Insights into Activation Dynamics and Functional Sites of Inwardly Rectifying Potassium Channel Kir3.2 by an Elastic Network Model Combined with Perturbation Methods.

The inwardly rectifying potassium channel Kir3.2, a member of the inward rectifier potassium (Kir) channel family, exerts important biological functions through transporting potassium ions outside of the cell, during which a large-scale synergistic movement occurs among its different domains. Currently, it is not fully understood how the binding of the ligand to the Kir3.2 channel leads to the structural changes and which key residues are responsible for the channel gating and allosteric dynamics. Here, we construct the Gaussian network model (GNM) of the Kir3.2 channel with the secondary structure and covalent interaction information considered (sscGNM), which shows a better performance in reproducing the channel's flexibility compared with the traditional GNM. In addition, the sscANM-based perturbation method is used to simulate the channel's conformational transition caused by the activator PIP2's binding. By applying certain forces to the PIP2 binding pocket, the coarse-grained calculations generate the similar conformational changes to the experimental observation, suggesting that the topology structure as well as PIP2 binding are crucial to the allosteric activation of the Kir3.2 channel. We also utilize the sscGNM-based thermodynamic cycle method developed by us to identify the key residues whose mutations significantly alter the channel's binding free energy with PIP2. We identify not only the residues important for the specific binding but also the ones critical for the allosteric transition coupled with PIP2 binding. This study is helpful for understanding the working mechanism of Kir3.2 channels and can provide important information for related drug design.

Core-genome-mediated promising alternative drug and multi-epitope vaccine targets prioritization against infectious Clostridium difficile.

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Dynamic Insights into the Self-Activation Pathway and Allosteric Regulation of the Orphan G-Protein-Coupled Receptor GPR52.

Within over 800 members of G-protein-coupled receptors, there are numerous orphan receptors whose endogenous ligands are largely unknown, providing many opportunities for novel drug discovery. However, the lack of an in-depth understanding of the intrinsic working mechanism for orphan receptors severely limits the related rational drug design. The G-protein-coupled receptor 52 (GPR52) is a unique orphan receptor that constitutively increases cellular 5'-cyclic adenosine monophosphate (cAMP) levels without binding any exogenous agonists and has been identified as a promising therapeutic target for central nervous system disorders. Although recent structural biology studies have provided snapshots of both active and inactive states of GPR52, the mechanism of the conformational transition between these states remains unclear. Here, an acceptable self-activation pathway for GPR52 was proposed through 6 µs Gaussian accelerated molecular dynamics (GaMD) simulations, in which the receptor spontaneously transitions from the active state to that matching the inactive crystal structure. According to the three intermediate states of the receptor obtained by constructing a reweighted potential of mean force, how the allosteric regulation occurs between the extracellular orthosteric binding pocket and the intracellular G-protein-binding site is revealed. Combined with the independent gradient model, several important microswitch residues and the allosteric communication pathway that directly links the two regions are both identified. Transfer entropy calculations not only reveal the complex allosteric signaling within GPR52 but also confirm the unique role of ECL2 in allosteric regulation, which is mutually validated with the results of GaMD simulations. Overall, this work elucidates the allosteric mechanism of GPR52 at the atomic level, providing the most detailed information to date on the self-activation of the orphan receptor.

Study of the Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase by Transfer Entropy via an Improved Gaussian Network Model and Co-evolution Analyses.

We propose an improved transfer entropy approach called the dynamic version of the force constant fitted Gaussian network model based on molecular dynamics ensemble (dfcfGNMMD) to explore the allosteric mechanism of human mitochondrial phenylalanyl-tRNA synthetase (hmPheRS), one of the aminoacyl-tRNA synthetases that play a crucial role in translation of the genetic code. The dfcfGNMMD method can provide reliable estimates of the transfer entropy and give new insights into the role of the anticodon binding domain in driving the catalytic domain in aminoacylation activity and into the effects of tRNA binding and residue mutation on the enzyme activity, revealing the causal mechanism of the allosteric communication in hmPheRS. In addition, we incorporate the residue dynamic and co-evolutionary information to further investigate the key residues in hmPheRS allostery. This study sheds light on the mechanisms of hmPheRS allostery and can provide important information for related drug design.

Key Residues in δ Opioid Receptor Allostery Explored by the Elastic Network Model and the Complex Network Model Combined with the Perturbation Method.

