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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,441 records · Page 80
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Supramolecular Energy Materials

Self-assembly is a bioinspired strategy to craft materials for renewable and clean energy technologies. In plants, the alignment and assembly of the light-harvesting protein machinery in the green leaf optimize the ability to efficiently convert light from the sun to form chemical bonds. In artificial systems, strategies based on self-assembly using noncovalent interactions offer the possibility to mimic this functional correlation among molecules to optimize photocatalysis, photovoltaics, and energy storage. Here, one of the long-term objectives of the field described here as supramolecular energy materials is to learn how to design soft materials containing light-harvesting assemblies and catalysts to generate fuels and useful chemicals. Supramolecular energy materials also hold great potential in the design of systems for photovoltaics in which intermolecular interactions in self-assembled structures, for example, in electron donor and acceptor phases, maximize charge transport and avoid exciton recombination. Possible pathways to integrate organic and inorganic structures by templating strategies and electrodeposition to create materials relevant to energy challenges including photoconductors and supercapacitors are also described. The final topic discussed is the synthesis of hybrid perovskites in which organic molecules are used to modify both structure and functions, which may include chemical stability, photovoltaics, and light emission.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Multi-modal characterization methods of solid-electrolyte interphase in silicon-graphite composite electrodes

Composite silicon-graphite (Si-Gr) anodes can improve battery energy density, due to Si's high gravimetric capacity, while mitigating mechanical degradation of the anode and solid-electrolyte interphase (SEI) caused by Si volumetric expansion. Optimizing these anodes is challenging, in part due to difficulty characterizing the SEI structure and composition. In this work, we present multi-modal characterization of the SEI on composite Si-Gr anodes to relate SEI chemical composition and structure to functional properties. Discrepancies in elemental concentrations from X-ray photoelectron spectroscopy, Auger electron spectroscopy, and energy-dispersive X-ray spectroscopy (EDS) are attributed to varying information depth and lateral resolution of the individual probes. However, by combining quantitative composition information with spatially resolved element mapping from scanning transmission electron microscopy, EDS, and electron energy loss spectroscopy, a holistic picture of the SEI emerges. We observe the bilayer SEI structure and a direct correlation between elemental Li and F, suggesting that most Li in the SEI exists as lithium fluoride (LiF). Further, LiF concentration is directly proportional to the maximum SEI resistivity, as determined by scanning spreading resistance microscopy. Lastly, there is an inverse relationship between lithium carbonate and LiF concentration in the SEI, providing insight into the detailed chemistry of SEI formation and evolution.

25 ENERGY STORAGE↗
DOE OSTI2025

Application of Amber Suppression To Study the Role of Tyr M210 in Electron Transfer in Rhodobacter sphaeroides Photosynthetic Reaction Centers

The initial light-induced electron transfer (ET) steps in the bacterial photosynthetic reaction center (RC) have been extensively studied and provide a paradigm for connecting structure and function. Although RCs have local pseudo-C 2 symmetry, ET only occurs along the A branch of chromophores. Tyrosine M210 is a key symmetry-breaking residue adjacent to bacteriochlorophyll B A that bridges primary electron donor P and bacteriopheophytin acceptor H A . Here, we used amber suppression to incorporate phenylalanine variants with different electron-withdrawing/donating capabilities at position M210. X-ray data generally reveal no appreciable structural changes due to the mutations. P* decay and P + H A - formation are multi-exponential (~2-9, ~10-60, and ~100-300 ps) and temperature dependent. The 1020 nm transient-absorption band of P + B A - is barely resolved for a few variants at 295 K and for none at 77 K. The results indicate a change from two-step ET for wild-type RCs to dominance of one-step superexchange ET for the mutants. Resonance Stark spectroscopy reveals that the free energy of P + B A - changes by -57 to +66 meV among the phenylalanine variants. Because P+BA apparently lies above P* in all phenylalanine variants, the perturbations primarily affect the energy denominator for superexchange mixing. The findings deepen insight into primary ET in the bacterial RC.

