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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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196 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2026

Isotopic Signature of Organic Molecules from Beyond the Solar System: An Enriched Methane D/H Ratio in the Interstellar Object 3I/ATLAS

Interstellar objects are interlopers from other planetary systems, and their volatile compositions provide a glimpse into planet formation around their host star. We present near-infrared spectra of the coma of interstellar object 3I/ATLAS measured with the James Webb Space Telescope. Our results demonstrate an unexpectedly high D/H = (3.31 ± 0.34)% for methane and represent an exceedingly rare detection of deuterated organic molecules in an interstellar object. This D/H ratio is a factor of 14 ± 2 higher than that measured in comet 67P/Churyumov-Gerasimenko by the Rosetta spacecraft, the only other comet for which CH3D has been detected, yet the ratio of deuteration in methane compared with water is consistent for both comets within 1.2σ. The D/H ratio in methane is observationally unconstrained in extrasolar sources to date, but the enriched ratio in 3I/ATLAS is most similar to those measured in other organic molecules toward primitive environments. The high D/H ratios of water and methane in 3I/ATLAS are a natural consequence of formation in a high D/H elemental ratio environment as a result of locally cold conditions in the protoplanetary disk and prior interstellar cloud. Thus, 3I/ATLAS formed in an environment very different from that in which our Sun and planets originated.

Interstellar Object 3I/ATLAS↗
DOE OSTI2022

Folding Coarse-Grained Oligomer Models with PyRosetta

Non-biological foldamers are a promising class of macromolecules that share similarities to classical biopolymers such as proteins and nucleic acids. Currently, designing novel foldamers is a non-trivial process, often involving many iterations of trial synthesis and characterization until folded structures are observed. In this work, we aim to tackle these foldamer design challenges using computational modeling techniques. We developed CG PyRosetta, an extension to the popular protein folding python package, PyRosetta, which introduces coarse-grained (CG) residues into PyRosetta, enabling the folding of toy CG foldamer models. Although these models are simplified, they can help explore overarching physical hypotheses about how oligomers can form. Through systematic variation of CG parameters in these models, we can investigate various folding hypotheses at the CG scale to inform the design process of new foldamer chemistries. In this study, we demonstrate CG PyRosetta’s ability to identify minimum energy structures with a diverse structural search over a range of simple models, as well as two hypothesis-driven parameter scans investigating the effects of side-chain size and internal backbone angle on secondary structures. We are able to identify several types of secondary structures from single- and double-helices to sheet-like and knot-like structures. Here, we show how side-chain size and backbone bond angle both play an important role in the structure and energetics of these toy models. Optimal side-chain sizes promote favorable packing of side chains, while specific backbone bond angles influence the specific helix type found in folded structures.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Discovery of Marburg virus neutralizing antibodies from virus-naïve human antibody repertoires using large-scale structural predictions

Marburg virus (MARV) disease is lethal, with fatality rates up to 90%. Neutralizing antibodies (Abs) are promising drug candidates to prevent or treat the disease. Current efforts are focused in part on vaccine development to induce such MARV-neutralizing Abs. We analyzed the antibody repertoire from healthy unexposed and previously MARV-infected individuals to assess if naïve repertoires contain suitable precursor antibodies that could become neutralizing with a limited set of somatic mutations. Here, we computationally searched the human Ab variable gene repertoire for predicted structural homologs of the neutralizing Ab MR78 that is specific to the receptor binding site (RBS) of MARV glycoprotein (GP). Eight Ab heavy-chain complementarity determining region 3 (HCDR3) loops from MARV-naïve individuals and one from a previously MARV-infected individual were selected for testing as HCDR3 loop chimeras on the MR78 Ab framework. Three of these chimerized antibodies bound to MARV GP. We then tested a full-length native Ab heavy chain encoding the same 17-residue-long HCDR3 loop that bound to the MARV GP the best among the chimeric Abs tested. Despite only 57% amino acid sequence identity, the Ab from a MARV-naïve donor recognized MARV GP and possessed neutralizing activity against the virus. Crystallization of both chimeric and full-length native heavy chain-containing Abs provided structural insights into the mechanism of binding for these types of Abs. Our work suggests that the MARV GP RBS is a promising candidate for epitope-focused vaccine design to induce neutralizing Abs against MARV.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Massively parallel, computationally guided design of a proenzyme