Opioid receptors, a kind of G protein-coupled receptors (GPCRs), mainly mediate an analgesic response via allosterically transducing the signal of endogenous ligand binding in the extracellular domain to couple to effector proteins in the intracellular domain. The δ opioid receptor (DOP) is associated with emotional control besides pain control, which makes it an attractive therapeutic target. However, its allosteric mechanism and key residues responsible for the structural stability and signal communication are not completely clear. Here we utilize the Gaussian network model (GNM) and amino acid network (AAN) combined with perturbation methods to explore the issues. The constructed fcfGNMMD, where the force constants are optimized with the inverse covariance estimation based on the correlated fluctuations from the available DOP molecular dynamics (MD) ensemble, shows a better performance than traditional GNM in reproducing residue fluctuations and cross-correlations and in capturing functionally low-frequency modes. Additionally, fcfGNMMD can consider implicitly the environmental effects to some extent. The lowest mode can well divide DOP segments and identify the two sodium ion (important allosteric regulator) binding coordination shells, and from the fastest modes, the key residues important for structure stabilization are identified. Using fcfGNMMD combined with a dynamic perturbation-response method, we explore the key residues related to the sodium ion binding. Interestingly, we identify not only the key residues in sodium ion binding shells but also the ones far away from the perturbation sites, which are involved in binding with DOP ligands, suggesting the possible long-range allosteric modulation of sodium binding for the ligand binding to DOP. Furthermore, utilizing the weighted AAN combined with attack perturbations, we identify the key residues for allosteric communication. This work helps strengthen the understanding of the allosteric communication mechanism in δ opioid receptor and can provide valuable information for drug design.

Allosteric mechanism for SL RNA recognition by polypyrimidine tract binding protein RRM1: An atomistic MD simulation and network-based study.

Polypyrimidine tract-binding protein (PTB), an RNA-binding protein, is involved in the regulation of diverse processes in mRNA metabolism. However, the allosteric modulation of its binding with RNA remains unclear. We explore the dynamic characteristics of PTB RNA recognition motif 1 (RRM1) in its RNA-free and wild-type/mutant RNA-bound states to understand the issues using molecular dynamics (MD) simulation, perturbation response scanning (PRS) and protein structure network (PSN) models. It is found that RNA binding strengthens RRM1 stability, while L151G mutation in α3 helix far away from the interface makes the complex unstable. The latter is caused by long-distance dynamic couplings, which makes intermolecular electrostatic and entropy energies unfavorable. The weakened couplings between interface ß sheets and C-terminal parts upon mutation reveal RNA recognition is co-regulated by these regions. Interestingly, PRS analysis reveals the allostery caused by the perturbation on α3 helix has already been pre-encoded in the equilibrium dynamics of the protein structure. PSN analysis shows the details of the allosteric signal transmission, revealing the necessity of strong couplings between α3 helix and interface for maintaining the high binding affinity. This study sheds light on the mechanisms of PTB allostery and RNA recognition and can provide important information for drug design.

Insight into the nucleoside transport and inhibition of human ENT1.

The human equilibrative nucleoside transporter 1 (hENT1) is an effective controller of adenosine signaling by regulating its extracellular and intracellular concentration, and has become a solid drug target of clinical used adenosine reuptake inhibitors (AdoRIs). Currently, the mechanisms of adenosine transport and inhibition for hENT1 remain unclear, which greatly limits the in-depth understanding of its inner workings as well as the development of novel inhibitors. In this work, the dynamic details of hENT1 underlie adenosine transport and the inhibition mechanism of the non-nucleoside AdoRIs dilazep both were investigated by comparative long-time unbiased molecular dynamics simulations. The calculation results show that the conformational transitions of hENT1 from the outward open to metastable occluded state are mainly driven by TM1, TM2, TM7 and TM9. One of the trimethoxyphenyl rings in dilazep serves as the adenosyl moiety of the endogenous adenosine substrate to competitively occupy the orthosteric site of hENT1. Due to extensive and various VDW interactions with N30, M33, M84, P308 and F334, the other trimethoxyphenyl ring is stuck in the opportunistic site near the extracellular side preventing the complete occlusion of thin gate simultaneously. Obviously, dilazep shows significant inhibitory activity by disrupting the local induce-fit action in substrate binding cavity and blocking the transport cycle of whole protein. This study not only reveals the nucleoside transport mechanism by hENT1 at atomic level, but also provides structural guidance for the subsequent design of novel non-nucleoside AdoRIs with enhanced pharmacologic properties.