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

Nutrient Exposure Alters Microbial Composition, Structure, and Mercury Methylating Activity in Periphyton in a Contaminated Watershed

The conversion of mercury (Hg) to monomethylmercury (MMHg) is a critical area of concern in global Hg cycling. Periphyton biofilms may harbor significant amounts of MMHg but little is known about the Hg-methylating potential of the periphyton microbiome. Therefore, we used high-throughput amplicon sequencing of the 16S rRNA gene, ITS2 region, and Hg methylation gene pair (hgcAB) to characterize the archaea/bacteria, fungi, and Hg-methylating microorganisms in periphyton communities grown in a contaminated watershed in East Tennes(see United States). Furthermore, we examined how nutrient amendments (nitrate and/or phosphate) altered periphyton community structure and function. We found that bacterial/archaeal richness in experimental conditions decreased in summer and increased in autumn relative to control treatments, while fungal diversity generally increased in summer and decreased in autumn relative to control treatments. Interestingly, the Hg-methylating communities were dominated by Proteobacteria followed by Candidatus Atribacteria across both seasons. Surprisingly, Hg methylation potential correlated with numerous bacterial families that do not contain hgcAB, suggesting that the overall microbiome structure of periphyton communities influences rates of Hg transformation within these microbial mats. To further explore these complex community interactions, we performed a microbial network analysis and found that the nitrate-amended treatment resulted in the highest number of hub taxa that also corresponded with enhanced Hg methylation potential. This work provides insight into community interactions within the periphyton microbiome that may contribute to Hg cycling and will inform future research that will focus on establishing mixed microbial consortia to uncover mechanisms driving shifts in Hg cycling within periphyton habitats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Longitudinal Multi-omics Reveal Phase-Dependent Viral Adaptive Strategies and Functional Potential During Formation of Algal-bacterial Granular Sludge

Virus-host interactions within microbial aggregates critically influence microbiome function and stability, yet how physicochemical stresses shape the interactive dynamics remains largely unexplored. Here, we investigated virus–host dynamics during the transition of algal-bacterial granular sludge (ABGS) from activated sludge under continuous hydraulic shear using integrated metagenomics and metatranscriptomics. Hydraulic stress initially reduced host a-diversity, which coincided with a marked increase in viral lysogenicity. During this host diversity bottleneck, viral microdiversity increased, and genes related to virion structure and DNA packaging were under positive selection (pN/pS >1). As host diversity recovered, viral microdiversity declined, while viral anti-defense systems (ADS) significantly increased in abundance. Lagged correlation analysis revealed a significant positive correlation between viral ADS and host defense systems (DS), suggesting an evolutionary arms race. Furthermore, active lysogenic infections were accompanied by enrichment of DS and auxiliary viral genes (AVGs) involved in genetic information processing and amino acid metabolism, potentially enhancing host fitness. Overall, our study unveils a phase-dependent co-evolutionary interplay between viruses and hosts during ABGS formation, providing insights into the development and maintenance of microbial structural and functional resilience in engineered ecosystems.

Qi, Huiyuan↗
DOE OSTI2024

Enhancing Biopreparedness through a Model System to Understand the Molecular Mechanisms that Lead to Pathogenesis and Disease Transmission: NW-BRaVE

The science of biopreparedness to counter biological threats hinges on understanding the fundamental principles and molecular mechanisms that lead to pathogenesis and disease transmission. Our vision to address this challenge is to create a powerful and user-friendly platform to elucidate the fundamental principles of how molecular interactions drive pathogen-host relationships and host shifts. We will enable groundbreaking discoveries by integrating a wide range of structural, genomics, proteomics, and other advanced omics measurements, along with evolutionary and artificial intelligence predictions. To make sure the system is applicable to real-world problems, we will develop it in the context of a tractable model system, the small, abundant, and accessible photosynthetic cyanobacteria and their constantly co-adapting viral pathogens, cyanophages. This model will maintain the system’s applicability to real-world problems and techniques, but the overall focus will be on elucidating general principles of detecting, assessing, and surveilling molecular interaction, adaptation, and coevolution that are system agnostic and therefore extensible to other viral-host interactions. Our overall objectives are to (1) identify the molecular complexes that comprise the cyanobacteria redox macromolecular subsystem and how they dynamically change with bacteriophage infection in situ, using cryo-electron tomography; (2) profile regulatory changes during infection using proteomics, multiomics, and experimental validation, and integrate the data with in situ structures; (3) use genomics and metagenomics to determine environmental and population factors across time scales that impact the interactions between marine cyanobacteria and their cyanophage parasites, predicting the evolutionary origins of in situ structural and functional interactions, convergence and coevolution; and (4) develop a data integration and transformation platform that facilitates the integration of in situ, proteomic, and evolutionary measurements of molecular interactions to surveil diverse hosts and parasites in various environmental contexts. These objectives address Focus Area 2 Reveal Molecular Interactions Across Biological Scales for Design of Targeted Interventions. Our powerful and user-friendly platform will enhance connections between the often-siloed fields of structure, molecular phenotype, and evolutionary genomics that are key to biopreparedness, but in need of integration (Figure 1). We will build an integrated navigation tool to facilitate the effective use of globally distributed experimental data for integrated analysis and predictive modeling. The project will develop, implement, and test a platform to assess host-pathogen molecular interactions, adaptation to hosts and host shifts, and coevolution between hosts and pathogens, successfully impacting the research community by revolutionizing abilities to study any host-pathogen interaction, encourage diverse community contributions, and gain fundamental insights into how proteins adapt to new contexts. This ability will be critical for designing early interventions to address future threats. We will build surveillance training capability, aiming for a fair and equitable response to future pandemics and biothreats.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Direct Mechanochemical Synthesis, Phase Stability, and Electrochemical Performance of α-NaFeO 2

