Novel potential inhibitors for adenylylsulfate reductaseto control souring of water in oil...

This article was downloaded by: [189.105.37.85]On: 09 October 2013, At: 15:31Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK

Journal of Biomolecular Structure and DynamicsPublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/tbsd20

Novel potential inhibitors for adenylylsulfate reductaseto control souring of water in oil industriesElias Silva dos Santos a , Leila Cristiane Virgens de Souza b , Patrícia Nascimento de Assis b ,Paulo Fernando Almeida b & Elias Ramos-de-Souza ca Instituto de Física, Universidade Federal da Bahia , Rua Barão de Geremoabo s/n – Ondina,Salvador , BA , 40.300-000 , Brasilb Instituto de Ciências da Saúde, Universidade Federal da Bahia , Av. Reitor Miguel Calmon,s/n – Vale do Canela, Salvador , BA , 40.300-100 , Brasilc Instituto Federal de Educação, Ciência e Tecnologia da Bahia , Campus de Salvador, RuaEmídio dos Santos s/n, Barbalho, Salvador , BA , 40.301-015 , BrasilPublished online: 13 Sep 2013.

To cite this article: Elias Silva dos Santos , Leila Cristiane Virgens de Souza , Patrícia Nascimento de Assis , Paulo FernandoAlmeida & Elias Ramos-de-Souza , Journal of Biomolecular Structure and Dynamics (2013): Novel potential inhibitors foradenylylsulfate reductase to control souring of water in oil industries, Journal of Biomolecular Structure and Dynamics, DOI:10.1080/07391102.2013.834850

To link to this article: http://dx.doi.org/10.1080/07391102.2013.834850

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) containedin the publications on our platform. However, Taylor & Francis, our agents, and our licensors make norepresentations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of theContent. Any opinions and views expressed in this publication are the opinions and views of the authors, andare not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon andshould be independently verified with primary sources of information. Taylor and Francis shall not be liable forany losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoeveror howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use ofthe Content.

This article may be used for research, teaching, and private study purposes. Any substantial or systematicreproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in anyform to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Novel potential inhibitors for adenylylsulfate reductase to control souring of water in oilindustries

Elias Silva dos Santosa, Leila Cristiane Virgens de Souzab, Patrícia Nascimento de Assisb

Paulo Fernando Almeidab* and Elias Ramos-de-Souzac

aInstituto de Física, Universidade Federal da Bahia, Rua Barão de Geremoabo s/n – Ondina, Salvador, BA 40.300-000, Brasil;bInstituto de Ciências da Saúde, Universidade Federal da Bahia, Av. Reitor Miguel Calmon, s/n – Vale do Canela, Salvador, BA40.300-100, Brasil; cInstituto Federal de Educação, Ciência e Tecnologia da Bahia, Campus de Salvador,Rua Emídio dos Santos s/n, Barbalho, Salvador, BA 40.301-015, Brasil

Communicated by Ramaswamy H. Sarma

(Received 9 May 2013; accepted 12 August 2013)

The biogenic production of hydrogen sulfide gas by sulfate-reducing bacteria (SRB) causes serious economic problemsfor natural gas and oil industry. One of the key enzymes important in this biologic process is adenosine phosphosulfatereductase (APSr). Using virtual screening technique we have discovered 15 compounds that are novel potential APSrinhibitors. Three of them have been selected for molecular docking and microbiological studies which have shown goodinhibition of SRB in the produced water from the oil industry.

Keywords: souring; SRB; inhibition APS reductase

Introduction

Sulfide production by sulfate-reducing bacteria (SRB)costs annually millions of dollars to the oil and gas indus-tries in order to control its generation. These bacteria arecommonly found and are active in aqueous systemsthroughout the world. Detrimental effects of SRB include:souring of oil and gas reservoirs and wells, souring of sur-face vessels of many types of reservoir and well pluggingthat results in reduced production, corrosion and scalingof metals, serious hazard to the personnel and the environ-ment (Almeida et al., 2006). According to many authors(Nemati, Jenneman, & Voordouw, 2001; Voordouw,Nemati, & Jenneman, 2002), current methods in industryare designed to either kill SRB or otherwise reduce theeffects of sulfides, but are only partially effective andoften prohibitively expensive. The demand is still high inmany industries for new, more effective and economicalsolutions to combat sulfide souring and corrosion.

The process is triggered when SRB, such as Desulf-ovibrio, Desulfotomaculum, Desulfobulbus, amongstothers, are present in the water of oil reservoirs reducesulfate into sulfide. These strict anaerobic bacteria attachto the walls of the reservoirs, ducts of transport, and tanksof storage, accumulating as biofilm and after detachment

they move to the other parts until they compromise theentire system.

Amongst responsible enzymes for the mechanism ofchemical reactions that culminate in the production ofthe hydrogen sulfide gas, the adenosine phosphosulfatereductase (APSr) is distinguished. The main characteris-tic of this enzyme is the presence of metallic ions orcofactors that play a very important role in the electrontransport system or in the stabilization of the enzyme–substrate structure.

Electron transport systems have a very important rolein biological processes (Marcus & Sutin, 1985). In suchsystems, enzyme redox chains have ions that arearranged in the direction to attend the electron flow andto generate charge separation which represents the firststep for capture of energy (Bendall, 1996; Bizzarri &Cannistraro, 1998). An electron moves from a donator(d) to an acceptor (a) flowing in a gradient of electricpotential through a chain of redox centers which can beseparated by a relatively slight distance (1–2 nm or more)(Marcus & Sutin, 1985).

The ability of these enzymes in carrying electronscan be modified by intervening in the oxi-reduction reac-tions. One could avoid the binding of the enzyme to the

*Corresponding author. Email: pfa@ufba.br

Journal of Biomolecular Structure and Dynamics, 2013http://dx.doi.org/10.1080/07391102.2013.834850

� 2013 Taylor & Francis

substrate in the bonding sites with the use of chelatingcompounds or antioxidants simply by modifying theproperties of the responsible cofactors for the electrontransport. It may cause a deceleration of the transport ofthese electrons and, therefore, a reduction in the speed ofthe oxi-reduction reactions.

The structure of the crystal of the APSr of the micro-organism Archaeoglobus fulgidus is formed by two sub-units α2β2. The α subunit, with a molecular mass of75KDa, contains a cofactor FAD. The β subunit, of20KDa, loads two iron-sulfur centers [4Fe-4S]+2, whichare responsible for the transport of two electrons fromthe surface of the enzyme to the isoalloxazine ring of theFAD. The active site is located between the centraldomain (A2-A261 and A394-487) and the domain ofcapping (A262-A393) mediated by water molecules. Thereaction starts with the nucleophilic attack of the N5

atom of the FAD, in the reduced state, on the sulphur ofadenosine phosphosulfate (APS) to form an intermediaryFAD–APS which decomposes spontaneously due to anelectronic reorganization in adenosine monophosphateand sulfite (Schiffer, Fritz, Kroneck, & Ermler, 2006).

