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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Chiral recognition and selection during the self-assembly process of protein-mimic macroanions

The research on chiral recognition and chiral selection is not only fundamental in resolving the puzzle of homochirality, but also instructive in chiral separation and stereoselective catalysis. Here we report the chiral recognition and chiral selection during the self-assembly process of two enantiomeric wheel-shaped macroanions, [Fe28(μ3-O)8(Tart)16(HCOO)24]20- (Tart=D- or L-tartaric acid tetra-anion). The enantiomers are observed to remain self-sorted and self-assemble into their individual assemblies in their racemic mixture solution. The addition of chiral co-anions can selectively suppress the self-assembly process of the enantiomeric macroanions, which is further used to separate the two enantiomers from their mixtures on the basis of the size difference between the monomers and the assemblies. We believe that delicate long-range electrostatic interactions could be responsible for such high-level chiral recognition and selection.

Yin, Panchao↗
DOE OSTI2022

Structural basis of R-loop recognition by the S9.6 monoclonal antibody

R-loops are ubiquitous, dynamic nucleic-acid structures that play fundamental roles in DNA replication and repair, chromatin and transcription regulation, as well as telomere maintenance. The DNA-RNA hybrid–specific S9.6 monoclonal antibody is widely used to map R-loops. Here, we report crystal structures of a S9.6 antigen-binding fragment (Fab) free and bound to a 13-bp hybrid duplex. We demonstrate that S9.6 exhibits robust selectivity in binding hybrids over double-stranded (ds) RNA and in categorically rejecting dsDNA. S9.6 asymmetrically recognizes a compact epitope of two consecutive RNA nucleotides via their 2'-hydroxyl groups and six consecutive DNA nucleotides via their backbone phosphate and deoxyribose groups. Recognition is mediated principally by aromatic and basic residues of the S9.6 heavy chain, which closely track the curvature of the hybrid minor groove. These findings reveal the molecular basis for S9.6 recognition of R-loops, detail its binding specificity, identify a new hybrid-recognition strategy, and provide a framework for S9.6 protein engineering.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Molecular basis of differential HLA class I-restricted T cell recognition of a highly networked HIV peptide

Cytotoxic-T-lymphocyte (CTL) mediated control of HIV-1 is enhanced by targeting highly networked epitopes in complex with human-leukocyte-antigen-class-I (HLA-I). However, the extent to which the presenting HLA allele contributes to this process is unknown. Here we examine the CTL response to QW9, a highly networked epitope presented by the disease-protective HLA-B57 and disease-neutral HLA-B53. Despite robust targeting of QW9 in persons expressing either allele, T cell receptor (TCR) cross-recognition of the naturally occurring variant QW9_S3T is consistently reduced when presented by HLA-B53 but not by HLA-B57. Crystal structures show substantial conformational changes from QW9-HLA to QW9_S3T-HLA by both alleles. The TCR-QW9-B53 ternary complex structure manifests how the QW9-B53 can elicit effective CTLs and suggests sterically hindered cross-recognition by QW9_S3T-B53. We observe populations of cross-reactive TCRs for B57, but not B53 and also find greater peptide-HLA stability for B57 in comparison to B53. These data demonstrate differential impacts of HLAs on TCR cross-recognition and antigen presentation of a naturally arising variant, with important implications for vaccine design.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Electron energy loss spectroscopy database synthesis and automation of core-loss edge recognition by deep-learning neural networks

Abstract The ionization edges encoded in the electron energy loss spectroscopy (EELS) spectra enable advanced material analysis including composition analyses and elemental quantifications. The development of the parallel EELS instrument and fast, sensitive detectors have greatly improved the acquisition speed of EELS spectra. However, the traditional way of core-loss edge recognition is experience based and human labor dependent, which limits the processing speed. So far, the low signal–noise ratio and the low jump ratio of the core-loss edges on the raw EELS spectra have been challenging for the automation of edge recognition. In this work, a convolutional-bidirectional long short-term memory neural network (CNN-BiLSTM) is proposed to automate the detection and elemental identification of core-loss edges from raw spectra. An EELS spectral database is synthesized by using our forward model to assist in the training and validation of the neural network. To make the synthesized spectra resemble the real spectra, we collected a large library of experimentally acquired EELS core edges. In synthesize the training library, the edges are modeled by fitting the multi-Gaussian model to the real edges from experiments, and the noise and instrumental imperfectness are simulated and added. The well-trained CNN-BiLSTM network is tested against both the simulated spectra and real spectra collected from experiments. The high accuracy of the network, 94.9%, proves that, without complicated preprocessing of the raw spectra, the proposed CNN-BiLSTM network achieves the automation of core-loss edge recognition for EELS spectra with high accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Structure of an engineered multidrug transporter MdfA reveals the molecular basis for substrate recognition

