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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Diversifying hierarchical ionic assembly by docking cations to anions as salt bridges

Here, we expand the diversity of building blocks available for ionic assembly by introducing tertiary (3°) ammonium cations into anion complexes. We use proton transfer between 3° amines and organo-phosphoric acids to generate H-bonding cations (R 3 NH + ) and anions (RHPO 4 – ) that co-assemble with cyanostar macrocycles into assemblies with 2:2:2 stoichiometry. At the heart is a supramolecular dimer where phosphate anions form salt bridges by H-bonding with cations. Unlike conventional 4° ammonium cations, 3,000 commercial amines provide diversity for high-throughput screening of 72 combinations (9 nitrogen bases and 8 acids), producing 13 privileged partners for quantitative assembly. Yields depend on the solvent and sterics of salt bridge formation. Ten more nitrogen bases connect to fluorophores (pyrene), photocatalysts (quinoline), drugs (Cipralex, Zytiga), and ionic liquids (imidazole). The synthesis and examination of 82 new salts exemplify how acid-base chemistry can open a pipeline to a diversity of building blocks for exploring hierarchical ionic assembly.

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

SARS-CoV2 Docking Dataset for MLMol Language Model (50M)

This is a processed molecular dataset from this https://doi.ccs.ornl.gov/ui/doi/348 adding up to 50M molecules for the training and 486K molecules for the validation. Instructions on how to use/run/train this dataset can be found here: https://code.ornl.gov/candle/mlmol

Tsaris, Aristeidis↗
DOE OSTI2024

Cycloturbine modular river current energy converter and method and apparatus for deploying marine hydrokinetic turbine assembly to harvest riverine and ocean tidal current energy

A hydrokinetic turbine system for harvesting energy from riverine and tidal sources, including a first floating dock, a marine hydrokinetic turbine mounted on the first floating dock, and a second floating dock. The system further includes a winch assembly mounted on the second floating dock and operationally connected to the first floating dock and a linkage assembly operationally connected to the first floating dock and to the second floating dock. The linkage assembly may be actuated to pull the first floating dock into contact with the second floating dock. The linkage assembly may be actuated to distance the first floating dock from the second floating dock, and the winch assembly may be energized to orient the first floating dock into a position wherein the marine hydrokinetic turbine is above the first floating dock and wherein the winch assembly may be energized to orient the first floating dock into a position wherein the marine hydrokinetic turbine is below the first floating dock.

Chen, Jun↗
DOE OSTI2021

Multiscale Models for Fibril Formation: Rare Events Methods, Microkinetic Models, and Population Balances

Amyloid fibrils are thought to grow by a two-step dock-lock mechanism. However, previous simulations of fibril formation (i) overlook the bi-molecular nature of the docking step and obtain rates with first-order units, or (ii) superimpose the docked and locked states when computing the potential of mean force for association and thereby muddle the docking and locking steps. Here, we developed a simple microkinetic model with separate locking and docking steps and with the appropriate concentration dependences for each step. We constructed a simple model comprised of chiral dumbbells that retains qualitative aspects of fibril formation. We used rare events methods to predict separate docking and locking rate constants for the model. The rate constants were embedded in the microkinetic model, with the microkinetic model embedded in a population balance model for “bottom-up” multiscale fibril growth rate predictions. These were compared to “top-down” results using simulation data with the same model and multiscale framework to obtain maximum likelihood estimates of the separate lock and dock rate constants. We used the same procedures to extract separate docking and locking rate constants from experimental fibril growth data. Our multiscale strategy, embedding rate theories, and kinetic models in conservation laws should help to extract docking and locking rate constants from experimental data or long molecular simulations with correct units and without compromising the molecular description.

Shayesteh Zadeh, Armin (ORCID:0000000299896710)↗
DOE OSTI2021

Biomechanical Comparison of Anatomic Restoration of the Ulnar Footprint vs Traditional Ulnar Tunnels in Ulnar Collateral Ligament Reconstruction

Background: Current techniques for ulnar collateral ligament (UCL) reconstruction do not reproduce the anatomic ulnar footprint of the UCL. The purpose of this study was to describe a novel UCL reconstruction technique that utilizes proximal-to-distal ulnar bone tunnels to better re-create the anatomy of the UCL and to compare the biomechanical profile at time zero among this technique, the native UCL, and the traditional docking technique. Hypothesis: The biomechanical profile of the anatomic technique is similar to the native UCL and traditional docking technique. Study Design: Controlled laboratory study. Methods: Ten matched cadaveric elbows were potted with the forearm in neutral rotation. The palmaris longus tendon graft was harvested, and bones were sectioned 14 cm proximal and distal to the elbow joint. Specimen testing included (1) native UCL testing performed at 90° of flexion with 0.5 N·m of valgus moment preload, (2) cyclic loading from 0.5 to 5 N·m of valgus moment for 1000 cycles at 1 Hz, and (3) load to failure at 0.2 mm/s. Elbows then underwent UCL reconstruction with 1 elbow of each pair receiving the classic docking technique using either anatomic (proximal to distal) or traditional (anterior to posterior) tunnel locations. Specimen testing was then repeated as described. Results: There were no differences in maximum load at failure between the anatomic and traditional tunnel location techniques (mean ± SD, 34.90 ± 10.65 vs 37.28 ± 14.26 N·m; P = .644) or when including the native UCL (45.83 ± 17.03 N·m; P = .099). Additionally, there were no differences in valgus angle after 1000 cycles across the anatomic technique (4.58°± 1.47°), traditional technique (4.08°± 1.28°), and native UCL (4.07°± 1.99°). The anatomic group and the native UCL had similar valgus angles at failure (24.13°± 5.86° vs 20.13°± 5.70°; P = .083), while the traditional group had a higher valgus angle at failure when compared with the native UCL (24.88°± 6.18° vs 19.44°± 5.86°; P = .015). Conclusion: In this cadaveric model, UCL reconstruction with the docking technique utilizing proximal-to-distal ulnar tunnels better restored the ulnar footprint while providing valgus stability comparable with reconstruction with the docking technique using traditional anterior-to-posterior ulnar tunnel locations. These results suggest that utilization of the anatomic tunnel location in UCL reconstruction has similar biomechanical properties to the traditional method at the time of initial fixation (ie, not accounting for healing after reconstruction in vivo) while keeping the ulnar tunnels farther from the ulnar nerve. Further studies are warranted to determine if an anatomically based UCL reconstruction results in differing outcomes than traditional reconstruction techniques. Clinical Relevance: Current UCL reconstruction techniques do not accurately re-create the ulnar UCL footprint. The UCL is a dynamic constraint to valgus loads at the elbow, and a more anatomic reconstruction may afford more natural joint kinematics. This more anatomic technique performs similarly to the traditional docking technique at time zero, and the results of this study may offer a starting point for future in vivo studies.

