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DOE OSTI2023

Entropic control of the free-energy landscape of an archetypal biomolecular machine

Biomolecular machines are complex macromolecular assemblies that utilize thermal and chemical energy to perform essential, multistep, cellular processes. Despite possessing different architectures and functions, an essential feature of the mechanisms of action of all such machines is that they require dynamic rearrangements of structural components. Surprisingly, biomolecular machines generally possess only a limited set of such motions, suggesting that these dynamics must be repurposed to drive different mechanistic steps. Although ligands that interact with these machines are known to drive such repurposing, the physical and structural mechanisms through which ligands achieve this remain unknown. Using temperature-dependent, single-molecule measurements analyzed with a time-resolution-enhancing algorithm, here, we dissect the free-energy landscape of an archetypal biomolecular machine, the bacterial ribosome, to reveal how its dynamics are repurposed to drive distinct steps during ribosome-catalyzed protein synthesis. Specifically, we show that the free-energy landscape of the ribosome encompasses a network of allosterically coupled structural elements that coordinates the motions of these elements. Moreover, we reveal that ribosomal ligands which participate in disparate steps of the protein synthesis pathway repurpose this network by differentially modulating the structural flexibility of the ribosomal complex (i.e., the entropic component of the free-energy landscape). Here, we propose that such ligand-dependent entropic control of free-energy landscapes has evolved as a general strategy through which ligands may regulate the functions of all biomolecular machines. Such entropic control is therefore an important driver in the evolution of naturally occurring biomolecular machines and a critical consideration for the design of synthetic molecular machines.

Science & Technology - Other Topics↗

DOE OSTI2023

Developing Enzyme Immobilization with Fibrous Membranes: Longevity and Characterization Considerations

Fibrous membranes offer broad opportunities to deploy immobilized enzymes in new reactor and application designs, including multiphase continuous flow-through reactions. Enzyme immobilization is a technology strategy that simplifies the separation of otherwise soluble catalytic proteins from liquid reaction media and imparts stabilization and performance enhancement. Flexible immobilization matrices made from fibers have versatile physical attributes, such as high surface area, light weight, and controllable porosity, which give them membrane-like characteristics, while simultaneously providing good mechanical properties for creating functional filters, sensors, scaffolds, and other interface-active biocatalytic materials. This review examines immobilization strategies for enzymes on fibrous membrane-like polymeric supports involving all three fundamental mechanisms of post-immobilization, incorporation, and coating. Post-immobilization offers an infinite selection of matrix materials, but may encounter loading and durability issues, while incorporation offers longevity but has more limited material options and may present mass transfer obstacles. Coating techniques on fibrous materials at different geometric scales are a growing trend in making membranes that integrate biocatalytic functionality with versatile physical supports. Biocatalytic performance parameters and characterization techniques for immobilized enzymes are described, including several emerging techniques of special relevance for fibrous immobilized enzymes. Diverse application examples from the literature, focusing on fibrous matrices, are summarized, and biocatalyst longevity is emphasized as a critical performance parameter that needs increased attention to advance concepts from lab scale to broader utilization. This consolidation of fabrication, performance measurement, and characterization techniques, with guiding examples highlighted, is intended to inspire future innovations in enzyme immobilization with fibrous membranes and expand their uses in novel reactors and processes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2012

The Origins of Transmembrane Ion Channels

Even though membrane proteins that mediate transport of ions and small molecules across cell walls are among the largest and least understood biopolymers in contemporary cells, it is still possible to shed light on their origins and early evolution. The central observation is that transmembrane portions of most ion channels are simply bundles of -helices. By combining results of experimental and computer simulation studies on synthetic models and natural channels, mostly of non-genomic origin, we show that the emergence of -helical channels was protobiologically plausible, and did not require highly specific amino acid sequences. Despite their simple structure, such channels could possess properties that, at the first sight, appear to require markedly larger complexity. Specifically, we explain how the antiamoebin channels, which are made of identical helices, 16 amino acids in length, achieve efficiency comparable to that of highly evolved channels. We further show that antiamoebin channels are extremely flexible, compared to modern, genetically coded channels. On the basis of our results, we propose that channels evolved further towards high structural complexity because they needed to acquire stable rigid structures and mechanisms for precise regulation rather than improve efficiency. In general, even though architectures of membrane proteins are not nearly as diverse as those of water-soluble proteins, they are sufficiently flexible to adapt readily to the functional demands arising during evolution.

