SEARCH · Engineering Papers

Results for “Biophysical Processes”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 469 records · Page 26

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Predicting the structural basis of targeted protein degradation by integrating molecular dynamics simulations with structural mass spectrometry

Targeted protein degradation (TPD) is a promising approach in drug discovery for degrading proteins implicated in diseases. A key step in this process is the formation of a ternary complex where a heterobifunctional molecule induces proximity of an E3 ligase to a protein of interest (POI), thus facilitating ubiquitin transfer to the POI. In this work, we characterize 3 steps in the TPD process. (1) We simulate the ternary complex formation of SMARCA2 bromodomain and VHL E3 ligase by combining hydrogen-deuterium exchange mass spectrometry with weighted ensemble molecular dynamics (MD). (2) We characterize the conformational heterogeneity of the ternary complex using Hamiltonian replica exchange simulations and small-angle X-ray scattering. (3) We assess the ubiquitination of the POI in the context of the full Cullin-RING Ligase, confirming experimental ubiquitinomics results. Differences in degradation efficiency can be explained by the proximity of lysine residues on the POI relative to ubiquitin.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR

The coupling of atomic force microscopy with infrared spectroscopy (AFM-IR) offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution. However, in order to fully utilize the measurement capability of AFM-IR, a three-dimensional dataset (2D map with a spectroscopic dimension) needs to be acquired, which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan. In this paper, we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm. This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image, greatly reducing data acquisition time. As a result, we are able to generate high-resolution maps of component distribution, produce chemical maps at any wavenumber available in the spectral range, and perform correlative analysis of the physical and chemical properties of the samples. We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition. We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.

36 MATERIALS SCIENCE↗

DOE OSTI2014

TH-E-BRD-01: Innovation in (gold) Nanoparticle-Enhanced Therapy

Radiation therapy relies on the concept of delivering high dose to tumor volumes whilst simultaneously aiming to minimize irradiation of healthy tissue. Gold and other metallic nanoparticles (GNPs) have the potential to greatly enhance dose depositions in their close proximity. While it was originally thought that this effect would only be significant for kV photon beams, it has been shown that GNPs also enhance dose and increase cell killing and survival fraction for MV photons as well as protons. GNPs have been shown to be preferentially taken up in tumors, depending on the GNP properties either internalized in the tumor cells or clustering in the tumor vasculature. Therefore GNPs offer an intriguing additional option to target the tumor while sparing healthy tissue. While a growing amount of research shows GNP induced enhancement factors in the order of 1.5 and higher, GNPs have not yet entered into clinical routine. In this symposium we will have three presentations discussing the current status of GNP based research, the potential to include GNPs in radiation therapy and the limitations and problems to use GNPs in the clinic. Physical and biological underpinnings of radiosensitization with gold nano particles An evolving body of recent literature alludes to the potential to sensitize tumors to radiation therapy using metallic nanoparticles. In preclinical studies, the techniques that hold promise for eventual clinical deployment are nanoparticle-assisted radiation dose enhancement and hyperthermic radiosensitization. To understand the underlying nanoparticle-radiation interactions, computational techniques offer an explanation for and predict the biophysical consequences at a nano-/meso-scopic scale. Nonetheless, there are persisting gaps in knowledge relating to the molecular mechanism of action of these radiosensitization approaches — some of these issues will be addressed. Since the literature relating to the diverse disciplines involved in these efforts spans across multiple specialties (clinical radiation oncology, radiation physics, radiation biology, nanotechnology, material science, biomedical engineering, pharmacology, chemistry, and tumor biology) and numerous specialty journals, there is no single compilation of extant research in this arena or forum for merging analogous concepts and paradigms. This symposium will provide such a venue — my presentation will start with familiarizing the audience with the potential applications of metallic nanoparticles in radiation therapy using specific illustrative examples and begin to explore ways to understand the underlying mechanisms of the effects observed. Biological effects of Gold nanoparticles in radiation therapy Gold nanoparticles (GNP) have been investigated as platforms to carry drugs or radio-sensitizing agents to tumors due to the biocompatibility of gold and relative ease of conjugation with therapeutic and targeting moieties. Recently, there has been interest in exploiting the physical properties of gold, specifically the high atomic number, to enhance radiation therapy. When irradiated, gold atoms will produce low energy electrons, depositing energy within a short distance. The ratio of dose deposited in the presence of the GNP to the dose deposited in the absence of GNP is referred to as the dose enhancement factor (DEF). This factor has been shown to depend on the concentration of GNP and the energy of the incident photons. The physics of this process, preliminary in vitro and in vivo experiments and future directions for this nascent field are described in this presentation. Gold Nanoparticles for improved therapeutic outcome in radiation therapy The application of nanoparticles (NPs) for improved therapeutics is at the forefront of cancer nanotechnology. Among other NP systems, gold nanoparticles (GNPs) are extensively used due to its impressive ability to act as both an anticancer drug carrier in chemotherapy and as a dose enhancer in radiotherapy. Cellular uptake of GNPs was dependent on their size. Among GNPs of diameter between 14–74 nm, GNPs of size 50 nm has the highest uptake. Radiosensitization was dependent on the size of the GNPs as well. GNPs of size 50-nm showed the highest radiosensitization enhancement factor compared to GNPs of 14 and 74 nm for lower- (105 kVp) and higher- (6 MVp) energy photons. GNPs used in those studies were predominantly localized in the cell cytoplasm. However, the therapeutic response can be further enhanced if NPs can be effectively targeted into the nucleus. Here, we present an effective strategy for designing a GNP-peptide complex for nuclear targeting. Two peptides were conjugated onto a GNP: One peptide enhanced the uptake while the other peptide enhanced the nuclear delivery. The nuclear-targeted cells displayed a fourfold increase in the therapeutic response when treated with radiation as compared to untargeted ones. DNA double-strand breaks were quantified using radiation-induced foci of γ- H2AX and 53BP1, and a modest increase in the number of foci per nucleus was observed in irradiated cell populations with internalized GNPs. This research will establish a more aggressive NP-based treatment approach for improved outcome in cancer therapy. Learning Objectives: Introduce radiosensitization concepts of metallic nanoparticle and provide the theoretical basis Provide an overview over the size and coating dependence for GNP uptake in cells Provide a compilation of the extant, multi-discipline research on metallic nanoparticles Understand the prospects for future studies and innovations and the potential for applications of metallic nanoparticles in radiation therapy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2014

