University Of Calif-Lawrenc Berkeley Lab

NIH Research Projects · FY 2026 · 2026-06

Project Summary (Abstract) Radiopharmaceutical therapies (RPT) are extremely promising for cancer treatment. RPT is a rapidly growing treatment modality that has demonstrated a great success in the treatment and care of multiple malignancies. The efficacy of RPTs relies on delivering a very targeted radiation dose to tumor cells, sparing healthy tissue. Using imaging to inform RPT treatment, known as theranostics (therapy + diagnostic), has shown a superior patient response than the regular one-size-fits-all approach for beta therapy with 177Lu and 131I. However, SPECT technology is very limited when it comes to imaging of alpha therapy radiopharmaceuticals since they are injected at very low activities (0.1-1 mCi), orders of magnitude lower than for 99mTc SPECT (1-10 mCi) or beta therapy (~100mCi). The typical SPECT sensitivity of 0.01% is not enough to precisely image alpha RPTs. On the other hand, time-of-flight positron emission tomography has emerged as the standard of care for a number of types of malignancies due to its superb image quality. However, it requires positron emitters, so it cannot be applied to the most popular RPT radionuclides presented above since they do not emit positrons. There is a need for an imaging modality capable of lowering the minimum imageable activities (MIA) and to obtain accurate images of RPTs. Thus, we propose a completely novel nuclear imaging modality (time-of-flight multi-photon emission tomography or TOF-MPET) that relies on emission of subnanosecond gamma-cascades to dramatically improve signal-to-noise ratio (SNR) and sensitivity to RPT radionuclides with respect to SPECT. All the top candidates for RPTs mentioned above present subnanosecond gamma-cascades, which allows to exploit coincidence detection and TOF to improve imaging of those RPT radionuclides. This technique can be also extended to other non-RPTs that present such photon cascades. This project aims to set the technical ground for TOF-MPET and to provide a demonstration of its performance with a minimal system. We envisioned two ways of exploiting TOF in gamma-cascade emitters: with and without a collimator. With a collimator, TOF-MPET uses TOF to provide depth perception and to form 3D images with a single detector view, resulting in an enhanced SNR. The rationale behind this technology is similar to the one that led from PET to TOF-PET. Without a collimator, TOF-MPET dramatically increases the sensitivity, as well as eliminating the trade-off between sensitivity and resolution introduced by the use of collimators. Our specific aims are (Aim 1) to demonstrate that collimated TOF-MPET can substantially improve SNR relative to SPECT and (Aim 2) to demonstrate that uncollimated TOF-MPET can dramatically lower the MIA.

NIH Research Projects · FY 2026 · 2026-06

PROJECT ABSTRACT Optical microscopy is ubiquitous in cell biology and is well-suited for studying cell signaling in live cells and animals, offering minimally invasive in situ monitoring, subcellular and millisecond resolution, and probes for specific receptors or other biomolecules. However, live cell imaging is constrained by the diffraction limit, by temporal limits of most super-resolution techniques, by probe stability, and by the UV or visible lasers used in most microscopy. These common microscopy lasers can be phototoxic, give rise to autofluorescent background, photobleach fluorophores to generate harmful free radicals, and lack the ability to penetrate to subsurface sites. Here, we address all of these limitations with unique new optical probes, avalanching nanoparticles (ANPs), that show the most nonlinear emission of any reported nanoscale material. ANPs upconvert incident light, which enables them to be imaged without autofluorescent background or photobleaching, and to be excited by near infrared (NIR) wavelengths, which are far less phototoxic than the lasers most common in bioimaging. The steep nonlinearity of ANP emission enables experimental realization of a revised Abbe equation of the diffraction limit, achieving real-time sub-70 nm spatial resolution without the need for special optics, computation, or imaging processing. To enable live-cell ANP imaging, we propose synthesis of biocompatible and antibody-functionalized ANPs for receptor targeting, as well as construction of a modified laser scanning confocal microscope, which addresses the innate slow kinetics of photon avalanching. Both breast cancer cells and neural microglia will be imaged to determine spatial localization and dynamics of cell surface receptors critical to their respective functions. ANPs may also be photoswitched indefinitely between bright and dim states entirely with NIR light, enabling sub-nanometer localization accuracies with the superresolution technique INPALM. Because ANPs do not measurably photobleach, collection of unlimited photons with controlled NIR toggling between bright and dim states is possible, leading to this exceptional precision. While these optical breakthroughs have the potential to fundamentally alter how high-resolution bioimaging is approached, ANPs have not been imaged in live cells and only in limited cases in fixed cells. We propose imaging of microglial receptors in live cells at 70-nm resolution and INPALM imaging of fixed cells at sub-nanometer precision with a confocal microscope customized for ANP imaging. The combination of receptor-targetable ANPs with a microscope designed for fast, high-resolution ANP imaging and for ANP superresolution leads to the ultimate goal of bringing these radical advances in nanoparticle optics into the realm of bioimaging.

