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At least 955 records · Page 53

Unraveling Bacterial Adaptation Strategies in the Microbiome Shaped by the Chemical Environment of the Plant Rhizosphere

The rhizosphere is a dynamic environment where rhizodeposits that include primary and secondary metabolites and mucilage serve as nutrient sources for soil microorganisms, attracting them toward plant roots. However, understanding how these microbes specifically respond to plant root chemical signals has been hindered by the challenges of disentangling physical and chemical interactions between the microbes and plant roots. To address this, we implemented an innovative filter-based experimental setup on plant roots that creates a physical barrier while facilitating the exchange of chemical signals. The proteomic analysis of 10 Populus root-associated bacterial strains grown in the presence or absence of a plant in either individual or mixed community conditions provided detailed insights into the functional responses of these strains to the root chemical environment. Additionally, this approach allowed us to discern the impact of root exudates on overall community dynamics. In particular, metaproteomic analyses revealed that each of these 10 microbial members responds uniquely to the presence of the plant, with Bacillus and Pantoea exhibiting the most dramatic favorable impact. Proteomic examination revealed the details of metabolism fine-tuning, including processes such as chemotaxis and ATP-binding cassette transporter proteins. This study demonstrates the application of a filter-based experimental setup to study microbial responses to plant chemicals and sheds light on adaptation strategies employed by various bacterial strains for survival in the rhizosphere.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗

Coupling of Charge Regulation and Geometry in Soft Ionizable Molecular Assemblies

The size, shape, and charge of structures, such as proteins and amphiphile assemblies, respond in an interconnected manner to solution ionic conditions. Here, we analyze assemblies of an amphiphile (C 16 K 2 ), with two ionizable amino acids [lysine (K)] coupled to a 16-carbon alkyl tail, via small-angle X-ray scattering (SAXS), nonlinear Poisson–Boltzmann theory (nl-PB), and hybrid Monte Carlo-molecular dynamics (MC-MD) simulations. SAXS revealed structural transitions from spherical micelles to cylindrical micelles to bilayers with increasing pH. By combining SAXS-determined structural information and nl-PB, we derived the molecular degree of ionization as a function of pH. The back-calculated titration curves matched the experimental data over an extended pH range, without adjustable parameters. Similarly, the SAXS data on the evolution of spherical micelle structure with ionic strength were combined with nl-PB and MC-MD to derive the bare and effective charges. MC-MD, which considered finite ion sizes, showed that bare and effective charges saturate quickly with increasing salt concentration. Furthermore, the calculated effective charges closely matched results from Zeta-potential measurements. The presented approach has advantages over customary methods for charge regulation, such as the Henderson–Hasselbalch (HH) or Hill models, where molecular ionization/deionization in assemblies is described by effective pKs that are distinct from the pK for isolated molecules. However, these models lack a physical explanation for these pK shifts. By contrast, our approach of combining structural details with an electrostatic model and simulations provides a more intuitive understanding of structure-charge coupling and a framework for understanding charge regulation in many synthetic and biological systems.

ionization↗

Decoding substrate specificity determining factors in glycosyltransferase-B enzymes – insights from machine learning models

Substrate specificity is an essential characteristic of any enzyme's function and an understanding of the factors that determine this specificity is crucial for enzyme engineering. Unlike the structure of an enzyme which is directly impacted by its sequence, substrate specificity as an enzyme attribute involves a rather indirect relationship with sequence as it also depends on structural aspects that dictate substrate accessibility and active site dynamics. In this study, we explore the performance of classifier-based machine learning models trained on curated sequence and structural data for a class of glycosyltransferases (GTs), namely GT-Bs, to understand their substrate specificity determining factors. GTs enable the transfer of sugar moieties to other biomolecules such as oligosaccharides or proteins and are found in all kingdoms of life. In plants, GTs participate in the biosynthesis of plant cell wall biopolymers (e.g.: hemicelluloses and pectins) and are an integral part of the enzymatic machinery that enables the storage of carbon and energy as plant biomass. To elucidate the substrate specificity of uncharacterized GT-Bs, we constructed multi-label machine learning models (Support Vector Classifier, K-Nearest Neighbors, Gaussian Naïve-Bayes, Random Forest) that incorporate both sequence and structural features. These models achieve good predictive accuracies on test datasets. However, despite our use of structural information, we highlight that there is further scope for improvement in training these models to draw interpretable relationships between sequence, structure and substrate specificity determining motifs in GT-Bs.

