Atomic structures of various transhydrogenases in different ligand-bound states have become available, and it is clear that the molecular mechanism involves major conformational changes. Here we utilized hydrogen/deuterium exchange mass spectrometry (HDX-MS) to map ligand binding sites and analyzed the structural dynamics of E. coli transhydrogenase. We found different allosteric effects on the protein depending on the bound ligand . The binding of either NADP+ or NADPH to domain III had pronounced effects on the transmembrane helices comprising the proton-conducting channel in domain II. We also made use of cyclic ion mobility separation mass spectrometry (cyclic IMS-MS) to maximize coverage and sensitivity in the transmembrane domain, showing for the first time that this technique can be used for HDX-MS studies. Using cyclic IMS-MS, we increased sequence coverage from 68 % to 73 % in the transmembrane segments. Taken together, our results provide important new insights into the transhydrogenase reaction cycle and demonstrate the benefit of this new technique for HDX-MS to study ligand binding and conformational dynamics in membrane proteins.Nicotinamide nucleotide transhydrogenases are integral membrane proteins that utilizes the proton motive force to reduce NADP In the current work, the ligand-induced conformational changes of the transhydrogenase from E. coli are explored using hydrogen/deuterium exchange mass spectrometry (HDX-MS). The results show global structural changes to the binding of NADP+ and NADPH to the bacterial enzyme.A low-resolution X-ray structure of the transhydrogenase from the hyperthermophilic bacterium E. coli along with X-ray structures of hydrophilic domains (I and III), but no structure of the entire enzyme is available. Sequence analysis E. coli transhydrogenase. In contrast to other bacterial enzymes, dI is connected with a linker to dII, which could make it possible for the transhydrogenase to exist in a double face down conformation. This double face down conformation is too found in the mammalian transhydrogenase, supporting a possible existence of a double-face down conformation in E. coliE. coli enzyme consists of two subunits. The α subunit contains domain I with the NAD(H) binding site as well as the first four transmembrane helices (TM1–TM4) of the membrane domain II. The β subunit contains the additional 9 transmembrane helices of domain II  plus domain III with the NADP(H) binding site. TM3, TM9 and TM13 form the core of the putative proton channel surrounded by TM4, TM10 and TM14 Considerable biochemical data are available for the transhydrogenase from 2O and contrasting changes in deuterium uptake in the presence or absence of a ligand provide information on binding sites and conformational dynamics. HDX-MS has become a routine method for soluble proteins Over the last years, HDX-MS became a widely used method to analyze ligand binding and structural dynamics in proteins Here we made use of a novel cyclic ion mobility separation (IMS) system to further improve resolution, coverage, and sensitivity for transmembrane segments in our HDX-MS data. Specifically, the higher sensitivity and ion mobility resolution enabled us to track smaller differences in deuterium uptake. We provide the first comprehensive map of the ligand-induced gating of the transmembrane proton channel and directly visualize ligand binding sites and the proton path through the membrane.22.1E. coli membranes containing wild-type transhydrogenase were thawed and dissolved in 2% DDM containing buffer to a final protein concentration of 4 mg/mL. Next, the protein was purified with a gravity flow Ni-NTA agarose column and pooled fractions were subjected to size exclusion chromatography (SEC). SEC was performed using an Äkta pure chromatography system with a HiLoad 16/600 Superdex 200 pg column. Protein-containing fractions were pooled, aliquoted in 100 µL fractions and stored at −80 °C for further experiments.2.2+) by NADPH at 375 nm at 37 °C using an Agilent 8453 UV–visible spectrophotometer and an absorbance coefficient of 6.07 mM-1cm−1. The reaction mixture included 25 mM HEPES (pH 7.5), 50 mM KCl, 125 μg/ml asolectin, 0.1% DDM and 100 μM NADPH, and the reaction was started by adding 100 μM APAD+.The reverse transhydrogenase activity was measured spectrophotometrically by following the reduction of acetylpyridine adenine dinucleotide  containing 1 mM of the respective ligand (binding) or without any ligand (control). The final sample concentration for the transhydrogenase was adjusted to 1.5 mg/mL.i, 0.02% DDM at pH 2.2). Then, 24 pmol of protein was injected into the chromatographic system. After online peptic digestion, trapping and C18 separation, the peptides were measured on a Synapt G2-Si mass spectrometer  in HDMSE mode (50–2000 m/z), including ion mobility for gas phase separation. Samples were measured in technical quadruplicates in randomized order.HDX mass spectrometry experiments were carried out on a fully automated HDX-2 system  previously described in To evaluate the system and measure protein-dependent deuterium back-exchange, we produced a fully deuterated reference sample and subjected it to the normal HDX-MS workflow.2.4E. coli transhydrogenase protein samples were prepared at a concentration of 4.0 mg/mL in phosphate storage buffer and transported on dry ice from MPI laboratories to Waters Corp. . Prior to use, the samples were diluted to 1.0 mg/mL using E-buffer. Aliquots containing 16 pmol of protein were injected onto the pepsin column.TM CyclicTM IMS mass spectrometer (Waters). Data were acquired in HDMSE mode (50–2000 m/z), with the cyclic ion mobility device operating a single pass separation routine.Details were the same as previously described. Peptides eluting from the analytical reversed phase column were infused into the electrospray source of a SELECT SERIES2.5t-test previously described by Houde et al.E. coli transhydrogenase built using UCSF Chimera Peptides were identified from non-deuterated measurements using ProteinLynx Global Server 3.0.3.  for each condition (control and binding). Mass spectra evaluation was performed using the software DynamX 3.0 (Waters). For statistically relevant differences in deuterium uptake all peptides and their respective spectra were subjected to a two-stage https://proteomecentral.proteomexchange.org) via the PRIDE partner repository Further and detailed information’s about experimental conditions and data evaluation are found in the supplement methods. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium  and PntB (β) subunits corresponding to their expected molecular weight of 57.1 kDa and 48.7 kDa, respectively B. The ret 30 s−1 C. After 3.2E. coli transhydrogenase. NADPH binding led to pronounced differential deuterium uptake in the protein. We observed a strong increase in deuterium uptake in key transmembrane helices in domain II: TM9, TM13 and TM14, as shown in Relative deuterium uptake was analyzed for all time points in the presence of 1 mM NADPH and compared to the apo-transhydrogenase reference sample. 3.3We then set out to maximize deuterium uptake information for the membrane-embedded segments using a new cyclic ion mobility mass spectrometer. Measurements with this instrument resulted in similar peptide numbers and redundancy, despite less protein injected. In addition, the sequence coverage of transmembrane segments increased from 68% to 73% and including now important segments. Deuterium uptake differences in the cyclic IMS data set were significantly higher for the whole protein complex compared to the Synapt G2-Si data set  binding pocket. Domain III, harboring the NADP(H) binding site, showed decreased deuterium uptake, especially in the loop D and loop E, both of which are involved in ligand binding. Also, helices hF and hA displayed strong decreased uptake The strongest effects were observed in domain II which displayed an increase in deuterium incorporation in TM9, TM10, TM13 and TM14. These effects are much more pronounced and are now present over the entire helices, in contrast to the initial dataset acquired on the Synapt G2-Si in which only individual segments of these helices showed a change A. In add3.4+, the E. coli transhydrogenase is proposed to adapt a different conformation compared to the apo-transhydrogenase. The map of those changes is shown in + and NADH/NADP+). NADP+ binding to domain III results in a mixed pattern of deuterium uptake rate in domain III. Key ligand binding segments have a reduced deuterium uptake rate (red) while several segments around helix 4 displayed an increased deuterium uptake (blue). A significant decrease in the rate of deuterium uptake was also observed in domain I near the interface between the two protomers of domain I. Domain II predominantly displayed an increased deuterium uptake rate in several key transmembrane helices , but the degree of the changes in domain II were not as strong as observed in the presence of NADPH.In the presence of 1 mM NADP+ binding. Incubation with 1 mM of NAD+ resulted in relatively minor conformational changes of the transhydrogenase  will undergo catalytic turnover which should reach equilibrium within a few minutes. After equilibration, the enzyme solution will contain 0.5 mM each NAD+, NADH, NADP+, and NADPH. We monitored deuterium uptake at equilibrium of the reaction and compared it to the apo-transhydrogenase after 45 min of labeling  X-ray structure of the apo-transhydrogenase from the hyperthermophilic bacterium +-bound, and NADPH-bound states E. coli transhydrogenase too express a linker between dI and dII, it could adapt a double face-down conformation, similar to the mammalian protein + in domain III, and (2) open the proton channel to the mitochondrial intermembrane space  while closing the channel gate on the matrix  +, depending on the protonation status of the histidine in the proton channel, is proposed to result in detachment of domain III from domain II  mitochondria in the apo-, NADP2 and to a smaller extent as state 5 (as classified in 5 with the protonated His91+. NADP+-binding is proposed to result in detachment only if the histidine is protonated (4 → 7). The structures determined at pH 7.4 show that in the presence of NADP+ about 60 % of the population is in the double face-down (attached) configuration, mostly state 1 with some 4 (7 and 8). Presumably, in the second protomer there are multiple detached conformations which are not resolved.The full set of equilibria between ligand-bound species is shown in otonated B, 4 → 7.h some 4 B [6]. In3 → 9) 9 in NADPH-binding is proposed to cause detachment only if the