The effect of lipidcomposition on models of the inner leafletof mammalian cell membranes has been investigated. Grazing incidenceX-ray diffraction and X-ray and neutron reflectivity have been usedto characterize lipid packing and solvation, while electrochemicaland infrared spectroscopic methods have been employed to probe phasebehavior in an applied electric field. Introducing a small quantityof the anionic lipid dimyristoylphosphatidylserine (DMPS)into bilayers of zwitterionic dimyristoylphosphatidylethanolamine(DMPE) results in a significant change in the bilayer response toan applied field: the tilt of the hydrocarbon chains increases beforereturning to the original tilt angle on detachment of the bilayer.Equimolar mixtures, with slightly closer chain packing, exhibit asimilar but weaker response. The latter also tend to incorporate moresolvent during this electrochemical phase transition, at levels similarto those of pure DMPS. Reflectivity measurements reveal greater solvationof lipid layers for DMPS > 30 mol %, matching the greater propensityfor DMPS-rich bilayers to incorporate water. Taken together, the dataindicate that the range of 10–35 mol % DMPS provides optimumbilayer properties (in flexibility and function as a barrier), whichmay explain why the DMPS content of cell membranes tends to be foundwithin this range. For this undertaking, itis common to use model systems where the composition and environmentcan be more precisely controlled,17 such as vesicles, monolayers,and supported bilayers.17 A range of structuralprobes may then be employed, including X-ray24 and neutron29 scattering techniques, vibrational spectroscopies,32 and imaging methods,36 inter alia. One of the fundamental questions remaining to be answeredis the reason for the structural diversity of lipids in nature. Experimentsinvestigating the effect of composition on lipid ensemble propertiescan shed light on the roles of different lipid types, on the propertiesof different membrane types, and lipid interactions with other functionalmolecules.Biological cell membranesare key to the function of the cell,forming a selective barrier between the cell and its environment orbetween the cytosol and the aqueous fluid phases of different compartmentswithin the cell.1 electrochemical experiments are employed to investigatethe barrier properties of membranes, using patch clamp methods,37 or of their mimics, using an array of electrochemicaltools to study suspended,40 supported,45 tethered,47 or floating48 monolayers or bilayers. The application of anelectrical potential difference across a bilayer also mimics the effectsof ion gradients or the charge asymmetry that arises from the asymmetricdistribution of charged lipids over the two halves of a bilayer.45 A continuously tunable electric field of similarmagnitude to those found in nature can be generated easily,49 and if the bilayer is supported on or in proximity to a flat surface,then in situ vibrational spectroscopy,55 neutron reflectivity,56 and scanning probe microscopies60 can be used to examine the effectof the field on the lipid organization and bilayer structure. Thisstrategy has been adopted to investigate the phase behavior of lipidlayers under the influence of an applied field,60 the effect of molecular structureon the phase behavior,53 and the interaction of smallpeptides with bilayers of various compositions.58 Two common lipid types in mammalian cells,phosphatidylethanolamine (PE) and phosphatidylserine(PS)  sideof the membrane induces a process leading to cell death. Some structuralstudies of PE61 and PS63 lipids and a few of PC/PS mixtures64 have been reported, but there are few on PE/PSmixtures.56 PE/PS mixtures are an interestingcase because the similarity in molecular geometry and headgroup structuremight be expected to result in ideal mixing of the two lipid types(both headgroups may participate in interheadgroup hydrogen bonding),although PS is anionic while PE is zwitterionic, which may affectthe distribution of PS and PE within the layer. That PE and PS lipidsare both predominantly found in the inner leaflet of the mammaliancell membrane suggests that benefit lies in the study of the mixtures,but comparatively few such studies exist.65 There are none, to our knowledge, exploring the effect of varyingthe anionic lipid content.As one of the primary functions of a cell membraneis to providea barrier to the passage of ions and water molecules,ine(PS) 1, haver53 whereas DMPS bilayers respond with an increase in the chaintilt angle and solvent content at negatively charged surfaces.54 In this study, we show that mixing of the lipidsleads to intermediate bilayer properties and a different structuralresponse to the applied field. To shed light on the differences betweenmixtures and their two components, we use a combination of grazingincidence X-ray diffraction (GIXD), X-ray reflectivity (XRR), andneutron reflectivity (NR) to characterize the structure of the monolayersfrom which our bilayers are formed, over a range of lipid phases.The addition of DMPS to a DMPE monolayer results in the formationof a solid phase over a wider range of molecular area. Subtle differencesin packing and in the tendency for monolayers at intermediate moleculararea to take up solvent are observed for some mixtures, which mayexplain the different responses and take-up of solvent observed formixed bilayers on increasing the applied electric field.In this work, we investigate the effect of compositionon the structureand properties of mixtures of dimyristoylphosphatidylethanolamine(DMPE) and dimyristoylphosphatidylserine (DMPS). Thesetwo lipids are structurally very similar 1 and hav22.1Dimyristoylphosphatidylethanolamine(DMPE), dimyristoylphosphatidylserine sodium salt (DMPS),and their perdeuterated analogues  were purchasedfrom Avanti Polar Lipids  and used as received. Solutionswere prepared in a 9:1 v/v ratio mixture of chloroform and methanol.2O or of air-contrast-matched water. Electrolytesolutions were prepared from sodium fluoride  at a concentration of 0.1 M in ultrapurewater or D2O.Ultrapure water was used throughout.A tandem Elix-Milli-Q A10 system  was used at Birmingham,a Millipore system was used at ISIS, and a Purelab Classic UV system was used at Diamond Light Source. Deuterium oxide (99.9%D) obtained from Sigma-Aldrich was employed for spectroelectrochemicaland neutron measurements. Neutron reflectivity measurements were carriedout with a subphase of DCaution!This is a highly exothermicprocess that maycause an explosion!), followed by rinsing thoroughly withultrapure water, soaking overnight in ultrapure water, and furtherrinsing with ultrapure water before use. All other glassware was cleanedby heating in a 1:1 mixture of nitric and sulfuric acids for ∼1h, followed by thorough rinsing and soaking overnight in ultrapurewater. PTFE and Kel-F parts of the spectroelectrochemical cell werecleaned with a 1:1 mixture of 30% ammonia solution and 30% hydrogenperoxide solution and then thoroughly rinsed with ultrapure water,soaked overnight in ultrapure water, rinsed again, and dried in adesignated clean oven.Volumetric glassware was cleaned withpiranha solution (2.268). The trough was cleaned with chloroform beforebeing filled withultrapure water and allowed to reach thermal equilibrium. The surfacewas checked for cleanliness by ensuring the pressure did not riseas the area was reduced. Next, a fixed volume of a chloroform/methanol solution of lipid was added to the watersurface. The solvent was allowed to evaporate, and isotherms wererecorded. To create lipid bilayers, a freshly cleaned Au(111) crystal(–1 (to match the pressure used in previous reports forthe pure lipids54). The crystal was withdrawn verticallythrough the interface at a rate of 2 mm min–1 (Langmuir–Blodgettdeposition), dried for 30 min in argon, and then lowered onto thewater surface at the same dipping rate in a Langmuir–Schaeferconfiguration  to deposit the second monolayer andso form a Y-type bilayer. (Au(111) is used for electrochemical structuralmeasurements because it is a relatively flat surface with a wide potentialwindow that facilitates the investigation of structure over a widerange of the applied electric field. Although it is less hydrophilicthan quartz (often used in reflectivity measurements), it is sufficientlyhydrophilic to allow the formation of Y-type bilayers.) The transferratio was 1, and bilayers were stable on the Au surface on the timescale of the electrochemical and IR measurements.A NimaLangmuir trough equipped with a Delrin barrier and a dipping mechanismwas used to record isotherms and to deposit bilayers on electrodes.A thermostatted water bath was used to control the subphase temperature.The temperature used for isotherm measurements was 19 °C, belowthe gel–liquid crystalline phase-transition temperature ofeither lipid , and those presented herein were acquired on the second compressionof each monolayer, for consistency with the X-ray and neutron measurements.The images shown were acquired at the same fixed molecular areas foreach composition, one chosen to be within the midplateau region ofthe isotherm at each composition and the other at the highest moleculararea used for X-ray and neutron measurements (Brewster anglemicroscopy (BAM) was carried out at Diamond LightSource with a Nanofilm EP3SE Imaging Ellipsometer (Accurion), equippedwith a 50 mW laser emitting light at 532 nm, a 50× magnificationobjective, a polarizer, an analyzer, and a CCD camera. p-Polarizedlight was used, incident upon the water surface of a Langmuir trough(Nima) at the Brewster angle for the air|water interface (53.15°).Images were recorded at a barrier compression speed of 45 cmurements 2.6.2.469 the crystal was flame-annealed and allowed tocool to ambient temperature; a drop of ultrapure water was then placedon the surface. The crystal was then heated gently to remove the drop,which was immediately replaced with a fresh drop. The crystal wasthen transferred to the cell or Langmuir trough with the drop of ultrapurewater to protect the surface from contamination. NaF (0.1 M) was usedas the electrolyte and was purged of oxygen by bubbling with argonfor at least 45 min before measurements. An argon blanket was maintainedover the solution for the duration of the measurements.Electrochemicalmeasurements were performed in an all-glass three-electrode cell,with a Au(111) single crystal, oriented to better than 0.5° , as a working electrode, a Au coil  as a counter electrode, and a saturated calomelelectrode (Hach Lange) as a reference electrode. The reference electrodewas immersed in saturated potassium chloride solution and connectedto