Moreover, the fabrication process can be easily accelerated by using multi pipettes working at the same time. The graphene spongy balls show energy storage with a specific capacitance of 124 F g−1 and oil adsorbing with a capacity of 105.4 times its own weight. This strategy can be a feasible approach to overcome the low efficiency in production and speed up the development of porous 3D graphene-based macrostructures in industrial applications.Porous three dimensional (3D) graphene macrostructures have demonstrated the potential in versatile applications in recent years, including energy storage, sensors, and environment protection, etc. However, great research attention has been focused on the optimization of the structure and properties of graphene-based materials. Comparatively, there are less reports on how to shape 3D graphene macrostructures rapidly and effortlessly, which is critical for mass production in industry. Here, we introduce a facile and efficient method, low temperature frying to form graphene-based spongy balls in liquid nitrogen with a yield of ~400 balls min Since great progress of graphene synthesis techniques has been made in recent years1261011et al.22Unlike other well-established methods15161819−1 per pipette, which can be further accelerated easily by multi-pipettes. Followed by freezing desiccation and chemical reduction, the graphene spongy balls (GSB) can be obtained. Due to the electrically conductive and the porous nature of GSB, they have been applied as electrodes for supercapacitor as well as efficient adsorbents for oils and organics. The GSB-based supercapacitor has shown a high specific capacitance of 124 F g−1. On the other hand, GSB showed an oil-adsorbing capacity as high as 105.4 times its own weight. Based on these promising properties, the present production strategy can be developed to an efficient technique to meet the criteria of labor-saving, massive and low-cost production in industrial practice.Here we report a novel assembling strategy aiming to realize rapid, continuous production of 3D graphene macrostructures through a liquid nitrogen-aided frying strategy. By dripping the GO dispersion drops directly into liquid nitrogen through a pipette, frozen GO balls of several millimeters in diameter can be continuously synthesized. In a typical experiment, the dripping speed is 0.5 mL s−1). The diameter of GSB could be controlled simply by varying the volume of each injection , which means that 1 L GO dispersion can be fried by the liquid nitrogen within 30 min. It is noteworthy that the preparation time can be further reduced, thus the yield can be easily multiplied by applying multi-pipettes working at the same time. After the frying process, GOFB were freeze-dried along with the container for 12 hours. The resulting light brown, GO spongy balls (GOSB) were synthesized after the sublimation of both ice and liquid nitrogen. Finally, by using hydrazine hydrate as reduction agent, GOSB were chemically reduced into GSB, with their color turning to black. Besides the apparent change of the color, the sharp decrease of oxygen atomic concentration , as shown in 3) and showed the great potential for industrial production. Therefore, our strategy may serve as a promising candidate to fabricate rGO-based, 3D macrostructures with both dimension and density under precise control.The production procedure of GSB was schematically demonstrated in njection . Typicalnjection . Either SEM and transmission electron microscopy (TEM) were performed in order to depict the microstructure of GSB. The low-magnification structure was shown in −3) electrodes was analyzed by cyclic voltammetry (CV) and galvanostatic charge/discharge measurements in 6 M KOH solution. In −1. The CV curves of GSB electrode showed roughly a rectangular shape without significant redox peaks, indicating a typical electrochemical double-layer capacitor (EDLC) behavior. Galvanostatic charge/discharge test was performed at different current densities from 1 to 8 A g−1  for the adsorption of oils and organic solvents. The process of adsorption was shown in 6A series of adsorption experiments were then carried out to test the capability and recyclability of GSB  and hexahydrobenzene (b. p. 80.7 °C). In a typical adsorption-desorption process, the heating temperature for vaporization was close to the boiling point of adsorbates. As an example, the GSB after adsorbing liquids were heated to 105 °C to release toluene vapor, or 75 °C to release hexahydrobenzene vapor. Then the heated GSB were weighed and compared with its original mass to determine the weight of ‘dead’ liquid resided in the adsorbent. The residual weights of both toluene and hexahydrobenzene in GSB were less than 3% for each adsorption-desorption cycle . As show−3 to 11 mg cm−3 by varying the experimental conditions. Compared with previous hydrothermal protocolsA mass continuous production strategy was introduced to realize the facile, massive production of rGO-based, 3D macrostructures. By continuously frying GO dispersion in liquid nitrogen, a large quantity of graphene oxide frozen balls (GOFB) can be synthesized. Followed by freezing drying and chemical reduction, graphene spongy balls, named GSB, can be obtained with a tunable density ranging from 1.8 mg cmGO was synthesized from natural graphite powder using a modified Hummers methodImages of oil on the surface of a glass plate were taken and analyzed to characterize the dynamic process of oil adsorption. hexahydrobenzene (1.5 g) was stained with Sudan red 5B (2 mg) to facilitate the evaluation of oil adsorption.−1 KOH aqueous solution was used as the electrolyte. GSB, Pt wire and Ag/AgCl electrode were used as the working, counter and reference electrodes, respectively. Cyclic voltammetry and Galvanostatic charge-discharge curves were measured within the potential window range from −1.0 to 0 V. Electrochemical impedance spectroscopy was obtained in a frequency range of 0.01 Hz–10 kHz. For two-electrode symmetrical test, the working electrodes were symmetrically assembled with a non-woven fabric as separator. The specific capacitance for single electrode  was calculated from the galvanostatic charge-discharge values by using the following equation:All electrochemical measurements were performed in a Solartron analytical equipment . The GSB without any additive were pressed into film-like graphene blocks and then sandwiched between the nickel foam to form a sandwich structure as the working electrode. The loading mass of GSB was about 2 mg. The as-formed electrode was then dried at 100 °C in a vacuum oven. For three-electrode test, 6 mol LI was the current density (A g−1), ΔV refered the potential window excluding the IR drop (V) within the discharge time Δt (s).Where E, W h kg−1) and power density  of one electrode were calculated according to:The energy density (Cp represented the capacitance and Δt (s) was the discharge time referring to the voltage interval of the above two-symmetric-electrode test.Where How to cite this article: Wan, S. et al. A facile strategy for rapid preparation of graphene spongy balls. Sci. Rep.6, 32746; doi: 10.1038/srep32746 (2016).