MdfA is a prototypical H + -coupled multidrug transporter that is characterized by extraordinarily broad substrate specificity. The involvement of specific H-bonds in MdfA-drug interactions and the simplicity of altering the substrate specificity of MdfA contradict the promiscuous nature of multidrug recognition, presenting a baffling conundrum. Here we show the X-ray structures of MdfA variant I239T/G354E in complexes with three electrically different ligands, determined at resolutions up to 2.2 Å. Our structures reveal that I239T/G354E interacts with these compounds differently from MdfA and that I239T/G354E possesses two discrete, non-overlapping substrate-binding sites. Our results shed new light on the molecular design of multidrug-binding and protonation sites and highlight the importance of often-neglected, long-range charge-charge interactions in multidrug recognition. Beyond helping to solve the ostensible conundrum of multidrug recognition, our findings suggest the mechanistic difference between substrate and inhibitor for any H + -dependent multidrug transporter, which may open new vistas on curtailing efflux-mediated multidrug resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

ChemPix: automated recognition of hand-drawn hydrocarbon structures using deep learning

Inputting molecules into chemistry software, such as quantum chemistry packages, currently requires domain expertise, expensive software and/or cumbersome procedures. Leveraging recent breakthroughs in machine learning, we develop ChemPix: an offline, hand-drawn hydrocarbon structure recognition tool designed to remove these barriers. A neural image captioning approach consisting of a convolutional neural network (CNN) encoder and a long short-term memory (LSTM) decoder learned a mapping from photographs of hand-drawn hydrocarbon structures to machine-readable SMILES representations. We generated a large auxiliary training dataset, based on RDKit molecular images, by combining image augmentation, image degradation and background addition. Additionally, a small dataset of ~600 hand-drawn hydrocarbon chemical structures was crowd-sourced using a phone web application. These datasets were used to train the image-to-SMILES neural network with the goal of maximizing the hand-drawn hydrocarbon recognition accuracy. By forming a committee of the trained neural networks where each network casts one vote for the predicted molecule, we achieved a nearly 10 percentage point improvement of the molecule recognition accuracy and were able to assign a confidence value for the prediction based on the number of agreeing votes. The ensemble model achieved an accuracy of 76% on hand-drawn hydrocarbons, increasing to 86% if the top 3 predictions were considered.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Recognition competes with hydration in anion-triggered monolayer formation of cyanostar supra-amphiphiles at aqueous interfaces

The triggered self-assembly of surfactants into organized layers at aqueous interfaces is important for creating adaptive nanosystems and understanding selective ion extraction. While these transformations require molecular recognition, the underlying driving forces are modified by the local environment in ways that are not well understood. Herein, we investigate the role of ion binding and ion hydration using cyanosurf, which is composed of the cyanostar macrocycle, and its binding to anions that are either size-matched or mis-matched and either weakly or highly hydrated. We utilize the supra-amphiphile concept where anion binding converts cyanosurf into a charged and amphiphilic complex triggering its self-organization into monolayers at the air–water interface. Initially, cyanosurf forms aggregates at the surface of a pure water solution. When the weakly hydrated and size-matched hexafluorophosphate (PF 6 - ) and perchlorate (ClO 4 - ) anions are added, the macrocycles form distinct monolayer architectures. Surface-pressure isotherms reveal significant reorganization of the surface-active molecules upon anion binding while infrared reflection absorption spectroscopy show the ion-bound complexes are well ordered at the interface. Vibrational sum frequency generation spectroscopy shows the water molecules in the interfacial region are highly ordered in response to the charged monolayer of cyanosurf complexes. Consistent with the importance of recognition, we find the smaller mis-matched chloride does not trigger the transformation. However, the size-matched phosphate (H 2 PO 4 - ) also does not trigger monolayer formation indicating hydration inhibits its interfacial binding. These studies reveal how anion-selective recognition and hydration both control the binding and thus the switching of a responsive molecular interface.

