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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Bacterial α-diglucoside metabolism: perspectives and potential for biotechnology and biomedicine

In a competitive microbial environment nutrient acquisition is a major contributor to the survival of any individual bacterial species, and the ability to access uncommon energy sources can provide a fitness advantage. One set of soluble carbohydrates that have attracted increased attention for use in biotechnology and biomedicine are the α-diglucosides. Maltose is the most well studied member of this class, however the remaining four less common α-diglucosides (trehalose, kojibiose, nigerose, and isomaltose) are increasingly used in processed food and fermented beverages. The consumption of trehalose has recently been shown to be a contributing factor in gut microbiome disease as certain pathogens are using α-diglucosides to outcompete native gut flora. Kojibiose and nigerose have also been examined potential prebiotics and alternative sweeteners for a variety of foods. Compared to the study of maltose metabolism our understanding of the synthesis and degradation of uncommon α-diglucosides is lacking and several fundamental questions that remain unanswered, particularly in regards to the regulation of bacterial metabolism for α-diglucosides. Therefore this minireview attempts to provide a focused analysis of uncommon α-diglucoside metabolism in bacteria, and suggests some future directions for this research area that could potentially accelerate biotechnology and biomedicine developments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Session Introduction: Graph Representations and Algorithms in Biomedicine

Connectivity is a fundamental property of biological systems: on the cellular level, proteins interact with each other to form protein-protein interaction networks (PPIs); on the organism level, neurons are arranged in a network; and on a community-level, species can have complex relationships with one another that drive the development and balance of an ecosystem. Graphs, representations of systems consisting of entities as vertices and their connections as edges, are a useful structure to characterize many such systems. Such models can be used to understand biological systems that naturally have a network structure, including PPIs, biological neurons, and ecosystems. In today’s information age, graph representations and algorithms (often in combination with machine learning techniques) are used to organize massive amounts of related data, much of which may be heterogeneous or unstructured, and identify patterns that represent novel biological insights. PSB’s 2023 session “Graph Representations and algorithms in Biomedicine,” encompasses modern developments in graph theory and its applications to various fields of biomedicine. This session includes a wide range of research - knowledge graphs built from text-mined health data, heterogeneous networks using multi-omic databases, and graphs refined to represent uncertainty or improve memory usage.

Chrisman, Brianna S.↗
DOE OSTI2020

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), the US data center for the global PDB archive and a founding member of the Worldwide Protein Data Bank partnership, serves tens of thousands of data depositors in the Americas and Oceania and makes 3D macromolecular structure data available at no charge and without restrictions to millions of RCSB.org users around the world, including >660 000 educators, students and members of the curious public using PDB101.RCSB.org. PDB data depositors include structural biologists using macromolecular crystallography, nuclear magnetic resonance spectroscopy, 3D electron microscopy and micro-electron diffraction. PDB data consumers accessing our web portals include researchers, educators and students studying fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. During the past 2 years, the research-focused RCSB PDB web portal (RCSB.org) has undergone a complete redesign, enabling improved searching with full Boolean operator logic and more facile access to PDB data integrated with >40 external biodata resources. New features and resources are described in detail using examples that showcase recently released structures of SARS-CoV-2 proteins and host cell proteins relevant to understanding and addressing the COVID-19 global pandemic.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Interfacing DNA nanotechnology and biomimetic photonic complexes: advances and prospects in energy and biomedicine

Abstract Self-assembled photonic systems with well-organized spatial arrangement and engineered optical properties can be used as efficient energy materials and as effective biomedical agents. The lessons learned from natural light-harvesting antennas have inspired the design and synthesis of a series of biomimetic photonic complexes, including those containing strongly coupled dye aggregates with dense molecular packing and unique spectroscopic features. These photoactive components provide excellent features that could be coupled to multiple applications including light-harvesting, energy transfer, biosensing, bioimaging, and cancer therapy. Meanwhile, nanoscale DNA assemblies have been employed as programmable and addressable templates to guide the formation of DNA-directed multi-pigment complexes, which can be used to enhance the complexity and precision of artificial photonic systems and show the potential for energy and biomedical applications. This review focuses on the interface of DNA nanotechnology and biomimetic photonic systems. We summarized the recent progress in the design, synthesis, and applications of bioinspired photonic systems, highlighted the advantages of the utilization of DNA nanostructures, and discussed the challenges and opportunities they provide. Graphical Abstract