The presence of tumour-infiltrating lymphocytes (TILs) is a favourable prognostic factor in patients with early breast cancer. Programmed cell death-1 (PD-1) and its ligand PD-L1 are associated with a variety of adverse features. The purpose of this study was to clarify the relationships between TILs, PD-1 and PD-L1 as well as their prognostic implications in early breast cancer.We investigated 180 patients with breast cancer who received neoadjuvant chemotherapy and underwent subsequent surgery for stage II–III invasive breast carcinoma between 1999 and 2007. TIL expression was classified as low or high using a previously reported scoring model. PD-1 and PD-L1 expression levels were determined by immunohistochemistry. The correlation between PD-1 expression in TILs and PD-L1 expression in cancer cells was also investigated.Higher tumour grade was significantly correlated with PD-L1 expression in tumours (p<0.0001). PD-1 and PD-L1 expression levels were associated with tumour subtype and were highest in triple-negative tumours (p<0.0001). Furthermore, expression of each of PD-1 and PD-L1 was significantly correlated with higher TIL expression and pathological complete response (pCR) (p<0.0001). PD-L1 expression in cancer cells was significantly correlated with PD-1 expression in TILs (p=0.03). The correlations between pCR and expression of each of PD-L1 and PD-1 were not significant.Expression of PD-L1 and PD-1 in early breast cancer is associated with higher TIL scores and pCR; conversely, expression of these proteins correlates with poor prognostic clinicopathological factors such as tumour grade and subtype. TILs, PD-1 and PD-L1 can potentially predict the response to treatment. The presence of tumour-infiltrating lymphocytes (TILs) is a prognostic factor in patients with early breast cancer.Programmed cell death-1 (PD-1) and its ligand PD-L1 are associated with a variety of adverse features.Expression of PD-L1 and PD-1 in early breast cancer is associated with higher TIL expression and pathological complete response (pCR).PD-L1 expression in cancer cells was significantly correlated with PD-1 expression in TILs.As immune-targeting therapies based on PD-1/PD-L1 blockade are being developed, understanding the roles of TILs, PD-1 and PD-L1 in breast cancer is critical. Our data show that PD-L1 and PD-1 are associated with poor clinicopathological features and  are indicators of good prognosis, higher TIL expression and pCR.Blocking the immune checkpoint receptor ‘programmed cell death-1’ (PD-1), and its ligands PD-L1 and PD-L2, is one of the most promising strategies in cancer immunotherapy.Tumour-infiltrating lymphocytes (TILs) comprise various types of lymphocytes; their activities are regulated by complex immune system activator and inhibitor pathways.et al also reported that high PD-1 and PD-L1 expression was associated with better overall survival and higher pCR rates in TNBC.Recent studies have revealed that PD-1 and PD-L1 expression levels in breast tumours are associated with adverse clinicopathological features.There has been lack of studies aimed at clarifying the relationship between PD-1, PD-L1 and TILs in patients with similar-stage breast cancer. Furthermore, the associations between the expression of these proteins and clinical outcomes in breast cancer subtypes other than TNBC have not been investigated. Therefore, we performed this study to identify clinicopathological factors associated with PD-L1 expression in tumour cells and PD-1 expression in TILs in early breast cancer and to reveal the association between clinical outcomes and PD-L1 or PD-1 expression.2 plus cyclophosphamide 600 mg/m2 (AC) or cyclophosphamide 600 mg/m2 plus epirubicin 100 mg/m2/5-fluorouracil 600 mg/m2 (FEC), followed by weekly paclitaxel 80 mg/m2 or triweekly docetaxel 75 mg/m2, or Adriamycin 50 mg plus docetaxel 60 mg/m2) an anthracycline-based regimen alone (AC or FEC) or a taxane-based regimen (weekly paclitaxel or triweekly docetaxel) alone. Trastuzumab was not used for 42 patients with HER2 because it was not approved for neoadjuvant chemotherapy in Japan at that time. The median follow-up time was 115 months (range: 4–202 months). All specimen were formalin fixed and paraffin embedded, and 4 µm thick sections were prepared for H&E staining. The immunohistochemistry (IHC) samples were reviewed by two observers including an experienced pathologist. The definition of pCR was ypT0/is ypN0/+ according to the National Adjuvant Breast and Bowel Project protocol.Our patient cohort was the same as that employed in our previous study on the association between TILs and neoadjuvant chemotherapy response.The present study was approved by the Institutional Review Board of the National Cancer Center Hospital.Histopathological assessment of predictive factors was performed for the CNB specimens. Histopathological parameters included histological grade,IHC of the CNB specimens was evaluated using the following primary antibodies: antioestrogen receptor (ER) , antiprogesterone receptor (PgR) , anti-HER2 , antihuman PD-1 monoclonal antibody  and antihuman PD-L1 polyclonal antibody . Antigen retrieval for PD-1 was performed in TRS buffer (Dako) at 121°C for 10 min; no antigen retrieval was performed for PD-L1. The peroxidase-based Envision System (Dako) was used for secondary detection of the antibody; 3, 3’-diaminobenzidine tetrahydrochloride was used for the immunoperoxidase reaction, and the samples were counterstained with haematoxylin. Tonsil tissues were used as positive controls, while similarly treated samples absent the primary antibody were used as secondary controls. ER and PgR were judged as positive if the Allred score was ≥3 and as negative if the score was ≤2.2 test or Fisher’s exact test. Correlations between PD-1 expression in TILs and PD-L1 expression in tumours were evaluated using the χ2 test. Univariate and multivariate binary logistic regression analyses were performed to identify independent predictive factors of pCR. Survival curves were constructed using the Kaplan-Meier method, and statistical differences between survival curves were calculated by using the log-rank test. Statistical significance was set at p<0.05 for all the analyses, which were performed using the JMP 1.2 software, V.12.Association between clinicopathological characteristics and expression of PD-1 and/or PD-L1 were evaluated using the χThe correlations between clinicopathological factors and PD-L1 expression in tumour cells or PD-1 expression in TILs are shown in One hundred and sixty-two tumours were evaluable for both PD-L1 expression in tumour cells and PD-1 expression in TILs among the 180 screened samples . PD-L1 e2 test  . FurtherOur data clarified the relationship between expression of PD-L1 in tumour cells and PD-1 in TILs. The association between certain clinicopathological factors and PD-L1 expression in breast cancer has been reported in various studies.Although PD-L1 expression in tumours is considered a diagnostic marker for anti PD-1 antibodies (such as pembrolizumab) in non-small cell lung cancer,Previous studies showed that PD-L1 expression correlated with that of TILs and to neoadjuvant chemotherapy response, particularly in HR− tumours and TNBCs.TP53 exhibit higher PD-1 in TILs compared with breast cancers that harbour other mutations  or those without mutations.PD-L1 and PD-1 were shown to be expressed concurrently in various tumours, including endometrial tumours (79%), malignant melanomas (58%), bladder cancer (55%), non-small cell lung carcinomas (43%), ovarian cancer (36%) and kidney cancer 33%).%.33 It wet al reported that PD-L1 mRNA upregulation was associated with better survival and response to chemotherapy in patients with TNBC.et al also reported that PD-L1 mRNA expression is associated with increased TILs and improved recurrence-free survival rates.TIL presence has been reported to be a positive prognostic factor for TNBC and HER2-positive breast cancerIn summary, we showed that expression of PD-L1 and PD-1 is associated with higher TIL scores and pCR; conversely, expression of these proteins correlated with poor prognostic clinicopathological factors such as tumour grade and subtype. PD-L1 expression in tumours and PD-1 expression in TILs were significantly correlated, and 34% of all tumours concurrently expressed both proteins. However, our results did not support the notion that PD-L1 and/or PD-1 expression are predictive markers for survival, which had previously been shown in a study of mRNA PD-L1 expression levels in TNBC.Our study has a number of limitations. One is its retrospective nature, as it was performed using archived specimens. A second limitation is that PD-L1 and PD-1 expression levels were evaluated by IHC and not transcriptionally. A third limitation is that our patients had received neoadjuvant chemotherapy using cytotoxic regimens, not immune checkpoint inhibitors. Namely, PD-L1 or PD-1 expression in this study does not reflect immune checkpoint inhibitor efficacy. Our data indicate that expression levels of PD-L1 and/or PD-1 are not sufficient predictive factors for survival; hence, alternative frameworks that include PD-L1, PD-1 and TILs combined with other microenvironmental factors are required for reliable prognostication.