97 MATHEMATICS AND COMPUTING↗

CryoEM Structures of the Nitrogenase Complex During Catalytic Turnover

The enzyme nitrogenase couples adenosine triphosphate (ATP) hydrolysis to the multi-electron reduction of atmospheric dinitrogen into ammonia. Despite extensive research, the mechanistic details of ATP-dependent energy transduction and dinitrogen reduction by nitrogenase are not well understood, requiring new strategies to monitor its structural dynamics during catalytic action. Here we report the cryogenic electron microscopic interrogation of the nitrogenase complex under enzymatic turnover conditions, which has enabled the structural characterization of the nitrogenase reaction intermediates at high resolution for the first time. Our structures show that asymmetry governs all aspects of nitrogenase mechanism including ATP hydrolysis, protein-protein interactions, and catalysis. Furthermore, they reveal several previously unobserved, mechanistically relevant conformational changes near the catalytic iron-molybdenum cofactor that are correlated with the nucleotide-hydrolysis state of the enzyme.

Hannah L. Rutledge↗

Failure Mode Identification of Insulin Drug Products – Impact of Relevant Stress Conditions on the Quality of the Drug

Fast-acting insulin drug products (DPs) are carried and administered by diabetic patients to maintain their blood glucose level throughout the day, exposing the DPs to stress conditions. Apidra, Novolog, and Humalog insulin DPs were tested under various stress conditions. Dynamic light scattering (DLS), and size exclusion chromatography (SEC) were used to monitor the stability and aggregation. Thermal stress alone did not influence the stability. However, 24 hr exposure to vigorous mechanical stress shifted the DLS size peaks of Novolog and Humalog from 5 ± 1 nm to > 50.9 ± 25.6 nm, and the SEC native protein peak areas decreased 52% for Novolog and 18.4% for Humalog. Combined stress accelerated protein aggregation more drastically. Novolog and Humalog size shifted (>75 nm) after 3 hr and the peak area decreased > 97.9% after 6 hr exposure, indicating that high temperature accelerated the aggregation triggered by agitation. Soluble aggregates were captured by DLS early on compared to SEC. Apidra was comparably stable indicating DP formulation plays a critical role in stability. Here this study provides a greater understanding of potential failure modes patients and care givers may encounter while handling insulin DPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Energetic and structural control of polyspecificity in a multidrug transporter

Multidrug efflux pumps are dynamic molecular machines that drive antibiotic resistance by harnessing ion gradients to export chemically diverse substrates. Despite their clinical importance, the molecular principles underlying multidrug promiscuity and energy efficiency remain poorly understood. Using multiparametric deep mutational scanning across eight substrates and two energy conditions, we deconvolute the contributions of substrate recognition, energetic coupling, and protein stability, providing an integrated, high-resolution view of multidrug transport. We find that substrate specificity arises from a distributed network of residues extending beyond the binding site, with mutations that reshape binding, coupling, conformational flexibility, and membrane interactions. Further, we apply a pH-based selection scheme to measure the effect of mutation on pH-dependent transport efficiency. By integrating these data, we reveal a fundamental relationship between efficiency and promiscuity: Highly efficient variants exhibit broad substrate profiles, while inefficient variants are narrower. In conclusion, these findings establish a direct link between energy coupling and polyspecificity, uncovering the biochemical logic underlying multidrug transport.

Biological Sciences↗

Small molecule biosensors

Biosensors for small molecules can be used in applications that range from metabolic engineering to orthogonal control of transcription. Biosensors are produced based on a ligand-binding domain (LBD) using a method that, in principle, can be applied for any target molecule. The LBD is fused to either a fluorescent protein or a transcriptional activator and is destabilized by mutation such that the fusion accumulates only in cells containing the target ligand. The power of this method is illustrated by developing biosensors for digoxin and progesterone. Addition of ligand to cells expressing a biosensor activates transcription in yeast, mammalian cells and plants, with a dynamic range of up to about 100-fold or more. The biosensors are used to improve the biotransformation of pregnenolone to progesterone in yeast and to regulate CRISPR activity in mammalian cells. This work provides a general methodology to develop biosensors for a broad range of molecules.