histidine is deprotonated B, 3 → 9 4.29) whereas the reference apo-transhydrogenase is in the double face-down/attached conformation, assuming the E. coli transhydrogenase is comparable to the mitochondrial enzyme (2). The transition from the attached/ face-down conformation to the detached conformation is accompanied by changes in the proton channel to change access to His91 from the periplasm to the cytoplasm. Consistent with this are the pronounced increased deuterium uptake rates in transmembrane helices TM9, TM13 and TM14 which form the core of the proton channel in domain II The highest difference in uptake were observed in the presence of NADPH. NADPH binding at pH 7.4 is expected to favor the detached conformation  binding site in domain I at the high concentrations used in these studies (1 mM). We observed the same effect in previous studies with these two ligands binding to other proteins 4.3E. coli transhydrogenase together with NADPH on a new cyclic IMS mass spectrometer with enhanced ion optics, detector, and a novel cyclic ion mobility cell. With these improvements, we were able to increase sequence coverage for transmembrane segments from 68% to 73% and could cover segments which were not previously detected. These new segments together with all other peptides displayed significantly higher intensities of peptide cluster peaks. Signal increase was 1 to 1.5 orders of magnitude higher compared to the Synapt G2-Si MS. This increase resulted in better signal-to-noise ratio and led to more precise identification of isotope envelopes of peptide signals.Membrane-embedded protein segments still remain a challenging target to analyze with HDX-MS because of their solvent inaccessibility, hydrophobicity and restricted proteolytic digestion efficiencies For the cytosolic domains I and III, we found similar effects compared to the dataset from the Synapt G2-Si MS. The NADP(H) binding site in domain III displayed decreased deuterium uptake rates in essential segments (loop E and loop D) involved in ligand binding In the Synapt G2-Si MS dataset, there was a marked increase in the deuterium uptake rate in domain II transmembrane helices TM9, TM13 and TM14. In addition, the increased sensitivity revealed changes also in TM4 and TM10 which also form a part of the proton channel and were not present in the Synapt G2-Si data set 4.4+ to domain III should favor the double face-down/attached conformation at pH 7.4 (1). However, a substantial fraction (18 %) of structures of the mitochondrial enzyme with bound NADP+ show one protomer in which domain III is detached. Hence, our HDX data could reflect these multiple conformational states, if the E. coli enzyme behaved similarly.Based on the structural data of the mitochondrial transhydrogenase + binding resulted in a significant increased rate of deuterium uptake in transmembrane helices TM9 (region near His91), TM13 and TM14 in domain II. The pattern was not identical to that observed in the presence of NADPH. However, the results are more pronounced on the cytoplasmic side of domain II near the interface with domain III. The HDX-MS data could result from a dynamic equilibrium in which the NADP+-bound transhydrogenase molecules spend a small fraction of time in a detached conformation. Alternatively, the presence of bound NADP+ in domain III, inserted into domain II in the attached conformation, might partially open the channel on the cytoplasmic side. In any event, NADP+ binding to domain II does perturb the conformation and increase D2O access to portions of domain II.The HDX-MS data showed NADP+. Stretches involved in NADP(H) binding (loop E and loop D) displayed a strong rate decrease due to shielding by the bound NADP+. Other segments in this domain  exhibited an increased rate of uptake, indicating a more flexible structure with increased solvent access compared to the control condition without ligand. This may be due to perturbation of the interaction between the diphosphate of NADP+ with R350 and Y315 resulting in an occluded but more “open” conformation of this portion of domain III. Changes in domain I were relatively small and attributed to minor binding of NADP+ to the NAD(H) binding site in domain I.HDX-MS identified some segments in domain III with increased and others with decreased deuterium uptake rates upon binding NADP+, and its effects on the protein. Binding of this ligand to domain I is not implicated in any perturbation to the proton channel in domain II + binding. Domain III displayed only minor changes, likely due to a small degree of cross binding of NAD+ to the NADP(H) binding site + binding in domain I alone does not affect the activation of the putative proton channel in the transmembrane domain II.Next, we analyzed the oxidized form of NAD(H), namely NAD4.5+, the transhydrogenase undergoes catalytic turnover and reaches equilibrium after a few minutes under the experimental conditions. After completion of the reaction, all four ligands are present at a concentration of 0.5 mM and a dynamic equilibrium is established with ligand exchanges and redox chemistry occurring with no net change. We performed this experiment to investigate whether any different conformational states can be detected that are not observed in the presence of individual ligands.In the presence of 1 mM each of NADH and NADP+ data, we observed increased deuterium uptake across the TM segments and thus full opening of the putative proton channel , comparable to the NADPH experiment.The results obtained under these conditions generally match the data obtained in presence of NADPH A and D. +/NADPH. This led to reduced differential uptake compared to the NADP+ and the NADPH experiment, respectively, as each state is only occupied transiently during the reaction cycle. Our findings could also indicate negative cooperativity between the binding sites, to avoid forming a dead-end complex consisting of either NADPH plus NADH or NADP+ plus NAD+Only minor changes were detected in domain III. During dynamic equilibrium, the NADP(H) binding site binds to and releases NADPNotably, there was a marked decrease in deuterium uptake in the NAD(H) binding site in domain I, presumably due to shielding by the bound ligand. Segments located at the surface of domain I displayed a minor increase in deuterium uptake. This may be due to active turnover, and only transient binding of NADH and electron transfer to NADPH. Overall, it is clear that the transhydrogenase undergoes major conformational changes during the reaction cycle.5+ shows different effect on domain II and III. Domain II shows pronounced increase in deuterium uptake on the cytoplasmic side indicating a partial opening of the putative proton channel to this site. Domain III might be in an occluded but more “open” conformation compared to the occluded and closed form with NADPH. These results are in good agreement with the previously proposed function published by Kampjut and Sazanov and give a detailed dynamic insight in the reaction cycle of E. coli transhydrogenase The current work shows that allosteric gating of the proton channel in domain II induced by specific ligand binding. NADPH binding in domain III clearly induces a full opening of the putative proton channel in domain II. However, the oxidized form NADPJonathan Zöller: Conceptualization, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization. Sangjin Hong: Validation, Formal analysis, Investigation, Data curation, Writing – review & editing. Martin L. Eisinger: Conceptualization, Methodology, Writing – review & editing, Supervision. Malcolm Anderson: Investigation, Writing – review & editing. Melanie Radloff: Investigation, Writing – review & editing. Kristina Desch: Formal analysis, Data curation, Writing – review & editing. Robert Gennis: Conceptualization, Resources, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition. Julian D. Langer: Conceptualization, Resources, Writing – review & editing, Visualization, Supervision, Project administration, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


SARS-CoV-2 spike protein, which forms the basis for high pathogenicity and transmissibility of the virus, is a prime target for the development of both diagnostics and vaccines for the debilitating disease caused by the virus. We present a full model of spike methodically crafted and used to study its atomic-level dynamics by multiple microsecond simulations. The results shed light on the impact of posttranslational modifications on the pathogenicity of the virus. We show how glycan–glycan and glycan–lipid interactions broaden the protein’s dynamical range and thereby, its effective interaction with the surface receptors on the host cell. Palmitoylation of the spike membrane domain, however, results in a unique deformation pattern that might prime the membrane for fusion. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein is the prime target for vaccines, diagnostics, and therapeutic antibodies against the virus. While anchored in the viral envelope, for effective virulence, the spike needs to maintain structural flexibility to recognize the host cell surface receptors and bind to them, a property that can heavily depend upon the dynamics of the unresolved domains, most prominently the stalk. Construction of the complete, membrane-bound spike model and the description of its dynamics are critical steps in understanding the inner working of this key element of the viral infection by SARS-CoV-2. Combining homology modeling, protein–protein docking, and molecular dynamics (MD) simulations, we have developed a full spike structure in a native membrane. Multimicrosecond MD simulations of this model, the longest known single trajectory of the full spike, reveal conformational dynamics employed by the protein to explore the surface of the host cell. In agreement with cryogenic electron microscopy (cryo-EM), three flexible hinges in the stalk allow for global conformational heterogeneity of spike in the fully glycosylated system mediated by glycan–glycan and glycan–lipid interactions. The dynamical range of the spike is considerably reduced in its nonglycosylated form, confining the area explored by the spike on the host cell surface. Furthermore, palmitoylation of the membrane domain amplifies the local curvature that may prime the fusion. We show that the identified hinge regions are highly conserved in SARS coronaviruses, highlighting their functional importance in enhancing viral infection, and thereby, provide points for discovery of alternative therapeutics against the virus. The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to impact nearly all aspects of