The proposed approach is capable of explaining the scatter of the results for computed stresses, energy and stiffness and provides the bounds on graphene elastic properties, which are quite important in modeling and simulation of the virtual experiments on graphene-based devices.In this paper, we provide the quantification of the linear and non-linear elastic mechanical properties of graphene based upon the judicious combination of molecular mechanics simulation results and homogenization methods. We clarify the influence on computed results by the main model features, such as specimen size, chirality of microstructure, the effect of chosen boundary conditions (imposed displacement  It is also a basic building block for carbon nano-structures of other dimensionality, since it can be wrapped up to form fullerenes, rolled into nanotubes or stacked into graphite. In other words, it is the building block of several carbon allotropes. Studies of graphene have been going on for more than fifty years. Nowadays, it is widely used for describing properties of various carbon-based materials. Although known as an integral part of carbon-based materials, graphene was initially presumed not to exist in the free state. Actually, in the early 1940s, scientists claimed that strictly 2D crystals could not exist, since they were thermodynamically unstable. Thus, graphene was usually described as an “academic” material that was believed to be unstable in the formation of “curved” structures, such as fullerene and carbon nanotube (CNT). When free-standing graphene was found in 2004 D honeyco,4). Cons a plane ,6. This E) and Poisson’s ratio (ν). The vast majority of previously proposed formulations and computational methods leads to radically different results regarding graphene elastic properties. We present in this paper a review of some recent research and the results of simulations, and more importantly, we identify the main mechanisms resulting in such a large dispersion of elastic properties.The practical application of graphene with its exceptional mechanical, thermal and electrical properties are yet to be discovered and flexible electronics, ultra-performance batteries, reinforcements in advanced composites and building blocks for nano-electro-mechanical systems (NEMS) are just some of the examples . For insE around 700 GPa and, then, those of around 1000 GPa. These correspond, respectively, to the minimized (equilibrium) and unminimized  configuration. The interatomic potential used therein is the Tersoff-Brenner [i.e., solved for unload configuration) yields a slightly different configuration than the initial formed of regular hexagonal structure. This effect is due to coordination number, i.e., the different number of neighboring atoms on the boundary, and is noticeable near the boundary. The bond length of interior bonds in the finite graphene is already close to that of infinite graphene . They conclude that the minimization of potential is one of the reasons for Young’s modulus scatter. The prescribed displacement is used on the edges of rather small graphene sample consisting of 120 atoms (around 1.5 nm × 1.5 nm). The results are in good agreement with the one presented in [i.e., stripes of graphene called graphene nanoribbon (GNR). They perform molecular dynamics (MD) uniaxial tensile tests under deformation-control with periodic boundary conditions to study the chirality effects on bulk (infinite size) graphene or force-control to study size and chirality effects of graphene nanoribbon (GNR) (finite). They show the convergence of E with the size of the GNR and the influence of the chirality (armchair versus zigzag) on the computed value of Young’s modulus. The results are in reasonable agreement with experiments, which report ab initio  and thickness (t). There are cases where there is no need to know E. However, if a specific value is needed, then an estimate of t is required to compute it. If a thickness equivalent to that of graphite interlayer spacing, around 0.34 nm, is assumed, E turns out to be roughly 1 TPa. In the case of the shell model, both the tension and bending rigidity needs to be calculated in order to obtain the thickness. In this case, the elastic modulus results in an estimate of 5–6 TPa. In [et al. [A large scatter of the Young’s modulus value was mentioned for the first time in . The sam-Brenner ). This pented in , they alented in , they usGPa (see ) and ab tio see ,15). Sim. SimE artio . A r [et al.  o calculab initio methods mentioned above. Each of these methods has known advantages and disadvantages and leads the differences in the elastic response of SLGS. The results discrepancy is partly attributed to a particular choice of intearatomic potential that drives the atomic system. Namely, while for SLGS, Tersoff-Brenner-like potential is usually the first choice, Morse, AMBER and second generation REBO potentials are also used. Furthermore, the dispersion of the equivalent mechanical properties of SLGS is related to the above-mentioned uncertainty of the thickness. Apart from these general reasons related more to the formulation of simulation method, there is a number of other mechanisms responsible for the scatter. They are related to size effects, relaxation (minimization of the energy due to coordination), chirality and edge passivation. The size effects results in size-dependent mechanical properties. Based on that observation, it is suggested that comparisons of results should be performed between graphene specimens of the same size. This mostly applies for sizes below 10 nm. The chirality is related to the intrinsic hexagonal structure and its orientation with respect to the load, while edge passivation concerns the boundary effects.We give a brief summary of the mechanisms causing the discrepancy presented above. The reason for the results scatter, obtained by different simulations, is first of all related to the formulation differences. This concerns MM, MD, continuum mechanics and the versus the real experimental measurements in load or displacement control in both a linear and non-linear regime. A procedure of this kind is of direct interest for the development of integrated graphene-based devices.In the previous works dealing with simulation of SLGS, the elastic modulus is calculated via average results for the stress and strain as the corresponding fit to the strain energy value. The latter is, in general, obtained from atomistic simulation. An alternative procedure to obtain the elastic properties is by averaging or homogenization of the discrete model. The latter can provide the homogenization bounds for the stiffness (see ), by makThe paper is organized as follows. In the following section, we introduce the numerical model, defining first the microstructure of graphene and the corresponding force field in agreement with the chosen interatomic potential. Next, the boundary conditions and computational procedures for the tensile tests are described. In N atoms placed within graphene microstructure. Let i, where i can be defined by U denotes the energy stored in the atomic bonds, as presented in sequel, and the second term on the right-hand side represents the external energy, k-th load increment, whereas We consider a domain, Ω, in a three-dimensional Euclidean space,  to zero:δEtot=∑iNUnlike conventional FEM for continuum mechanics , we derEquation . Due to Equation  and geomThe initial iteration, r, to its first neighbor, whereas the second depends upon angle θ between particular atom bonds. Thus, for atom i, j, and θ is the current angle between the bonds, The interatomic potential is assumed to be an at least twice continuously differentiable function, which ensures that the stiffness matrix, Equation  is definEquation , this mal  are: Dei.e., bond energies, i, due to pair interaction in the bond, j from the pair potential is given as:i.e., angle We present a detailed derivation of the residual force and tangent stiffness matrices related to the chosen atom in Equation . These r before ,34) that thatUp illustrated in echanics , and it i.e., Let vely see a.i, in load direction. For the “R” and “m” cases, we assume to have a uniaxial stress state, while for the “V” case, a biaxial state is assumed, where the average stress in the i, in direction, In the case of BC labeled as “m” and “V”, we impose the corresponding atom displacements; thus, the forces are obtained as reactions on the constrained atoms, s e.g., ,18,25), ,25, i,∀igraphite . Thus, tIn this section, we present numerical results for average elastic properties of SLGS under the uniaxial tensile test as a function of size, chirality and BC type using MM simulations. We show, first, the linear elastic mechanical behavior characterized by predicted Youngs modulus, with an emphasis on BC choice. The influence of the BC case is also examined in a non-linear regime, characterized by the tangential modulus value corresponding to the stress-strain relation for moderate strains. The study is concluded with detailed deformation analysis of carbon (C-C) bonds and convergence in energy, depending on the BC case. The geometry and size of the SLGS lattices used in numerical examples is depicted in i.e., Reuss for the computed Young’s modulus, i.e., Voigt, i.e., for the strains lower than approximately 3%) seems to remain nearly constant. By looking more precisely at the value of the factor that includes the ratio of We seek to verify that the linear elastic stiffness obeys the theoretical bounds proposed in  and in lEquation , the valulus see . From thWe further discuss how the three BC cases would influence the stiffness bounds in the nonlinear regime for the case of moderate strain. In such a case, we must employ the modified Morse potential, as described in i.e., it reaches its maximum, the difference between the stress of upper bound, “V”, and the lower one, “m” and “R”, becomes negligible. Furthermore, for even larger strains .Through the study of intearatomic bond structure, the corresponding deformation and energy, we can confirm the importance of the type of the BC imposed on the lattice. This is certainly one of the main sources for the computed response discrepancy that is typical in the currently available literature.