Pohorille, Andrew↗

DOE OSTI2023

A Simple and Flexible Infusion Platform for Automated Native Mass Spectrometry Analysis

High throughput native mass spectrometry analysis of proteins and protein complexes has been enabled by recent development of infusion and liquid chromatography (LC) systems, which often include complete LC pumps without fully utilizing their gradient flows. We demonstrated a lower-cost infusion cart for native mass spectrometry applications using a single isocratic solvent pump that can operate at both nano- and high-flow configurations (0.05-150 $μ$L/min) for both infusion and online buffer exchange experiments. In conclusion, the platform is controlled via open-source software and can potentially be expanded for customized experimental designs, offering a lower cost alternative to laboratories with limited budgets and/or needs in student training.

47 OTHER INSTRUMENTATION↗

DOE OSTI2026

Protein Adhesion on Semi-Fluorinated Polystyrene Surfaces in Static and Dynamic Measurements

Reducing protein adhesion is a critical strategy in fouling-resistant material innovation, with broad applications spanning biomedical and healthcare devices, biosensors, industrial and environmental systems, and other important technological domains. Here, in this study, we elucidated protein adhesion behavior on polystyrene-based thin films by neutron reflectometry (NR) and quartz crystal microbalance with dissipation (QCM-D), using both lysozyme and bovine serum albumin (BSA) as model proteins. To this end, semifluorinated polystyrene thin films with gradient wettability and surface energy were fabricated through dry processing using plasma oxidation and gas-phase deposition. Although it is believed that a fully fluorinated alkyl chain offers extremely low surface energy, thus rejecting foulants, and has been used in many fouling-resistant surface designs, enhanced protein–surface interactions were observed consistently in NR and QCM-D results, due to the combined effects of surface morphology and chemistry. On the contrary, depositing shorter fluorinated silane onto a hydrophilic PS surface contributed to a more homogeneous nanoscale fluorine coating, resulting in less initial protein adsorption and improved surface recovery. Comparative analysis of proteins with different sizes on the nanopatterned semifluorinated surface revealed the influence of molecular characteristics on surface interactions. Lysozyme, being smaller and more compact, showed faster adsorption kinetics and higher surface coverage but largely reversible binding, whereas BSA, with its larger and more flexible structure, formed broader and more stable interfacial layers. This study fills the gap in understanding protein adhesion within the range of hydrophobicity (water contact angle ∼90°), as current strategies often associate with extreme hydrophilic and superhydrophobic surfaces due to hydration or low-surface-energy rejection mechanisms, respectively. It also provides in-depth insights into current combinatorial fouling-resistant surface design.

Yuan, Yue [Oak Ridge National Laboratory (ORNL), O↗

DOE OSTI2022

High-throughput native mass spectrometry as experimental validation for in silico drug design

In this project, we developed automated workflows for both experimental validation and computational prediction of protein-ligand interactions. The ultimate goal is to establish an integrated pipeline for high throughput design of inhibitors to enzymes relevant to all areas of biological research. Our experimental approach is based on native mass spectrometry (native MS), which measures accurate masses and quantify the relative abundance of protein-ligand complexes to define binding affinity. We set up an in-house built autosampler with highly flexible configurations to minimize the manual steps for high throughput native MS. In parallel, we also performed manual native MS to characterize the binding of substrates and inhibitors of SARS-Cov-2 nonstructural protein nsp10/16 in order to optimize the experimental parameters for future automation. On the computational side, we streamlined the pipeline to achieve minimal manual intervention for predicting enzyme inhibitors via simulation, using the same nsp10/16 system as an example. Using the native MS method we examined 8 top-ranked designed compounds, 2 of which showed weak binding of ~50 µM. The information from native MS experiment provided critical insights and the foundation for a fully integrated workflow for enzyme inhibitor design.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Predicting receptor-ligand pairing preferences in plant-microbe interfaces via molecular dynamics and machine learning

