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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Structural Characterization of Disulfide-Linked p53-Derived Peptide Dimers

Disulfide bonds provide a convenient method for chemoselective alteration of peptide and protein structure and function. We previously reported that mild oxidation of a p53-derived bisthiol peptide (CTFANLWRLLAQNC) under dilute non-denaturing conditions led to unexpected disulfide-linked dimers as the exclusive product. The dimers were antiparallel, significantly α-helical, resistant to protease degradation, and easily reduced back to the original bisthiol peptide. Here, in this work, we examine the intrinsic factors influencing peptide dimerization using a combination of amino acid substitution, circular dichroism (CD) spectroscopy, and X-ray crystallography. CD analysis of peptide variants suggests critical roles for Leu6 and Leu10 in the formation of stable disulfide-linked dimers. The 1.0 Å resolution crystal structure of the peptide dimer supports these data, revealing a leucine-rich LxxLL dimer interface with canonical knobs-into-holes packing. Two levels of higher-order oligomerization are also observed in the crystal: an antiparallel “dimer of dimers” mediated by Phe3 and Trp7 residues in the asymmetric unit and a tetramer of dimers mediated by Trp7 and Leu10. In CD spectra of Trp-containing peptide variants, minima at 227 nm provide evidence for the dimer of dimers in dilute aqueous solution. Importantly, and in contrast to the original dimer model, the canonical leucine-rich core and robust dimerization of most peptide variants suggests a tunable molecular architecture to target various proteins and evaluate how folding and oligomerization impact various properties, such as cell permeability.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Distributed optimization for multi-commodity urban traffic control

A distributed method for concurrent traffic signal and routing control of traffic networks is proposed. The method is based on the multi-commodity store-and-forward model, in which the destinations are the commodities. The system benefits from the communication between vehicles and infrastructure, providing optimal signal timings to intersections and routes to vehicles on a link-by-link basis. Using the augmented Lagrangian to model the constraints into the objective, the baseline centralized problem is decomposed into a set of objective-coupled subproblems, one for each intersection, enabling the solution to be computed by a distributed- gradient projection algorithm. Further, the intersection agents only need to communicate and coordinate with neighboring intersections to ensure convergence to the optimal solution while tolerating suboptimal iterations that offer more flexibility, unlike other distributed approaches. Through microsimulation, we demonstrate the effectiveness of the proposed algorithm in traffic networks with time-varying demand. Computational analysis shows that the distributed problem is suitable for real-time applications. A robustness analysis show that the distributed formulation enables a graceful degradation of the system in case of failure.

Augmented Lagrangian↗
DOE OSTI2021

Bringing discrete-time Langevin splitting methods into agreement with thermodynamics

In light of the recently published complete set of statistically correct Grønbech–Jensen (GJ) methods for discrete-time thermodynamics, we revise a differential operator splitting method for the Langevin equation in order to comply with the basic GJ thermodynamic sampling features, namely, the Boltzmann distribution and Einstein diffusion, in linear systems. This revision, which is based on the introduction of time scaling along with flexibility of a discrete-time velocity attenuation parameter, provides a direct link between the ABO splitting formalism and the GJ methods. This link brings about the conclusion that any GJ method has at least weak second order accuracy in the applied time step. It further helps identify a novel half-step velocity, which simultaneously produces both correct kinetic statistics and correct transport measures for any of the statistically sound GJ methods. Explicit algorithmic expressions are given for the integration of the new half-step velocity into the GJ set of methods. Finally, numerical simulations, including quantum-based molecular dynamics (QMD) using the QMD suite Los Alamos Transferable Tight-Binding for Energetics, highlight the discussed properties of the algorithms as well as exhibit the direct application of robust, time-step-independent stochastic integrators to QMD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Harnessing Heterologous Bacterial Two-Component Systems as Biosensors to Address Challenges in Fermentation Scale-Up