In this direction, the procedure of docking of mole-cules with chelating capacity is one technique that canhelp in the prediction of protein–ligand interactions.

Although docking studies are important as a screen-ing procedure to search for inhibitors, the experimentalevaluation of their inhibitory action against the sulfate-reducing micro-organisms is a complementary and veryimportant process. The importance of these studies relyon the large groups of sulfate-reducing micro-organisms,differences in their structures and composition, as wellas by the fact that the inhibitors compounds have toenter the outer cell structures to exert their effects in theelectron transport chain of the SRB cells. Furthermore,the distribution of SRB in the oil and gas environmentoccurs in water with different levels of metallic ioniccompounds as well as in biofilms which makes difficultthe action of the inhibiting compounds to attain the cells.By all the reasons mentioned above, we decided toexplore microbiological studies and ways in which theselected compounds could reach the SRB cells.

Material and methods

Protein preparation

The three-dimensional structure of the APSr in complexwith the APS was obtained from the Protein Date Bank(PDB code 2FJA) in order to model and to simulate theA and B chains, Figure 1. Ligand APS was removed butthe water molecules of the active site was kept. Hydro-gen atoms were added and bonded using the Autodock-Tools (Morris et al., 2009). Partial charges werecalculated using the Gasteiger method. As this methoddo not calculate charges for the iron-sulfur centers due to

missing parameters, we used the charges values previ-ously calculated (dos Santos, Gritta, Taft, Almeida, &Ramosde-Souza, 2010) and edited the enzyme file in thePDBQT. Then the enzyme was subjected to one shortminimization using the steepest descent method. Thebinding site selected for docking is formed by the FADand the residues His-A398, Trp-A234, Arg-A265, Val-A273, Gly-A274, Glu-A141, Gln-A145, Asn-A74, andPhe-A448 plus some water molecules. When water mole-cules are found in crystalline protein structures, they tendto occupy conserved positions and, therefore, they playan important role for the binding of the substrate andprobably in the process of catalysis. When a ligand bindsto a protein, water molecules in the binding site are dis-located or kept to mediate hydrogen bonds between theligand and the protein to stabilize the complex (Barillari,Taylor, Viner, & Essex, 2007).

Preparation of the ligands

Once the structure of the target protein is known, thesearch for inhibitors usually follows well-establishedvirtual screening techniques applied early during the dock-ing protocol to reduce the size of large compoundlibraries. The 15 compounds obtained by the virtualscreening method were first analyzed regarding their activ-ity, toxicity, and physicochemical properties (Pinsetta,Taft, & Silva, 2013). From the 15 compounds obtained weselected three.

The three ligands with chelating and/or anti-oxidizingcharacteristics 2-[2-[bis(carboxymethyl)amino]ethyl-car-boxymethyl)amino]acetic acid (EDTA), 2-[2 bis(carboxy-methyl)amino]ethyl-(2-hidroxymethyl)amino]acetic acid(DETAROL), and 2-[2-(1,2-dicarboxyethylamino)ethyl-amino]butanedioic acid (EDDS) (Figure 2) in the sdf

Figure 1. Three-dimensional structure of A and B chainsAPSr.

2 E.S. dos Santos et al.

format had been obtained from the PubChem BioAssaydatabase. Openbabel software version 2.3.1 (O’Boyleet al., 2011) was used to convert the ligands in the pdbformat. They had been visually checked to correct even-tual structural errors. Hydrogens atoms and Gasteigercharges were added for all the ligands. We used theempirical field force MMFF94 for the conformationalmodeling of ligands. Each ligand was subjected to oneshort minimization using the steepest descent method.

Redocking

The simulations were made using the Autodock Vina(Trott & Olson, 2010). To evaluate the validity of the sys-tem and to adjust the parameters for the docking of inhibi-tors, the substrate was removed from the crystallographicstructure and recoupled to the enzyme through a redockingsimulation. The simulation box was defined around theprotein binding site whose center of the box is under thecoordinates of the APS (22.446, �13.037, 128.373).The size of the box in the x, y, and z directions is 40Å.

The parameters used in the redocking simulations areshowed in Table 1. The ‘energy range’ describes themaximum energy difference between the best bindingmode and the worst one displayed. The ‘num modes’parameter is the maximum number of binding modes togenerate. The ‘exhaustiveness’ is related with thorough-ness of search, roughly proportional to time and ‘seed’ isthe seed of the random number generator. The resultsshow that the conformer of the crystallography is veryclose to the conformer docked as deduced through theoverlapping of the two structures (Figure 3). The refer-ring parameters to the lesser root-mean-square deviation(RMSD) of 0.74Å found during the redocking simula-tions were used for adjustment of the simulations.

Protein–ligand docking methods

Three simulations for each protein–ligand complex werecarried out. All the complexes were visualized in the

Pymol version 1.4.1. (DeLano, 2011) to find the bestmodes of binding. The best conformation was selectedtaking into account the lowest energy and RMSD. There-fore, optimum model shows low value of energy (highaffinity), low distance RMSD lower bound (l.b.), andlow RMSD upper bound (u.b.).

Initially the solutions with the same conformationwere removed and we placed them in families. Identicalsolutions in conformation that show minor energy andRMSD were removed and visually compared with theAPS. The visual comparison in the Pymol helped us tofind a conformation that adequately binds to the bindingsite. The best conformations were then separated usingthe auxiliary program vina_split to generate the threefinal models after docking.

Thus, to refine the docking results (to relax the bondsin the models and to accommodate water molecules inthe binding site), the systems were submitted to a newminimization with the steepest descent method and after

Table 1. Parameters used in simulation redocking.

Parameters Value

Energy range 10Num modes 20Exhaustiveness 150Seed �8841

Figure 3. Overlapped structure of APS. The APS docked thiscolored in red.

Figure 2. Three-dimensional structures of (a) DETAROL, (b)EDDS, and (c) EDTA.

Inhibitors for APS reductase to control souring 3

this an energy analysis was carried out with the softwarePEARLS (program of energetic analysis for ligand–receptor system) (Han et al., 2006).

