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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structure-aware annotation of leucine-rich repeat domains

Protein domain annotation is typically done by predictive models such as HMMs trained on sequence motifs. However, sequence-based annotation methods are prone to error, particularly in calling domain boundaries and motifs within them. These methods are limited by a lack of structural information accessible to the model. With the advent of deep learning-based protein structure prediction, existing sequenced-based domain annotation methods can be improved by taking into account the geometry of protein structures. We develop dimensionality reduction methods to annotate repeat units of the Leucine Rich Repeat solenoid domain. The methods are able to correct mistakes made by existing machine learning-based annotation tools and enable the automated detection of hairpin loops and structural anomalies in the solenoid. The methods are applied to 127 predicted structures of LRR-containing intracellular innate immune proteins in the model plant Arabidopsis thaliana and validated against a benchmark dataset of 172 manually-annotated LRR domains.

Xu, Boyan↗
DOE OSTI2025

Investigation of Site Amplifications Using Ambient-Noise-Derived Shallow Velocity Structures Under a Dense Array in Oklahoma

The shear-wave velocity (V S ) structure plays an important role in characterizing site amplification. The Large-n Seismic Survey in Oklahoma (LASSO; 1820 stations) revealed large vertical ground-motion variability in a 25 km × 32 km area in northern Oklahoma. The LASSO array has a relatively simple and flat topography, typical in a sedimentary basin environment in the central United States. In this study, we use the dense array to investigate the velocity structure under the LASSO array and how vertical ground motions relate to the shallow-to-deep structures. We extract the fundamental-mode Rayleigh wave by cross-correlating one month of ambient noise (0.7–5 Hz). We use double-beamforming to measure the group and phase velocities and anisotropy. By jointly inverting the group and phase velocities, we obtain the V S structure. Here, we observe correlations between V S at depths of 0.1–1.5 km and vertical ground motions using sites on the stiffer Permian formations. The shallow Quaternary alluvium and terrace deposits can amplify vertical ground motions by a factor of 2–4.5 between 2 and 25 Hz and attenuate signals above 25 Hz. We use 1D V S profiles to simulate the SV-wave transfer functions. An average V S of 250 m/s in the upper 20–40 m may cause the observed amplification between 2 and 40 Hz. V S estimated by topographic slopes cannot predict the relative amplification. Our results highlight the large variability of site-dependent ground motion in a small local region and the importance of characterizing shallow structures to estimate seismic hazards. Small thickness variations of the shallow formation can significantly change the resonance amplitude and frequency, which likely reduces the coherency of the Rayleigh waves extracted from ambient noise.

Chang, Hilary [Massachusetts Inst. of Technology (↗
DOE OSTI2024

Deep Learning Prediction of Protein Complex Structures

Proteins interact to form protein complex to carry out biological functions such as catalytic chemical reaction. Therefore, it is important to develop computational methods to predict protein-protein interaction and the structures of protein complexes to study and enhance protein function. In this project, we successfully developed several deep learning methods to predict inter-protein contacts and the reinforcement learning and optimization methods to reconstruct protein complex structures from predicted inter-chain contacts. The methods were integrated with the MULTICOM protein complex structure prediction system and applied to predict the complex structures of biomass production-related proteins of green algae. During the two and a half years of research and development, all the specific milestones of the project were achieved successfully. 16 publications/manuscripts were produced. 10 software tools were developed. A patent application was submitted. Our MULTICOM predictors leveraging some tools developed in this project were ranked among the top predictors in the 15th Critical Assessment of Techniques for Protein Structure Prediction (CASP15) in 2022.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Non-destructive structural characterization of graphite components using mechanical resonance and deep learning

