several factors or parameters that are more critical when hydrogen production is aimed at a large scale.Various parameters influence hydrogen production in different ways, a few selected parameters are presented with examples:A. Environmental conditions: Several environmental conditions such as light, temperature, salinity, nutrient availability, gaseous atmosphere play a role in hydrogen production. Requirement of different cyanobacterial species are different for optimum hydrogen production.Arthrospira platensis) produces hydrogen under anaerobic conditions, both in the dark and in the light [Synechococcus PCC7942 occurs under in the dark under anaerobic condition [Spirulina platensis can produce hydrogen optimally at 32°C in complete anaerobic and dark condition [Anabaena variabilis ATCC 29413 and in its mutant PK84 [Anabaena variabilis PK84 with air containing 2% CO2 was stimulated by light [Nostoc muscorum is catalyzed by nitrogenase more hydrogen is produced in this strain in the light than in the dark [Anabaena cylindrica, produces hydrogen under an argon atmosphere for 30 days in light limited (luminous intensity 6.0 W/m-2) and 18 days under elevated light (luminous intensity 32 W/m-2) [Anabaena cylindrica for a prolonged period under light limited condition occurs in the absence of exogenous nitrogen [2 to 60 W/m2 [I) Light: Although most cyanobacterial species preferentially absorb red light near 680 nm , light rhe light  but sevehe light . Hydrogeondition . Spiruliondition . The higthe dark . Anabaen2 W/m-2) . Continunitrogen . The effnitrogen . Nitroge 60 W/m2 .Nostoc cultured at 22°C showed higher rates of hydrogen production than at 32°C [Nostoc muscorum SPU004 showed optimum hydrogen production at 40°C [Anabaena variabilis SPU 003 on the other hand show optimum hydrogen production at 30°C [II) Temperature: The optimum temperature for hydrogen production for most cyanobacterial species is between 30–40°C and varies from species to species of cyanobacteria. For example,  at 32°C , while N at 40°C . Anabaen at 30°C .+ ions from within the cells or prevention of Na+ influx [III) Salinity: Salinity does effect hydrogen production by cyanobacteria . In gene+ influx .Anabaena variabilis SPU003 is highly sensitive to Co, Cu, Mn, Zn, Ni, Fe ions and shows no hydrogen production at concentrations below 10 mM for these ions [Anabaena cylindrica grown with 5.0 mg of Ferric ions per liter produce hydrogen at a rate about twice that of culture with 0.5 mg of Ferric ions per liter [IV) Micronutrients: Trace elements such as cobalt (Co), copper (Cu), molyblednum (Mo), zinc (Zn), and nickel (Ni) effects hydrogen production . Many ofese ions . A cultuer liter .V) Carbon source: Carbon sources are also known to influence the hydrogen production considerably by influencing nitrogenase activity . PresencAnabaena variabilis SPU003 and Anabaena cylindrical [Anabaena cylindrical ammonium addition (0.2 mM NH4+) at a given time point eventually suppresses hydrogen production, but periodic addition of lower amounts (0.1 mM ammonium chloride) do not inhibit hydrogen evolution [Anabaena cylindrica culture, oxygen production gets dominated with the incremental addition of ammonium chloride (from 0.1 mM to 0.5 mM) [VI) Nitrogen source: Several inorganic nitrogenous compound influence hydrogen production rates in many ways. Nitrite, nitrate and ammonia have been reported to inhibit nitrogenase in indrical ,46. Geneindrical . Althougvolution  while otvolution . In Anab 0.5 mM) . The hyd 0.5 mM) .VII) Molecular nitrogen: Molecular nitrogen is a competitive inhibitor for hydrogen production and removal of molecular nitrogen if often very necessary for hydrogen production. Hydrogen production may be considerably inhibited in presence of molecular nitrogen .VIII) Effect of oxygen on hydrogen production: Due to their extreme sensitivity to oxygen, hydrogen photo evolution catalyzed by nitrogenases or hydrogenases can function only under anaerobic conditions  As oxygeGloeocapsa alpicola and Synechocystis PCC 6803). It is possible to inhibit the oxygenic photosynthesis and enhance hydrogen production by incubating the cynobacteria in nutrients that lack sulfur [IX) Effect of sulfur on hydrogen production: Sulfur starvation enhances the rate of hydrogen production in several cyanobacterial species  Effect of Methane: Increased hydrogen production (up to four times) is observed in lination .B. Intrinsic factors affecting hydrogen production: There are several intrinsic factors such as genetic components or sensitive proteins in cyanobacteria that may affect hydrogen production.I) Presence of uptake hydrogenase and decreased hydrogen yield: The net hydrogen yield is affected in strains containing uptake hydrogenase. Much of the produced hydrogen is lost due to the activity of the uptake hydrogenase . KnockinII) Sensitivity of hydrogenase and nitrogenase to molecular oxygen: The molecular oxygen acts as an inhibitor for hydrogenase and nitrogenase. However, innovative technical interdisciplinary solutions, as described below, are now available to reduce or eliminate presence of molecular oxygen and increase yield of hydrogen .a) With the advances in nanotechnology it has become possible to build semi-permeable membranes around the organisms. An example is a membrane that discriminates by size with an active transport system . NanotechydS and hydL from Thiocapsa roseopersicina into sensitive organisms may help reducing the oxygen sensitivity [b) Problem to oxygen sensitivity can also be addressed by using sulfur stress phenomenon to down-regulate photosynthesis as described before. This creates the anaerobic conditions required for hydrogen production. This problem may also be addressed by engineering the native hydrogenase. Engineering oxygen-tolerant hydrogenase genes, for example, sitivity . An exprsitivity .2 fixation [hetR . Recombinant strains can be created with HetR protein harboring vectors that may allow with variable and controllable heterocyst frequency [III) Heterocystous cyanobacteria are more efficient to produce hydrogen than cyanobacteria with vegetative cells ,51. Thesrequency .Bioreactors are essential for large-scale production of hydrogen. Since light is an essential parameter for cyanobacterial growth so all such bioreactors must be transparent and hence are called photobioreactors ,55. All Several types of bioreactors have been used for hydrogen production. These can be mainly divided into three types of photobioreactors (PBRs): vertical column reactor, tubular type and flat panel photobioreactor. A reactor for photobiological hydrogen production must meet several conditions:1) Photobioreactor should be an enclosed system so that the produced hydrogen may be collected without any loss.2) The reactor design must allow sterilization with convenience and ease.3) To maximize the area of incident light  photobioreactor design should provide high surface to volume ratio.Rhodobactor sp. [(a) Vertical column reactor (air-lift loop reactor and bubble column): Such PBRs consists of a transparent column usually made up of high quality glass and surrounded by a water jacket that while allowing maintenance of the temperature with circulating water allows adequate entry of light. Reactor top has provision for medium inlet and outlets for the gases such as argon and for the hydrogen. Fresh medium is added from a reservoir from above the PBR ,58. Micrctor sp. ,57,58. T2. Alternately red light emitting diode (LED peaking at 665 nm) is used as the light source on one side [(b) Flat panel Photobioreactor: A typical flat-panel PBR consists of a stainless-steel frame and three polycarbonate panels . The reaone side . A membrone side . The pro(c) Tubular photobioreactors: Tubular PBRs consists of long transparent tubes with diameters ranging from 3 to 6 cm, and lengths ranging from 10 to 100 m . The culCyanobacterial hydrogen production is poised to be a very useful commodity provided various effective utilization of the produced hydrogen is devised. There are various applications where the process of biological hydrogen production by cyanobacteria can be well utilized. The examples can be included from food and chemical industries, which employ the process of hydrogenation to produce derivatives that are used as food additives, commodities, and fine chemicals:5 and C4 polyols can be obtained from cheap C6 carbohydrates by oxidative decarbonylation followed by hydrogenation. These polyols blends are useful for the manufacture of polyesters or alkyl resins employed in the manufacture of paints. Sorbitol is another important polyol produced from hydrogenation of glucose, which is used industrially in a variety of physical and chemical processes, . Derivatives are also used in protecting coatings, plasticizers, emulsifiers and detergents.