Mechanistic determinants of the directionality and energetics of active export by a heterodimeric ABC transporter

The ATP-binding cassette (ABC) transporter associated with antigen processing (TAP) participates in immune surveillance by moving proteasomal products into the endoplasmic reticulum (ER) lumen for major histocompatibility complex class I loading and cell surface presentation to cytotoxic T cells. Here we delineate the mechanistic basis for antigen translocation. Notably, TAP works as a molecular diode, translocating peptide substrates against the gradient in a strict unidirectional way. We reveal the importance of the D-loop at the dimer interface of the two nucleotide-binding domains (NBDs) in coupling substrate translocation with ATP hydrolysis and defining transport vectoriality. Substitution of the converved aspartate, which coordinates the ATP-binding site, decreases NBD dimerization affinity and turns the unidirectional primary active pump into a passive bidirectional nucleotide-gated facilitator. Thus, ATP hydrolysis is not required for translocation per se , but is essential for both active and unidirectional transport. As a result, our data provide detailed mechanistic insight into how heterodimeric ABC exporters operate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2012

Application of DEN refinement and automated model building to a difficult case of molecular-replacement phasing: the structure of a putative succinyl-diaminopimelate desuccinylase from Corynebacterium glutamicum

Phasing by molecular replacement remains difficult for targets that are far from the search model or in situations where the crystal diffracts only weakly or to low resolution. Here, the process of determining and refining the structure of Cgl1109, a putative succinyl-diaminopimelate desuccinylase from Corynebacterium glutamicum , at ~3 Å resolution is described using a combination of homology modeling with MODELLER , molecular-replacement phasing with Phaser , deformable elastic network (DEN) refinement and automated model building using AutoBuild in a semi-automated fashion, followed by final refinement cycles with phenix.refine and Coot . This difficult molecular-replacement case illustrates the power of including DEN restraints derived from a starting model to guide the movements of the model during refinement. The resulting improved model phases provide better starting points for automated model building and produce more significant difference peaks in anomalous difference Fourier maps to locate anomalous scatterers than does standard refinement. This example also illustrates a current limitation of automated procedures that require manual adjustment of local sequence misalignments between the homology model and the target sequence.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Structure and regulation of human epithelial cell transforming 2 protein

