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NASA NTRS1998

New AFM Techniques for Investigating Molecular Growth Mechanisms of Protein Crystals

Atomic Force Microscopy (AFM) has emerged as a powerful technique for investigating protein crystal growth. Earlier AFM studies were among the first to demonstrate that these crystals grew by dislocation and 2D nucleation growth mechanisms [1]. These investigations were restricted to the micron range where only surface features, such as dislocation hillocks and 2D islands are visible. Most AFM instruments can scan at higher resolutions and have the potential to resolve individual protein molecules at nanometer ranges. Such scans are essential for determining the molecular packing arrangements on crystal faces and for probing the growth process at the molecular level. However, at this resolution the AFM tip influences the image produced, with the resulting image being a convolution of the tip shape and the surface morphology [2]. In most studies this problem is resolved by deconvoluting the image to obtain the true surface morphology. Although deconvolution routines work reasonably well for simple one- dimensional shapes, for complex surfaces this approach does not produce accurate results. In this study we devised a new approach which takes advantage of the precise molecular order of crystal surfaces, combined with the knowledge of individual molecular shapes from the crystallographic data of the protein and the AFM tip shape. This information is used to construct expected theoretical AFM images by convoluting the tip shape with the constructed crystal surface shape for a given surface packing arrangement. By comparing the images from actual AFM scans with the constructed ones for different possible surface packing arrangements, the correct packing arrangement can be conclusively determined. This approach was used in this study to determine the correct one from two possible packing arrangements on (I 10) faces of tetragonal lysozyme crystals. Another novel AFM technique was also devised to measure the dimension of individual growth units of the crystal faces. Measuring these units was not attempted before and most studies have assumed that the growth unit consisted of individual protein molecules. The linescan mode of AFM instruments allows the crystal surface to be scanned along a single line. By scanning across a growth step an image showing the motion of the step is obtained. Normally such an image shows a straight line for continuous and constant step velocity. In this study by increasing the scan rate and by decreasing the step velocity (by decreasing the supersaturation), we were able to capture images of individual growth events, shown by jump discontinuities in the step line. By suitable integration of the image the growth unit dimension in the scanned direction can be obtained. Since multiple units can be involved in the growth process it is necessary to collect a statistically relevant sample before drawing conclusions about the growth mechanism. This technique was successfully employed to obtain the dimensions of growth units for the (110) face, showing that they consisted of various aggregates corresponding to the 43 helices in the crystal structure.

Li, Huayu↗

NASA NTRS1999

Effective Forces Between Colloidal Particles

Colloidal suspensions have proven to be excellent model systems for the study of condensed matter and its phase behavior. Many of the properties of colloidal suspensions can be investigated with a systematic variation of the characteristics of the systems and, in addition, the energy, length and time scales associated with them allow for experimental probing of otherwise inaccessible regimes. The latter property also makes colloidal systems vulnerable to external influences such as gravity. Experiments performed in micro-ravity by Chaikin and Russell have been invaluable in extracting the true behavior of the systems without an external field. Weitz and Pusey intend to use mixtures of colloidal particles with additives such as polymers to induce aggregation and form weak, tenuous, highly disordered fractal structures that would be stable in the absence of gravitational forces. When dispersed in a polarizable medium, colloidal particles can ionize, emitting counterions into the solution. The standard interaction potential in these charged colloidal suspensions was first obtained by Derjaguin, Landau, Verwey and Overbeek. The DLVO potential is obtained in the mean-field linearized Poisson-Boltzmann approximation and thus has limited applicability. For more precise calculations, we have used ab initio density functional theory. In our model, colloidal particles are charged hard spheres, the counterions are described by a continuum density field and the solvent is treated as a homogeneous medium with a specified dielectric constant. We calculate the effective forces between charged colloidal particles by integrating over the solvent and counterion degrees of freedom, taking into account the direct interactions between the particles as well as particle-counterion, counterion-counterion Coulomb, counterion entropic and correlation contributions. We obtain the effective interaction potential between charged colloidal particles in different configurations. We evaluate two- and three-body forces in the bulk as well as study the influence of soft walls. We qualitatively explain the effects of the walls on the forces and demonstrate that many-body effects are negligible in our system. With adjustments in the parameters, the DLVO pair-potential can describe the results quantitatively. Besides electrostatic interactions, entropic depletion effects that arise from (hard-core) exclusion play an important role in determining the behavior of multi-component colloidal suspensions. A standard theory for depletion forces is due to Asakura and Oosawa and is based on the ideal gas approximation. To go beyond this approximation, we have studied entropic forces in molecular dynamics simulations of systems of hard spheres (the effects of the solvent have been ignored). The effective depletion forces for these systems can be found either from equilibrium distribution functions or from direct momentum transfer calculations. Our results obtained by either method show qualitative differences from the Asakura-Oosawa forces, indicating a longer range, higher value at contact and most importantly a more complicated structure, comprising of several maxima and minima. Our calculations include the determination of effective forces between two spheres, a hard sphere and a wall, and the behavior of a hard sphere near a step-edge and a corner. We also demonstrate that such entropic forces do not necessarily satisfy pairwise additivity.