(a) Hydrogenation of cheap carbohydrates into high valued derivatives: High value-added C(b) Hydrogenation of fatty acids: Hydrogenation of fatty acids is used to manufacture margarines, shortenings, and shortening oils. The hydrogenation of oils converts liquid oils into hard fats by adding hydrogen to the fat molecule. Oils can be hydrogenated to varying degrees, depending on the hardness. These fats are desirable for its melting point, allowing for high temperature cooking and frying. Hydrogenation involves the artificial saturation of unsaturated bond(s) present in fatty acids. In the process fatty acids are put under pressure, using hydrogen gas at temperatures of 120–210°C (248–410°F) in the presence of a metal catalyst   for six All these process requires the rigorous hydrogenation. If hydrogen produced by cyanobacteria is very economical in comparison to the traditional large-scale hydrogen production. Cyanobacterial hydrogen produced in a photobioreactor can easily be directed to separate compartments containing the substrate for hydrogenation and specific catalysts. Provisions of external heating as well as purifying the products from different contaminants  may be easily provided as well. Hydrogen generated in near vicinity in PBRs will reduce requirement for transport and hazards associated with transport. Apart from hydrogenation, hydrogen is combustible so it can be well applied as a substitute for conventional fuel or may be used in fuel cells to generate electricity.2 in combustion, liberates large amounts of energy per unit weight in combustion, easily converted to electricity, and is an inexhaustible resource. Biological hydrogen production has several advantages over other conventional hydrogen production processes. It requires the use of a simple photobioreactor akin to a transparent closed box, with low energy requirements and it is very cost effective. Electrochemical hydrogen production via solar battery-based water splitting requires the use of solar batteries with high-energy requirements. Low conversion efficiencies of biological systems can be compensated for, by low energy requirements and reduced initial investment costs. The most appealing aspect of the biological hydrogen production is the source of hydrogen, which is nothing but water. With the existing knowledge of bioengineering it possible to obtain sufficient amount of hydrogen that on combustion will liberate energy and therefore could act as a substitute of coal in several operations [3) is much lower compared to that produced by photovoltaic splitting of water ($170/m3) [Hydrogen gas is seen, as a future energy carrier by virtue of the fact that it does not evolve the "greenhouse gas" COerations . Reporte$170/m3) . Though 2 Carbon dioxide; ATP Adenosine triphosphate; NOx Nitrogen oxides; Mo Molybdenum; Fe Iron; V Vanadium; PBR Photo bioreactor; H2 Hydrogen; O2 Oxygen; N2 Nitrogen; NADP Nicotinamide Adenine di nucleotide Phosphate; Ni Nickel; Co Cobalt; Cu Copper; Zn Zinc; Mn Manganese; Na+ Sodium ion; CH4 Methane; C4 Four carbon compounds; C5 Five carbon compounds; C6 Six-carbon compounds.ADP Adenosine di-phosphate; CODD and DD collected and read the papers and prepared a rough skeleton of manuscript. SC co-directed and supervised the work. SKB directed and prepared the final manuscript.


An important element in homology modeling is the use of rotamers to parameterize the sidechain conformation. Despite the many libraries of sidechain rotamers that have been developed, a number of rotamers have been overlooked, due to the fact that they involve hydrogen atoms.We identify new, well-populated rotamers that involve the hydroxyl-hydrogen atoms of Ser, Thr and Tyr, and the sulfhydryl-hydrogen atom of Cys, using high-resolution crystal structures (<1.2 Å). Although there were refinement artifacts in these structures, comparison with the electron-density maps allowed the placement of hydrogen atoms involved in hydrogen bonds. The χ2 rotamers in Ser, Thr and Cys are consistent with tetrahedral bonding, while the χ3 rotamers in Tyr are consistent with trigonal-planar bonding. Similar rotamers are found in hydrogen atoms that were computationally placed with the Reduce program from the Richardson lab.Knowledge of these new rotamers will improve the evaluation of hydrogen-bonding networks in protein structures. One important conformational parameter of a protein structure is the sidechain χ torsion angle . In crysDue to the difficulty in placing hydrogen atoms in protein electron density maps, it has long been customary to omit hydrogen atoms in reporting the crystal structure of a protein. However, Richardson and co-workers showed that positions of hydrogen atoms in high-resolution crystal structures can be confidently projected from the topology of the heavy atoms . The proOne of the reasons why the projection of hydrogen atoms works so well is that the positions of most of the sidechain hydrogen atoms are stereo-chemically restricted. For example, the Hβ atom of Val can only adopt one tetrahedral-bonding position off the Cβ position given that 3 other C atoms are also bound to Cβ. For other hydrogen atoms, symmetry between equivalent methyl-hydrogen atoms results in similar restrictions. For example, in Val, the three equivalent Hγ1 atoms bound to Cγ1 saturate the three available tetrahedral-bonding positions at Cγ1. Nevertheless, there exist four types of sidechain hydrogen atoms in which there is ambiguity in projecting their positions. For instance, in Ser, there are three different ways to place the Hγ atom onto the tetrahedral-bonding positions of the Oγ atom. This freedom is also found in the hydroxyl-hydrogen atoms of Thr and Tyr, and in the sulfhydryl-hydrogen in Cys.As the positions of most of the sidechain hydrogen atoms are so restricted, little attention has been paid to their conformation in crystal structures. However, given the growing number of structures containing hydrogen atoms in the data bank, it has become practical to revisit the question of sidechain hydrogen rotamers for the four classes of ambiguous sidechain hydrogen atoms. The positions of these sidechain hydrogen atoms should be parameterized by χ torsion angles, and we would like to know if these angles display rotameric preferences. Here, we study the distributions of these χ torsion angles in three data-sets: (1) high-resolution X-ray structures that contains explicit hydrogen atoms, (2) neutron diffraction structures and (3) structures with computationally-placed hydrogen positions.1AHO, 1DY5, 1JM1, 1M40, 1RW1, 2AXW, 2FDN, 1BXO, 1EUW, 1KQP, 1MUW, 1TT8, 2BF9, 2FFY, 1C75, 1F94, 1L9L, 1O7J, 1UCS, 2CAL, 2H5C, 1CEX, 1GQV, 1LS1, 1RB9, 2AWK, 2ERL.For the first part of the analysis, we use high-resolution X-ray structures that have explicit hydrogen atoms in the Ser, Thr, Tyr and Cys residues. As such, the data-set consists of structures found in the RCSB.ORG website  with resWe first generate the χ-angle distributions of the hydrogen atom positions directly from the coordinates reported in the high-resolution structures Figure . Hydroge0-Fc maps for the hydrogen atoms of the 27 structures in this data-set and discovered that no electron density was observable for any of these hydrogen atoms at normal contour levels of the maps  where the hydrogen atom positions have been carefully refined against the chemistry of nearby atoms.In many cases with high-resolution structures, the hydrogen atoms are not actually refined, but are instead placed automatically. As such their positions can be unreliable. To check this, we carefully inspected the 2FGiven the absence of agreement with the other distributions (below), and incompatibilities with tetrahedral-bonding and trigonal-planar bonding, it must be concluded that in Ser, the two rotamers at χ2 = -120° Figure  and χ2 =0-Fc electron density for the hydroxyl-hydrogen atoms is too weak to be observed at contour levels that are typically used. However, in these high-resolution structures, even at a contour level of σ = 0, there are regions of the 2F0-Fc map that result in well-defined contours of the molecule. Given that heavy atoms can be clearly distinguished in these maps, in regions where the map is well defined, the position of hydrogen atoms can be determined from density between the donor and acceptor atoms of a hydrogen bond , provided by the Richardson lab , where mIn the structures with Reduce-placed hydrogen atoms, we found an artifact in the surface hydrogen atoms. As there are few neighboring contacts on the surface to help determine the position of hydrogen atoms, many surface hydrogen atoms remain at the default value, resulting in a pronounced peak at 180° (data not shown). This peak can be removed if we eliminate surface residues. Furthermore, as the Reduce algorithm uses steric contacts to optimize hydrogen positions, we need to use well-packed hydrogen atoms. Consequently, we only consider buried interior hydrogen atoms, defined as atoms with > 8 neighboring atoms, where a neighboring atom is defined if it is within 3.5 Å of another atom. We also filter out residues with alternate conformations and atoms where the B-factor > 40. There were hydrogen atoms from 5768 Ser, 5932 Thr, 3645 Tyr and 660 Cys residues in 480 structures. The large size of this data set gives the most reliable statistics.The χ-angle distributions and rotamers were calculated from the Reduce-placed hydrogen atoms Figure . In the Given the robust performance of Reduce, we investigated the position of sulfhydryl-hydrogen atoms in the set of structures provided