Epithelial cell transforming 2 (Ect2) protein activates Rho GTPases and controls cytokinesis and many other cellular processes. Dysregulation of Ect2 is associated with various cancers. Here, we report the crystal structure of human Ect2 and complementary mechanistic analyses. The data show the C-terminal PH domain of Ect2 folds back and blocks the canonical RhoA-binding site at the catalytic center of the DH domain, providing a mechanism of Ect2 autoinhibition. Ect2 is activated by binding of GTP-bound RhoA to the PH domain, which suggests an allosteric mechanism of Ect2 activation and a positive-feedback loop reinforcing RhoA signaling. Furthermore this bimodal RhoA binding of Ect2 is unusual and was confirmed with Förster resonance energy transfer (FRET) and hydrogen–deuterium exchange mass spectrometry (HDX-MS) analyses. Several recurrent cancer-associated mutations map to the catalytic and regulatory interfaces, and dysregulate Ect2 in vitro and in vivo. Together, our findings provide mechanistic insights into Ect2 regulation in normal cells and under disease conditions.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2015

Investigation of North American Vegetation Variability under Recent Climate: A Study Using the SSiB4/TRIFFID Biophysical/Dynamic Vegetation Model

Recent studies have shown that current dynamic vegetation models have serious weaknesses in reproducing the observed vegetation dynamics and contribute to bias in climate simulations. This study intends to identify the major factors that underlie the connections between vegetation dynamics and climate variability and investigates vegetation spatial distribution and temporal variability at seasonal to decadal scales over North America (NA) to assess a 2-D biophysical model/dynamic vegetation model's (Simplified Simple Biosphere Model version 4, coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics Model (SSiB4/TRIFFID)) ability to simulate these characteristics for the past 60 years (1948 through 2008). Satellite data are employed as constraints for the study and to compare the relationships between vegetation and climate from the observational and the simulation data sets. Trends in NA vegetation over this period are examined. The optimum temperature for photosynthesis, leaf drop threshold temperatures, and competition coefficients in the Lotka-Volterra equation, which describes the population dynamics of species competing for some common resource, have been identified as having major impacts on vegetation spatial distribution and obtaining proper initial vegetation conditions in SSiB4/TRIFFID. The finding that vegetation competition coefficients significantly affect vegetation distribution suggests the importance of including biotic effects in dynamical vegetation modeling. The improved SSiB4/TRIFFID can reproduce the main features of the NA distributions of dominant vegetation types, the vegetation fraction, and leaf area index (LAI), including its seasonal, interannual, and decadal variabilities. The simulated NA LAI also shows a general increasing trend after the 1970s in responding to warming. Both simulation and satellite observations reveal that LAI increased substantially in the southeastern U.S. starting from the 1980s. The effects of the severe drought during 1987-1992 and the last decade in the southwestern U.S. on vegetation are also evident from decreases in the simulated and satellite-derived LAIs. Both simulated and satellite-derived LAIs have the strongest correlations with air temperature at northern middle to high latitudes in spring reflecting the effect of these climatic variables on photosynthesis and phenological processes. Meanwhile, in southwestern dry lands, negative correlations appear due to the heat and moisture stress there during the summer. Furthermore, there are also positive correlations between soil wetness and LAI, which increases from spring to summer. The present study shows both the current improvements and remaining weaknesses in dynamical vegetation models. It also highlights large continental-scale variations that have occurred in NA vegetation over the past six decades and their potential relations to climate. With more observational data availability, more studies with differentmodels and focusing on different regions will be possible and are necessary to achieve comprehensive understanding of the vegetation dynamics and climate interactions.

climate variability↗

DOE OSTI2013

Seizure-induced alterations in fast-spiking basket cell GABA currents modulate frequency and coherence of gamma oscillation in network simulations