the rest of the cell via a salt bridge. Although the saturatedcalomel electrode was used for electrochemical measurements, a Ag|AgCl|3M KCl electrode was used for infrared measurements and so potentialsin this work will be reported vs Ag|AgCl|3 M KCl. The Au(111) crystalwas cleaned using a method previously described in the literature:53 The potential was held at a basepotential of −0.06 V, stepped to the potential of interestand held for 3 min ,and then stepped to a potential at which desorption takes place beforebeing returned to the base potential. The current transients wererecorded during the desorption step. The sequence was recorded forpotentials every 0.05 V, starting from 0.44 V and moving in the cathodicdirection. The current transients were integrated to give the totalcharge passed during each potential step. The resulting relative chargedensities were then converted to absolute charge densities using thepotential of zero charge (pzc) of the bare electrode (0.315 V).Thepotentiostat used was a Heka PGStat 590 , controlledwith a PC with in-house-written software  and a data acquisition board .Chronocoulometry measurements consisted of applying a series of potentialsteps and recording the resulting current transients, as describedpreviously.2.52O. Spectra wererecorded at a temperature of 19 °C (±1 °C), where bothlipids are in the gel phase.68A custom-builtspectroelectrochemical cell was used for polarization-modulatedinfrared reflection absorption spectroscopy (PM-IRRAS) measurements.The working electrode was a Au(111) crystal, oriented to better than0.5° , and was preparedas described in –1 for investigatingthe C–H stretching region of the spectrum and to 1600 cm–1 for the C=O stretching region. Spectra wereacquired at an instrumental resolution of 2 cm–1. The angle of incidence was 51° for the C–H stretchingregion and 61° for the C=O stretching region; the optimumthicknesses of electrolyte between the working electrode and the windowwere taken as 1.9 and 3.6 μm, respectively.70 Fresnel 1 software,71 kindlyprovided by Dr V. Zamlynny , was employedto determine the electrolyte thickness and to simulate theoreticalspectra of randomly oriented molecules for the cell configurationsused in the experiments. These simulated spectra are used in the analysisof PM-IRRA spectra, as described by Zamlynny and Lipkowski.72 The isotropic optical constants required forthese simulations were as acquired previously;54 weighted averages were calculated for the mixtures from the opticalconstants of the pure components. Spectra were demodulated and correctedfor the response of the photoelastic modulator as described in ref  and a synchronous samplingdemodulator (GWC Technologies) to obtain the difference signal. Thehalf-wave retardation was set to 2900 cm2.673 and SURF74 beamlinesat ISIS Pulsed Neutron and Muon Source . Data wereacquired in time-of-flight mode over two angles (0.8 and 2.3°)at INTER and three angles  at SURF at aresolution of 7%; both beamlines employ 3He detectors.In each case, a large-area (700 cm2), temperature-controlledLangmuir trough (Nima) was used and encased in a box to reduce theexchange of D2O with atmospheric water. The in-house writtensoftware Mantid76 was used for data reduction.Neutron reflectivity measurementswere carried out at the INTER77 X-rays  were directed onto the water surface with a double-crystaldeflector system.78 A large-area (700 cm2) Langmuir trough (Nima) with temperature control was employedand encased in a box with a He atmosphere to reduce background scatteringand beam damage. XRR data were collected over a qz range of 0.018 to ∼0.6 Å–1, where qz is momentum transfer normal to the surface, and were reduced withan in-house Python script. GIXD was measured with an angle of incidencecorresponding to qz =0.018 Å–1 and with a pinhole setup,23 allowing the acquisition of diffraction images.These were subsequently scaled and spliced to produce an image overa qz range of 0–0.8Å–1, using an in-house-written MATLAB script.Reflectivity data were fitted using RasCal.79 GIXD data were integrated and fitted using MATLAB scripts and OriginPro.X-ray reflectivity and grazing incidence X-ray diffraction measurementswere carried out on the I07 beamline at Diamond Light Source .–1, to match the depositionpressure used for the bilayers, and 40 mN m–1, whichis comfortably in the solid phase for each composition and often usedfor the deposition of bilayers in other electrochemical structuralmeasurements.45 The fixed molecular areaswere chosen to span the remaining condensed-phase region of the isotherm:38, 42, 44, and 46 Å2 on the isotherm, hereafter referredto as areas A1, A2, A3, and A4, respectively. Neutron measurementswere carried out for a subset of these points at 40 mN m–1 and at the highest and lowest fixed areas (A1 and A4). Two contrastswere employed: lipids with perdeuterated chains (d-lipids) on D2O and d-lipids on air-contrast-matched water (ACMW). X-raymeasurements employed undeuterated lipids (h-lipids) for all compositions,but measurements were also carried out with d-lipids for the compositionsand pressures used for NR measurements to ensure that the structureswere comparable.X-ray measurements were carried out at two fixed pressuresandat four fixed molecular areas. The fixed pressures were both in thesolid phase, 47 mN m33.1Figure S1 of the Supporting Information. The DMPE and DMPS isothermsare consistent with those in our previous publications.54 The surface pressure of the phase transition between the liquidexpanded (Le) and liquid condensed (Lc) phases(the plateau in the isotherm) decreases as the proportion of DMPSis increased. Likewise, the pressure at which the Lc phasecondenses into the solid phase (the kink in the isotherm) also decreaseswith increasing DMPS content . The limiting areas per molecule (obtained by extrapolating thesolid-phase portion of the isotherm to the abscissa) are all similarat around 40 Å2. The gradient of the isotherm at agiven point is related to the compressibility of the monolayer: thesteeper the gradient, the less compressible the monolayer. The conceptis quantified through differentiation of the isotherm and calculationof the compressibility modulus, Cs–1, from AM is the area per molecule, N is the number of molecules,and T is the absolutetemperature. Cs–1 is plotted as a function of surface pressure in the inset to Cs–1 are around 700–800 mN m–1 and vary littlewith composition, which indicates that the monolayers have similarmechanical strength.81 presumably because the molecules are of similarsize and shape and their intermolecular interactions have similarnatures. An indication of the mixing behavior in planar layers canbe obtained by considering the excess area and excess Gibbs energyof mixtures. The excess area Aexc is thedifference in the measured area per molecule of the mixture (A12) and the weighted-average molecular areasof the constituent molecules (A1 and A2)82xi is the mole fraction of species i. At allpressures, Aexc of each mixture is smalland of similar magnitude to the expected experimental error. Mostmixtures have slightly positive Aexc,but the magnitude of the values suggests that mixing is close to idealfor these lipids.Calorimetry results reportedin the literature for aqueous dispersionssuggest that the mixing of DMPE and DMPS is close to ideal,Figure S2 for the monolayersat a surface pressure of 47 mN m–1. If it is assumedthat the excess Gibbs energy is related to differences in intermolecularinteractions between molecules in mixed or segregated films, thenthe data show that the mixing of DMPE and DMPS is close to but notquite ideal. At most compositions, the excess Gibbs energy is positive,an indication that the DMPE–DMPS interactions are less favorablethan in the ideal mixture, the weighted sum of the DMPE–DMPEand DMPS–DMPS interactions. This may be a result of the disruptionof an organized hydrogen bonding network by accommodating slightlydifferent headgroup geometries. Both DMPE and DMPS have strong dispersioninteractions between their chains and hydrogen bonding interactionsbetween their headgroups. Hydrogen bonding interactions are directionaland can result in particular headgroup conformations that may differfor DMPE and DMPS (the latter has two hydrogen bond acceptors andso more possible arrangements). Introduction of DMPS molecules intoDMPE monolayers (or vice versa) may disrupt the headgroup organizationto some extent, resulting in a slightly positive excess area and excessGibbs energy without preventing mixing .If the excess Gibbs energy is added to the Gibbs energy of an idealmixture, RT(x1 ln x1 + x2 ln x2), to obtain the total Gibbs energy of mixing,then the values are all negative, which indicates that the two componentsstill mix. Mixing is most favorable at the 1:1 ratio (with a veryslightly negative excess energy), which may indicate that moleculescan adopt an optimum arrangement of headgroups in this composition.The observation that the excess Gibbs energy tends to be more positivefor mixtures of low DMPS content than for those of high DMPS contentsuggests that the disruption of the network of DMPE headgroups ismore unfavorable than the disruption of the packing of DMPS headgroupsor that the latter is offset to some extent by the dilution of chargedspecies within the monolayer. Wydro studied the mixing of cholesterolwith monolayers formed of dipalmitoyl PE and PS on different subphases.65 On water, the binary mixture of xPS/xPE = 0.53 exhibited negativeexcess Gibbs energy at 32.5 mN m–1 , which was attributed to the ability of both moleculesto form intermolecular hydrogen bonds.65The excess Gibbs energy of a mixture was calculatedfrom 3.2Figure S3. The mixtures have similar electrochemical phase behavior to oneanother and to that reported in previous publications.56 The plots measured for surfacescoated in lipid bilayers show two steps: the first (into region II)arises from the adsorption/desorption of the lipid bilayer and thesecond (between regions II and I) is from a phase change between twodifferent structures of the bilayer.49 In situ NRstudies for the case of DMPC/cholesterol bilayers have shown thatin the potential range positive of −0.4 V (region I) the bilayeris directly adsorbed on the Au(111) surface and in the range between−0.4 V and desorption (region II) the bilayer is separatedfrom the electrode surface by a cushion of electrolyte. The step incharge density is related to the movement of electrolyte through thebilayer to form this cushion.49 For the adsorbed layer,the slope of the charge density–potential plot gives the capacitanceof the interface, which is lower in the presence of lipid than inthe absence of lipid because the planes of charge  are farther separated and the average permittivityof the organic layer is lower than that of interfacial water. Thedata for the PE/PS mixtures show that these slopes are similar forthe different lipid compositions up to 50% DMPS but are higher for70% DMPS and pure DMPS. The higher interfacial capacitance for DMPShas previously been attributed to higher solvent content within thebilayer, which increases the average permittivity of the bilayer.54 Therefore, the data in Figure S3 suggest that the bilayer solvent content changes little with compositionbetween 10 and 50% DMPS.