Tehver, Riina↗

DOE OSTI2020

Three dimensional cluster analysis for atom probe tomography using Ripley’s K-function and machine learning

The size and structure of spatial molecular and atomic clustering can significantly impact material properties and is therefore important to accurately quantify. Ripley’s K-function (K(r)), a measure of spatial correlation, can be used to perform such quantification when the material system of interest can be represented as a marked point pattern. This work demonstrates how machine learning models based on K (r)-derived metrics can accurately estimate cluster size and intra-cluster density in simulated three dimensional (3D) point patterns containing spherical clusters of varying size; over 90% of model estimates for cluster size and intra-cluster density fall within 11% and 18% error of the true values, respectively. These K (r)-based size and density estimates are then applied to an experimental APT reconstruction to characterize MgZn clusters in a 7000 series aluminum alloy. Here we find that the estimates are more accurate, consistent, and robust to user interaction than estimates from the popular maximum separation algorithm. Using K (r) and machine learning to measure clustering is an accurate and repeatable way to quantify this important material attribute.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Mass spectrometry and spectroscopic characterization of a tetrameric photosystem I supercomplex from Leptolyngbya ohadii , a desiccation-tolerant cyanobacterium

Cyanobacteria inhabiting desert biological soil crusts face the harsh conditions of the desert. They evolved a suite of strategies toward desiccation-hydration cycles mixed with high light irradiations, etc. In this study we purified and characterized the structure and function of Photosystem I (PSI) from Leptolyngbya ohadii, a desiccation-tolerant desert cyanobacterium. We discovered that PSI forms tetrameric (PSI-Tet) aggregate. We investigated it by using sucrose density gradient centrifugation, clear native PAGE, high performance liquid chromatography, mass spectrometry (MS), time-resolved fluorescence (TRF) and time-resolved transient absorption (TA) spectroscopy. MS analysis identified the presence of two PsaB and two PsaL proteins in PSI-Tet and uniquely revealed that PsaLs are N-terminally acetylated in contrast to non-modified PsaL in the trimeric PSI from Synechocystis sp. PCC 6803. Chlorophyll (Chl) a fluorescence decay profiles of the PSI-Tet performed at 77 K revealed two emission bands at ∼690 nm and 725 nm with the former appearing only at early delay time. The main fluorescence emission peak, associated with emission from the low energy Chls a, decays within a few nanoseconds. TA studies demonstrated that the 725 nm emission band is associated with low energy Chls a with absorption band clearly resolved at ∼710 nm at 77 K. As a result, our work suggests that the heterogenous composition of PsaBs and PsaL in PSI-Tet is related with the adaptation mechanisms needed to cope with stressful conditions under which this bacterium naturally grows.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Super-resolution and segmentation deep learning for breast cancer histopathology image analysis

Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

3-O-Methyltolcapone and Its Lipophilic Analogues Are Potent Inhibitors of Transthyretin Amyloidogenesis with High Permeability and Low Toxicity

Transthyretin (TTR) is an amyloidogenic homotetramer involved in the transport of thyroxine in blood and cerebrospinal fluid. To date, more than 130 TTR point mutations are known to destabilise the TTR tetramer, leading to its extracellular pathological aggregation accumulating in several organs, such as heart, peripheral and autonomic nerves, and leptomeninges. Tolcapone is an FDA-approved drug for Parkinson’s disease that has been repurposed as a TTR stabiliser. We characterised 3-O-methyltolcapone and two newly synthesized lipophilic analogues, which are expected to be protected from the metabolic glucuronidation that is responsible for the lability of tolcapone in the organism. Immunoblotting assays indicated the high degree of TTR stabilisation, coupled with binding selectivity towards TTR in diluted plasma of 3-O-methyltolcapone and its lipophilic analogues. Furthermore, in vitro toxicity data showed their several-fold improved neuronal and hepatic safety compared to tolcapone. Calorimetric and structural data showed that both T4 binding sites of TTR are occupied by 3-O-methyltolcapone and its lipophilic analogs, consistent with an effective TTR tetramer stabilisation. Moreover, in vitro permeability studies showed that the three compounds can effectively cross the blood-brain barrier, which is a prerequisite for the inhibition of TTR amyloidogenesis in the cerebrospinal fluid. Our data demonstrate the relevance of 3-O-methyltolcapone and its lipophilic analogs as potent inhibitors of TTR amyloidogenesis.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS1989

