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At least 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mapping Hsp104 interactions using cross‐linking mass spectrometry

Molecular machines from the AAA+ (ATPases Associated with diverse cellular Activity) superfamily of protein disaggregases play important roles in protein folding, disaggregation and DNA processing. Recent cryo-EM structures of AAA+ molecular machines have uncovered nuanced changes in their conformation that underlie their specialized functions. Structural knowledge of these molecular machines in complex with substrates begins to explain their mechanism of activity. Here, we explore how cross-linking mass spectrometry (XL-MS) can be used to interpret changes in conformation induced by ATP in Hsp104 and how a substrate may interact with Hsp104. We applied a panel of cross-linking reagents to produce cross-linking maps of Hsp104 and interpret our data on previously determined X-ray and cryo-EM structures of Hsp104 from a thermophilic yeast, Calcarisporiella thermophila. We developed an analysis pipeline to differentiate between intra-subunit and inter-subunit contacts within the hexameric homo-oligomer. We identify cross-links that break the asymmetry that is present in Hsp104 in an ATP-hydrolysis competent conformation but is absent in an ATP-hydrolysis-defective mutant. Finally, we identify contacts between Hsp104 and a selected protein (proprotein convertase subtilisin/kexin type 9 PCSK9) to reveal contacts on the central channel of Hsp104 across the length of this protein indicating that we might have trapped interactions consistent with its translocation. Our simple and robust XL-MS-based experiments and methods help interpret how these molecular machines change conformation and bind to other proteins even in the context of homo-oligomeric assemblies enabling coupling state-of-the-art modeling approaches with XL-MS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

An efficient protocol for excited states of large biochromophores

Efficient energy transport in photosynthetic antenna is a long-standing source of inspiration for artificial light harvesting materials. However, characterizing the excited states of the constituent chromophores poses a considerable challenge to mainstream quantum chemical and semiempirical excited state methods due to their size and complexity and the accuracy required to describe small but functionally important changes in their properties. In this paper, we explore an alternative approach to calculating the excited states of large biochromophores, exemplified by a specific method for calculating the Q y transition of bacteriochlorophyll a, which we name Chl-xTB. Using a diagonally dominant approximation to the Casida equation and a bespoke parameterization scheme, Chl-xTB can match time-dependent density functional theory’s accuracy and semiempirical speed for calculating the potential energy surfaces and absorption spectra of chlorophylls. We demonstrate that Chl-xTB (and other prospective realizations of our protocol) can be integrated into multiscale models, including concurrent excitonic and point-charge embedding frameworks, enabling the analysis of biochromophore networks in a native environment. We exploit this capability to probe the low-frequency spectral densities of excitonic energies and interchromophore interactions in the light harvesting antenna protein LH2 (light harvesting complex 2). The impact of low-frequency protein motion on interchromophore coupling and exciton transport has routinely been ignored due to the prohibitive costs of including it in simulations. Our results provide a more rigorous basis for continued use of this approximation by demonstrating that exciton transition energies are unaffected by low-frequency vibrational coupling to exciton interaction energies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Students authoring molecular case studies as a partial course-based undergraduate research experience (CURE) for lab instruction

Understanding the relationship between protein structure and function is a core-learning goal in biochemistry. Students often struggle to visualize proteins as three-dimensional objects that interact with other molecules to affect its biochemical consequences. We describe here a partial course-based undergraduate research experiences that has students exploring protein structure and function hands-on while authoring a molecular case study intended for others to use.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

GRIK phosphorylates and activates KIN10 which also promotes its degradation

The sensor kinase Sucrose Non-fermenting-1-Related Kinase 1 (SnRK1) plays a central role in energy and metabolic homeostasis. KIN10 is a major catalytic (a) kinase subunit of SnRK1 regulated by transcription, posttranslational modification, targeted protein degradation, and its subcellular localization. Geminivirus Rep Interacting Kinase 1 and 2 (GRIK1 and 2) are immediate upstream kinases of KIN10. In the transient protein expression assays carried out in Nicotiana benthamiana (N. benthamiana) leaves, GRIK1 not only phosphorylates KIN10 but also simultaneously initiates its degradation. Posttranslational GRIK-mediated KIN10 degradation is dependent on both GRIK kinase activity and phosphorylation of the KIN10 T-loop. KIN10 proteins are significantly enriched in the grik1-1 grik2-1 double mutant, consistent with the transient assays in N. benthamiana. Interestingly. Among the enriched KIN10 proteins from grik1-1 grik2-1, is a longer isoform, putatively derived by alternative splicing which is barely detectable in wild-type plants. The reduced stability of KIN10 upon phosphorylation and activation by GRIK represents a mechanism that enables the KIN10 activity to be rapidly reduced when the levels of intracellular sugar/energy are restored to their set point, representing an important homeostatic control that prevents a metabolic overreaction to low sugar conditions. Since GRIKs are activating kinases of KIN10, KIN10s in the grik1 grik2 double null mutant background remain un-phosphorylated, with only their basal level of activity, are more stable, and therefore increase in abundance, which also explains the longer isoform KIN10L which is a minor isoform in wild type is clearly detected in the grik1 grik2 double mutant.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