Specific recognition between YTHDF3 and m6 A-modified RNA: An all-atom molecular dynamics simulation study.

The YTH domain of YTHDF3 belongs to a class of protein "readers" recognizing the N6-methyladenosine (m6 A) modification in mRNA. Although static crystal structure reveals m6 A recognition by a conserved aromatic cage, the dynamic process in recognition and importance of aromatic cage residues are not completely clear. Here, molecular dynamics (MD) simulations are performed to explore the issues and negative selectivity of YTHDF3 toward unmethylated substrate. Our results reveal that there exist conformation selectivity and induced-fit in YTHDF3 binding with m6 A-modified RNA, where recognition loop and loop6 play important roles in the specific recognition. m6 A modification enhances the stability of YTHDF3 in complex with RNA. The methyl group of m6 A, like a warhead, enters into the aromatic cage of YTHDF3, where Trp492 anchors the methyl group and constraints m6 A, making m6 A further stabilized by π-π stacking interactions from Trp438 and Trp497. In addition, the methylation enhances the hydrophobicity of adenosine, facilitating water molecules excluded out of the aromatic cage, which is another reason for the specific recognition and stronger intermolecular interaction. Finally, the comparative analyses of hydrogen bonds and binding free energy between the methylated and unmethylated complexes reveal the physical basis for the preferred recognition of m6 A-modified RNA by YTHDF3. This study sheds light on the mechanism by which YTHDF3 specifically recognizes m6 A-modified RNA and can provide important information for structure-based drug design.

Computer-Aided Rational Construction of Mediated Bioelectrocatalysis with π-Conjugated (Hetero)cyclic Molecules: Toward Promoted Distant Electron Tunneling and Improved Biosensing.

Highly π-conjugated (hetero)cyclic molecules having delocalized orbitals and tunable charge mobilities are attractive redox relays for mediated bioelectrocatalysis in manifold applications. As rigid molecules, their dynamics within the soft but confined intraprotein space becomes the crucial determinant of the enzyme-mediator electron-tunneling efficiency. However, it is rarely investigated in designing the mediated interface with a particular biocatalyst (e.g., oxidoreductase), which remains an empirical but try-and-error process. Herein, we present the computer-aided exploration of interactions between a flavin-containing reductive synthase and structurally diverse π-extended (hetero)cyclic mediators to realize reversed bioelectrocatalytic oxidation at low overpotentials. Compared to ring-fused systems with unbroken molecular planarity, heteroatom-bridged cyclic, and rotatable conjugated structures (e.g., indophenols) can experience unusually large dynamic torsion under biased forces of hydrogen bonding with enzyme residues. This behavior led to fast intraprotein reorientation (<50 ps) that shortened the electron-tunneling distance from 12 to 9 Å. Meanwhile, the lowest unoccupied molecular orbital level upon molecular torsion was decreased by 0.5 eV to further promote electron abstraction from the reduced flavin cofactor. An efficient distant electron tunneling also obviated mediator transport through the substrate channel, thus avoiding competitive inhibition on enzyme kinetics to broaden the operating concentration range. The resulting bioelectrocatalytic interface enables low-potential biosensing of glutamate with improved selectivity. Our finding provides new structural insights into constructing efficient long-range heterogeneous charge transport with biomacromolecular catalysts.

Dynamics of binding interactions of TDP-43 and RNA: An equally weighted multiscale elastic network model study.

Transactive response DNA binding protein 43 (TDP-43), an alternative-splicing regulator, can specifically bind long UG-rich RNAs, associated with a range of neurodegenerative diseases. Upon binding RNA, TDP-43 undergoes a large conformational change with two RNA recognition motifs (RRMs) connected by a long linker rearranged, strengthening the binding affinity of TDP-43 with RNA. We extend the equally weighted multiscale elastic network model (ewmENM), including its Gaussian network model (ewmGNM) and Anisotropic network model (ewmANM), with the multiscale effect of interactions considered, to the characterization of the dynamics of binding interactions of TDP-43 and RNA. The results reveal upon RNA binding a loss of flexibility occurs to TDP-43's loop3 segments rich in positively charged residues and C-terminal of high flexibility, suggesting their anchoring RNA, induced fit and conformational adjustment roles in recognizing RNA. Additionally, based on movement coupling analyses, it is found that RNA binding strengthens the interactions among intra-RRM ß-sheets and between RRMs partially through the linker's mediating role, which stabilizes RNA binding interface, facilitating RNA binding efficiency. In addition, utilizing our proposed thermodynamic cycle method combined with ewmGNM, we identify the key residues for RNA binding whose perturbations induce a large change in binding free energy. We identify not only the residues important for specific binding, but also the ones critical for the conformational rearrangement between RRMs. Furthermore, molecular dynamics simulations are also performed to validate and further interpret the ENM-based results. The study demonstrates a useful avenue to utilize ewmENM to investigate the protein-RNA interaction dynamics characteristics.

