Melanoma differentiation associated gene 5 (MDA5) and retinoic acid-inducible gene-I (RIG-I) selectively sense cytoplasmic viral RNA to induce an antiviral immune response. Infectious bronchitis virus (IBV) is one of the most important infectious agents in chickens, and in chicken cells, it can be recognized by MDA5 to activate interferon production. RIG-I is considered to be absent in chickens. However, the absence of RIG-I in chickens raises the question of whether this protein influences the antiviral immune response against IBV infection.Here, we showed that chicken cells transfected with domestic goose RIG-I (dgRIG-I) exhibited increased IFN-β activity after IBV infection. We also found that IBV can cleave MAVS, an adaptor protein downstream of RIG-I and MDA5 that acts as a platform for antiviral innate immunity at an early stage of infection.Although chicken MDA5 (chMDA5) is functionally active during IBV infection, the absence of RIG-I may increase the susceptibility of chickens to IBV infection, and IBV may disrupt the activation of the host antiviral response through the cleavage of MAVS.The online version of this article (doi: 10.1186/s12917-017-1253-7) contains supplementary material, which is available to authorized users. Infectious bronchitis (IB) is a serious and highly contagious disease in chickens that is caused by the infectious bronchitis virus (IBV) . AlthougThe innate immune system plays a critical role in the detection and elimination of invading pathogens, especially the IFN antiviral immune response . To actiAlthough both RIG-I and MDA5 are closely related, exhibiting 25% and 40% identities in their N-terminal CARD and C-terminal helicase domains , 17, theTo explore the mechanisms that control the chicken immune response to IBV infection with regard to the RIG-like helicase, we have cloned chicken MDA5 (chMDA5) and domestic goose RIG-I (dgRIG-I) and demonstrated that they act as positive regulators in the activation of IFN-β induced by IBV. Furthermore, the knockdown or overexpression of chMDA5 has no effect on IBV replication. In this study, we also investigated the potential role of MAVS in the MDA5-mediated antiviral signaling pathway after IBV infection and demonstrated a positive regulatory role of MAVS.The JS/2010/12 strain of IBV was previously characterized as nephropathogenic by our laboratory, and its genomic sequence was determined (GenBank accession No. JQ900122.1). In this study, the stock of JS/2010/12 strain propagated in 10-day-old SPF Line 22 of White Leghorn chicken embryos for 5 passages (P5) was used. The 50% tissue culture infective dose (TCID50) of the IBV strain was determined by identifying the cytopathic effect (CPE) induced by the virus in CEK cells. The DF1 chicken fibroblast cell line was used for all transfection-based assays. The cells were maintained in Dulbecco’s modified Eagle’s medium  containing 10% FBS. CEK cells were aseptically generated from 20-day-old SPF chicken embryos. The cell suspension was obtained by trypsinization of kidneys for 30 min at 37 °C and subsequent filtration with a 100-μm mesh. Then, the cells were cultured in M199 media (HyClone) containing 3% FBS (HyClone).EcoR V and Xba I, then was inserted into the pcDNA-5′-Flag plasmid that had been digested with the same enzymes. The chTLR3 ORF was PCR amplified from CEK cells with the primer pair chTLR3 F/R, and the product was cloned into the p3 × flag-CMV-7.1 vector (Invitrogen) at the Not I and EcoR V sites. The dgRIG-I ORF (GenBank accession No. JF804977) was amplified from goose splenic cDNA using primers dgRIG-I F/R, and the fragment was inserted into the p3 × flag-CMV-7.1 vector at the EcoR I and BamH I sites. The small interfering RNAs (siRNA) targeting the chicken chMDA5, chTLR3, and chMAVS mRNAs as well as control siRNA were synthesized by Santa Cruz Biotechnology and have been previously described [The chMDA5 ORF was amplified from CEK cells by overlap PCR with primers chMDA5-F1/R1 and chMDA5-F2/R2, producing a 3006 bp MDA5 PCR product (GenBank accession No. GU570144.1). The PCR product was digested with escribed .Plasmids and siRNA were transfected into cells with Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions. Briefly, plasmid DNA (2 μg for a 6-well plate) and siRNA  were diluted with opti-MEM. Lipofectamine 2000 (5 μl for a 6-well plate) was also diluted with opti-MEM. Diluted DNA was added to the diluted Lipofectamine 2000 reagent (1:1) and was incubated for 5 min, then inoculated into cells and incubated for 24 h before further treatments.LightCycler 2.0 System . The relative expression ratios of the target genes chMDA5, chIFN-β, chIFN-λ and chMx were calculated using the △△Ct method. To assess IBV replication in ovo and in vitro, real-time PCR was performed by absolute quantitation PCR [To quantitate gene expression and IBV replication from IBV-infected CEK cells and chicken embryos, primers and probes specific for chMDA5 , chIFN-βtion PCR .Table 1SEight housekeeping were screened to identify the most stably expressed reference genes in different chicken embryo tissues: β-actin (ACTB) , testis-CEK cells were infected with IBV, and at different time points post-infection, the cells were lysed with RIPA lysis buffer . The cell lysates were analyzed for N proteins by Western blot with anti-N antibody (1:1000) (Prepared by our laboratory). Actin was detected using a β-actin antibody  as a protein loading control. An anti-MAVS antibody  was used to detect the MAVS protein.3 EID50 via allantoic cavity. Three embryos inoculated with PBS served as a negative control. Three embryos from each group were killed at 72 h post-inoculation to determine chMDA5, chIFN-β, chIFN-λ and antiviral protein chMx transcription levels in the trachea, lung, liver, kidney, muscle and intestine of embryos, with all tissues being immediately processed for RNA extraction.Eleven-day-old SPF chicken embryos were inoculated with IBV at 10CEK cells were infected with JS/2010/12 and harvested at 72 h post-inoculation. Viral stocks were prepared by freezing/thawing cells three times; the initial JS/2010/12 stock inoculated CEK cells for 5 passages. Then, DF1 cells were infected with this viral stock, and RNA was extracted at different time points. The viral genome copy number was quantified by qPCR. Although no CPE was observed, an increase in the viral genome copy number can be seen over time. CEK cells in 24-well plates were infected with IBV at an MOI of 1 and were incubated at 37 °C for 1 h. Cells that were inoculated with PBS served as a negative control. At different time points post-infection  and kidney (P ≤ 0.05). IBV induced higher chIFN-β .IBV could replicate sufficiently in 9–11 day old chicken embryos, to evaluate the relationship between antiviral response and viral replication in different embryo tissues, chicken embryos were inoculated with IBV, then the trachea, intestine, kidney, lung, liver, and muscle of the embryos were collected 72 h post-infection. The replication ability of IBV was determined by absolute quantification real-time PCR. IBV can be detected in all tissues, and the viral genome load was higher in the lung and trachea compared with other tissues Fig. . Eight hN-β Fig. , chIFN-λN-β Fig.  and chMxN-β Fig.  transcriTo investigate the replication ability of IBV in vitro, CEK cells were infected with IBV at an MOI of 1. The replication of IBV was quantified by RT-qPCR to detect the IBV genome load in cell culture supernatants, and N protein was detected by a Western blot assay. We observed a significant increase in the IBV genome load in cell culture supernatants, with the highest level occurring at 60 h post-infection for the IBV-infected cells compared to the other time points Fig. . The IBVP ≤ 0.05) post-infection, consistent with IBV replication.To monitor the kinetics of the chMDA5 and IFN response in relation to IBV replication in vitro, the transcription of chMDA5, chIFN-β, chIFN-λ and chMx was quantified in IBV-infected CEK cells Fig. . The exp−2, 10−1, 100, 101 and 102 MOI for 36 h. As shown in Fig. 1 and 102 MOI (P ≤ 0.01). Overall, IBV significantly induced the activation of chMDA5, chIFN-β, chIFN-λ and chMx in a dose-dependent manner.To determine the relationship between the antiviral immune response and virus titer, CEK cells were infected with IBV at 10P ≤ 0.05) , and dgRIG-I induced chIFN-β expression after IBV infection to a greater extent than chMDA5. Expression of chTLR3 did not enhance chIFN-β transcription after being infected with IBV. These results appear to indicate that IBV stimulates chIFN-β transcription via MDA5. Similarly, the IBV-induced chIFN-β transcription was further increased by the overexpression of dgRIG-I. These data show that chMDA5 and dgRIG-I act as positive regulators of the IBV-induced chIFN-β signaling pathway  Fig. . We alsoway Fig. .Fig. 5DgTo investigate whether chMDA5 and dgRIG-I can influence the replication of IBV, DF1 cells were transfected with chMDA5 or dgRIG-I, then were infected with IBV at an MOI of 1. The supernatants were harvested and titrated onto CEK cells. The test results indicated that neither the expression of chMDA5 nor dgRIG-I in CEK cells affected viral replication Fig. .RIG-I and MDA5 interact with MAVS via CARD-CARD domains at their N-terminal, which is essential for the activation of the NF-κB and IRF3/7 downstream signaling pathway. MAVS plays a critical role in antiviral activity. To determine whether chMAVS is involved in the antiviral immune response to IBV infection, the expression level of chMAVS in IBV-infected CEK cells was investigated. We found that the cleavage of MAVS was induced by IBV at 8 h and 12 h post-infection but was not cleaved after 24 h Fig. . We infeA gene silencing technique was also used to identify whether chMDA5, chTLR3 and chMAVS influence the antiviral immune response mediated by IBV. First, to assess the effect of chMDA5, chTLR3 and chMAVS on IBV-induced IFN-β transcription, the expression of chMDA5, chTLR3 and chMAVS was knocked down. Compared with the control group, silencing of chMAVS or chMDA5 mRNA in DF1 cells significantly reduced IBV-induced IFN-β transcription. The simultaneous silencing of chMDA5, chTLR3 and chMAVS reduced the transcription of IFN-β significantly Fig. . These rTo further understand the role of chMDA5 and chMAVS during IBV infection, chMDA5 and chMAVS were knocked down in CEK cells, then the cells were infected with IBV at an MOI of 1. IBV in supernatants was titrated onto CEK cells at 48 h post-infection while the IBV genome loads were quantitated by real-time PCR. The results indicated that the observed viral replication in chMDA5 and chMAVS knockdown cells was the same as in the control group Fig. . OverallIn ovo and in vitro study, we show that infection with IBV leads to a considerable activation of the type I IFN and antiviral immune response [response . Among bresponse . RIG-I aresponse , 11, 12.response . In thisTissue distribution is an important characteristic of MDA5 function, as it