As compared to conventional nuclear reactors, microreactors have the potential to significantly reduce construction timelines and capital costs, decreasing the barriers for advanced nuclear reactor technologies. However, the lower power output of these microreactors (typically < 20 MWe) creates challenging economics if operation and maintenance costs cannot be sufficiently reduced. The compact size of these designs presents an opportunity for comprehensive in-situ structural health monitoring to provide real-time feedback in order to reduce operational costs associated with maintenance and downtime. Many microreactor concepts use graphite for both in-core neutron moderation and as a structural material, which has typically required some form of periodic and laborious inspection. This report provides a description and assessment of recent work with graphite to couple acoustic-based experimental measurements and characterization with machine learning models to mature structural health monitoring capabilities and generate benefits for the nuclear microreactor industry. With resilient embedded sensors in development in other programs funded by the US Department of Energy’s Office of Nuclear Energy and elsewhere, the work described herein builds upon previously funded efforts to mature non-destructive testing technology that relates measured vibrational signatures to structural changes, using a combination of new experimental measurements and machine learning processing. Building on past successful demonstrations of predictive workflows to identify structural changes in a hexagonal stainless steel test article with excellent acoustic propagation, we first performed baseline characterization on graphite samples with canonical geometries to ensure compatibility and confidence in the applied techniques for a material with distinctly different mechanical properties. In contrast to efforts in previous years, we worked exclusively with unidirectional vibration data that is more comparable to those expected from the existing embedded sensor technologies which are suitable for deployment in a reactor setting. Established acoustic and modern machine-learning-based characterization approaches were applied to the resulting datasets from these simple geometries. Both approaches were found to be highly capable of detecting even small geometric irregularities amongst nominally identical samples. As such, we then moved to testing these approaches for detection of artificial local stress perturbations introduced into a more complex geometry: a hexagonal block with drilled holes. A main outcome of this work is that a generalizable ML workflow can be used to detect and predict the characteristics of small artificial anomalies in a graphite component with a relevant geometry. While this work was performed using surficial vibration data, we expect the approach to be flexible and viable for other monitoring scenarios, such as those with different arrangements or types of sensor arrays. As compared to previously funded efforts, an existing ML workflow based on neural networks was enhanced through the addition of recently developed Fourier neural operators. As applied to previously collected and new vibration datasets, prediction accuracies of anomaly characterizations were greatly improved with minimal added computational cost. As trained on small durations of vibration data (tens of seconds) collected over a realistic number of locations, the model was able to reliably determine the presence of a subtle stress anomaly and begin to provide location estimates. Such an approach is likely to be viable for more relevant reactor damage scenarios for graphite components, such as progressive crack growth or creep.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structural Insights into Mechanisms Underlying Mitochondrial and Bacterial Cytochrome c Synthases

Mitochondrial holocytochrome c synthase (HCCS) is an essential protein in assembling cytochrome c (cyt c) of the electron transport system. HCCS binds heme and covalently attaches the two vinyls of heme to two cysteine thiols of the cyt c CXXCH motif. Human HCCS recognizes both cyt c and cytochrome c1 of complex III (cytochrome bc1). HCCS is mutated in some human diseases and it has been investigated recombinantly by mutational, biochemical, and reconstitution studies in the past decade. Here, we employ structural prediction programs (e.g., AlphaFold 3) on HCCS and its two substrates, heme and cytochrome c. The results, when combined with spectroscopic and functional analyses of HCCS and variants, provide insights into the structural basis for heme binding, apocyt c binding, covalent attachment, and release of the holocyt c product. Results from in vitro reconstitution of purified human HCCS using cyt c and cyt c1 peptides as acceptors are consistent with the structural modeling of substrate binding. Reconstitution of HCCS and cyt c1 provides an approach to studying cyt c1 assembly, which has been refractile to recombinant in vivo reconstitution (unlike HCCS and cyt c). We propose a structural basis for release of the holocyt c product from HCCS based on in vitro studies and on cryoEM structures of the bacterial cyt c synthase (CcsBA) active site. We analyze the kinetoplastid mitochondrial synthase (KCCS), and hypothesize a molecular evolutionary path from mitochondrial endosymbiosis to the current HCCS.