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

Geolocation tracking for human identification and activity recognition using radar deep transfer learning

Abstract Human identification and activity recognition (HIAR) is crucial for many applications, such as surveillance, smart homes, and assisted living. As a sensing modality, radar has many unique characteristics including privacy protection, and contactless sensing. Single classification systems have shown to be accurate, but for long‐term solutions both human identification (ID) and human activity recognition (HAR) will need to be integrated in one system where it can be utilised simultaneously. In this article, a novel radar‐based human tracking system is presented where three classifiers are utilised to identify the subject and his/her behaviour. For any kind of motion, the system tracks the subject and detect the type of his/her motion. Based on the detected type of motion, the three classifiers are utilised for identification and activity recognition. The classifiers are built utilising deep transfer learning where three radar datasets are established to train and validate each of the deep networks. To recognise six activities and 10 human subjects, the three classifiers, namely, HAR, Gait ID, and Heart sound ID, achieve superior performance compared to the best reported results in literature with classification accuracies of 97.6%, 100%, and 41.8% respectively. Three successful examples are presented to demonstrate the introduced concept.

Alkasimi, Ahmad↗
DOE OSTI2015

Structure and dynamics of H{sub 2}O vis-á-vis phenylalanine recognition at a DPPC lipid membrane via interfacial H-bond types: Insights from polarized FT-IRRAS and ADMP simulations

Preferential and enantioselective interactions of L-/D-Phenylalanine (L-Phe and D-Phe) and butoxycarbonyl-protected L-/D-Phenylalanine (LPA and DPA) as guest with 1,2-dipalmitoyl-sn-glycero-3-phosphocholine (L-DPPC) as host were tapped by using real time Fourier transform infrared reflection absorption spectroscopy (FT-IRRAS). Polarization-modulated FT-IRRAS of DPPC monolayers above the phenylalanine modified subphases depicted fine structure/conformation differences under considerations of controlled 2D surface pressure. Selective molecular recognition of D-enantiomer over L-enantiomer driven by the DPPC head group via H-bonding and electrostatic interactions was evident spectroscopically. Accordingly, binding constants (K) of 145, 346, 28, and 56 M{sup −1} for LPA, DPA, L-Phe, and D-Phe, respectively, were estimated. The real time FT-IRRAS water bands were strictly conformation sensitive. The effect of micro-solvation on the structure and stability of the 1:1 diastereomeric L-lipid⋯, LPA/DPA and L-lipid⋯, (L/D)-Phe adducts was investigated with the aid of Atom-centered Density Matrix Propagation (ADMP), a first principle quantum mechanical molecular dynamics approach. The phosphodiester fragment was the primary site of hydration where specific solvent interactions were simulated through single- and triple- “water-phosphate” interactions, as water cluster’s “tetrahedral dice” to a “trimeric motif” transformation as a partial de-clusterization was evident. Under all the hydration patterns considered in both static and dynamic descriptions of density functional theory, L-lipid/D-amino acid enantiomer adducts continued to be stable structures while in dynamic systems, water rearranged without getting “squeezed-out” in the process of recognition. In spite of the challenging computational realm of this multiscale problem, the ADMP simulated molecular interactions complying with polarized vibrational spectroscopy unraveled a novel route to chiral recognition and interfacial water structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Structural basis of substrate recognition by a bacterial deubiquitinase important for dynamics of phagosome ubiquitination

Manipulation of the host’s ubiquitin network is emerging as an important strategy for counteracting and repurposing the posttranslational modification machineries of the host by pathogens. Ubiquitin E3 ligases encoded by infectious agents are well known, as are a variety of viral deubiquitinases (DUBs). Bacterial DUBs have been discovered, but little is known about the structure and mechanism underlying their ubiquitin recognition. In this report, we found that members of the Legionella pneumophila SidE effector family harbor a DUB module important for ubiquitin dynamics on the bacterial phagosome. Structural analysis of this domain alone and in complex with ubiquitin vinyl methyl ester (Ub-VME) reveals unique molecular contacts used in ubiquitin recognition. Instead of relying on the Ile44 patch of ubiquitin, as commonly used in eukaryotic counterparts, the SdeA Dub module engages Gln40 of ubiquitin. The architecture of the active-site cleft presents an open arrangement with conformational plasticity, permitting deubiquitination of three of the most abundant polyubiquitin chains, with a distinct preference for Lys63 linkages. We have shown that this preference enables efficient removal of Lys63 linkages from the phagosomal surface. Remarkably, the structure reveals by far the most parsimonious use of molecular contacts to achieve deubiquitination, with less than 1,000 Å 2 of accessible surface area buried upon complex formation with ubiquitin. In conclusion, this type of molecular recognition appears to enable dual specificity toward ubiquitin and the ubiquitin-like modifier NEDD8.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Structures of the peptide-modifying radical SAM enzyme SuiB elucidate the basis of substrate recognition