Mandell, Daniel J.↗

ezAlign: A Tool for Converting Coarse-Grained Molecular Dynamics Structures to Atomistic Resolution for Multiscale Modeling

Soft condensed matter is challenging to study due to the vast time and length scales that are necessary to accurately represent complex systems and capture their underlying physics. Multiscale simulations are necessary to study processes that have disparate time and/or length scales, which abound throughout biology and other complex systems. Herein we present ezAlign, an open-source software for converting coarse-grained molecular dynamics structures to atomistic representation, allowing multiscale modeling of biomolecular systems. The ezAlign v1.1 software package is publicly available for download at github.com/LLNL/ezAlign. Its underlying methodology is based on a simple alignment of an atomistic template molecule, followed by position-restraint energy minimization, which forces the atomistic molecule to adopt a conformation consistent with the coarse-grained molecule. The molecules are then combined, solvated, minimized, and equilibrated with position restraints. Validation of the process was conducted on a pure POPC membrane and compared with other popular methods to construct atomistic membranes. Additional examples, including surfactant self-assembly, membrane proteins, and more complex bacterial and human plasma membrane models, are also presented. By providing these examples, parameter files, code, and an easy-to-follow recipe to add new molecules, this work will aid future multiscale modeling efforts.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Room-temperature structural studies of SARS-CoV-2 protein NendoU with an X-ray free-electron laser

NendoU from SARS-CoV-2 is responsible for the virus’s ability to evade the innate immune system by cleaving the polyuridine leader sequence of antisense viral RNA. Here we report the room-temperature structure of NendoU, solved by serial femtosecond crystallography at an X-ray free-electron laser to 2.6 Å resolution. The room-temperature structure provides insight into the flexibility, dynamics, and other intrinsic properties of NendoU, with indications that the enzyme functions as an allosteric switch. Functional studies examining cleavage specificity in solution and in crystals support the uridine-purine cleavage preference, and we demonstrate that enzyme activity is fully maintained in crystal form. Optimizing the purification of NendoU and identifying suitable crystallization conditions set the benchmark for future time-resolved serial femtosecond crystallography studies. This could advance the design of antivirals with higher efficacy in treating coronaviral infections, since drugs that block allosteric conformational changes are less prone to drug resistance.

59 BASIC BIOLOGICAL SCIENCES↗

Mapping microhabitats of lignocellulose decomposition by a microbial consortium

The leaf-cutter ant fungal garden ecosystem is a naturally evolved model system for efficient plant biomass degradation. Degradation processes mediated by the symbiotic fungus Leucoagaricus gongylophorus are difficult to characterize due to dynamic metabolisms and spatial complexity of the system. Herein, we performed microscale imaging across 12-µm-thick adjacent sections of Atta cephalotes fungal gardens and applied a metabolome-informed proteome imaging approach to map lignin degradation. This approach combines two spatial multiomics mass spectrometry modalities that enabled us to visualize colocalized metabolites and proteins across and through the fungal garden. Spatially profiled metabolites revealed an accumulation of lignin-related products, outlining morphologically unique lignin microhabitats. Metaproteomic analyses of these microhabitats revealed carbohydrate-degrading enzymes, indicating a prominent fungal role in lignocellulose decomposition. Integration of metabolome-informed proteome imaging data provides a comprehensive view of underlying biological pathways to inform our understanding of metabolic fungal pathways in plant matter degradation within the micrometer-scale environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Context-dependent coordination of TOR and SnRK1 signaling under carbon and nitrogen perturbations

Target of rapamycin (TOR) and sucrose non-fermenting 1–related protein kinase 1 (SnRK1) are conserved regulators of plant growth and metabolism and are often portrayed as functionally antagonistic under nutrient limitation. However, how this relationship operates across different nutrient contexts remains poorly defined. Here, we generated an Arabidopsis dual-reporter line that enables simultaneous monitoring of TOR and SnRK1 activities and profiled their dynamics under carbon and nitrogen perturbations. We found that TOR and SnRK1 activities overall exhibit a negative relationship during the transition from carbon starvation to carbon abundance; however, their temporal dynamics during that transition do not support a strictly inverse correlation. Under dark conditions, TOR activity is gradually repressed, while SnRK1 is initially repressed in the early hours and subsequently activated during extended darkness. During nitrogen starvation, TOR activity is progressively repressed, whereas SnRK1 is activated during early hours and then becomes repressed. In vitro , recombinant SnRK1α1 directly inhibits the activity of immunoprecipitated TOR (IP-TOR), whereas IP-TOR does not directly affect SnRK1α1 activity. Together, these results support a nutrient-dependent model in which TOR and SnRK1 are coordinated primarily by cellular metabolic status.