Biochemistry & Molecular Biology↗
DOE OSTI2026

Fyn–Saracatinib Complex Structure Reveals an Active State-like Conformation

Fyn is a Src-family tyrosine kinase implicated in synaptic dysfunction and neuroinflammation across multiple neurodegenerative disorders, including Alzheimer’s disease (AD) and Parkinson’s disease (PD). Saracatinib (AZD0530) is a potent Src-family inhibitor that has been explored as a repurposed therapeutic; however, its clinical utility is limited by poor kinase selectivity caused by high sequence conservation within Src-family ATP-binding sites. Here, we combine surface plasmon resonance (SPR) and X-ray crystallography to define saracatinib recognition by the Fyn kinase domain (KD). SPR single-cycle kinetics shows that saracatinib binds the isolated Fyn KD and full-length Fyn with low-nanomolar affinity, whereas dasatinib binds with subnanomolar affinity and markedly slower dissociation. We determined the crystal structure of the Fyn KD-saracatinib complex at 2.22 Å resolution. The kinase adopts an active-like conformation with the DFG motif and αC-helix in the ‘in’ state and a conserved β3 αC Lys-Glu salt bridge. Saracatinib occupies the adenine and ribose pockets, and engages the hinge through direct and water-mediated hydrogen bonding while complementing a hydrophobic back pocket by van der Waals contacts. Comparison with reported saracatinib-bound structures of other kinases suggests that the active-state geometry observed for Fyn creates a pocket not observed in inactive-like complexes, providing a structural handle for designing Fyn-selective inhibitors. Comparison with all saracatinib-bound kinase co-structures currently available in the PDB (ALK2 and PKMYT1) indicates a conserved monodentate hinge binding mode but kinase-dependent αC-helix conformations, providing a structural rationale for designing Fyn-selective analogues.

AZD0530↗
DOE OSTI2024

Feasibility of Recovering and Recycling Polymer Composites from End-of-Life Marine Renewable Energy Structures: A Review

Over the last few decades, several marine renewable energy (MRE) technologies, such as wave energy converters (WECs) and current energy converters (CECs), have been developed. As opposed to traditional materials such as metal alloys, the structure of these technologies is made up of polymer and polymer composite materials. Most structures have been made using thermoset polymer composites; however, since thermoset polymer composites are not recyclable and lack sustainability, and with recent innovations in recyclable resins, bio-based resins, and the development of additive manufacturing technologies, thermoplastic polymers are increasingly being used. Nevertheless, the methodologies for identifying end-of-life options and recovering these polymer composites, as well as the recycling and reuse processes for MRE structures, are not well-studied. Specifically, since these MRE structures are subjected to salinity, moisture, varying temperature, biofouling, and corrosion effects depending on their usage, the recyclability after seawater aging and degradation needs to be explored. Hence, this review provides an in-depth review of polymer composites used in marine applications, the hygrothermal aging studies conducted so far to understand the degradation of these materials, and the reuse and recycling methodologies for end-of-life MRE structures, with a particular emphasis on sustainability.

16 TIDAL AND WAVE POWER↗
DOE OSTI2025

Detection of supernova magnitude fluctuations induced by large-scale structure

The peculiar velocities of supernovae and their host galaxies are correlated with the large-scale structure of the Universe, and can be used to constrain the growth rate of structure and test the cosmological model. In this work, we measure the correlation statistics of the large-scale structure traced by the Dark Energy Spectroscopic Instrument Bright Galaxy Survey Data Release 1 sample, and magnitude fluctuations of type Ia supernova from the Pantheon+ compilation across redshifts z < 0.1. We find a detection of the cross-correlation signal between galaxies and type Ia supernova magnitudes. Fitting the normalised growth rate of structure f sigma_8 to the auto- and cross-correlation function measurements we find f sigma_8 = 0.384 +0.094 -0.157, which is consistent with the Planck LambdaCDM model prediction, and indicates that the supernova magnitude fluctuations are induced by peculiar velocities. Using a large ensemble of N-body simulations, we validate our methodology, calibrate the covariance of the measurements, and demonstrate that our results are insensitive to supernova selection effects. We highlight the potential of this methodology for measuring the growth rate of structure, and forecast that the next generation of type Ia supernova surveys will improve f sigma_8 constraints by a further order of magnitude.

Nguyen, A. [Swinburne U., Ctr. Astrophys. Supercom↗
DOE OSTI2026

Dark Matter Constraints from Small-Scale Cosmic Structure

Small-scale cosmic structure provides a powerful test of the fundamental nature of dark matter (DM). A wide range of DM models impact matter clustering on small scales, including warm, fuzzy, and (self-)interacting DM. In these scenarios, DM physics such as free-streaming, wave interference, and self/Standard Model interactions alter the abundance and internal structure of DM halos. Cosmological and astrophysical probes of nonlinear structure---including dwarf galaxies, strong lensing, the Lyman-$α$ forest, stellar streams, and high-redshift galaxies---are therefore sensitive to these effects. Here, we review DM constraints provided by small-scale structure, focusing on observables that probe scales smaller than $\sim 1~\mathrm{Mpc}$, which define the frontier of current measurements. We summarize how these constraints have been translated to limits on microphysical DM models, and we discuss key modeling uncertainties and observational systematics. Finally, we highlight the growing importance of probe combination and simulation-based inference for this field, and we overview upcoming observational facilities that will sharpen small-scale structure tests of DM physics.