human life , 2. SARSSeveral SARS-CoV-2 spike cryo-EM structures have been resolved –15. AlthAdditionally, the assembly and budding of the virus are known to take place in the endoplasmic reticulum–Golgi intermediate compartment (ERGIC) , 23, an Computational simulations offer an effective technique for modeling complex systems like the spike at a detailed level. Recent classical molecular dynamics (MD) simulations of the spike head –29 as we302633We constructed a full-length, membrane-embedded spike and investigated its dynamics by atomistic MD simulations. Taking a hybrid approach combining homology modeling, protein–protein docking and MD, and the available experimental data –40, a fuHere, we first develop a full spike structure in its membrane-bound state and use it for microsecond-scale MD simulations to characterize its conformational dynamics. We describe these conformational changes in terms of bending and twisting motions of the spike head with respect to the stalk and examine the potential role of glycan–lipid and glycan–glycan interactions in these motions. Sequence comparison with other human coronaviruses allows us to delineate multiple conserved hinge regions in the stalk that likely promote the conformational motions of the spike head. Furthermore, we quantify the effects of palmitoylation in the TM domain, offering support for the role of this posttranslational modification in modulating the local membrane curvature.SI Appendix, Fig. S1). Additionally, we carried out an expansive procedure for deriving a stable model of the TM domain in a native membrane , where we combined sequence-based secondary structure prediction for the TM region  with protein–protein docking for generating the initial configurations of TM trimers  followed by MD relaxation of suitable TM trimeric configurations in membrane . The above-described modeling procedure was combined with the biochemical data on the palmitoylation sites  to develop the full-length, palmitoylated, and fully glycosylated spike structure . The HR2 domain in the previous studies was modeled using a coiled-coil template from Salmonella autotransporter adhesin . As SARS-CoV and SARS-CoV-2 display complete sequence identity in the HR2 domain, our stable model is expected to provide a closer physiological representation of this domain. The TM domain in the previous spike models . The correct arrangement of the different stalk domain structures, including the trimeric HR2 and TM domains, can be critical to the correct description of the spike’s global dynamics.We have also carried out structural comparison of our full-length spike model with previously published models , 31, 41. adhesin . Here, w adhesin , resultie models , 31 was e models . It is ne models . Conseque models . As the e models A, its coe models A and B. Movie S1). These motions were quantified in terms of head-twist and head-bend angles as well as head distances calculated separately with respect to the spike neck, the HR2 domain, or the TM domain (xy plane) with respect to both the HR2 and TM domains, highlighting the large conformational flexibility available along this degree of freedom . A wide normal-like distribution of head-twist angles is observed with respect to the HR2 domain, with the highest sampled configurations turned by almost Upper and E). These results can be directly related to the length of the linkers connecting the different domains of spike. The domains around which the spike head displays larger motions (HR2 and TM domains) are connected by longer linkers  compared with the domain around which the spike head displays relatively more restricted motions (spike neck), which are connected to the spike head by a smaller linker (linker 1: 7 residues). Furthermore, direct comparison of the head-twist motions in the glycosylated form with the control nonglycosylated system shows surprisingly that these motions become restricted in the latter (Lower), especially with respect to the head-twist angles calculated around the HR2 and TM domains. These results point to an unexpected enhancing role of glycans in the overall dynamics of the spike.To further describe the dynamics employed by the spike to sample the crowded cellular surface, we characterized its global motions in terms of the movements of the spike head with respect to the stalk region during the simulations . Comparatively, smaller head-bend motions are observed around the spike neck and the TM domain, with the respective populations showing distributions over a range of 0∘ to 60∘ angles . The extent of these bending angles is also directly related to the changes in the head distances with respect to these domains, with the largest distance changes observed in conformations with the largest head-bend angles (Upper). Thus, the spike head can move as much as 125 Å with respect to the HR2 domain compared with its starting configuration. Again, comparison of the glycosylated and nonglycosylated forms shows that the spike domains rigidify  in the absence of glycans (Lower). The potential role of the glycans in modulating global motions of the spike is further discussed in later sections.Similarly, the spike head displays a broad range of head-bend angles with respect to the HR2 domain, sampling configurations almost orthogonal to the starting structure   of the spike head represent major degrees of freedom for the RBD (located in the spike head) to locate and bind to the target ACE2 receptors in the host cell membranes. As spike proteins have been found to be sparsely distributed on the virion envelope , thce area) , 46 or oce area) –29 and ice area) , 31, in In order to examine potential generality of the observed global motions in the SARS-CoV-2 spike, we analyzed the sequence similarity of the spike protein, specifically focusing on the flexible regions connecting the different domains. These include the small connecting linkers between the spike head and the neck (linker 1: NTVYDPL), between the spike neck and the HR2 domain (linker 2: HTSPDVDLGDISG), and between the HR2 domain and the TM domain (linker 3: GKYEQYIKWP). The three linkers are found to be rich in Pro and Gly, residues known to contribute to the conformational flexibility in proteins , 48 FigA. AdditiTo further elucidate the possible functional importance of these residues in the mobility of the spike, multiple sequence alignment was carried out with other coronaviruses infecting humans. Interestingly, all Pro and Gly residues present in the flexible regions are completely conserved in the homologous bat-CoV and human SARS-CoV and to a large extent, in human MERS-CoV, where only Pro-1163 is not conserved B. Most oSI Appendix, Fig. S6A). The accessibility of the spike protein, measured independently for the glycosylated and nonglycosylated systems, is found to increase monotonically with increasing probe radius , consistent with the previous MD results  receptor, and S2, which includes the stalk domain along with the fusion machinery that mediates the viral membrane fusion after cleavage and separation from the S1 subunit. Recent studies have shown that antibodies targeting the S2 subunit exhibit specific neutralizing activity against SARS-CoV-2 . AnalyziThe extensive glycosylation of the spike surface is key to the viral escaping from the immune system , 41, 46.Movies S2 and S3). To obtain deeper insights into the impact of these glycan interactions, especially with respect to the spike global motions, we evaluated the relationship between the inclination of the different stalk domains and the glycan–lipid or glycan–glycan contacts formed both in the native glycosylated spike simulations and in the control nonglycosylated spike. In the glycosylated spike when the inclination angle of the HR2 domain exceeded a threshold of around We noted that the glycans also form contacts with each other, as well as with the lipid bilayer, during the simulations  A and C, N-glycans have been reported to be involved in mediating receptor recognition and facilitating cell adhesion (N-linked complex glycans with multiple N-acetylglucosamine (GlcNAc) termini . Additionally, these glycans are completely conserved in human coronaviruses . The majority of the interactions between glycans and lipids consist of hydrogen bonds formed between O or N atoms of the N-acetyl-D-neuraminic acid or D-galactose sugars present in the glycans and N atoms from PE head group or O atoms in the POPE/POPC phosphate groups. These interactions may further stabilize the inclined conformations of the HR2 domain with respect to the membrane. The contacts with cholesterol are minimal, which is expected as cholesterol is known to insert more deeply in the membrane core compared with the phospholipids . A strong Pearson’s coefficient of 0.84 ±0.07 between the Head1 and TM vectors , but at the same time, the correlation between them is also reduced, with the nonglycosylated spike showing a relatively lower Pearson’s coefficient, 0.76 ±0.07, for the in-plane motion of the spike head with respect to the HR2–TM domain bending. In general, the correlated motions between the different spike domains can be attributed to its unique tertiary structure and can be considered an intrinsic property of the protein, but at the same time, glycosylation may further amplify these motions through the extensive glycan–lipid and glycan–glycan interactions in the system  indicates concerted motions of the spike head and the TM domain about the HR2 domain. Here, the orthonormal vectors representing the spike head and the TM domain are oriented in the same direction about the HR2 domain for 88% of the simulation in the fully glycosylated spike , the concerted, opposing bending of the spike head and the TM domain may pull the head back to an upward position with respect to the membrane, preventing it from severe inclination and falling into the membrane and thus, maintaining a proper configuration for binding with the ACE2 receptor.The Gram–Schmidt analysis  measurements have shown membrane curvature induced by hemagglutinin (HA) palmitoylation . Apart fThe spike protein of SARS-CoV-2 constitutes a major component in the viral infection and therefore, the main target for both diagnostics and vaccines developed for the disease caused by the virus. To better understand the many mechanistic steps the spike is involved in, it is imperative to characterize its structure and dynamics in the most detailed manner and under the most realistic conditions possible. We report here a full model for the full-length spike in its native, glycosylated, palmitoylated, and membrane-bound form, which we use for several microseconds of atomistic simulations highlighting some of the dynamics employed by the spike to search for and locate the host cell receptors effectively . The hinSI Appendix.An extended version of the methods is available as As a first step, a full-length spike was constructed using recent cryo-EM structures of the spike head from SARS-CoV-2 [Protein Data Bank (PDB) ID codes 6VYB and 6VXX ], SARS-CSI Appendix, Table S1). After 100 ns of relaxation of the monomer in the membrane, multiple trimeric configurations were generated using Multimer Docking in ClusPro  using Glycan Reader & Modeler  were then performed in NAMD , 83 usinSI Appendix, Fig. S10) of the protein were characterized in terms of the positional and orientational behavior of the spike head with respect to the different stalk domains. The potential effect of glycosylation on these motions was analyzed in relation to their direct interactions with the lipid bilayer as well as contacts between the glycans at the interfaces of different domains. Since the palmitoylation tails contribute to membrane deformation (The global dynamics (ormation , we alsoormation  and visuormation . The solormation .Supplementary FileSupplementary FileSupplementary FileSupplementary FileSupplementary File