To better understand polymorph control in transition metal oxides, the mechanochemical synthesis of NaFeO 2 was explored. In this paper, we report the direct synthesis of α-NaFeO 2 through a mechanochemical process. By milling Na 2 O 2 and γ-Fe 2 O 3 for 5 h, α-NaFeO 2 was prepared without high-temperature annealing needed in other synthesis methods. While investigating the mechanochemical synthesis, it was observed that changing the starting precursors and mass of precursors affects the resulting NaFeO 2 structure. Density functional theory calculations on the phase stability of NaFeO 2 phases show that the α phase is stabilized over the β phase in oxidizing environments, which is provided by the oxygen-rich reaction between Na 2 O 2 and Fe 2 O 3 . This provides a possible route to understanding polymorph control in NaFeO 2 . Annealing the as-milled α-NaFeO 2 at 700 °C has resulted in increased crystallinity and structural changes that improved electrochemical performance in terms of capacity over the as-milled sample.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Plant diversity and functional identity drive grassland rhizobacterial community responses after 15 years of CO 2 and nitrogen enrichment

Abstract Improved understanding of bacterial community responses to multiple environmental filters over long time periods is a fundamental step to develop mechanistic explanations of plant–bacterial interactions as environmental change progresses. This is the first study to examine responses of grassland root‐associated bacterial communities to 15 years of experimental manipulations of plant species richness, functional group and factorial enrichment of atmospheric CO 2 (eCO 2 ) and soil nitrogen (+N). Across the experiment, plant species richness was the strongest predictor of rhizobacterial community composition, followed by +N, with no observed effect of eCO 2 . Monocultures of C 3 and C 4 grasses and legumes all exhibited dissimilar rhizobacterial communities within and among those groups. Functional responses were also dependent on plant functional group, where N 2 ‐fixation genes, NO 3− ‐reducing genes and P‐solubilizing predicted gene abundances increased under resource‐enriched conditions for grasses, but generally declined for legumes. In diverse plots with 16 plant species, the interaction of eCO 2 +N altered rhizobacterial composition, while +N increased the predicted abundance of nitrogenase‐encoding genes, and eCO 2 +N increased the predicted abundance of bacterial P‐solubilizing genes. Synthesis : Our findings suggest that rhizobacterial community structure and function will be affected by important global environmental change factors such as eCO 2 , but these responses are primarily contingent on plant species richness and the selective influence of different plant functional groups.

Revillini, Daniel↗
DOE OSTI2022

Multi-head attention-based U-Nets for predicting protein domain boundaries using 1D sequence features and 2D distance maps

Abstract The information about the domain architecture of proteins is useful for studying protein structure and function. However, accurate prediction of protein domain boundaries (i.e., sequence regions separating two domains) from sequence remains a significant challenge. In this work, we develop a deep learning method based on multi-head U-Nets (called DistDom) to predict protein domain boundaries utilizing 1D sequence features and predicted 2D inter-residue distance map as input. The 1D features contain the evolutionary and physicochemical information of protein sequences, whereas the 2D distance map includes the structural information of proteins that was rarely used in domain boundary prediction before. The 1D and 2D features are processed by the 1D and 2D U-Nets respectively to generate hidden features. The hidden features are then used by the multi-head attention to predict the probability of each residue of a protein being in a domain boundary, leveraging both local and global information in the features. The residue-level domain boundary predictions can be used to classify proteins as single-domain or multi-domain proteins. It classifies the CASP14 single-domain and multi-domain targets at the accuracy of 75.9%, 13.28% more accurate than the state-of-the-art method. Tested on the CASP14 multi-domain protein targets with expert annotated domain boundaries, the average per-target F1 measure score of the domain boundary prediction by DistDom is 0.263, 29.56% higher than the state-of-the-art method.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Living Filter Designs for In-Line Recovery and Sorting of Critical materials