Scaling up bacterial fermentation from bench to industrial scale often results in unpredictable performance losses, possibly in part due to changes in microenvironmental conditions such as pH. To investigate this, we developed a suite of pH-sensitive biosensors from bacterial two-component systems (TCSs) that provide a dynamic, fluorescent readout in response to extracellular pH changes. TCSs consist of a periplasmic sensor histidine kinase (HK) that, in response to an extracellular stimulus, autophosphorylates intracellularly and subsequently transfers the phosphate to a cognate response regulator (RR) that modulates transcription of target genes. We utilized three pH-responsive TCSs (referred to here as CVJ1, CVJ30, and CVJ79) and linked their output to GFP. This was achieved by placing the RR promoter upstream of GFP or by constructing a chimeric RR composed of the native receiver domain and the DNA-binding domain of another well-characterized RR with a defined promoter. All components - HK, RR (native or chimeric), and GFP under its corresponding promoter - were cloned into a broad-host-range plasmid. Sensors were validated in Escherichia coli and Pseudomonas putida, including the muconic acid-producing strain P. putida TL207. All three biosensors successfully reported pH, with fluorescence (normalized to optical density) correlating strongly with media pH. Among the native sensors, CVJ79 showed the most robust performance while CVJ1 also performed best in its native form; CVJ30 exhibited improved functionality as a chimera, suggesting that modular RR design can enhance compatibility in some heterologous hosts. Further, CVJ79 was activated by alkaline conditions, while CVJ30 responded to acidic environments. Notably, CVJ1 was induced by high pH in wild-type E. coli and P. putida, but low pH in TL207. The observed differences in sensor activation between strains - particularly the divergent response of CVJ1 - suggest that host-specific regulatory pathways may influence how cells perceive and adapt to pH stress. Moving forward, these biosensors can be used to guide the rational design of more robust strains, optimize process conditions in real time, and inform strategies to minimize physiological heterogeneity during scale-up. Integrating these tools into high-throughput screening and bioreactors will be a key step toward improving predictability and performance in industrial bioprocesses.

09 BIOMASS FUELS↗
DOE OSTI2026

Revealing the complex chemistry of grain boundaries in K-doped BaFe 2 As 2 with atom probe tomography

Iron-based superconductors have attractive properties for high-field applications, but there is a lack of understanding of the effect of grain boundary chemistry on the in-field performance. The near atomic-scale resolution, ppm sensitivity and 3D analysis offered by atom probe tomography make it a powerful tool to investigate the nanoscale structure and chemistry of these defects in fine-grained K-doped BaFe 2 As 2 samples. A computational method to systematically extract and compare the Gibbsian interfacial excess of chemical species across grain boundaries has been explored in this work. The robustness of the method has been tested by evaluating the effects of selected variables on simulated APT datasets. The accuracy and precision of the calculated Gibbsian interfacial excess were found to be stable over a range of analysis conditions: varying grain boundary widths and detection efficiencies, spatial precisions below 1.5 nm, and bin widths between 1.2 and 1.6 nm. For the K-doped BaFe 2 As 2 samples studied, segregation of As, Ba, K and impurities of O, Na, and Sb were found at grain boundaries. The Gibbsian excess values were found to vary widely between different boundaries, showing the complexity of the grain boundary chemistry in this material. Possible links between the observed critical current density (Jc) of these samples and their nano- and micro-structure have also been investigated and discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2021

SuFExable polymers with helical structures derived from thionyl tetrafluoride

Sulfur(VI) fluoride exchange (SuFEx) is a category of click chemistry that enables covalent linking of modular units through sulfur(VI) connective hubs. The efficiency of SuFEx and the stability of the resulting bonds have led to polymer chemistry applications. Now, we report the SuFEx click chemistry synthesis of several structurally diverse SOF 4 -derived copolymers based on the polymerization of bis(iminosulfur oxydifluorides) and bis(aryl silyl ethers). This polymer class presents two key characteristics. First, the [-N=S(=O)F-O-] polymer backbone linkages are themselves SuFExable and undergo precise SuFEx-based post-modification with phenols or amines to yield branched functional polymers. Second, studies of individual polymer chains of several of these new materials indicate helical polymer structures. The robust nature of SuFEx click chemistry offers the potential for post-polymerization modification, enabling the synthesis of materials with control over composition and conformation.