influences the capacity of MDA5 to capture different viruses as they enter and proliferate in different tissues , 23. UndMDA5 and RIG-I are the two major PRRs for detecting RNA viruses. They can both detect RNA viruses and activate a signaling pathway that leads to the production of type I interferon and the initiation of antiviral activities , 37. ConAlthough chMDA5 was found to be capable of sensing IBV, leading to the induction of chIFN-β expression in chicken cells, the overexpression of chIFN-β induced by chMDA5 and dgRIG-I did not affect the viral replication in our experiment. Silencing of chMDA5 also had little impact on IBV replication, and a subtle role for chMDA5 may be masked by incomplete silencing of chMDA5, resulting in trace levels of IFN activity. Nevertheless, our data gave a similar result as mammalian models showing that MDA5 is not critical to combat the influenza virus . It is kInfectious diseases are a manifestation of constant battles between the host and pathogenic microbes. This host-pathogen antagonism is demonstrated by the interaction between viruses and MAVS, a critical molecule that is downstream of MDA5 and RIG-I , 41. ForTaken together, our study represents a comprehensive analysis of the host antiviral immune response against IBV infection. We show that IBV induces activation of the IFN response in CEK cells and chicken embryos. We also found that chMAVS acts as a key modulator in antiviral signaling by positively regulating chMDA5-mediated signaling. dgRIG-I and chMDA5 have similar roles in the IBV-induced IFN-β signaling pathway, the absence of RIG-I may increase the susceptibility of chickens to IBV infection. The exact contributions of these PRRs and MAVS are worth exploring in future studies.Additional file 1:Supplementary information, selection of appropriate reference genes. (DOCX 36 kb)Additional file 2: Figure S1.Transcriptional stability of eight candidate reference genes in different chicken embryo tissues. Three eleven-day-old SPF chicken embryos were inoculated with PBS. Then, the kidney (A and B), liver (C and D), muscle (E and F), intestine (G and H), lung (I and J) and trachea (K and L) tissues were collected from the embryos 72 h post-inoculation, and RNA was extracted. Candidate reference gene mRNA was amplified by real-time PCR. The transcriptional stability of the candidate reference genes was measured using geNorm and NormFinder software. (A), (C), (E), (G), (I) and (K) are the geNorm analysis results. Average expression stability M of all eight reference genes. The most stably expressed genes have lower M values. (B), (D), (F), (H), (J) and (L) are the NormFinder analysis results. The lower stability value indicates a gene that is more stable. (TIFF 730 kb)Additional file 3: Figure S2.n=3) . (TIFF 1330 kb)IBV induces chMDA5, chIFN-β, chIFN-λ and chMx expression in chicken embryos. In this experiment three embryos were inoculated with IBV, and three embryos were inoculated with PBS, which served as negative controls. Then, the trachea, intestine, kidney, lung, liver, and muscle tissues were collected from the embryos 72 h post-infection. (A) chMDA5, (B) chIFN-β, (C) chIFN-λ and (D) chMx were calculated as fold change of the infected group relative to the uninfected group and normalized against three different reference genes. In the kidney, liver and muscle tissues chMDA5, chIFN-β, chIFN-λ and chMx transcription was normalized to ACTB (1), EFF1 (2) and RPL5 (3) respectively. In the intestine chMDA5, chIFN-β, chIFN-λ and chMx transcription was normalized to ACTB (1), GAPDH (2) and RPL32 (3) respectively. In the lung chMDA5, chIFN-β, chIFN-λ and chMx transcription was normalized to ACTB (1), TUBAT (2) and RPL5 (3) respectively. In the trachea chMDA5, chIFN-β, chIFN-λ and chMx transcription was normalized to ACTB (1), EFF1 (2) and RPL32 (3) respectively. Data are shown as the mean ± SD.


Immobilized hOPN enhanced the adhesion, but suppressed the release of IL-5, IL-8, and TNF-α of anti-IgE-activated HCMC while soluble hOPN failed to demonstrate any significant effects. By employing cyclic RGD peptide and neutralizing antibodies against different classes of integrin and CD44, we demonstrated that the interaction of immobilized hOPN and HCMC was mediated by the RGD domain of hOPN and integrin but not CD44 on HCMC. Our results suggest that immobilized hOPN anchored to extracellular matrix can regulate adaptive immunity in humans by retaining mast cells at the site of inflammation and suppressing anti-IgE-induced cytokine release from HCMC.Osteopontin (OPN) is an Arg-Gly-Asp (RGD)-containing extracellular matrix protein which is upregulated in inflamed tissues and has been reported to modulate mast cell activities in mice. Due to the known heterogeneity among mast cells of different species and the important roles of mast cells in allergic reactions, we investigated the effects of human OPN (hOPN) on human mast cell activities. Mature primary human cultured mast cells (HCMC) were derived from peripheral blood CD34 All experiments were carried out in accordance with the approved guidelines and regulations. Mature primary human cultured mast cells (HCMC) were derived from progenitors isolated from fresh buffy coat collected from the Hong Kong Red Cross as previously described . Briefly4) suspended in 100 µl IMDM with 100 ng/ml SCF and 10% FBS were then seeded into both control and hOPN-coated culture wells. Following incubation in a CO2 incubator for 1 h, the cells were further incubated with 1 µg/ml of anti-IgE  for 2 h. Finally, the wells were rinsed three times with PBS to remove non-adherent cells. The density of adhered cells was determined fluorometrically following staining of cellular nucleic acids with the CyQUANT Cell Proliferation Assay kit . Results are expressed as the percentage of total cells adhered to culture surface which was calculated with reference to the fluorescence emitted by 5 × 104 HCMC.Cell adhesion assay was performed as described by Ariel et al. . Briefly5 cells/ml. The cells were then incubated with hOPN either in suspension or immobilized on culture plate surface for 1 h before being activated by anti-IgE. For histamine release, HCMC were incubated with anti-IgE for 30 min prior to addition of ice-cold buffer followed immediately by centrifugation. The cell pellets and supernatants were collected separately and the contents of histamine were measured spectrofluorometrically using a Bran + Luebbe AutoAnalyzer 3 . The percentage of total cellular histamine released into the supernatant was then calculated and expressed as [histamine release (%)] and was corrected for spontaneous release that was in general less than 5%. For the release of cytokines, HCMC were incubated with anti-IgE for 24 h and the supernatants were then collected and analyzed to determine the amounts of cytokines released by using commercially available EIA kits  and the results were presented as . All incubation procedures were carried out at 37°C in 5% CO2 incubator.IgE-sensitized HCMC were suspended in IMDM with 100 ng/ml SCF and 10% FBS at a cell density of 5 × 10For studying the effects of blocking antibodies or inhibitors, IgE-sensitized HCMC were pre-incubated with 1 µg/ml of the antibody or the appropriate concentration of the inhibitor for 1 h at 37°C before performing adhesion or mediator release assays. The results are expressed as the percentage of the anti-IgE induced level of mediator released that was reduced in the presence of immobilized hOPN (% inhibition). The antibodies against various potential OPN receptors are the same as those employed for immunostaining. The inhibitors include the αvβ3 integrin binding cyclic RGD peptide and the corresponding control cyclic RAD peptide .IgE-sensitized HCMC were induced to adhere to immobilized hOPN by anti-IgE as described above. The adhered cells were directly fixed with 4% formaldehyde for 15 min at room temperature. The fixed cells were washed with distilled water followed by blocking with peroxidase blocker for 5 min and then washed with PBS. The blocked cells were then incubated with 1 µg/ml of different antibodies for 1 h at room temperature followed by washing with PBS. Binding of antibody was visualized with DAKO immunostaining kit as instructed by the manufacturer. The antibodies employed for staining were those against α1, α2, α3, α4, α5, αvβ3, β1, β2, β3, β4, CD44, and CD44v6 and were clone FB12, P1E6, P1B5, P1H4, P1D6, 23C6, P4G11, P4H9, 25E11, ASC-9, G44-26, and 2F10, respectively. Antibody of α1, α2, α3, α4, α5, β1, β2, β3, and β4 were obtained from Merck Millipore, αvβ3 and CD44v6 antibody were from R&D systems and CD44 antibody was from BD Bioscience.t-test where appropriate. Results are presented as mean ± SEM for the number of experiments reported and p < 0.05 was considered statistically significant. Different donors were used in each experiment.Statistical analysis was performed with Prism software . Differences between treatment groups were analyzed with repeated measure one-way ANOVA or paired 6 cells of VEGF but no detectable levels of IL-8, TNF-α, and IL-5 in culture medium alone. While incubation with either form of hOPN did not significantly affect the basal spontaneous release of these mediators, anti-IgE activation significantly increased the release of histamine and VEGF and induced the release of IL-5, IL-8, and TNF-α. Preincubation of HCMC with soluble hOPN (1–10 µg/ml) for 1 h did not have any effect on the levels of anti-IgE-induced release of the various inflammatory mediators   (cRGD), a highly potent and selective inhibitor for integrin αvβ3 also demonstrated significant suppression of the mast cell modulating actions of immobilized hOPN but not the control peptide cRAD Figure . FollowiWe have demonstrated for the first time that OPN can modulate human mast cell functions, but only in the immobilized adhesion molecule configuration, not when it is in the soluble cytokine configuration. The immobilized OPN facilitated the adhesion of anti-IgE-activated HCMC while suppressing the subsequent syntheses of cytokines, including IL-5, IL-8, and TNF-α but with no effect on the immediate release of preformed granular mediators such as histamine. Our results are contradictory to those reported by Nagasaka et al. who employed mast cells cultured from fetal skin of OPN knockdown mouse and soluble mouse recombinant OPN . They dide novo synthesis and release by resident epithelial, endothelial, and smooth muscle cells as well as a host of infiltrating immune cells (in vivo post-translation modification in determining the functions of OPN. The failure of soluble hOPN to induce significant actions on HCMC implicates that the RGD region of the protein is not normally exposed for the interaction with integrin receptors on HCMC surface. Our results suggested that attachment to the culture well surface orientated the hOPN molecule in such a way that the integrin interacting region is exposed to enhance HCMC adhesion and the subsequent functional