This survey first summarizes the background of recent research on unsupervised domain adaptation with time series sensor data, the types of sensors used, the domain gap between the source and target domains, and commonly used datasets. Then, the paper classifies and compares different unsupervised domain adaptation methods according to the way of adaptation and summarizes different adaptation settings based on the number of source and target domains. Finally, this survey discusses the challenges of the current research and provides an outlook on future work. This survey systematically reviews and summarizes recent research on unsupervised domain adaptation for time series sensor data to provide the reader with a systematic understanding of the field.Sensors are devices that output signals for sensing physical phenomena and are widely used in all aspects of our social production activities. The continuous recording of physical parameters allows effective analysis of the operational status of the monitored system and prediction of unknown risks. Thanks to the development of deep learning, the ability to analyze temporal signals collected by sensors has been greatly improved. However, models trained in the source domain do not perform well in the target domain due to the presence of domain gaps. In recent years, many researchers have used deep unsupervised domain adaptation techniques to address the domain gap between signals collected by sensors in different scenarios,  Sensors capture physical parameters from the observed environment and convert them into observable electrical pulses ,2,3,4. AIn recent years, the analysis techniques for time series data have been rapidly developed. Commonly used models for processing time series data are spectral and wavelet analysis ,20,21, 1This survey collects papers on unsupervised domain adaptation with time series sensor data from academic studies in 2018 to date. First, this survey divides them by industry, listing the background of different applications, sensors often used, the sources of data discrepancies, and the commonly used research datasets. Then, this survey classifies these different methods for unsupervised domain adaptation for time series data, including domain adaptation methods aligned in the input space, feature space, and output space, and model-based domain adaptation methods. After that, the paper classifies the existing domain adaptation settings according to the number of sources and target domains and analyzes their advantages. Finally, the survey summarizes the existing methods and looks into possible future research directions. Unlike previous reviews ,34,35 thThe remainder of the paper is structured as follows see . SectionTo facilitate the understanding of this paper, this section explains some basic concepts, including what a sensor is, the definition of time series sensor data, what a domain is, and what domain adaptation and deep unsupervised domain adaptation are.A sensor converts physical phenomena into a measurable digital signal, which can then be displayed, read, or processed further . Accordin observed by the sensor can be expressed ast. When d indicates the dimension of The time series of length Data collected under a certain distribution is referred to as a domain, and this distribution can be understood as a specific data collection scenario . For exaIn the concept of domain adaptation, scholars usually refer to the training set as the source domain and the test set as the target domain. Assuming that the model is trained with data collected at the arm (source domain) and later tested with data collected at the leg (target domain), the model does not perform as well. This is because the deep learning model can only recognize test data with the same distribution as the training data. One way to improve the performance of the model in the target domain is to label a large amount of data in the target domain and fine-tune the model trained in the source domain using a supervised approach. However, labeling a large amount of data requires significant human resources.Domain adaptation see  refers tAlthough there are many papers dedicated to UDA of time series sensor data, they have different application backgrounds and research directions. This section systematically analyzes the applications of UDA with time series sensor data in recent years and broadly divides them into three application areas, namely industry, transportation, and biosignal. Each field contains several specific application directions. Moreover, this section summarizes in detail which sensors are included in each application direction, what causes the domain gaps, and which datasets are publicly available. The overall classification results are shown in This section introduces several applications of UDA in industry, including fault diagnosis of rolling bearings, fault diagnosis of power plant thermal systems, and diagnosis of ball screw faults.Background. A rolling bearing is a precision mechanical component that changes the sliding friction between the running shaft and the shaft seat, thereby reducing frictional losses [l losses ,130,131.Sensors. Common types of sensors used for rolling bearing signal acquisition are accelerometer sensors [ sensors ,52 as we sensors . AccelerDomain Gap. The reasons for the inconsistent distribution of training and test data come from three main sources: different working conditions, different locations of sensors, and different machines.(1)Different Working Conditions. Due to the influence of speed, load, temperature, etc., working conditions often vary during the monitoring period. Collected signals may contain domain shift, which means that the distribution of data may differ significantly under different working conditions. The aim of deep unsupervised domain adaptation-based fault diagnosis is that the model trained using signals under one working condition can be transferred to signals under another different working condition.(2)Different Locations of Sensors. Since sensors installed on the same machine are often responsible for monitoring different components, sensors located near the fault component are more suitable to indicate the fault information. However, key components have different probabilities of failure rates, leading to the situation where signals from different locations have different numbers of labeled data. The aim of unsupervised domain adaptation-based fault diagnosis is that the model trained with plenty of labeled data from one location can be transferred to the target domain with unlabeled data from other locations.(3)Different Machines. For testing cost and safety reasons, it is difficult to collect sufficient samples of marked failures from test machines. In addition, sufficient labeled data can be generated from dynamic simulations or fault seeding experiments. However, the distribution of data from dynamic simulations or fault seeding experiments is different from that of real machines due to similar structure and measurement situations, which is one of the sources of the domain gap.Datasets. Some common open-source rolling bearing fault diagnosis datasets are the Case Western Reserve University (CWRU) Dataset [(1)CWRU [CWRU . The CWR(2)PU [PU . PU bear(3)IMS [IMS . The IMS Dataset  and the  Dataset .(1)CWRUBackground. Power units are developing towards large scale, integration, and complexity. The thermal system is one of the most critical components of a large-scale power unit [wer unit . In recewer unit . Due to Sensors. The monitoring of thermal power unit operating conditions includes many physical parameters, such as the temperature and pressure of high-pressure heaters, and the flow rate of condensate, etc. Therefore, the commonly used sensors in the fault diagnosis of the power plant thermal system are temperature sensors, pressure sensors, flow rate sensors, etc.Domain Gap. In the fault diagnosis of the power plant thermal system, the domain gap comes from two main areas.(1)Fault Severity. For example, for feedwater leak faults, different leak volumes represent different levels of severity [severity ,61. Alth(2)Different Load Conditions. The distribution of the fault data collected by the power plant thermal system is different under different load conditions [nditions ,61, whicBackground. Ball screws are important mechanical devices capable of converting rotary motion into high-precision linear motion, and they are widely used in feed drive systems for machine tools [ne tools . Ball scSensors. During the operation of ball screws, vibration signals are often used to determine if a ball screw is faulty. Therefore, a common sensor used for ball screw fault diagnosis is the accelerometer sensor NASA Battery Dataset [ Dataset . The NAS(2)CALCE Battery Dataset [ Dataset . The CAL Battery  and CALC Battery .(1)NASABackground. Remaining Useful Lifetime (RUL) relates to the amount of time left before a piece of equipment is considered not to perform its intended function [function ,140,141.Sensors. Common applications in RUL estimation include RUL estimation of turbofan engines and RUL estimation of bearings. Commonly used sensors for the former are temperature sensors, pressure sensors, flow sensors, etc. The latter commonly used sensors include acceleration sensors and temperature sensors.Domain Gap. In the task of RUL estimation, what leads to the domain gap are the operating conditions and failure modes.