The systematic study of nanoparticle–biological interactions requires particles to be reproducibly dispersed in relevant fluids along with further development in the identification of biologically relevant structural details at the materials–biology interface. Here, we develop a biocompatible long-term colloidally stable water dispersion of few-layered graphene nanoflakes in the biological exposure medium in which it will be studied. We also report the study of the orientation and functionality of key proteins of interest in the biolayer (corona) that are believed to mediate most of the early biological interactions. The evidence accumulated shows that graphene nanoflakes are rich in effective apolipoprotein A-I presentation, and we are able to map specific functional epitopes located in the C-terminal portion that are known to mediate the binding of high-density lipoprotein to binding sites in receptors that are abundant in the liver. This could suggest a way of connecting the materials' properties to the biological outcomes. The production of graphene nanoflakes for biological studies is usually done in a controlled chemical environment. Here, the authors develop a method to produce graphene nanoflakes in biofluids and find that their protein coats are different from that of other materials. Among the other nanomaterials, the distinctive properties of graphene5 have attracted an immense scientific interest and have raised high expectations for its potential applications not only in the field of electronics, photonics, composite materials, and energy generation and storage, but also in biological fields9.Recent years have seen very active interest in understanding the factors that influence nanoparticle interactions with living systems12, one could expect distinctive interactions because of the distinctive shape, size15, surface functionalization18, as well as the surface presentations to cellular- processing machinery21. The question of nanosurface presentation is not unique to graphene, but the challenges here are more significant than for most systems. Thus, we now believe that in contact with biological media, the bare nanoparticle surface induces the formation of a relatively slowly exchanging layer of molecules derived from the environment, often modeled simply by animal serum or plasma. This has been named the “biomolecular corona”24. Increasingly, we recognize that besides shape, it is the biomolecular recognition motifs conferred by this layer which lead to most early-stage impacts . Indeed, recent capacity to “read” or map out these aspects of the corona27 reinforces the role that the proteins play in most early stages of biological interactions28. The nature of the graphene surface makes these issues even more significant, as it is difficult for first-exposure (exfoliation) dispersants, once they have formed such an adsorbed layer to be displaced. In usual circumstances though, one may expect the (surface-induced) biological identity to be determined by the first dispersant (or exposure scenario) encountered. Thus, one has the choice between progressive dispersion of a more weakly interacting dispersant that is then subsequently displaced in exposure  or creating dispersions directly in the exposure medium of interest29. It would in general be desirable to do both, and establish the nature of the receptor interactions that result30. Typically, it is also understood that the source of the media  should be matched to the origin of cells in any in vitro study, as proteins and other biomolecules from different species may interact differently with receptors from another species. One should be aware that the organization of the recognition motifs on the nanoparticle surface may also be affected by the nature of the displacement or attachment, and in any case, it will be important to use biological media that reflect the real exposure scenario. Thus, lung fluids will be important for inhalation scenarios, and in situ exfoliation may be most appropriate31. These are all highly significant issues that touch on the whole validity and durability of such studies32.In evaluating the interaction of graphene with cellsIn the present paper , we show that it is possible to exfoliate directly in the presence of a biological milieu, without undergoing uncontrolled oxidation/reduction of functionalization processes (therefore affecting the graphene properties) and without compromising the protein functionality, thereby allowing for an interesting and useful model of biological exposure scenarios. We also show that it is possible to characterize the dispersed graphene, both in relation to size, and the number of layers in the nanoflakes. Finally, we isolate the graphene–corona complexes, analyzing the macroscopically averaged composition of the hard-corona layer. While some of the proteins identified are common to many other nanomaterials, some typical ones are absent , and some are unusually abundant . These differences may be reflected in the short-term biological outcomes for graphene.33. Top-down approaches for graphene nanoflakes production have focused on the separation of graphite planes using, e.g., ultrasonic or shear exfoliation in organic solvents or water-based surfactant solutions36. Liquid-phase exfoliation (LPE) of graphite is a common method to obtain graphene water dispersions, and the use of biomolecules as a means to produce biocompatible graphene dispersions has recently attracted increasing attention40. A common choice has been to disperse graphene , making use of single proteins. However, because our strategy is to explore biological interactions, we employ the full-protein portfolio (complete serum) as a model for realistic biological exposure scenarios. This leads to presentation of appropriate biological recognition motifs.Graphene can be produced starting from graphite, by a large variety of techniques, even though reliable production of biologically dispersed single-layer graphene samples in high quality and yield is still a challengeIn this work, highly stable colloidal dispersions of graphene nanoflakes in aqueous solutions were prepared by 1–4 h of bath sonication of natural flake graphite in complete serum and phosphate-buffered saline (PBS). Different kinds of serum and concentrations were investigated. This process, described in more detail in Methods, resulted in a mixture of graphite and graphene with different thicknesses and lateral sizes. The dispersions were therefore subjected to a size selection by centrifugation  at different concentrations Fig. , while t41.The proteins surrounding the flakes were denatured (procedure described in the Methods section) and separated from graphene flakes using 1D sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), as reported in Fig. Figure To better resolve the protein composition on the graphene nanoflakes, mass-spectrometry (MS) analysis was performed, as detailed in the Methods section. Three bioinformatics analyses were used for comparison purposes (more details in the Methods section). Table 46; (ii) lipoproteins and immunoglobulins played a major role when HS was used for exfoliation, while, as shown in Supplementary Fig. 48; however, the data at the present stage do not allow to conclude a general trend for carbon-based materials in a competitive environment such as full serum. Apolipoprotein A-I, the major component of high-density lipoprotein (HDL), was found to be highly abundant on the nanoflakes exfoliated with HS. It is also interesting to note that apolipoprotein B100  was nearly absent even though it is commonly found in the corona of several nanoparticle types, such as silica and amine-modified polystyrene nanoparticles50. However, we now understand that the composition of the corona may reflect little on the abundance of specific recognition motifs present in endogenous HDL, and therefore consider that question in more detail later in the paper.The pie charts in Fig. 52, and plays a major role not only in the lipid metabolism (cholesterol efflux) but also in the innate immunity53. Therefore, this result is even more interesting, considering the reported affinity between