modulation. In fact, the orientation of OPN following attachment to biomaterial surfaces has been demonstrated to be an important factor in determining the biological functions of OPN (The level of OPN is increased during inflammation through ne cells . Both prne cells . In addine cells , 16. Ours of OPN . Furthers of OPN . It was Immunohistochemical studies revealed that HCMC expressed integrin αvβ3, α5, β2, β4, and CD44. Further studies with neutralizing antibodies of these receptors revealed that only the antibody against integrin αvβ3 could reverse both the enhanced adhesion and the inhibition of cytokine release of anti-IgE-activated HCMC mediated by immobilized hOPN, suggesting that integrin αvβ3 is the main functional cell surface receptor that interacts with hOPN. However, α5 and β2 may also contribute to the regulatory action of OPN on HCMC cytokine release as the inhibitory effect of hOPN on anti-IgE induced release of cytokines was also reversed by the antibodies against integrin α5 and β2. The inconsistency in the reversing effect of integrin antibodies on immobilized hOPN-mediated adhesion and cytokine release suggests that the suppressing effect of immobilized hOPN on anti-IgE-activated HCMC is not necessarily dependent on adhesion. The failure of the antibody against CD44 to reverse any of the mast cell modulatory actions of immobilized hOPN despite of the positive immunostaining of HCMC for CD44 suggested that the CD44 isoform expressed is not the variants that optimally interact with OPN. Human mast cells have been demonstrated to express only the standard form of CD44 under normal conditions , while tin vivo studies employing OPN knockout mice and OPN antibodies which suggested that OPN could suppress allergic reactions (In summary, our studies have demonstrated that OPN does not suppress human mast cell degranulation which contributes to the acute phase of allergic reaction but is capable of suppressing IgE-mediated release of cytokines from human mast cells that maintains the chronic phase of allergic reactions. While the results are contradictory to those observed in mouse mast cells , they areactions , 23. Theeactions . In addieactions , direct eactions , 25. Maseactions , 27, andCN: planning and performing experiments, data collection, data analysis, making figures, and writing manuscript. IT: data analysis and reviewing manuscript. SS: mast cell culturing and morphology study. YY: data analysis. HL: planning the study and supervision of writing manuscript.None of the authors have any conflict of interest, nor have they received any money for this study. Research is part of their daily activities. All authors had full access to all data and take responsibility for the integrity and accuracy of the data analysis.


Tissue engineering, gene therapy, drug screening, and emerging regenerative medicine therapies are fundamentally reliant on high‐quality adherent cell culture, but current methods to cryopreserve cells in this format can give low cell yields and require large volumes of solvent “antifreezes”. Herein, we report polyproline as a minimum (bio)synthetic mimic of antifreeze proteins that is accessible by solution, solid‐phase, and recombinant methods. We demonstrate that polyproline has ice recrystallisation inhibition activity linked to its amphipathic helix and that it enhances the DMSO cryopreservation of adherent cell lines. Polyproline may be a versatile additive in the emerging field of macromolecular cryoprotectants. Tissue engineering, gene therapy, therapeutic protein production, and transplantation rely on the successful storage and transport of donor cells.−1 and enhances the cryopreservation of cells in suspension.Antifreeze (glyco)proteins (AF(G)Ps) are potent ice recrystallisation inhibitors (IRIs), but are unsuitable for cryopreservation applications owing to their potential toxicity/immunogenicity and their secondary effect of dynamic ice shaping (DIS), which leads to needle‐like ice crystals that pierce cell membranes.There are no crystal structures for AFGPs but solution‐state NMR and circular dichroism (CD) spectroscopy suggest a polyproline II (PP II)‐type helix.Herein, we introduce polyproline as a minimum (bio)synthetic antifreeze protein mimic. We demonstrate that polyproline has ice recrystallisation inhibition activity, which is linked to its amphipathic PP II helix structure. Polyproline was found to improve the post‐cryopreservation recovery of cell monolayers compared to DMSO alone, demonstrating a new macromolecular approach for the storage of complex cells to enable next‐generation therapies.l‐, d‐, and (racemic) d/l‐polyproline were synthesised by condensation polymerisation using 1‐ethyl‐3‐(3‐dimethylaminopropyl) carbodiimide 15 gave the mirror spectrum whilst the d/l racemic mixture showed no secondary structure. This series of peptides were subsequently tested for IRI activity using a splat assay.CD spectroscopy confirmed that PPro10) lost activity below 10 mg mL−1, but the longer ones retained activity at 5 mg mL−1. The magnitude of activity was significantly smaller than for AF(G)Ps, which function at concentrations as low as 0.14 μg mL−1,15 and two α‐helical poly(amino acid)s.50) and poly(glutamic acid) (PGlu110) showed no IRI activity. PPro15 was found to be more active than poly(hydroxyproline) of higher molecular weight. This finding confirmed that hydroxyl groups are not essential for activity in IRI‐active compounds. P(d‐Pro15) and P(d/l‐Pro21) had statistically identical activity to PPro15, suggesting that local rather than long‐range order is crucial for activity.All polyproline variants were found to display dose‐dependent activity but weak molecular‐weight dependence in the range tested Figure  B. The s10 was compared to a non‐glycosylated type I sculpin AFP10 by mapping their hydrophobic/hydrophilic domains  has charged groups around the core of the helix, which prevents the presentation of hydrophobic domains. This agrees with our previous study on nisin A, which has IRI activity associated with segregated domains,We hypothesise that IRI activity requires segregated hydrophilic and hydrophobic domains (amphipathy).s Figure . The typs Figure  A posses0 Figure  B also p19 did not induce shaping, supporting the concept that polyproline inhibits ice crystal growth without inhibiting the formation of a specific plane of ice; however, as these are relatively weak IRIs, the concentrations needed for ice shaping would be very high.Aside from IRI activity, AF(G)Ps display unwanted ice shaping, which promotes the formation of needle‐like ice crystals, which damage cell membranes.m (23 mg mL−1) proline . After 10 min exposure to this solution, all excess solvent was removed, and the cells were subjected to controlled‐rate freezing at 1 °C min−1 to −80 °C. Following storage at −80 °C, the cells were thawed by addition of warm medium (37 °C), and the total number of viable cells was determined by trypan blue staining 24 h after thawing. Figure m proline for 24 h before treatment with 10 mg mL−1 PPro11/10 % DMSO, the cell recovery doubled to 53 %. Increasing the concentration of polyproline beyond 10 mg mL−1 did not increase recovery further, suggesting that the additive benefits plateau at 10 mg mL−1.m (>500 mg mL−1) plus 10 wt % (ca. 100 mg mL−1) of the polymer, and there was a reduction in the post‐thaw proliferation rate associated with the large solvent volumes, which may limit practical applications. In our PPro system introduced here, the total recovery levels were less, but far lower concentrations of DMSO were employed (10 wt %/ca. 100 mg mL−1), and the total exposure time to this potentially toxic component was only 10 min. To critically compare PPro, another batch (PPro10–25) was synthesised and tested for cytotoxicity and heamocompatibility. A549 monolayers were exposed to PPro for 24 h, and the cell viability was assessed (see the Supporting Information). This extended exposure period led to a reduction in alamar blue to 60 % for 5 mg mL−1 PPro, suggesting some cytotoxicity if exposed to elevated concentrations for long periods of time. It is important to note that in this cryopreservation procedure, PPro is only in contact with the cells for 10 min before the excess is removed and the cells are frozen. Red blood cell heamolysis experiments (see the Supporting Information) showed this was not due to any inherent membrane activity of the (amphipathic) PPro.To explore polyproline as a macromolecular cryopreservative, A549 cells were employed as a prototypical adherent cell line.In summary, we have demonstrated that polyproline is a potent additive for cell‐monolayer cryopreservation when appropriate freezing conditions are employed. Polyproline has moderate ice recrystallisation inhibition activity, which was hypothesised to be due to its “patchy” amphipathic structure associated with its PP II helix. Addition of polyproline to adherent cell cultures led to an increase from 20 % to >50 % in total cell recovery post‐cryopreservation, which is significantly better than for the use of DMSO alone. This increase in recovery is thought to be associated with the inhibition of ice recrystallisation. Short exposure times of just 10 min to the polyproline/DMSO solution, followed by removal of the excess solvent, reduced the cytotoxicity associated with long‐term (24 h) exposure to elevated levels of polyproline. The minimal solvent exposure times may give benefits in downstream processing and biomedical applications compared to current high‐solvent‐concentration methods using vitrification. Polyproline is appealing compared to other macromolecular cryoprotectants as it only comprises native amino acids and can be obtained by chemical and biochemical methods.The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