–2 and +15 μC cm–2 (region I), which is typicalfor phospholipid bilayers supported on Au(111) surfaces.56 This range of charge densitycorresponds to the range of the electric field within which naturalcell membranes are stable.49 The potential of zerocharge (pzc) is shifted in the presence of molecules to more negativepotentials, as has been observed previously for supported lipid bilayers.The shift indicates a small charge asymmetry across the bilayer.51 For zwitterionic molecules, this has been interpretedas a different orientation of the headgroup dipoles in each leafletof the bilayer. For the anionic molecules, a much larger shift wouldbe expected.84 The small size of the shiftindicates that the molecules are probably coadsorbed with the counterions.85 The shift in pzc is largest for DMPS. The mixtures with DMPS content<30% exhibit a similar shift to DMPE, and the 70% mixture has asimilar shift to DMPS. The 40 and 50% DMPS mixtures have intermediateshifts. Bearing in mind that the step size in these experiments is50 mV, small differences in pzc should not be overinterpreted. However,it is possible to comment from these data that at low DMPS concentrationswithin the bilayer the surface potential is not strongly dependenton composition and that differences emerge at 40% DMPS and above  move deeper into the bilayer,as suggested by Moncelli et al.,86 or ifester groups have different orientations. It should also be notedthat there is a possibility that the distribution of the anionic lipidacross the two halves of the bilayer changes with time, as “flip-flop”of lipids between two halves of a supported bilayer has been observedpreviously with sum frequency vibrational spectroscopy89 and NR.90 Such a change in lipid distributionwould alter the charge distribution across the bilayer and so affectthe pzc. However, our previous electrochemical study of asymmetricbilayers of DMPE and DMPS showed that there was consistently lessnegative charge density when the bilayer was prepared with DMPS onthe Au-facing side of the interface than when it was prepared withDMPS on the solution-facing side or with the lipids mixed in bothsides .85 That the curves did not merge as the experimentproceeded indicates that the rate of flip-flop is slow on the timescale of these measurements (∼3 h), at least for these lipids.The slow rate of flip-flop was attributed to tight packing of thelipids within the bilayer, which raises the activation barrier fortraversing the membrane. The flip-flop of lipids observed with NRwas shown to take place only for lipids in their fluid phases,90 and in other cases, bilayer asymmetry couldbe maintained over many hours.91 In addition, previousin situ IR studies of bilayers on Au(111), where one-half of the bilayeris deuterated and the other not,93 have shownthat tilt angles of the undeuterated chains were different for theAu-facing and solution-facing monolayers across the whole range ofpotentials studied. Given that the IR measurement is longer in durationthan the electrochemistry measurement, this would also suggest thatthe lipids are not redistributing over the time scale in the electrochemicalmeasurements reported here. Mixing DMPS with DMPC in different waysalso resulted in subtle differences in the electrochemical response,while the asymmetric bilayers formed from DMPE and DMPC (both zwitterionic)were very similar in their responses.85 These previous reports thus indicate that the changes in pzc withanionic lipid content are more likely to arise from differences inhow DMPS packs within a mixed monolayer than from any change in thedistribution of anionic lipid across the bilayer.The chronocoulometry data show that the lipidsare adsorbed withina range of charge density of ca. −10 μC cmnd above 3b. Monce3.33.3.1Figure S4). The methyl groups on the ends of the hydrocarbon chains have twovibrational modes in this region: the symmetric stretching mode at∼2870 cm–1 and the asymmetric stretchingmode at ∼2960 cm–1.96 The methylene CH2 groups have a symmetric stretching mode, υs, at ∼2850 cm–1 and an asymmetric stretchingmode, υas, at ∼2920 cm–1.96 There are also two Fermi resonances in this region of the spectrum,which result from the combination of the CH2 bending modeovertone with the symmetric stretching mode.97 The spectra were fitted to six peaks, using a mixed Gaussian–Lorentzianline shape, as described previously.53 Anexample is given in the Supporting Information, Figure S5. The peak positions provide information on the degreeof ordering in the hydrocarbon chains: a higher wavenumber is associatedwith a larger number of gauche conformers in thechains.95 The peak positions for the 1:1 mixture vary littleacross the potential range. The average positions for the symmetricand asymmetric stretching modes are 2851.6 (±0.4) and 2920.5(±0.4) cm–1, respectively. There is more variationin the symmetric stretching mode for the 9:1 mixture, with a slightincrease in wavenumber for the desorbed bilayer, but the change iswithin error. The average positions are 2851.6 (±0.9) and 2919.9(±0.4) cm–1. The symmetric stretching modein the mixtures is similar to that previously observed for DMPE bilayersand 0.5–1.0 cm–1 higher than for DMPS bilayers;the position of the asymmetric stretching mode is around 1.0–1.5cm–1 higher than for DMPE and DMPS bilayers. Forcomparison, DMPC stretching modes are at higher wavenumbers (2852.5–2854.0and 2922.0–2923.0 cm–1) because DMPC moleculespack less tightly than DMPE or DMPS. The results indicate that lipidmolecules in mixtures of DMPE and DMPS have relatively few gauche conformers and are in the gel state,95 as are the pure components andas would be expected at this temperature. The peak width gives anindication of the degree of mobility of the molecules. For the 1:1mixture, the full widths at half-maximum (fwhm) are 9.8 (±0.8)and 16.3 (±1.0) cm–1 for the symmetric andasymmetric stretching modes, respectively, similar to the values reportedfor DMPE at negative potentials.53 Thefwhm values in the spectra of the 9:1 mixture show a small increasewhen the bilayer is detached, suggesting a slightly greater degreeof mobility of the molecules in this state, but the values for theadsorbed bilayer (9.1 and 18.9 cm–1) are similarto those of the 1:1 mixture and to DMPE at positive potentials. Apparently,the mixing of the molecules does not result in a significant changein chain organization or mobility for the PE/PS mixtures. PS is oftenstudied in the presence of different metal ions, particularly calciumbecause of its importance in physiological processes such as membranefusion and signal transduction.98 Calciumhas an ordering effect on PS lipids relative to the ammonium or sodiumsalts, binding to the phosphate group by replacing a water moleculeand causing crystallization of the lipid.98 The effect of this ordering on the C–H stretching regionof the spectrum is mainly to increase the phase-transition temperature,with a slight lowering of the CH2 symmetric stretchingband position in the gel phase (∼1 cm–1).98 A study of the effect of calcium on our bilayersis complicated by the specific adsorption of the counterions on theelectrode surface, so the present study was restricted to sodium-containingelectrolyte.Supporting Information).72 This chain tilt angle is plotted in 53 andDMPS54 are included for comparison. Althoughthe applied potential is the directly controlled variable, the resultingcharge density is the driving force for the observed changes in molecularorientation and solvent distribution at the interface. As the chargeon the metal becomes negative, the tilt angle of the chains increasesand then falls to its original value when the molecules are detachedfrom the surface. The response is seen for both mixtures but is muchstronger for the 9:1 mixture. The behavior differs from those of theconstituent molecules: DMPE exhibits almost no response to the appliedfield,53 whereas the tilt angle of DMPSincreases at negative charge densities and remains high when the moleculesare detached.54 However, the behavior issimilar to that observed for DPPC bilayers99 and asymmetric DMPE bilayers where one of the monolayers is deuterated.93 The negligible response of the DMPE bilayerto the applied field has previously been attributed to a combinationof tight packing between molecules and strong hydrogen bonding interactionsbetween the headgroups. DMPS bilayers differ in that the anionic headgrouprequires stronger solvation at negatively charged surfaces. It islikely that the addition of a small quantity of DMPS into the DMPEbilayer disrupts the packing and allows the bilayer to respond tothe field or that the DMPS headgroups respond to the changing field,causing the change in orientation of the ensemble of chains. The smallerresponse of the 1:1 mixture may be because the average area per moleculeat the transfer pressure is slightly smaller–molecules wouldbe more tightly packed than in the 9:1 mixture and this small differencein packing is enough to alter bilayer properties. The question thenarises as to why the 1:1 mixture responds where pure DMPE does notand is most likely answered by considering the interaction betweenthe DMPS headgroups and the negative surface charge. The interplaybetween chain–chain interactions and headgroup–headgroupand headgroup–field interactions is clearly complex. The tiltangles determined with in situ IR (at low charge densities) are largerthan would be expected for monolayers in a solid phase. (Below wesee that GIXD measurements indicate perpendicular chains for the solidphase.) This observation may suggest a small change in molecular orientationon transfer of molecules to the substrate, perhaps as a result ofthe interaction of the headgroup with the substrate. However, thedifference is minor and the IR peak positions and widths indicatethat the extent of ordering remains very high when monolayers aretransferred, so we conclude that there is no significant change instructure on transfer of the monolayers.Finally, the integrated areas of the peaks canbe used todeterminethe