NIH Research Projects · FY 2026 · 2026-03

ABSTRACT Brain functions, ranging from perception to cognition and action, are produced by the collective dynamics of populations of neurons. High-dimensional neural population data are commonly analyzed in a lower-dimensional subspace, which enables characterization of dynamic neural phenomena. However, subspaces themselves do not provide a principled explanation of those phenomena. Normative theories, on the other hand, explain brain function in terms of computational principles. However, we currently lack normative theories that both make precise predictions that can be tested in experimental data and enable principled understanding of neural population dynamics. A purpose of the brain is to produce behaviors, such as reaching to grasp food, and this is accomplished using feedback. Feedback can be used to correct observed errors in the output of a system relative to a target. For example, at a behavioral level, sensory consequences of arm reaches provide feedback used to control the arm, and (optimal) feedback control provides a normative explanation of many aspects of reaching. Likewise, at a neural systems level, anatomical feedback loops are present both within and between brain area, and the same neural populations can perform diverse target functions on demand (e.g., reaching to different targets). This suggests that neural population dynamics can be controlled by other brain areas with feedback (i.e., are feedback controllable). If and how the principle of feedback control shapes neural population dynamics is unknown. The long-term goal of our research is to arrive at a principled understanding of neural population activity. The goal of this research is to elucidate the control principles underlying neural population dynamics. Control theory distinguishes between systems that use feedback to correct errors (feedback control, e.g., reach to target) and those that do not (feed-forward control, e.g., eye blink reflex). We will design objective functions for extracting subspaces from neural population data that are most feed-forward vs. feedback controllable. Our central hypothesis is that feedback control is a normative principle of neural population dynamics. The following aims develop theory and analysis methods to test this hypothesis in neural population data from monkey primary motor/somatosensory cortex (M1/S1) and inferior temporal cortices (IT) during reaching and face recognition respectively, as well as rat medial prefrontal cortex/hippocampus (mPFC/HC) during maze navigation.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The goal of this project is to enhance the sustainability and impact of the Hierarchical Data Modeling Framework (HDMF), an open-source software tool for standardizing scientific data that forms the foundation of the Neurodata Without Borders (NWB) data standard and software ecosystem for neurophysiology. Supported by the NIH BRAIN Initiative, NWB has become the leading open data standard in neurophysiology, with over 310 public datasets from more than 150 labs already available on the DANDI data archive, and more than 35 community tools supporting NWB. As the HDMF and NWB user base continues to grow, maintaining its code base has become increasingly challenging. Key HDMF features that did not exist in other tools when HDMF was initially developed are now better addressed by newer, widely used technologies. As NWB becomes increasingly adopted as the primary format for sharing neurophysiology data, and as interest grows in using HDMF for other scientific data applications, we must ensure the software remains maintainable, interfaces well with other popular technologies, and is user-friendly. The project has two main objectives: Aim 1 focuses on enhancing interoperability of HDMF by implementing support for LinkML as an additional schema language. This will simplify harmonization and exchange of HDMF-based standards and data models with other data standards and formats, enable use of ontologies and controlled terminologies in HDMF schema, support the design of more expressive and rigorous data models, and improve HDMF’s sustainability and maintainability. Aim 2 focuses on improving the usability and maintainability of the HDMF API through integration with the Pydantic library. We will update the HDMF data interfaces with Pydantic models and replace the custom type validation and documentation system in HDMF with Python type hints, Pydantic validation, and autodoc-pydantic documentation generation. These changes will also make HDMF more accessible to the broader open-source community, enabling developers to better understand the codebase and contribute new features and bug fixes more effectively. We will also create test suites to ensure the stability and compatibility of HDMF for downstream applications. These improvements will address current challenges, including the high maintenance cost of custom code in HDMF, the need to interface with modern data modeling technologies, and the demand for better usability and stability. By integrating HDMF with modern, widely used, well-tested technologies such as LinkML and Pydantic, we will ensure the sustainability of HDMF for the next 5-10 years. This modernization effort will strengthen HDMF's position as a valuable tool for standardizing scientific data while enhancing the infrastructure that supports the NWB neurophysiology data standard.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY We aim to develop a non-invasive, painless biodosimeter for use during triage after a radiological emergency. Our preliminary data showed that Fourier transform infrared attenuated total reflection (FTIR-ATR) imaging coupled to statistical machine learning models is capable of distinguishing irradiated from control mice at doses as low as 0.1 Gy for as long as 90 days after radiation exposure. To further develop this signature, and to determine the ability to discriminate between radiation doses, we will first increase our power of detection by expanding our studies to human skin explants. Statistical machine learning models will be used to develop an FTIR-based signature that can distinguish irradiated from unirradiated animals and accurately determine radiation dose and time since exposure for triage. We will then use ambient infrared laser ablation mass spectrometry to identify the biomolecules that underly the radiation-specific response signature in the skin and address confounders. This novel, non-invasive procedure will lay the groundwork for future applications of deployable imaging devices for biodosimetry at population scales – a valuable tool for radiobiology, epidemiology, and monitoring. Further, because only hundreds of samples were required to learn highly discriminative signatures in our pilot study, this study will contribute to developing human-relevant diagnostic capabilities.