Molecular dynamics of TWEEN and PEG

Preliminary in vitro assays using selected potential inhib-itors alone against SRB have shown low inhibitoryactivities. So, it was decided to use some delivery agents(Brazilian Patent number BR1013438-7) to see if theycould facilitate entry or at least to deliver the selectedinhibitors inside the outer membrane to improve theactivity of the chelating agents against APSr. So weused the technique of Carvalho, Almeida, Souza, andRoque (2010) which consist of complexing these com-pounds with 2-[2-[3, 4-bis(2-hydroxyethoxy)oxolan-2-yl]-2-(2-hydroxyethoxy)ethoxy]ethyl(E)-octadec-9-enoate(TWEEN) and propane 1,2-diol (PEG). Furthermore,molecular dynamics studies of these compounds weredone to tentatively explain their action. The two matrices(PEG and TWEEN) in the sdf format were obtainedfrom the PubChem BioAssay database. Openbabel soft-ware version 2.3.1 (O’Boyle et al., 2011) was used toconvert the matrices for the pdb format.

The simulations were performed using a dual corecomputer with 2Gb of RAM memory, using the ffg43a1force field code of the Groningen Machine for ChemicalSimulations software package, version 3.3. (Van derSpoel, Lindahl, & Berendsen, 2005). The analysis wasperformed for 1.5 ps at 300K with 1 bar of pressure. Thetopology of the matrices was obtained from the webserver PRODRG (data not shown) (Schuettelkopf & vanAalten, 2004).

The systems were solvated with water molecules ofthe spc (simple point charge) type and minimized withthe steepest descent algorithm in a dodecahedron boxwith an approximate volume of 10.39 nm3 and with aminimum distance of 1.0 nm between the matrices andthe walls of the box. The whole systems were balancedmaintaining restricted position for 200 ps with 1 fs timesteps. The SETTLE algorithm (Ryckaert, Ciccotti, &Berendsen, 1977) was used for the bonds of the watermolecules. The unbounded interactions were cut at1.2 nm and the temperature was maintained through theweak coupling of Berendsen with a constant time ofTT = .1 ps. The initial velocity used in the equilibration ofthe system was generated using the Maxwell distribution.The pressure was kept constant with Berendsen’s weakcoupling barostat with a constant time of Tp = 1 ps.

In vitro microbiological assays

From the 15 chelating and/or anti-oxidizing compoundsobtained by virtual screening technique, we selectedDETAROL, EDDS, and EDTA based on their solubility,price, and toxicity to evaluate the inhibitory efficiency inlaboratory assays through the analysis of their activity to

reduce the biogenic sulfide production. Each laboratoryexperiment was carried out with a definite population ofSRB in produced water (PW) samples from the oilindustry. Preliminary assays were made with a mixtureof the three chosen chelating compounds in concentra-tions between 250 and 750mgL�1 of each one, and hadshown to be efficient in the reduction of the104MPNmL�1 of a mix SRB population. For this rea-son, it was chosen 250 and 750mgL�1 as the minimumand maximum concentrations to evaluate the efficiencyof each one and combinations of those selectedcompounds.

Samples of PW collected from oil wells of the Rec-ôncavo Baiano basin, Brazil were used in the experi-ments. The SRB counting have showed a naturalpopulation of 10 and 103 cells of SRB mL�1 as deter-mined by fluorescence in situ hybridization techniqueaccording to Santos, Batista et al. (2010. In view of thelow numbers of SRB, aliquots of a mixture of previouslypure-activated cultures of SRB were added in order toobtain a concentration of 104 and 107 cells mL�1,respectively, to the water samples of oil wells determinedby most probable number (APHA, 1989). For eachexperiment, PW samples were enriched with a filter ster-ilized solution containing (mg L�1) sodium salts of lac-tate (1681.2), citrate (4411.0), acetate (410.2) as well asthe compounds toluene (525.0) and ferrous sulfate(600.0). The pH was previously adjusted to 7.8 to allowthe growth of any SRB already present in the water.Also, in order to improve the inhibitory efficiency of thechelating compounds we complexed them with TWEENand PEG at a concentration of 100mgL�1 (dos Santos,Ramos-de-Souza, & Almeida, 2010). Treatments weredivided in eight groups, each one with the addition ofeither TWEEN or PEG. According to Table 6, six groupswere carried out with a single chelating agent and theother two groups with a mixture containing the threecompounds. SRB counts from produced water andcultures were performed in triplicate in tubes of16� 150mm with 10mL of the supplemented PW addedto 1 mL of the activated SRB mixture cultures inPostgate medium (Postgate, 1984). After serial decimaldilution the SRB concentration was determined by themost probable number technique in Postgate medium. Inall groups, series of one hundred micro liters were trans-ferred to 900μL of Postgate medium in microplaques inorder to obtain the concentration of 104 and 107MPN ofSRB per mL. The inhibitors were added previously inPostgate medium at the concentration cited in Table 6.Positive and negative controls were made with and with-out SRB cultures. Tween and PEG controls were madein cultures without the inhibitors. The tubes were incu-bated at an anaerobic atmosphere in an anaerobic cabinetBACTRON IV (SHELLAB) at 38 °C for 11 days. At theend of the experiments, aliquots of each tube were

submitted to serially decimal dilution and inoculated inplates of 96 wells contained Postgate medium to countthe survivors. Positive and negative control assays weredone as well without the chelating compounds and addi-tion of SRB, respectively. Results of the triplicate assayswere registered by making growth lectures at 24 h inter-vals during the experiments by observing the develop-ment or not of a black color resulting from the reductionof sulfate by SRB generating sulfides which precipitatewith the metallic compounds present in enriched PW.

Results and discussion

SRB are the most known and studied bacteria implicatedin the biocorrosion (Zuo et al., 2004). They have beenfrequently found in facilities such as transportation, stor-age, oil and gas producing facilities, and are the mostprobable cause for corrosion and problems such as sour-ing and biofouling in these areas (Beech & Sunner,2004). Currently it is tried to skirt the problem by threeforms. The first one, applied to the gaseous phase, usesscavenging substances which do not eliminate the cause

but the effect, capturing the produced hydrogen sulfidegas through chemical reaction. Second approaches act onthe cause, i.e. against the SRB through the use of antimi-crobials (biocides) and metabolic inhibitors with the pur-pose to destroy or to inhibit, respectively, the SRB. Thebiocides, beyond having raised costs, are used as empiri-cal form, many of them are toxic and carcinogenic,attack the environment, demanding special care and,most of the time, are not capable to solve the problemdue to the sprouting of resistant SRB (Almeida et al.,2006). For this second option there is also an alternativeto use nutrients such as nitrate that stimulate the nitrate-reducing bacteria (NRB) already present in the fields,which generate nitrite that increase redox potential of theenvironment, inhibiting the growth of the SRB(Voordouw et al., 2002). This last strategy is consideredpromising, however, some NRB reduce nitratecompletely to nitrogen gas or much worse until nitrogenoxides which can promote bigger corrosive effect ofpipelines than sulfide (Vik et al., 2007). Moreover, notalways the NRB are present in the fields and also haveSRB that use these composites, becoming the technique