The purpose of this study was to compare the clinical and radiological differences among three advanced guided technologies in adult degenerative scoliosis. A total of 1012 pedicle screws were inserted in 83 patients using a spine robot (group A), 886 screws were implanted in 75 patients using a drill guide template (group B), and 1276 screws were inserted in 109 patients using CT-based navigation (group C). Screw positions were evaluated using postoperative CT scans according to the Gertzbein and Robbins classification. Other relevant data were also collected. Perfect pedicle screw insertion (Grade A) accuracy in groups A, B, and C was 91.3%, 81.3%, and 84.1%, respectively. Clinically acceptable accuracy of screw implantation (Grades A + B) respectively was 96.0%, 90.6%, and 93.0%. Statistical analysis showed the perfect and clinically acceptable accuracy in group A was significant different compared with groups B and C. Group A exhibited the lowest intra-op radiation dose and group B showed the shortest surgical time compared with the other two groups. Robotic-assisted technology demonstrated significantly higher accuracy than the drill guide template or CT-based navigation systems for difficult screw implantations in adult degenerative scoliosis and reduced the intra-op radiation dose, although it failed to reduce surgery time. In particular, the introduction of a robot-assisted pedicle screw fixation system , a spinous process-mounted miniature robot, holds great potential as an advanced idea, with a nearly 99% accuracy of clinically acceptable screw implantation. Though robotic-assisted surgery is an emerging field, similar to a baby making its first steps3, some superior features, such as minimal invasiveness and a high accuracy of screw implantation, are critical reasons why some “pioneering” surgeons are dedicated to this new field.Recently, major advances in biotechnology, such as image-guided, computer-assisted and robot-assisted systems, have been introduced in spine surgery, potentially resulting in better surgical outcomes with higher accuracy, smaller incisions, faster healing times, reduced trauma to normal tissues, and better pain relief5. These methods have been used for spine diseases such as fractures, degenerative diseases and so on, providing surgeons with visualization and guidance both preoperatively and intraoperatively in situations of normal spinal anatomy structures. However, it is unknown whether advanced RenaissanceTM will continue to maintain its advantages in adult degenerative scoliosis with difficult screw insertions when compared with patient-specific drill guide templates or image-guided navigation systems.Other guided methods, such as patient-specific templates and CT-based navigation systems, provide increased accuracy compared with conventional fluoroscopy guided methods, as demonstrated by a series of low- to high-level studies6. Prior characterization of vertebra deformities leads to difficult screw implantations; therefore, the purpose of this study was to investigate clinical and radiological differences between three advanced technologies in scoliosis by reviewing recorded information.Adult degenerative scoliosis is a complicated spinal disorder, resulting from a combination of osteoporosis and asymmetric disc degeneration, with rotatory subluxation of multiple lumbar functional spinal unitsA careful search of the Case Review Digital system of our hospital found that between 2009 and 2016, 286 patients were diagnosed with severe adult degenerative scoliosis. Patients meeting the following characteristics were eligible: (1) degenerative scoliosis often superimposed on a preexisting scoliosis with greater rotational deformity and a greater loss of lordosis; (2) imbalance in the coronal and sagittal planes; (3) Cobb angle >30°; and (4) obvious lower back pain and radiculopathy. Nineteen patients were excluded for several reasons, as follows: (1) degenerative scoliosis with a fracture, infection, tumor, discitis, or vertebral tuberculosis; (2) previous surgery; or (3) incomplete data in the review case. Finally, 3174 pedicle screws were inserted in 267 adult patients  to reestablish both regional and global spinal balance in the frontal and sagittal planes and reconstruct three-column stability of the vertebral body  that were acquired of the patient’s anatomy for registration of the robot. Basic steps in the RenaissanceTM open-PLIF operation are summarized as follows: 1. preoperative planning; 2. posterior exposure and approach establishment; 3. attachment to the spinous process; 4. image acquisition and registration; 5. robot assembly and motion; 6. drilling and tubing system placement; 7. cannula introduction and K-wire placement; and 8. screw insertion. This was followed by decompression or posterior osteotomy for mobilization of the curves, interbody cage insertion and rod fixation after correction.In the robot-PLIF cohort, preoperative computed tomography (CT) slice scans 1 mm thick were obtained for the 3D-reconstruction of the target level. CT data were transferred to a personal computer and imported in the Renaissance7.The major procedures of patient-specific drill guide template assistance for screw implantation are as follows: 1. CT scanning with thin slices to target the spinal vertebral body; 2. reconstruction of captured data as a three-dimensional pattern to identify an optimal entry hole and calculation of the width, height and length of the target pedicle using special software; 3. design of a digital template contrary to the anatomical structure of the target level; 4. printing of the digital template, except a pre-reserved column, by a 3D-printer; 5. preoperative sterilization of the solid template; 6. Intraoperative clearance of the soft tissue behind the intended vertebral plate and spinous process; and 7. attachment of the template to the bone structure, followed by the insertion of screws through the pre-designed trajectory. Decompression and fusion procedures were performed, if necessary, along with a subsequent osteotomy for better scoliosis correction. More detailed surgical procedures have been described in a previous study8.The assistance of a CT-based navigation system for surgery included 4 steps: 1. a reference clamp was attached to the spinous process of the vertebra; 2. intraoperative all-spine scanning; 3. obtained CT data were automatically matched in the navigation system; 4. fiberoptic markers were fixed on the navigation instrument; 5. the entry point was identified using a marked navigation probe; 6. screw trajectory was established during real-time visualization of the intraosseous position of the navigation tip; and 7. pedicle screw placement. If necessary, a subsequent osteotomy was performed for better scoliosis correction, followed by decompression and fusion, interbody cage insertion and rod fixation. More detailed surgical procedures have been described in a previous study9 using axial, coronal and sagittal reconstruction views of the CT scans . Scoliosis correction parameters were assessed with plain radiographs preoperatively and postoperatively, which included measurements of the coronal plane deformity according to the Cobb method and the evaluation of spinopelvic parameters such as pelvic tilt (PT), lumbar lordosis (LL), and sacral slope (SS). We also collected the time of surgery, adverse events, cases of revision surgeries and intraoperative blood loss.Categorical and continuous variables were respectively analyzed by ANOVA and SNK tests. Mean values are presented as the mean ± SD. All statistical analyses were performed using the SPSS 20.0.0 statistics package , with an alpha level of significance set at 0.05.The mean patient age was 63.2 years (range: 45–80), and the gender ratio (m/f) was 113/154. The average BMI was 26.5. The mean BMI between group A and group C, the number of 5.5 mm screws between group B and group C and the number of upper or lower instrumented vertebra between three groups showed significant differences. The rest of the baseline parameters did not significantly differ between the 3 groups. The screw diameter varied from 5.5 to 6.5 mm, and the fusion level varied from 1–3 levels. Detailed parameters are shown in Table As shown in Table et al.10  in the robot-PLIF group in this study were considered to be “clinically acceptable” by us, less than the 98% reviewed by Devito because he identified pedicle cortical breaches <4 mm without neurological complications as clinically acceptable accuracy; on the other hand, he used the robot technique only for normal lumbar pedicle screw insertion. Strictly speaking, a screw that breaches the pedicle’s cortex by ≥2 mm is subject to a risk of long-term screw-related neurological complications. In our experience, we only classify Grade A + B as an appropriate accuracy criterion for “clinically acceptable” screw placement. Interestingly, the accuracy of perfect placements in the drill guide template group (81.3%) was not significantly different compared with the robot-assisted group (84.1%), possibly because, during the last decade, gradual advances in computer and additive manufacturing technologies have resulted in a more practical and sophisticated application of individual templates in spine surgery, with hopeful outcomes16. Otherwise, three-dimensional fluoroscopic image guidance systems demonstrated a significantly higher pedicle screw placement accuracy than CT-based navigation system and 2D fluoroscopic image guidance methods, what is the result when it is compared with the spine robot, this is a subject worth further studying17.In our review, RenaissanceWe also wonder if an association exists between the accuracy of screw placement and results of scoliosis correction, but it seems that differences in accuracy did not lead to variations in Cobb angle, PT, LL or SS. Though Group B showed differences in the changes of the Cobb angle values compared to Group C, this may because the preoperative Cobb angles were not aligned. Proximal facet joint violations still failed to be identified as different between the three groups, and it is unclear that if this situation results from the high accuracy of screw insertions.18. We also found this issue in the research process, but due to the above mentioned reasons for these systems, the routine usage rate is relatively low in our health care, system need continuous improvement to increase its use. The robot-PLIF group showed a small intra-op radiation dose that was similar to the template-PLIF group, and they all demand fluoroscopy for preoperative location and postoperative confirmation of the placement of every screw as well as the extra fluoroscopy needed for matching in the robotic process20. Regarding adverse events, blood loss, revision surgery and length of stay, the open robot technique failed to show an advantage over the other two techniques, but the lowest rate of intraoperative revision of screws occurred in the robot-PLIF; surely, the high accuracy of screw implantation reduces screw revisions due to malpositioning. Revised screws were included in the analysis as poor screw positioning that was clinically unacceptable.As shown in Fig. TM platform uses a computerized mechanical positioning system that assists surgeons in inserting implants along the planned trajectory. There are two advantages; first, it requires registration without using bony landmarks that can be applied even in difficult insertions and deformities of the spine. Second, the robot system works without relying on a camera tracking mechanism and avoids all the problems of reconnaissance between cameras and navigation instruments or targets21.The Renaissance22. Normally, when this occurs lateral to the facet joint, it demands careful attention when using the robot. In addition, the relatively expensive costs for the robot’s supplies and maintenance add an extra financial burden to patients.Drawbacks in the process of the robotic system usage include a few patients with severe degenerative scoliosis who were operated on but failed to match the preoperative CT scanning with the intraoperative fluoroscopy images; some time was wasted, and the surgeon had to change the surgical technique to free hand. Involving all search processes from the Case Review Digital system, 8 such cases occurred. Although there is good registration, there could still be a possible phenomenon where the cannula slides off an angled bone surface, resulting in difficult-to-prevent lateral screw inaccuraciesTwo limitations of our study should be mentioned. The first limitation is that patient characteristics, such as mean BMI, did not align between group A and group C, and a similar condition occurred with screw diameter and upper/lower instrumented vertebra. Another limitation is that all studies derived from a database that included four spine surgeries, nearly 40 surgeons participated in the employment of the three assistive tools, and the variances in the proficiency of tool employment and surgical skills may add a slight bias to the primary or secondary endpoints. Therefore, additional prospective, randomized, controlled studies are required.Robotic-assisted technology showed obviously higher accuracy than drill guide template or CT-based navigation systems for difficult screw implantations in adult degenerative scoliosis; the method also reduced intra-op radiation dose, but it did not reduce time for surgery.Supplementary Figure S1