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Biological toxicity and environmental hazards associated with PLGA nanoparticles

There have been tremendous advances in nanotechnology. More recently, the use of nanoparticles has expanded to applications including materials, packaging, energy, and medical uses such as drug delivery, diagnostics, and therapeutics. The American Society for Testing and Materials (ASTM) and the International Organization for Standardization (ISO) define nanoparticles as particles with at least one dimension measuring between 1 and 100 nm (2), but this view is generally too limited, and recommendations have been made to consider all materials with a dimension measuring under 1,000 nm as nanoparticles. The biomedical uses of nanoparticles are particularly promising because of their ability to reach and target various sites and organs. However, some nanoparticles can be composed of toxic materials or are limited by issues of biodistribution and bioaccumulation, which have hampered their use in biomedicine. The copolymer poly lactic-co-glycolic acid (PLGA) has gained use in biomedical applications as a delivery system because it is considered biocompatible and can be formulated with controlled degradation in physiological environments. PLGA has a history in biomedicine that dates to the 1970s when biodegradable sutures were developed using PLGA. In addition, the United States (US) Food and Drug Administration (FDA) and the European Medicine Agency (EMA) have approved various PLGA particle formulations as therapeutic delivery vehicles. Interest in biocompatible materials to deliver a range of therapeutic drugs, proteins, nucleic acids, and other molecules has risen recently. In addition to drug delivery, sustained drug release can be achieved by tuning the physical properties of PLGA, such as the molecular weight, ratio of lactic to glycolic acid, drug concentration, stabilizing molecules, and particle size. Given the rise of nanoparticles, human and environmental exposure to them is inevitable as more applications use free, unbound, and highly mobile particles. Although PLGA is generally considered safe, a detailed understanding of how PLGA and the other components used to help formulate this copolymer into a nanoparticle delivery vehicle is useful for assessing potential harmful effects. Despite recent advances in PLGA nanoparticle formulations, residual stabilizing molecules, inconsistent preparations, and batch-to-batch variations can lead to toxicity. Poor preparations of PLGA can lead to common mechanisms of toxicity observed with nanoparticles, such as inflammation and oxidative stress. Consistency in PLGA formulations and accurate methods to evaluate nanoparticle toxicity are needed to ensure safety. In order to better assess PLGA nanoparticles as non-toxic delivery systems, it is critical to understand their physical and chemical properties and the formulation protocols that may introduce toxic components into particles.

Biology, nanoparticle, toxicity↗
DOE OSTI2024

Simultaneous Separation of Protactinium-230 and Uranium-230 Isotopes from a Proton-Irradiated Thorium Matrix

The use of radioisotopes in cancer therapy is becoming increasingly important. As a potential candidate for targeted alpha therapy, 230 U (t 1/2 = 20.8 d), the decay daughter of 230 Pa (t 1/2 = 17.4 d) is currently being investigated for cancer treatment. For radioisotopes to be used in biomedicine, they must be radiochemically pure and free from carrier interference. This can be challenging given their short half-life. Thus, in this study, radiological separation methods for harvesting isotopes for use in biomedicine must be simple, fast, and capable of obtaining the required purity levels. Herein, we demonstrate a new rapid method for the separation of 230 U and 230 Pa from a proton-irradiated 232 Th metal target and from coproduced fission products with high recovery and purity. A dual-column approach was used, in which the first column was an anion exchange (AX) column and the second column was a DGA column. The MP-1 AX resin was used for the primary separation of the three major components (U, Pa, and Th, as well as fission products), and the DGA column was used for further purification of the separated 230 U. The method is simple and allows for straightforward separation of U, Pa, Th, and fission products; also, the entire process from target dissolution to shipment of 230 U could be completed in 3 days with an overall 230 U recovery yield of 96 ± 3%. Additionally, separated 230 Pa fractions can be used to harvest ingrown 230 U after initial separation.