Size distribution of dust grains: A problem of self-similarity

Distribution functions describing the results of natural processes frequently show the shape of power laws, e.g., mass functions of stars and molecular clouds, velocity spectrum of turbulence, size distributions of asteroids, micrometeorites and also interstellar dust grains. It is an open question whether this behavior is a result simply coming about by the chosen mathematical representation of the observational data or reflects a deep-seated principle of nature. The authors suppose the latter being the case. Using a dust model consisting of silicate and graphite grains Mathis et al. (1977) showed that the interstellar extinction curve can be represented by taking a grain radii distribution of power law type n(a) varies as a(exp -p) with 3.3 less than or equal to p less than or equal to 3.6 (example 1) as a basis. A different approach to understanding power laws like that in example 1 becomes possible by the theory of self-similar processes (scale invariance). The beta model of turbulence (Frisch et al., 1978) leads in an elementary way to the concept of the self-similarity dimension D, a special case of Mandelbrot's (1977) fractal dimension. In the frame of this beta model, it is supposed that on each stage of a cascade the system decays to N clumps and that only the portion beta N remains active further on. An important feature of this model is that the active eddies become less and less space-filling. In the following, the authors assume that grain-grain collisions are such a scale-invarient process and that the remaining grains are the inactive (frozen) clumps of the cascade. In this way, a size distribution n(a) da varies as a(exp -(D+1))da (example 2) results. It seems to be highly probable that the power law character of the size distribution of interstellar dust grains is the result of a self-similarity process. We can, however, not exclude that the process leading to the interstellar grain size distribution is not fragmentation at all. It could be, e.g., diffusion-limited growth discussed by Sander (1986), who applied the theory of fractal geometry to the classification of non-equilibrium growth processes. He received D=2.4 for diffusion-limited aggregation in 3d-space.

Henning, TH.↗

DOE OSTI2023

Convergent behavior of extended stalk regions from staphylococcal surface proteins with widely divergent sequence patterns

Staphylococcus epidermidis and Staphylococcus aureus are highly problematic bacteria in hospital settings. A major challenge is their ability to form biofilms on abiotic or biotic surfaces. Biofilms are well-organized, multicellular bacterial aggregates that resist antibiotic treatment and often lead to recurrent infections. Bacterial cell wall-anchored (CWA) proteins are important players in biofilm formation and infection. Many have putative stalk-like regions or regions of low complexity near the cell wall-anchoring motif. Recent work demonstrated the strong propensity of the stalk region of S. epidermidis accumulation-associated protein (Aap) to remain highly extended under solution conditions that typically induce compaction. This behavior is consistent with the expected function of a stalk-like region that is covalently attached to the cell wall peptidoglycan and projects the adhesive domains of Aap away from the cell surface. In this study, we evaluate whether the ability to resist compaction is a common theme among stalk regions from various staphylococcal CWA proteins. Circular dichroism spectroscopy was used to examine secondary structure changes as a function of temperature and cosolvents along with sedimentation velocity analytical ultracentrifugation, size-exclusion chromatography, and SAXS to characterize structural characteristics in solution. All stalk regions tested are intrinsically disordered, lacking secondary structure beyond random coil and polyproline type II helix, and they all sample highly extended conformations. Remarkably, the Ser-Asp dipeptide repeat region of SdrC exhibited nearly identical behavior in solution when compared to the Aap Pro/Gly-rich region, despite highly divergent sequence patterns, indicating conservation of function by various distinct staphylococcal CWA protein stalk regions.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRSDate not supplied

Soluble Organic Matter (SOM) analysis of the Hayabusa2 samples: The first results