graphene (and GO) surface and lipids. It has been reported that both the materials can promote the formation of supported lipid bilayers54, self-organization of phospholipids on their surface55, or vesicles in lipid layers56. Moreover, they can promote the disruptive extraction of phospholipid molecules from the lipid bilayers by phospholipid interaction onto its own surfaces57.HDL is a complex of small lipoproteins containing an outer-shell layer of phospholipids, free cholesterol stabilized by apolipoproteins (apoA-I for the 70%), and a hydrophobic lipid core of cholesterol esters and triglycerides58. Various studies in the past have suggested that HDL elasticity and the capacity to adopt such flattened structure could affect cellular uptake, as well as interendothelial transport59. Whether such capabilities could be transferred onto graphene flakes in the relevant media is unknown, but it will at least be of interest to understand the nature of the dispersion and the biological presentation.The presence of apolipoprotein A-I could suggest the binding of intact HDL lipoprotein complexes onto the nanomaterial, but whatever the source, depending on how the apolipoprotein A-I is presented, combined with the flat shape of graphene nanoflakes, suggests that they might be biologically recognized as HDL complexes, though without the capacity to modify its structure that HDL possesses62. Figure −1 (in the absence of other plane defects), while the number of graphene layers modify the shape and intensity of the 2D band (~2750 cm−1). Monolayer graphene presents a single narrow 2D peak with twice the intensity of the G band (~1580 cm−1)62. The overall spectral pattern (D/G intensity and shape and relative intensity of the 2D band) is consistent with the presence of few-layer graphene41. More spectra and their I2D/IG ratios are shown in Supplementary Fig. To characterize the obtained graphene nanoflakes dispersions, Raman spectroscopy, atomic force microscopy (AFM), differential centrifugal sedimentation (DCS), and ultraviolet/visible (UV/Vis) extinction spectroscopy were used, as shown in Fig. D band ~250 cm−1. AFM confirmed the presence of few-layer graphene nanoflakes Fig. . Lateral63, allowing to distinguish different populations and to detect aggregation.Although the size distribution given by DCS is not formally correct in the case of nonspherical particles, still it can produce very consistent and repeatable results even when applied to graphene65. By further AFM analysis presented in Supplementary Fig. 68, the number of graphene layers was estimated to range between 2 and 10 layers.DCS and AFM showed comparable results for size distributions with a maximum peak around 250 nm for graphene exfoliated in 100% v/v HS after 2 h of ultrasonication Fig. . These sDespite the clear challenge for such a complex suspension, the ζ potential for the dispersions at pH = 7 was measured, resulting in −28 mV. In such complex mixtures, the meaning and validity of zeta potential should be considered with some caution. A more pertinent point is that the effects that stabilize the nanomaterials in the presence of proteins  are believed to be similar to those that stabilize the protein solution: a mixture of charge  and hydrogen bonding, as well as entropic interactions  and other forces are believed to play a significant role.It is frequently observed that changing the media composition can impact the colloidal stability of the nanomaterial; therefore, it is essential to assess the nanomaterial stability in a given biological media over time to translate the material toward in vitro testing. The stability of graphene nanoflakes was tested in the biological medium serum-free minimum essential medium (MEM) and supplemented with 50% v/v of serum (complete MEM or CMEM).DCS measurements were performed after 24 h of incubation at 37 °C in MEM and CMEM, and any shaking was performed during the incubation or prior to the analysis, to be closer to in vitro exposure conditions. In serum-free MEM after 24 h, a deposit of flakes can be clearly noticed, and almost nothing can be measured in the supernatant. After few seconds of vortexing, the sample can be measured, but it results in larger aggregates  and reporting functions (say gold nanoparticles (GNPs) or QDs) that recognize sites close to the reported recognition domains for relevant receptors, but in any case, they are capable of giving information on the general interface organization. Prior to the experiment, we made sure that the effect of prolonged sonication did not affect the protein conformation. To this aim, we exploited tryptophan fluorescence emission and circular dichroism as an indicator of the tertiary structure for some proteins. The results reported in Supplementary Fig. It is now widely accepted that the slowly exchanging biomolecular part of nanomaterial–biomolecular complexes determines the biological identity of the nanomaterial via the presentation of key recognition motifs in the corona that interact with relevant receptorsPreliminary evidence on the availability of exposed epitopes of interest on the graphene nanoflakes surface was obtained by immuno dot-blot assay, as described in Methods. The results are reported in Fig. Since the exfoliated graphene produces a black spot if adsorbed onto the polyvinylidene fluoride membranes (PVDF), making the fluorescence of the secondary antibody undetectable, a different strategy was used for this immunoassay. The dark color of graphene was therefore exploited as a signal, and a dark circle can be clearly seen when the interaction with antibodies occurs Fig. . An empt27. In this paper, we used two different immunoprobes  that consisted of GNPs or QDs with a nominal diameter of about 4 nm functionalized with a specific monoclonal antibody; in this case, again the monoclonal anti-apoA-I was able to recognize amino acids 113–243 of apoA-I of human origin (proxy for the HDL receptor-binding domains)69.To understand the likelihood of biological recognition of exfoliated graphene nanoflakes , we aimed to map out the relevant recognition motifs at the surface of graphene by using recently reported immunomapping methods27. Negative controls , and then washed an increasing number of times, following the procedure described elsewhereols Fig.  suggest Thus, as shown in Fig. In the case of IG mapping, as shown in Fig. To confirm these data, IQDs were also used to detect the availability of apoA-I functional epitopes, as described in All these evidences suggested the widespread surface presentation of the relevant apoA-I epitopes, able to promote specific recognition by, for instance, suitable cell receptors.14 functional apoA-I epitopes per mg of exfoliated graphene nanoflakes, as detailed in Supplementary Fig. The estimation of an average number of exposed epitopes per particle is extremely challenging in the case of graphene flakes. The wide distribution of the flakes in terms of size and shape made it very difficult to reasonably estimate the number of particles. Also, the drying effect on the TEM grid made it very difficult to isolate a statistically significant number of single flakes. However, based on the samples mass concentration, and the QDs emission intensity, it was possible to estimate about 1 × 10The preparation of biologically relevant dispersions of nanoparticles has grown in significance and substance as the route to developing our understanding of the nanomaterial–biological interactions, prior to degradation of such layers, typically in lysosomes. Progressively, we have understood that, besides the size and shape, such particles act as scaffolds on which biomolecular recognition motifs (derived from the exposure medium) are presented, and that these factors likely combine to determine receptor interactions, and thereby likely various aspects of liver clearance and other organ-level interactions. Furthermore, the typical surface-free energies of nanoparticles are sufficiently large  that for many practical purposes of biological study, the corona is essentially fixed after its initial formation, so that the biomolecular components from which the corona is formed become a critical