DGA resin↗
DOE OSTI2021

Propagation of positive discharges in an air bubble having an embedded water droplet

Low temperature plasmas generated inside gas bubbles immersed in water is an effective method of rapidly transferring plasma generated reactive species to the water for applications in biomedicine, agriculture and environment. Reactive species are generally produced in the gas phase plasma and then solvate into the liquid. The large surface-to-volume ratio (SVR) of the bubble accelerates this process. In generating bubbles in water, aerosols and droplets are also contained within the bubble. These droplets also have a large SVR and so can be rapidly plasma activated. However, the presence of the droplets can also impact the propagation of the plasma in the bubble. In this paper, results are discussed from computational and experimental investigations of the formation and evolution of discharges in an air bubble immersed in water with an embedded water droplet. The computations were performed with a two-dimensional plasma hydrodynamics model. Experiments were performed with a quasi-2D bubble apparatus. In bubbles having a droplet, a plasma filament typically bridges from the powered electrode to the droplet, and then from the droplet to the bubble surface. A surface-hugging streamer also occurs on the inner bubble surface and on the surface of the droplet. Both surface streamers result in part from surface charge accumulation and can dominate the formation of reactive species that transport into the droplet. Increasing droplet conductivity suppresses propagation of the surface discharge and leads to a lower density of aqueous reactive species. Increasing conductivity of the surrounding water does not change the overall structure of the discharge but does slightly elevate the discharge intensity. Finally, the size and shape of the embedded droplet can significantly affect the formation and propagation of the streamer.

2D fluid modelling↗
DOE OSTI2021

Multiscale Modeling Meets Machine Learning: What Can We Learn?

Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learn- ing to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.

machine learning, multiscale modeling, physics-bas↗
DOE OSTI2021

Poly(vinyl alcohol) Hydrogels with Broad-Range Tunable Mechanical Properties via the Hofmeister Effect

Hydrogels, exhibiting wide applications in soft robotics, tissue engineering, implantable electronics, etc., often require sophisticately tailoring of the hydrogel mechanical properties to meet specific demands. For examples, soft robotics necessitates tough hydrogels; stem cell culturing demands various tissue-matching modulus; and neuron probes desire dynamically tunable modulus. Herein, a strategy to broadly alter the mechanical properties of hydrogels reversibly via tuning the aggregation states of the polymer chains by ions based on the Hofmeister effect is reported. An ultratough poly(vinyl alcohol) (PVA) hydrogel as an exemplary material (toughness 150 ± 20 MJ m -3 ), which surpasses synthetic polymers like poly(dimethylsiloxane), synthetic rubber, and natural spider silk is fabricated. With various ions, the hydrogel's various mechanical properties are continuously and reversibly in situ modulated over a large window: tensile strength from 50 ± 9 kPa to 15 ± 1 MPa, toughness from 0.0167 ± 0.003 to 150 ± 20 MJ m -3 , elongation from 300 ± 100% to 2100 ± 300%, and modulus from 24 ± 2 to 2500 ± 140 kPa. Importantly, the ions serve as gelation triggers and property modulators only, not necessarily required to remain in the gel, maintaining the high biocompatibility of PVA without excess ions. Overall, this strategy, enabling high mechanical performance and broad dynamic tunability, presents a universal platform for broad applications from biomedicine to wearable electronics.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Engineering In Situ Catalytic Cleaning Membrane Via Prebiotic-Chemistry-Inspired Mineralization