Microbiome assembly, structure, and dynamics significantly influence plant health. Secreted microbial signaling molecules initiate and mediate symbiosis by binding to structurally compatible plant receptors. For example, lipo-chitooligosaccharides (LCOs), produced by nitrogen-fixing rhizobial bacteria and various fungi, are recognized by plant lysin motif receptor-like kinases (LysM-RLKs), which activate the common symbiotic pathway. Accurately predicting these molecular interactions could reveal complementary signatures underlying the initial stages of endosymbiosis. Despite the breakthrough in protein-ligand structure prediction with deep learning-based tools, such as AlphaFold3, the large size and highly flexible nature of signaling compounds like LCOs present major challenges for detailed structural characterization and binding-affinity prediction. Typical structure-/physics-based methods of ligand virtual screening are designed for small, drug-like molecules, often rely on high-resolution, experimentally determined structures of the protein receptors, and rarely achieve sufficient sampling to obtain converged thermodynamic quantities with large ligands. In this study, we developed a hybrid molecular dynamics/machine learning (MD/ML) approach capable of predicting binding affinity rankings with high accuracy in systems involving large, flexible ligands, despite limited experimental structural information. Using coarse initial structural models, the predictions using the MD/ML workflow achieved strong alignment with experimental trends, particularly in the top-affinity tier for four legume LysM-RLKs (LYR3) binding to LCOs and a chitooligosaccharide. Furthermore, the MD-based conformation selection protocol provided critical structural insights into substrate specificity and binding mechanisms. This study demonstrates a powerful method to screen for challenging cognate ligand-receptors and advance our understanding of the molecular basis of microbial colonization in plants.

Lipo-chitooligosaccharides↗

DOE OSTI2024

Asymmetric oligomerization state and sequence patterning can tune multiphase condensate miscibility

Endogenous biomolecular condensates, composed of a multitude of proteins and RNAs, can organize into multiphasic structures with compositionally distinct phases. This multiphasic organization is generally understood to be critical for facilitating their proper biological function. However, the biophysical principles driving multiphase formation are not completely understood. Here we use in vivo condensate reconstitution experiments and coarse-grained molecular simulations to investigate how oligomerization and sequence interactions modulate multiphase organization in biomolecular condensates. We demonstrate that increasing the oligomerization state of an intrinsically disordered protein results in enhanced immiscibility and multiphase formation. Interestingly, we find that oligomerization tunes the miscibility of intrinsically disordered proteins in an asymmetric manner, with the effect being more pronounced when the intrinsically disordered protein, exhibiting stronger homotypic interactions, is oligomerized. Our findings suggest that oligomerization is a flexible biophysical mechanism that cells can exploit to tune the internal organization of biomolecular condensates and their associated biological functions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS1998

The Development of an Edible Peanut Protein Film

The peanut is one of the crops chosen for use in NASA's Advanced Life Support Program (ALS). The peanut is a source of both oil and protein. After oil is extracted from the peanut, a protein rich flour remains. An edible peanut protein film is one use for this flour. Two types of film are developed for this study, one set of film contains 10% fat while the other set contains no fat. For film without fat the defatting of the peanut by the Soxhlet method is the first step in the manufacturing process of the film. Secondly, the protein is precipitated at its isoelectric point (pH 4.5) and centrifuged to separate the protein from the non-protein. After freeze-drying the protein it is milled in a ceramic ball mill to decrease particle size and sifted through a series of sieves to determine particle size distribution. Those particles retained on the 100 mesh sieves are utilized for film formation. Larger particles are re-ground and sifted. Five grams of protein is mixed with 50 mL of distilled water, 70 mL of 80% ethanol, 15 mL of 6N ammonium hydroxide and a plasticizer. This mixture is heated for 30 minutes until the temperature reaches 70 C. The mixture is then poured onto a level Teflon coated glass surface. After allowing the film to form overnight under a ventilation hood, it is manually removed from the plate. The processes and methods adopted have created flexible films of uniform thickness that are free of air bubbles. Thickness of films made from defatted peanut protein and partially defatted peanut protein were 0.10 Lm and 0.13 Lm respectively. Films with natural peanut fat are approximately three times as flexible and almost four times as strong as the films made without fat. Further research will be performed to evaluate its mechanical properties. This paper will greatly contribute to food preservation and waste management. Potential applications of this film are edible/biodegradable containers, wrapping for food preservation (against water, oxygen and oil), storage packets for seasonings or other ingredients and encapsulation for pharmaceutical use.