NIH Research Projects · FY 2025 · 2025-09

PROJECT SUMMARY The goal of this project is to enhance the sustainability and impact of AqNWB, an open C/C++ API for enabling direct data acquisition in the Neurodata Without Borders (NWB) data standard. With the increasing adoption of NWB as the leading format for sharing neurophysiology data, there is a pressing need to support its use throughout the entire data lifecycle—from acquisition and processing to analysis, publication, and reuse. Currently, researchers typically acquire data in system-specific formats and convert their data to NWB later for analysis and sharing, a process which is inefficient and error-prone. AqNWB aims to address this challenge by allowing direct data acquisition in NWB, thus streamlining laboratory practices and promoting FAIR data principles. This proposal will enable us to ensure AqNWB’s readiness for production use (Aim1) and to promote its adoption through integration with leading neurophysiology data acquisition software (Aim 2). Specifically, Aim 1 focuses on enhancing production readiness of AqNWB by improving reliability through enhanced testing workflows, simplifying installation via standardized deployment processes, and increasing usability with better documentation and tutorials. Additionally, Aim 1 will enhance support for multi-modal neurophysiology data acquisition by enhancing AqNWB to support optical physiology and behavioral NWB data types. Aim 2 aims to facilitate the integration of AqNWB with leading neurophysiology data acquisition systems to promote broader adoption. This work will involve the development of advanced I/O features for large-scale data acquisition, including customization of chunking and compression settings and integration of Zarr as a new storage backend to enhance parallel write and cloud support. AqNWB will be integrated with OpenEphys in the first year, and further integration targets (e.g., SpikeGadgets, ScanImage, and BPOD) will be prioritized in the second year. By leveraging best practices in open-source software development and collaborations with leading data acquisition systems, this project will enhance the sustainability and impact of AqNWB as a valuable tool for neuroscience research and promote the widespread adoption of NWB. Beyond the innovations in the AqNWB software, this project will remove the need for post-acquisition data conversion, thus streamlining laboratory data management practices and promoting FAIR data practices by enabling use of NWB throughout the data lifecycle.

NIH Research Projects · FY 2026 · 2025-07

SUMMARY Developmental anomalies of the midface, including clefts of the lip and palate, are a common feature of congenital disorders. They can substantially impact respiration, feeding, speech, and social connection, and often require stressful and expensive surgical interventions in affected children. Despite their clinical importance, the genetic basis of most midface defects remains unclear due to our limited understanding of the genes involved, as well as their cell type-specific regulation in time and space during human face development. This proposal will generate resources to address the current lack of a cell type-resolved understanding of gene regulation in human midface development and apply these findings for in-depth computational and experimental studies of individual enhancers involved in human midface development. We propose to generate a single-cell multimodal atlas of human craniofacial development, followed by evolution-driven comparative analysis to prioritize enhancers likely to affect midface development, and detailed hypothesis-driven interrogation of enhancers using transgenic and knock-in mouse models. First, we will apply a suite of state-of-the-art multimodal single-cell assays to human craniofacial tissues at stages critical for midface development. This will include single-cell assays developed by the investigators and uniquely available in their laboratories. Using a data integration strategy demonstrated in preliminary studies, this will provide a detailed atlas of the transcriptome and enhancer landscape at cell type resolution, including the genomic location of enhancers, their activity states (poised or active), and their 3D interactions with target promoters. This information cannot be gained from currently available bulk tissue-derived human data but is critical for studies of enhancers affecting human midface development. Building on this unique resource, we will use the substantial diversity in midface morphology present in hundreds of mammalian species with sequenced genomes to identify enhancers that show accelerated evolutionary sequence signatures that correlate with recurrent changes in midface morphology. Intersecting such evolutionary signatures with cell type-resolved maps of human midface enhancers will identify high-confidence candidate midface enhancers at loci implicated in human midface variation and birth defects. Finally, we will use mouse transgenic assays, single-cell reporter assays, and knock-in enhancer replacement engineering, to validate predicted enhancers in vivo, characterize their activity in craniofacial development, and study their impact on midface development through landmark-based 3D morphometrics of knock-in mice. We will reserve a proportion of our transgenic and mouse engineering capabilities under this proposal to support ongoing collaborative studies with promising initial data, as well as new nominations from the community. Consistent with our commitment to community support and FAIR data principles, we will make all data from this proposal available to the craniofacial community through FaceBase, with which Dr. Visel has been continuously involved since its inception.