orientation of the associated transition dipole moments and, fromthese, the chain tilt angle with respect to the surface normal  is associated with carbonyl groups that arenot partaking in hydrogen bonding, and the lower-wavenumber peaksare associated with carbonyl groups that are involved in hydrogenbonding with water .100 The shapes of the peaks in the spectra suggest that the mixed bilayerscontain more solvent than DMPE and less solvent than DMPS. By dividingthe area of the 1740 cm–1 peak by the total areaof the two or three peaks, an estimate of the degree of solvationof the ester region of the lipid bilayer can be obtained.54 This proportion is plotted for each bilayer as a function of chargedensity in 53 and DMPS54 are included for comparison. This plot showsthat the bilayers have similar hydrogen bonding interactions at smallcharge densities. At negative charge densities, the hydrogen bondinginteractions of the 9:1 mixture are similar to those of DMPE, whilethose of the 1:1 mixture are similar to those of DMPS. Apparently,the tendency of the bilayer to incorporate water as the charge densityincreases is dependent on the DMPS content: the higher the DMPS content,the more solvent is incorporated into the headgroups. This is likelyto be related to the greater need of the anionic headgroups to bescreened from the negative charge density on the metal.54 Bilayers of the zwitterionic molecule DMPC,which have a stronger response to the electric field than DMPE bilayers,decrease in solvent content at negative charge densities.51 This behavior was attributed to the egress ofsolvent from the bilayer to form a cushion between the bilayer andthe surface,49 based on complementary neutronreflectivity studies on similar bilayers.49 The combination of the electrochemical and IR data in the presentwork suggests that the DMPE:DMPS 9:1 mixture is a more flexible versionof a DMPE bilayer, more able to respond to an electric field, whilethe 1:1 mixture is intermediate between DMPE and DMPS, with a smallerresponse and with similar solvent content to DMPS bilayers.The differences in behavior betweenthe mixtures and their individualcomponents were investigated further using X-ray and neutron reflectivityand X-ray diffraction (vide infra). Specifically, the aims of theseexperiments were (i) to ascertain whether packing of the mixtureswas looser than that of DMPE itself, which might explain their abilityto respond to the application of an applied field, and (ii) to determinewhether the monolayers used for deposition had different headgroupsolvation, which might be incorporated into the deposited bilayer.Alternatively, a trend in solvation might reveal different tendenciesfor the bilayers to incorporate solvent. The treatment of the IR spectraabove gives an indication of solvation that is useful for identifyingtrends and comparing bilayers, whereas reflectivity methods give directinformation on the volume fraction of solvent contained within theheadgroup region.3.42). These imagesshow extended condensed phase regions within the fluid phase for DMPEand DMPS, similar to structures reported previously for DMPE monolayers.101 Smaller structures have been reported for DMPS102 (but at higher temperature than in our measurements),which are similar to some of our structures for DMPS-rich monolayersat higher molecular area. The condensed regions are branched, an indicationof relatively fast growth kinetics and low line tension between thetwo phases.103 For mixtures, the domainsize is smaller and the extent of branching (or length of the branches)less. It was suggested for DMPE, based on the formation of fractal-likestructures and the length of time required for them to relax to compactdomains, that the line tension was weak or the DMPE molecules hadlow mobility.101 McConnell and co-workershave reported that the shapes of domains can be predicted by consideringthe balance between intermolecular repulsions and line tension,105 and Andelman et al. extendedthe theory to include a consideration of charged molecules (in thelimits of low and high screening of charge by the subphase medium).106 The fingers of the DMPS domains are less roundedthan those of the DMPE domains, which may be a result of greater repulsionbetween molecules, but the mixtures do not display a trend in shapefrom DMPE to DMPS. Instead, as the DMPS content is increased, thesize of the domains and the length of the branches decrease to a minimumat 60% DMPS and then increase again. The relatively small size ofthe domains of the nearly equimolar mixtures suggests relatively higherrates of nucleation or slower rates of growth than for mixtures witha majority of one or the other lipid.BAM wasused to investigate further the differences in isotherms of the mixturesat lower surface pressures, where the liquid expanded and liquid condensedphases coexist. Selected BAM images are presented in 3.5–1 and 42 Å2 (area A2). The single peak in the image at 47 mN m–1 indicates that hydrocarbon chains are organized in a hexagonal arrangement,where the d spacing of the {1 1}, {1 0}, and {0 1} sets of planes is the same.107 At area A2, the chains are tilted, the hexagonal unit cellis distorted, and the degeneracy is broken. The positions of the peakscan be determined and analyzed to provide information on the interchainspacing and tilt angle from the surface normal.107 An explanation of the analysis used is given in the Supporting Information. Plots of integrated intensityvs qxy for each compositionare presented in Figure S9.) For each composition, a single Braggpeak is observed at 47 and 40 mN m–1. At area A1,a single peak is observed for every monolayer except for pure DMPE,where two peaks are observed. At area A2, three peaks are observed,whose positions are consistent with an oblique unit cell formed oftilted hydrocarbon chains.107 At a largerarea per molecule, diffraction was weak, if present at all, and typicallyonly a weak feature corresponding to the {1 1} d spacing was observed, suggesting very little long-rangeorder as chains tilt farther from the surface normal. Unit cell parametersderived from fitting the Bragg peaks (to a Voigt function) are givenin Tables S1–S8 in the SupportingInformation. In the solid phase, the areas are similar across thecomposition range, as was observed in isotherm data, and the valuesfor DMPE are similar to those reported by Helm et al.18 However, the plot of area vs composition in c phase, at a lower surfacepressure than the other monolayers. The observation of tilting ofhydrocarbon chains at this molecular area is consistent with a monolayerin the Lc phase. The larger calculated area per molecule(42.2 Å2 compared with 41.1 Å2 forPS) reflects the tilt of the chains; the cross-sectional area of thechains matches the molecular areas determined for the other compositions.The molecular areas of most of the different compositions are similar.At area A2, it can be observed that the areas per molecule and chaintilt angle for the mixtures decrease from DMPE to DMPS , although,again, the changes are small. There is more variation in packing atthis lower surface pressure than at the higher pressures. At thisarea per molecule, there is a variation in the slopes of the isotherms,as the surface pressures of the plateaux are lower for DMPS-rich samplesthan for DMPE-rich samples. There may be different degrees of condensation,and the isotherm area is an average of the monolayer while diffractionindicates the packing of ordered phases only. Only weak diffractionwas observed at area A3. Three peaks could be observed for three compositions,but only the {1 1} reflection was observed forothers, with low intensity. The molecular areas derived from the imageswith three peaks were around 44 Å2, similar to theisotherm molecular area. No diffraction was observed for the highestarea, A4. GIXD was also measured for the deuterated lipids at thecompositions used in the NR measurements to ensure that the structureswere comparable. There is little difference in the data between deuteratedand undeuterated lipids at 40 mN m–1. At area A1,the deuterated 9:1 mixture showed some distortion of the peak, whichlikely corresponds to a slight tilting of the chains (∼9°).A slight tilt was also observed for deuterated DMPS, but the resultsfor DMPE and for the 1:1 mixture were similar to those of undeuteratedsamples. Deuterated lipids generally gave peaks of slightly lowerintensity, suggesting a lower degree of ordering.At area A1, DMPE is inthe L3.6–1, area A1 and area A4 for DMPE, DMPS, and the9:1, 7:3, and 1:1 mixtures. NR data acquired at area A1 are givenin the left panels of 2O anddeuterated lipids on ACMW. The middle and right panels of –1 and area A2, respectively. The remaining data are given in the Supporting Information. Data were fitted usinga two-slab model comprising a headgroup slab and a tailgroup slab.The headgroup slab included the carbonyl groups of the ester linkages;the tailgroup slab contained the remaining carbon and hydrogen (ordeuterium) atoms in the chains. It is generally assumed that deuteratedand undeuterated samples do not differ in structure, at least in thesolid and Lc phases, and it is common practice to cofitNR data for multiple contrasts including deuterated and undeuteratedlipids; on occasion, this practice is explicitly considered and justified.108 To determine whether our NR data for deuteratedsamples and XRR data for undeuterated samples could be compared, XRRdata were also acquired for some of the deuterated systems used inNR experiments, since X-rays are not sensitive to isotopic substitution.The results were not different at 40 mN m–1 in thesolid phase. At area A1, small differences were observed between deuteratedand undeuterated samples for the 1:1 and DMPS samples, but the DMPE-richsamples were very similar. Problems with beam damage were experiencedfor some of the lower pressures, and time constraints precluded ourinvestigating differences in these structures more thoroughly. However,the different positions of the reflectivity minima do indicate differencesbetween deuterated and undeuterated samples. This will be followedup in a future study.NRwas measured for a subset of the points on the isotherm: at 40 mNmqz as the monolayer is expanded to lowsurface pressure. This behavior indicates that the monolayer becomesthinner and headgroups less dense at these higher molecular areas,consistent with the observation of increasing tilt angle of the hydrocarbonchains. At a given pressure, there is a small shift in the positionof the minimum as the DMPS content is increased, which indicates asmall increase in monolayer thickness with increasing DMPS content.For each