Figure 4. Binding modes of inhibitors in the binding site (a) DETAROL, (b) EDDS, and (c) EDTA. The inhibitors are representedin sticks and colored in blue. The FAD molecule represented in sticks and colored in magenta. Hydrogen bonds represented bydashed yellow lines.

inefficient (Greene, Hubert, Nemati, Jenneman, & Voor-douw, 2003). Finally, the third is associated with physi-cal methods such as nanofiltration that removes sulfatefrom the water (Mulder, van Voorthuizen, & Peeters,2005, chap. 5), but this procedure is of high cost. Forthe above cited reasons, still it has necessity of newstrategies for the effective control of souring in the oilindustry. Since souring is associated with the transfer ofelectrons, studies that could find the inhibitors of theelectronic transport can result in the control of the meta-bolic activities of the SRB.

Figure 4 shows that inhibitors occupied exactly theregion of the binding site suggesting that our dockingprocedure was successful. The best modes for the threeinhibitors exhibited in the Table 2 were submitted thesecond minimization.

In the process of protein–ligand complexation, manyenergetic contributions are responsible for the stability ofthe ligand in the binding site. The free energy of bindingresults from the balance of these contributions. From thepoint of view of the solvent, there is an entropic andenthalpic gain when the water molecules around theligand and the protein active site are released to diffusein solution (Guimarães & Cardozo, 2008). These multi-ple contributions are associated with the unusual andamazingly unique properties of water. Its small size, thedipolar nature caused by its charge distribution, thehighly directional hydrogen bonds as both donor andacceptor, and the entropic gain associated with therelease to bulk solution of water molecules bound to bio-macromolecules and ligands (Cozzini et al., 2004). Fromthe point of view of the ligand and the protein, entropictranslational and rotational losses are mitigated in thecomplexation by vibrational residual entropy and finallythe enthalpic gain from protein–ligand intermolecularinteractions (Guimarães & Mathiowetz, 2010). Theligands that interact most favorably with the protein arenot entropically penalized due to the restriction of theirvibrational modes in the binding site. Also, the ligandswith higher molecular weight that tend to display a morefavorable hydrophobic effect and van der Waals interac-tions are not penalized due to a greater loss of transla-tional and rotational entropies upon binding (Guimarães& Mathiowetz, 2010).

These losses are due to the restriction of translationaland rotational motions of the binding ligands in the com-plex relative to the freely moving state. Hydrogen bonds

restrict the movement of both ligands and protein bind-ing site causing greater limitations on degree freedomand, therefore, entropic translational and rotationallosses. To compute the energies of interaction betweenprotein and the inhibitors we used the software PEARLS.The energy was calculated from the equationDG ¼ DH � TDS, where T is the absolute temperature,DS is the entropy change, DH is the enthalpy change,and DG is the Gibbs free energy change. This softwaretakes into account the components of energy thatincludes van der Waals interactions, the electrostaticligand-receptor forces, the hydrogen bonds, and themetal-ligand interactions, in addition to changes in thefree energy of solvation and in the conformationalentropy (Han et al., 2006). The contribution fromchanges in conformational entropy during the ligand-binding process can be estimated by using the empiricalformula of Filikov et al. (2000). Also, the solvation freeenergy change resulting from the molecular binding canbe estimated by Eisenberg’s method of atomic solvationparameters (Eisenberg & McLachlan, 1986; Wesson &Eisenberg, 1992) including the solvation accessibleatomic surface area (Lee & Richards, 1971).

The Table 3 summarizes these interactions in theprotein-ligand complex energy profile.

The experiment revealed that the presence of watermolecules in the binding site contributed to the stabilityof inhibitors. Hydrogen bonding interactions mediated bywater molecules were observed in all the complexes asshown in Table 4. A water molecule (W812) wasobserved in all the complexes. The water moleculeW812 binds to the oxygen (O) of the DETAROL and tothe oxygen (OE1) of Glu141A. Oxygen (O) of theW812 and W814 water molecules bind OE1 of Glu141and OG1 of Tyr95, N and O of the EDDS, the OE1 of

Table 3. Energy profile of the individual contributions of eachenergy in protein-ligand complex.

Energetic profile of ligand-receptor interactions (kcal/mol)

Inhibitors

DETAROL EDDS EDTA

Total ligand-receptor interactionenergy

�2.53 �10.31 �6.80

Ligand-receptor van der Waalsenergy

�5.96 �7.78 �6.54

Ligand-receptor eletrostaticenergy

�.73 �2.57 �1.54

Ligand-receptor hydrogen bondenergy

�1.01 �1.14 �1.63

Ligand-receptor solvatation freeenergy

1.00 1.16 .75

Ligand–water–receptor bindingenergy

�1.56 �1.27 �3.57

Ligand-receptor conformationalentropy

5.73 1.29 5.73

Table 2. Affinities of the best modes for the three inhibitorsdistance of RMSD values.

Inhibitors Affinities (kcal/mol) RMSD (l.b.) RMSD (u.b.)

EDDS �6.1 1886 5987EDTA �5.3 1865 5752DETAROL �5.3 1731 1008

Glu141A, and OG1 of Thr314A. EDTA binds to theenzyme through oxygens OG1 and OE1 of Thr314 andGlu141, respectively. Hydrogen bonds between thereceptor and the inhibitor, as well as the distancesbetween the receptor and donor atoms are shown inTable 5.

The hydrophobic effect also plays a significant rolein stabilizing the complex. The van der Waals interactionis the factor with the highest level of contribution.Hydrophobic contacts were observed between the DETA-ROL and Leu278A, Phe277A and Trp234A, while in thecase of EDTA contacts with Gln145A, Gly274A,Leu278A, Gln143A and Phe277A were generated. EDDSis connected by hydrophobic contacts Gln145A residues,His398A, Pro272A, Gly274A, Val273A, Leu278A, andthe molecule of FAD.