NIH Research Projects · FY 2026 · 2025-02

Project Summary/Abstract The goal of this proposal is to develop new methods for X-ray absorption spectroscopy (XAS) that can use the ultra-short, femtosecond pulses from X-ray free electron lasers (XFELs) to help us understand the mechanism of redox-active metalloenzymes by following the catalytic reaction, in real time, at room temperature. Unlike X-ray emission spectroscopy (XES), which has been used at XFELs, XAS is much more versatile for studying the electronic structure with X-ray near edge spectroscopy (XANES) and extended X-ray absorption fine structure (EXAFS) for determining metal-metal, and metal-ligand distances. Unlike crystallography, XAS can be conducted using solution samples and often yields higher resolution for specific metal-distances than X-ray diffraction. Nevertheless, XAS has so far seen only very limited use for room temperature time-resolved studies of metalloenzymes using XFELs. The challenges of XAS at XFELs arise from the requirement to scan a wide energy range of incident X-rays; significant shot-to-shot spectral, temporal, and intensity fluctuations in the pulses; and data collection of dilute metalloenzyme systems with limited sample volumes. While progress has been made overcoming these impediments, the most important issue that has not been addressed adequately until now is how to mitigate the contribution of background scattering, which leads to very poor S/N ratios. This is largely because the high count rate of photons at XFELs leads to pulse pile-up and does not allow us to use the energy discriminating, single-photon counting detectors that are usually employed in synchrotron facilities to discriminate the signal from background photons at XFELs. In this proposal, we will focus on the remaining obstacle to XAS at XFELs by minimizing background scattering through designing and building a robust and versatile spectrometer, with a large solid angle and energy resolution of ~10 eV, to discriminate between the background scatter and signal photons and capture the entire metal Ka fluorescence from the metal, which will be collected using a position-sensitive detector. This instrument will be tested using model compounds and then will be used to collect time-resolved XANES and EXAFS data from two important metalloenzymes: the Mn4Ca complex in photosystem II involved in the oxidation of water to oxygen, and the ribonucleotide reductase class of enzymes with binuclear Fe/Fe, Mn/Fe, or Mn/Mn active sites involved in the conversion of ribonucleotides to deoxy-ribonucleotides, the building blocks of DNA. In PS II, which will be triggered by light flashes, we will focus on the time-points in the S3 to S0 transition reached after illumination with three flashes, the last step in the catalytic cycle where the critical step of O-O formation occurs. The XANES and EXAFS data from these timepoints will be important for unravelling the mechanism of the photosynthetic water oxidation reaction. In RNR, which will be triggered by in situ O2 addition, we will focus on the intermediate state, known as ‘X’, which is transiently generated and whose exact structure is not known and has been the subject of much study.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract The human virome, i.e. the collection of viruses found in or on humans, is a complex set of viral communities whose diversity is only now starting to be explored and described. Along with viruses infecting humans and causing diseases, the human virome includes many viruses infecting microorganisms part of the human microbiome as well as transient viruses originating from e.g. food or drinking sources. Most of these viruses are currently known only through metagenomics, i.e. assays through which the genomes of viruses present in a given sample are sequenced directly without the need for laboratory cultivation or isolation. Because of challenges in analyzing this broad diversity of viral genomes, however, the biological information extracted from these metagenome-assembled viral genomes remains limited at this stage. As the field of viromics was being established, most of the effort so far has been focused on the development of methods to comprehensively identify the genomes of known and novel viruses in metagenomes. This resulted in multiple efficient tools for viral sequence detection, and the creation of large-scale catalogs of genomes from different parts of the human virome. Critical for understanding the biology of these viruses, however, will be our ability to classify these new viruses in a robust taxonomic framework, link these viruses to their host(s), and functionally annotate a majority of the genes they encode. Some approaches have been proposed to address these questions, but current tools are inadequate either in terms of resolution, accuracy, and/or throughput. Moreover, some of the most promising methods, such as the use of innovative AI for genome annotation, are only available as experimental software and not ready for large-scale application. Here, we aim to establish the necessary tools, curated databases, and integrated pipelines to enable any researcher with a set of viral genomes to (i) classify these viruses in quasi-taxa enabling robust comparison to other similar studies at multiple ranks, (ii) predict the most likely host(s) taxa and/or strains infected by these viruses, and (iii) identify the genetic potential and thus potential role(s) and impact(s) of these viruses. We will build this work on our previous experience in developing advanced viromics tools and databases, as well as recent developments in viral taxonomy, large-scale comparative genomics, and machine-learning for sequence analysis. Specifically, we intend to develop new genome comparison and clustering approaches to provide a comprehensive genome-based viral taxonomy database and an associated toolkit; expand the current host prediction tools by integrating large-scale CRISPR detection and viral phenotype prediction in a virus-host network framework to enable virus-host linkage at the strain level; and establish a new functional annotation pipeline leveraging protein structure prediction and genomic neighborhood. We intend to develop these new tools in close collaboration with members of the Human Virome Program to build a robust viromics toolkit enabling researchers to thoroughly investigate the direct and indirect impact(s) of these viruses on humans.