The Hayabusa2 spacecraft successfully collected the surface and possible sub-surface materials of the asteroid 162173 Ryugu. Ryugu is a C-type asteroid characterized by a low-albedo surface probably consisting of hydrous minerals and carbonaceous materials. [1] The direct optical and spectral analysis of the returned samples indicates that Ryugu material is dominated by hydrous carbonaceous chondrite-like matter (similar to CI chondrites) [2]. Since carbonaceous chondrites have generally yielded various types of organic matter, the collected Ryugu grains are expected to contain diverse types of organic compounds including bio-related molecules. The occurrence of organic compounds in the Ryugu surface will provide clues to the evolution of prebiotic molecules and their preservations associated with aqueous alteration of the primitive asteroid. The initial analysis of soluble organic matter (SOM) of the Hayabusa2-returned samples has been performed by an international team consisting of 32 members. Because the sample amount available for comprehensive SOM analyses is limited, and because the SOM is expected to be present as a complex mixture of various types of organic compounds with very small concentrations of each compound, high-sensitivity and high-resolution analytical techniques have been developed using carbonaceous meteorites [e.g. 3]. Two aggregate samples of the Ryugu grains (A106 and C107) were allocated for the solvent extractions and bulk carbon (C), hydrogen (H), nitrogen (N), sulfur (S) and oxygen (O) measurements. The A106 sample was collected during the 1st sampling in February 2019 and the C107 sample was collected during the 2nd sampling in July 2019 after the Small Carry Impactor (SCI) operation. They consist mainly of particles smaller than 1 mm in diameter, and each sample mass was 38-39 mg. They were firstly investigated spectroscopically in the near infrared wavelength range by the Stone Team prior to the solvent extractions. Other small grains (A0080 and C0057) were also allocated for this study to investigate the spatial distribution of organic compounds on the sample surface. The extraction and analytical measurements implemented by the SOM Team are summarized in Figure 1. Each powder sample was extracted sequentially with non-polar to polar solvents, i.e., hexane, dichloromethane (DCM), methanol (MeOH) and H2O, for non-targeted analysis to reveal the compound composition. Each solvent extract was analyzed by solution state nuclear magnetic resonance (NMR) spectroscopy [4], Fourier transform- ion cyclotron resonance/mass spectrometry (FT-ICR/MS) with ESI and APPI ionization [5] and by high-resolution mass spectroscopy using Orbitrap MS coupled with nano-liquid chromatography (nanoLC/Orbitrap MS) [6], and using two dimensional gas chromatography/mass spectrometry (GC×GC/MS). The extracted residues were passed to the Chemistry Team for further inorganic element analysis. The other powder sample was subjected to the hot water extraction for amino acid analyses including chiral isomer separation, which was performed by three-dimensional (3D) high-performance liquid chromatography (HPLC) with high-sensitivity fluorescence detection (FD) [7] and by HPLC/FD coupled with quadrupole-time of flight/mass spectrometry (QToF/MS) [8]. After the hot water extraction, the residue was split into two halves. One half was further extracted with hydrochloric acid (HCl) to analyze for amino acids in bound-form. The other half was sequentially extracted with DCM/MeOH (1/1) to analyze semi-polar compounds such as polycyclic aromatic hydrocarbons (PAHs) by GC/MS, followed by further extraction with formic acid to analyze polar heterocyclic compounds, and subsequent extraction with HCl to detect bound-form polar compounds. The extracted residues were passed to the IOM Team for the analysis of insoluble organic matter (IOM). Compound-specific stable isotope analyses will be performed using GC/combustion/isotope ratio mass spectrometry (GC/C/IRMS) if the compound concentration is high enough to enable such an isotopic measurement. All extraction procedures were performed on an ISO 6 (Class 100) clean bench inside an ISO 5 (Class 1000) clean room. Baked serpentine powder was also analyzed as a procedural blank. In situ organic compound analysis with the molecular imaging was performed using desorption electrospray ionization (DESI) equipped with Orbitrap MS [9, 10], followed by spatial imaging of organic compounds using ToF/secondary ion mass spectrometry (ToF/SIMS) [11]. The bulk chemical and isotopic compositions of CNS and HO were determined using nano-elemental analysis/isotope ratio mass spectrometry (nanoEA/IRMS) [12] and EA/pyrolysis/IRMS, respectively. We have identified a variety of indigenous organic compounds in the extracts of both A106 and C107 samples. The Ryugu grains host organic molecules under the high-vacuum and cosmic-ray irradiation environment of the asteroid surface. The analysis of extracted molecules is in progress, and the first results will be presented at the symposium.