Chang, Edward S.↗
DOE OSTI2022

Speed vs Accuracy: Effect on Ligand Pose Accuracy of Varying Box Size and Exhaustiveness in AutoDock Vina

Abstract Structure‐based virtual high‐throughput screening involves docking chemical libraries to targets of interest. A parameter pertinent to the accuracy of the resulting pose is the root mean square deviation (RMSD) from a known crystallographic structure, i. e., the ‘docking power’. Here, using a popular algorithm, Autodock Vina, as a model program, we evaluate the effects of varying two common docking parameters: the box size (the size of docking search space) and the exhaustiveness of the global search (the number of independent runs starting from random ligand conformations) on the RMSD from the PDBbind v2017 refined dataset of experimental protein‐ligand complexes. Although it is clear that exhaustiveness is an important parameter, there is wide variation in the values used, with variation between 1 and >100. We, therefore, evaluated a combination of cubic boxes of different sizes and five exhaustiveness values (1, 8, 25, 50, 75, 100) within the range of those commonly adopted. The results show that the default exhaustiveness value of 8 performs well overall for most box sizes. In contrast, for all box sizes, but particularly for large boxes, an exhaustiveness value of 1 led to significantly higher median RMSD (mRMSD) values. The docking power was slightly improved with an exhaustiveness of 25, but the mRMSD changes little with values higher than 25. Therefore, although low exhaustiveness is computationally faster, the results are more likely to be far from reality, and, conversely, values >25 led to little improvement at the expense of computational resources. Overall, we recommend users to use at least the default exhaustiveness value of 8 for virtual screening calculations.

Agarwal, Rupesh↗
DOE OSTI2023

PECAN2

PECAN2, Pose Classification with 3D Atomic Network 2, presents an innovative approach to pose classification in computational modeling, particularly in the context of molecular docking. Traditional methods like docking, which rely on physics-based calculations, are prone to inaccuracies in predicting binding poses during experimental testing. While machine learning (ML) approaches, such as ML-driven pose classification, have been introduced to address these issues, they often depend on the availability of crystal structure data. In contrast, PECAN2 introduces a novel pose classification method that operates independently of crystal structures. Instead, it utilizes a 3D atomic neural network with Point Cloud Network (PCN) to establish correlations between docking scores and experimental data. This approach marks a departure from previous studies that heavily relied on crystal structures for labeling. The key innovation lies in the ability of PECAN2 to enhance the performance of molecular docking by filtering out false positives and false negatives through the correlation between docking scores and experimental data.

Shim, Heesung↗
DOE OSTI2020

Ligand-Binding-Site Refinement to Generate Reliable Holo Protein Structure Conformations from Apo Structures

The first important step in a structure-based virtual screening is the judicious selection of a receptor protein. In cases where the holo protein receptor structure is unavailable, significant reduction in virtual screening performance has been reported. In this work, we present a robust method to generate reliable holo protein structure conformations from apo structures using molecular dynamics (MD) simulation with restraints derived from holo structure binding-site templates. We perform benchmark tests on two different datasets: 40 structures from a directory of useful decoy-enhanced (DUD-E) and 84 structures from the Gunasekaran dataset. Our results show successful refinement of apo binding-site structures toward holo conformations in 82% of the test cases. In addition, virtual screening performance of 40 DUD-E structures is significantly improved using our MD-refined structures as receptors with an average enrichment factor (EF), an EF1% value of 6.2 compared to apo structures with 3.5. Docking of native ligands to the refined structures shows an average ligand root mean square deviation (RMSD) of 1.97 Å (DUD-E dataset and Gunasekaran dataset) relative to ligands in the holo crystal structures, which is comparable to the self-docking (i.e., docking of the native ligand back to its crystal structure receptor) average, 1.34 Å (DUD-E dataset) and 1.36 Å (Gunasekaran dataset). On the other hand, docking to the apo structures yields an average ligand RMSD of 3.65 Å (DUD-E) and 2.90 Å (Gunasekaran). These results indicate that our method is robust and can be useful to improve virtual screening performance of apo structures.

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