by the Richardson lab. The hydrogen atoms are considered only if there are no other Cys residues within 4.5 Å of the SG atom of the Cys in order to avoid disulfide-bonded Cystines. The position of the sulfhydryl-hydrogen in Cys is determined by the χ2 = Cα-Cβ-Sγ-Hγ angle. The Cys χ2 distribution show a dominant rotamer at χ2 = 181° (Figure Data was collected from PDB structures using in-house Python scripts. The distributions in Figure Based on experimental data, we find that certain sidechain torsion angles involving hydrogen atoms have strongly preferred orientations and should thus be considered rotameric. Although there were serious artifacts found in the reported coordinates of high-resolution X-ray structures, reliable hydrogen atom positions could be directly derived from the electron-density maps of hydrogen-bonded hydrogen atoms. The χ-angle distributions of these hydrogen-bonded hydrogen atoms match the distribution of hydrogen atoms that were computationally placed by the program Reduce .BKH carried out the study and drafted the manuscript. DAA provided support and guidance. This work was supported by the Howard Hughes Medical Institute.Manually-fitted hydrogen atoms in high resolution structures. In these high-resolution X-ray structures, the hydroxyl hydrogen atoms were removed if there is no corresponding electron density but if there is sufficient electron density due to hydrogen-bonding, the hydrogen atom position were fitted manually.Click here for fileList of hydroxyl-hydrogen χ rotamers in high-resolution PDB files. List of the residues used to calculate the hydroxyl-hydrogen χ-angle rotamers in Ser, Thr and Tyr. Data-sets provided for both the manually-placed hydrogen atoms in the high-resolution structures, and the Reduce-placed hydrogen atoms in the Richardson data-set.Click here for file


A method for rapid model building of β-sheets at moderate resolution is presented. β atoms along the strand averaged over all repeats present in the strand. The β-strands obtained are then assembled into a single atomic model of the β-sheet regions. The method was tested on a set of 42 experimental electron-density maps at resolutions ranging from 1.5 to 3.8 Å. The β-sheet regions were nearly completely built in all but two cases, the exceptions being one structure at 2.5 Å resolution in which a third of the residues in β-sheets were built and a structure at 3.8 Å in which under 10% were built. The overall average r.m.s.d. of main-chain atoms in the residues built using this method compared with refined models of the structures was 1.5 Å.A method for rapidly building β-sheets into electron-density maps is presented. β-Strands are identified as tubes of high density adjacent to and nearly parallel to other tubes of density. The alignment and direction of each strand are identified from the pattern of high density corresponding to carbonyl and C O .One way to look for a specific feature in a map is to use an FFT-based convolution search  Identification of the location of sheets based on the presence of nearly parallel tubes of density.β atoms along the strand averaged over all repeats present in the strand.(ii) Identification of β-strand alignment and direction using the pattern of high density corresponding to carbonyl and C(iii) Assembly of β-strands into a single model.The result of this process is a model of the β-sheet portions of the structure. It can be used as a starting point for further model building and map interpretation in combination with a model of the α-helical portions of the structure. The steps carried out are described in detail below.Our approach for modeling the β-sheets in an electron-density map of a protein focuses on speed by examining the map for characteristic features of these structures. The method consists of three steps.2.1.a) illustrates a model of an antiparallel β-sheet and corresponding density at a resolution of 2.5 Å. The first step in our procedure for building β-sheets is the identification of where strands are located in the electron-density map. At moderate resolution (2.5–4 Å) the polypeptide backbone resembles a tube of density and for β-strands the tubes have only a small amount of curvature. In β-sheets these strands are arranged in a nearly parallel or antiparallel fashion, with a small  inclination between adjacent strands. To simplify the identification of strands in a map and to make it as rapid as possible, a pair of strands in a β-sheet is therefore initially considered to consist of two tubes of density that are nearly parallel and that are separated by approximately 4.5 Å at their closest approach. In this analysis, tubes of density are identified, then pairs of nearly parallel tubes are found and finally the tubes are extended into density, allowing curvature of the tubes.Fig. 1b). All pairs of points that are connected by high density are then identified. The criteria for two points being connected are that (i) the density ρ sampled along the line connecting the points has a value of at least ρmax × cut1, where ρmax is the higher of the densities at the two end points and cut1 typically has a value of 0.5, and (ii) that the mean density ρmean along the line is at least ρmax × cut2, where the typical value of cut2 is 0.75. These pairs of connected points and the lines connecting them represent the locations and directions of tubes of density that might be β-strands.Tubes of density are found in the electron-density map by finding points along ridgelines of high density. Firstly, a set of points in the map that are on ridgelines of high density and are separated typically by 2 Å are identified . The cosine of the angle between the two tubes of density is typically required to be at least 0.5. The distance between the tubes of density is typically required to be 4.5 ± 2.0 Å at their closest approach. These tubes representing high density in the map are then extended into the available density, allowing the curvature of the tubes to match the high density in the map, as illustrated for the two tubes of density identified by red spheres in Fig. 1b). To simplify the analysis, this curvature is only allowed in the direction perpendicular to a line connecting the midlines of the two tubes of density at their closest approach. This direction was chosen because the strands in β-sheets typically have a curvature that is roughly perpendicular to the plane of the β-sheets.Next, pairs of nearly parallel tubes of density separated by about 4.5 Å are identified. This is performed by finding two nearby nearly parallel pairs of connected points (representing two tubes of density) with no high-density connections between the pairs  then the tube of density is discarded as a candidate β-strand position. Otherwise, the score for the tube consists of the mean density along the axis of the tube multiplied by the square root of the length of the tube of density. This is similar to the scoring procedure that we have used previously to evaluate the quality of fit of a model to density  shows a close-up view of a model strand and of the curved axis of the tube of density corresponding to it. In this step the goal is to start with the density map and the points marking the tube of density and to end with a strand fitted into the density. One way to do this would be to model a strand in all possible positions near the axis of the tube of density and find the one that fits the best. We chose instead to use a faster but less comprehensive method in which the density near the curve marking the tube of density is examined for periodic patterns corresponding to the pattern of carbonyl O and Cβ atoms along a β-sheet.Once tubes of density that could represent β-strands have been identified as described above, they are each considered individually for their fit to a model of a β-strand, allowing the curvature of the strand to match the curvature of the tube of density. Fig. 1c) illustrates the features of the electron density that we used in this process. The carbonyl O atoms of the strand in the middle of the figure point alternately up towards the β-strand above it and down towards the β-strand below it in the figure. The Cβ atoms point alternately into the page and out from it. Note that the Cβ atoms have a specific relationship to the direction of the chain and the positions of the carbonyl atoms: they are located about two-thirds of the distance from one carbonyl to the next going from right to left (N-terminus to C-terminus) along the chain in the middle of Fig. 1c). This relationship is what we use to identify the positioning and direction of the β-strand.Fig. 1c). Note that the red spheres very nearly coincide with the main-chain atoms of the strand. As the periodicity of the β-strand is known (about 6.7 Å), it is simple and rapid to average the density near the strand over all corresponding locations along the strand. This produces average density for one repeat of the strand. Then, as the direction towards the neighboring strand is already known, the positioning of the carbonyl atoms can readily be identified as being where the density approximately 1.5 Å from the axis of the strand in the direction of the neighbouring strand is maximal . With the same alignment, another carbonyl O atom points down towards the strand on the other side and this density should be offset by half of the period of the β-strand. In our approach, if these two estimates of the locations of the carbonyls agree to within approximately 1/12 of the period then the identification of the location is considered to be a possible match.The representation of the density for this strand as a tube is marked by the red spheres in Fig. 1β atoms and is offset by about 1/3 of a period from that of the carbonyl O atoms . The pattern of high density and direction along the β-strand going from the N-terminus to the C-terminus can now be readily seen. For the β-strand in the middle of Fig. 1c), starting at the carbonyl O atom pointing up at the right of the figure and moving to the left one atom at a time, it may be seen that the pattern of high density will be (i) up at position 1 (at the carbonyl C atom), (ii) right at atom 3 (the Cβ atom into the plane of the figure), (iii) down at atom 4 (the next carbonyl), (iv) left at atom 6 (the next Cβ atom) and then up again at atom 7 (the next carbonyl O atom pointing up). Note that if the strand were in the opposite direction then the pattern would be different. Consequently, the position and direction of the strand can be identified. In our procedure, if all the positions of highest density in this pattern are aligned within a tolerance of 1/12 of the period of the β-strand then the position and direction are considered to be likely to be correct .The same approach can then be applied to examine the density, again about 1.5 Å from the axis of the strand, this time in the directions perpendicular to the plane of the β-sheet. This density corresponds to that of the Cms Fig. 1c. The pc) and the curve corresponding approximately to the main-chain atoms , an idealized β-strand can be placed. In cases where the direction cannot be identified using the method described above, two candidate β-strands are created, one in each direction. Note that if the curvature is substantial then there can be some distortion of the strand.Given the direction and alignment of a strand as in Fig. 12.3.RESOLVE software to assemble these fragments and resolve any overlaps  shows the result of carrying this out when the strands found from analyses of this map using data to a resolution of 2.5, 3 and 4 Å are merged in this assembly process (the default procedure).The analysis described above yields a group of modelled β-strands that match the electron density. However, these strands may contain overlapping fragments. We use the main-chain assembly routines in the 3.PHENIX structure library previously solved by MAD, SAD, MIR and a combination of SAD and SIR procedures with data extending to high resolutions ranging from 1.5 to 3.8 Å. Maps were calculated with the PHENIX AutoSol wizard , the quality of the map , the r.m.s. coordinate difference between main-chain atoms in the modeled β-strands compared with those in the refined structure and the correlation between the map and a map calculated from the β-strand model.Table 1DSSP were built using our approach. Of these, 60% of the residues built were in fact in β-strands (the Cα atom was within 3 Å of a Cα atom of a β-strand residue in the refined structure of the protein). The 40% of the residues built by our procedure that did not match β-strands as identified by DSSP were either incorrectly built (e.g. traced into helices) or were built into less regular secondary structure (such as loop regions). Therefore, the method built β-sheets reasonably well, but some β-strands were missed and some residues were identified as β-strand that were in fact another type of structure. Overall, the r.m.s.d. between modelled β-strands and refined coordinates was about 1.5 Å. The CPU time required (using 2.9 GHz Intel Xeon processors) to analyze all 42 maps was 66 min or about 0.8 s per residue of β-strand placed.On average, 58% of the residues in β-strands as identified by PHENIX AutoBuild wizard  of 0.95 Å and required 43 h for the entire set of structures.To compare these results with a standard procedure for automated model building, the same 42 maps were analyzed with the 1oel; Braig et al., 1995et al., 2000et al., 1977One structure for which most β-strand residues were missed was the GroEL structure ; however, there were other places where β-strands have been built into density that corresponds to α-helices or to side-chain density .In a few cases significantly more β-strand residues were built than were identified by ly Fig. 2a; howevty Fig. 2b.1lql; Choi et al., 2003DSSP) with an r.m.s.d. from the refined model of 1.3 Å. The structure has tenfold NCS but this was not used in the model-building process. A ribbon diagram of this model is shown in Fig. 2c).The procedure produced very complete structures of the β-sheets in many cases. The largest number of β-sheet residues built was for the structure 1038B at a resolution of 3 Å  between the electron-density map and the β-sheet model, only including points in the map that are near (within 2 Å) of an atom in the model. Fig. 3a) illustrates the mean value of the r.m.s.d. between main-chain atoms of the β-sheet models built and the corresponding atoms in refined models as a function of this parameter and Fig. 4b) illustrates the number of residues built. The accuracy generally improves with increasing stringency, but as expected the number of residues built decreases. Values of cc_strand_min in the range 0.3–0.5 would appear to be reasonable compromises between these competing effects.One adjustable parameter in this procedure that would be expected to affect both the accuracy of the models and the number of residues built is cc_strand_min, the minimum correlation between the density near a potential strand and that of an idealized tube of density. Fig. 44.b), but in general it builds β-sheets very well .The procedure that we have developed for modelling β-sheets is quite rapid and reasonably accurate. It identifies most of the β-sheets in the tests we have carried out. The method does show some overprediction and can accidentally build β-structure into helical or side-chain density in some cases Fig. 2b, but ill Fig. 2c.Several improvements can readily be imagined for this procedure. One would be to take account of the hydrogen-bonding pattern in β-sheets, which would be expected to improve the register and alignment of the models. Another would be to explicitly look for deviations from regular β-structure, such as β-bulges or the start of helices, so as to more precisely define the start and end of regular β-strands.c) is not likely to be found unless this is the case. Secondly, the procedure can be part of a rapid scoring procedure for evaluation of electron-density maps by analysis of the regular secondary structure evident in the maps. Lastly, the procedure could be used as part of a more complete model-building process in which the secondary structure built with this method is used as a starting point for chain extension and further model building.The method may be useful in several ways. Firstly, it can be a good indicator of whether a structure has been solved, as a picture such as that in Fig. 2


A method for rapid model building of α-helices at moderate resolution is presented. A method for the identification of α-helices in electron-density maps at low resolution followed by interpretation at moderate to high resolution is presented. Rapid identification is achieved at low resolution, where α-helices appear as tubes of density. The positioning and direction of the α-helices is obtained at moderate to high resolution, where the positions of side chains can be seen. The method was tested on a set of 42 experimental electron-density maps at resolutions ranging from 1.5 to 3.8 Å. An average of 63% of the α-helical residues in these proteins were built and an average of 76% of the residues built matched helical residues in the refined models of the proteins. The overall average r.m.s.d. between main-chain atoms in the modeled α-helices and the nearest atom with the same name in the refined models of the proteins was 1.3 Å. Atomic models can be simple and readily visualized representations of the structures of macromolecules and are commonly used as the primary means of conveying structural information about a macromolecule.e.g. O . Additionally, they all become progressively less effective as the resolution of the map decreases, although some progress has recently been made in this regard (DiMaio O software has shown that the direction (and placement) of α-helices in a map can be accurately identified by averaging the electron density near several sequential Cα positions by applying a transformation corresponding to the relationship between sequential residues in an α-helix  resolution  Identification of α-helical density and modeling of α-helical axes and extent using maps with varying low-resolution cutoffs.(ii) Determination of α-helix placement  using the full available resolution.