element of the study.The evidence we have accumulated from such samples here shows that in sharp contrast to many other nanomaterials, graphene nanoflakes present a negligible proportion of apoB100-type recognition motifs, but are rich in effective apoA-I presentation. This certainly suggests that graphene could have distinct early biological interactions, both at the cell and organ level. One should be cautious though. Even if such differences are real, they will not be superficially obvious. That is, graphene will still be accumulated in cells, and within organs  system, including liver and spleen), as with all other nanomaterials, but the detailed mechanisms, cell types, and processes by which this occurs could be different. That is something which will have to be investigated in some detail.In any case, a full understanding of the biological identity of graphene will likely include its shape, size, and the fact that certain components (in this case apoA-I derived from HDL) and potentially other less-abundant proteins, are presented at the surface. The simple, inexpensive, and scalable way for the graphene production and the resulting dispersions presented here are reproducible and very stable over time, and constitute the basis of a rational approach to the formation of biologically relevant samples for the study of graphene.Dispersions of graphene in aqueous solution were prepared by 1–4 h of ultrasonication of 10% w/v of natural flake graphite  dispersed in a solution of serum at different concentrations. The ultrasound-assisted exfoliation was performed using both FBS and HS at the concentrations of 10%, 50%, and 100% v/v in PBS. FBS was purchased from Gibco by Life Technologies , and HS off the clot was purchased from Millipore .For the exfoliation, a bath sonicator Fisherbrand FB11207 was used at the frequency of 37 kHz and 100% of power. The temperature was kept around 15 °C by a mixture of water and ice (70:30) in the bath which was frequently replaced.The centrifugation steps for size selection were performed using an Eppendorf 5810 R centrifuge. An Eppendorf 5410 R centrifuge with a fixed rotor 1195-A and a 1.5-mL Eppendorf™ LoBind microcentrifuge tube were used for the washing procedure. Size selection and washing procedures are described in detail in To measure the mass concentration of exfoliated graphene nanoflakes, an ultrabalance Sartorius, Cubis® and aluminum boat (Lüdi Swiss) were used.The final dispersions have been fully characterized by DCS, Raman spectroscopy, AFM, and absorption spectroscopy, as shown in Fig. −1, refractive index of 2.377, and a non-sphericity factor of 3 were used. The rotational speed of the disk was set to 18,000–20,000 rpm.DSC experiments were performed with a CPS Disc Centrifuge DC24000 (CPS Instruments). A total of 100 μL of sample were injected in an 8–24% PBS-based sucrose gradient. Density values of 1.75 g × mL2 layer. This technique accelerated the drying, minimizing the aggregations. The wafer was rinsed with Milli-Q water after the deposition.AFM was carried out in tapping mode on a Bruker Innova system using MPP-11123-10 tapping-mode probes. A drop of the dispersion (10 μL) was deposited on a pre-heated (120 °C) Si wafer with a 300-nm-thick SiOUV–Vis extinction spectra were recorded using a Varian Cary 6000 UV-Vis spectrophotometer in a 1-cm path quartz cuvette.Raman spectroscopy was performed using a Horiba Jobin Yvon LabRAM HR800 with a 514-nm excitation laser in air under ambient conditions. The Raman emission was collected by ×100 objective lens (N.A. = 0.8). To avoid sample heating, Raman experiments were carried out at 10% of maximum laser power (<2 mW). The spectra were recorded on a dried droplet and 50 spectra were averaged to obtain a representative mean.For electron microscopy analysis, a drop of concentrated sample was deposited onto a glow-discharged holey film grid . The grid was kept in a humidified environment for 45 min and then rinsed with three drops of Milli-Q water. The grid was subsequently dried and visualized using FEI Tecnai G2 20 Twin TEM.ζ potential of the NPs suspension was measured by Zetasizer Nano ZS by using a disposable capillary zeta cell. ζ potential measurements reported are an average of five independent measurements, with each measurement consisting of an accumulation of ten runs.Circular dichroism spectra were recorded on a J-810 (JASCO) Spectropolarimeter. The spectra were recorded at 25 ± 0.2 °C using a quartz cuvette of 1-mm path length ; the temperature was maintained by a Peltier thermostat, and the spectra were averaged over eight measurements.After the washing procedure  for each protein in the samples. The method of spectral counting (SpC) representing the total number of the MS/MS spectra for all the peptides attributed to a matched protein was mainly used. By applying Eqn 71 performed by MaxQuant. This method, able to accurately and robustly quantify small fold changes on a proteome scale, has the prerequisite that a majority population of proteins exists that is not changing between the samples, therefore, it has been applied to the samples incubated with the same serum at different percentages.The obtained results have been compared with the method of the label-free quantificationFor the immuno dot blot and immunolabeling experiments, apo A-I antibody (A 5.4), cat. num. sc-13549, mouse monoclonal IgG, and apoB100 antibody (A-6), cat. num. sc-393636 from Santa Cruz Biotechnology were used.−1. A membrane without an antibody was used as a further negative control. The blots were blocked in 5% skimmed milk in PBS for 1 h at room temperature, washed three times in PBS, and incubated with the graphene nanoflakes at the concentration of about 20 μg mL−1. After 1 h of incubation with the exfoliated graphene solution in PBS at room temperature, the blots were washed five times for 10 min, dried, and scanned under white light using a G:Box Chemi XT4 (Syngene) to detect the presence of graphene bounded to the primary antibody in the spot on the membranes.For the immuno dot blot, the monoclonal anti-apoA-I and anti-apoB100 antibodies were spotted on the PVDF membrane at the concentration of 2 μg mL26. For antibody (IgG) conjugation, a carbodiimide-based strategy was adopted for both GNPs and QDs, with small differences. Briefly, 0.2 nmol (1.65 nmol) of GNPs (QDs) were mixed with 0.8 mg of EDC and 1.6 mg of Sulfo-NHS in MES buffer, pH 6.5 (PBS pH 7.4), and the mixture was incubated at 37 °C for 30 min. The activated GNP (QD) solution was purified from the unreacted EDC/Sulfo-NHS by passing it through a PD-10 column  using MES (PBS) as exchange buffer. Then IgG was added to the GNPs (QDs) in a 0.8:1 molar ratio and the mixture was stirred at 37 °C for 1 h. The ratio Ab/GNP (Ab/QD) was optimized to get one antibody per nanoparticle. Subsequently, the excess of activated carboxylic groups was blocked by the addition of 4-aminophenyl-β-D-galactopyranoside (5 mg·mL−1), and the mixture was incubated overnight. IG  and IQDs  were stored at 4 °C.For IG and IQDs preparation, 4-nm GNPs and 4-nm CdTe QDs were synthesized as described elsewhere−1) were incubated with 100 µL of IG at 37 °C for 1 h and then washed two times with fresh PBS by centrifugation at 20,000 × g for 10 min. Then 20 µL of BS(PEG)9 (bis-N-succinimidyl-(nonaethylene glycol) ester, 21582 (ThermoFisher) were added to the sample and incubated at 37 °C for 2 h, and then washed three times with fresh PBS by centrifugation at 10000 × g for 10 min prior to the preparation of the TEM grid.For IG mapping experiments, 20 µL of graphene nanoflakes dispersion (conc. 200 µg  mL−1) were incubated with 80 µL of IQDs for 1 h at 37 °C under shaking and then washed two times with fresh PBS by centrifugation at 8000 × g for 10 min. The concentration of graphene flakes after the washing steps was measured by UV–Vis spectroscopy. Fluorescence spectroscopy measurements were performed with a Horiba Jobin Yvon Fluorolog-3 fluorimeter using a 45-μL quartz ultra-micro cuvette of 3-mm path length . For each sample, emission spectra were recorded using λex = 375 nm as the excitation wavelength. QDs functionalized with bovine serum albumin (QD-BSA) were used as control for unspecific binding.For IQDs mapping experiments, 10 µL of graphene nanoflakes dispersion (conc. 700 µg mLData supporting the findings of this study are available within the article (and its Supplementary Information files) and from the corresponding author upon reasonable request.Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2