The conformational entropy had a large importancein the calculation of the binding energy. It is related tothe change in mechanical nature of the folded (morerigid) and unfolded (more flexible) states. Moreover,conformational entropy is invariably a critical factor toprotein function (Frederick, Marlow, Valentine, & Wand,2007), either because the protein must be rigid in spe-cific regions, or have intrinsic flexibility in other specificregions to facilitate molecular recognition. A polymer

with many rigid parts interspersed with few flexible con-necting regions will have (more, less) conformationalentropy than if it was completely rigid or flexible (Vorov,Livesay, & Jacobs, 2008). A feature common to thenative structures of proteins is a backbone and sidechains that are tightly packed, with little interior space(Fleming & Richards, 2000; Klapper, 1971; Tsai, Taylor,Chothia, & Gerstein, 1999; Zhou et al., 1999). Thismeans that protein folding results in a large loss ofconformational entropy for the protein molecule (Harano,2012). Although it is well known that the ‘protein fold-ing problem‘ has still not been elucidated, over the lastdecade a large number of workers have successfully usedvirtual screening, minimizations, and other techniques ina wide range of areas to correctly predict the inhibitors(in agreement with experiment) including lead drugs(Taft & da Silva, 2013).

A number of empirical energy models used in virtualligand screening include a term to account for thisentropic penalty, but the underlying physics is not wellcharacterized and hence merits critical examination. Mostenergy models assume that the ligand’s entropy changecan be decomposed into additive components, althoughcorrelated motions could lead to none additively. Also,energy models often account for changes in torsionalentropy with a term related to the number of rotatablebonds in the ligand, based on reasoning about thenumber of rotamers which each bond can adopt and acomputational analysis of changes in vibrational andconformational entropy on protein folding (Karplus,Ichiye, & Pettitt, 1987). However, the physical rationaleand accuracy of this approach is largely unexamined,especially in the context of protein-ligand binding.Similarly, the common assumption that changes inrotational and translational entropy are constant from oneligand to another appears to be unsupported (Chia-en,Chang, & Michael, 2007).

Quantum methods actually seem to be more accuratethan the canonical partition functions commonly used toquantify these entropic contributions (Grigoriev,Luschekina, Romanov, Sulimov, & Nikitina, 2007).However, in our model where flexible ligands are boundto relatively large length or high molecular weight

Table 5. Energy of hydrogen bonds between the receptor andligands with their respective distances between donor andacceptor atom.

Inhibitors

Individual H-bonds

Ligandatom

Receptoratom

Energy(kcal/mol)

Donor-aceptordistance(A)

DETAROL O OD1 (ASN74) �.62 2.51O OD1 (ASN74) �.10 3.28O OH (TYR95) �.29 3.26N OD1 (ASN74) �.35 3.17

EDDS OD2 NE (ARG265) �.51 2.98OXT OH (TYR95) �.27 3.78

EDTA O NH2(ARG265)

�.43 3.07

O ND2 (ASN74) �.55 2.94

Table 4. Number of hydrogen bonds mediated by water molecules.

Inhibitors

Energy of ligand–water–receptor H-bonds

Ligand atom Water Receptor atom Energy (kcal/mol) L-W distance (A) W-R distance (A)

DETAROL O O (W812) OE1 (GLU141) �.22 2.3 2.3OXT O (W812) OH (TYR95) �.22 2.5 2.4

EDDS N O (W812) OE1 (GLU141) �.19 2.4 3.0O O (W814) OG1. (THR314) �.22 2.7 2.5

EDTA O O (W811) OG1 (THR314) �.22 2.3 2.7O O (W812) OE1 (GLU141) �.22 2.3 2.4

compounds, under severe restriction of movement andthus with a reduction of the vibrational entropy,canonical functions could be a good option.

For each of the docking complexes, we also analyzethe distance between the two redox centers and the

isoalloxazine ring. The distance between the two redoxcenters is a crucial parameter for electron transferefficiency of the resulting complexes (Marcus & Sutin,1985; Moser, Keske, Warncke, Farid, & Dutton, 1992).We analyze the distance between the iron-sulfur centers

Table 6. Anti-microbial and anti-biogenic sulfide activities of the three chelating/anti-oxidizing compounds in viable economicconcentration against 104 and 107MPNmL�1 of a mixture species of SRB found or added to PW samples from oil industrysupplemented with TWEEN and PEG.

Chelating agentsand SRB MPNmL�1

Concentration (mgL�1)

100 250 500 750 1500 2250

104 107 104 107 104 107 104 107 104 107 104 107

EDTA TW NA NA + + + + + + + + + + + 0 + + + + + + ND ND ND NDEDTA PEG NA NA + + + +0 0 0 0 0 + 0 0 + + 0 + + 0 ND ND ND NDEDDS TW NA NA + + + + + + + 0 0 + + + 0 0 0 + + 0 ND ND ND NDEDDS PEG NA NA + + + + + + + + + + + + + + + + + + ND ND ND NDDETAROL TW NA NA + + + + + + + + + + + 0 + + + + + 0 ND ND ND NDDETAROL PEG NA NA + + + + + + + + + + + + + + + + + + ND ND ND NDMIXTURE TW NA NA ND ND ND ND 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0MIXTURE PEG NA NA ND ND ND ND + + + + + + 0 0 0 0 0 0 0 0 0 0 0 0TW Control + + + + + + NA NA NA NA NA NA NA NA NA NAPEG Control + + + + + + NA NA NA NA NA NA NA NA NA NA

Notes: TW – TWEEN; NA – not applicable; ND – not determined; + – SRB growth and H2S production; 0 – no SRB growth or H2S production.

Figure 5. Distance represented by dashed yellow line between the iron-sulfur centers after the docking (a) DETAROL, (b) EDDS,and (c) EDTA.

and between the Center I and the methyl residue of theisoalloxazine ring from the crystallographic structure.According to data, the iron-sulfur centers have a side-by-side distance of 10.61Å. The distance between the S3from the Center I and the methyl group C8M of FAD is15.78Å.

After a visual inspection in Figures 5 and 6, a smallreorientation of the isoalloxazine ring and a distancingeffect between the iron-sulfur centers can be assessed.The distances observed between the centers with theinhibitors docked were 10.38, 9.94, and 8.80Å forDETAROL, EDDS, and EDTA, respectively. If weobserve the distances between the Center I and themethyl group of the FAD, they reach 15.76 and 14.79Åfor the inhibitors DETAROL and EDDS, respectively,but increases to 17.60Å for the EDTA. Such modifica-tions are local and are possibly due to the presence ofligands and can be better investigated by moleculardynamics simulations.

In vitro microbiological assays

Computational findings shown in this work suggest thataccording to the hypothesis raised in the beginning, the

use of the chelating and/or antioxidant agents selected bymolecular docking should interfere in electron transportmechanisms that occur in oxidation–reduction reactions,which could contribute therefore to reduce the activity ofSRB and, consequently to reduce the amount of H2S toacceptable industrial and environment levels. Further-more, in vitro experiments have an essential role in thevalidation of the models developed and can providecriteria to improve the methodology of an in silicoscreening process and verify the efficiency of thesecompounds at a laboratory level.