Nadler, Ethan O. [UC, San Diego] (ORCID:0000000211↗
DOE OSTI2025

Distinctive features of fluorescence and waveguides in magnesium aluminate spinel crystals driven by structural discrepancy

Transparent polycrystalline ceramics are of significant importance for a wide range of scientific and industrial applications. Developing a deeper understanding of their thermodynamic behavior is essential for achieving their maximum output performance in technological applications. This study provides a systematic investigation into the thermal excitation-induced fluorescence kinetics and waveguide characteristics of Magnesium Aluminate Spinel (MgAl 2 O 4 ) single crystals, with a focus on the intricate relationship between structure and properties. High-temperature extreme environments through irradiation with swift heavy ions 645.0 MeV Xe and 352.8 MeV Fe ions were created; the atomic deposition energy threshold (E th ) associated with disorder morphologies was assessed between 0.91 and 0.99 eV atom –1 . The track prediction model was developed to support the theoretical prediction of track formation. Electronic energy loss (E ele ) disrupts the balance of the initial structure through the thermal spike effect, leading to the formation of absorption-related F and F + color centers. These defects enhance photoluminescence in the visible spectrum and result in an effective modulation of the intrinsic bandgap. Moreover, as ion beams penetrate into the material, the uneven damage distribution induces the formation of waveguide structures. In conclusion, these findings provide valuable insights into the fabrication of functional devices through irradiation technologies and the structural changes of MgAl 2 O 4 at high temperatures within extreme environments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

RSPECT: A PROSPECT-based model incorporating the real structure of rice leaves

Radiative transfer models (RTMs) describe how light is absorbed, scattered, and transmitted within leaves by simulating mechanistic light propagation processes. The PROSPECT model is based on measurable parameters (the leaf biochemical content) and a non-measurable parameter (the leaf anatomical structure represented by the leaf structure parameter (N)). The effect of N on the optical properties of leaves has been investigated through a number of local and global sensitivity analyses. Other studies have directly evaluated the effect of the leaf anatomical structure on spectral reflectance, particularly in the near infrared region. However, the relationship between N and the anatomical structure is unclear. Here, in this study, we leveraged eLeaf, a ray tracing-based 3D rice leaf simulator, to establish relationships between leaf anatomical features and spectral properties, enabling us to replace N in the PROSPECT-4 model with measurable leaf anatomical parameters and develop the RSPECT model. The leaf thickness at minor vein, leaf thickness at bulliform cells, mesophyll thickness at minor vein, and distance between two minor veins could be used to predict N effectively. The RSPECT model achieved spectral simulation accuracy comparable to PROSPECT-4 and was more suitable for parameter inversion of the physical and chemical properties of rice leaves, with relative root mean square errors of 7.4% for chlorophyll content, 5.6% for equivalent water thickness, and 7.5% for dry matter content. In conclusion, RSPECT improves radiative transfer modeling by integrating measurable anatomical features and provides a framework for extending this approach to other vegetation types.

hyperspectral↗
DOE OSTI2024

Exploration of Tertiary Structure in Sequence-Defined Polymers Using Molecular Dynamics Simulations

Peptoids are a class of sequence-defined biomimetic polymers with peptide-like backbones and side chains located on backbone nitrogens rather than alpha carbons. These materials demonstrate a strong ability for precise control of single-chain structure, multiunit self-assembly, and macromolecular assembly through careful tuning of sequence due to the diversity of available side chains, although the driving forces behind these assemblies are often not understood. Prior experimental work has shown that linked 15mer peptoids can mimic the protein helical hairpin structure by leveraging the chirality-inducing nature of bulky side chains and hydrophobicity, but there are still gaps in our understanding of the relationship between sequence, stability, and particular secondary or tertiary structure. Here, we present a molecular dynamics (MD) study on the folding behavior of these polymers into hairpins, discussing the differences in structure from sequences with various characteristics in water and acetonitrile, and then compare the handedness preference of common helical motifs between solvents.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Evolution of Structural Order and Magnetic Anisotropy in Yb 0.5 (Co 1– x Fe x ) 3 Ge 3 through Doping of a Kagome Lattice