composition, the minimum in the reflectivityobservedin the XRR data becomes shallower and shifts to higher 108 The data were fitted with the roughness fixed and then with the tailgroupslab SLD also fixed, for a range of different values of SLD that correspondedto 5 or 10 Å3 increments in molecular volume. Thedata were fitted in RasCal79 using theBayesian MCMC algorithm. Areas per molecule from the diffraction measurements(where diffraction was observed) or from the isotherms (if diffractionwas not observed) were used to assess the acceptability of the fit.The details of the procedure adopted are explained in the Supporting Information. In general, fitting ofthe NR data yielded higher molecular areas for the tailgroup slabthan the XRR data but the tailgroup slab thicknesses were similar.It was possible to obtain good fits to the XRR data with molecularareas in good agreement with GIXD or isotherm results.The parameters used to describe the model were the scattering lengthdensities (SLDs) of each slab, the thicknesses of each slab, and theroughness. The roughness was kept the same for each interslab interface.2O in that subphase62 and so alter the average scattering length of the dry headgroup,although most studies appear to assume a scattering length based onno H/D exchange. With three unknown parameters and two subphase contrasts,it is not possible to determine unambiguously the solvent volume fraction.Therefore, we have assumed a solvated headgroup volume from the tailgrouparea and headgroup slab thickness and used that in conjunction withthe SLD from the D2O measurement and the scattering lengthsof lipid and solvent to obtain a volume for the unsolvated headgroupand the number of water molecules per lipid. Similar calculationswere carried out using the molecular area determined independentlyfrom GIXD or isotherm measurements. The SLD from the ACMW measurementwas then used to confirm that the molecular area for this fit wasin reasonable agreement with that used in the D2O calculation.For XRR data, a similar calculation to that used for the D2O parameters was carried out. These calculations are explained inthe Supporting Information. Solvation levelsfor the D2O and XRR calculations were found to be comparablewhere up to one headgroup hydrogen atom was assumed to exchange withdeuterium. When a headgroup scattering length corresponding to greaternumbers of exchanged hydrogen atoms was used, unphysical values ofwater content resulted, so we conclude the level of exchange to below in these measurements.For theheadgroup slab, the solvated headgroup volume was obtainedfrom the tailgroup molecular area and the headgroup slab thickness,as the molecular area must be the same in each slab. This headgroupvolume contains a contribution from the volume of the “dry”or unsolvated lipid headgroup and the water molecules solvating it.Ideally, the SLD could then be used to calculate the volume fractionof water from the SLDs of water and the unsolvated headgroups. Unfortunately,independent values for headgroup volume are not always available inthe literature or there are conflicting values. Moreover, the averageheadgroup volume in a mixture may not always be the same as the theoreticalweighted average of the two components, although our diffraction datasuggest that this may not be a problem for DMPE:DMPS mixtures. Inthe case of NR data, different subphase contrasts can be used to helpto distinguish a change in headgroup volume from a change in solventcontent . In the present case,this process is complicated by the ability of the ammonium protonsto exchange with Dc and solid phases,18 and the trendswith surface pressure or molecular area are very similar. The tailgroupslab increases in thickness, as the tilt angle of chains decreases,and the headgroup slab decreases in thickness. That observation wasattributed to a flattening of the headgroup on compression.18 At lower surface pressures, the solvent contentof the headgroup slab increases, as the monolayers are more expandedwith larger average molecular area. In the solid phase, the wateris squeezed out of the headgroup slab. Roughness increases upon compression,as headgroups may stagger in order to maximize chain packing in thetailgroup slab. Similar general behavior has been reported for DMPS,63 but those measurements were carried out on anelectrolyte subphase, which alters the structure of the monolayerat higher molecular area, and so the values are not necessarily directlycomparable. All of the mixtures displayed similar behavior to thepure components on compression. NR data are relatively insensitivefor the headgroup slab but suggested a greater slab thickness forDMPS layers than for other samples. In the XRR data, a weak tendencywas observed for the headgroup slab thickness to increase with increasingmole fraction of DMPS . Asthe molecular areas are similar, this increase in thickness resultsfrom an increase in headgroup molecular volume. At higher molecularareas, the increase in molecular volume with DMPS content is a resultof an increase in solvation. The unsolvated headgroup volume appearsto change little at these areas. For DMPE, the value obtained is ∼250–260Å3 at these areas, close to that reported by Marsh,109 and for DMPS, a value of ∼250 Å3 is obtained, similar to that reported by Petrache110 and used by Campbell et al. to fit NR dataobtained for DMPS at 10 mN m–1.108 Hence, the DMPE and DMPS headgroups are of comparable sizeand the average size of the headgroups varies little with composition.The anionic DMPS headgroups attract more solvent, to screen the charges,and the solvated volume increases, particularly in the range of upto 30 mol % DMPS. In the solid phase, the situation is different;the monolayers contain relatively little solvent as there is littlecapacity to accommodate it. Yet the average solvated headgroup volumestill increases and appears to be a result of an increase in DMPSheadgroup volume to values approaching those reported by Pan et al.65 (278 Å3) or Fragneto et al.64 (321 Å3). The effect is observedin DMPS-rich samples and appears to be counterintuitive, but it wasnot possible to obtain acceptable fits for solid phase samples usingan unsolvated headgroup volume of ∼244 Å3 orfor the more expanded phases with the larger volume of ∼305Å3. We tentatively suggest a different DMPS headgroupconformation in the solid phase from the Lc phase to accommodateinterlipid hydrogen bonding interactions between headgroups (cf. thelower density of ice than water).The structural parameters obtainedfor XRR data of DMPE are similarto those reported by Helm et al. for DMPE monolayers in the L54 and intermediate solvation of the mixtures. Some reportsattribute the lowest-wavenumber band (1710 cm–1)to hydrogen bonding interactions with other headgroups rather thanto water; a similarity in solvent content between DMPE, DMPS, andmixtures would support that assignment. Alternatively, the structureof a monolayer may alter slightly on transfer to the substrate, perhapsif there is a specific interaction between the headgroup and the substrate.However, the analysis in Figure S29) suggestsa greater propensity for water to interact with the anionic headgroups,which could explain the greater degree of interaction with solventwhen the supported bilayer is exposed to a strong electric field and/orthe anionic headgroups experience greater repulsion from the negativelycharged surface.The similarity in headgroupslab solvation at higher pressuresappears at first sight to be inconsistent with the differences inthe IR spectra of the supported bilayers. The appearance of the IRspectra suggested a lower solvation of DMPE than DMPS4110 few investigations of their mixtures or the effect of compositionhave been reported.65 In this study, structural informationacquired for lipid monolayers has been used to explain the electrochemicalphase behavior of bilayers of the corresponding compositions. Surfacepressure–area isotherms of DMPE/DMPS mixtures show small positiveexcess areas at most compositions and a tendency for phase transitionsto occur at lower surface pressure as the mole fraction of the anioniclipid DMPS is increased. Electrochemical measurements of supportedbilayers indicate similar general electrochemical phase behavior ateach composition but with a change in the potential of zero chargeat ∼40–50 mol % anionic lipid (DMPS). In situ electrochemicalinfrared data show marked differences in the response of the differentlipid bilayers to increasing electric field strength: as the surfacebecomes negatively charged, mixed bilayers show an increase in thehydrocarbon chain tilt angle, while DMPE bilayers appear unperturbed.On increasing the field strength further, the chains of mixed bilayersreturn to their original tilt angle, unlike DMPS bilayer chains, whichremain tilted. The solvent content around the ester groups in DMPSand DMPE:DMPS 1:1 mixtures increases as the charge on the electrodeis made more negative. X-ray and neutron reflectivity measurementsof the corresponding monolayers show little variation in solvent contentat the surface pressure used to deposit the bilayers (in the solidphase), but in the Lc phase, the monolayers with >30%anioniclipid contained more solvent than the monolayers with lower DMPS content.GIXD measurements gave small positive excess areas for most compositions,apart from the equimolar mixture, and slightly greater tilt anglesfor DMPE-rich monolayers for a given area in the Lc phase.Both GIXD and IR indicate highly organized lipid films, with onlya minor increase in tilt angle on transfer to the substrate. Takentogether, the data show that seemingly minor structural differencesbetween monolayers can still result in strong differences in the behaviorof the bilayers they are used to form. The differences in packinginferred from isotherm and diffraction data were on the limit of theexperimental error, yet a mixture comprising 10% DMPS exhibited astronger response to an externally applied electric field than boththe pure DMPE and the 50% DMPS mixture. Similarly, there was no trendin the solvation of monolayers in the solid phase, but the bilayersshowed different tendencies to take up water, which matched the dataobtained for monolayers at lower surface pressure. This result showsthat measurements over a range of surface pressures can be very helpfulfor understanding the behavior of supported bilayers. The surfacepressure of the supported lipid bilayers (the area between the chronocoulometrycurves in the presence and absence of lipid) is lower as the surfacecharge is made more negative, so the monolayers at lower surface pressuregive useful insight into the abilities of