(iii) Assembly of α-helices, elimination of overlaps and joining of adjacent segments.The result of this process is a model of the α-helical portions of the structure that can be used as a starting point for further model building and map interpretation. These steps are described in detail below.Our approach for modeling the α-helices in an electron-density map of a protein consists of three steps. These are as follows.2.1.a), at a resolution of 7 Å an α-helix appears as a tube of density , so that finding the α-helices can be quite straightforward.The first step in our process for modeling α-helices in the electron-density map of a protein is to identify the α-helices using a set of maps with low-resolution cutoffs from about 5 to 8 Å. While at high resolution an α-helix has a rather complicated pattern of density Fig. 1a, at a ty Fig. 1b, so thb) and a set of points is identified along the axis of the tubes of density corresponding to α-helices. The points are chosen to be a set for which (i) each point is in relatively high density , (ii) no more than one point that is adjacent to a chosen point has an electron-density value that is greater than the value at the chosen point and (iii) each chosen point is at least a specified distance  from each other chosen point. The second criterion is chosen to ensure that the chosen points are either at a peak of density or along a line of high density. A set of points satisfying these criteria for the map in Fig. 1b) is shown in Fig. 1c).A map is calculated  at low resolution  are used to guess the location and direction of the axis of the tube of density. Each point is considered as a possible marker of the center of a tube of density and the directions to every other point  are considered, one at a time, as the direction of the tube of density. The center and direction are scored by calculating the electron density at intervals of typically 2 Å along the line they define and identifying the longest segment that satisfies the criteria that (i) every point along the line has a density ρ of at least ρmean × cut1, where ρmean is the mean density in the segment and cut1 has a typical value of 0.5, and (ii) the points on the ends have densities of at least ρmean × cut2, where the value of cut2 is typically 0.75. These are the same criteria as used previously in building protein main-chain segments  are saved.This process is typically repeated with maps with resolution cutoffs from about 5–8 Å and all the resulting α-helices are considered in the following steps.2.2.The second overall step in α-helix identification is to use the high-resolution electron-density map to determine how an α-helix could be optimally placed in the electron density given the helix axis and the ends of the helical segment. This is performed in three stages. Firstly, the positioning along the helix axis of the tubes of density in the map corresponding to the main-chain atoms in each  helix is determined. The direction of the α-helix is then identified and finally the positioning of an idealized α-helix is identified.d) illustrates the approach used to position the helix axis of a segment in ideal α-helical density. The blue mesh corresponds to a contour of ideal density from an α-helical segment and the gray helix is an ideal helix with a radius of 2 Å and a pitch of 5.4 Å. The parameter that is optimized in this step is the translation of the gray ideal helix along the helix axis, with a score given by the mean density along the gray ideal helix multiplied by the square root of its length. As in the previous overall step, the ends of the helix are chosen to maximize its length, while requiring that the density at all intermediate points and at the ends be at least cut1 or cut2 times the mean in the segment, respectively.Fig. 1β atoms would be located given the location of the gray helix representing main-chain atoms as identified above. Fig. 1d) illustrates this process. Two helices  are constructed based on the gray helix. Each of these helices has a radius of 4 Å and a pitch of 5.4 Å. They are offset by ±1 Å along the helix axis from the gray main-chain helix. Depending on the direction of the helix, one of these two helices  will typically be in much higher average density than the other, allowing the direction of the helix to be identified. A Z score is estimated reflecting the confidence in this difference from the ratio of the difference between the scores for the two directions to the estimated standard deviation of this ratio for random helix placements. This standard deviation is estimated from the variance of the values of the scores obtained for both directions, assuming incorrect periodicities of a helix of 80°, 90°, 110° and 120°. If the Z score was 2 or larger, the assignment of the direction was considered to be likely to be correct.The direction of the α-helix is identified by maximizing the density at the positions where Ce) shows the position of the model polymethylalanine α-helix used to generate the density for Fig. 1The positioning and extent of an idealized polyalanine α-helix in the high-resolution electron density is then identified by a simple search over rotations about the helix axis and translations along the helix axis, trimming the ends in the same fashion as described above and scoring by the mean value of electron density at the coordinates of atoms in the idealized α-helix multiplied by the square root of the number of atoms. Fig. 12.3.RESOLVE software  are found. The resulting set of α-helices is saved.The previous steps result in a collection of α-helices that match the electron density but that may contain overlapping or otherwise incompatible fragments of α-helix. The assembly of all these fragments and the resolution of overlaps is carried out by the main-chain assembly routines in the 3.et al., 2004PHENIX AutoSol wizard . Tubes of density corresponding to helices are readily identifiable in the map. Fig. 2b) shows the map at high resolution, along with the α-helices that were identified using the procedure described here (in yellow) and the α-helices from the refined structure , the quality of the map , the r.m.s. coordinate difference between main-chain atoms in the modeled α-helices compared with those in the refined structure and the correlation between the map and a map calculated from the α-helix model.Table 1α atom within 3 Å of a Cα atom in an α-helix in the refined structure. The remaining 24% were built into structure that was not identified as α-helical by DSSP. The overall r.m.s.d. between modeled α-helices and refined coordinates  was 1.3 Å. The CPU time (using 2.9 GHz Intel Xeon processors) required to analyze all 42 maps was 28 min or about 0.2 s per residue of α-helix placed. To provide a frame of reference for these results, we carried out one cycle of automated model building applying the PHENIX AutoBuild wizard  of 0.95 Å, requiring 43 h for the 42 maps.Overall, 63% of the 11 233 residues in α-helices in the refined structures were found. Viewed differently, 76% of the residues that were built using the present method in fact corresponded to α-helical segments of the refined structures, with a Ca) shows that for this set of maps the quality of the map has only a small effect on the quality of the α-helices built, as reflected in the r.m.s.d. between the main-chain atoms in the α-helices found and those in the corresponding refined models. Similarly, the resolution of the map, in the range 1.5–3.8 Å, had little effect on the quality of the models . However, it was possible to tell which models were accurate. Fig. 3c) shows that the map–model correlation based on the coordinates of the α-helices that were built is inversely related to the r.m.s.d. between those coordinates and those of the corresponding refined structures. Those models with a model–map correlation of greater than about 0.45 generally had an r.m.s.d. of less than about 1.5 Å and those with lower model–map correlation generally had an r.m.s.d. of greater than 1.5 Å.The maps used in this analysis were of fair to excellent quality, with correlations to model maps based on the corresponding refined structures of 0.53–0.89. Fig. 3ls Fig. 3b. Howeva) shows the overall r.m.s.d. of main-chain atoms from those in corresponding refined models and Fig. 4b) shows the total number of residues built. Increasing the threshold correlation results in more accurate models but fewer residues built and the default value of 0.5 appears to be a reasonable compromise between these effects.One parameter that might be particularly important in determining both the accuracy of the procedure and the number of residues built is the map-correlation cutoff used to choose the density at low resolution (cc_helix_min). The default value is a correlation of 0.5. We tested a range of values of cc_helix_min for the set of 42 maps in Table 14.The procedure described here for the rapid placement of α-helices in electron-density maps may be useful in several contexts. Firstly, it may be useful as a method for the evaluation of map quality. Secondly, it may be useful in giving a rapid indication to a crystallographer as to whether they have successfully determined the structure in their crystals. Thirdly, it may be a useful approach to generating a partial model of a protein that can then be extended with other model-building tools.