Plasmodium falciparum SpdS (PfSpdS) transfers the aminopropyl group of decarboxylated S-adenosylmethionine (dcAdoMet) to putrescine or to spermidine to form spermidine or spermine, respectively. In an effort to understand why efficient inhibitors of PfSpdS have been elusive, the present study uses enzyme activity assays and isothermal titration calorimetry with verified or predicted inhibitors of PfSpdS to analyze the relationship between binding affinity as assessed by KD and inhibitory activity as assessed by IC50. The results show that some predicted inhibitors bind to the enzyme with high affinity but are poor inhibitors. Binding studies with PfSpdS substrates and products strongly support an ordered sequential mechanism in which the aminopropyl donor (dcAdoMet) site must be occupied before the aminopropyl acceptor (putrescine) site can be occupied. Analysis of the results also shows that the ordered sequential mechanism adequately accounts for the complex relationship between IC50 and KD and may explain the limited success of previous efforts at structure-based inhibitor design for PfSpdS. Based on PfSpdS active-site occupancy, we suggest a classification of ligands that can help to predict the KD−IC50 relations in future design of new inhibitors. The present findings may be relevant for other drug targets that follow an ordered sequential mechanism.The aminopropyltransferase spermidine synthase (SpdS) is a promising drug target in cancer and in protozoan diseases including malaria.  Plasmodium falciparum. Recent reports of artemisinin-resistant P. falciparum strains in Southeast Asia emphasize the urgent need for new antimalarial drugs  or whenD: 2PT6, ] shows iD: 2PT6, ,20. Thes50 and KD values for inhibitory compounds is not a simple one. In particular, the quantitative relationship between these two measures depends on a number of factors including: the kinetic reaction mechanism, i.e., single or multi-substrate, sequential or random ordered; the nature of the inhibition, i.e., competitive, non-competitive, or un-competitive; and the assay conditions, i.e., substrate concentrations. Particularly relevant for the present analysis is the fact that the correlation between inhibition and binding in an ordered sequential mechanism is different for compounds competing with the first or second binding substrate, or with a product. Based on these considerations the results presented here identify three inhibitor types depending on whether binding occurs when the dcAdoMet site is free, occupied by the first substrate dcAdoMet, or occupied by the product MTA.As noted over 40 years ago by Cheng and Prusoff , the rel50 values depend on KD values as follows:D,A (3.7 μM); [B] is the concentration of the second substrate, here putrescine with Michaelis-Menten constant KM,B (35 μM); KD is the binding affinity of the inhibitor. Eq  [PfSpdS comprises several sites and cavities  ,20 or wicavities . The typInhibitor group I1 contains compounds that bind to ligand-free PfSpdS and compete with dcAdoMet for binding to the free enyzme. A further sub-division considers whether both the dcAdoMet and putrescine sites are occupied (Subgroup I1a), the ligand binds to the central aminopropyl cavity and the putrescine site (Subgroup I1b), or the dcAdoMet site only is occupied (Subgroup I1c). Subgroup I1a contains ligands like AdoDATO that occupy both the dcAdoMet and putrescine sites according to the crystal structure [Subgroup I1b includes ligands that bind in the putrescine site and also occupy the central aminopropyl cavity, but not the MTA cavity, of the dcAdoMet site; BIPA is such a ligand according to the crystal structure [Subgroup I1c contains ligands like MTA that bind in the dcAdoMet site. Adenosine, which is described as a binder by Jacobsson et al. [PfSpdS [3.tructure . Subgroutructure . Subgroun et al.  and is p [PfSpdS , also beInhibitor group I2 contains ligands that bind in the presence of dcAdoMet and can be seen as polyamine substrate competitors. A further sub-division considers whether the distal aminopropyl cavity is free, mimicking the position of putrescine (Subgroup I2a), or is occupied by the ligand, mimicking the position of spermidine in presence of dcAdoMet (Subgroup I2b). Subgroup I2a includes ligands that bind in the putrescine site only when dcAdoMet is bound and that can be considered as putrescine substrate analogs. Previously studied representatives of this group are the known putrescine competitors 4MCHA and 4MAN for which crystal structures in presence of dcAdoMet are available [Subgroup I2b includes compounds that occupy the distal aminopropyl cavity in addition to the putrescine site and thus can be considered spermidine substrate analogs. This subgroup may contain inhibitors that are specific for the PfSpdS and do not inhibit human SpdS, which lacks the additional aminopropyl cavity. NAC is the only representative of this subgroup to date and it does inhibit PfSpdS and human SpmS, but not human SpdS [In silico prediction [vailable ,20. Subgman SpdS . In siliediction  suggestsInhibitor group I3 contains compounds that bind to PfSpdS in the presence of MTA and can be seen as polyamine product analogs as they occupy the central aminopropyl cavity partially or fully. This group contains compounds like 4AMA and NACD that in presence of MTA bind in the putrescine and central aminopropyl cavity and can be considered spermidine product analogs. 4AMA binds relatively strongly in the presence of MTA but not to the free or dcAdoMet-bound enzyme, and is a poor inhibitor. NACD, which binds with very high affinity in presence of MTA, is a weak inhibitor similar to 4AMA. A further differentiation of group I3 into subgroups depends on the occupancy of the distal aminopropyl cavity, analogous to subgroups I2a and I2b. Members of subgroup I3a include spermidine product analogs like 4AMA and NACD, and subgroup I3b members include spermine analogs occupying the central aminopropyl cavity, the putrescine site, and the distal aminopropyl cavity. Subgroup I3a shares similarities with subgroup I1c, as these ligands occupy the central aminopropyl cavity and the putrescine site. Subgroup I1c, however, has measurable affinity for the free enzyme. Future compounds predicted to bind the central aminopropyl cavity and the putrescine site will fall under subgroup I1c when they have measurable affinity to the free enzyme and into subgroup I3a when they bind in presence of MTA.The findings of this work may apply as well to other enzymes that follow an ordered sequence of binding and where the transferred group is large enough to cause steric interference. In such cases the product after transfer is expected to have reduced affinity in presence of the donor substrate due to steric clash. Many enzymes with sequential substrate binding transfer only electrons, and these arguments may not apply unless the change in redox state causes large structural change.per se. Understanding the correlation between inhibition (IC50) and binding (KD) should improve the outcome of the search for potent inhibitors. Cheng and Prusoff [in vivo conditions may show weak or no inhibition in an in vitro assay. A more reliable selection of compounds for further investigation can be achieved not only by choosing inhibitors with the lowest IC50 values in the assay but also by taking into account their KD values, which can be directly measured or estimated from their IC50 values. In silico drug design should therefore consider not only the enzyme mechanism but also the binding affinity and concentrations of substrates present in the assays used to identify inhibitors, as well as the sites or sub-sites occupied by the potential inhibitors in presence and absence of substrates. Thus the present results have relevance for the development of inhibitors of other enzymes with complex binding sites and mechanisms.A systematic classification like that proposed here may have predictive value in virtual screening aimed at identifying inhibitors of enzymes that follow a sequential binding mechanism. The scoring functions used for ligand screening are designed primarily to detect potential binders , whereas Prusoff  explaine