the bilayers to take upwater.Theeffect of composition on lipid phase behavior in models ofthe inner leaflet of mammalian cell membranes has been studied. Theinner leaflet contains predominantly PE and PS lipids; while theselipid types have been studied using various methods,c–Sphase transition in the isotherm decreases in a linear fashion to40 mol %, at which point a decrease in the slope occurs. A similartrend is seen for the onset of the Le–Lc phase transition. The potential of zero charge for the bilayer-coatedelectrode changes at >30–40 mol % PS. The solvation of theLc phase appears to change at ∼30–40 mol% DMPS, and the propensity for perturbed bilayers to take up solventchanges either side of this composition. PS lipids make up ∼15–33%of the cytosolic leaflet of a mammalian cell membrane, depending onthe type of cell.111 The changes in propertieswe observe between ∼10 and ∼40 mol % DMPS may explainwhy cells restrict their PS content to that range. PS is requiredfor the function of some proteins, and the bilayer containing ∼10mol % DMPS is close in structure and barrier properties to pure DMPEbut more flexible, while the bilayers containing >30–40mol% have a greater propensity to incorporate water when perturbed byan external field. The uptake of solvent in the presence of an electricfield, which could result from an ion gradient or even the chargeasymmetry conferred by the asymmetric distribution of PS lipids themselvesin the natural membrane, would damage the barrier integrity. Althoughthe models used are simplified, they do show that bilayers withinthe range of 10–30 mol % PS appear to provide the best balanceof properties for the cell membrane. The results highlight the valueof investigating the phase behavior of an extended range of lipidcompositions if we are to acquire a detailed understanding of structure–propertyrelationships. They also illustrate the benefits of using complementarymethods to understand membrane behavior: both Langmuir monolayer studiesand electrochemical methods provide a means to control the chargedensity and/or surface pressure of a monolayer or bilayer. The structuralinformation that can be acquired for monolayers over a range of pressuresheds light on the reasons for differences between bilayers exposedto strong electric fields. In turn, electrochemical and associatedin situ structural methods can reveal profound differences in behaviorwhen bilayers that initially appear to be similar in structure areperturbed by such fields.We note that several parameters appear to change at composition∼40 mol % DMPS. The surface pressure of the L


The revolutionary success of AlphaFold2 in single-domain structure prediction showed potential to extend deep-learning techniques for multi-domain structure modeling. This work presents a significantly improved method, DEMO2, which integrates analogous template structural alignments with deep-learning techniques for high-accuracy domain structure assembly. Starting from individual domain models, inter-domain spatial restraints are first predicted with deep residual convolutional networks, where full-length structure models are assembled using L-BFGS simulations under the guidance of a hybrid energy function combining deep-learning restraints and analogous multi-domain template alignments searched from the PDB. The output of DEMO2 contains deep-learning inter-domain restraints, top-ranked multi-domain structure templates, and up to five full-length structure models. DEMO2 was tested on a large-scale benchmark and the blind CASP14 experiment, where DEMO2 was shown to significantly outperform its predecessor and the state-of-the-art protein structure prediction methods. By integrating with new deep-learning techniques, DEMO2 should help fill the rapidly increasing gap between the improved ability of tertiary structure determination and the high demand for the high-quality multi-domain protein structures. The DEMO2 server is available at   Deep-learning techniques have dramatically promoted the progress of protein structure prediction , especiaWe previously developed a pipeline, DEMO , for conIn this work, we developed a significantly improved method, DEMO2, which integrates analogous template alignments with deep-learning techniques for high-accuracy domain structure assembly. Starting from individual domain models, inter-domain spatial restraints are first predicted with deep residual convolutional networks, where full-length structure models are constructed by simultaneously searching for the optimal positions and orientations of all domains using Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) simulations, under the guidance of a hybrid energy function combining deep-learning restraints and analogous template alignments. The results on the large-scale benchmark and CASP blind tests showed that DEMO2 significantly improves the performance compared to its predecessor and outperforms the best linker-based domain assembly methods and the state-of-the-art structure modeling tools.DEMO2 is a pipeline for automated assembly of full-length structural models of multi-domain proteins by integrating analogous template structural alignments with deep-learning predicted inter-domain spatial restraints. As shown in Figure Compared to the previous DEMO , four madth domain model and the template structure. According to the TM-scoreh, the top 500 templates are then selected to perform the global evaluation, where the overlap between the alignments of different domains is not allowed , the templates of the two broken parts will be independently detected  simulations in DEMO, DEMO2 performs an L-BFGS simulation to simultaneously assemble all domain structures. Starting from the initial full-length model built from the template, the full-length structure decoy is created according to the corresponding inter-domain rotation angles and translation vectors, as sampled by the L-BFGS simulation for each domain , which iWhen the cryo-EM data is available for the target, DEMO2 employs a new strategy to process the rigid-body domain assembly simulation. Instead of fitting the initial full-length model created by the template into the density map in DEMO, DEMO2 creates the initial model by matching each domain model into the density map based on the best position and orientation identified by L-BFGS, where the L-BFGS simulation is purely guided by the density correlation  between In order to facilitate the interpretation of the predicted models, the estimated TM-score (eTM-score) and RMSD (eRMSD) are introduced to DEMO2 to estimate the accuracy of the assembled model. They are calculated according to the significance of the templates identified by analogous structural alignments using all domain models, the consistency between the deep-learning predicted inter-domain distances/contacts and those in the assembled model, the convergence of the domain model assembly simulations, and the estimated accuracy of the individual domain models by ResQ . DetailsTo evaluate DEMO2, we tested it on a comprehensive benchmark set which is extended from the original DEMO benchmark set  and withmentclasspt{minimaWe first compare DEMO2 with its predecessor DEMO  and the Figure Ornithine Acetyltransferase (PDBID: 1vz6A), a protein with two continuous domains, and methicillin resistant Staphylococcus aureus (PDBID: 4ewtA), a protein that consists of a continuous domain and discontinuous domain, respectively. For 1vz6A  and trRosetta (110), respectively. In the aspect of inter-domain orientations, DEMO2 models also obtain a higher rTM-score (0.48) than either DMPfold (0.33) or trRosetta (0.41).In addition, we also compared DEMO2 with DMPfold and trRosetta on a subset of 162 proteins for which all proteins with sequence identity > 30% to the DeepPotential training set have been excluded. As shown in Given that both DEMO2 and trRosetta/DMPfold simulations utilized the deep-learning restraints, these results reflect the advantage of the DEMO2 protocol which split the proteins into domains and then model the domain structure individually. Since the deep-learning methods are trained mainly on the single-domain proteins , this may result in the incorrect folding for some domains when directly modelling full-length structures of the multi-domain proteins. Since the D-I-TASSER deep-learning model is also trained through single-domain proteins, the DEMO2-based domain split allows it to construct better models for individual domains. In NKT TCR-CD1d-alpha- galactosylceramide analogue-OCH (PDBID: 3arbA) and the Endoglucanase 9G (PDBID: 1g87B). For 3arbA, all methods correctly modelled the individual domain structures with TM-score > 0.8. However, the domain orientations were not correctly predicted by DMPfold and trRosetta as indicated by the rTM-score (0.31 and 0.29), which resulted in the full-length model with a relative low TM-score = 0.61 for both DMPfold and trRosetta  in fully automated protein structure prediction categories. Here we take Zhang-Server as an example since it utilized the D-I-TASSER algorithm to build the domain level models. Since both DeepPotential and DEMO2 were trained before CASP14, none of the CASP14 targets was included in their training dataset. DEMO2 is also compared with the latest version of AlphaFold2  and Rosehttps://zhanggroup.org/D-I-TASSER/), and then upload the domain models into DEMO2 server for full-length structure assembly. A checkbox , where the model is assembled by selecting the option of ‘remove templates from protein sharing > 30% sequence identity with target’. The result page of the example is available at https://zhanggroup.org/DEMO/example/.The results page consists of 5 sections, including the full-length sequence and domain boundaries, user submitted domain models, the deep-learning predicted residue-residue distance and contact maps, the top full-length structurally analogous templates, and the top final full-length models assembled by DEMO2. Figure As shown in Figure The second section Figure  shows thAs shown in Figure h reported in the fourth column of the table. The subsequent three columns of the table show the TM-score, RMSD, and sequence identity between the DEMO2 assembled model and the templates. The eighth column is the coverage rate of the aligned regions determined by TM-align. These parameters indicate the conservation of spatial motifs in the model and the structurally analogous proteins. Users can download the PDB file with the DEMO2 model structurally aligned to the corresponding template by clicking the link in the last column of ‘Download Alignment’.The fourth section Figure  summarizThe last section Figure  presentsThis work presents