The treatments with single compounds and with thecombination at different concentrations (250 until750mgL�1) showed some anti-microbial activities tocontrol souring when used with isolated pure cultures,but not with the mixed inoculates (data not showed).This could be explained by the differences exhibited bythe different micro-organisms. In silico studies suggestedthat all the three compounds were able to impair electrontransport chain in SRB, an important metabolic pathwayto reduce sulfate and get energy by these micro-organ-isms. Although their activity to inhibit SRB is unques-tionable, the activities of chelating and/or anti-oxidizing

Figure 6. Distance represented by dashed yellow lines between the S3 atom and atom of the center I C8M isoalloxazine ring.

molecules have to occur inside the cell in order to inhibitthe SRB growth at expenses of respiratory metabolism.This is why we included the nanometric matrices ordelivery molecules as TWEEN and PEG. They also havethe property to protect the chelating/anti-oxidizingcompounds against the cations usually present in the PWin variable concentrations. Their activities however weresubstantially improved when used together with TWEENand PEG. EDTA showed better activity when PEG wasused, however results are inconclusive due to the fail atthe concentration of 750mgL�1 in the cell concentrationof 104 (Table 6). On the contrary, EDDS when evaluatedper si was effective at a 750mgL�1 concentrationtogether with the TWEEN, showing no activity whenused with PEG at the same concentration. For all othercombinations, TWEEN was a better delivery carrier. Thiscould be due to the activity of these matrices in thepermeabilization of the membranes, competing withdivalent cations usually present in the PW or even toprotect their properties to impair electron transport.

As we can see in Table 6, the mixture of all antimi-crobials in the concentrations of 750, 1500, and2250mgL�1 worked best when TWEEN was used,inhibiting biogenic sulfide generation in populations of104 and 107NMPmL�1 of SRB, even at the lowest con-centration used (750mgL�1). This means that there is asynergy between the compounds tested and this situationis improved in the presence of TWEEN, since the same

result was only found for concentrations above1500mgL�1 when PEG was used.

Chelating compounds EDTA and EDDS (Figure 7(a)), acting alone, have low effectiveness on biogenicsulfide generation inhibition (series in which the growthand production of sulfide by SRB viewed through theblack precipitates). This can be due to excessiveconcentration of compounds with a positive electricalcharge (calcium, potassium, iron, barium, among others)present in the PW (data not showed). However, whenevaluated per si, EDDS was effective when used at aconcentration of 750mgL�1 together with the TWEEN,showing no activity when used with the PEG at thisconcentration.

Molecular dynamics of PEG and TWEEN

As was demonstrated in laboratory experiments, the twocomplex matrices added to the three chelating agentsshowed different behavior in inhibiting biogenic sulfideproduction. Given this situation, we carried out two sim-ulations with the purpose of analyzing the behavior ofthe PEG and TWEEN solvated in aqueous solution.

For both simulations, the formation and break downof hydrogen bonds (lifetime) with water molecules weremonitored using the criterion of 0.35 nm distancebetween the atoms that form the bonds and are shown inFigure 8. It is noted that the time for formation and rup-ture of hydrogen bonds is very small (on average), withthe number of bonds reaching zero around 2.5 ps simula-tion time for both matrices.

The radial distribution function G(r) represents ameasure of water molecules closer to those matrices,forming layers of hydration (Figure 9), taken in relationto the center of mass of the PEG and TWEEN matrices.Interaction with water molecules to distance values near.11 nm indicates that little or no hydrogen bond can beformed. At the distance of .45 nm, it is observed for both

Figure 7. Pictures show the activities of each inhibitor and amixture of the three compounds against a mixture of SRB.Black means the growth and production of H2S. Yellow orpale means no growth and no production of H2S. (a) WithoutTWEEN or PEG, (b) Isolated chelating with TWEEN or PEG,and (c) shows the activities of a mixture of chelating and/orantioxidizing compounds that have been complexed withTWEEN or PEG against the SRB mixtures. Phothographieswere taken with the microplaques inside the anaerobic cabinet.

Figure 8. Lifetime of the hydrogen bonds formed between thewater molecules and polymers, where the curves for the PEGand TWEEN are in dotted and continuous lines, respectively.The plotting represents the average number of hydrogen bonds.

matrices the second layer of hydration. Above 1.0 nm,the third layer of hydration extends to 2.5 nm.

The idea now is to analyze the volumes of the matri-ces during the simulations based on the behavior of thegyration radius. Figure 10 shows a decreased gyrationradius of approximately .15 nm for the PEG whilegyration radius of TWEEN decreased about .10 nm.Comparison with crystal structures and electronic micro-

graphs indicates that the protein pore radius is largerthan the effective hydrodynamic radius of PEG and thenit would be able to diffuse through the pores of proteins(Decard & Nikaido, 1976; Robertson et al., 2007). Evenfor a short simulation, it looks like PEG tends to stabi-lize its gyration radius on a lower value than that ofTWEEN. The diffusion constant was estimated throughthe mean square displacement of atomic positions duringthe simulations, with respect to the centers of mass ofthe matrices as shown in Figure 11.

Diffusion constants for TWEEN and PEG werecalculated for 1.5 ns simulation time. The diffusionconstant to the PEG was estimated to be (.645 ± .215)10�5 cm2/s, calculated in the range between 500 and900 ps, while for the TWEEN this value is (1.783± .176) 10�5 cm2/s, calculated in the range of 1.0–1.2 nsshowing that the TWEEN spreads more easily throughthe channels of the pores in the membrane of the micro-organism in passing without substantial distortionsregarding the PEG.

Conclusion

This study demonstrates the effectiveness of molecularmodeling and virtual screening in searching potentialinhibitors for control of biogenic sulfide production. Thediscovery of these inhibitors leads to the proposition of anew technological alternative for the control of biogenicsulfide generation based on the use of chelating and/orantioxidant agents to inhibit specifically the reactions ofreduction of sulfate to sulfide. As an assessment criterionused for the selection of inhibitors, the in silico experi-ments indicated that the three compounds demonstratedto be efficient in the control of biogenic sulfide produc-tion. The results of laboratory tests carried out with threeselected compounds in molecular docking studies corrob-orated the hypothesis that the use of chelating agents canlead to the inhibition of the sulfate reduction reactionsby capturing electrons which otherwise could result insulfide.

In comparison to this new method which involvesthe blocking of the transfer of electrons is better thanothers because it is specifically directed to avoid thereduction of sulfate and it is useful even against the SRBthat show activities of nitrate and/or nitrite reductaseenzymes. Besides, it does not depend on the presence ofNRB to be effective. It also can be used in conjunctionwith other methods as a new alternative to controlsouring in oil and gas facilities.