Pressure-driven membrane separation promises a sustainable energy-water nexus but is hindered by ubiquitous fouling. Natural systems evolved from prebiotic chemistry offer a glimpse of creative solutions. Herein, a prebiotic-chemistry-inspired aminomalononitrile (AMN)/Mn 2+ -mediated mineralization method is reported for universally engineering a superhydrophilic hierarchical MnO 2 nanocoating to endow hydrophobic polymeric membranes with exceptional catalytic cleaning ability. Green hydrogen peroxide catalytically triggered in-situ cleaning of the mineralized membrane and enabled operando flux recovery to reach 99.8%. The mineralized membrane exhibited a 9-fold higher recovery compared to the unmineralized membrane, which is attributed to active catalytic antifouling coupled with passive hydration antifouling. Electron density differences derived from the precursor interaction during mediated mineralization unveiled an electron-rich bell-like structure with an inner electron-deficient Mn core. This work paves the way to construct multifunctional engineered materials for energy-efficient water treatment as well as for diverse promising applications in catalysis, solar steam generation, biomedicine, and beyond.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Two–Photon Printing of Shape–Memory Microstructures and Metasurfaces via Radical–Mediated Thiol–Vinyl Hydrothiolation

Shape-memory resists capable of high-resolution curing into arbitrarily designed structures are increasingly demanded for soft robotics, optical sensors, microscale manufacturing, and biomedicine. Amorphous, shape-memory thiol-vinyl networks were printed using two-photon polymerization (2PP) curing of a simple resin formulated with commercially available reagents. The ability to print high-resolution feature sizes down to 200 nm is attributed to the use of radicalmediated, thiol-vinyl step-growth polymerization that quickly crosslinks the resin, limiting diffusive transport. The thermomechanical behavior of the 2PP-cured material analyzed in compression, tension and three-point bending is similar to the behavior of the UV-polymerized samples. To demonstrate the ability to design, field, and test 4D responsive microstructures, an array of nine springs with coil diameters of 330 μm was printed. Following compressive shapefixing, printed arrays can release 11 μJ of stored elastic strain energy when reheated. Further, a new concept of dichroic-memory of a metamaterial device is demonstrated by printing a twisted woodpile structure with circular dichroism as characterized by Mueller Matrix ellipsometry. Furthermore, our results demonstrate how combining high-resolution 2PP curing with stimuli-responsive molecular architectures can further the engineering of responsive microstructures and metamaterials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Electrochemical and Optical Spectroscopic Probing of Transition–Sized Au 130 (SR) 50 Nanoclusters

Ultrasmall metal nanoclusters (NCs) exhibit a quantized conduction band, hence, a distinct HOMO-LUMO gap (E g ). Such a quantized electronic structure gives rise to multiple discrete peaks in the optical absorption spectrum of the NCs. As the size grows to 130 gold atoms (Au 130 protected by ligands), electrical charging and optical behaviors seem to show certain metal-like features (hence, transition-sizes). To probe such behaviors, especially the potential ligand effect, we have devised the synthesis of Au 130 NCs protected by phenylethanethiolate and naphthalenethiolate, respectively, with the former having a nonconjugated separation between the aromatic molecular group and the metal core while the latter being in direct bonding. A careful comparison of these two Au 130 nanoclusters with the earlier reported analogues is carried out, including the structurally characterized Au 130 (pMBT) 50 and the aqueous counterpart. While all of these nanoclusters possess the same 80 free electron counts in the core, some notable differences in electrochemical and optical properties are found, which are attributed to the ligand effects. The obtained insights may stimulate further interest in the transition-sized nanoclusters and also promote their applications in optics, energy conversion, and biomedicine.

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

From PINNs to PIKANs: recent advances in physics-informed machine learning

Physics-Informed Neural Networks (PINNs) have emerged as a key tool in Scientific Machine Learning since their introduction in 2017, enabling the efficient solution of ordinary and partial differential equations using sparse measurements. Over the past few years, significant advancements have been made in the training and optimization of PINNs, covering aspects such as network architectures, adaptive refinement, domain decomposition, and the use of adaptive weights and activation functions. A notable recent development is the Physics-Informed Kolmogorov-Arnold Networks (PIKANS), which leverage a representation model originally proposed by Kolmogorov in 1957, offering a promising alternative to traditional PINNs. In this review, we provide a comprehensive overview of the latest advancements in PINNs, focusing on improvements in network design, feature expansion, optimization techniques, uncertainty quantification, and theoretical insights. We also survey key applications across a range of fields, including biomedicine, fluid and solid mechanics, geophysics, dynamical systems, heat transfer, chemical engineering, and beyond. Lastly, we review computational frameworks and software tools developed by both academia and industry to support PINN research and applications.