Accurate prediction of active sites is an important tool in bioinformatics. Here we present an improved structure based technique to expose active sites that is based on large changes of solvent accessibility accompanying normal mode dynamics. The technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique is trained using a small 133 enzyme dataset and tested using a large 845 enzyme dataset, both with known active site residues. EXPOSITE is also tested in a benchmark protein ligand dataset (PLD) comprising 48 proteins with and without bound ligands. EXPOSITE is shown to successfully locate the active site in most instances, and is found to be more accurate than other structure-based techniques. Interestingly, in several instances, the active site does not correspond to the largest pocket. EXPOSITE is advantageous due to its high precision and paves the way for structure based prediction of active site in enzymes. In this paper, we present an improved technique to predict active sites in enzymes. Our technique is based on changes of solvent accessibility that accompany normal mode dynamics. We assert the technique strength using several enzyme datasets with known catalytic residues. We show the technique successfully locates the active site in most cases, and consistently surpasses the accuracy of other techniques. We show how the technique is advantageous and paves the way for high precision prediction of active sites. Prediction of functional sites in proteins is essential for a range of bioinformatics applications such as molecular docking, and structure based drug design. Traditional methods for predicting functional sites include three approaches: 1). The first approach uses sequence homology to find evolutionary conserved residues with functional activity. 2). The second approach utilizes structural homology with other proteins of known function to locate functional regions. 3). The third and last approach uses geometry and physico-chemical attributes of the protein structure and sequence to identify areas with functional activity.Over the years, several techniques based on the third approach have been developed. These techniques include LIGSITE , POCKET When used in combination with sequence and structure homology, geometric techniques are enhanced and prediction is improved. Some techniques use a vast combination of parameters ranging from conservation, residue type, accessibility, 2D structure propensity, cleft depth, B-factors, etc. to predict active site residues. Using such parameters, Gutteridge et al. predicted the location of active sites in enzymes using a neural network and spatial clustering . SimilarNormal-mode analysis is one of the standard techniques for studying long time dynamics and, in particular, low-frequency motions. In contrast to molecular dynamics, normal-mode analysis provides a very detailed description of the dynamics around a local energy minimum. Even with its limitations, such as the neglect of the solvent effect, the use of harmonic approximation of the potential energy function, and the lack of information about energy barriers and crossing events, normal modes have provided much useful insight into protein dynamics. Over the past years, several techniques have been described to calculate large-scale motions using simplified normal-mode analysis –51. BaseRecently, several studies have drawn attention to the allosteric effect of ligand binding on normal modes dynamics . From thIn this paper we present a novel structure based technique using normal modes to predict the location of active sites in enzymes. The technique exploits the normal mode opening and closing motion of enzymes and the accompanied change of solvent accessibility and highlights residues of the active site. The idea behind the presented technique is that active sites pockets become exposed in normal mode dynamics .The hypothesis that active sites are surrounded by a shell of flexibility is not new and has been proposed in the dynamic lock-and-key model for biomolecular interactions. The shell of flexibility allows the enzyme to adapt to its ligand through an induced fit. The hypothesis was demonstrated in several studies notably by Weng et al. in a recent study on the flexibility of enzyme active sites , and lesThe technique which detects EXPOsure of active SITes through normal modEs is named EXPOSITE. The technique may also be used in association with other methods to rank geometrically calculated pockets according to their solvent exposure. First, the prediction strength of EXPOSITE is trained extensively in a dataset containing 133 enzymes with known active sites from the Catalytic Site Atlas (CSA) database . Then, ETo assemble a training dataset containing 133 enzymes with known active sites, enzymes were selected from the CSA database , versionTo assemble a test dataset containing 845 enzymes, enzymes were selected from the CSA database , versionst and 14th structure out of 40 respectively. At these extremes, the structures look fully “distorted” from each other. For ElNemo, the opposite extremes of the harmonic motion are the 1st and 10th structure out of 10.To calculate normal modes of the dataset enzymes, two programs were utilized namely STAND  and ElNeTo calculate the solvent accessible surface (SAS) area of amino acids in the generated PDB files, the DSSP program was used . For eacTo calculate pockets, LIGSITE  was run 2The predicted active site was defined as the geometrical center (centroid) of the Cα coordinates of all residues with a solvent exposure |ΔSAS|, in the range 20-40ÅThe observed active site was defined as the geometrical center (centroid) of the Cα coordinates of the active site residues specified in the CSA database .The predicted and observed active sites were represented each by a single coordinate in Cartesian space. The distance between these two coordinates was defined as the distance between the predicted and observed sites.The success of a prediction was based on the distance between the predicted and observed sites in the training and test datasets. If the distance between the predicted and observed sites was less than 12Å, then a prediction was considered successful. Conversely, if the distance was larger than 12Å, then a prediction was deemed incorrect.In the special case of the PLD dataset and for easy comparison with other techniques, a prediction was considered successful if any atom coordinate of the ligand was within 4Å of the predicted site. If no atom coordinate of the ligand was within 4Å of the predicted site, then the prediction was considered wrong.To compare EXPOSITE with other techniques, several software were run on all datasets namely, the training dataset of 133 enzymes, and the testing dataset of 845 enzymes, as well as a dataset containing 48 proteins derived from the PLD dataset  and engiTo reliably assess the success rate of our technique in an sizeable ensemble, two datasets were assembled from the CSA database . The CSACSC [A number of programs were tested to calculate geometric pockets of biomolecular structures, i.e. POCKET , LIGSITECSC  providesSurprisingly, there are significant differences between SAS of residues calculated by DSSP and other techniques such as ENCAD, CNS, and Accelrys. These differences arise from the different approaches used in calculating SAS. Nonetheless, when calculating the change of surface areas, ΔSAS, these differences cancel out and all programs produce comparable ΔSAS values.2.Biologically relevant modes are not always represented in the lowest frequency modes. Sampling more data, i.e. by calculating more modes could provide better results. Similarly, changing the |ΔSAS| thresholds could also lead to a higher success rate by allowing more exposure data to be included. To test this assertion and optimize the success rate of EXPOSITE the following parameters were varied: the threshold value of |ΔSAS| and the number of normal modes sampled. The number of modes sampled was varied from 0 to 10 and the |ΔSAS| minimum and maximum thresholds were changed from 0 to 60 Å2 respectively. Below the threshold of 10 Å2, normal exposure fluctuations contribute little to EXPOSITE’s accuracy. Above the threshold of 40 Å2, exposure changes arise from the normal mode tip effect (bond breaking and exaggerated exposure) and contribute little to the EXPOSITE accuracy. For STAND, the optimal |ΔSAS| threshold values were 20 and 40 Å2 respectively. This difference of |ΔSAS| thresholds between STAND and ElNemo is due to the fact that STAND uses coarse graining, inherently reducing the surface area, whereas ElNemo does not. STAND uses coarse graining and represents each amino acid with a single bead, while ElNemo uses a heavy atoms representation. In both cases, the maximum deformation amplitude were not chosen and default values were used. Also, the maximum deformation amplitude was not scaled in this study.As seen in The optimal number of mode sampling peaks to a plateau around modes 8, 9, and 10 for both STAND and ElNemo . Below tEXPOSITE uses solvent accessibility changes in normal-modes to predict the location of active sites in enzymes. As seen in On average, the predicted and observed active sites in the training dataset are separated by 7.9 Å, and a standard deviation of 4.4 Å .The maximum success rate of EXPOSITE in the training dataset consisting of 133 enzymes was 92%. Curiously, in the training dataset, the binding pocket coincides mostly with the largest pocket (82%) but not always (18%). This finding accounts for the pitfall of other techniques which rely on pocket size only for ranking.3. Such pockets are too small to accommodate ligands and validate our convention of discarding them as insignificant.Also interesting is the fact that no active site was found in pockets with a size less than 7 ÅShown in To test the robustness of EXPOSITE, we tested its success rate in a dataset containing 845 enzymes . Not surEXPOSITE highlights the binding site of proteins of the Protein Ligand Dataset (PLD) published elsewhere . Shown iOn average, the predicted and observed centers in the protein PLD dataset are separated by 7 Å with a standard deviation of 3.3 Å. Intriguingly, the separation in the PLD dataset is smaller than that of the CSA dataset by almost 1 Å, and it is probably a flaw due to the handpicked nature of the PLD dataset.To accurately and robustly compare EXPOSITE with other techniques, all other software were run on all datasets namely the training dataset of 133 enzymes, the testing dataset of 845 enzymes. A prediction was considered accurate if the distance between the predicted and observed sites was less than 12Å. If the distance was larger than 12Å, then a prediction was considered inaccurate. The calculated prediction accuracies are listed in When compared to other geometric techniques EXPOSITE is advantageous due to its high success rate. As seen in To accurately and robustly compare EXPOSITE with other techniques, all other software were run on the bound and unbound PLD dataset . A prediIntriguingly, the classically accepted metric for binding site prediction is 4Å, and we used this metric in the classical PLD dataset when comparing the classical performance of EXPOSITE, Ensite, VICE, Fpocket, PocketPicker, LIGSITEcs, CAST, PASS and SURFNET . HoweverGenerally speaking, the success rate in the handpicked PLD dataset is higher than in the non-handpicked 845 test dataset. This discrepancy suggests that the PLD dataset was not randomly picked, and could artificially increase prediction success rates.EXPOSITE’s feature, of highlighting active sites is very useful for ranking pockets. Indeed, the technique is capable of ranking enzyme pockets according to their degree of exposure in normal mode dynamics. This ranking enables EXPOSITE to choose the correct binding pockets from a list of pockets calculated by LIGSITE. The assumption that the active site pockets is usually in the largest pocket , 4, 64 iThe rationale behind the success rate of EXPOSITE is fairly simple. For proper enzyme activity, protection from the surrounding water is often necessary as shown by normal modes closure of the active site. Proteins in general and enzymes in particular often act as environment protectors. They envelop substrates to catalyze chemical reaction that would otherwise not take place in aqueous solution. They conceal prosthetic groups to coordinate binding thus increasing affinity which is negligible in water. They act as small shielding cases displaying alternating motions of opening and closing to allow ligand entrance and protection respectively. Throughout