Kagome materials provide fruitful grounds for exploring the intersection of topology and magnetism. In this article, the single crystal growth of Yb 0.5 (Co 1–x Fe x ) 3 Ge 3 (x = 0.00, 0.25, 0.50, 0.75, and 1.00) is reported. As Fe is substituted into the Co-containing kagome net, the structure transforms from the disordered Y 0.5 Co 3 Ge 3 /CoSn-type hybrid structure to the ordered HfFe 6 Ge 6 -type structure. Diffusive scattering is observed in all doped concentrations that eventually converge to a single reflection in the Fe end member, ultimately doubling the unit cell along the c-axis. Anisotropic magnetic measurements were performed to evaluate how the magnetism of the kagome lattice is influenced by Fe substitution. Magnetic interactions are primarily observed along the c-axis. Additionally, a reorientation of the magnetic easy axis is observed with increasing Fe incorporation, highlighting how the magnetism of this material can be chemically tuned. Resistivity with unusual behavior observed in the doped compositions is also reported. Furthermore, the rationale behind the structural evolution from disordered to ordered is discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Structural Analysis of 23S rRNA Methylating Enzyme Cfr Reveals RNA-Binding Determinants for Methylation Regiospecificity and Antibiotic Resistance

The 23S rRNA methylating enzyme Cfr, found in pathogens including Staphylococcus aureus, Clostridium difficile, Escherichia coli, and Klebsiella pneumoniae, confers resistance to phenicols, lincosamides, oxazolidinones (including linezolid), pleuromutilins, and streptogramins A (the PhLOPS A phenotype). Cfr catalyzes methylation of the C8 position of the A2503 base in 23S rRNA, the recognition site of the above antibiotic classes. Along with the RlmN housekeeping enzyme, Cfr can also promote methylation of the C2 position of the same base. The molecular and structural basis of Cfr’s dual substrate specificity is not known, which hinders our ability to design Cfr-targeting inhibitors necessary to curb PhLOPS A resistance. Here, we present the first crystal structure of Cfr and a detailed analysis of its possible interactions with rRNA. Using structure-guided mutagenesis, mass spectrometry analysis of in cellulo 23S rRNA methylated species, and in cellulo resistance studies, we identify the key amino acids essential for Cfr methylation and multidrug resistance activity. In particular, we found that Cfr’s Q329 residue is important for C8- specific methylation. These data provide a framework for further studies of the biochemistry, structure, and inhibition of this important resistance determinant.

AlphaFold↗
DOE OSTI2025

Dimensional Control in Phase-Pure Coevaporated Quasi-2D Ruddlesden–Popper Structures

Fast, uncontrolled crystallization with several competing pathways makes solution-processing of phase-pure quasi-two-dimensional (quasi-2D) metal halide Ruddlesden–Popper thin films challenging. Typically, solution-processing results in the formation of different structural phases with varying dimensionality ranging from 2D, to quasi-2D, and 3D, introducing bandgap disorder and inhibiting charge transport. In this work, we eliminate interactions between precursor salts and solvents by using controlled thermal coevaporation to grow quasi-2D thin films that show high phase purity and narrow phase distribution. We study the structural landscape using synchrotron-based X-ray scattering and charge-carrier dynamics using ultrafast pump–probe spectroscopy. We then demonstrate a strategy to control the crystallographic phase of the film through phosphonic acid-based surface modification. We use density functional theory to study the interactions between propylphosphonic acid and the organic precursors and find that the interactions of loosely bound phosphonic acid molecules with evaporated precursors, followed by the migration of phosphonic acids through the deposited thin film, dictate the film structure between 2D and quasi-2D phases. These findings introduce new solvent-free methods for the fabrication of phase-pure quasi-2D Ruddlesden–Popper thin films and control phase selectivity across different dimensional (2D and quasi-2D) structures.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Combining Theory and Experiment to Map the Atomic-Level Structure–Energy Pathways of Adsorbate-Mediated Phase Changes in a Cooperatively Flexible Metal–Organic Framework