Hiroshi Naraoka↗

DOE OSTI2024

HydraGNN_Predictive_GFM_2024 - Ensemble of predictive graph foundation models for ground state atomistic materials modeling

We provide the ensemble of fifteen pre-trained graph foundation models (GFMs) for atomistic materials modeling applications. Each one of the fifteen GFMs has been trained on five open-source datasets that (once aggregated) amount to over 154 million atomistic structures, which cover over two-thirds of the natural elements of the periodic table and that comprises a broad set of organic and inorganic compounds. This vast set of atomistic structures comprises ground state configurations that are dynamically stable (i.e., equilibrated structures with atomic forces approximately close to zero values) as well as dynamically unstable structures (i.e., non-equilibrium structures with non-negligible non-zero values of atomic forces). The ensemble of datasets aggregated does NOT include excited states. The datasets have been curated to remove atomistic structures with spectral norm of the force tensor above 100 eV/angstrom. Moreover, a linear term of the energy was computed for each dataset using a linear regression model that uses the chemical concentration of each natural element as regressor. The linear term predicted by the linear regression model has been subtracted from each original energy value to perform a re-alignment of the energy values across different electronic structures approximation theories performed to generate the diverse multi-source, multi-fidelity datasets. The folder "ADIOS_files" contains the set of pre-processed datasets in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used for the development and training of GFMs in this work. The "ADIOS_files" directory contains 6 sub-directories named as follows: - ANI1x-v3.bp - MPTrj-v3.bp - OC2020-20M-v3.bp - OC2020-v3.bp - OC2022-v3.bp - qm7x-v3.bp Each sub-directory contains the pre-processed datasets converted in Adaptable I/O System (ADIOS) format (https://www.exascaleproject.org/research-project/adios/) that have been used to the development, training, and performance testing of the ensemble go predictive graph foundation models. Each GFM was developed using HydraGNN (https://github.com/ORNL/HydraGNN) as underlying graph neural network (GNN) architecture. The multi-task learning (MTL) capability of HydraGNN was used to simultaneously train the GFMs on labeled values for direct predictions of energy (a total system property of an atomistic structure that measures the chemical stability) and atomic forces (an atomic level property of an atomistic structure that measures the dynamical stability). The hyper parameters of the GFM have been tuned using scalable hyperparameter optimization (HPO) algorithms implemented in the software DeepHyper (https://github.com/deephyper/deephyper). The pre-training of each HPO trial was performed using distributed data parallelism (DDP) to scale the training across 128 compute nodes of the exascale OLCF supercomputer Frontier. Each HPO trial was trained only for 10 epochs and an early stopping was performed to avoid wasting significant computational resources on GNN architectures that were clearly underperforming. For each HPO trial, the 'omnistat' tool developed by (AMD Research - Advanced Micro Device) was used to measure the total energy consumption in kWh. The ensemble of GFMs was obtained by selecting the fifteen best performing HPO trials. Four models have been selected for their clear advantage in accuracy, and these are the GFMs with IDs 229, 156, 147, 260. Additional eleven models have been selected based on judicious balance between accuracy and energy consumption needed for training, and these are the GFMs with IDs 165, 78, 137, 1, 175, 171, 181, 67, 179, 167, 351. Each selected GFM of the ensemble was continued to cumulate a total of at most 30 epochs. In some cases, the total number of epochs actually performed was les than 30 due to two combined factors: (1) the size of the GFM (i.e., the number of model parameters to train) and (2) the total wall-clock time for which the computational resources could be allocated on OLCF-Frontier. The "Ensemble_of_models" directory contains 15 sub-directories named as follows: - gfm_0.229 - gfm_0.156 - gfm_0.147 - gfm_0.260 - gfm_0.165 - gfm_0.78 - gfm_0.137 - gfm_0.1 - gfm_0.175 - gfm_0.171 - gfm_0.181 - gfm_0.67 - gfm_0.179 - gfm_0.167 - gfm_0.351 Each one of these sub-directories refers to one of the fifteen HPO trials that have been selected to continue the pre-training with at most 30 epochs. With each sub-directory associated with a specific HPO trial, the following files can be found: - config.json: file for argument parsing to develop and train an HydraGNN architecture - gfm_0.ID_epoch_N.pk: file with model parameters for HPO ID trial after N epochs of training The ensemble of fifteen GFM architectures was used for (1) ensemble averaging to stabilize the predictions of energy and atomic forces after pre-training for post-processing analysis and (2) ensemble uncertainty quantification (UQ). The code used to develop, pre-train, and load the pre-trained models for post-processing analysis is available on the ORNL-GitHub at the following link: https://github.com/ORNL/HydraGNN/tree/Predictive_GFM_2024

36 MATERIALS SCIENCE↗