this motion, protein residues located at various distances from the active site are exposed to the solvent to a different degree. Residues in proximity to the active site are exposed more than those faraway. This idea lays down the foundation for EXPOSITE suggesting the pocket closest to the maximum exposure center is the active site.The change in solvent accessibility between the X-ray structure and the largest deformation of either of the normal mode extremes could also have been used. However, the maximum effect of motion is observed between the two extremes which vibrate around the X-ray structure corresponding to a local minimum.EXPOSITE takes into account several parameters such as accessibility change in normal modes, centroid distance from pockets, as well as pocket size. Normal modes by their own virtue take into account more parameters such as contact network and distances. Together, these parameters resemble those used in neural network techniques , 34 wherThe success rate is not affected by the different types of normal modes techniques, STAND and ElNemo. The success rate remains unchanged even when STAND and ElNemo are used in different combinations with accessibility calculators . The cutoff should minimize the effect of loose and flexible termini with exaggerated exposure change. A complete list of success and failures is provided in Care should be taken when using our technique on structures composed of several domains. Practical interpretation of normal modes of multi-domain structures tend to be problematic in the sense that bending and twisting of one domain relative to another tend to overshadow modes with biological meaning. One way to circumvent this problem is to run normal modes of single domains to predict its active site. We excluded multi chain enzymes which are biologically active in oligomeric states from our CSA dataset. Similarly, care should be taken when using EXPSOITE on structures with elongated termini or exceedingly flexible loops. Such structures often present odd normal modes around these areas which tend to overshadow modes with biological meaning. Some strongly recommended ways to circumvent the problem of exaggerated motion is simply to clip out (or edit out) the stretches and rerun normal mode computation or to set an upper value for the cutoff of |ΔSAS| of 75 ÅA distinction should be made between the concepts “binding site” and “active site”. Usually, an active site is found in a single copy in an enzyme, while binding sites may be present in multiple copies in proteins. Thus, prediction of active sites and ligand binding sites are very different, and whereas only one prediction is correct for enzymes, several predictions are correct for proteins. To complicate things further, some enzymes are composed of multiple chains, each equipped with a distinct active site, and so much care should be taken so as not to over interpret a prediction. As a rule of thumb structure based predictions  are more accurate in single chain polypeptide enzymes.3 was from LIGSITE. These parameters all reflect on the size of the active site yet there is no obvious correlation among them. There was no correlation (R = 0.12) between pocket size and the number of active site residues. This is partially due to fractionation of active sites into adjacent pocket (POK) which decrease “real” active site size. This fractioning of active sites is a problem often encountered in pocket calculating programs. Adjoining sizes of vicinal pockets did not improve the correlation significantly.In an attempt to correlate between pocket size and active site, the following parameters of active site were calculated in the PLD dataset: 1). The number of Cα atoms of the active site was derived from the CSA database. 2). The number of heavy atoms in the substrate was calculated from the PLD database. 3). The number of residues of with high accessibility change was calculated from EXPOSITE. 4). The size of the predicted pocket in ÅOver the past years normal modes have enjoyed a revival. In this article, the biological relevance of normal modes is illustrated in a new technique. The presented technique exposes active sites of enzymes with high success rates. As pocket detection methodologies normal mode techniques improve so will our technique. In the future, EXPSOITE is expected to become publicly available as a basic tool (website and/or program) for predicting active sites of enzymes. The Perl code used in this study is freely available in the supplementary data. Note that DSSP, LIGSITE, ElNemo, and/or STAND must be obtained from third parties, and that the time bottleneck of the method is normal mode calculation.S1 FigThe distribution of distances between the predicted and observed active sites is shown. Note that 92% of the predictions fall within 12 Å of the observed active site.(TIF)Click here for additional data file.S2 FigShown are four additional EXPOSITE predictions for the enzymes (A) 2pk4, (B) 1ulb, (C) 1stp, and (D) 1apu of the PLD dataset. The predicted and observed binding sites are indicated by green stars and red ligands respectively, and LIGSITE pockets are displayed as white spheres. In cyan and green, are residues displaying large changes of accessibility in normal modes, and in blue, are residues which display little or no change of exposure. Note that the ligand (in red) is within 4Å of the predicted site (green star). The figure was prepared using Pymol.(TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 Table(DOCX)Click here for additional data file.S6 Table(DOCX)Click here for additional data file.S7 Table(DOCX)Click here for additional data file.S1 Code(ZIP)Click here for additional data file.


However, present-day increased mass loss was found by previous studies predominantly in west Antarctica and the Antarctic Peninsula. The newly discovered increased mass loss in Wilkes Land suggests that the ocean heat flux may already be influencing ice dynamics in the marine-based sector of the East Antarctic ice sheet (EAIS). The marine-based sector could be adversely impacted by ongoing warming in the Southern Ocean, and this process may be conducive to destabilization.We constructed Antarctic ice velocity maps from Landsat 8 images for the years 2014 and 2015 at a high spatial resolution (100 m). These maps were assembled from 10,690 scenes of displacement vectors inferred from more than 10,000 optical images acquired from December 2013 through March 2016. We estimated the mass discharge of the Antarctic ice sheet in 2008, 2014, and 2015 using the Landsat ice velocity maps, interferometric synthetic aperture radar (InSAR)-derived ice velocity maps (~2008) available from prior studies, and ice thickness data. An increased mass discharge (53 ± 14 Gt yr Studies of Antarctic ice sheet processes since the 1990s using satellite, airborne and in situ observations8 have reported present-day ice sheet changes. The West Antarctic ice sheet has experienced a negative mass balance, extensive dynamic thinning on the periphery9, accelerated mass loss8, and grounding line retreat in the Amundsen Sea sector10, all of which have highlighted the long standing concerns about the marine based ice sheet instability11. In contrast, it is generally considered that the EAIS, which encompasses a larger ice mass, has been in a state of mass equilibrium or has had a slightly positive mass balance over the past two decades12. However, recent observations have detected thinning in some glaciers on the EAIS, resulting in a negative mass balance in the ice catchments13. Satellite observations and ice-proximal evidence have demonstrated repeated large-scale advances and retreats of glacier termini18. Ice dynamic studies based on ice sheet modelling also indicate sensitivity to ocean forcing22. All of these findings suggest that the EAIS has a highly dynamic nature and may be more vulnerable to climatic and oceanography forcing than previously thought. However, the mass balance of the predominantly land-based EAIS remains less clear due to the lack of observable evidence19. It is also unclear whether the rate of Antarctic ice loss/gain has increased/decreased over the past two decades23. Furthermore, the underlying drivers of ice sheet changes remain poorly understood24. All these limitations make it difficult to determine the future behaviour of the ice sheet. The key to understanding the dynamics of the Antarctic ice sheet involves more accurately determining its mass budget using extended observations to provide a longer and higher-resolution observational record and improve the understanding of ice sheet evolution, which is crucial for accurate future projections of global sea level6.A considerable challenge associated with rigorous sea level projections in the 2125, has been determined from traditional ground-based measurements  since the 1970s in the Antarctic ice sheet. However, the sporadic and discontinuous observations prohibit the study of the ice sheet mass balance as a whole. Not until recently have glaciologists begun to present a complete picture of ice velocity in Antarctica using multi-satellite interferometric synthetic aperture radar (InSAR) with a long time span (1996–2009)26. However, such a snapshot of the ice motion in Antarctica is insufficient for providing clear insight into the spatial and temporal characteristics of ice dynamics. Furthermore, the lack of high-resolution ice velocity data limits a thorough investigation of localized ice dynamics28, such as crevasse production, the role of ice rises on the stability of ice sheets, etc. These limitations highlight the need for a new set of high-resolution ice velocity observations over Antarctica.Ice velocity, one of the critical ice dynamic parameters that affects estimates of the ice sheet mass balance and the corresponding sea level rise29, and ice discharges are calculated from all three years using a compilation of ice thickness data. Furthermore, the mass balances of the Antarctic ice sheet are estimated by comparing the mass discharges with the latest ice sheet surface mass balance (SMB) data derived from a regional atmospheric climate model (RACMO2.3)30 based on an input-output method (IOM)31.Here, we present two ice velocity maps covering the years 2014 and 2015 for all of Antarctica inferred from Landsat 8 (L8) images acquired by the Operational Land Imager (OLI). The ice velocity data from 2014 and 2015 are then compared with an existing InSAR-derived ice velocity dataset from 2008−1 (see Methods and Supplementary Discussions 4–8). Our results exhibit a similar pattern in ice velocities compared with published InSAR studies29, but they yield better coverage at the ice sheet margins. Moreover, the spatial resolution of our velocity maps is 4 to 10 times higher than that of the InSAR-derived ice velocity maps29.We construct two present-day ice velocity maps spanning the years 2014 and 2015 for all of Antarctica (except for the area south of 82.5° S) inferred from L8 images. These velocity