Gamma frequency oscillations have been proposed to contribute to memory formation and retrieval. Fast-spiking basket cells (FS-BCs) are known to underlie development of gamma oscillations. Fast, high amplitude GABA synapses and gap junctions have been suggested to contribute to gamma oscillations in FS-BC networks. Recently, we identified that, apart from GABAergic synapses, FS-BCs in the hippocampal dentate gyrus have GABAergic currents mediated by extrasynaptic receptors. Our experimental studies demonstrated two specific changes in FS-BC GABA currents following experimental seizures [Yu et al., J. Neurophysiol. 109, 1746 (2013)]: increase in the magnitude of extrasynaptic (tonic) GABA currents and a depolarizing shift in GABA reversal potential (E{sub GABA}). Here, we use homogeneous networks of a biophysically based model of FS-BCs to examine how the presence of extrasynaptic GABA conductance (g{sub GABA-extra}) and experimentally identified, seizure-induced changes in g{sub GABA-extra} and E{sub GABA} influence network activity. Networks of FS-BCs interconnected by fast GABAergic synapses developed synchronous firing in the dentate gamma frequency range (40–100 Hz). Systematic investigation revealed that the biologically realistic range of 30 to 40 connections between FS-BCs resulted in greater coherence in the gamma frequency range when networks were activated by Poisson-distributed dendritic synaptic inputs rather than by homogeneous somatic current injections, which were balanced for FS-BC firing frequency in unconnected networks. Distance-dependent conduction delay enhanced coherence in networks with 30–40 FS-BC interconnections while inclusion of gap junctional conductance had a modest effect on coherence. In networks activated by somatic current injections resulting in heterogeneous FS-BC firing, increasing g{sub GABA-extra} reduced the frequency and coherence of FS-BC firing when E{sub GABA} was shunting (−74 mV), but failed to alter average FS-BC frequency when E{sub GABA} was depolarizing (−54 mV). When FS-BCs were activated by biologically based dendritic synaptic inputs, enhancing g{sub GABA-extra} reduced the frequency and coherence of FS-BC firing when E{sub GABA} was shunting and increased average FS-BC firing when E{sub GABA} was depolarizing. Shifting E{sub GABA} from shunting to depolarizing potentials consistently increased network frequency to and above high gamma frequencies (>80 Hz). Since gamma oscillations may contribute to learning and memory processing [Fell et al., Nat. Neurosci. 4, 1259 (2001); Jutras et al., J. Neurosci. 29, 12521 (2009); Wang, Physiol. Rev. 90, 1195 (2010)], our demonstration that network oscillations are modulated by extrasynaptic inhibition in FS-BCs suggests that neuroactive compounds that act on extrasynaptic GABA receptors could impact memory formation by modulating hippocampal gamma oscillations. The simulation results indicate that the depolarized FS-BC GABA reversal, observed after experimental seizures, together with enhanced spillover extrasynaptic GABA currents are likely to promote generation of focal high frequency activity associated with epileptic networks.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Differential sensitivity to oxygen among the bacteriochlorophylls g in the type-I reaction centers of Heliobacterium modesticaldum

The type-I, homodimeric photosynthetic reaction center (RC) of Heliobacteria (HbRC) is the only known RC in which bacteriochlorophyll g (BChl g) is found. It is also simpler than other RCs, having the smallest number of protein subunits and bound chromophores of any type-I RC. In the presence of oxygen, BChl g isomerizes to 8 1 -hydroxychlorophyll aF (Chl a F ). This naturally occurring process provides a way of altering the chlorophylls and studying the efect of these changes on energy and electron transfer. Transient absorbance diference spectroscopy reveals that triplet-state formation occurs in the antenna chlorophylls of HbRCs but does not provide site-specifc information. Herein, we report on an extended optically detected magnetic resonance (ODMR) study of the antenna triplet states in HbRCs with difering levels of conversion of BChl g to Chl a F . The data reveal pools of BChl g molecules with diferent triplet zero-feld splitting parameters and diferent susceptibilities to chemical oxidation. Finally, by relating the detailed spectroscopic characteristics derived from the ODMR data to the recently solved crystallographic structure, we have tentatively identifed BChl g molecules in which the probability of triplet formation is high and sites at which BChl g conversion is more likely, providing useful information about the fate of the excitation in the complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Model-based approach to investigate equipment-induced error in pressure-waveform derived hemodynamic measurements