The hydrogen sulfate–sulfate anion is a hydrogen-bond acceptor for the three independent cations and the uncoordinated water molecule, the hydrogen-bonding interactions giving rise to a three-dimensional hydrogen-bonded network. In the hydrogen sulfate–sulfate species, one of the sulfate groups is disordered in respect of its O atoms in a 2:1 ratio.In the crystal structure of the title salt, 3C DOI: 10.1107/S1600536809048545/xu2676Isup2.hklStructure factors: contains datablocks I. DOI:  crystallographic information; 3D view; checkCIF reportAdditional supplementary materials:


Inflammation and its associated pathologies are increasingly suggested as risk factors for colorectal cancer (CRC) development. Previous research from our group has shown that increased levels of circulating, pro-inflammatory cytokines IL-6 and TNFα promote colorectal adenoma risk. Emerging data in mice and humans suggest that Suppressor of Cytokine Signaling 3 (SOCS3) may act as a tumor suppressor in the intestine, and decreased SOCS3 expression may promote CRC. As SOCS3 has been shown to inhibit the actions of IL-6 and TNFα in the intestine, we hypothesized that decreased SOCS3 expression in normal mucosa may predispose to adenomas and thus increase risk for CRC.We examined SOCS3 mRNA levels in normal mucosa biopsies of 322 screening colonoscopy patients (93 with adenoma and 229 without adenoma) using real-time qRT-PCR. Logistic regression analysis was used to generate odds ratios (OR) and 95% confidence intervals to determine if low SOCS3 expression was associated with adenoma status. Median SOCS3 values did not differ between patients with or without adenoma. Logistic regression analysis showed no association (unadjusted or adjusted for age and sex) between SOCS3 and colorectal adenomas.Low SOCS3 mRNA expression is not an independent biomarker of colorectal adenoma risk in the normal mucosa. SOCS3 silencing likely occurs later in CRC progression. Recent evidence in mice and humans suggest that the anti-inflammatory protein Suppressor of Cytokine Signaling 3 (SOCS3) may act as a tumor suppressor in the colon ,2. SpeciPrior studies from our group demonstrated that increased systemic levels of pro-inflammatory cytokines IL-6 and TNFα correlate with risk of colorectal adenoma ,7-12. BaAll eligible subjects provided written, informed consent. Consenting participants were enrolled in Diet and Health Study IV at University of North Carolina Hospitals as previously described . BrieflyRNA from four pooled, normal colon biopsies per subject was extracted using Qiagen's RNeasy kit  and reverse transcribed with AMV-Reverse Transcriptase (Promega) as previously described .SOCS3 mRNA abundance was determined using the ABI Prism 7900 HT  and Platinum Quantitative PCR SuperMix-UDG . Human SOCS3 (NM_003955.3) was quantified using Applied Biosystem primer/probe set targeting exon 2. The housekeeping gene hydroxymethylbilane synthase  was chosen as a low-abundance, invariant control. Standard curves for each primer/probe set were generated using gel-isolated, sequence-confirmed PCR products. Cycling included initial denaturation at 95°C for 5 minutes followed by 45 cycles of 95°C denaturation for 15 seconds and 60°C annealing for 45 seconds. Threshold cycles analysis was performed using Applied Biosystem SDS v2.2.2 software and values are expressed as copy number relative to HMBS. All PCR runs included standards and inter-run calibrator controls (pooled sample cDNA), as well as non-reverse transcribed (no-RT) and water controls. Samples were run in triplicate.Means and standard errors were generated for continuous variables, and frequencies and percentages were generated for categorical variables. T-tests and Mann-Whitney tests were used to compare cases with controls on continuous variables. SOCS3 values were log-transformed to normalize the distribution. Chi-Square tests were used to compare cases and controls on categorical variables. Logistic regression was used to test for an association between case/control status and SOCS3. Levels of SOCS3 were categorized into tertiles based on control values. Age, race, sex, WHR, NSAIDS, IL-6, TNFα, and family history were assessed as potential confounders of SOCS3-adenoma association. Each variable was put into a model separately with SOCS3, and if one of the two dummy variables for SOCS3 changed by at least 15% compared to when only SOCS3 was in the model, then that co-variable passed the first stage for being a confounder. All such variables were then entered into a model with SOCS3 and a backwards, stepwise regression was done with the SOCS3 variable being forced into the model. Only age and sex met these criteria for confounding and thus they were included in the final model.Descriptive characteristics of the population in the SOCS3-adenoma study are shown in Table To determine if low SOCS3 is associated with having an adenoma, odds ratios and 95% confidence intervals were generated using logistic regression analysis Table . There wIdentifying local factors that predispose patients to early, pre-cancerous lesions may make it possible to stratify screening based on risk, but risk factors that occur early in CRC development are not well defined. Our group has shown that patients with adenoma have reduced apoptosis in their normal mucosa, demonstrating a field effect that predisposes the individual to a higher risk of developing precancerous adenomas ,15. We hPatients with inflammatory bowel diseases (Crohn's disease or ulcerative colitis) have an increased risk of developing CRC, which is associated with the degree and duration of intestinal inflammation (reviewed in ). In addOne potential limitation of the study is possible effect of standard bowel preparation on SOCS3 expression. While we have not directly tested this for SOCS3, similar studies of other genes have shown that there is no significant change in mucosal gene expression in patients using bisacodyl or polyethylene glycol bowel preparations . AnotherThe authors declare that they have no competing interests.KEH carried out the molecular studies, participated in data analyses, and drafted the manuscript. PKL and RSS participated in the study conception, design, and coordination. JAG performed the statistical analyses. TOK participated in the study conception, design, and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.