Patrick, N.↗

DOE OSTI2026

CAHS: Context-Aware Homology Search

Protein homology search is foundational to bioinformatics: it supports annotation transfer, structure/function inference, and evolutionary analysis over rapidly expanding sequence repositories (e.g., UniProtKB). Profile hidden Markov models (pHMMs), as implemented in HMMER, remain the most widely trusted approach because they provide statistically calibrated E-values; however, their gap behavior is fixed once a profile is trained, despite biological evidence that insertion/deletion tolerance varies across flexible loops and intrinsically disordered regions. We present CAHS (Context-Aware Homology Search), a lightweight query-time adapter for pHMM search that incorporates learned and biologically motivated signals without changing HMMER's downstream search pipeline or its calibrated E-value reporting. Given a query sequence, CAHS computes per-residue representations from a protein language model and a disorder predictor, maps these to profile coordinates, and modulates only match-state transition rows (gap-open and gap-extension probabilities) while preserving Plan7 constraints. We comprehensively evaluate CAHS across six structurally diverse protein families and multi-domain architectures against a 570k-sequence target corpus. CAHS expands detection capability, retrieving thousands of additional remote homologs at relaxed thresholds by maintaining alignment quality through flexible regions. For multi-domain proteins, context-aware modulation resolves 94% of fragmented alignments. Crucially, CAHS preserves hit-set invariance at stringent operating points (E<10-10), demonstrating increased statistical confidence without inflating false positives. Furthermore, sharper statistical distinction between homologs and background noise during early filter stages yields up to a 3.87× acceleration in end-to-end wall-clock time on high-performance computing clusters. Overall, CAHS illustrates a practical AI-for-science design pattern: augmenting a trusted probabilistic model with query-specific learned signals to improve interpretable, reproducible inference in data-rich biology.

Bhattaram, Swethasree [Georgia Institute of Techno↗

DOE OSTI2021

Expanding the application of a UV-visible reporter for transient gene expression and stable transformation in plants

Abstract Green fluorescent protein (GFP) has been widely used for monitoring gene expression and protein localization in diverse organisms. However, highly sensitive imaging equipment, like fluorescence microscope, is usually required for the visualization of GFP, limitings its application to fixed locations in samples. A reporter that can be visualized in real-time regardless the shape, size and location of the target samples will increase the flexibility and efficiency of research work. Here, we report the application of a GFP-like protein, called eYGFPuv, in both transient expression and stable transformation, in two herbaceous plant species ( Arabidopsis and tobacco) and two woody plant species (poplar and citrus). We observed bright fluorescence under UV light in all of the four plant species without any effects on plant growth or development. eYGFPuv was shown to be effective for imaging transient expression in leaf and root tissues. With a focus on in vitro transformation, we demonstrated that the transgenic events expressing 1x eYGFPuv could be easily identified visually during the callus stage and the shoot stage, enabling early and efficient selection of transformants. Furthermore, whole-plant level visualization of eYGFPuv revealed its ubiquitous stability in transgenic plants. In addition, our transformation experiments showed that eYGFPuv can also be used to select transgenic plants without antibiotics. This work demonstrates the feasibility of utilizing 1x eYGFPuv in studies of gene expression and plant transformation in diverse plants.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Reducing Communication in Graph Neural Network Training