Engineerable β-turn motif is reported that modulates the extent of right-handed twist in β-sheets. d-Pro–l-Pro, d-Pro–Gly or Asn–Gly as the turn inducing motif restricting their biological application and physicochemical modulation. In this report, for the first time we elucidate that N-methylation of heterochiral amino acids in linear peptides nucleates β-sheet conformation without the necessity of having a ring or covalent constraint at the reverse turn. Our results show that d-Pro can be conveniently substituted by any other N-methylated d-amino acid followed by an N-methylated l-amino acid or sarcosine to adopt a βII′ turn inducing the β-sheet folding. Furthermore, we reveal that a single amino acid either at the i + 1 or i + 2 site of the reverse turn can modulate the right-handed twist, which eventually dictates the extent of the foldedness of the β-hairpin.There is a lack of functional group diversity in the reverse turn motifs nucleating a β-sheet conformation in designed peptides, proteins and foldamers. The majority of these sequences consist of  We were thus keen to study the influence of N-methylation on the turn-inducing residues in linear β-hairpin peptides as there is no such report and the few known facts about the N-methylation of the turn residues are conflicting.N-methylation on a varied selection of heterochiral residues in linear peptides using circular dichroism and NMR based structure calculations. We show that N-methylation indeed nucleates β-hairpin conformation irrespective of the amino acids present at the i + 1 and i + 2 positions of the β-turn. Furthermore, most of these hairpins are conformationally homogeneousN-methylated heterochiral amino acids. This indicates the wide applicability of the newly identified turn inducing motif towards peptide/protein engineering studies in lipophilic (e.g. membrane) and hydrophilic (e.g. cytosol) environments.10N-methylation on β-hairpin induction in apolar conditions, we selected the model octapeptide 1,d-Pro (i + 1) and l-Pro (i + 2) at the reverse turn with various N-methylated amino acids  were indistinguishable from the chemical shifts measured after 100-fold dilution. For the hydrophilic peptides, 17–21, the chemical shifts measured at the concentration employed for 2D NMR studies were indistinguishable from the chemical shifts measured after ∼20-fold dilution.The hydrophobic amino acids: Ala, Val, Ile, Leu and Phe were chosen as substituents since they display the least propensity to occur at the β-turn region, according to Chou–Fasman's analysis of β-turns in protein structures.i + 2 and i + 1 .The initial qualitative assessment of their potential to form β-hairpins was done using far UV (190–260 nm) CD measurements in methanol. The CD spectra of all of the nd i + 1  variants1–16 were carefully designed, so that if these were well folded, besides the characteristic NOEs (1H NMR of 1 (in CDCl3) showed well dispersed signals in the Hα and HN region with 3JN–HαH > 8 Hz suggestive of a well-defined and extended conformation  Hα–(i + 1st)Hα NOE confirming the presence of an all-trans conformer.3JN–HαH values in 2 were higher than the corresponding values in 1. The NOEs ala′Hα–Ala′5NMe, ala′Hα–Leu6HN and Ala′5NMe–Leu6HN along with solvent shielded Leu6HN clearly define a βII′ turn.N–Val8HN, Phe2Hα–Phe7Hα, Val3HN–Leu6HN and NHAc–OMe additionally suggest the antiparallel strand registry in the molecule. The conformation of 2 (Φi+1)( and Ψi+1),4 and 5 both have an N-methylated β-branched amino acid at the i + 2 position and display a similar CD profile although with strikingly low intensity. However, we could assign the characteristic NOEs in both of the compounds, and the high 3JN–HαH suggested the presence of a hairpin conformation. It was interesting to note that in these compounds the Leu1HN and Leu6HN are comparatively less solvent shielded than in 1, 2 and 3 ( and Ψi+2)( (4 and 5 are quite similar with a backbone RMSD of 0.45 Å and a stark right handed twist (Compound tic NOEs  with a c Table S2. In thesd Ψ(i+2) . The coned twist .i + 2 position (6), results in less deviation from the optimal Φ and Ψ values , display a similar solution conformation to 6, suggesting the compatibility of the appended side chains at the i + 2 site.Introduction of a γ-branched residue at the Ψ values  in the tN-methylated glycine (sarcosine)  at the i + 2 position to assess its suitability in nucleating the β-hairpin. In spite of the absence of any side chain constraint, 9 exhibited conformational homogeneity in CDCl3 with all of the characteristic NOEs. To our surprise, the dihedral angles about the turn region of 9  and displays the presence of a well-folded conformation . The solvent-shielded Leu1HN, Val3HN, Leu6HN and Val8HN along with the 3JN–HαH > 8 Hz implies a putative β-hairpin structure. The structure of 10  indicates the differential behavior of valine at the i + 1 and i + 2 sites.The β-branched le to 10  suggesti1) in 11  showed a5, compound 12 with the Ile′ shows a CD spectrum with a high intensity. The explanation was beautifully provided by the solution structure of 12, where the Φi+1)( and Ψi+2)( is a result of the strong steric interaction between the ile′Cγ1 and ile′NMe (γ2 and Ala′5NMe resulting in a higher Ψi+2).Unlike  ile′NMe  that eve13 is less intense than the CD spectrum of the corresponding i + 2 analog 6. This observation is strikingly opposite to the trend observed for the β-branched analogs 5 and 12. The solution structures of these compounds revealed the presence of a considerable right-handed twist in 13 as compared to 6, validating the CD spectrum. The basis of this twist in 13 is the steric interaction between the isopropyl and the N-methyl group of leu′ resulting in a slightly higher value of Φi+1)–N–Cα–Cβi+2)( than C(NMe)–N–Cα–Cβi+1)( (due to the opposite stereochemistry at the i + 1 and i + 2 site) resulting in the flattened hairpin in 6.The CD spectrum of the γ-branched analog 14 showed a lower intensity in comparison to all other i + 1 analogs. Nevertheless, we could identify the characteristic NOEs, and the solution conformation of 14 is almost identical to that of 13 (backbone RMSD of 0.24 Å) displaying the right handed twist. On the other hand the aromatic i + 1 γ-branched analog 15 shows the most intense CD spectrum amongst all of the i + 1 congeners indicating the occurrence of a flatter hairpin. However, the conformation of 15 and the solvent exposure of amide protons show a striking similarity with the corresponding i + 2 analog 8 (backbone RMSD of 0.26 Å), which shows a less intense CD spectrum. This re-emphasizes the anomalous behavior of the electronic CD for peptides with aromatic residues.Surprisingly, the CD spectrum of i + 1 side chain on the induction of the βII′ turn and the subsequent folding of the hairpin, we synthesized compound 16 with sarcosine at the i + 1 position. Although the CD spectrum of 16 is comparable to other i + 1 analogs, it has considerably less intensity than the CD spectrum of 9. This observation directly correlates with the flexibility of 16 in CDCl3,3JN–HαH and solvent shielded HN) of a β-hairpin. It is interesting to note that in spite of the absence of any side chains at the i + 1 site, there is a clear indication of a βII′ turn as suggested by the following NOEs: Gly′Hα1–Ala′5NMe, Gly′Hα1–Leu6HN, Ala′5NMe–Leu6HN and a shorter distance for Ala′5NMe–Ala′5Hβ than Ala′5NMe–Ala′5Hα. However, there is a substantial increase in the Φi+1)( and Φi+2)( in 2 and 4 in comparison to Hα(Pro–R)–Cα–C–Oi+1)( in 16).To determine the importance of the CO group  –N–Cα–Cβi+1)( and C(NMe)–N–Cα–Cβi+2) at the i + 1 site than at i + 2. This leads to the differential behavior of certain substituents at these two sites (e.g.4 and 11).The tolerance of various e β-turn . Any relposition  allows