Allosteric Mechanism of Human Mitochondrial Phenylalanyl-tRNA Synthetase: An Atomistic MD Simulation and a Mutual Information-Based Network Study.

Aminoacyl-tRNA synthetases (aaRSs), a family of ubiquitous and essential enzymes, can bind target tRNAs and catalyze the aminoacylation reaction in genetic code translation. In this work, we explore the dynamic properties and allosteric communication of human mitochondrial phenylalanyl-tRNA synthetase (hmPheRS) in free and bound states to understand the mechanisms of its tRNAPhe recognition and allostery using molecular dynamics simulations combined with the torsional mutual information-based network model. Our results reveal that hmPheRS's residue mobility and inter-residue motional coupling are significantly enhanced by tRNAPhe binding, and there occurs a strong allosteric communication which is critical for the aminoacylation reaction, suggesting the vital role of tRNAPhe binding in the enzyme's function. The identified signaling pathways mainly make the connections between the anticodon binding domain (ABD) and catalytic domain (CAD), as well as within the CAD composed of many functional fragments and active sites, revealing the co-regulation role of them to act coordinately and achieve hmPheRS's aminoacylation function. Besides, several key residues along the communication pathways are identified to be involved in mediating the coordinated coupling between anticodon recognition at the ABD and activation process at the CAD, showing their pivotal role in the allosteric network, which are well consistent with the experimental observation. This study sheds light on the allosteric communication mechanism in hmPheRS and can provide important information for the structure-based drug design targeting aaRSs.

Insight into Shared Properties and Differential Dynamics and Specificity of Secretory Phospholipase A2 Family Members.

Understanding generic mechanisms of functions shared by the secretory phospholipase A2 (sPLA2) family involved in the lipid metabolism and cell signaling and the molecular basis of function specificity for family members is an intriguing but challenging problem for biologists. Here, we explore the issue through extensive analyses using a combination of structure-based methods and bioinformatics tools on130 sPLA2 family members. The principal component analysis of the structure ensemble reveals that the enzyme has an open-close motion which helps widen the substrate binding channel, facilitating its binding to phospholipid. Performing elastic network model and sequence analyses found that the residues critical for family functions, such as cysteine and catalytic residues, are highly conserved and undergo minimal movements, which is evolutionarily essential as their perturbation would impact the function, while the four residue regions involved in the association with the calcium ion/membrane are lowly conserved and of high mobility and large variations in low-to-intermediate frequency modes, which reflects the specificity of members. The analyses from perturbation response scanning also reveal that the above four regions with high sensitivity to an external perturbation are member-specific, suggesting their different roles in allosteric modulation, while the minimal sensitive residues are the shared characteristics across family members, which play an important role in maintaining structural stability as the folding core. This study is helpful for understanding how sequences, structures, and dynamics of sPLA2 family members evolve to ensure their common and specific functions and can provide a guide for accurate design of proteins with finely tuned activities.

Edaphic variables are better indicators of soil microbial functional structure than plant-related ones in subtropical broad-leaved forests.