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making GNNs harder to scale to high concurrencies than convolutional or fully-connected neural networks. Here, we introduce a family of parallel algorithms for training GNNs and show that they can asymptotically reduce communication compared to previous parallel GNN training methods. We implement these algorithms, which are based on 1D, 1. 5D, 2D, and 3D sparse-dense matrix multiplication, using torch.distributed on GPU-equipped clusters. Our algorithms optimize communication across the full GNN training pipeline. We train GNNs on over a hundred GPUs on multiple datasets, including a protein network with over a billion edges.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Conceptual Basis and Techno-Economic Modeling for Integrated Algal Biorefinery Conversion of Microalgae to Fuels and Products (2019 NREL TEA Update: Highlighting Paths to Future Cost Goals via a New Pathway for Combined Algal Processing)

The report documents the conceptual basis for a new potential Combined Algal Processing design strategy which may allow more flexibility in accommodating different algal biomass feedstock compositions, by enabling upgrading of both protein and carbohydrates in a single step, without a strict requirement for either component to be in soluble or monomeric form, while maintaining effective wet lipid extraction techniques to enable high lipid recoveries. In light of previously-established constraints around algal biomass costs (which are significantly higher than lignocellulosic terrestrial biomass), the present CAP processing strategy reflects an integrated biorefinery concept producing both fuels and value-added chemical coproducts as a means to improve profitability and generate coproduct revenues to help drive down the minimum fuel selling price (MFSP) towards economically viable levels. Namely, this report highlights an integrated CAP biorefinery process and associated technical targets that would be required to achieve U.S. Department of Energy target MFSP goals of $2.5/gallon gasoline equivalent by 2030. This is accomplished by a process involving low-cost seasonal storage of algal biomass during high-growth seasons, rapid flash hydrolysis pretreatment of the biomass, solvent extraction of pretreated biomass, cleanup and fractionation of lipids into triglyceride and free fatty acid fractions, and a series of thermochemical conversion steps to upgrade carbohydrates and protein to hydrocarbon fuels. These steps include mild oxidative treatment (MOT), a process originally investigated at NREL for upgrading lignin, followed by catalytic ketonization and hydrotreating of MOT products to fuels. Isolated triglycerides are sent to a coproduct train, with the base case focused on upgrading to polyurethane foams as a high-value, high-market-volume coproduct.

09 BIOMASS FUELS↗

NASA NTRS1996

STS-72 Flight Day 1

On this first day of the STS-72 mission, the flight crew, Cmdr. Brian Duffy, Pilot Brent W. Jett, and Mission Specialists Leroy Chiao, Daniel T. Barry, Winston E. Scott, and Koichi Wakata (NASDA), were shown in prelaunch and launch activities. This was the tenth flight of the Space Shuttle Orbiter Endeavour. The primary objectives of this mission were the retrieval of the Japanese Space Flyer Unit (SFU) spacecraft, the deployment and retrieval of the NASA Office of Aeronautics and Space Technology Flyer (OAST-FLYER) spacecraft, and two 6 1/2 hour spacewalks to test hardware and tools that will be used to assemble the International Space Station. Secondary objectives included the Shuttle Solar Backscatter Ultraviolet (SSBUV-8), the Shuttle Laser Altimeter GAS(5) (SLA-01/GAS(5)), the National Institutes of Health-R3 (NIH-R3), the Space Tissue Loss (STL/NIH-C), and Thermal Energy Storage (TES-2) experiments. Get-Away-Specials (GAS) included the USAF Academy G-342 Flexible Beam Experiment (FLEXBEAM-2), the Society of Japanese Aerospace Companies G-459 Protein Crystal Growth Experiments, and the Jet Propulsion Laboratory (JPL) GAS Ballast Can with Sample Return Experiment. This night launch was shown at various angles and distances from the launching pad.