(1)Different Failure Modes. Taking RUL estimation of bearings as an example, the degradation of a bearing running to failure is usually associated with different failure modes, including inner failure, outer failure, etc., it is not a single failure behavior. Bearings in failure behavior under the action of the pressure and thermal strain usually lead to other failure modes. The degradation of bearings exhibits different behaviors due to different failure behaviors and, therefore, may result in differences in the distribution of features.(2)Different Operating Conditions. In many practical applications, complete life degradation data are only available for bearings under certain operating conditions. However, the degradation process is often different when the operating conditions are different. Therefore, different operating conditions can also lead to a domain gap.Datasets. Commonly used datasets in the estimation of RUL include the C-MAPPS dataset [(1)C-MAPPS [C-MAPPS . Commerc(2)IEEE PHM Challenge 2012 bearing dataset [ dataset . The IEE dataset , which i dataset  and the  dataset .(1)C-MABackground. In the mechanical transmission system, the gearbox is a frequently used transmission type that has been widely used in wind turbine [ turbine ,143, hel turbine ,145, aut turbine ,147, et Sensors. In fault diagnosis of the gearbox, accelerometers are commonly used to obtain the vibration signal of the gearbox. Usually, one accelerometer is fixed in the X-axis direction and one in the Y-axis direction to obtain vibration information in different directions.Domain Gap. The domain gap in gearbox fault diagnosis comes from different operating conditions, including speed and load. This frequent change in speed and load will greatly affect the domain distribution.This section presents two research directions of UDA in biosignal, namely EEG-based brain-computer interface (BCI), human activity recognition (HAR), EMG-based muscle-computer interface (MCI), and gait analysis.Background. Brain-computer interface is an important issue in biosignal and it enables users to communicate directly with computers using brain signals [ signals ,149,150.Sensors. EEG signals usually are collected using a special device called an electroencephalogram. This device uses the principle of differential amplification or recording voltage differences between different points using a pair of special metal plate electrodes that compares one active exploring electrode site with another neighboring or distant reference electrode.Domain Gap. The domain gap of EEG-based BCI is mainly reflected in different subjects and different sessions.(1)Different Subjects. Brain signals show high variability among subjects due to inherent background neural activities, concentration levels, fatigue, etc. In this regard, deep learning-based BCI faces a great challenge in that classifiers trained on one subject cannot be directly used to decode brain signals from other subjects.(2)Different Sessions. EEG signals are weak, susceptible to interference and noise contamination, nonstationary for the same subject, and varying across different sessions. Therefore, there are also differences in the distribution of signals collected at different sessions on the same subject.Datasets. The common applications of EEG-based BCI are emotion recognition and motor imagery. The datasets related to emotion recognition include SEED [(1)SEED [SEED . Shangha(2)DEAP [DEAP . The Dat(3)BCI Competition IV-IIa [n IV-IIa . The BCI(4)BCI Competition IV-IIb [n IV-IIb . The BCIude SEED  and DEAPude SEED . The datude SEED  and BCI ude SEED .(1)SEEDBackground. Human activity recognition (HAR) is the recognition of user behavior from observed data collected by a set of sensors [ sensors ,153,154.Sensors. In the task of human activity recognition, commonly used sensors are accelerometers and gyroscopes. There are also some recent studies [ studies ,106 usinDomain Gap. In the field of human activity recognition, domain gaps exist mainly in the differences between sensor signals acquired by different body parts of the same user and differences in sensor signals acquired by different users. Besides, there are also some differences in the signals acquired by different sensors, which can also lead to the domain shift of the model.(1)Different Body Parts. Users often change the position of a sensor or wearable device based on their preferences and current activity. Despite doing the same activity, the sensor signals obtained from different body parts are very different. Therefore, transferring the activity model learned at one body location to another body location can reduce annotation costs and improve recognition accuracy and robustness to wearable device position changes.(2)Different Users. Even when doing the same activity, there is some variation in the sensor signals collected by different users. A HAR system has a large number of users, and it is impractical or infeasible to collect enough ground truth from each user to build a model of the activity. Therefore, it is also very important to reduce the domain shift of the model between different users.(3)Different Sensors. There are some differences in the data collected by different sensors . Therefore, how to improve the performance of the model on newly deployed sensors without collecting any activity labels for new sensors is also a very important issue.(4)Different Environments. When using WIFI sensors to identify human activities, different WIFI environments often result in biased data collection. For example, different indoor structures have different signal strengths and distributions of WIFI reflections [lections . Anotherlections .Datasets. Common datasets in the field of human activity recognition include Opportunity [(1)Opportunity [ortunity . The Opp(2)HHAR [HHAR . The Het(3)PAMAP2 [PAMAP2 . The phy(4)RealWorld [ealWorld . The Reaortunity , HHAR [1ortunity , PAMAP2 ortunity , and Reaortunity .(1)OppoBackground. The muscle-computer interface (MCI) is an interaction method that translates myoelectrical signals directly from a mere reflection of muscle activity to an interactive command that conveys the intent of the user’s movement. It does not depend on actions performed by the user on a physical device, nor on externally visible or audible actions, thus enabling a readily available input mechanism in myoelectric control. As the technical core of non-invasive MCI, electromyography (EMG) technology is a technique that uses one or more electrodes to measure the electrical activity of muscles from the skin surface. Due to its safety and convenience, it is increasingly being used for MCI tasks.Sensors. EMG signals are usually collected using a special device called an electromyogram. When muscles contract, they release a burst of electrical activity or electrical impulses that circulate through adjoining bones and tissues. The myoelectric potential is detected with surface and reference electrodes, and this potential is amplified using differential amplifiers to detect muscle or limb activation.Domain Gap. The domain gap of EMG-based MCI is mainly reflected in different sessions and different subjects.(1)Different Sessions. In practice, the electrode position may change when the EMG is taken off or put on, sweating, etc. This change is mainly caused by changes in electrode conductivity, electrophysiological changes, and electrode movement. This inherent non-stationarity leads to great variability in the signals collected by the EMG.(2)Different Subjects. Since the EMG signal is a biological signal, the obtained signal varies considerably between subjects due to their physical condition. In this case, the variability of the data comes from the differences in the human body.Datasets. Common datasets for EMG-based MCI include the CapgMyo dataset [(1)CapgMyo [CapgMyo . CapgMyo(2)NinaPro [NinaPro . The Nin(3)CSL-HDEMG [SL-HDEMG . The CSL dataset , the Nin dataset  and the  dataset .(1)CapgBackground. Walking and running are the most frequent movements in human life. Identifying gait phases is useful in a variety of applications. For example, physical therapists analyze gait phases to identify abnormal walking patterns. In sports science, gait phases are used to improve the skills and physical condition of athletes. Gait phases can also be used to synchronize the assistive forces of a wearable robot with the user’s movements.Sensors. The sensors used for gait analysis include IMU sensors (including accelerometers and gyroscopes), infrared (IR) sensors, radar sensors, cameras, and EMG electrodes.Domain Gap. For gait analysis, the domain gap mainly comes from the different positions of sensor placement, different subjects, and different moving states.(1)Different Positions of Sensors. Such a problem is common in IMU sensor-based gait analysis. The position of the IMU may drift due to motion or personal preference, and it is difficult to precisely control the fixture of the IMU on users in real-world settings. Since IMU signals are location sensitive, the signals recorded at different locations can vary dramatically.(2)Different Subjects. Human gait is highly individualized; each individual has his or her own unique gait pattern. Therefore, the sources of domain gaps in gait analysis also include different subjects.(3)Different Moving States. In radar sensor-based gait analysis, differences in domains often stem from different states of motion. This is because when the test subject changes his or her motion state , the radar echo intensity, Doppler bandwidth, or motion period will change. This also means that the distribution of gait data will also be shifted.Datasets. Common gait analysis datasets include the Daphnet dataset [(1)Daphnet [Daphnet . This da(2)OU-ISIR [OU-ISIR . The OU-(3)CASIA-B [CASIA-B . The CAS(4)CASIA-C [CASIA-C . The CAS dataset , the OU- dataset , the CAS dataset , and the dataset .