Posttranslational modification of ribosomally synthesized peptides provides an elegant means for the production of biologically active molecules known as RiPPs (ribosomally synthesized and posttranslationally modified peptides). Although the leader sequence of the precursor peptide is often required for turnover, the exact mode of recognition by the modifying enzymes remains unclear for many members of this class of natural products. In this report, we have used X-ray crystallography and computational modeling to examine the role of the leader peptide in the biosynthesis of a homolog of streptide, a recently identified peptide natural product with an intramolecular lysine–tryptophan cross-link, which is installed by the radical S -adenosylmethionine (SAM) enzyme, StrB. We present crystal structures of SuiB, a close ortholog of StrB, in various forms, including apo SuiB, SAM-bound SuiB, and a complex of SuiB with SAM and its peptide substrate, SuiA. Although the N-terminal domain of SuiB adopts a typical RRE (RiPP recognition element) motif, which has been implicated in precursor peptide recognition, we observe binding of the leader peptide in the catalytic barrel rather than the N-terminal domain. Computational simulations support a mechanism in which the leader peptide guides posttranslational modification by positioning the cross-linking residues of the precursor peptide within the active site. Together the results shed light onto binding of the precursor peptide and the associated conformational changes needed for the formation of the unique carbon–carbon cross-link in the streptide family of natural products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Recognition of nonproline N-terminal residues by the Pro/N-degron pathway

Eukaryotic N-degron pathways are proteolytic systems whose unifying feature is their ability to recognize proteins containing N-terminal (Nt) degradation signals called N-degrons, and to target these proteins for degradation by the 26S proteasome or autophagy. GID4, a subunit of the GID ubiquitin ligase, is the main recognition component of the proline (Pro)/N-degron pathway. GID4 targets proteins through their Nt-Pro residue or a Pro at position 2, in the presence of specific downstream sequence motifs. Here we show that human GID4 can also recognize hydrophobic Nt-residues other than Pro. One example is the sequence Nt-IGLW, bearing Nt-Ile. Nt-IGLW binds to wild-type human GID4 with a $K_d$ of 16 μM, whereas the otherwise identical Nt-Pro–bearing sequence PGLW binds to GID4 more tightly, with a $K_d$ of 1.9 μM. Despite this difference in affinities of GID4 for Nt-IGLW vs. Nt-PGLW, we found that the GID4-mediated Pro/N-degron pathway of the yeast Saccharomyces cerevisiae can target an Nt-IGLW–bearing protein for rapid degradation. We solved crystal structures of human GID4 bound to a peptide bearing Nt-Ile or Nt-Val. We also altered specific residues of human GID4 and measured the affinities of resulting mutant GID4s for Nt-IGLW and Nt-PGLW, thereby determining relative contributions of specific GID4 residues to the GID4-mediated recognition of Nt-Pro vs. Nt-residues other than Pro. These and related results advance the understanding of targeting by the Pro/N-degron pathway and greatly expand the substrate recognition range of the GID ubiquitin ligase in both human and yeast cells.

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

30S subunit recognition and G1405 modification by the aminoglycoside-resistance 16S ribosomal RNA methyltransferase RmtC

Acquired ribosomal RNA (rRNA) methylation has emerged as a significant mechanism of aminoglycoside resistance in pathogenic bacterial infections. Modification of a single nucleotide in the ribosome decoding center by the aminoglycoside-resistance 16S rRNA (m 7 G1405) methyltransferases effectively blocks the action of all 4,6-deoxystreptamine ring-containing aminoglycosides, including the latest generation of drugs. To define the molecular basis of 30S subunit recognition and G1405 modification by these enzymes, we used a S-adenosyl-L-methionine analog to trap the complex in a postcatalytic state to enable determination of a global 3.0 Å cryo-electron microscopy structure of the m 7 G1405 methyltransferase RmtC bound to the mature Escherichia coli 30S ribosomal subunit. This structure, together with functional analyses of RmtC variants, identifies the RmtC N-terminal domain as critical for recognition and docking of the enzyme on a conserved 16S rRNA tertiary surface adjacent to G1405 in 16S rRNA helix 44 (h44). To access the G1405 N7 position for modification, a collection of residues across one surface of RmtC, including a loop that undergoes a disorder-to order transition upon 30S subunit binding, induces significant distortion of h44. This distortion flips G1405 into the enzyme active site where it is positioned for modification by two almost universally conserved RmtC residues. Further, these studies expand our understanding of ribosome recognition by rRNA modification enzymes and present a more complete structural basis for future development of strategies to inhibit m 7 G1405 modification to resensitize bacterial pathogens to aminoglycosides.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Functionally critical residues in the aminoglycoside resistance-associated methyltransferase RmtC play distinct roles in 30S substrate recognition