A DE1 BINDING FACTOR 1–GLABRA2 module regulates rhamnogalacturonan I biosynthesis in Arabidopsis seed coat mucilage

Abstract The mucilage surrounding hydrated Arabidopsis thaliana seeds is a specialized extracellular matrix composed mainly of the pectic polysaccharide rhamnogalacturonan I (RG-I). Although, several genes responsible for RG-I biosynthesis have been identified, the transcriptional regulatory mechanisms controlling RG-I production remain largely unknown. Here we report that the trihelix transcription factor DE1 BINDING FACTOR 1 (DF1) is a key regulator of mucilage RG-I biosynthesis. RG-I biosynthesis is significantly reduced in loss-of-function mutants of DF1. DF1 physically interacts with GLABRA2 (GL2) and both proteins transcriptionally regulate the expression of the RG-I biosynthesis genes MUCILAGE MODIFIED 4 (MUM4) and GALACTURONOSYLTRANSFERASE-LIKE5 (GATL5). Through chromatin immunoprecipitation-quantitative PCR and transcriptional activation assays, we uncover a cooperative mechanism of the DF1–GL2 module in activating MUM4 and GATL5 expression, in which DF1 binds to the promoters of MUM4 and GATL5 through interacting with GL2 and facilitates the transcriptional activity of GL2. The expression of DF1 and GL2 is directly regulated by TRANSPARENT TESTA GLABRA2 (TTG2) and, in turn, DF1 directly represses the expression of TTG2. Taken together, our data reveal that the transcriptional regulation of mucilage RG-I biosynthesis involves a regulatory module, comprising DF1, GL2, and TTG2.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Alternative splicing and allosteric regulation modulate the chromatin binding of UHRF1

UHRF1 is an important epigenetic regulator associated with apoptosis and tumour development. It is a multidomain protein that integrates readout of different histone modification states and DNA methylation with enzymatic histone ubiquitylation activity. Emerging evidence indicates that the chromatin-binding and enzymatic modules of UHRF1 do not act in isolation but interplay in a coordinated and regulated manner. Here, we compared two splicing variants (V1, V2) of murine UHRF1 (mUHRF1) with human UHRF1 (hUHRF1). We show that insertion of nine amino acids in a linker region connecting the different TTD and PHD histone modification-binding domains causes distinct H3K9me3-binding behaviour of mUHRF1 V1. Structural analysis suggests that in mUHRF1 V1, in contrast to V2 and hUHRF1, the linker is anchored in a surface groove of the TTD domain, resulting in creation of a coupled TTD-PHD module. This establishes multivalent, synergistic H3-tail binding causing distinct cellular localization and enhanced H3K9me3-nucleosome ubiquitylation activity. In contrast to hUHRF1, H3K9me3-binding of the murine proteins is not allosterically regulated by phosphatidylinositol 5-phosphate that interacts with a separate less-conserved polybasic linker region of the protein. Our results highlight the importance of flexible linkers in regulating multidomain chromatin binding proteins and point to divergent evolution of their regulation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The obligate intracellular bacterium Orientia tsutsugamushi differentiates into a developmentally distinct extracellular state