NIH Research Projects · FY 2025 · 2024-09

Project Summary/Abstract X-ray crystallography is a powerful tool for determining the atomic positions of proteins, used by researchers at synchrotrons and X-ray free electron lasers. Typically, the crystal is exposed to X-rays, which diffract and are collected on a detector to create diffraction patterns. These patterns are processed by software designed to seek out weak signal on the images and create datasets from which the protein structure can be solved. The DIALS diffraction data processing package is a mature product produced in collaboration between LBNL and Diamond Light Source (UK) that has primarily been supported in the US by research funding. DIALS is used at a number of synchrotrons world-wide for regular processing of user datasets and has been used at X- ray free electron lasers for fast processing of large datasets collected at hundreds to thousands of images per second. The program allows fast feedback of data quality when incorporated into automated processing that allows users to quickly make decisions about beamline operation and experimental direction. This has allowed researchers to produce high-impact structures in general biological fields, including human diseases such as COVID-19 and malaria, and energy research such as photosynthesis. This proposal would create a US R24 National Resource for the DIALS diffraction data processing package. The Resource would move DIALS funding in the US from primarily R01 research funding to a combination of separate research funding and operational funding from this proposal. The operational funding would be to support codebase optimization, maintenance, and refactoring, a build-and-release schedule, and new robust and adaptable user interfaces. Further, it would provide user outreach and training, both for general users, and through on-site training for beamline scientists to help with software integration into existing pipelines. The end result will be a well-maintained and documented software package used at synchrotrons and XFELs for routine data analysis without user intervention, and robust support for difficult cases.

NIH Research Projects · FY 2025 · 2024-04

Project Summary Clinical time-of-flight positron emission tomography (TOF-PET) systems capable of excellent coincidence time resolution (CTR) promise to drastically enhance effective 511 keV photon sensitivity. The ability to more precisely localize annihilation origins along system response lines constrains event data, providing improved signal-to- noise ratio (SNR) and reconstructed image quality by associating 511 keV photons more closely to their true origin. This SNR enhancement increases as CTR is improved, and a major goal of ongoing PET instrumentation research and development is to push system CTR ≤100 ps full-width-at-half-maximum (FWHM). At this level of performance, events are constrained ≤1.5 cm, providing more than a five-fold increase in SNR relative to a system with no TOF capability. Advanced systems capable of ≤100 ps FWHM CTR would more than double or quadruple the effective 511 keV system sensitivity, in comparison to state-of-the-art, clinical TOF-PET systems (250-400 ps FWHM CTR). Thus, advancing CTR is also a pathway for greatly improved system sensitivity without increasing detection volume and associated costs. This level of timing performance can be achieved with state- of-the-art (SoA) electronic readout for silicon photomultiplier (SiPM)-based scintillation detectors in single pixel, bench top coincidence measurements. Readout capable of demonstrating experimental limits in achievable CTR leverage low noise, high frequency signal processing to facilitate a single photon time response that is near the limit of the SiPMs architecture. This readout strategy can optimally exploit fast luminescence and prompt optical photon populations, and promising measurements show detector concepts employing this readout can greatly advance TOF-PET detector CTR, relative to SoA in clinical systems. However, the technique employs power hungry components which make the electronics chain impractical for channel-dense TOF-PET detectors and systems. If compact, tractable readout topologies that achieve this performance can be created, they offer a platform for the development and translation of novel detector concepts to push system CTR ≤100 ps. We propose to design and experimentally evaluate an analog, multichannel application specific integrated circuit (ASIC) that implements SoA front end signal processing and time pickoff methods into a compact form factor, capable of bringing SoA CTR demonstrated for new PET detector concepts into systems. Thus, when coupled with existing high resolution, multichannel time-to-digital converters (TDCs), this new development thereby offers a direct pathway to realize greatly advanced CTR in large scale, clinical PET imagers.