Advanced hemodynamic monitoring systems have provided less invasive methods for estimating pressure-derived measurements such as pressure-derived cardiac output (CO) measurements. These devices apply algorithms to arterial pressure waveforms recorded via pressure recording components that transmit the pressure signal to a pressure monitor. While standards have been developed for pressure monitoring equipment, it is unclear how the equipment-induced error can affect secondary measurements from pressure waveforms. We propose an approach for modelling different components of a pressure monitoring system and use this model-based approach to investigate the effect of different pressure recording configurations on pressure-derived hemodynamic measurements. The proposed model-based approach is a three-step process. (1) Modelling the response of pressure recording components using bench tests; (2) verifying the identified models through nonparametric equivalence tests; and (3) assessing the effects of pressure recording components on pressure-derived measurements. To delineate the application of this approach, we performed a series of model-based analyses to quantify the combined effect of a wide range of tubing configurations with various damping ratios and natural frequencies and monitors with different bandwidths on pressure waveforms and CO measurements by six pulse contour algorithms. Model-based results show the error in pressure-derived CO measurements because of tubing configurations with different natural frequencies and damping ratios. Tubing configurations with low natural frequencies (<23 Hz) altered characteristics of pressure waveforms in a way that affected the CO measurement, some by as much as 20%. Our method can serve as a tool to quantify the performance of pressure recording systems with different dynamic properties. In conclusion, this approach can be applied to investigate the effects of physiologic signal recording configurations on various pressure-derived hemodynamic measurements.

42 ENGINEERING↗

DOE OSTI2017

Mesoscopic Adaptive Resolution Scheme toward Understanding of Interactions between Sickle Cell Fibers

Understanding of intracellular polymerization of sickle hemoglobin (HbS) and subsequent interaction with the membrane of a red blood cell (RBC) is important to predict the altered morphologies and mechanical properties of sickle RBCs in sickle cell anemia. However, modeling the integrated processes of HbS nucleation, polymerization, HbS fiber interaction, and subsequent distortion of RBCs is challenging as they occur at multispatial scales, ranging from nanometers to micrometers. To make progress toward simulating the integrated processes, we propose a hybrid HbS fiber model, which couples fine-grained and coarse-grained HbS fiber models through a mesoscopic adaptive resolution scheme (MARS). To this end, we apply a microscopic model to capture the dynamic process of polymerization of HbS fibers, while maintaining the mechanical properties of polymerized HbS fibers by the mesoscopic model, thus providing a means of bridging the subcellular and cellular phenomena in sickle cell disease. At the subcellular level, this model can simulate HbS polymerization with preexisting HbS nuclei. At the cellular level, if combined with RBC models, the generated HbS fibers could be applied to study the morphologies and membrane stiffening of sickle RBCs. One important feature of the MARS is that it can be easily employed in other particle-based multiscale simulations where a dynamic coarse-graining and force-blending method is required. As demonstrations, we first apply the hybrid HbS fiber model to simulate the interactions of two growing fibers and find that their final configurations depend on the orientation and interaction distance between two fibers, in good agreement with experimental observations. We also model the formation of fiber bundles and domains so that we explore the mechanism that causes fiber branching.

Biophysics↗

DOE OSTI2024

HDBind: encoding of molecular structure with hyperdimensional binary representations

Traditional methods for identifying “hit” molecules from a large collection of potential drug-like candidates rely on biophysical theory to compute approximations to the Gibbs free energy of the binding interaction between the drug and its protein target. These approaches have a significant limitation in that they require exceptional computing capabilities for even relatively small collections of molecules. Increasingly large and complex state-of-the-art deep learning approaches have gained popularity with the promise to improve the productivity of drug design, notorious for its numerous failures. However, as deep learning models increase in their size and complexity, their acceleration at the hardware level becomes more challenging. Hyperdimensional Computing (HDC) has recently gained attention in the computer hardware community due to its algorithmic simplicity relative to deep learning approaches. The HDC learning paradigm, which represents data with high-dimension binary vectors, allows the use of low-precision binary vector arithmetic to create models of the data that can be learned without the need for the gradient-based optimization required in many conventional machine learning and deep learning methods. This algorithmic simplicity allows for acceleration in hardware that has been previously demonstrated in a range of application areas (computer vision, bioinformatics, mass spectrometery, remote sensing, edge devices, etc.). To the best of our knowledge, our work is the first to consider HDC for the task of fast and efficient screening of modern drug-like compound libraries. We also propose the first HDC graph-based encoding methods for molecular data, demonstrating consistent and substantial improvement over previous work. We compare our approaches to alternative approaches on the well-studied MoleculeNet dataset and the recently proposed LIT-PCBA dataset derived from high quality PubChem assays. We demonstrate our methods on multiple target hardware platforms, including Graphics Processing Units (GPUs) and Field Programmable Gate Arrays (FPGAs), showing at least an order of magnitude improvement in energy efficiency versus even our smallest neural network baseline model with a single hidden layer. Our work thus motivates further investigation into molecular representation learning to develop ultra-efficient pre-screening tools. We make our code publicly available at https://github.com/LLNL/hdbind.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2019