data have the highest spatial resolution of 100 m achieved to date and are assembled from 10,690 scenes of displacement vectors. The vectors are inferred from more than 10,000 orthorectified panchromatic bands with a 15-m spatial resolution acquired by the OLI on Landsat 8 from December 2013 to March 2016 using the optical offset method. Our maps cover nearly all the ice shelves and ice streams and the majority of the ice sheet with an accuracy of 0 to 20 m yr29 (Table σ)  Fig. . Glacier37 using ice velocity measurements from 2014 and 2015; the InSAR-derived ice velocity in ~2008 (see Supplementary Discussion 2); a compilation of ice thickness data, mainly derived from our analysis of Cryosat-2 (CS2) radar altimeter measurements associated with Bedmap238; and ice-penetrating radar (IPR) thickness values from multiple campaigns from 2002 to 2014 from the IceBridge project42 (see Methods and Supplementary Discussion 3). We compare the ice sheet discharges with new SMB data (1979–2014)30 to estimate the Antarctic mass balance using the input-output method31. The mass discharges across the Antarctic grounding lines43 are derived from the flux gate method44 using a newly developed procedure (see Methods and Supplementary Discussion 9). Here, we calculate the ice sheet inflow mass for 27 glacier drainage basins37 using the new SMB data at a horizontal resolution of 27.5 km produced by the updated regional Atmospheric Climate Model RACMO2.3  and accounted for 2/3 of the total imbalance (−230 ± 71 Gt yr−1) of the entire Antarctic ice sheet. In addition to the Amundsen Sea sector, another significant negative imbalance (−86 ± 33 Gt yr−1) was observed in the East Indian Ocean sector of East Antarctica. In contrast, the West Indian Ocean sector exhibited a clear positive mass balance (51 ± 31 Gt yr−1). The Weddell sector exhibited a slight mass gain, whereas the Ross Sea and the Bellingshausen Sea sectors exhibited no significant mass changes. The mass balance values in the Bellingshausen Sea sector are likely underestimated because the summer meltwater is not considered (see Supplementary Discussion 10).We calculate ice discharges at the drainage basin scale.3 Table . Figure −1 in 2015 compared with −149 ± 71 in 2008, which shows that mass loss from the Antarctic ice sheet is still accelerating. This finding is similar to the results of many previous studies49. A pronounced change in the mass balance occurred only in the East Indian Ocean sector, where the change reached −53 ± 47 Gt yr−1. In addition, we found that the mass discharge from the East Indian Ocean sector increased by as much as 53 ± 14 Gt yr−1, which is attributed to unexpected widespread glacier acceleration in Wilkes Land, East Antarctica. The underlying cause for this increased mass discharge is likely linked to the incursion of warm CDW towards the outlet glacier termini50, as well as sea ice reduction and break-up51. Recent oceanographic observations have confirmed that warm water has intruded into the cavities beneath the Totten Ice Shelf through deep channels, as in West Antarctica and the AP50. In Wilkes Land, the large mass discharge increases, together with anomalous glacier retreat18, contemporary thinning along the margins3, grounding line retreat52 and unstable inland-sloping bedrock topography, suggest potential instability in the marine-based sector of the EAIS under warmer ocean currents53. Once the marine sector began an unstable retreat, as has occurred for the West Antarctic ice sheet, the likelihood of rapid mass loss increased under unstable bedrock configurations, partially due to the sensitivity to oceanic conditions55. This situation would increase the possibility of abrupt and irreversible ice loss in the marine sector. However, due primarily to the current lack of adequate observational evidence and insufficient understanding of these processes, process-based modelling of the instability projections of marine sector glaciers remains elusive. Consequently, the lack of knowledge regarding glacier instability in this region considerably increases the difficulty of future global sea level projection2. Of particular importance are the parts of the EAIS that are underlain by extensive marine-based subglacial basins. The Aurora Subglacial Basin (ASB) in western Wilkes Land is located to the northeast of elevated Dome A and Ridge B on the Antarctic ice sheet  of eastern Wilkes Land, which holds an ice mass equivalent to a 19-m sea level rise19 and drains through marginal glaciers  near George V Land, functions as an ice plug to support the marine-based WSB. However, we find that these glaciers exhibit evidently increased mass discharges, which may already be influencing ice dynamics in the marine-based sector of the EAIS. In contrast, the other five sectors exhibit no significant changes in mass discharge. Notably, the increased mass discharges over the last seven years in the Pine Island (basin 21) and Thwaites catchment (basin 22), West Antarctica, and the AP are found to be 13 ± 4 Gt yr−1, 15 ± 54 Gt yr−1, and 21 ± 10 Gt yr−1, respectively. The values are notably less than the previous estimates of 46 ± 5 Gt yr−1, 46 ± 23 Gt yr−1 and 29 ± 13 Gt yr−1, respectively, from 1996–200631. However, the exact underlying causes are unclear.We further analysed the change in the mass balance of the Antarctic ice sheet from 2008 to 2015 . More importantly, we found that Wilkes Land in the East Indian Ocean sector contributed most to increased mass loss, reaching 53 ± 47 Gt yr−1. The significant increase in mass discharge in Wilkes Land suggests the potential risk of the destabilization of the marine-based sector of the EAIS, which has large subglacial basins, inland-sloping bedrock and deep troughs, i.e., an instable bedrock configuration similar to that in West Antarctica. Our new high-resolution ice velocity maps together with historic InSAR ice velocity data allow us to determine the first continent-wide changes in ice velocity and ice discharge over the past seven years. These results contribute to our understanding of Antarctic-wide ice dynamics and can potentially improve ice sheet modelling and future sea level projections.In this contribution, we constructed two Antarctic-wide high spatial resolution (100 m) ice velocity maps covering the years 2014 and 2015. These maps provide a new tool for obtaining a more comprehensive understanding of the current ice dynamics in the area. We found a significantly increased mass discharge of 53 ± 14 Gt yr56, which is freely available from http://www.tectonics.caltech.edu/slip_history/spot_coseis/index.html. Further information about the methodology can be found in the Supplementary Information.The optical image correlation technique is used to determine the horizontal ice displacement field. This technique measures the displacement pixel-by-pixel between optical satellite images acquired at different times. The local frequency content is calculated at the same location in both images within a specific sliding window. Horizontal displacement maps, including North-South (N-S) and East-West (E-W) components, are then determined from the phase shift of the low-frequency content using the convention that eastward and northward are positive. The technique allows us to co-register optical satellite images with unprecedented accuracy to produce high-quality displacement measurements between pairs of images. Specifically, the approach enables us to resolve subpixel displacements of less than 1/20 of the Landsat 8 pixel resolution (15 m) at a high signal-to-noise ratio (SNR), which is generally greater than 0.9. All processes are performed using the COSI-Corr  software package developed at the California Institute of Technology−1. For InSAR-derived ice velocity, we used annual ice velocity data from 2007/08 and 2008/09 to produce a full mosaic of the entire Antarctic ice sheet. Further information about the methodology can be found in the Supplementary Information.The ice velocity is obtained by applying the optical offset method to orthorectified panchromatic bands with a 15-m spatial resolution acquired by the OLI on the Landsat 8 satellite from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center between December 2013 and March 2016. To enhance the accuracy of the mosaicked ice velocity, all displacement scenes are stacked based on a set of algorithms developed for the seamless generation of ice velocity mosaics under the consideration of absolute calibration. Simultaneously, the procedure can also output spatially varying error estimates for the ice velocity with magnitudes of 0–20 m yr30. The SMB values are estimated for the 27 glacier drainage basins. The ice discharge flux across grounding lines is calculated using the rigorous flux gate method44 in combination with ice velocity values, a compilation of CS2 ice thicknesses, Bedmap-2 ice thicknesses, and IPR track measurements from the IceBridge project. CS2 ice thickness data are adjusted for ice flux estimates in corresponding periods using 18-year observations of ice shelf thickness changes at a 27-km spatial resolution57.The mass budget method relies on quantifying the difference between ice sheet mass gained (input) through snowfall, with considering sublimation and melt water runoff, and the perimeter ice discharge flux (output). The ice sheet mass gained, which is also called the net SMB, is generally simulated using a regional atmospheric climate modelhttp://nsidc.org/data/docs/measures/nsidc0484_rignot/, http://nsidc.org/data/nsidc-0720, and http://nsidc.org/data/velmap/. To obtain the velocity products, please contact the author. The ice thickness products are provided by the Bedmap programme at http://www.antarctica.ac.uk/bas_research/data/ access/bedmap/download/ and by IceBridge (IPR data) at http://nsidc.org/data/docs/daac/icebridge/irmcr3/, https://nsidc.org/data/docs/daac/icebridge/brmcr2/, http://nsidc.org/data/ir1hi2, and https://nsidc.org/data/ir2hi2. The optical satellite images are from http://glovis.usgs.gov and http://earthexplorer.usgs.gov/ for Landsat. Other data are from http://icesat4.gsfc.nasa.gov/cryo_data/ant_grn_drainage_systems.php for Antarctic drainage basins, http://nsidc.org/data/atlas/news/antarctic_coastlines.html for the MOA grounding line and coastline, http://nsidc.org/data/nsidc-0489 for the ASAID project grounding line, http://nsidc.org/data/docs/measures/nsidc0498_rignot/ for the DInSAR grounding line, http://pangaea.de/ for the grounding line provided by Depoorter et al.43, http://nsidc.org/data/nsidc-0082 for the Antarctica DEM, http://www.staff.science.uu.nl/ for the FDM and SMB data, http://nsidc.org/data/docs/agdc/nsidc0280/index.html for MODIS Mosaic of Antarctica58, and http://woceatlas.tamu.edu/ for the potential temperature of seawater and bathymetry.The data used in this paper include ice velocity data, ice thickness data, optical satellite images and grounding line products. The ice velocity data are from Supplementary informationSupplementary Tables