With regard to in situ utilization of the compoundsthat prove effective, it is necessary to define whichsubstances may be effectively used in the oil and gasindustry, considering the environmental, economic,social, and human health aspects.

Figure 9. Radial distribution function of water moleculesaround the polymers. The dotted and continuous lines representthe curves of the fluctuation of hydration layers for the PEGand TWEEN, respectively.

Figure 11. Fluctuation displacement of root-mean-square ofatoms of polymers. The curves of the fluctuation of the atomsof the PEG and TWEEN are dotted and continuous lines,respectively.

Figure 10. Behavior of polymers gyration radius. The curvesof the radius of gyration are dotted for PEG and continuous forthe TWEEN.

Acknowledgments

We would like to acknowledge Dr. Ernesto Rául Caffarena forcollaboration with the dynamic molecular analysis.

Funding

This research was supported by the Financiadora de Estudos eProjetos (FINEP), Petróleo Brasileiro S.A. (PETROBRAS), andAgência Nacional de Petróleo (ANP). Project numbers: FINEP0.1.05.0974.00/PETROBRAS 0050. 001843106.4/SAP contractnumber 4600202295).

ReferencesAlmeida, P. F., Almeida, R. C. C., Carvalho, E. B.,

Ramos-de-Souza, E., Carvalho, A. S., Silva, C. H. T. P., &Taft, C. A. (2006). Overview of sulfate-reducing bacteria andstrategies to control biosulfide generation in oil waters. In C.A. Taft (Ed.), Modern biotechnology in medicinal chemistryand industry (pp. 183–195). Kerala: Research Signpost.

American Public Health Association. (1998). Standard methodsfor the examination of water and wastewater (20th ed.).Washington, DC: Author.

Barillari, C., Taylor, J., Viner, R., & Essex, J. W. (2007).Classification of water molecules in protein binding sites.Journal American Chemical Society, 129, 2577–2587.

Beech, I. B., & Sunner, J. (2004). Biocorrosion: Towardsunderstanding interactions between biofilms and metals.Current Opinion in Biotechnology, 15, 181–186.

Bendall, D. S. (1996). Interprotein electron transfer. In D. S.Bendall (Ed.), Protein electron transfer (pp. 43–64).Oxford: Bios Scientific.

Bizzarri, A. R., & Cannistraro, S. (1998). Molecular dynamicssimulation of plastocyanin potential energy fluctuations: 1/fnoise. Physics Letters A, 267, 257–270.

Carvalho, A. S., Almeida, P. F., Souza, E. R., & Roque, M. R.A. (2010). Brazil Patent No. BR 1013438-7. Rio deJaneiro: Instituto Nacional de Propriedade Intelectual.

Chia-en, A., Chang, W. C., & Michael, K. G. (2007). Ligandconfigurational entropy and protein binding. Proceeding ofthe National Academy of Sciences of the United States ofAmerica, 104, 1534–1539.

Cozzini, P., Fornabaio, M., Marabotti, A., Abraham, D. J.,Kellogg, G. E., & Mozzarelli, A. (2004). Free energy ofligand binding to protein: Evaluation of the contribution ofwater molecules by computational methods. CurrentMedicinal Chemistry, 11, 3093–3118.

Decard, G. M., & Nikaido, H. (1976). Outer membrane ofgram-negative bacteria molecular-sieving function ofcell-wall. Journal Bacteriology, 128, 325–336.

DeLano, W. L. (2011). The PyMOL molecular graphics system(Version 1.4.1). San Carlos, CA: DeLano Scientific LLC.Retrieved from www.pymol.org

dos Santos, E. S., Gritta, D., Taft, C., Almeida, P. F., & Ramos-de-Souza, E. (2010). Molecular dynamics simulation of theadenylylsulfate reductase from hyperthermophilic Archaeo-globus fulgidus. Molecular Simulation, 36, 190–203.

dos Santos, E. S., Ramos-de-Souza, E., & Almeida, P. F.(2010). Brazil patent No. BR1005035-3. Rio de Janeiro:Instituto Nacional de Propriedade Intelectual.

Eisenberg, D., & McLachlan, A. D. (1986). Solvation energyin protein folding and binding. Nature, 319, 199–203.

Filikov, A. V., Mohan, V., Vickers, T. A., Griffey, R. H., Cook,P. D., Abagyan, R. A., & James, T. L. (2000). Identificationof ligands for RNA targets via structure-based virtualscreening: HIV-1 TAR. Computer-Aided Molecular Design,14, 593–610.

Fleming, P. J., & Richards, F. M. (2000). Protein packing:Dependence on protein size, secondary structure andamino acid composition. Journal of Molecular Biology,299, 487–498.

Frederick, K. K., Marlow, M. S., Valentine, K. G., & Wand, A.J. (2007). Conformational entropy in molecular recognitionby proteins. Nature, 448, 325–329.

Grigoriev, F. V., Luschekina, S. V., Romanov, A. N., Sulimov,V. B., & Nikitina, E. A. (2007). Computation of entropycontribution to protein–ligand binding free energy.Biochemistry (Moscow), 72, 785–792.

Greene, E. A., Hubert, C., Nemati, M., Jenneman, G. E., &Voordouw, G. (2003). Nitrite reductase activity of sulphate-reducing bacteria prevents their inhibition bynitrate-reducing, sulphide oxidizing bacteria. EnvironmentalMicrobiology, 5, 607–617.

Guimarães, C. R. W., & Mathiowetz, A. M. (2010). Addressinglimitations with the MM-GB/SA scoring procedure using theWaterMap method and free energy perturbation calculations.Journal of Chemical Information and Modeling, 50, 547–559.

Guimarães, C. R. W., & Cardozo, M. (2008). MM-GB/SArescoring of docking poses in structure-based leadoptimization. Journal of Chemical Information andModeling, 48, 958–970.

Han, L. Y., Lin, H. H., Li, Z. R., Zheng, C. J., Cao, Z. W.,Xie, B., & Chen, Y. Z. (2006). PEARLS: Program for ener-getic analysis of receptor-ligand system. Journal of Chemi-cal Information and Modeling, 46, 445–450.

Harano, Y. (2012). Application of hydration thermodynamics tothe evaluation of protein structures and protein-ligandbinding. Entropy, 14, 1443–1468.

Karplus, M., Ichiye, T., & Pettitt, B. M. (1987). Configurationalentropy of native proteins. Biophysical Journal, 52, 1083–1085.

Klapper, M. H. (1971). On the nature of protein interior.Biochemica et Biophysica Acta, 229, 557–566.