To compare how and to what extent ingestion of hydrogen water and milk increase breath hydrogen in adults.Five subjects without specific diseases, ingested distilled or hydrogen water and milk as a reference material that could increase breath hydrogen. Their end-alveolar breath hydrogen was measured.Ingestion of hydrogen water rapidly increased breath hydrogen to the maximal level of approximately 40 ppm 10–15 min after ingestion and thereafter rapidly decreased to the baseline level, whereas ingestion of the same amount of distilled water did not change breath hydrogen (p < 0.001). Ingestion of hydrogen water increased both hydrogen peaks and the area under the curve (AUC) of breath hydrogen in a dose-dependent manner. Ingestion of milk showed a delayed and sustained increase of breath hydrogen in subjects with milk intolerance for up to 540 min. Ingestion of hydrogen water produced breath hydrogen at AUC levels of 2 to 9 ppm hour, whereas milk increased breath hydrogen to AUC levels of 164 ppm hour for 540 min after drinking.Hydrogen water caused a rapid increase in breath hydrogen in a dose-dependent manner; however, the rise in breath hydrogen was not sustained compared with milk. It is widely accepted that breath hydrogen reflects carbohydrate fermentation in the colon.Recent studies revealed that hydrogen gas plays an important role in inactivating oxidative stresses such as hydroxyl radicals in animal models of cerebral infarction,11Since hydrogen solubility in water at 25 °C is 0.0176 vol gas STP/vol liquid at 1 atm partial pressure,In the present study, in order to examine whether ingestion of commercial hydrogen water increased breath hydrogen and the duration for which it was maintained, the effects of hydrogen water on breath hydrogen was examined. The effect of milk ingestion on breath hydrogen was also examined for comparison as milk increases breath hydrogen.15Commercial hydrogen water was purchased at the general consumer level and fresh hydrogen water (0.6 mM) was obtained from Blue Mercury Inc. . Hydrogen water was made by dissolving hydrogen into water under high pressure, as described by Nagata et al.Five healthy adult volunteers (two men and three women: mean age 29 ± 14 years) participated in this study. They did not have any specific diseases based on detailed questionnaires and examination by physicians. The subjects randomly volunteered to be assigned to consecutive studies from experiment one to three. All the subjects eventually showed lactose deficiency as described below, possibly due to the high incidence of lactose deficiency in Japanese people.18The subjects refrained from food, supplements and drugs, except water, for at least 15 hours before the experiments. On the day of the experiment, the subjects rested in the sitting position for at least 30 min and then drank 300 ml of milk, hydrogen water or distilled water within one min. Immediately after drinking, the subjects rinsed their oral cavity with 50 ml of distilled water. End-alveolar breath was obtained in a breath sampling bag  every 5 min from 30 min before drinking until the end of each experiment as described below. All subjects underwent training to obtain end-alveolar breath before these experiments.For the dose-response experiment, hydrogen water for general consumers was used within one week after shipping. Hydrogen concentration was 0.40 ± 0.07 mM (N = 18). Three subjects had hydrogen water in volumes of 100, 200 and 300 ml every hour, with two repetitions.To confirm that breath hydrogen originates from hydrogen water, five subjects had 300 ml of distilled water and then changes in breath hydrogen were observed for 90 min. After an hour , the same subject ingested hydrogen water (0.4 mM).To compare the hydrogen-producing capability of hydrogen water and milk, four subjects had 300 ml of milk and their breath hydrogen was monitored for up to 540 min. On separate experimental days, the same two subjects drank 300 ml of hydrogen water and repeated the same protocols at three different concentration levels  of hydrogen water.The breath was immediately transferred to a gas-tight glass syringe and 1 ml was injected into the gas chromatograph with a semiconductor detector  to measure breath hydrogen. Breath concentrations of each gas were calculated by subtracting ambient air collected in the same sampling bag. Hydrogen concentration of the milk, distilled or hydrogen water at 25 °C were measured for each subject by head-space gas sampling in a sealed glass vial with a small amount of the materials. Analysis was performed using the same sensor as mentioned above.Statistical values are expressed as the mean ± standard deviations of the mean. Statistical analysis was performed by a paired t-test or two-way analysis of variance and significance was accepted at p < 0.05.Hydrogen water significantly increased both hydrogen peak  and the area under the curve (AUC) of breath hydrogen  in a dose-dependent manner .Effects of distilled and hydrogen water on breath hydrogen were compared. Ingestion of hydrogen water (0.4 mM) rapidly increased breath hydrogen to the maximal level of 36 ppm 15 min after ingestion and thereafter rapidly decreased to the baseline level, whereas the same amount of distilled water ingestion did not change breath hydrogen . Furthermore, in the recent experiment, it was estimated that after ingestion of hydrogen water, approximately 0.1% of hydrogen was released from the whole body surface and the amount was considerably negligible, which will be reported elsewhere . On the other hand, it is known that hydrogen produced by colonic fermentation is partially consumed by bacterial flora in the colon.The present study supports the conclusion that ingestion of commercial hydrogen definitely increases hydrogen concentration in the body; however, the rise in breath hydrogen was transient and the hydrogen-producing capability of hydrogen water was less than that of milk in subjects with hypolactasia. Since a higher increase in breath hydrogen was expected, fasted subjects had test items at the rate of 300 ml/60 sec, i.e. 5 ml/sec. However, the rise did not reach the reported level of 55 ppm by ingestion of 300 ml of hydrogen-rich water by a recent clinical trial of Kajiyama et al.On the other hand, Nagata et al.The same group revealed that hydrogen gas plays an important role in inactivating oxidative stresses such as hydroxyl radicals.Ingestion of milk in subjects with hypolactasia caused a marked and sustained rise in breath hydrogen, as shown in the present and previous studies.This study was performed under special conditions, refraining from unabsorbable foods before the experimental days and eating and drinking nothing except water and the test items. However, in everyday life, breath hydrogen levels fluctuate markedly, ranging from several ppm to 50 ppm or more,Hydrogen water caused a rapid increase in breath hydrogen in a dose-dependent manner; however, the rise in breath hydrogen was not sustained compared with the same amount of milk in subjects with milk intolerance.