a significantly extended server, DEMO2, which integrates analogous template alignments with deep-learning techniques for high-accuracy multi-domain protein structure assembly. Instead of constructing the full-length model by iteratively assembling pair-wise consecutive domain models in the previous DEMO, DEMO2 extended the pipeline to build the full-length model by simultaneously modeling the optimal positions and orientation of all domains through an L-BFGS simulation. Accordingly, the global analogous templates that cover multiple domains are identified in DEMO2 to guide the initial model construction and the assembly simulation. Meanwhile, the inter-domain spatial restraints predicted by the deep residual convolutional networks were coupled with the inter-domain distance profiles collected from the analogous multi-domain templates, and physics-based steric potentials to guide the L-BFGS simulation. In addition, a new confidence score system is introduced to DEMO2 to facilitate the use and interpretation of the assembled models.DEMO2 was tested on a comprehensive benchmark set of 461 non-homologous proteins containing various numbers and types of domain structures. The results showed that DEMO2 assembled more accurate full-length models with an average TM-score 12.7% and 36.5% higher than DEMO and the linker-based domain assembly method AIDA, respectively. If we compare the rTM-score which counts for the inter-domain orientations more specifically (Eq. S16), the improvement of DEMO2 will be 26.3% and 108.7% over DEMO and AIDA, respectively. DEMO2 was also compared with two widely used deep-learning structure modeling methods, DMPfold and trRosetta. DEMO2 correctly predicted the full-length structure with a TM-score > 0.5 in 84.2% cases, which is 44.2% and 19.0% higher than the full-length models directly modelled by DMPfold and trRosetta, respectively. DEMO2 was also used to assemble structures of all multi-domain targets for ‘Zhang-Server’ in CASP14, which achieved the best performance on the protein modeling among all server groups in the experiment.To better guide the user to use the method through the online server, we optimized the server interface which includes (i) updating the option for cryo-EM and cross-linking data assisted modeling, (ii) rearranging the result page by adding the superposition of the assembled model, analogous templates, and the accuracy estimated system for the final model. The result page now includes five sections to display the query sequence and domain boundaries, the individual domain models, the residue-residue distance and contact maps, the analogous templates, and the final full-length models.Despite the progress, the DEMO2 server could be further improved in several aspects. First, the current inter-domain restraints are predicted by DeepPotential, which is developed for the residue-residue restraints prediction for the full-length protein. A specific deep-learning network for predicting the inter-domain restraints should improve the performance. Second, the test of AlphaFold2 has shown the ability to predict the global structure of multi-domain proteins. Although the accuracy of multi-domain models is still considerably worse than that of the individual domains , it coulhttps://zhanggroup.org/DEMO/.The webserver, standalone package, and dataset are available at gkac340_Supplemental_FileClick here for additional data file.


This paper presents a contribution to the study of bibliographic corpora through science mapping. From a graph representation of documents and their textual dimension, stochastic block models can provide a simultaneous clustering of documents and words that we call a domain‐topic model. Previous work investigated the resulting topics, or word clusters, while ours focuses on the study of the document clusters we call domains. To enable the description and interactive navigation of domains, we introduce measures and interfaces that consider the structure of the model to relate both types of clusters. We then present a procedure that extends the block model to cluster metadata attributes of documents, which we call a domain‐chained model, noting that our measures and interfaces transpose to metadata clusters. We provide an example application to a corpus relevant to current science, technology and society (STS) research and an interesting case for our approach: the abstracts presented between 1995 and 2017 at the American Society of Clinical Oncology Annual Meeting, the major oncology research conference. Through a sequence of domain‐topic and domain‐chained models, we identify and describe a group of domains that have notably grown through the last decades and which we relate to the establishment of “oncopolicy” as a major concern in oncology. We then proceed to study in detail one of the notable shifts, namely, the rise of “oncopolicy” as several associated domains, a central issue nowadays as ASCO officially transitions from being a “mostly research” to being a “research and policy debate” organization ASCO, .It was during the preparation of this paper that Gerlach independently published  the graph's nodes into blocks. In the nested case, this includes further partitioning the blocks themselves into higher‐level blocks for each nested level. Given these partitions, their blocks' degree sequences and the connection probabilities between blocks are simply traced from the edges in the graph. A model that best fits the graph is then searched following a minimal description length (MDL) approach, by seeking a partition that minimizes the combined informational costs of describing the graph given the model, and of describing the choice of partitions and other model parameters Peixoto, . Notablygeneral connectivity patterns; resolving the issue of treating different types of nodes: documents, terms, and metadata.They detect nonparametric; in particular, both the number of blocks and levels in the hierarchy are directly inferred from the data.They are not overfit: by accounting for the information cost of model parameters when maximizing model probability, they only infer statistically significant structures.They do They provide a nested, multi‐level abstraction of terms and documents, allowing the investigation of topics and domains at different scales.This class of models provides our domain‐topic model with a set of desirable properties:Another major concern in the study of corpora is the ease and reproducibility of result interpretation. This concern guides the following two additional choices regarding the model. First, stochastic models can be employed either by searching for a single model that best fits the data or by averaging the values of interest over a distribution of models yielded according to their fitness. In this paper, we choose to work with the single best‐known fit for our data. Second, the model adopted allows for overlapping as well as nonoverlapping blocks. In this paper, we have worked with nonoverlapping blocks. Contrary to what one might expect, it has been shown that nonoverlapping models are often a better fit than overlapping ones Peixoto, . This rehttps://doi.org/10.5281/zenodo.3596035]). The work presented here has received ethical approval by the Institutional Review Board of the Faculty of Medicine of McGill University (IRB Study Number A07‐E55‐15B).In conclusion, we obtain our domain‐topic models by fitting the aforementioned choice of SBM to a document‐term graph, as pictured in Figure 2.2Research domains also carry social and contextual dimensions, given that they are grounded in the historically situated, socially contingent activities of specific teams and institutions, whose results are made public at professional conferences and in scholarly journals. These dimensions are typically reflected in metadata such as authorship, institutional affiliations, funding sources, or year of publication. Our present purpose is to study them through the lens of the substantive content of the corpus, that is, we want to cluster metadata elements that entertain similar connections across the domains of a domain‐topic model.inference chain toward the documents' metadata. We begin by restricting the original graph  between these distributions, in order to quantify the contribution of each topic to the overall information gain incurred when we describe topic probabilities using the domain's actual distribution instead of a higher‐level superdomain's broader distribution. That contribution is the expectation‐weighted pointwise relative entropy of a topic. For the nested specificity, to account for all scales while still accentuating more local ones, we average this contribution across the superdomain ladder. The nested specificity of a topic We must now account for expectation, so that our nested commonality measure corresponds to a topic's contribution to this quantity's expected value. Consistent with the above, we treat the subdomains as equivalent units, and consider a topic's probability as the probability of first uniformly choosing a subdomain, and then sampling a topic from it. We finally obtain:2.3.2The domain‐topic table answers the need for a simple, static, and publication friendly interface. It features a chosen group of domains together with their constitutive level 1 subdomains, and it uses the measures defined above to list, for each domain in the group, the topics common to its immediate subdomains, as well as the specific topics of the level 1 subdomains. For example, if we are interested in a level 3 domain, the domain‐topic table lets us examine the group of its level 2 subdomains. For each level 2 domain, it would display what its subdomains have in common, and what is specific to the level 1 domains composing it. In this case, the table can also present what the level 2 domains have in common among themselves, as subdomains of the level 3 domain  for the domain in question, until those topics account for half the sum of the positive specificities  over all topics. For terms from a topic, we pick those terms whose values are higher than half the highest value. The difference in criteria for topics and terms are a consequence of what they represent: distinct topics display different connectivity patterns, so we account for enough topics to cover a majority of their weight; meanwhile, terms within the same topic display similar connectivity patterns, so we simply account for those that contribute the most.As with our two measures, these tables can be similarly constructed for other pairs of block types.2.3.3Domain‐topic tables are useful when one already has a broad understanding of the corpus they are studying, and has chosen