Kolmogorov-Arnold networks↗
DOE OSTI2021

Submonomer synthesis of sequence defined peptoids with diverse side-chains.

Peptoids are a diverse family of sequence-defined oligomers of N-substituted glycine monomers, that can be readily accessed by the solid-phase submonomer synthesis method. Due to the versatility and efficiency of this chemistry, and the easy access to hundreds of potential monomers, there is an enormous potential sequence space that can be explored. This has enabled researchers from many different fields to custom-design peptoid sequences tailored to a wide variety of problems in biomedicine, nanoscience and polymer science. Here we provide detailed protocols for the synthesis of peptoids, using optimized protocols that can be performed by non-chemists. The submonomer method is fully compatible with Fmoc-peptide synthesis conditions, so the method is readily automated on existing automated peptide synthesizers using protocols provided here. Although the submonomer synthesis for peptoids is well established, there are special considerations required in order to access many of the most useful and desirable sidechains. Here we provide methods to include most of the amino-acid-like side chains, some of the most important non-natural monomer classes, as well as the creation of peptoid conjugates and peptide-peptoid hybrids.

Connolly, Michael D↗
DOE OSTI2026

From atomistic models to machine learning: Predictive design of nanocarbons under extreme conditions

The formation of technologically valuable nanocarbon structures under extreme conditions, such as those produced during high-explosive detonations, remains poorly understood but holds significant potential for the development of controlled synthesis pathways. While detonation shockwaves provide the high-pressure, high-temperature environment required for nanodiamond formation, subsequent cooling and decompression dictate whether the diamond phase is preserved or transformed into other nanocarbon structures. Here, in this study, we employ GPU-accelerated reactive molecular dynamics (ReaxFF) simulations to investigate the graphitization and structural remodeling of detonation nanodiamond under nonlinear quench and pressure-release trajectories. We further investigate how the initial nanodiamond morphology; cuboctahedral, octahedral, or hexagonal prism influences the resulting transformation products. Evolution of nanostructure, allotrope (via simulated x-ray diffraction), carbon hybridization, and ring statistics are tracked during a two-stage quench from 5000 K to 60 GPa. Rapid cooling combined with slow decompression optimizes cubic diamond retention, whereas slow cooling with rapid pressure release promotes surface-to-core graphitization, producing concentric sp 2 -hybridized layers and hollowed inner shells. Octahedral nanodiamonds evolve into carbon nano-onions, initially forming bucky diamonds that progressively transform into fully sp 2 -hybridized structures, while hexagonal prisms preferentially form parallel-stacked graphite layers resembling carbon dots. Transient hexagonal diamond (lonsdaleite) emerges as an interfacial phase, suggesting potential reversibility in the shock-induced graphite-to-diamond transformation pathway transformation route. To extend predictive capabilities, we trained machine learning (ML) regressors on over 10 5 node-hours of molecular dynamics (MD) trajectories. A multilayer perceptron (MLP) model reliably predicts the number of graphitized layers from temperature–pressure trajectories with a coefficient of determination (R 2 ) exceeding 0.90. This high predictive fidelity enables efficient, high-throughput mapping of the synthesis parameter space for optimized graphitization outcomes. Collectively, morphological control combined with optimized quench–decompression conditions promote the selective synthesis of nanocarbon allotropes. This work establishes a data-driven framework for the rational, a priori design of carbon nanomaterials for applications in energy storage, sensing, and biomedicine.