NIH Research Projects · FY 2026 · 2024-03

Project Summary / Abstract Under present R01 funding, the Sauter group has focused on the use of serial crystallography to discover the structure-function relationships of large biomolecules. While specializing in technology related to data analysis, we have enabled new science at X-ray free-electron laser (XFEL) lightsources, where the X-ray pulse structure permits time resolution down to picoseconds, at normal physiological temperature where the full range of available molecular conformations can be revealed, and with essentially no radiation damage, ideal for studying metalloenzymes in their functional redox states. Our software CCTBX.XFEL gives a workflow to process big data up to 100 Terabytes on supercomputing platforms, while applying algorithms that are customized for the “still shots” of serial crystallography, distinct from the rotating crystal geometry used at synchrotron sources. Considering the enormous cost of XFEL crystallography in terms of labor and competitive beamtime allocation, the 10 minute turnaround time of our pipeline affords a significant mitigation of the risks, allowing the experimental team to adjust data collection parameters and reprioritize samples. We have collaborated on the development of new instrumentation to observe enzymatic reaction progress triggered by laser pump, mix-and-inject, or gas incubation. We’ve implemented new serial crystallography modalities such as X-ray emission spectroscopy to monitor catalytic metal sites, and chemical crystallography to determine small molecule structures. Under the proposed R35, the goal is to gain greater detail in the molecular model, in comparison to present results. There is potential for improvement because today’s algorithms still inherit assumptions from traditional crystallography (such as monochromatic beam), while the plan is to introduce a new Bayesian inference model, accounting for every detail of the diffraction pattern down to the pixel level. We will also develop the related capability of using the protein crystal as an X-ray spectrometer, thus revealing detail about the electronic environment of catalytic metals (using X-rays tuned to the metal absorption edge), a measurement that has not yet been achieved at ambient temperatures or in the time domain. Our goals also include the observation of diffuse scatter (diffraction intensity between the lattice of Bragg spots), which reflects correlated motions within and between protein molecules, and further to probe macromolecule flexibility by infrared beam temperature-jump experiments. Finally, we hope to explore new computational directions for high-throughput interpretation of cryo-electron tomography (cryoET) data. The unifying theme between serial crystallography and cryoET is the desire to learn the biological role of structural variability. The X-ray data processing improvements will allow us to model the small changes that contribute to a reaction mechanism, e.g., an amino acid sidechain rotation, a change in water occupancy, or even the displacement of a single electron. High-throughput cryoET will sample the variability and heterogeneity of cellular structures, giving a spatiotemporal understanding of living systems and how they respond to genes, regulation and environment.