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

Autonomous Cyber Defense Against Dynamic Multi-strategy Infrastructural DDoS Attacks

Dynamic Infrastructural Distributed Denial of Service (I-DDoS) attacks constantly change attack vectors to congest core backhaul links and disrupt critical network availability while evading end-system defenses. To effectively counter these highly dynamic attacks, defense mechanisms need to exhibit adaptive decision strategies for real-time mitigation. This paper presents a novel Autonomous DDoS Defense framework that employs model-based reinforcement agents. The framework continuously learns attack strategies, predicts attack actions, and dynamically determines the optimal composition of defense tactics such as filtering, limiting, and rerouting for flow diversion. Our contributions include extending the underlying formulation of the Markov Decision Process (MDP) to address simultaneous DDoS attack and defense behavior, and accounting for environmental uncertainties. We also propose a fine-grained action mitigation approach robust to classification inaccuracies in Intrusion Detection Systems (IDS). Additionally, our reinforcement learning model demonstrates resilience against evasion and deceptive attacks. Evaluation experiments using real-world and simulated DDoS traces demonstrate that our autonomous defense framework ensures the delivery of approximately 96 - 98% of benign traffic despite the diverse range of attack strategies.

Dutta, Ashutosh↗
DOE OSTI2023

Novel Module Architecture for Lower CapEx and Improved Recyclability for c-Si PV Modules

Photovoltaic (PV) energy production is currently increasing at a rate at which recycling is becoming necessary. A novel module architecture has been demonstrated that has potential for high value recycling for c-Si PV. This architecture eliminates the vacuum lamination process and cross-linked en capsulants. Functioning prototypes of c-Si have been fabricated for stress testing in collaboration with NREL. These modules are being tested against traditionally manufactured modules. Based on preliminary results, this module architecture is a potentially viable solution for improving the manufacturing cost and recyclability of PV modules while retaining module performance.

computer architecture↗
DOE OSTI2024

Evaluating Optical Techniques to Characterize Solid State Samples for the Pu-238 Supply Program

This report documents steps that have been taken to establish advanced characterization capabilities for solid-state samples to support the 238 Pu Supply Program. The program has already invested significantly in spectroscopy and online monitoring development for liquid processes. Many of the data, analytical methods, and equipment could also be leveraged for the analysis of Np/Pu oxides and Np pellets to help classify material properties and link them to operations. The techniques described in this work include diffuse reflectance spectroscopy (DRS), Raman spectroscopy, and laser-induced breakdown spectroscopy (LIBS). Each technique is sensitive to various chemical and/or elemental species and can provide complimentary information to more standard methods such as powder x-ray diffraction and scanning electron microscopy. These options are remotely deployable via fiber-optic cables. DRS and Raman spectroscopy are already approved for glove box and hot cell work, but LIBS requires US Department of Energy approval for incorporation. Raman spectroscopy does require laser shielding, which means that DRS is the easiest method to implement. However, each technique provides complimentary data, and the most robust sample characterization could be achieved when all three, among other methods, are harnessed together. This report documents some of the progress made testing these capabilities, some of the challenges, and next steps to optimize the technology and integration strategies.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2024

DECOVALEX-2023: Task G, SAFENET Final Report

DECOVALEX Task G deals with fracture mechanics at several scales using a combined approach of experimental work, related model development, benchmarking and experimental analysis. Fig. 0.1 provides a graphical abstract for Task G. The experimental basis for the related mechanical (M), hydro-mechanical (HM), and thermo-mechanical (TM) processes comes from the rock mechanics laboratories of Universities of Freiberg (TUBAF) and Edinburgh, as well as the Korea Institute of Civil Engineering and Building Technology (KICT). The experimental work in the rock laboratories is closely linked to the underground research laboratories (URLs) Reiche Zeche (Germany), KURT (Korea), and Mont Terri (Switzerland). The scientific key questions are related to fracture permeability evolution under THM conditions, anisotropy effects on fracturing processes, and thermal fracture slip, which are being addressed in specific steps of the task. A large variety of numerical methods have been developed and applied for experimental analysis, ranging from continuum to discontinuum approaches. A detailed comparison of mechanical and hydro-mechanical processes is given in the section 3 via the benchmarking exercises. As a result of DECOVALEX-2023 Task G we further improved our understanding and predictability of fracturing processes under THM conditions. This was based on robust numerical simulation methods and in-depth experimental analysis.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Advancing Li-plating detection: Motivating a multi-signal correlation approach