f17, with d-Ala–l-Ala as the turn inducing motif, since in small cyclic peptides heterochirality is enough to induce the formation of a βII′ turn.1Next, to assess the suitability of the designed reverse turn motif in the context of protein engineering, we studied several hydrophilic peptides with a common strand sequence17  showing the maximum and the β-branched analog (19) showing the minimum intensity. It was also encouraging to observe the differential behavior of the i + 2 and i + 1 β-branched analogs 19 and 21, respectively, that followed the trend as noted for the hydrophobic peptides.The CD spectrum of 17  showed ttructure . Further1H spectrum of 17 showed clear indications of an unfolded structure, with the absence of any upfield shifted methyl groupsN-methylated analogs show the presence of two upfield shifted methyl groups that progressively increase in the order 19 < 21 < 18 < 20. This order also correlated with the enhanced dispersion of chemical shifts in the amide region of these compounds.To obtain additional insights into the folding behavior of these compounds, we performed NMR spectroscopy in acetate buffer (pH 3.8). The N-methylated d-residue was substituted with an N-methylated l-residue)19 < 21 ≈ 18 < 20, suggesting the increased foldedness of the β-sheet from left to right.via a single substitution at the i + 1 or i + 2 site, which would find enormous utility in protein engineering.The secondary chemical shifts of these compounds (obtained from the respective unfolded controls, where the 18 at 25 °C. The conformation of 18 (β–ala′NMe, ala′Hα–Ala′7NMe, ala′Hα–Lys8HN, and Ala′7NMe–Lys8HN NOEs, while Tyr2Hα–Leu11Hα, Tyr2Hα–Gln12HN, and Val5HN–Lys8HN NOEs confirmed the strand registry.18 was calculated utilizing the distances derived from the NOEs, which showed the presence of a β-hairpin conformation with a central βII′ turn. It was gratifying to see that 18 displayed significant structural similarity with two different β-sheet peptidesFinally, to validate the broad scope of our design strategy in foldamer design, we calculated the solution structure of on of 18  was wellN-methyl groups in conjunction with the Cβ at the i + 1 and i + 2 amino acid side chains provide sufficient steric locking to fold a linear peptide into a β-hairpin. The introduction of heterochirality in linear peptides alone is not enough to induce the formation of β-sheets, unlike in cyclic peptides. Our engineering strategy is quite modular in terms of decorating the reverse turn with different functional groups to alter the physicochemical properties of the designed β-sheets and attach various probes for biophysical studies. Furthermore, branched amino acids at the reverse turn add another dimension to its modularity by introducing a varying extent of twist that could be utilized to probe the structure activity relationship of designed β-sheets and the modulate folding of proteins. N-methylation has found tremendous utility in cyclic peptides, however, its use in linear peptides was limited. With this report we strongly believe that this simple strategy will not only find enormous utility in foldamer design28In conclusion, we show that the Supplementary informationClick here for additional data file.


Tumours with high somatic mutation rates escape immune surveillance by upregulating receptors and ligands such as programmed death receptor-1 and its ligand (PD-1/PD-L1). Checkpoint inhibitors (ICI) provide encouraging therapeutic results in non-small cell lung cancers (NSCLC) and may soon be used in 2nd or 1st line therapy. Currently PD-L1 immunohistochemistry (IHC) expression assessed on tumour cells is used as a predictive biomarker, since better patient outcomes are often, but not always associated with increased tumour cell PD-L1 IHC expression. However pre-analytical variables, different anti-PD-L1 clones used on different staining platforms, different specimens types, as well as intra- and interobserver variability influence the results. We will only understand PD-L1 expression on tumour cells if we accept that PD-L1 is an inducible pathophysiological factor with variable levels of PD-L1 expression depending on the immunological status. Should we test PD-L1 during initial diagnostic work up before, or at the point when immune checkpoint therapy is considered? Taking all arguments into account the value of PD-L1 as a predictive biomarker is questionable. Other predictive biomarkers such as high mutation burden, mRNA expression, neo-antigens and the diversity of tumour antigen-specific T cells should be evaluated in the future. Here we review results presented in 30 journal articles and three reviews covering this topic in the last 3 years. Within their microenvironment tumour cells can modify the immune response, which comprises a dynamic balance of multifactorial interactions with stimulating and inhibitory receptors and ligands of immune cells. The same mechanisms help create immune tolerance and prevent autoimmune diseases . ParticuToday numerous PD-1 inhibitors are available such as nivolumab  and pembrolizumab  or PD-L1 inhibitors such as durvalumab , atezolizumab  and avelumab . Since checkpoint inhibitors may be used in 2nd or 1st line therapy in the near future, it is important to define a reliable predictive biomarker. One candidate is the expression of PD-L1 assessed by immunohistochemistry (IHC) especially on tumour cells , 7. A nuThe results of “The Blueprint PD-L1 IHC Assay Comparison Project”, a phase I study, were presented by Dr. Hirsch et al. at the recent 2016 Annual Meeting of the AACR. In this study three of four assays were analytically similar for tumour cell staining, i. e. SP263, 28-8 and 22C3, but no clinical diagnostic cut-off was applied in the project. At the same AACR meeting, M. Ratcliffe presented “A Comparative Study of PD-L1 Diagnostic Assays”. Evaluating 500 biopsy samples including both squamous and non-squamous histology and showed that a 25 % cut-off point using Ventana SP263 was similar to the results obtained from a DAKO 28-8 test at 10 % cut-off mark. The results from the SP263 and the Dako 22C3 tests were similar at a cut-off of 50 %. All three tests agreed overall in more than 90 % of cases.In a review by Kerr et al. , the calp < 0.001). The authors concluded that 5 % tumour PD-L1 expression as a threshold of PD-L1 expression may be optimal.Khunger et al. presented “Meta-analysis of tumour PD-L1 expression as a predictive biomarker of benefit from PD-1/PD-L1 axis inhibitors in solid tumours” at ASCO 2016. The analysis evaluated 18 studies with 2731 patients. Inclusion criteria were different tumours with high mutational burden, with 9 studies of NSCLC with known IHC PD-L1 status and PD1/PDL1 inhibitor treatment. A threshold of 5 % PD-L1 IHC expression was highly predictive for different drugs as nivolumab, pembrolizumab, atezolimumab, durvalumab and avelumab. The largest therapeutic effect was seen in NSCLC   translocation or epidermal growth factor receptor (EGFR) mutation does. Nevertheless PD-L1 IHC expression seems to be the best currently available biomarker and may be indicative of a dose–response relationship between PD-L1 expression and drug efficacy. A low threshold such as a PD-L1 TPS of 1 % allows us to include nearly all patients who may really benefit from these therapies. However, since it may be inappropriate to select patients for ICI therapy solely on the basis of PD-L1 expression other predictive biomarkers should be established. In advanced lung cancer plasma PD-L1 protein could provide a promising alternative for monitoring PD-L1 levels. PDL1-enzyme linked immunosorbent assay (PDL1-ELISA) can analyse PDL1 quantitatively or qualitatively in plasma and PDL1 western blot might help to detect specific proteins in tissue homogenate. Mutational findings from targeted NGS panels can be correlated with response, but until today targeted NGS panels were not able to predict response to checkpoint inhibitors. Looking at DNA only provides limited information therefore, if we understand mRNA as a molecule reflecting the dynamic nature of a cancer cell, we should focus on investigating the cancer transcriptome in future.