(1)DaphIn this section, unsupervised domain adaptation methods for time series sensor data are investigated. The most commonly used methods include adaptation in the input space, adaptation in the feature space, adaptation in the output space, and model-based adaptation see .i-th sample as defined in Equation , multi-kernel MMD (MK-MMD), correlation alignment (CORAL), etc.MMD. Maximum mean discrepancy is a two-sample statistical test of the hypothesis that two distributions are equally based on observed samples from the two distributions. The test is computed from the difference between the mean values of a smooth function on the samples from two domains. If the means are different, then the samples are most likely not from the same distribution. The smooth functions chosen for MMD are unit balls in feature reproducing kernel Hilbert spaces (RKHS) since it can be proven that the population MMD is zero if and only if the two distributions are equal. Assuming that the marginal distributions of the source and target domains are k (the Gaussian kernel is often used as the kernel) and essed asLmapping=C is the number of classes. Combining the Equation ,,9), the Like mapping-based methods, there are a large number of methods in UDA that are based on adversarial learning. For example, Wang et al.  uses advPseudo-label-based domain adaptation methods ,57,58 arPseudo-label-based domain adaptation methods also have some applications in solving the domain gap problem of time series sensor data. For example, Song et al.  uses a pThe model-based domain adaptation approach is to reduce the domain shift of a model by imposing constraints on its parameters. For example, Jimenez et al.  use adapThis section describes the differences in adaptation settings caused by different numbers of domains in unsupervised domain adaptation, i.e., single-source single-target domain adaptation, multi-source single-target domain adaptation, and single-source multi-target domain adaptation. The differences between the different settings are shown in The vast majority of current domain adaptation algorithms focus on a single-source single-target setup. This setup is relatively simple, assuming only one source and one target domain. The single domain gap motivates a large number of approaches, including domain adaptation in the input space, domain adaptation in the feature space, etc. However, this single-source single-target setup can have limitations. In reality, the sensor deployment environment is always changing, and models adapted in a single target domain are difficult to transfer to other target domains (data collected under changing sensor deployment conditions). Moreover, during domain adaptation, the available supervised samples may come from multiple source domains with different distributions. Therefore, it is also important to utilize multiple source domains with labels to improve the performance of the target domain.Unlike single-source domain adaptation, multi-source domain adaptation can obtain more comprehensive knowledge by fusing supervised samples from multiple source domains and mitigate the risk of overfitting problems by having more training samples, which is beneficial to improving the performance of the target domain.In order to effectively fuse different source domains, Zhu et al.  proposesAlthough multi-source domain adaptation achieves better results compared to single source domain adaptation, this setting may lead to negative adaptation, i.e., some source domains may not have a positive effect on improving the model’s performance in the target domain. To address this issue, Wei et al.  proposesIn practical application scenarios, different working conditions and different environments result in different domain gaps. Therefore, it is important to train a model to have a good performance on multiple target domains at the same time. There are two naive ways of directly extending domain-specialized UDA to work on multiple target domains, that are (1) training a single model on combined data from multiple target domains, and (2) training multiple models individually for each target domain. Unfortunately, these methods are not appropriate to handle multi-target domain adaptation problems because they would suffer from performance degradation due to the mismatching of multi-target domains .How to explore the potential connection between multiple target domains is an important research topic. There is very limited research on multi-target-based domain adaptation in time series sensor data. Ref.  proposesAs shown in In addition to the applications of UDA, there will be some room for exploration in the UDA approach. Most of the deep models used by existing methods are CNN and RNN, and if the backbone network is replaced with Transformer  that deaIn terms of UDA settings, existing methods rarely explore multi-source to multi-target domain adaptation. This adaptation setting would include the advantages of both multi-source adaptation and multi-target adaptation, i.e., the source domain contains data collected under multiple conditions and can simultaneously contribute to improving the model performance in the target domain. Also, since multiple target domains are included, the adapted model can be generalized to multiple operating conditions.Complex environments and variable working conditions result in deep learning models trained with time series sensor data collected in one condition performing poorly when tested in another. In recent years, more and more scholars have adopted the unsupervised domain adaptation approach to solve the domain shift problem of deep models. This survey systematically studied, analyzed and summarized the unsupervised domain adaptation technique based on time series sensor data from three aspects: application, method, and setup. In particular, this survey analyzed and summarized which sensors are used in different application directions, where the domain gaps are, and what data are publicly available. Moreover, this survey classified and compared different unsupervised domain adaptation methods and discussed and summarized the advantages of various settings. This survey also provided an outlook on potential application areas, explored feasible research directions, and discussed additional settings for unsupervised domain adaptation. Through this survey, we hope to provide a comprehensive and systematic understanding to readers who are investigating the direction of unsupervised domain adaptation with time series sensor data.


Rhodopseudomonas palustris . Experiments were conducted in a TPBR, and response surface methodology (RSM), varying biomass concentration, and light intensity and temperature were employed to determine the operating conditions for the enhancement of both hydrogen production as well as biomass suspension. Biomass concentration was found to have had the most pronounced effect on both hydrogen production as well as biomass suspension. RSM models predicted maximum specific hydrogen production rates of 0.17 mol m−3h−1 and 0.21 mmol gCDW−1h−1 at R. palustris concentrations of 1.21 and 0.4 g L−1, respectively. The experimentally measured hydrogen yield was in the range of 45 to 77% (±3.8%), and the glycerol consumption was 8 to 19% (±0.48). At a biomass concentration of 0.40 g L−1, the highest percentage of biomass (72.3%), was predicted to remain in suspension in the TPBR. Collectively, the proposed novel photobioreactor was shown to produce hydrogen as well as passively circulate biomass.A thermosiphon photobioreactor (TPBR) can potentially be used for biohydrogen production, circumventing the requirement for external mixing energy inputs. In this study, a TPBR is evaluated for photofermentative hydrogen production by  Hydrogen has gained increasing interest as a potential energy carrier . Moving R. palustris shows great promise for photofermentative hydrogen production, due to its acclimation ability to light intensity [R. palustris is principally facilitated by the photoheterotrophic metabolic route, meaning metabolism in the presence of light, a suitable carbon substrate, and under an anaerobic atmosphere. However, strain-dependent R. palustris has been shown to produce hydrogen in the temperature range of 30 to 42 °C, with 42 °C also being its physiological upper limit, beyond which the bacterial cells start to die [R. palustris is not associated with photoinhibition, due to it not being an oxygen-evolving microorganism, and also due to its ability to dissipate excess energy from high light intensities as heat through the use of carotenoids [−2 [R. palustris cells have been shown to achieve hydrogen production rates of up to 1.96 mol m−3h−1 [R. palustris has been shown to achieve hydrogen production rates of up to 1.2 mol m−3h−1 [Rhodobacteror capsulatus, 0.31–1.3 mol m−3h−1 [Rhodobacter spaeroides, 0.45 mol m−3h−1 [Rhodopseudomonas spaeroides, up to 4.5 mol m−3h−1 [Dark fermentation has been widely investigated and proven to be the most suitable method for sustainable biohydrogen production ,4. Dark ntensity ,11 and tntensity . Hydroget to die . R. paluotenoids ; howeveroids [−2 ,14. Undel m−3h−1 . Under ol m−3h−1 , while ol m−3h−1 ,22,23,24l m−3h−1 ,26; Rhodl m−3h−1 ).At present, the energy requirements for biohydrogen production are still predominantly greater than the energy output of the hydrogen product ,28. A reR. palustris. The combination of industrial waste streams as a carbon substrate and a cost-efficient PBR would not only decrease the cost of photofermentative hydrogen production [Crude glycerol, a by-product of the biodiesel industry, has gained increasing attention as a substrate in biological waste to value-added product processes—some of these processes include biogas production by anaerobic digestion ,36,37, loduction , but alsOne PBR implementing natural mixing is the thermosiphon photobioreactor (TPBR) . Such a R. palustris (NCIMB 11774) cells (concentration of 0.5 g L−1) in free suspension over a period of 4 h [−1 was reported [−1), half (0.11 m s−1), and alternately (0.36 m s−1) aerated airlift PBRs [−1) [R. palustris can optimally produce hydrogen. Consequently, for R. palustris to be able to produce hydrogen in a TPBR, insight is required into the effect of these conditions.Recently, a study on a prototype TPBR reported a promising ability for the circulation of biomass, where the TPBR (1 L working volume) was able to maintain up to 83% of active d of 4 h . Using creported  which faift PBRs  and tubuBRs [−1) . HoweverBRs [−1) . To add R. palustris under suitable operating conditions. Response surface methodology is applied to systematically determine the effect of some of the most critical factors, such as light intensity, temperature, and biomass concentration, affecting photofermentative hydrogen production as well as biomass circulation in a thermosiphon photobioreactor. In addition, suggestions are also made on operating conditions to enhance hydrogen production as well as biomass suspension in a TPBR, adding to the understanding and development of more cost-effective photobioreactors for sustainable hydrogen production.The overall research hypothesis of this study is that a novel thermosiphon photobioreactor using passive circulation, can be implemented for the application of photofermentative hydrogen production by Rhodopseudomonas palustris strain NCIMB 11774 was