This project explored “bio-mining” of critical materials from electronic waste (E-waste) streams by developing living filters arrays capable of recovering these materials, focusing on the platinum group metals (PGMs) and rare earth elements (REEs). While highly toxic, E-waste is considered a valuable “urban mine” as it contains critical materials such as PGMs and REEs with orders of magnitude higher purity than the richest ores. The aim of the project was to: (1) develop mechanically robust, silk-based biomaterial filtration membranes that can capture REEs from dilute aqueous waste; (2) design and build 3D bioprinted living filters containing encapsulated electrochemically active bacteria (EAB) capable of bio-reducing PGMs and recovering them from waste streams; and (3) constructing combined living filter arrays using both components to efficiently capture REE and PGM from the same waste stream. The developed silk-based filtration membranes were self-assembled using silk-nanofibrils (SNFs) derived from silkworm (Bombyx mori) cocoons in conjunction with recombinant silk-elastin-like proteins (SELPs), which contained lanthanide-binding peptide tags (LBTs) with a high affinity and specificity towards REEs. These 100% biodegradable protein-based membranes were capable of recovering up to 85% of model REE ions (Tb 3+ ) filtered through the membrane, with ~50% recovery achieved in the presence of high concentrations (100X) of common interfering metals (Ca 2+ , Cu 2+ , Fe 3+ , Zn 2+ ). REE captured by the membranes were easily recovered by applying a low pH desorption buffer. The membranes also demonstrated substantial reusability, with only a 30% loss in binding capacity after 4 cycles of REE binding and recovery. To recover the PGMs, we created a bottom-up assembling strategy to construct a living hydrogel composed of a seamlessly integrated living catalyst, Shewanella loihica PV-4 (PV-4), for metal reduction, and their structural and functional linkers, bio-reduced graphene oxide (B-rGO). This hydrogel demonstrated a close to 90% recovery of model metal ions, Pd, from a simulated e-waste leaching stream with minimum-to-no biomass production. It’s also worth noting that the Pd recovery is initiated immediately after the introduction of living hydrogel, compared to conventional biocarriers that required start-up times within hours to days. Overall, these living hydrogels demonstrated superior bioactivity, structural integrity, and agility over existing biocarriers, which offers extensive opportunities to advance the biological metal recovery with unparallel efficiency, reduced energy/material consumption, and minimal environmental impact.

36 MATERIALS SCIENCE↗
NASA NTRS1996

The shuttle main engine: A first look

Anyone entering the Space Shuttle Main Engine (SSME) team attends a two week course to become familiar with the design and workings of the engine. This course provides intensive coverage of the individual hardware items and their functions. Some individuals, particularly those involved with software maintenance and development, have felt overwhelmed by this volume of material and their lack of a logical framework in which to place it. To provide this logical framework, it was decided that a brief self-taught introduction to the overall operation of the SSME should be designed. To aid the people or new team members with an interest in the software, this new course should also explain the structure and functioning of the controller and its software. This paper presents a description of this presentation.

Schreur, Barbara↗
DOE OSTI2024

Uniaxial stress effect on the electronic structure of quantum materials

Uniaxial stress has proven to be a powerful experimental tuning parameter for effectively controlling lattice, charge, orbital, and spin degrees of freedom in quantum materials. In addition, its ability to manipulate the symmetry of materials has garnered significant attention. Recent technical progress to combine uniaxial stress cells with quantum oscillation and angle-resolved photoemission techniques allowed to study the electronic structure as function of uniaxial stress. This review provides an overview on experimental advancements in methods and examines studies on diverse quantum materials, encompassing the semimetal WTe 2 , the unconventional superconductor Sr 2 RuO 4 , Fe-based superconductors, and topological materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Causal discovery from data assisted by large language models

Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

Causal inference↗
DOE OSTI2021

Ytterbium ion trap quantum computing: The current state-of-the-art

In this work, we present an overview of contemporary quantum computing with ytterbium ion traps, placing the emphasis on industry implementations. We provide brief, concrete descriptions of various key features, such as trap loading, electronic structure, qubit function, gates, error analysis, and benchmarking. We focus on the underlying science and current technologies to provide readers with a holistic picture of available techniques for using ytterbium in contemporary ion trap designs.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Label-free characterization of organic nanocarriers reveals persistent single molecule cores for hydrocarbon sequestration

Self-assembled molecular nanostructures embody an enormous potential for new technologies, therapeutics, and understanding of molecular biofunctions. Their structure and function are dependent on local environments, necessitating in-situ/operando investigations for the biggest leaps in discovery and design. However, the most advanced of such investigations involve laborious labeling methods that can disrupt behavior or are not fast enough to capture stimuli-responsive phenomena. We utilize X-rays resonant with molecular bonds to demonstrate an in-situ nanoprobe that eliminates the need for labels and enables data collection times within seconds. Our analytical spectral model quantifies the structure, molecular composition, and dynamics of a copolymer micelle drug delivery platform using resonant soft X-rays. We additionally apply this technique to a hydrocarbon sequestrating polysoap micelle and discover that the critical organic-capturing domain does not coalesce upon aggregation but retains distinct single-molecule cores. This characteristic promotes its efficiency of hydrocarbon sequestration for applications like oil spill remediation and drug delivery. Such a technique enables operando, chemically sensitive investigations of any aqueous molecular nanostructure, label-free.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Mechanistic Studies of a Primitive Homolog of Nitrogenase Involved in Coenzyme F430 Biosynthesis (Final Report)

Methyl-coenzyme M reductase (MCR) is the key enzyme in the biological formation and anaerobic oxidation of methane (AOM). Methane is a potent greenhouse gas and the major component of natural gas. Given the abundance of natural gas reserves in remote areas, there is great current interest in a scalable bio-based process for the conversion of methane to liquid fuel and other high-value chemicals. MCR holds much promise for use in such a methane bioconversion strategy. However, MCR cannot currently be produced in an active form in a heterologous host, due in large part to the lack of genetic and biochemical information about the production of holo MCR. In an effort to overcome this deficiency, our laboratory recently elucidated the biosynthetic pathway of the unique nickel-containing coenzyme of MCR, F430. The key step in coenzyme F430 biosynthesis (Cfb) was found to involve an unprecedented reductive cyclization reaction. This remarkable transformation, which involves a 6-electron reduction, the formation of a γ-lactam ring, and the generation of 7 stereocenters, is catalyzed by a primitive homolog of nitrogenase (CfbCD). Nitrogenase is a two-component metalloenzyme that catalyzes the adenosine triphosphate (ATP)-dependent reduction of dinitrogen to ammonia (nitrogen fixation). Homologs of nitrogenase are also involved in the biosynthesis of the photosynthetic pigments chlorophyll and bacteriochlorophyll. Phylogenetic analysis of the CfbCD complex suggests that it is representative of a more ancient lineage of the nitrogenase superfamily, and a thorough investigation of its structure and function is likely to shed light on the mechanisms and evolution of these important metalloenzymes. Moreover, a detailed understanding of the mechanism of the CfbCD complex may aid in the development of specific inhibitors to help reduce natural greenhouse gas emissions and can be exploited for the heterologous production of MCR for methane bioconversion. Towards these goals, specific aims were pursued for the 1) identification of physiological electron donors and in vivo coenzyme F430 synthesis, 2) analysis of the iron sulfur centers, structure, and oligomerization state changes, and 3) characterization of transient intermediates and the intercomponent electron transfer.

03 NATURAL GAS↗
DOE OSTI2021

J-domain proteins interaction with neurodegenerative disease-related proteins

HSP70 chaperones, J-domain proteins (JDPs) and nucleotide exchange factors (NEF) form functional networks that have the ability to prevent and reverse the aggregation of proteins associated with neurodegenerative diseases. JDPs can interact with specific substrate proteins, hold them in a refolding-competent conformation and target them to specific HSP70 chaperones for remodeling. Thereby, JDPs select specific substrates and constitute an attractive target for pharmacological intervention of neurodegenerative diseases. This, under the condition that the exact mechanism of JDPs interaction with specific substrates is unveiled. In this review, we provide an overview of the structural and functional variety of JDPs that interact with neurodegenerative disease-associated proteins and we highlight those studies that identified specific residues, domains or regions of JDPs that are crucial for substrate binding.

60 APPLIED LIFE SCIENCES↗