a set of domains to investigate. But we still need a tool for interactively exploring the corpus in its different scales and dimensions. Domain‐topic maps provide such a tool, depicted in Figure cursor over a domain: displays the domain's specific (or common) topics and terms.cursor over a topic: displays the most frequently employed terms from the topic;scroll: zooms in and out of lower levels, following the hierarchy;changes the opposite map to display colors relative to the block, see Figure in a secondary view, displays general information on the block plus titles of pertinent documents, linked to their URL;when selecting a domain, restricts the histogram to its documents.select a block:search: selects a topic by entering a term it contains.In them, color represents the relevance of a block, normalized so that the greatest value within each level corresponds to the strongest color. Before selecting a block, this relevance corresponds, for domains, to the fraction of the corpus they contain, and for topics, to the fraction of term usage originating from that topic. By interacting with the map, one may:These maps can also be built pairing domains and metadata blocks, providing the same kind of interactivity.33.1To test and exemplify our approach, we analyzed a dataset of conference abstracts from the ASCO Annual Meeting between 1995 and 2017.Why conference abstracts rather than journal articles? First, because scientific and clinical gatherings are a major forum for the introduction of the latest clinical and scientific research results, including some that are preliminary, will not necessarily be confirmed, and will therefore remain unpublished  and “pk” (pharmacokinetics) for D50, “rfa” (radiofrequency ablation) for D40, and imaging, hormones, and hormone‐related cancers  for D41. In between those sectors, we see the small but increasing D52, linked to trials of more recent hormonal treatments for prostate cancers, and to their right also increasing is D45, associated with modern imagery and surgery for brain cancers. Still toward the lower right, a small group concerns the study of cells and their physiology, from D42 to D57, showing a lesser decrease. This latter group shares aspects with the large and strongly rising D47, associating cells, and genetics. From D47 to D43, we can see on the upper‐right the rising world of genetics and genomics, complemented by the relatively large D54, which ties clinical trials to genotyping, besides which we find and small domain, D46, linked to “qol”  and clinical trials. Finally, toward the top we find two domains, D44 and D48, sharing concerns about populations and participants, but with a different emphasis (see below our discussion of “oncopolicy”).We start our inquiry by employing the domain‐topic and domain‐period maps. Because domain‐topic maps offer the opportunity to delve into the details of the entire corpus, they afford two complementary disciplined uses: on the one hand, they allow researchers to perform an exhaustive analysis of its domains, which in our case encompasses the numerous developments at clinical oncology's research front over 23 years; and, on the other hand, they allow researchers interested in a specific set of questions to uncover the relevant domains on which to focus their analysis. The present paper explores this latter usage, because it allows us to demonstrate the specific task of determining what portion of the map to focus on, but also to exemplify the tasks and procedures involved in the exhaustive approach while conveniently restricting our scope to fewer domains.L2P1 and (2006–2017)L2P2. We have added labels singling out some notable domains. At level 1, the domain with the highest growth refers to survival and prognosis across different cancersL1D808, a finding that corresponds to a renewed interest in prediction as discussed in , or the meteoric growth of cancer genomics—are far from surprising for readers familiar with oncology, the fact that the domain‐topic model was able to capture them demonstrates its ability to single out key developments that have characterized the field. Besides these well‐known trends, we also find surprising or less obvious ones. In particular, at level 3 we notice a high‐growth domain, L3D44, that we initially had trouble characterizing, as it consists of a number of miscellaneous contributions related to public health, healthcare policy, healthcare services, and cost analysis. Not only did this domain refer to issues that one would not necessarily expect to see discussed at a clinical research meeting, but, as we will see below, it also appeared to do so by embedding these policy topics into activities such as clinical trials or other biomedical investigations such as epidemiological surveys. This particular result prompted us to launch a qualitative investigation that led us to a set of activities to which oncologists refer by the umbrella term “oncopolicy.” It is important to emphasize at this stage that the qualitative investigation was spurred by the computational results and, in turn, as detailed below, led us back to a computational exploration, resulting in an iterative process of aligning these two approaches . We then considered the later of these oncopolicy‐specific periods to represent its more current research trends and restricted once more the corpus, keeping only documents found in both oncopolicy and (2013–2017). To this last subcorpus, we fitted a domain‐chained model of the countries found in the affiliation metadata, thus clustering them from their insertion in “current oncopolicy.” Using a country‐domain table to investigate these clusters shows, for instance, that the bulk of European countries, together with Brazil, form a single cluster displaying a slight tendency toward “Meta‐analysis of clinical trials”5We have shown how domain‐topic models and domain‐chained models, allied with measures, tables, and maps tailored to render model states associated with data accessible to human researchers, afford a fine‐grained and multifaceted view over the scientific dynamics of the ASCO Annual Meeting. The domain‐topic model formed the basis of our approach. By abstracting topic‐wise similar documents into domains, and domain‐wise similar terms into topics, at different levels of scale, it systematized and facilitated the navigation and characterization of the corpus.Applying a domain‐chained model to the meeting's years allowed us to identify content based periods, whose consistent chronology reflects the conference's unfolding dynamics. Through these periods we located and broadly described a few shifting domains, including a growing level 3 domain related to “public health” and of interest to major current events in ASCO and other oncology organizations. We chose to focus on this issue, and by inspecting the topics of this domain we located and expanded our scope to another, strongly related level 3 domain. We investigated these two domains upward from their concrete elements, characterizing their direct subdomains, the domains themselves, and the ensemble we label “oncopolicy.” In sequence, by crossing periods and characterized subdomains, we provided an analytical picture of the evolution of “oncopolicy.” And, lastly, we have briefly exemplified how to employ sequences of operations with domain‐chained models and the production of targeted sub‐corpora in order to perform more complex inquiries into the data.The types of research cited by the CPGs were primarily clinical trials, as well as three treatment modalities . Genetics, palliative care and quality of life were largely neglected.While one would expect that articles on clinical trials and the three major treatment modalities will act as primary evidentiary sources for CPGs, it is somewhat surprising that this is not the case for contributions on topics such as “quality of life,” which, as we saw, are increasingly discussed during ASCO meetings. This raises a number of interesting questions, for instance, how to account for such a discrepancy between these two evidentiary settings? And, relatedly, what translational mechanisms are implemented to operationalize “matters of concern” (Callon & Rabeharisoa, To further illustrate the heuristic value of our approach, we refer to a recent article (Pallari et al., In line with a recent special issue of Quantitative Science Studies (Leydesdorff et al., This work also hints at a few questions to be addressed by further research, in respect to the approach we introduced and the corpus we studied. Concerning the approach, although we make a strong case for our choice of model, one may ask whether other models, such as those discussed in the introduction, could be expanded to perform a similar role. As a result, a quantitative or systematic method of comparison with such alternatives is desirable. Another interesting avenue would be to consider small corpora that do not generate a detailed topic partition on their own, yet may borrow a reference topic partition from a larger and thematically related corpus, which could afford the discovery of domains in the small corpus by employing the same procedure as the chained‐model presented here. Concerning the corpus, it would be interesting to compare the ASCO Annual Meeting with other conferences such as ESMO or the smaller ASCO meetings, by modeling them together and observing, for example, their differential insertion in domains. Beyond that, studying conferences, such as the ASCO Annual Meeting, together with related grant proposals and publications may provide an original view into research cycles if one can account for the diversity of these corpora and their metadata, a task for which our approach seems an able candidate. We conclude with a note, that the methods introduced, while built around the study of scientific activities, may be applicable to the study of other subjects, so long as the subject is associated with a corpus whose documents play a connective role across its dimensions.The authors declare no potential conflict of interest.Alexandre Hannud Abdo: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Software, Validation, Visualization, Writing—original draft, Writing—review & editing. Jean‐Philippe Cointet: Conceptualization, Funding acquisition, Methodology, Resources, Validation, Writing —original draft, Writing—review & editing. Pascale Bourret: Conceptualization, Methodology, Resources, Validation, Writing—original draft, Writing—review & editing. Alberto Cambrosio: Conceptualization, Funding acquisition, Methodology, Project administration, Resources, Supervision, Validation, Writing—original draft, Writing—review & editing.