Integrin nanoclusters can bridge thin matrix fibres to form cell–matrix adhesions

Integrin-mediated cell-matrix adhesions are key to sensing the geometry and rigidity of extracellular environments and influence vital cellular processes. In vivo, the extracellular matrix is composed of fibrous arrays. To understand the fibre geometries that are required for adhesion formation, we patterned nanolines of various line widths and arrangements in single, crossing or paired arrays with the integrin-binding peptide Arg-Gly-Asp. Single thin lines (width ≤ 30 nm) did not support cell spreading or formation of focal adhesions, despite the presence of a high density of Arg-Gly-Asp, but wide lines (>40 nm) did. Using super-resolution microscopy, we observed stable, dense integrin clusters formed on parallel (within 110 nm) or crossing thin lines (mimicking a matrix mesh) similar to those on continuous substrates. These dense clusters bridged the line pairs by recruiting activated but unliganded integrins, as verified by integrin mutants unable to bind ligands that coclustered with ligand-bound integrins when present in an active extended conformation. Furthermore, in a fibrous extracellular matrix mesh, stable integrin nano-clusters bridge between thin (≤ 30 nm) matrix fibres and bring about downstream consequences of cell motility and growth.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Ultrafast Carrier Self-Trapping Driven by Strong Exciton–Phonon Coupling in 2D MA 3 Bi 2 I 6 Cl 3 Perovskite

Two-dimensional (2D) hybrid bismuth halide perovskites have emerged as promising lead-free materials for optoelectronic applications due to their solution processability and tunable structures. Here, in this study, we investigate 2D layered hybrid perovskite MA 3 Bi 2 I 6 Cl 3 using temperature-dependent photoluminescence (PL) and femtosecond transient absorption spectroscopy. Our results reveal strong coupling between excitons and phonons, evidenced by giant Huang–Rhys factors, coherent phonon oscillations, and ultrafast carrier self-trapping into small-polaron and self-trapped exciton (STE) states. These processes appear as time-dependent ground-state bleach and photoinduced absorption features, highlighting the influence of the lattice in carrier dynamics. Wavelength- and time-resolved measurements reveal that PL emission is dominated by STEs, while free exciton emission is weak and short-lived. By comparing 2D MA 3 Bi 2 I 6 Cl 3 with 0D MA 3 Bi 2 I 9 , which contains molecularly isolated [BiI 6 ] 3− octahedra and 2D MA 3 Bi 2 Br 9 perovskites, we demonstrate how halide composition and structural dimensionality influence the balance between free exciton populations and carrier localization. These insights uncover the intrinsic kinetic landscape of photoexcited states in MA 3 Bi 2 I 6 Cl 3 . Overall, our study contributes to a mechanistic understanding of exciton–phonon interactions in lead-free 2D perovskites.

Pradeep, Kodimana Ramakrishnan [Northwestern Univ.↗

DOE OSTI2016

Computational approaches to detect allosteric pathways in transmembrane molecular machines

Many of the functions of transmembrane proteins involved in signal processing and transduction across the cell membrane are determined by allosteric couplings that propagate the functional effects well beyond the original site of activation. Data gathered from breakthroughs in biochemistry, crystallography, and single molecule fluorescence have established a rich basis of information for the study of molecular mechanisms in the allosteric couplings of such transmembrane proteins. The mechanistic details of these couplings, many of which have therapeutic implications, however, have only become accessible in synergy with molecular modeling and simulations. Here, we review some recent computational approaches that analyze allosteric coupling networks (ACNs) in transmembrane proteins, and in particular the recently developed Protein Interaction Analyzer (PIA) designed to study ACNs in the structural ensembles sampled by molecular dynamics simulations. The power of these computational approaches in interrogating the functional mechanisms of transmembrane proteins is illustrated with selected examples of recent experimental and computational studies pursued synergistically in the investigation of secondary active transporters and GPCRs. This article is part of a Special Issue entitled: Membrane Proteins edited by J.C. Gumbart and Sergei Noskov.