Lee, B., & Richards, F. M. (1971). The interpretation of proteinstructures: Estimation of static accessibility. Journal ofMolecular Biology, 55, 379–400.

Marcus, R. A., & Sutin, N. (1985). Electron transfers in chemis-try and biology. Biochimica Biophysica Acta, 811, 265–322.

Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew,R. K., Goodsell, D. S., & Olson, A. J. (2009). Autodock4and AutodockTools4: Automated docking with selectivereceptor flexiblity. Journal Computational Chemistry, 16,2785–2791.

Moser, C. C., Keske, J. M., Warncke, K., Farid, R. S., &Dutton, P. L. (1992). Nature of biological electron transfer.Nature, 366, 796–802.

Mulder, M. H. V., van Voorthuizen, E. M., Peeters, J. M. M.(2005). Membrane Characterization. In: A. I. Schafer, A.G. Fane, T. D. Waite (Eds). Nanofiltration principles andapplicatons, chapter 5 (pp. 89–117). Sydney: Elsevier.

Nemati, M., Jenneman, G. E., & Voordouw, G. (2001). Impactof nitrate-mediated microbial control of souring in oil reser-voirs on the extent of corrosion. Biotechnology Progress,17, 852–859.

O’Boyle, N. M., Banck, M., James, C. A., Morley, C., Vander-meersch, T., & Hutchison, G. R. (2011). Open Babel: An openchemical toolbox. Journal of Cheminformatics, 3, 1–14.

Pinsetta, F. R., Taft, C. A., & Silva, C. H. T. P. (2013).Rational design of novel potential p38α MAPK inhibitorswith drug-like properties using pharmacophore and similar-ity-based virtual screening procedures. Current BioactiveCompounds, 9, 3–13.

Postgate, J. R. (1984). The sulphate-reducing bacteria (2nded.). Cambridge: Cambridge University Press.

Robertson, J. W. F., Rodrigues, C. G., Stanford, V. M.,Rubison, K. A., Krasilnikov, O. V., & Kasianowicz, J. J.(2007). Single-molecule mass spectrometry in solutionusing a solitary nanopore. Proceedings of the NationalAcademy of Sciences of the USA, 104, 8207–8211.

Ryckaert, J. P., Ciccotti, G., & Berendsen, H. J. C.(1977). Numeral integration of the Cartesian equationsof motion of a system with constraints; moleculardynamics of n-alkanes. Journal of ComputationalPhysics, 23, 327–341.

Santos, A. F. J., Batista, L. L. F., Lima, J. B. T., Almeida, R.C. C., Roque, M. R. A., Ramos-de-Souza, E., & Almeida,P. F. (2010). Evaluation of the fluorescence in situ hybrid-ization technique for detection of eubacteria and sulfate-reducing bacteria from samples of water in oil fields.Chemical Engineering Transactions, 20, 139–144.

Schuettelkopf, A. W., & van Aalten, D. M. F. (2004).PRODRG: A tool for high-throughput crystallography ofprotein–ligand complexes. Acta Crystallographica, D60,1355–1363.

Schiffer, A., Fritz, G., Kroneck, P. M. H., & Ermler, U. (2006).Reaction mechanism of the iron-sulfur flavoenzyme adeno-sine-5-phosphosulfate reductase based on the structuralcharacterization of different enzymatic states. Biochemistry,45, 2960–2967.

Taft, C. A., & da Silva, C. H. T. P. (2013). Comments on thepaper ‘Levinthal’s question, revisited, and answered’.Journal of Biomolecular Structure and Dynamics, J31,1001–1002.

Tsai, J., Taylor, R., Chothia, C., & Gerstein, M. (1999). Thepacking density in proteins: Standard radii and volumes.Journal of Molecular Biology, 290, 253–266.

Trott, O., & Olson, O. J. (2010). Autodock Vina: Improvingspeed and accuracy of docking with a new scoringfunction, efficient optimization and multithreading. Journalof Computation Chemistry, 31, 455–461.

Van der Spoel, D., Lindahl, E., & Berendsen, H. J. C. (2005).GROMACS: Fast, flexible, and free. Journal ofComputational Chemistry, 26, 1701–1718.

Vik, E. A., Janbu, A. O., Garshol, F., Henninge, L. B.,Engebretsen, S., Kuijvenhoven, C., …, Hendriks, W. P.(2007). Nitrate based souring mitigation produced water –Side effects and challenges from the Draugen producedwater re-injecting pilot. Houston, TX: SPE InternationalSymposium on oilfield chemistry.

Voordouw, G., Nemati, M., & Jenneman, G. E. (2002). Use ofnitrate reducing, sulfide-oxidising bacteria to reduce souringin oil fields: Interactions with SRB and effects oncorrosion. Corrosion, 34, 1–6.

Vorov, O. K., Livesay, D. R., & Jacobs, D. J. (2008).Conformational entropy of an ideal cross-linking polymerchain. Entropy, 10, 285–308.

Wesson, L., & Eisenberg, D. (1992). Atomic solvationparameters applied to molecular dynamics of proteins insolution. Protein Science, 1, 227–235.

Zhou, Y., Vitkup, D., & Karplus, M. (1999). Native proteinsare surface-molten solids: Application of the Lindemanncriterion for the solid versus liquid state. Journal ofMolecular Biology, 285, 1371–1375.

Zuo, R., Ornek, D., Syrett, B. C., Green, R. M., Hsu, C.-H.,Mansfeld, F. B., & Wood, T. K. (2004). Inhibiting mild steelcorrosion from sulfate-reducing bacteria using antimicrobial-producing biofilms in Three-Mile-Island process water.Applied Microbiology and Biotechnology, 64, 275–283.

Novel potential inhibitors for adenylylsulfate reductaseto control souring of water in oil...

Documents

Transcript of Novel potential inhibitors for adenylylsulfate reductaseto control souring of water in oil...

UU 32 2004

92 • noviembre 2014 • $3,36

Durango, Colorado - CATALOG 1991-92

IR Society – Informed 92

The Southern Insurgency in the American Revolution 1780 ...

92-25380 92 9 17 O03 - DTIC

32-SIGALOVADA SUTTA

Changes in food consumption in Iceland 1780-1940

Series CCS-32/CS-32 - Mouser Electronics

Claustre et al MC 92

Cultural Practices of Illumination, London 1780-1840

WHO DMP CFD 92 fre

Mantenimiento Productivo 92

AND ANNUAL PLAN 1990-91 & 92

Communication 32 1983

B-1780 - LegiStorm

Boletin CEUR 32

ANNUAL PLAN 1991-92-PUNJAB.pdf

la conjunción en la gramática escolar (1780-1830 ... - Dialnet

32 Stabilization