Source record↗

DOE OSTI2023

De novo design of high-affinity binders of bioactive helical peptides

Many peptide hormones form an α-helix on binding their receptors, and sensitive methods for their detection could contribute to better clinical management of disease. De novo protein design can now generate binders with high affinity and specificity to structured proteins. However, the design of interactions between proteins and short peptides with helical propensity is an unmet challenge. Here we describe parametric generation and deep learning-based methods for designing proteins to address this challenge. We show that by extending RFdiffusion to enable binder design to flexible targets, and to refining input structure models by successive noising and denoising (partial diffusion), picomolar-affinity binders can be generated to helical peptide targets by either refining designs generated with other methods, or completely de novo starting from random noise distributions without any subsequent experimental optimization. The RFdiffusion designs enable the enrichment and subsequent detection of parathyroid hormone and glucagon by mass spectrometry, and the construction of bioluminescence-based protein biosensors. The ability to design binders to conformationally variable targets, and to optimize by partial diffusion both natural and designed proteins, should be broadly useful.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Flexible linkers in CaMKII control the balance between activating and inhibitory autophosphorylation

The many variants of human Ca2+/calmodulin-dependent protein kinase II (CaMKII) differ in the lengths and sequences of disordered linkers connecting the kinase domains to the oligomeric hubs of the holoenzyme. CaMKII activity depends on the balance between activating and inhibitory autophosphorylation (on Thr 286 and Thr 305/306, respectively, in the human α isoform). Variation in the linkers could alter transphosphorylation rates within a holoenzyme and the balance of autophosphorylation outcomes. We show, using mammalian cell expression and a single-molecule assay, that the balance of autophosphorylation is flipped between CaMKII variants with longer and shorter linkers. For the principal isoforms in the brain, CaMKII-α, with a ~30 residue linker, readily acquires activating autophosphorylation, while CaMKII-β, with a ~200 residue linker, is biased towards inhibitory autophosphorylation. Our results show how the responsiveness of CaMKII holoenzymes to calcium signals can be tuned by varying the relative levels of isoforms with long and short linkers.

Life Sciences & Biomedicine - Other Topics↗

DOE OSTI2025

Multifunctional Silk Fibroin Hydrogels with Strong Adhesion for Tissue Sealing and Wearable Electronic Sensors

Multifunctional hydrogels with excellent adhesion, biodegradability, and conductivity are essential for overcoming the obstacles of postoperative secondary injury, flexible sensing instability, and so on. Herein, we develop a multifunctional silk fibroin (SF) hydrogel modified with poly(acrylic acid). Owing to the stable chemical cross-linking network and the abundant carboxylic acid groups of the SF network, the SF hydrogel exhibits a high tensile strength of 74.34 kPa due to sufficient cohesion and interfacial interactions. Additionally, the tensile strain reaches a maximum of 414.6%, the compressive strength is 0.9 MPa, and the shear adhesive strength for pig skin tissues is as high as 64 kPa. Compared with most hydrogels, our multifunctional SF hydrogel with a low swelling ratio provides excellent adhesion, biodegradation, and conductivity, which shows advantages in terms of invasive tissue sealing. Furthermore, the use of self-adhesive SF hydrogels as conductive hydrogels in flexible sensors also benefits the collection of physiological electricity and human motion signals in the field of wearable and implantable electronic devices.

adhesive↗

DOE OSTI2026

Activation dynamics of a water-soluble human mu-opioid receptor

The mu-opioid receptor (MOR), a class A G protein-coupled receptor mediates opioid analgesia and remains a central target for pain therapeutics. While crystal structures of MOR exist, they provide limited insight into the receptor’s dynamic conformational landscape underlying function. Here, we engineered a thermostable water-soluble MOR variant (wsMOR) that retains native-like ligand-binding and activation dynamics. This variant enables high-yield production and detailed solution-phase structural studies that are challenging with membrane-embedded MOR, providing a valuable tool for studying receptor activation and aqueous-phase drug screening. Using a combined computational and experimental approach, we performed long-timescale all-atom molecular dynamics simulations together with neutron scattering and single-molecule FRET, revealing a structurally stable receptor with a diverse ensemble of conformations at different temporal resolutions. In the ligand-free state, wsMOR displayed high conformational flexibility, which decreased upon agonist binding, particularly in transmembrane helix 6, a hallmark of G protein-coupled receptor activation. Positive allosteric modulation and G protein binding further stabilized active-like states. These findings highlight wsMOR’s conformational plasticity across picosecond to millisecond timescales and provide a foundation for structure-guided development of next-generation opioid ligands with improved efficacy and safety.

E, Agyemang [University of Tennessee Knoxville]↗