Methylation of the small ribosome subunit rRNA in the ribosomal decoding center results in exceptionally high-level aminoglycoside resistance in bacteria. Enzymes that methylate 16S rRNA on N7 of nucleotide G1405 (m 7 G1405) have been identified in both aminoglycoside-producing and clinically drug-resistant pathogenic bacteria. Using a fluorescence polarization 30S-binding assay and a new crystal structure of the methyltransferase RmtC at 3.14 Å resolution, here we report a structure-guided functional study of 30S substrate recognition by the aminoglycoside resistance-associated 16S rRNA (m 7 G1405) methyltransferases. We found that the binding site for these enzymes in the 30S subunit directly overlaps with that of a second family of aminoglycoside resistance-associated 16S rRNA (m 1 A1408) methyltransferases, suggesting that both groups of enzymes may exploit the same conserved rRNA tertiary surface for docking to the 30S. Within RmtC, we defined an N-terminal domain surface, comprising basic residues from both the N1 and N2 subdomains, that directly contributes to 30S-binding affinity. In contrast, additional residues lining a contiguous adjacent surface on the C-terminal domain were critical for 16S rRNA modification but did not directly contribute to the binding affinity. The results from our experiments define the critical features of m 7 G1405 methyltransferase–substrate recognition and distinguish at least two distinct, functionally critical contributions of the tested enzyme residues: 30S-binding affinity and stabilizing a binding-induced 16S rRNA conformation necessary for G1405 modification. Our study sets the scene for future high-resolution structural studies of the 30S-methyltransferase complex and for potential exploitation of unique aspects of substrate recognition in future therapeutic strategies.

(ribosomal RNA) ribosome RNA methylation↗
DOE OSTI2017

Enhanced spontaneous DNA twisting/bending fluctuations unveiled by fluorescence lifetime distributions promote mismatch recognition by the Rad4 nucleotide excision repair complex

Rad4/XPC recognizes diverse DNA lesions including ultraviolet-photolesions and carcinogen-DNA adducts, initiating nucleotide excision repair. Studies have suggested that Rad4/XPC senses lesion-induced helix-destabilization to flip out nucleotides from damaged DNA sites. However, characterizing how DNA deformability and/or distortions impact recognition has been challenging. Here, using fluorescence lifetime measurements empowered by a maximum entropy algorithm, we mapped the conformational heterogeneities of artificially destabilized mismatched DNA substrates of varying Rad4-binding specificities. The conformational distributions, as probed by FRET between a cytosine-analog pair exquisitely sensitive to DNA twisting/bending, reveal a direct connection between intrinsic DNA deformability and Rad4 recognition. High-specificity CCC/CCC mismatch, free in solution, sampled a strikingly broad range of conformations from B-DNA-like to highly distorted conformations that resembled those observed with Rad4 bound; the extent of these distortions increased with bound Rad4 and with temperature. Conversely, the non-specific TAT/TAT mismatch had a homogeneous, B-DNA-like conformation. Molecular dynamics simulations also revealed a wide distribution of conformations for CCC/CCC, complementing experimental findings. We propose that intrinsic deformability promotes Rad4 damage recognition, perhaps by stalling a diffusing protein and/or facilitating ‘conformational capture’ of pre-distorted damaged sites. Surprisingly, even mismatched DNA specifically bound to Rad4 remains highly dynamic, a feature that may reflect the versatility of Rad4/XPC to recognize many structurally dissimilar lesions

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

SU-D-201-05: On the Automatic Recognition of Patient Safety Hazards in a Radiotherapy Setup Using a Novel 3D Camera System and a Deep Learning Framework