59 BASIC BIOLOGICAL SCIENCES↗

Atomic Simulation of Complex DNA DSBs and the Interactions with the Ku70/80 Heterodimer

DNA double strand breaks (DSBs) induced by ionizing radiation (IR) usually contain modified bases such as 8-oxo-7,8-dihydroguanine (8-oxoG) and thymine glycol, apurinic/apyrimidinic (AP) sites, 2-deoxyribonolactone, or single-strand breaks (SSBs). The presence of such lesions in close proximity to the DSB terminus makes the DNA nicks more difficult to repair and rejoin than endogenously induced simple DSBs, and as such a major determinant of the biological effects of high linear energy transfer (LET) radiation as encountered in space travel. In this study we conducted molecular dynamics simulations on a series of DNA duplexes with various complex lesions of 8-oxoG and AP sites, in an effort to investigate the effects of such lesions to the structural integrity and stability of DNA after insulted by IR. We also simulated the interaction of such complex DSBs with the Ku70/80 heterodimer, the first protein in mammalian cells to embark the non-homologous end joining (NHEJ) DNA repair pathway. The results indicate, compared to DNA with simple DSBs, the complex lesions can enhance the hydrogen bonds opening rate at the DNA terminus, and increase the mobility of the whole duplex, thus they present more deleterious effects to the genome integrity if not captured and repaired promptly in cells. Simulations also demonstrate the binding of Ku drastically reduces structural disruption and flexibility caused by the complex lesions, and the interactions of Ku with complex DSBs have a different potential energy landscape from the bound structure with simple DSB. In all complex DSBs systems, the binding of DSB terminus with Ku70 is softened while the binding of the middle duplex with Ku80 is tightened. This energy shift may help the Ku protein to secure at the DSB terminus for a longer time, so that other end processing factors or repair pathways can proceed at the lesions before NHEJ repair process starts. These atomic simulations may provide valuable new insight into the selective action of repair proteins on damaged DNA.

Hu, Shaowen↗

Data for "Soil carbon dynamics during drying vs. rewetting: Importance of antecedent moisture conditions"

This dataset contains data used for the paper "Soil carbon dynamics during drying vs. rewetting: importance of antecedent moisture conditions". The Related References field will be updated with a full citation when available.Soil moisture influences soil carbon dynamics, including microbial growth and respiration. The response of such ‘soil respiration’ to moisture changes is generally assumed to be linear and reversible, i.e. to depend only on the current moisture state. Current models thus do not account for antecedent soil moisture conditions when determining soil respiration or the available substrate pool. We conducted a laboratory incubation to determine how the antecedent conditions of drought and flood influenced soil organic matter (SOM) chemistry, bioavailability, and respiration. We sampled soils from an upland coastal forest, Beaver Creek, WA USA, and subjected them to drying and rewetting treatments. For the drying treatment, field moist soils were saturated and then dried to 75, 50, 35, and 5 % saturation. In the rewetting treatment, field moist soils were air-dried and then rewet to 35, 50, 75, and 100 % saturation. We measured respiration and water extractable organic carbon (WEOC) concentrations and used 1H-NMR and FT-ICR-MS to characterize the WEOC pool across the treatments. The drying vs. wetting treatment strongly influenced SOM bioavailability, as rewet soils (with antecedent drought) had greater WEOC concentrations and respiration fluxes compared to the drying soils (with antecedent flood). In addition, air-dry soils had the highest WEOC concentrations, and the NMR-resolved peaks showed a strong contribution of protein groups in these soils. Both NMR and FT-ICR-MS analyses indicated increased contribution of complex aromatic groups/molecules in the rewet soils, compared to the drying soils. We suggest that drying introduced organic matter into the WEOC pool via desorption of aromatic molecules and/or by microbial cell lysis, and this stimulated microbial mineralization rates. Our work indicates that even short-term shifts in antecedent moisture conditions can strongly influence soil C dynamics at the core scale. The predictive uncertainties in current soil models may be reduced by a more accurate representation of soil water and C persistence that includes a mechanistic and quantitative understanding of the impact of antecedent moisture conditions.This dataset contains a compressed (.zip) archive of the data and R scripts used for this manuscript. The dataset includes files in .csv and .txt format, which can be accessed and processed using MS Excel or R. NMR data are provided as raw output data (accessed in Bruker TopSpin or MestreNova) as well as the MestreNova-processed files. This archive can also be accessed on GitHub at https://github.com/kaizadp/hysteresis_and_soil_carbon (DOI: 10.5281/zenodo.4432885).