Allelopathy  has been largely studied as an autecological process, often assuming simplistic associations between pairs of isolated species. The growth inhibition of a species in filter paper bioassay enriched with a single chemical is commonly interpreted as evidence of an allelopathic interaction, but for some of these putative examples of allelopathy, the results have not been verifiable in more natural settings with plants growing in soil.Festuca rubra ssp. commutata. We re-examined the allelopathic effects of m-tyrosine to understand its dynamics in soil environment. Allelopathic potential of m-tyrosine with filter paper and soil (non-sterile or sterile) bioassays was studied using Lactuca sativa, Phalaris minor and Bambusa arundinacea as assay species. Experimental application of m-tyrosine to non-sterile and sterile soil revealed the impact of soil microbial communities in determining the soil concentration of m-tyrosine and growth responses.On the basis of filter paper bioassay, a recent study established allelopathic effects of m-tyrosine, a component of root exudates of F. rubra ssp. commutata rhizosphere. We hope that our study will motivate researchers to integrate the role of soil microbial communities in bioassays in allelopathic research so that its importance in plant-plant competitive interactions can be thoroughly evaluated.Here, we show that the allelopathic effects of m-tyrosine, which could be seen in sterilized soil with particular plant species were significantly diminished when non-sterile soil was used, which points to an important role for rhizosphere-specific and bulk soil microbial activity in determining the outcome of this allelopathic interaction. Our data show that the amounts of m-tyrosine required for root growth inhibition were higher than what would normally be found in  Since 8-hydroxyquinoline has affinity for cations such as aluminum, iron or magnesium, the addition of EDTA (disodium ethylenediaminetetraacetate) to the extraction solution improved the recovery of 8-hydroxyquinoline probably due to dissociation of 8-hydroxyquinoline-metal cation complex C. diffusa-invaded soil and since the influence of microbial communities were excluded from this work, it remains an open question whether 8-hydroxyquinoline is allelopathic in soil.Phytotoxicity of 8-hydroxyquinoline, a root-exuded metabolite from Festuca rubra ssp. commutata. These results were extrapolated to the development of a biorational approach for weed control On the basis of filter paper and agar bioassays, Bertin and colleagues Lactuca sativa), littleseed canarygrass  and bamboo (Bambusa arundinacea) using filter paper and soil (non-sterile or sterile) bioassays. Since m-tyrosine was suggested to be a potent allelopathic and herbicidal compound We tested the phytotoxic effects of m-tyrosine on lettuce  or 30°C (for bamboo). Three seeds of lettuce, littleseed canarygrass or bamboo were sown in 10 g non-sterile soil and inoculated at 22 (lettuce and littleseed canarygrass) or 30°C (bamboo). After 8 d of seed germination, the non-sterile soil was treated with 0, 4.25, 8.5, 17, 34, 68 or 136 µg m-tyrosine/g soil and incubated under same conditions for 24 h. In order to investigate the recovery of m-tyrosine in soil devoid of microbes, sterile and non-sterile soils without assay species were treated with 0, 4.25, 8.5, 17, 34, 68 or 136 µg m-tyrosine/g soil and incubated at 22 or 30°C for 24 h. Each m-tyrosine treatment was replicated thrice.After an incubation period of 24 h, soil samples were extracted in 5 ml water, and briefly mixed by vigorous vortexing. After centrifugation for 10 min at 4500 rpm, supernatant was passed through 0.2 µm filters (Sartorius) and placed in sample vials for high-pressure liquid chromatography (HPLC) analysis. m-Tyrosine recovery was measured by HPLC with an Agilent-HPLC, 1100 series equipped with an ODS inertsil C-18 column  protected by a Phenomenex Security Guard C18 pre-column. The mobile phases were (A) water: 0.05% formic acid and (B) acetonitrile: 0.05% formic acid. The elution system was as follows: 0–3 min, 3% of B; 3–12 min, 3–50% of B; 12–15 min, 50% of B. The flow rate was 1 mL/min, the injection volume was 10 µL, and the column oven was set at 24°C, and the elutent was monitored at 280 nm. m-Tyrosine concentration among or between treatments was analyzed by means of one-way ANOVAs or t-tests, respectively F. rubra ssp. commutata were sown in 10 g soil at 22°C. Eight days after the germination of seeds, seedlings were removed, and soil was extracted with 10 mL water. Soil extracts were then subjected to HPLC as described above.In order to examine the root exudation of m-tyrosine in non-sterile soil, seeds  of 2 respiration by chemical titration is an effective method to compare microbial activity in different soils 2. Three replicates were used. Soil was incubated at 22°C or 30°C, respectively for 24 h, and was terminated by adding 1 mL of 0.1 N BaCl2 to NaOH. A 10 mL of NaOH taken from blank, control or treatment was titrated against 0.1 N HCl, and amount of CO2 released was calculated Measuring soil COlettuce = 56.65, df = 6, 284; P<0.001; Flittleseed canarygrass = 301.1; df = 6, 318; P<0.001; Fbamboo = 16.97, df = 6, 230, P<0.001) or in sterile soil treated with m-tyrosine   and lettuce  was inhibited in soils amended with 68 or 136 µg/g tyrosine  . No significant difference in m-tyrosine recovery was observed among three assay species at 34 or 68 µg m-tyrosine/g soil . HoweverWe also examined the recovery of m-tyrosine in sterile and non-sterile soil without assay species when treated with 0, 4.25, 8.5, 17, 34, 68 or 136 µg m-tyrosine/g soil and incubated at 22 or 30°C. A higher availability of m-tyrosine was observed in non-sterile soil, incubated at 22°C, treated with 17 (12.1%), 34 (22.1%), 68 (29.7%) or 136 (40.3%) µg m-tyrosine/g non-sterile soil compared to non-sterile soil, incubated at 30°C, treated with 17 (0%), 34 (13.1%), 68 (18.5%) or 136 (21.8%) µg m-tyrosine/g non-sterile soil . The rec2 release  = 0.035) compared to untreated soil incubated at 22°C (2 release in treated soil incubated at 30°C was significantly (P<0.0001) higher compared to untreated soil incubated at 30°C. No significant differences (except 4.25 or 8.5 µg m-tyrosine/g soil) were observed in the CO2 released from m-tyrosine-treated soil incubated at 22°C compared to untreated soil incubated at 22°C. A significantly higher CO2 release from soil treated with 17 <0.0001), 34 <0.0001), 68 <0.0001) or 136 <0.0001) µg m-tyrosine/g soil was observed at 30°C compared to respective soils incubated at 22°C  in non-sterile soil with bamboo compared to non-sterile soil with lettuce (31.1%) or littleseed canarygrass (34.1%) . The low2 efflux is a reliable technique for comparing microbial activity in different soils 2 release in untreated and m-tyrosine-treated soil incubated at 22 or 30°C. We observed a higher CO2 release in the untreated soil incubated at 30°C (4±0.2 µg CO2 released/g soil/h) compared to 22°C (2.6±0.6 µg CO2 released/g soil/h). A higher m-tyrosine recovery of 12.1, 22.1, 29.7 and 40.3% was observed in non-sterile soil treated with 17, 34, 68 or 136 µg m-tyrosine/g soil at 22°C compared to 0, 13.1, 18.5 or 21.8%, respectively in m-tyrosine treated soil at 30°C. The lower recovery was associated with higher CO2 efflux suggesting higher microbial activity in soil treated with 17 (47.4%), 34 (59.1%), 68 (81.1%) or 136 (61.4%) µg m-tyrosine/g soil at 30°C compared to respective soils incubated at 22°C (2 release (except from the soil treated with 8.5 µg m-tyrosine/g soil) compared to soil treated with 17, 34, 68 or 136 µg m-tyrosine/g soil at 22°C. No differences in the CO2 release were observed among soils treated with 17, 34, 68 or 136 µg m-tyrosine/g soil at 22°C  reflect the amount actually released from the plant that could interact with competing plants. Moreover, it is difficult to collect root surface washings in a system without causing physical damage to the roots, which is unlikely to occur in the artificial soil system. In natural conditions, biotic stresses such as herbivory, however, might cause a local release of m-tyrosine. Ecologists have often criticized the extraction procedures used in past experiments claiming the demonstration of allelopathic effects. Harper Almost all species can, by appropriate digestion, extraction and concentration, be persuaded to yield a product that is toxic to one species or another”. These comments hold for most of the recent claims of allelopathic function F. rubra ssp. commutata could have some ecological function, which yet remains to be discovered.We did not detect m-tyrosine in non-sterile soils with 3, 6 or 9 seedlings of While m-tyrosine may exert allelopathic effects at high concentrations, our research suggests that the quantities of m-tyrosine released from the plant are unlikely to persist in soil to exert phytotoxic effects. Soil microbial communities form a major sink that influences accumulation of a metabolite to phytotoxic levels . Microbiin vitro demonstrations of allelopathic activity of a chemical can rarely be applied to field situation, the most recent studies published in high profile journals in vitro experiments. It is our hope that this study will motivate researchers to integrate the role of soil microbial communities in bioassays in allelopathic research so that its importance in plant-plant competitive interactions can be thoroughly evaluated.Bioassays for allelopathy should investigate rhizosphere-specific and bulk soil microbial activity; and also temperature-driven microbial activity. The differences between species that are ‘sensitive’ or ‘resistant’ to a putative allelochemical, as is commonly invoked in invasion ecology, may reflect differences in microbial communities harbored by different plant species. Although it is well known that