Orientia tsutsugamushi (Ot) is an obligate intracellular bacterium in the family Rickettsiaceae that causes scrub typhus, a severe mite-borne human disease. Its mechanism of cell exit is unusual amongst Rickettsiaceae, as Ot buds off the surface of infected cells enveloped in plasma membrane. Here, we show that Ot bacteria that have budded out of host cells are in a distinct developmental stage compared with intracellular bacteria. We refer to these two stages as intracellular and extracellular bacteria (IB and EB, respectively). These two forms differ in physical properties: IB is both round and elongated, and EB is round. Additionally, IB has higher levels of peptidoglycan and is physically robust compared with EB. The two bacterial forms differentially express proteins involved in bacterial physiology and host-pathogen interactions, specifically those involved in bacterial dormancy and stress response, and outer membrane autotransporter proteins ScaA and ScaC. Whilst both populations are infectious, entry of IB Ot is sensitive to inhibitors of both clathrin-mediated endocytosis and macropinocytosis, whereas entry of EB Ot is only sensitive to a macropinocytosis inhibitor. Our identification and detailed characterization of two developmental forms of Ot significantly advances our understanding of the intracellular lifecycle of an important human pathogen.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Structural basis for ATG9A recruitment to the ULK1 complex in mitophagy initiation

The assembly of the autophagy initiation machinery nucleates autophagosome biogenesis, including in the PINK1- and Parkin-dependent mitophagy pathway implicated in Parkinson’s disease. The structural interaction between the sole transmembrane autophagy protein, autophagy-related protein 9A (ATG9A), and components of the Unc-51–like autophagy activating kinase (ULK1) complex is one of the major missing links needed to complete a structural map of autophagy initiation. We determined the 2.4-Å x-ray crystallographic structure of the ternary structure of ATG9A carboxyl-terminal tail bound to the ATG13:ATG101 Hop1/Rev7/Mad2 (HORMA) dimer, which is part of the ULK1 complex. We term the interacting portion of the extreme carboxyl-terminal part of the ATG9A tail the “HORMA dimer–interacting region” (HDIR). This structure shows that the HDIR binds to the HORMA domain of ATG101 by β sheet complementation such that the ATG9A tail resides in a deep cleft at the ATG13:ATG101 interface. Disruption of this complex in cells impairs damage-induced PINK1/Parkin mitophagy mediated by the cargo receptor NDP52.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Siderocalin fusion proteins enable a new 86 Y/ 90 Y theranostic approach

The mammalian protein siderocalin binds bacterial siderophores and their iron complexes through cation-π and electrostatic interactions, but also displays high affinity for hydroxypyridinone complexes of trivalent lanthanides and actinides. In order to circumvent synthetic challenges, the use of siderocalin-antibody fusion proteins is explored herein as an alternative targeting approach for precision delivery of trivalent radiometals. We demonstrate the viability of this approach in vivo, using the theranostic pair 90 Y (β − , t 1/2 = 64 h)/ 86 Y (β + , t 1/2 = 14.7 h) in a SKOV-3 xenograft mouse model. Ligand radiolabeling with octadentate hydroxypyridinonate 3,4,3-LI(1,2-HOPO) and subsequent protein binding were achieved at room temperature. The results reported here suggest that the rapid non-covalent binding interaction between siderocalin fusion proteins and the negatively charged Y(III)-3,4,3-LI(1,2-HOPO) complexes could enable purification-free, cold-kit labeling strategies for the application of therapeutically relevant radiometals in the clinic.

Cosby, Alexia G.↗
DOE OSTI2020

Identification of preferred multimodal ligand‐binding regions on IgG1 F C using nuclear magnetic resonance and molecular dynamics simulations

Abstract In this study, the binding of multimodal chromatographic ligands to the IgG1 F C domain were studied using nuclear magnetic resonance and molecular dynamics simulations. Nuclear magnetic resonance experiments carried out with chromatographic ligands and a perdeuterated 15 N‐labeled F C domain indicated that while single‐mode ion exchange ligands interacted very weakly throughout the F C surface, multimodal ligands containing negatively charged and aromatic moieties interacted with specific clusters of residues with relatively high affinity, forming distinct binding regions on the F C . The multimodal ligand‐binding sites on the F C were concentrated in the hinge region and near the interface of the C H 2 and C H 3 domains. Furthermore, the multimodal binding sites were primarily composed of positively charged, polar, and aliphatic residues in these regions, with histidine residues exhibiting some of the strongest binding affinities with the multimodal ligand. Interestingly, comparison of protein surface property data with ligand interaction sites indicated that the patch analysis on F C corroborated molecular‐level binding information obtained from the nuclear magnetic resonance experiments. Finally, molecular dynamics simulation results were shown to be qualitatively consistent with the nuclear magnetic resonance results and to provide further insights into the binding mechanisms. An important contribution to multimodal ligand‐F C binding in these preferred regions was shown to be electrostatic interactions and π–π stacking of surface‐exposed histidines with the ligands. This combined biophysical and simulation approach has provided a deeper molecular‐level understanding of multimodal ligand–F C interactions and sets the stage for future analyses of even more complex biotherapeutics.