1H-NMR↗

Transient local secondary structure in the intrinsically disordered C‐term of the Albino3 insertase

Albino3 (Alb3) is an integral membrane protein fundamental to the targeting and insertion of light‐harvesting complex (LHC) proteins into the thylakoid membrane. Alb3 contains a stroma‐exposed C‐terminus (Alb3‐Cterm) that is responsible for binding the LHC‐loaded transit complex before LHC membrane insertion. Alb3‐Cterm has been reported to be intrinsically disordered, but precise mechanistic details underlying how it recognizes and binds to the transit complex are lacking, and the functional roles of its four different motifs have been debated. Using a novel combination of experimental and computational techniques such as single‐molecule fluorescence resonance energy transfer, circular dichroism with deconvolution analysis, site‐directed mutagenesis, trypsin digestion assays, and all‐atom molecular dynamics simulations in conjunction with enhanced sampling techniques, we show that Alb3‐Cterm contains transient secondary structure in motifs I and II. The excellent agreement between the experimental and computational data provides a quantitatively consistent picture and allows us to identify a heterogeneous structural ensemble that highlights the local and transient nature of the secondary structure. This structural ensemble was used to predict both the inter‐residue distance distributions of single molecules and the apparent unfolding free energy of the transient secondary structure, which were both in excellent agreement with those determined experimentally. We hypothesize that this transient local secondary structure may play an important role in the recognition of Alb3‐Cterm for the LHC‐loaded transit complex, and these results should provide a framework to better understand protein targeting by the Alb3‐Oxa1‐YidC family of insertases.

Okoto, Patience S.↗

How cholesterol stiffens unsaturated lipid membranes

Cholesterol is an integral component of eukaryotic cell membranes and a key molecule in controlling membrane fluidity, organization, and other physicochemical parameters. It also plays a regulatory function in antibiotic drug resistance and the immune response of cells against viruses, by stabilizing the membrane against structural damage. While it is well understood that, structurally, cholesterol exhibits a densification effect on fluid lipid membranes, its effects on membrane bending rigidity are assumed to be nonuniversal; i.e., cholesterol stiffens saturated lipid membranes, but has no stiffening effect on membranes populated by unsaturated lipids, such as 1,2-dioleoyl- sn -glycero-3-phosphocholine (DOPC). This observation presents a clear challenge to structure–property relationships and to our understanding of cholesterol-mediated biological functions. Here, using a comprehensive approach—combining neutron spin-echo (NSE) spectroscopy, solid-state deuterium NMR ( 2 H NMR) spectroscopy, and molecular dynamics (MD) simulations—we report that cholesterol locally increases the bending rigidity of DOPC membranes, similar to saturated membranes, by increasing the bilayer’s packing density. All three techniques, inherently sensitive to mesoscale bending fluctuations, show up to a threefold increase in effective bending rigidity with increasing cholesterol content approaching a mole fraction of 50%. Our observations are in good agreement with the known effects of cholesterol on the area-compressibility modulus and membrane structure, reaffirming membrane structure–property relationships. The current findings point to a scale-dependent manifestation of membrane properties, highlighting the need to reassess cholesterol’s role in controlling membrane bending rigidity over mesoscopic length and time scales of important biological functions, such as viral budding and lipid–protein interactions.

59 BASIC BIOLOGICAL SCIENCES↗