Detonation nanodiamond remodeling↗
DOE OSTI2025

Polylactic acid (PLA)-based multifunctional and biodegradable nanocomposites and their applications

Polylactic acid (PLA)-based nanocomposites are emerging as multifunctional, biodegradable materials, offering sustainable alternatives to petroleum-based plastics. This review examines recent advancements in PLA nanocomposites, focusing on enhanced mechanical strength, thermal stability, and biodegradability achieved through nanofillers like metallic particles, carbon-based materials, and ceramics. Techniques such as in situ polymerization, melt mixing, and electrospinning enable application-specific improvements. PLA's limitations, including brittleness and low barrier properties, are addressed to support diverse applications: in packaging (e.g., extended shelf life), biomedicine (e.g., degradable implants), and electronics (e.g., flexible devices). Despite challenges with filler dispersion and thermal resistance, continued innovations expand PLA's potential across multiple industries, contributing to a sustainable, circular economy.

Biodegradable polymers↗
DOE OSTI2025

Innovative use of CaO in combination with amino acid salt to convert CO 2 as CaCO 3 nanoparticles under mild pH and low temperature

The increasing demand for sustainable CO 2 management has driven the development of innovative methods that can convert point source CO 2 into value-added products. In this study, CaO in combination with amino acid salt was used to convert CO 2 into CaCO 3 nanoparticles. Different from the conventional method where CO 2 diffusion presents a major challenge in reacting with CaO to form CaCO 3 , amino acid salt solvent was applied to absorb CO 2 first and then rapidly reacted with CaO to form CaCO 3 nanoparticles (∼50 nm) at a low temperature (e.g., 60 °C). Our experiments showed that at a glycine (Gly)/NaOH ratio of 2:1 or 3:1, the solution pH values during the CO 2 absorption and conversion were about 8–9 at 60 °C, while at a ratio of 1:1, the solution pH values were about 9–11; without Gly, the solution pH values were about 12. Gly-optimized solvent substantially reduced corrosion risk to reactors. In addition, the use of amino acid (i.e., Gly) led to much smaller CaCO 3 particles, distinctly different chemical phases, and fundamentally different chemical reactions. Moreover, in the presence of Gly, the solution pH was completely reversed and the solution was regenerated for cyclic use when CaO was added. The solvent was recyclable and reusable, highlighting the cost-effectiveness and sustainability of this approach. The Gly-modulated CaCO 3 nanoparticles may have significant potential for industrial applications in the biomedicine, construction, plastics, and rubber industries.

36 MATERIALS SCIENCE↗
DOE OSTI2025

rcsb-api : Python Toolkit for Streamlining Access to RCSB Protein Data Bank APIs

The Protein Data Bank (PDB) was founded in 1971 as the first open-access digital data resource in biology to serve as the single global archive for three-dimensional (3D) macromolecular structure data. Current PDB holdings exceed 230,000 experimentally determined structures of proteins, nucleic acids, viruses, and macromolecular machines. The RCSB Protein Data Bank RCSB.org research-focused web portal facilitates search, analyses, and visualization of every PDB structure along with more than one million Computed Structure Models from AlphaFold DB and the ModelArchive. It is powered by a set of publicly available Application Programming Interfaces (APIs) that both support RCSB.org users and provide programmatic access to PDB data. Given the breadth and levels of granularity encompassed in this rich data collection, efficiently accessing the information programmatically may be challenging for new users. RCSB PDB has developed a Python software package, rcsb-api , that facilitates easy and efficient use of RCSB PDB APIs within a Python environment. This software tool is designed to streamline access to the extensive corpus of data housed within the PDB, enabling researchers to search, retrieve, and analyze 3D biostructure data seamlessly. Its use will accelerate research in structural biology, molecular biology and biochemistry, drug discovery, and bioinformatics by providing more efficient tools for data integration and analysis. The new toolkit is available on GitHub (github.com/rcsb/py-rcsb-api) and published to the public Python package repository (PyPI) to foster wider usage and support basic and applied research in fundamental biology, biomedicine, and the energy sciences.

FAIR principles↗