used for this study. R. palustris cells were precultured in a fast-growing Van Niels medium (ATCC® medium 112) containing (per liter): 1 g K2HPO4, 0.5 g MgSO4, 10 g yeast extract and filled up with deionised water [−2 (±20 W m−2) in the spectral range of 500 to 1100 nm. The cultures were allowed to grow for five days up until the mid-logarithmic phase was reached.The purple non-sulfur bacterium Rhodospirillaceae medium—modified minimal media containing (per liter): 0.6 g K2HPO4, 1.7 g KH2PO4, 0.02 g, MgSO4.7H2O, 0.005 g CaCl2.2H2O, 0.4 g NaCl, 0.3 g Na2S2O3, 0.0005 g ferric citrate, 0.0002 g para-aminobenzoic acid, and 1 mL of trace element solution containing: (per liter) 70 mg ZnCl2, 100 mg MnCl2.4H2O, 60 mg H3BO3, 200 mg CoCl2.6H2O, 20 mg CuCl2.2H2O, 20 mg NiCl2.6H2O, and 40 mg NaMoO4.2H2O [R. palustris cells. Precultured cells were centrifuged in autoclaved 250 mL centrifuge bottles  at 3500× g for 15 min , after which the supernatant was removed aseptically inside a sterile laminar flow cabinet, and the cell pellets were washed and resuspended in sterile minimal media. This procedure was repeated three times to ensure that no Van Niels media, and therefore, also no nitrogen remained on the cell pellets—this was to ensure a constant biomass concentration throughout each experimental run, i.e., no growth of R. palustris. After washing, the cell pellets were resuspended in sterile non-growing minimal media. Chemically pure, biological grade glycerol  was used throughout in this study.Hydrogen production experiments were conducted using a non-growing oO4.2H2O . The minoO4.2H2O . This me−1. It should be noted that the chiller was only used for the prototype TPBR—to reduce all costs associated with the TPBR, a cooling system operating without external energy inputs, e.g., cooling fins, will be employed in the future.The laboratory-scale prototype TPBR comprised a glass tubular loop (diameter of 24 mm), a cooling water jacket, and a GL45 polypropylene lid at the top of the reactor, which was modified by adding gastight stainless steel sampling ports. The riser section (length of 600 mm) was illuminated by a bank of four halogen floodlights , while the rest of the reactor, including the downcomer section (length of 450 mm) was insulated and shaded from the light. Cooling water was pumped from a water chiller , circulated through a cooling water jacket (volume of 442 mL) on the reactor and returned to the chiller at a fixed flow rate of 0.5 L minEvolved gas was collected in an inverted measuring cylinder (1 L), immersed in a water bath. The cylinder was connected to the PBR through low hydrogen-permeability tubing  which was fitted with a one-way valve to prevent reverse flow into the reactor. The volume of evolved gas was quantified via the water displacement method. For gas analysis, samples were taken from the gas sampling port situated at the top of the gas collecting chamber. Liquid samples (biomass and glycerol concentration) were taken aseptically from the liquid sampling port at the top of the reactor. The reactor temperature was monitored using three strategically positioned temperature sensors  connected to a data logging system. R. palustris cells suspended in 1 L of modified minimal media was aseptically added to an autoclaved  TPBR. The reactor was sparged with filter sterilised  argon gas (>99.9%) for 10 min, to ensure a dinitrogen-free atmosphere required for hydrogen production. The experimental run was initialized by switching on the halogen floodlights and cooling water to the reactor. Liquid samples were taken in time intervals of approximately 24 h, over a duration of 208 h. Similarly, the volume of evolved gas was also noted approximately every 24 h. All experimental runs were conducted in batch-mode, following the Box–Behnken experimental design with three center-point replications, allowing for the determination of statistical significance and standard deviation.A specified concentration of 660nm − 0.007 (Van Niels medium), R2 = 0.9981; CDW = 0.6391 × OD660nm + 0.0619 , R2 = 0.9996. The concentration of glycerol in the samples was measured using high-performance liquid chromatography (Dionex UltiMate 3000 HPLC). Samples were passed through disposable syringe filters  to remove all solid particles and to avoid blocking the HPLC column. Samples were then injected into the HPLC column  operating at a temperature of 65 °C, using an ERC Refracto Max520 RI detector. The mobile phase in the HPLC was a 0.005M H2SO4 solution at a flow rate of 0.6 mL min−1. Evolved gas samples were taken with a gastight gas sampling syringe, and analyzed using a gas chromatograph . The GC was equipped with a thermal conductivity detector (110 °C), using packed columns . Argon was used as the carrier gas (45 kPa), using 50 µL injections at 60 °C, with a split of 5 mL min−1. The oven temperature was set to 65 °C, the filament temperature was at 210 °C, and a reference flow rate of 1 mL min−1 was used. Since R. palustris only produces H2 and CO2, other gases present in the gas samples were not taken into account, and the GC values were normalized for H2 and CO2. The PBRs were illuminated by halogen flood lights. The light intensity was measured using a handheld spectrometer  with an optical fiber probe.To determine the cell dry weight (CDW), a CDW versus optical density (OD) standard curve was developed. OD measurements were made using a UV/Vis-spectrophotometer (Model AE-S60-4U), and converted to CDW using the following correlations: CDW = 0.7126 × OD2 m−3reactorh−1); (ii) the specific rate of hydrogen production (mmol H2 gCDW−1h−1); (iii) the hydrogen yield (%); (iv) the substrate consumption (%); and (v) the biomass suspension (%).RSM is a useful tool for the investigation of the effect of specific independent factors on a response, as well as for the investigation of the interaction between certain independent factors ,46. For Y denotes the response parameter, β0 is the offset term, jβ and jjβ the linear and quadratic coefficients, respectively, ijβ the interaction coefficient, and x the independent input variables. In Equation (1), R. palustris [R. palustris has been shown to grow and produce hydrogen. As mentioned, this range is approximately 70 to 600 W m−2 [R. palustris has been shown to be more productive in the higher end of this range. Though it would have been interesting to see what the response surfaces would have looked like when extending the range to the lower end (closer to the minimum of 70 W m−2), such low light intensities would not have been of much interest in terms of hydrogen productivity by R. palustris, as photo-saturation has been shown to start at approximately 200 W m−2 [R. palustris. The cells produce hydrogen in the range of 30 to 42 °C [−3 would have been relatively low for sufficient hydrogen production in the reactor, while concentrations greater than 1.2 kg m−3 would have resulted in all the light being attenuated through the cross-section of the riser of the reactor. As a result, the rear-end of the riser section would have been in the dark, essentially being a dead zone with no productivity.The ranges chosen for the input values were based on previous work on the batch photofermentation of glycerol using alustris ,12,48,49alustris ,43, keep00 W m−2 ; therefoto 42 °C . The bioR2 and adjusted R2-values, together with the model’s p-statistic, are given. A model exhibiting R2-values greater than 0.95 shows a good fit to the experimental data, while a p-value of less than 0.05 suggests a statistically significant correlation between the model/independent variable and the response [p-values greater than 0.05 were eliminated to produce models consisting only of statistically significant terms.To test the statistical significance of the regression models, an analysis of variance (ANOVA) was conducted. For each regression model, the response . RegressV) (Equation (2)), and the biomass concentration (m) (Equation (3)), using the total amount of hydrogen, in moles (Δn), produced over the course of the experimental run, together with the final time of 208 h .The rate of hydrogen production was assessed, both in terms of the reactor working volume (3H8O3 + 3H2O → 3CO2 + 7H2. The molar volume of hydrogen (at NTP) was determined using the composition of hydrogen in the evolved gas. The hydrogen content in the evolved gas varied between 88% and 94% (±1%), with the balance being carbon dioxide.Hydrogen yield (Equation (4)) was determined as the molar ratio of hydrogen produced to glycerol, consumed as a percentage of the theoretical maximum, per the stoichiometric conversion of glycerol to hydrogen: Ct, to the glycerol initially in the system (Equation (5)).Glycerol consumption was evaluated as the molar ratio of glycerol consumed at time t (tc), to the initial biomass concentration (oc) measured before each experimental run (Equation (6)). A liquid sample was taken at the top of the reactor to determine the biomass concentration in free suspension at any time, while the initial biomass concentration remained constant throughout each experimental run, due to the use of non-growing culture medium—this was verified by also measuring the biomass concentration at the end of each run.Biomass suspension was assessed as the ratio of the concentration of bacterial cells in free suspension at time −1)—this is expected to have had a slight effect on the calculated standard deviations; however, the standard deviation was still reported to give a good guideline of the variance in the data.The standard deviations of the time profiles referred to in R2- and adjusted R2-values of 0.969 and 0.957, respectively, and an overall p-value of 1.63 × 10−7. The prediction model for specific hydrogen production per biomass concentration had R2- and adjusted R2-values that were slightly lower −0.893 and 0.850, and a p-value of 0.0000767.To determine the effects of the mentioned operating parameters, predictive models were developed for response parameters, such as rate of hydrogen production (Equations (8) and (9)). The model generated for the rate of hydrogen production per reactor volume fitted the experimental data well, with R. palustris to produce hydrogen in the temperature range of 30 to 42 °C [−3h−1 can be achieved at a biomass concentration of 1.16 g L−1. This was expected, as more bacterial cells were present in the reactor to produce hydrogen, while the reactor working volume remained constant. An experimentally measured time-profile of the cumulative hydrogen production at similar conditions to that predicted with the response model (Equation (8)) is provided in −3h−1 at similar conditions.The rate of hydrogen