NIH Research Projects · FY 2025 · 2023-09

SUMMARY Structural birth defects (SBDs) encompass a spectrum of congenital abnormalities affecting a wide range of human organ systems. Progress in sequencing technologies has enabled significant advances in the discovery of coding mutations underlying SBDs through whole-exome sequencing. Nonetheless, to date most cases continue to remain “unsolved”, creating a major barrier to diagnostic interpretation and therapeutic development. In particular, the identification and interpretation of mutations in noncoding sequence, which constitutes 98% of the human genome, has presented a formidable challenge. The present proposal addresses the hypothesis that noncoding sequence represents a major reservoir of causal mutations explaining many unsolved SBD cases. Specifically, we will focus on distant-acting transcriptional enhancers, a predominant class of noncoding genome elements with critical regulatory functions in embryonic development. There are isolated examples of SBD- causing enhancer mutations, but three principal hurdles have prevented their identification at scale: a) the lack of whole genome sequence data (WGS) from unsolved cases; b) inadequate annotations of noncoding genome functions; c) the lack of testing pipelines to assess the in vivo relevance of enhancer mutations and determine their causality. In this proposal, we address these challenges by creating an integrated pipeline for the identification, function-based prioritization, and in vivo validation of causality of enhancer mutations in SBD cases. This proposal will take advantage of growing aggregated WGS data, advanced analysis pipelines for mutation identification, a unique catalog of prioritized predictions of developmental in vivo enhancers, and advanced mouse engineering capabilities for in vivo validation of enhancers and enhancer mutations. Our specific aims include: 1) Prioritize de novo noncoding gene regulatory mutations identified in growing WGS catalogs in SBD patients. Taking advantage of preexisting aggregated WGS genetic data and innovative analysis strategies, we will identify noncoding mutations in SBD at unprecedented scale. Noncoding findings will be interpreted and prioritized using DevCisReg, a comprehensive catalog of gene regulatory sequences we developed from analysis of >800 human and mouse epigenomic data sets. 2) Functionally test prioritized SBD noncoding mutations for impacts on gene expression in scaled transgenic mouse enhancer assays. We will use a targeted CRISPR-enabled transgenic approach to characterize 200 candidate enhancer alleles in mice and determine which mutations impact on gene expression in vivo. 3) Functionally model prioritized SBD noncoding mutations in knockin mice. We will create and phenotype 40 knockin mouse lines with human alleles to test the in vivo impact of regulatory mutations in live animals. We will focus on mutations from SBDs that can be modeled and studied by streamlined phenotyping in mice to increase the likelihood we can detect a defect in vivo. Together, these efforts will create an integrated mutation-to-phenotype identification and testing pipeline that will provide conclusive in vivo evidence for establishing the causality of enhancer mutations in SBD.

NIH Research Projects · FY 2025 · 2023-09

ABSTRACT The ultimate goal of structural biology is to visualize biomolecules in action in their native environment and to establish their structure-function relationship. Cellular cryo-electron and X-ray tomography have emerged as powerful techniques for imaging complex biological samples such as intact cells, organelles, macromolecular machines, and for quantifying the internal organization of biological objects in their native states in situ at resolutions ranging from a few microns to tens of nanometers with X-rays to tens of angstroms with electrons. However, compared to the mature techniques of X-ray crystallography and single-particle cryo-electron microscopy, cellular tomography is yet to reach its potential due to a severe degradation in the resolution of reconstructions because of the effects of mechanical misalignment and non-rigid sample deformation due to radiation damage, missing-wedge artifacts, low signal-noise ratio in a crowded environment, and unresolved conformational heterogeneity. To address these issues, we propose to leverage our new approach for automated joint 3D alignment and regularized reconstruction that combines advances in iterative projection methods and convex optimization to achieve better than state-of-the-art reconstruction resolution from severely misaligned data. Infusing our framework with new advances in mathematical modeling and machine learning provides a clear path to a host of new model-based and data-driven algorithms that could address current challenges and bottlenecks in the analysis of cellular tomography data. In particular we propose to (1) develop techniques for improved tilt-series alignment that account for rigid-body motion of the sample and recover the anisotropic effects of radiation-induced warping by using optical flow alignment; (2) develop a decoder that leverages the full frequency information contained in randomly oriented macromolecules in the cell volume to constrain the effects of the missing-wedge; and (3) improve the resolution of subtomograms extracted from the refined volume by developing a volume-encoder--deformation-decoder deep neural network to model conformational heterogeneity. By developing new data-driven methods that constrain the missing-wedge information and treat shape variability as a continuum of non-rigid deformations rather than discrete clusters, our algorithmic framework will provide significant improvements in the resolution and quality of reconstructions over currently existing methods for data analysis that neglect these effects. As the structural biology community is increasingly focusing on cellular tomography, there is a growing need for easy to use, automated software amenable to both experienced and novice users. (4) To this end, algorithms resulting from this proposal will be turned into GPU- enabled open-source user-friendly software to accelerate the analysis of the growing pool of imaging data. Ultimately, our algorithmic framework will be capable of yielding high-resolution structures from noisy, incomplete and complex data, thereby enhancing the predictive power of cellular tomography to answer important biological questions.