Purpose: Patient safety hazards such as a wrong patient/site getting treated can lead to catastrophic results. The purpose of this project is to automatically detect potential patient safety hazards during the radiotherapy setup and alert the therapist before the treatment is initiated. Methods: We employed a set of co-located and co-registered 3D cameras placed inside the treatment room. Each camera provided a point-cloud of fraxels (fragment pixels with 3D depth information). Each of the cameras were calibrated using a custom-built calibration target to provide 3D information with less than 2 mm error in the 500 mm neighborhood around the isocenter. To identify potential patient safety hazards, the treatment room components and the patient’s body needed to be identified and tracked in real-time. For feature recognition purposes, we used a graph-cut based feature recognition with principal component analysis (PCA) based feature-to-object correlation to segment the objects in real-time. Changes in the object’s position were tracked using the CamShift algorithm. The 3D object information was then stored for each classified object (e.g. gantry, couch). A deep learning framework was then used to analyze all the classified objects in both 2D and 3D and was then used to fine-tune a convolutional network for object recognition. The number of network layers were optimized to identify the tracked objects with >95% accuracy. Results: Our systematic analyses showed that, the system was effectively able to recognize wrong patient setups and wrong patient accessories. The combined usage of 2D camera information (color + depth) enabled a topology-preserving approach to verify patient safety hazards in an automatic manner and even in scenarios where the depth information is partially available. Conclusion: By utilizing the 3D cameras inside the treatment room and a deep learning based image classification, potential patient safety hazards can be effectively avoided.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

SU-F-T-20: Novel Catheter Lumen Recognition Algorithm for Rapid Digitization

Purpose: Manual catheter recognition remains a time-consuming aspect of high-dose-rate brachytherapy (HDR) treatment planning. In this work, a novel catheter lumen recognition algorithm was created for accurate and rapid digitization. Methods: MatLab v8.5 was used to create the catheter recognition algorithm. Initially, the algorithm searches the patient CT dataset using an intensity based k-means filter designed to locate catheters. Once the catheters have been located, seed points are manually selected to initialize digitization of each catheter. From each seed point, the algorithm searches locally in order to automatically digitize the remaining catheter. This digitization is accomplished by finding pixels with similar image curvature and divergence parameters compared to the seed pixel. Newly digitized pixels are treated as new seed positions, and hessian image analysis is used to direct the algorithm toward neighboring catheter pixels, and to make the algorithm insensitive to adjacent catheters that are unresolvable on CT, air pockets, and high Z artifacts. The algorithm was tested using 11 HDR treatment plans, including the Syed template, tandem and ovoid applicator, and multi-catheter lung brachytherapy. Digitization error was calculated by comparing manually determined catheter positions to those determined by the algorithm. Results: he digitization error was 0.23 mm ± 0.14 mm axially and 0.62 mm ± 0.13 mm longitudinally at the tip. The time of digitization, following initial seed placement was less than 1 second per catheter. The maximum total time required to digitize all tested applicators was 4 minutes (Syed template with 15 needles). Conclusion: This algorithm successfully digitizes HDR catheters for a variety of applicators with or without CT markers. The minimal axial error demonstrates the accuracy of the algorithm, and its insensitivity to image artifacts and challenging catheter positioning. Future work to automatically place initial seed positions would improve the algorithm speed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Structure of the saxiphilin:saxitoxin (STX) complex reveals a convergent molecular recognition strategy for paralytic toxins

Dinoflagelates and cyanobacteria produce saxitoxin (STX), a lethal bis-guanidinium neurotoxin causing paralytic shellfish poisoning. A number of metazoans have soluble STX-binding proteins that may prevent STX intoxication. However, their STX molecular recognition mechanisms remain unknown. Here, we present structures of saxiphilin (Sxph), a bullfrog high-affinity STX-binding protein, alone and bound to STX. The structures reveal a novel high-affinity STX-binding site built from a “proto-pocket” on a transferrin scaffold that also bears thyroglobulin domain protease inhibitor repeats. Comparison of Sxph and voltage-gated sodium channel STX-binding sites reveals a convergent toxin recognition strategy comprising a largely rigid binding site where acidic side chains and a cation-π interaction engage STX. These studies reveal molecular rules for STX recognition, outline how a toxin-binding site can be built on a naïve scaffold, and open a path to developing protein sensors for environmental STX monitoring and new biologics for STX intoxication mitigation.

60 APPLIED LIFE SCIENCES↗