production was affected both by biomass concentration as well as cooling water inlet temperature and the interaction between biomass concentration and light intensity. The actual operating temperature inside the TPBR, defined as the average of the three temperatures measured in the reactor ). The model had an overall p-value of 0.0008, suggesting a statistically significant prediction model.From experimental measurements, between 8 and 19% (±0.48%) of the initial glycerol in the system (50 ± 4.3 mM) was consumed by the −2 (±20 W m−2), the light intensity had no statistically significant effect on glycerol consumption. This was also the case for the cooling water inlet temperature, while the initial biomass concentration had the most significant effect. The optimal biomass concentration for glycerol consumption by R. palustris was approximately 1.15 g L−1, predicting a glycerol consumption of 17.5% of the initial concentration in the system.In the range of 400 to 600 W mR2-value of 0.441 and an even lower adjusted R2-value of 0.348. The reduced prediction model produced a p-value of 0.030.Of the 8 to 19% of glycerol that was consumed during each experimental run, approximately 45 to 77% (±3.8%) of that glycerol was converted to hydrogen gas, depending on the conditions—this was slightly lower than the literature-reported crude glycerol conversion efficiency of 90% [In the ranges investigated, the experimentally determined hydrogen yield was relatively low ,15 prompR. palustris tends to generate internal storage products, specifically poly-hydroxybutyrate (PHB), glycogen, and trehalose [It has also been shown that rehalose ,55 when rehalose ,57. SincR2-value of 0.843, an adjusted R2-value of 0.800, and a p-value of 0.0000998, indicating a statistically significant regression model.The percentage of bacterial cells in suspension decreased substantially over the duration of the experimental runs, with the final measured suspension values (at 208 h) ranging from approximately 42 to 75% (±2.9%), depending on the conditions. From visual observation, the cells seemed to settle on all horizontal/inclined areas, presumably due to insufficient fluid velocity. The predictive model for biomass suspension is given by Equation (16). The model produced an −1, the lowest concentration in the range investigated, maintaining approximately 72.3% of the biomass in suspension over a period of 208 h.As for glycerol consumption and hydrogen yield, the light intensity did not have a statistically significant effect on the suspension of biomass in the reactor, while biomass concentration had the most pronounced effect. According to the prediction model, as well as the response surface plot provided in R. palustris cells being under sub-optimal conditions—e.g., nutrient starvation, mutual shading, excessive light intensity, inefficient mixing, and/or operating temperatures outside the physiological limits of the cells—the cells tend to lump together, forming small clumps with higher terminal settling velocities than for single bacterial cells [A biomass circulation being better at lower biomass concentrations was to be expected; however, the measured suspension values were still relatively low. This can be attributed to the upward circulation velocity in the reactor being slower than the terminal settling velocities of the bacterial cells, and/or clumps of cells forming. It is also inferred that the cells in suspension had mostly been motile daughter cells, rather than the larger mother cells. The daughter cells are smaller than the mother cells , meaningal cells . This isR. palustris. The effects of light intensity, inlet cooling water temperature, and biomass concentration were evaluated with RSM. The predictive regression models generated were used to investigate the effect of the abovementioned operating conditions on hydrogen production, as well as biomass circulation in the proposed TPBR.In this work, hydrogen production experiments were conducted at various independent operating conditions to evaluate the use of a prototype TPBR for photofermentative hydrogen production by Biomass concentration was found to have had the most significant effect on the rate of hydrogen production and glycerol consumption, as well as biomass suspension. The effect of light intensity was expected to be more pronounced; however, it was only significant for the rate of hydrogen production when interacting with biomass concentration. Further investigations into light intensity and light distribution in the TPBR would be beneficial to the understanding of this system. The inlet cooling water temperature had little effect on the evaluated responses.R. palustris cells, i.e., approximately 0.4 g L−1; however, in terms of maximum hydrogen production and carbon substrate consumption, the overall system performed better at higher biomass concentrations of approximately 1.2 g L−1. The system was able to utilize approximately 8 to 19% of the carbon substrate present. Under the conditions investigated, R. palustris converted approximately 45 to 77% of the glycerol to hydrogen gas, while redirecting a portion of the consumed glycerol to the production of PHB. Though not the focus of the present study, PHB has gained increasing attention in the field of bioplastics, and it can therefore also add value to this proposed system, allowing for further investigation. For bioremediation and hydrogen production, it is recommended that the TPBR be operated at higher light intensities and biomass concentrations, provided that these conditions are within the physiological limits of R. palustris. Collectively, the overall research hypothesis of the study was verified—R. palustris cells were demonstrated to be able to produce hydrogen in the proposed TPBR, while, with a few alterations, the TPBR has also been proven to be a suitable prospect for the application of photofermentative hydrogen production. Though the hydrogen productivity of the system were slightly lower than that achieved by conventional photobioreactors, the proposed photobioreactor still merits consideration as an alternative photobioreactor for sustainable biohydrogen production—it is currently the only photobioreactor with the prospect of operating without any external energy inputs, which could, in the future, balance out its lower efficiency of hydrogen productivity.In the ranges investigated, the proposed TPBR generated satisfactory fluid flow and was able to maintain up to 77% of biomass in suspension. The TPBR itself performed best when containing lower concentrations of


The K-homology (KH) domains are small, structurally conserved domains found in proteins of different origins characterized by a central conserved βααβ “core” and a GxxG motif in the loop between the two helices of the KH core. In the eukaryotic KHI type, additional αβ elements decorate the “core” at the C-terminus. Proteins containing KH domains perform different functions and several diseases have been associated with mutations in these domains, including those in the fragile X mental retardation protein (FMRP). FMRP is an RNA-binding protein crucial for the control of RNA metabolism whose lack or mutations lead to fragile X syndrome (FXS). Among missense mutations, the R138Q substitution is in the KH0 degenerated domain lacking the classical GxxG motif. By combining equilibrium and kinetic experiments, we present a characterization of the folding mechanism of the KH0 domain from the FMRP wild-type and of the R138Q variant showing that in both cases the folding mechanism implies the accumulation of an on-pathway transient intermediate. Moreover, by exploiting a battery of biophysical techniques, we show that the KH0 domain has the propensity to form amyloid-like aggregates in mild conditions in vitro and that the R138Q mutation leads to a general destabilization of the protein and to an increased fibrillogenesis propensity.  The K-homology (KH) domain is a small, structurally conserved domain originally identified in the human heterogeneous nuclear ribonucleoprotein K (hnRNP K) ,2. This βα, prokaryotic type II KH  or type III KH, where the two additional elements are split between the N-terminus (the α-helix) and C-terminus (the β-strand)) . Fluorescence emission (λex = 440 nm) with a FluoroMax-4 spectrofluorometer  using a 1 cm path-length quartz cuvette in 20 mM sodium phosphate pH 7.2, 100 mM NaCl. Aggregation kinetic experiments at different temperatures were carried out in the same conditions by adding a stoichiometric amount of ThT to a KH0 or KH0-R138Q (10 μM) preincubated at 37 °C or 55 °C. The kinetics were followed by recording fluorescence spectra over time and then plotting the emission at 482 nm as a function of time.Thioflavin T  stock solution (2.5 mM) was added to proteins pre-incubated for 10 min at 20 °C or 70 °C . ThT fluorescence spectra were recorded between 460 and 550 nm , provided with a tungsten hexaboride filament and a Phurona (Emsis) CMOS camera for image acquisition. The images were processed using ImageJ 1.53k to measure fibril dimensions and to subtract the noisy background .2 diameter. Diffraction experiments were carried out at the XRD2 beamline at Elettra synchrotron, Trieste  . Data weKH0 and KH0-R138Q (100 μM) were heated at 70 °C for 10 min or 37 °C for 50 h, centrifugated, and then pellets were washed three times with MQ water to remove buffer residues. Kapton mesh loops  were used as support to let 10 μL protein aliquots run dry. To collect diffraction images, the resulting dried thin layer of randomly oriented fibrils was characterized without spinning, using an exposure time of 300 s. One-dimensional diffraction patterns were obtained by radial integration of the area detector images using FIT2D program .Standard experimental setup calibration was performed using a capillary filled with lanthanum hexaboride (LaB6-NIST 660a) powder .Unf is the free energy change for unfolding for a given denaturant concertation, ΔGH2O the free energy change for unfolding in the absence of denaturant and m a term which quantifies the change in ΔGUnf as function of denaturant, R the gas constant, T the temperature (K), and KUnf the equilibrium constant for unfolding.All urea-induced equilibrium unfolding transitions monitored by far-UV CD ellipticity or intrinsic fluorescence emission were analyzed by fitting the baseline and transition region data to a two-state linear extrapolation model  accordinKinetic data were analyzed on the basis of a three-state model, with either an on- or off-pathway intermediate, assuming a linear dependence of the logarithm of the microscopic rate constants on the denaturant concentration .