Gudhka, Ronak B.↗
DOE OSTI2021

Interpreting the Lipidome: Bioinformatic Approaches to Embrace the Complexity

Background Improvements in mass spectrometry (MS) technologies coupled with bioinformatics developments have allowed considerable advancement in the measurement and interpretation of lipidomics data in recent years. Since research areas employing lipidomics are rapidly increasing, there is a great need for bioinformatic tools that capture and utilize the complexity of the data. Currently, the diversity and complexity within the lipidome is often concealed by summing over or averaging individual lipids up to (sub)class-based descriptors, losing valuable information about biological function and interactions with other distinct lipids molecules, proteins and/or metabolites. Aim of review To address this gap in knowledge, novel bioinformatics methods are needed to improve identification, quantification, integration and interpretation of lipidomics data. The purpose of this mini-review is to summarize exemplary methods to explore the complexity of the lipidome. Key scientific concepts of review Here we describe six approaches that capture three core focus areas for lipidomics: (1) lipidome annotation including a resolvable database identifier, (2) interpretation via pathway- and enrichment-based methods, and (3) understanding complex interactions to emphasize specific steps in the analytical process and highlight challenges in analyses associated with the complexity of lipidome data.

Kyle, Jennifer E.↗
DOE OSTI2025

Quick-and-Easy Validation of Protein–Ligand Binding Models Using Fragment-Based Semiempirical Quantum Chemistry

Electronic structure calculations in enzymes converge very slowly with respect to the size of the model region that is described using quantum mechanics (QM), requiring hundreds of atoms to obtain converged results and exhibiting substantial sensitivity (at least in smaller models) to which amino acids are included in the QM region. As such, there is considerable interest in developing automated procedures to construct a QM model region based on well-defined criteria. However, testing such procedures is burdensome due to the cost of large-scale electronic structure calculations. Here, we show that semiempirical methods can be used as alternatives to density functional theory (DFT) to assess convergence in sequences of models generated by various automated protocols. The cost of these convergence tests is reduced even further by means of a many-body expansion. We use this approach to examine convergence (with respect to model size) of protein–ligand binding energies. Fragment-based semiempirical calculations afford well-converged interaction energies in a tiny fraction of the cost required for DFT calculations. Two-body interactions between the ligand and single-residue amino acid fragments afford a low-cost way to construct a “QM-informed” enzyme model of reduced size, furnishing an automatable active-site model-building procedure. This provides a streamlined, user-friendly approach for constructing ligand binding-site models that needs neither a priori information nor manual adjustments. Extension to model-building for thermochemical calculations should be straightforward.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Protein–Ligand Binding Free-Energy Calculations with ARROW–A Purely First-Principles Parameterized Polarizable Force Field

Protein–ligand binding free-energy calculations using molecular dynamics (MD) simulations have emerged as a powerful tool for in silico drug design. Here, we present results obtained with the ARROW force field (FF)–a multipolar polarizable and physics-based model with all parameters fitted entirely to high-level ab initio quantum mechanical (QM) calculations. ARROW has already proven its ability to determine solvation free energy of arbitrary neutral compounds with unprecedented accuracy. The ARROW FF parameterization is now extended to include coverage of all amino acids including charged groups, allowing molecular simulations of a series of protein–ligand systems and prediction of their relative binding free energies. We ensure adequate sampling by applying a novel technique that is based on coupling the Hamiltonian Replica exchange (HREX) with a conformation reservoir generated via potential softening and nonequilibrium MD. ARROW provides predictions with near chemical accuracy (mean absolute error of ~0.5 kcal/mol) for two of the three protein systems studied here (MCL1 and Thrombin). The third protein system (CDK2) reveals the difficulty in accurately describing dimer interaction energies involving polar and charged species. Overall, for all of the three protein systems studied here, ARROW FF predicts relative binding free energies of ligands with a similar accuracy level as leading nonpolarizable force fields.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

In Vitro Encapsulation of Functionally Active Abiotic Photosensitizers Inside a Bacterial Microcompartment Shell