Soil microorganisms play important roles in the ecosystem functioning of subtropical broad-leaved forests (SBFs). However, the patterns and environmental indicators of soil microbial functional structure remain unclear in SBFs. In the present work, we used a functional microarray (GeoChip 4.0) to examine the soil microbial functional structure of three types of SBFs, including a deciduous broad-leaved forest (DBF), a mixed evergreen-deciduous broad-leaved forest (MBF), and an evergreen broad-leaved forest (EBF). We found that microbial functional structure was significantly different among SBFs (P < 0.05). Compared to the DBF and the EBF, the MBF had higher functional α-diversity (P = 0.001, F = 12.55) but lower ß-diversity (P < 0.001, F = 61.09), and showed more complex functional gene networks. Besides, the MBF had higher relative abundances of functional genes for carbon (C) decomposition, C fixation, nitrogen (N) cycling, sulfur (S) cycling, and phosphorus (P) cycling (P < 0.05), indicating stronger microbial functional capabilities of nutrient cycling processes. Edaphic variables (i.e., soil pH and soil nutrient content) were revealed as better indicators of soil microbial functional structure than plant-related ones (i.e., vegetation type and plant diversity) in SBFs. For example, functional gene structure of the DBF was significantly related to soil total S (P = 0.041), that of the MBF was significantly related to soil organic C (P = 0.027) and plant available P (P = 0.034), and that of the EBF was significantly related to soil pH (P = 0.006) and total potassium (K) (P = 0.038). Overall, through the analysis of microbial functional gene profiles, this study yields unique insights into the environmental indicators of patterns and mechanisms of soil microbial functional structure in SBFs.

Equally Weighted Multiscale Elastic Network Model and Its Comparison with Traditional and Parameter-Free Models.

Dynamical properties of proteins play an essential role in their function exertion. The elastic network model (ENM) is an effective and efficient tool in characterizing the intrinsic dynamical properties encoded in biomacromolecule structures. The Gaussian network model (GNM) and anisotropic network model (ANM) are the two often-used ENM models. Here, we introduce an equally weighted multiscale ENM (equally weighted mENM) based on the original mENM (denoted as mENM), in which fitting weights of Kirchhoff/Hessian matrixes in mENM are removed since they neglect the details of pairwise interactions. Then, we perform its comparison with the mENM, traditional ENM, and parameter-free ENM (pfENM) in reproducing dynamical properties for the six representative proteins whose molecular dynamics (MD) trajectories are available in http://mmb.pcb.ub.es/MoDEL/. In the results, for B-factor prediction, mENM performs best, while the equally weighted mENM performs also well, better than the traditional ENM and pfENM models. As to the dynamical cross-correlation map calculation, mENM performs worst, while the results produced from the equally weighted mENM and pfENM models are close to those from MD trajectories with the latter a little better than the former. Furthermore, encouragingly, the equally weighted mANM displays the best performance in capturing the functional motional modes, followed by pfANM and traditional ANM models, while the mANM fails in all the cases. This work is helpful for strengthening the understanding of the elastic network model and provides a valuable guide for researchers to utilize the model to explore protein dynamics.

Study on functional sites in human multidrug resistance protein 1 (hMRP1).

Human multidrug resistance protein 1 (hMRP1) is an important member of the ATP-binding cassette (ABC) transporter superfamily. It can extrude a variety of anticancer drugs and physiological organic anions across the plasma membrane, which is activated by substrate binding, and is accompanied by large-scale cooperative movements between different domains. Currently, it remains unclear completely about how the specific interactions between hMRP1 and its substrate are and which critical residues are responsible for allosteric signal transduction. To the end, we first construct an inward-facing state of hMRP1 using homology modeling method, and then dock substrate proinflammatory agent leukotriene C4 (LTC4) to hMRP1 pocket. The result manifests LTC4 interacts with two parts of hMRP1 pocket, namely the positively charged pocket (P pocket) and hydrophobic pocket (H pocket), similar to its binding mode with bMRP1 (bovine MRP1). Additionally, we use the Gaussian network model (GNM)-based thermodynamic method proposed by us to identify the key residues whose perturbations markedly alter their binding free energy. Here the conventional GNM is improved with covalent/non-covalent interactions and secondary structure information considered (denoted as sscGNM). In the result, sscGNM improves the flexibility prediction, especially for the nucleotide binding domains with rich kinds of secondary structures. The 46 key residue clusters located in different subdomains are identified which are highly consistent with experimental observations. Furtherly, we explore the long-range cooperation within the transporter. This study is helpful for strengthening the understanding of the work mechanism in ABC exporters and can provide important information to scientists in drug design studies.

An investigation into the allosteric mechanism of GPCR A2A adenosine receptor with trajectory-based information theory and complex network model.