Facilitating fast charging in lithium-ion batteries (LiBs) is often linked to Li-plating, which harms performance, longevity, and safety. Early detection of Li-plating is essential for rapid technological development and for preventing performance deterioration and ensuring safety during operation. Fast and real-time detection, using commonly collected measurements like voltage (V), current (I), temperature (T), and pressure (P), is highly desirable. Existing standalone methods relying on electrochemical and mechanical signatures, using half, smaller, or specially designed cells often operated at lower temperatures, fail to account for real-world fast-charging conditions. These signatures may also have inherent unreliability in aged LiBs, a phenomenon currently not-well understood. All these uncertainties have complicated practical implementation of a robust Li-plating detection technique. This study, through multiple case studies involving real-world fast-charging conditions using automotive-grade 11.6 Ah LiBs, shows that many single signal-based diagnostic techniques may be inadequate to detect Li-plating. Among various signatures, end-of-charge rest pressure, differential pressure-sensing, and end-of-charge rest voltage were identified as particularly useful in detecting Li-plating. Furthermore, A multi-signal-based detection technique is shown to be more robust in detecting Li-plating. Using both fresh and aged cells, the results and analysis highlight how detection capabilities are influenced by various factors, including battery design, size, charging speed, operating temperature, and degradation level. Adopting such a multi-signal Li-plating detection approach may be instrumental in the rapid development of battery technology in laboratories, as well as ensuring the enhanced safety of next-generation LiBs in real-world fast-charging applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Technology Integration to Gain Commercial Efficiency for the Urban Goods Delivery System, Meet Future Demand for City Passenger and Delivery Load/Unload Spaces, and Reduce Energy Consumption

This three-year project supported by the U.S. Department of Energy Vehicle Technologies Office has the potential to radically improve the urban freight system in ways that help both the public and private sectors. Working from 2018-2021, project researchers at the University of Washington’s Urban Freight Lab and collaborators at the Pacific Northwest National Laboratory have produced key data, tested technologies in complex urban settings, developed a prototype parking availability app, and helped close major knowledge gaps. All the fruits of this project can be harnessed to help cities better understand, support and actively manage truck load/unload operations and their urban freight transport infrastructure. Project learnings and tools can be used to help make goods delivery firms more efficient by reducing miles traveled and the time it takes to complete deliveries, benefitting businesses and residents who rely on the urban freight system for supplies of goods. And, ultimately, these project learnings and tools can be used to make cities more livable by minimizing wasted travel, which, in turn, contributes to reductions in fuel consumption and emissions. Cities today are challenged to effectively and efficiently manage their infrastructure to absorb the impacts of ever-increasing e-commerce-fueled delivery demand. All delivery trucks need to park somewhere to unload and load. Yet today’s delivery drivers have no visibility on available parking until they arrive at a site, which may be full. That means they can wind up cruising for parking, which wastes time and fuel and contributes to congestion. Once drivers do find parking, the faster they can unload at the spot, the faster they free up space for other drivers, helping others avoid circling for parking. This makes the parking space—and thus the greater load/unload network—more productive. To this end, the research team successfully met the project’s three goals, developing and piloting strategies and technologies to: 1. Reduce parking-seeking behavior in the study area by 20%; 2. Reduce parcel truck dwell time (the time a truck spends in a spot to load/unload) in the study area by 30%; 3. Increase curb space, alley space and private loading bay occupancy rates in the study area. The research team met these goals by creating and piloting on Seattle streets OpenPark, a first-of-its-kind real-time and forecasting curb parking app customized for commercial delivery drivers—giving drivers the “missing link” in their commonly used routing tools that tell them how best to get to delivery locations, but not what parking is available to use when they get there. Installing in-ground sensors on commercial vehicle load zones (CVLZs) and passenger load zones (PLZs) in the 10-block study area in Seattle’s downtown neighbourhood of Belltown let researchers glean real-time curb parking data. The research team also met project goals by piloting three parcel lockers in public and private spaces open to any delivery carrier, creating a consolidated delivery hub that lets drivers complete deliveries faster and spend less time parked. Researchers collected and analysed data to produce the first empirical, robust, statistically significant results as to the impact of the lockers, and app, on on-the-ground operations. In addition to collecting and analyzing sensor and other real-time and historical data, researchers rode along with delivery drivers to confirm real-world routing and parking behavior. Researchers also surveyed building managers on their private loading bay operations to understand how to boost usage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Diverse PFAS produce unique transcriptomic changes linked to developmental toxicity in zebrafish