Biochemistry & Molecular Biology↗

DOE OSTI2023

Building a Computational and Experimental Rapid Response Pipeline to Counter the Coronavirus Disease 2019 Outbreak and Emerging Biothreats

The COVID-19 pandemic underscored the promise of monoclonal antibody-based prophylactic and therapeutic drugs, especially where protective candidates can be rapidly identified or developed for emerging biothreats and escape variants. Current cutting-edge technologies for this purpose still rely on pathogen-exposed convalescent volunteers and a large screening effort to find a proverbial needle in a haystack. Computational design of protective antibodies based on pre-existing templates skips those requirements and allows for greater control over the breadth and target epitope, while also co-optimizing for potency and developability or other biophysical characteristics. We approached this problem by building and expanding an in vitro experimental rapid antibody production and characterization pipeline to support development of an autonomous, closed loop, active learning software system based on structural simulation and ground truth experimental data to design and evaluate antibody antigen interactions. Starting from early in the pandemic, we targeted SARS-CoV-2, the causative agent of COVID-19, by re-purposing neutralizing antibodies against SARS-CoV-1 that had been identified in the wake of that outbreak in the early 2000’s. We successfully re-targeted three different anti-SARS-CoV-1 antibodies to neutralize SARS-CoV-2 in vitro, where the antibodies were generated externally and tested through conventional binding and neutralization assays internally or with collaborators. As antibodies were identified from the blood of humans infected with SARS-CoV-2, we shifted to collaborate with academic partners to develop improved versions of their human-derived antibodies. This work reached its most important stage in rapid response to the emergence of the Omicron variant of concern (VOC) in late 2021. In a matter of weeks, enabled by on demand innovation to our screening pipeline, we computationally designed and experimentally characterized derivative antibodies of COV2-2130, one of two antibodies from Vanderbilt that form the basis of the AstraZeneca Evusheld prophylactic drug product. This drug product suffers a serious loss of efficacy against Omicron BA.1 and BA.1.1, the first Omicron strains. Due to tight integration of computational design and experimental evaluation, we were able to identify a pair of designs with potent neutralization of the main targets Omicron BA.1 and BA.1.1; but also the earlier Delta variant, and subsequent Omicron strains including BA.2, BA.4, BA.5, and BA.2.75, demonstrating that our multi-target design process can, by its nature, produce robust antibody designs that strictly improve over the parental antibody. These results, recognized by a 2022 Director’s Science and Technology award, have enabled the follow-on GUIDE program, to commence in FY23. While earlier design campaigns were substantially outsourced, we have engineered better and faster processes internally to better compliment, calibrate, and speed computational designs. As part of the follow-on GUIDE program, we will stand up a rapid and high-throughput antibody production and characterization facility staffed with the expertise and capabilities to foster our current collaboration across PLS and ENG as well as other partnerships toward computational design of biologics.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2014

CrowdPhase: crowdsourcing the phase problem

The idea of attacking the phase problem by crowdsourcing is introduced. Using an interactive, multi-player, web-based system, participants work simultaneously to select phase sets that correspond to better electron-density maps in order to solve low-resolution phasing problems. The human mind innately excels at some complex tasks that are difficult to solve using computers alone. For complex problems amenable to parallelization, strategies can be developed to exploit human intelligence in a collective form: such approaches are sometimes referred to as ‘crowdsourcing’. Here, a first attempt at a crowdsourced approach for low-resolution ab initio phasing in macromolecular crystallography is proposed. A collaborative online game named CrowdPhase was designed, which relies on a human-powered genetic algorithm, where players control the selection mechanism during the evolutionary process. The algorithm starts from a population of ‘individuals’, each with a random genetic makeup, in this case a map prepared from a random set of phases, and tries to cause the population to evolve towards individuals with better phases based on Darwinian survival of the fittest. Players apply their pattern-recognition capabilities to evaluate the electron-density maps generated from these sets of phases and to select the fittest individuals. A user-friendly interface, a training stage and a competitive scoring system foster a network of well trained players who can guide the genetic algorithm towards better solutions from generation to generation via gameplay. CrowdPhase was applied to two synthetic low-resolution phasing puzzles and it was shown that players could successfully obtain phase sets in the 30° phase error range and corresponding molecular envelopes showing agreement with the low-resolution models. The successful preliminary studies suggest that with further development the crowdsourcing approach could fill a gap in current crystallographic methods by making it possible to extract meaningful information in cases where limited resolution might otherwise prevent initial phasing.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