G protein-coupled receptors (GPCRs), a large superfamily of transmembrane (TM) proteins, allosterically transduce the signal of ligand binding in the extracellular (EC) domain to couple to effector proteins in the intracellular (IC) domain, therefore forming the largest class of drug targets. The A2A adenosine receptor (A2AAR), a class-A GPCR, has been extensively studied as it offers numerous possibilities for therapeutic applications. However, the mechanism of allosteric communication between EC and IC domains is not completely clear. In this work, we utilize torsional mutual information to quantify the correlated motions of residue pairs from its molecular dynamics (MD) simulation trajectories, and further use the complex network model to obtain allosteric pipelines and hubs. The identified allosteric communication pipelines mainly transmit the signal from EC domain to the cytoplasmic ends of TM helix 5 (TM5), TM6 and TM7. The allosteric hubs, mostly located at TM5, TM6 and TM7, play an important role in mediating allosteric signal transmission to keep the receptor rigid and prevent G protein from binding to IC domain, which can explain the reason why their mutations distant from ligand-binding site do not affect the ligand binding affinity but affect the ligand efficacy. Additionally, we identify the key residues located in antagonist ZM241385 binding pocket which mediate multiple allosteric pathways and have been experimentally proven to play a critical role in affecting the ligand potency. This study is helpful for understanding the allosteric communication mechanism of A2AAR, and can provide valuable information for the structure-based drug design of GPCRs.Communicated by Ramaswamy H. Sarma.

Interpreting the Dynamics of Binding Interactions of snRNA and U1A Using a Coarse-Grained Model.

The binding interactions of small nuclear RNAs (snRNA) and the associated protein factors are critical to the function of spliceosomes in alternatively splicing primary RNA transcripts. Although molecular dynamics simulations are a powerful tool to interpret the mechanism of biological processes, the atomic-level simulations are, however, too expensive and with limited accuracy for the large-size systems, such as snRNA-protein complexes. We extend the coarse-grained Gaussian network model, which models the RNA-protein complexes as a harmonic chain of Cα, P, and O4' atoms, to investigating the impact of the snRNA-binding interaction on the dynamic stability of the human U1A protein, which is a major component of the spliceosomal U1 small nuclear ribonucleoprotein particle. The results reveal that the first and third loops and the C-terminal helix regions of the U1A domain undergo a significant loss of flexibility upon the RNA binding due to the forming of mostly electrostatic and hydrogen bond interactions with RNA 5' stem and loop. By examining the residues whose mutations significantly change the binding free energy between U1A and snRNA, the Gaussian network model-based calculations show that not only the residues at the binding sites that are traditionally considered to play a major role in U1A-RNA association but also those residues that are far away from the RNA-binding interface can participate in the long-range allosteric signal transmission; these calculations are quantitatively consistent with the data observed in the recent snRNA binding experiments. The study demonstrates a useful avenue to utilize the simplified elastic network model to investigate the dynamics characteristics of the biologically important macromolecular interactions.

Exploring Ferredoxin-Dependent Glutamate Synthase as an Enzymatic Bioelectrocatalyst.

Ferredoxin-dependent glutamate synthase (Fd-GltS) is reported as an enzymatic bioelectrocatalyst for the first time. By configuring mediated electrochemical interfaces with mediators of different redox potentials, we realize bioelectrosynthesis or bioelectrooxidation of glutamate with recombinant Fd-GltS from cyanobacteria. Particularly, bioelectrocatalytic oxidation of glutamate by Fd-GltS is demonstrated to be oxygen independent. This study reinforces a new catalytic option for developing enzymatic bioelectronic devices for powering, sensing or synthesis.

Physicochemical differences between sorghum starch and sorghum flour modified by heat-moisture treatment.

Sorghum starch and sorghum flour were modified by heat-moisture treatment (HMT) at two different moisture contents, 20% and 25%. The result showed that solubility and swelling power of modified samples decreased. In addition, the pasting viscosities of most modified samples were lower than that of native samples. The onset, peak and conclusion temperatures of gelatinization, and the enthalpy of samples modified by HMT increased. The crystallinity of the modified samples was higher than that of control samples. HMT had a far greater effect on the solubility, swelling power, setback viscosity, through viscosity, enthalpy and crystallinity of sorghum flour than of sorghum starch. On the granules surface there were more holes for the HMT starches than for HMT flours. The microstructure of HMT sorghum starch gel had a more orderly and smaller holey structure. The sorghum flour gel had originally a crackled structure, but after the HMT treatment, it had many ordered and small holes.