Bacterial microcompartments (BMCs) are self-assembling, selectively permeable protein shells that encapsulate enzymes to enhance catalytic efficiency of segments of metabolic pathways through means of confinement. The modular nature of BMC shells' structure and assembly enables programming of shell permeability and underscores their promise in biotechnology engineering efforts for applications in industry, medicine, and clean energy. Realizing this potential requires methods for encapsulation of abiotic molecules, which have been developed here for the first time. We report in vitro cargo loading of BMC shells with ruthenium photosensitizers (RuPS) by two approaches-one involving site-specific covalent labeling and the other driven by diffusion, requiring no specific interactions between cargo molecules and shell proteins. The highly stable shells retain encapsulated cargo over 1 week without egress and preserve RuPS photophysical activity. Finally, this study is an important foundation for further work that will converge biological BMC architecture with synthetic chemistry to facilitate biohybrid photocatalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Closing the Gap Between Modeling and Experiments in the Self-assembly of Biomolecules at Interfaces and in Solution

Molecular self-assembly is a powerful tool in materials design, wherein non-covalent interactions like electrostatic, hydrophobic, hydrogen bonding, and van der Waals can be exploited to produce supramolecular nanostructures that are functional and highly tunable. Biomolecules are attractive building blocks, as they are biocompatible, biodegradable and adopt a wide array of higher order structures. Moreover, naturally occurring protein systems display a manifold of structures and interactions that can be replicated in synthetic biomolecules. In this perspective, we highlight advances in multiscale simulation techniques across broad spatiotemporal scales that can aid in characterizing self-assembly of hybrid and hierarchical bionanomaterial systems, with an emphasis on physics-based simulation approaches currently employed to study biomolecules at mineral interfaces. The power of these approaches is highlighted across a few recent areas where molecular simulations have advanced our understanding of self-assembly spanning peptides to protein self-assembly. Looking forward, we discuss how in the near future emerging methods in statistical and machine learning will advance this research field in all areas from expanding the capabilities of physics-based simulation methods to enabling new analyses of high throughput experiments. These advances will pave the way for understanding the molecular recognition patterns in systems that are dictated by self-assembly - biomineralizing peptides, hierarchical peptoids, and large protein assemblies, and will aid in the development of a new synthesis science for achieving precise molecular control in materials design

Sampath, Janani↗
DOE OSTI2026

Impact of solvent forces and broken symmetry on the assembly of designed proteins at a liquid-solid interface

The era of protein design has enabled the creation of hybrid protein-inorganic interfaces, leading to both surface-directed self-assembly of de novo protein architectures and protein-directed formation of inorganic materials. However, the resulting patterns of protein assembly are often unexpected, implying that essential interactions are not accounted for in current design platforms. Here, we use high-speed atomic force microscopy (AFM) analyzed through machine learning to follow the assembly of protein nanorods in aqueous electrolytes on two types of mica exhibiting disparate symmetry elements, which are imprinted on the overlying hydration structure. Using Monte Carlo simulations, we reproduce the observed phases and show that an observed smectic phase, previously thought to be unstable for non-interacting rods in two dimensions, emerges when crystal symmetry introduces a directional bias. The findings demonstrate the importance of incorporating solvent forces as modulated by the hydration structure inherent to interfacial systems when designing protein assemblies at liquid-crystal interfaces. Coupling physics-based simulations that can account for these factors to de novo protein design algorithms can lead to improved design platforms for bio-inspired, hybrid materials.

bioinspired materials↗
DOE OSTI2021

Identifying key determinants and dynamics of SARS-CoV-2/ACE2 tight interaction

SARS-CoV-2 virus, the causative agent of Covid-19, has fired up a global pandemic. The virus interacts with the human receptor angiotensin-converting enzyme 2 (ACE2) for an invasion via receptor binding domain (RBD) on its spike protein. To provide a deeper understanding of this interaction, we performed microsecond simulations of the RBD-ACE2 complex for SARS-CoV-2 and compared it with the closely related SARS-CoV discovered in 2003. We show residues in the RBD of SARS-CoV-2 that were mutated from SARS-CoV, collectively help make the RBD anchor much stronger to the N-terminal part of ACE2 than the corresponding residues on RBD of SARS-CoV. This would result in a reduced dissociation rate of SARS-CoV-2 from human receptor protein compared to SARS-CoV. The phenomenon was consistently observed in simulations beyond 500 ns and was reproducible across different force fields. Altogether, our study adds more insight into the critical dynamics of the key residues at the virus spike and human receptor binding interface and potentially aids the development of diagnostics and therapeutics to combat the pandemic efficiently.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