Per- and polyfluoroalkyl substances (PFAS) are a widespread and persistent class of contaminants posing significant environmental and human health concerns. Comprehensive understanding of the modes of action underlying toxicity among structurally diverse PFAS is mostly lacking. To address this need, we recently reported on our application of developing zebrafish to evaluate a large library of PFAS for developmental toxicity. In the present study, we prioritized 15 bioactive PFAS that induced significant morphological effects and performed RNA-sequencing to characterize early transcriptional responses at a single timepoint (48 h post fertilization) after early developmental exposures (8 h post fertilization). Internal concentrations of 5 of the 15 PFAS were measured from pooled whole fish samples across multiple timepoints between 24–120 h post fertilization, and additional temporal transcriptomics at several timepoints (48–96 h post fertilization) were conducted for Nafion byproduct 2. A broad range of differentially expressed gene counts were identified across the PFAS exposures. Most PFAS that elicited robust transcriptomic changes affected biological processes of the brain and nervous system development. While PFAS disrupted unique processes, we also found that similarities in some functional head groups of PFAS were associated with the disruption in expression of similar gene sets. Body burdens after early developmental exposures to select sulfonic acid PFAS, including Nafion byproduct 2, increased from the 24–96 h post fertilization sampling timepoints and were greater than those of sulfonamide PFAS of similar chain lengths. In parallel, the Nafion byproduct 2-induced transcriptional responses increased between 48 and 96 h post fertilization. PFAS characteristics based on toxicity, transcriptomic effects, and modes of action will contribute to further prioritization of PFAS structures for testing and informed hazard assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

ZTF SN Ia DR2: An environmental study of Type Ia supernovae using host galaxy image decomposition

The second data release of Type Ia supernovae (SNe Ia) observed by the Zwicky Transient Facility has provided a homogeneous sample of 3628 SNe Ia with photometric and spectral information. This unprecedented sample size enables us to better explore our currently tentative understanding of the dependence of the host environment on SN Ia properties. In this paper, we make use of two-dimensional image decomposition to model the host galaxies of SNe Ia. We model elliptical galaxies as well as disc and spiral galaxies with or without central bulges and bars. This allows for the categorisation of SN Ia based on their morphological host environment, as well as the extraction of intrinsic galaxy properties corrected for both cosmological and atmospheric effects, through point-spread-function (PSF) convolution. We find that although this image decomposition technique leads to a significant bias towards elliptical galaxies in our final sample of processed galaxies, the overall results are still robust. By successfully modelling 728 host galaxies, we find that the photometric properties of SNe Ia found in discs and in elliptical galaxies correlate fundamentally differently with their host environment. We identified strong linear relations between light-curve stretch and our model-derived galaxy colour for both the elliptical (16.8σ) and disc (5.1σ) subpopulations of SNe Ia. Lower-stretch SNe Ia are found in redder environments, which we identify as an age and/or metallicity effect. Within the subpopulation of SNe Ia found in disc-containing galaxies, we find a significant linear trend (6.1σ) between light-curve stretch and model-derived local r-band surface brightness, which we link to the age and metallicity gradients found in disc galaxies. SN Ia colour shows little correlation with the host environment, as is seen in the literature. We do identify a possible dust effect in our model-derived surface brightness (3.3σ) for SNe Ia in disc galaxies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Dark matter density profiles in dwarf galaxies: linking Jeans modelling systematics and observation

ABSTRACT The distribution of dark matter in dwarf galaxies can have important implications on our understanding of galaxy formation as well as the particle physics properties of dark matter. However, accurately characterizing the dark matter content of dwarf galaxies is challenging due to limited data and complex dynamics that are difficult to accurately model. In this paper, we apply spherical Jeans modelling to simulated stellar kinematic data of spherical, isotropic dwarf galaxies with the goal of identifying the future observational directions that can improve the accuracy of the inferred dark matter distributions in the Milky Way dwarf galaxies. We explore how the dark matter inference is affected by the location and number of observed stars as well as the line-of-sight velocity measurement errors. We use mock observation to demonstrate the difficulty in constraining the inner core/cusp of the dark matter distribution with data sets of fewer than 10 000 stars. We also demonstrate the need for additional measurements to make robust estimates of the expected dark matter annihilation signal strength. For the purpose of deriving robust indirect detection constraints, we identify Ursa Major II, Ursa Minor, and Draco as the systems that would most benefit from additional stars being observed.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