An important subclass of metal–organic frameworks (MOFs) exhibits cooperative flexibility, wherein individual crystallites undergo global structural phase changes in response to external stimuli. Where cooperative flexibility results in reversible changes between crystalline states of distinct accessible porosity, these frameworks can exhibit rare yet desirable behaviors that cannot be explained by local dynamics alone. Yet, the chemical and structural origins of cooperative flexibility and how frameworks undergo these reversible phase changes at the atomic level remain poorly understood. Deliberate design for specific applications is therefore exceedingly difficult, and there is great impetus to develop a fundamental understanding of this phenomenon. Here, an effective and widely accessible computational approach is developed, which is designed to provide microscopic resolution via direct comparison to experimental data along the desorption-guided pathway. The strategy is applied to explain the desorption-induced phase change in an experimentally well-characterized framework, CdIF-13 (sod-Cd(benzimidazolate)2), where experiment alone was unable to resolve the atomistically detailed phase change landscape. Our findings reveal that the cooperative phase change pathways are adsorbate dependent with thermodynamics of intermediate structural states dictated by a nuanced interplay of ligand orientation, skeletal symmetry, and modes of surface adsorption. The results reveal that this isotropically flexible framework is “chaperoned” through a complex energy landscape by specific adsorbates, revealed by the reported computational approach with atomic-level insight and validated by experimentally determined structures. Thus, this work facilitates both understanding and future design of flexible materials for applications in gas storage, transport, delivery, and separation technologies.

03 NATURAL GAS↗
DOE OSTI2025

Higher Dimensionality in the Mg–Co–B System: Synthesis and Structure of Incommensurate Composite Mg 1+ε Co 4 B 4

Guided by high-temperature in situ X-ray diffraction, the discovery and synthesis of Mg 1+ε Co 4 B 4 (ε ≈ 0.272) using a MgH 2 hydride precursor is reported, along with a detailed crystal structure description and measurement of magnetic properties. The mismatch in lattice periodicities between Mg and Co–B substructures places Mg 1+ε Co 4 B 4 in the family of incommensurate composite crystals and prompted structural refinement in a (3 + 1)-dimensional model. The structure of Mg 1+ε Co 4 B 4 (P4 2 /ncm(00γ)s00s, a = 6.75847(7) Å, c = 3.94007(8) Å, q = (0, 0, 1.2721(3))) was refined from neutron powder diffraction and high-resolution powder X-ray diffraction data and confirmed by scanning transmission electron microscopy and electron diffraction. Mg 1+ε Co 4 B 4 is isostructural to Nd 1+ε Fe 4 B 4 and several related ternary borides with 0.07 ≤ ε ≤ 0.17, with Mg occupying the rare-earth site. Satellite reflections in the electron diffraction patterns hinted at positional modulation of the transition metal–boron substructure by Mg atoms, but this could not be refined from the neutron or X-ray diffraction data. Low-temperature magnetic measurements show no indications of long-range magnetic ordering or superconductivity down to 5 K. DFT calculations confirmed the absence of a magnetically ordered ground state and the stability of a 5:4 supercell (ε = 0.25) relative to the fully commensurate structure. Neutron diffraction and synthesis from elemental Mg demonstrated that Mg 1+ε Co 4 B 4 is not a hydrogen-stabilized phase. Mg 1+ε Co 4 B 4 represents the second compound reported in the Mg–Co–B system and the first superspace symmetry model of a Nd 1+ε Fe 4 B 4 -type incommensurate composite compound refined from powder diffraction data.

chemical structure↗
DOE OSTI2024

Structural basis of divergent substrate recognition and inhibition of human neurolysin

A zinc metallopeptidase neurolysin (Nln) processes diverse bioactive peptides to regulate signaling in the mammalian nervous system. To understand how Nln interacts with various peptides with dissimilar sequences, we determined crystal structures of Nln in complex with diverse peptides including dynorphins, angiotensin, neurotensin, and bradykinin. The structures show that Nln binds these peptides in a large dumbbell-shaped interior cavity constricted at the active site, making minimal structural changes to accommodate different peptide sequences. The structures also show that Nln readily binds similar peptides with distinct registers, which can determine whether the peptide serves as a substrate or a competitive inhibitor. We analyzed the activities and binding of Nln toward various forms of dynorphin A peptides, which highlights the promiscuous nature of peptide binding and shows how dynorphin A (1–13) potently inhibits the Nln activity while dynorphin A (1–8) is efficiently cleaved. Our work provides insights into the broad substrate specificity of Nln and may aid in the future design of small molecule modulators for Nln.

59 BASIC BIOLOGICAL SCIENCES↗