Proteins have shown promise as therapeutics and diagnostics, but their effectiveness is limited by our inability to spatially target their activity. To overcome this limitation, we developed a computationally guided method to design inactive proenzymes or zymogens, which are activated through cleavage by a protease. Since proteases are differentially expressed in various tissues and disease states, including cancer, these proenzymes could be targeted to the desired microenvironment. We tested our method on the therapeutically relevant protein carboxypeptidase G2 (CPG2). We designed Pro-CPG2s that are inhibited by 80 to 98% and are partially to fully reactivatable following protease treatment. The developed methodology, with further refinements, could pave the way for routinely designing protease-activated protein-based therapeutics and diagnostics that act in a spatially controlled manner. Confining the activity of a designed protein to a specific microenvironment would have broad-ranging applications, such as enabling cell type-specific therapeutic action by enzymes while avoiding off-target effects. While many natural enzymes are synthesized as inactive zymogens that can be activated by proteolysis, it has been challenging to redesign any chosen enzyme to be similarly stimulus responsive. Here, we develop a massively parallel computational design, screening, and next-generation sequencing-based approach for proenzyme design. For a model system, we employ carboxypeptidase G2 (CPG2), a clinically approved enzyme that has applications in both the treatment of cancer and controlling drug toxicity. Detailed kinetic characterization of the most effectively designed variants shows that they are inhibited by ∼80% compared to the unmodified protein, and their activity is fully restored following incubation with site-specific proteases. Introducing disulfide bonds between the pro- and catalytic domains based on the design models increases the degree of inhibition to 98% but decreases the degree of restoration of activity by proteolysis. A selected disulfide-containing proenzyme exhibits significantly lower activity relative to the fully activated enzyme when evaluated in cell culture. Structural and thermodynamic characterization provides detailed insights into the prodomain binding and inhibition mechanisms. The described methodology is general and could enable the design of a variety of proproteins with precise spatial regulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody–Antigen Interactions

This perspective sheds light on the transformative impact of recent computational advancements in the field of protein therapeutics, with a particular focus on the design and development of antibodies. Cutting-edge computational methods have revolutionized our understanding of protein–protein interactions (PPIs), enhancing the efficacy of protein therapeutics in preclinical and clinical settings. Central to these advancements is the application of machine learning and deep learning, which offers unprecedented insights into the intricate mechanisms of PPIs and facilitates precise control over protein functions. Despite these advancements, the complex structural nuances of antibodies pose ongoing challenges in their design and optimization. Our review provides a comprehensive exploration of the latest deep learning approaches, including language models and diffusion techniques, and their role in surmounting these challenges. We also present a critical analysis of these methods, offering insights to drive further progress in this rapidly evolving field. The paper includes practical recommendations for the application of these computational techniques, supplemented with independent benchmark studies. These studies focus on key performance metrics such as accuracy and the ease of program execution, providing a valuable resource for researchers engaged in antibody design and development. Through this detailed perspective, we aim to contribute to the advancement of antibody design, equipping researchers with the tools and knowledge to navigate the complexities of this field.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2013

Design of Quasi-Terminator Orbits near Primitive Bodies

Quasi-terminator orbits are a class of quasi-periodic orbits around a primitive body that exist in the vicinity of the well-known terminator orbits. The inherent stability of quasi-terminator trajectories and their wide variety of viewing geometries make them a very compelling option for primitive body mapping missions. In this paper, we discuss orbit design methodologies for selection of an appropriate quasi-terminator orbit that would meet the needs of a specific mission. Convergence of these orbits in an eccentric, higher-fidelity model is also discussed with an example case at Bennu, the target of the upcoming NASA's OSIRIS-REx mission.

mapping↗