ReaxFF reactive force field model enables accurate prediction of physiochemical and mechanical properties of crystalline and amorphous shape‐memory polyurethane

Abstract We investigate reactive force field (ReaxFF) prediction of physiochemical and mechanical properties, and stimuli response behavior of a shape‐memory polyurethane (SMPU) rigid segment. We used SMPUs as a platform to link the rigid/hard segment domain crystallinity with properties that can also be experimentally measured. Specifically, we developed models for crystalline and amorphous forms of a common rigid segment of SMPUs, for example, 4,4′‐diphenylmethane diisocyanate (MDI) with n‐butanediol (BDO), denoted as MDI‐BDO. ReaxFF molecular dynamics simulations with equilibrium dynamics are performed by controlling mass, temperature, and pressure or volume to predict the structural and physiochemical properties of crystalline and amorphous rigid segments of SMPU systems, followed by simulating their XRD and FTIR patterns, respectively. Non‐equilibrium ReaxFF simulations are implemented by using uniaxial box deformation technique to study response‐behavior of crystalline SMPU under tensile loading and during stress‐relaxation. The model and simulation scheme presented in this study demonstrates a robust theoretical pathway toward application‐dictated, on‐demand design of high‐performance SMPUs in applications such as soft robotics.

Afroz, Mohammad M.↗
DOE OSTI2024

APOA2 increases cholesterol efflux capacity to plasma HDL by displacing the C-terminus of resident APOA1

The ability of high-density lipoprotein (HDL) to promote cellular cholesterol efflux is a more robust predictor of cardiovascular disease protection than HDL-cholesterol levels in plasma. Previously, we found that lipidated HDL containing both apolipoprotein A-I (APOA1) and A-II (APOA2) promotes cholesterol efflux via the ATP-binding cassette transporter (ABCA1). In the current study, we directly added purified, lipid-free APOA2 to human plasma and found a dose-dependent increase in whole plasma cholesterol efflux capacity. APOA2 likewise increased the cholesterol efflux capacity of isolated HDL with the maximum effect occurring when equal masses of APOA1 and APOA2 coexisted on the particles. Follow-up experiments with reconstituted HDL corroborated that the presence of both APOA1 and APOA2 were necessary for the increased efflux. Using limited proteolysis and chemical cross-linking mass spectrometry, we found that APOA2 induced a conformational change in the N- and C-terminal helices of APOA1. Using reconstituted HDL with APOA1 deletion mutants, we further showed that APOA2 lost its ability to stimulate ABCA1 efflux to HDL if the C-terminal domain of APOA1 was absent, but retained this ability when the N-terminal domain was absent. Based on these findings, we propose a model in which APOA2 displaces the C-terminal helix of APOA1 from the HDL surface which can then interact with ABCA1—much like it does in lipid-poor APOA1. These findings suggest APOA2 may be a novel therapeutic target given this ability to open a large, high-capacity pool of HDL particles to enhance ABCA1-mediated cholesterol efflux.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

APACE: AlphaFold2 and advanced computing as a service for accelerated discovery in biophysics

The prediction of protein 3D structure from amino acid sequence is a computational grand challenge in biophysics and plays a key role in robust protein structure prediction algorithms, from drug discovery to genome interpretation. The advent of AI models, such as AlphaFold, is revolutionizing applications that depend on robust protein structure prediction algorithms. To maximize the impact, and ease the usability, of these AI tools we introduce APACE, AlphaFold2 and advanced computing as a service, a computational framework that effectively handles this AI model and its TB-size database to conduct accelerated protein structure prediction analyses in modern supercomputing environments. We deployed APACE in the Delta and Polaris supercomputers and quantified its performance for accurate protein structure predictions using four exemplar proteins: 6AWO, 6OAN, 7MEZ, and 6D6U. Using up to 300 ensembles, distributed across 200 NVIDIA A100 GPUs, we found that APACE is up to two orders of magnitude faster than off-the-self AlphaFold2 implementations, reducing time-to-solution from weeks to minutes. This computational approach may be readily linked with robotics laboratories to automate and accelerate scientific discovery.

97 MATHEMATICS AND COMPUTING↗