NIH Research Projects · FY 2026 · 2023-05

Project Summary/Abstract Metalloproteins containing Mn and Fe in a redox-active role are involved in a variety of physiologically important reactions of dioxygen metabolism and activation. Perhaps the most complex is the Mn4CaO5 cluster that is involved in the oxidation of water to dioxygen in photosystem II (PS II), a multi-subunit membrane protein complex. The water-oxidation reaction in PS II involves removal of four electrons from two water molecules, in a stepwise manner by light-induced oxidation, to produce a molecule of oxygen. PS II and the Mn4CaO5 cluster generate almost all of the dioxygen that supports aerobic life, and it is abundant in the atmosphere because of its constant regeneration by the oxidation of water. The light-induced oxidation of water to dioxygen is one of the most important chemical processes occurring on such a large scale in the biosphere. Although the structure of PS II and the chemistry at the catalytic site have been studied intensively, understanding the sequence in the chemistry at atomic-scale from light absorption to water-oxidation requires a new approach beyond the conventional steady state X-ray crystallography and X-ray spectroscopy at cryogenic temperatures. Following the dynamic changes in the structure of PS II and the Mn4CaO5 cluster at ambient conditions at physiological temperatures, while overcoming the severe X-ray damage to the redox active center is key for deriving the mechanism. The very intense, ultra-short femtosecond (fs) X-ray pulses from a X-ray free electron laser (XFEL) provide an opportunity to overcome the current limitations in room temperature data collection for biological samples at traditional X-ray sources. The fs X-ray pulses allow us to acquire the signal before the sample is destroyed, thus making the light-induced snapshot study possible. The objective of this proposal is to study the protein structure and dynamics of PS II with X-ray diffraction, as well as the chemical structure and changes in the Mn4CaO5 cluster (charge and spin density, and covalency) with X-ray spectroscopy during the light-driven process of PS II. We will use the XFEL facilities at Stanford and elsewhere to collect X-ray diffraction and emission spectra simultaneously, and X-ray absorption spectra of the Mn cluster in its native and intermediates states at room temperature in a time-resolved manner, to capture short-lived intermediates and the step that includes the O-O bond formation. We have also started studying the chemistry of other Mn/Fe/Ni containing metalloenzymes of importance such as methane monooxgygenase, ribonucleotide reductase, isopenicillin N synthase and other related enzymes. These studies have the potential to provide an unprecedented combination of correlated data between the proteins and the metal co-factors, providing the geometric and electronic structure and the changes that occur during the catalytic cycle, all of which are necessary for a complete understanding of the mechanism of the enzymatic reactions.

NIH Research Projects · FY 2025 · 2022-09

Project Summary Clinical time-of-flight positron emission tomography (TOF-PET) systems capable of excellent coincidence time resolution (CTR) promise to drastically enhance effective 511 keV photon sensitivity. The ability to more precisely localize annihilation origins along system response lines constrains event data, providing improved signal-to- noise ratio (SNR) and reconstructed image quality by associating 511 keV photons more closely to their true origin. This SNR enhancement increases as CTR is improved, and a major goal of ongoing PET instrumentation research and development is to push system CTR ≤100 ps full-width-at-half-maximum (FWHM). At this level of performance, events are constrained ≤1.5 cm, providing more than a five-fold increase in SNR relative to a system with no TOF capability. Advanced systems capable of ≤100 ps FWHM CTR would effectively more than double or quadruple the effective 511 keV system sensitivity, in comparison to state-of-the-art, clinical TOF-PET systems (250-400 ps FWHM CTR). Thus, advancing CTR is also a pathway for greatly improved system sensitivity without increasing detection volume and system cost. Standard PET detectors comprising segmented arrays of high-aspect-ratio scintillation crystal elements cannot achieve this level of performance and are ultimately limited by poor light collection efficiency and depth-dependent scintillation photon transit time jitter seen by the photodetector. To address this, we propose to develop a new detector readout concept which allows scintillation photons to be counted and a unique timestamp to be assigned for the first arriving photon at each photosensor pixel. We will leverage this new advancement in scalable PET detector readout and produce PET detector modules capable of high resolution, three-dimensional positioning capabilities and 100 ps FWHM CTR in a design that also makes no sacrifices on 511 keV photon detection efficiency. The new detector design will be integrated into large area detector modules that span the full axial extent (>20 cm) of a clinical PET system, including front-end signal and back-end data processing. We will construct a prototype tomographic imaging setup and quantify relevant system performance metrics and the imaging performance of future clinical systems made from this new detector. The proposed PET detector technologies can have a significant impact on quantitative PET imaging. The image SNR enabled by the significant boost in effective sensitivity can be employed to substantially reduce tracer dose and shorten scan time/increase patient throughput, or to better visualize and quantify smaller lesions/features in the presence of significant background, which are important features that can make PET more practical and accurate, as well as help to expand its roles in patient management.

NIH Research Projects · FY 2025 · 2022-09

Project Summary (Abstract) We propose to build a novel gamma imaging device based on the combination of Compton and proximity reconstructions in order to achieve unprecedented sensitivities that will enable in vivo imaging of biodistributions of 225Ac, a promising Targeted Alpha Therapy (TAT) isotope. TAT has demonstrated a remarkable efficacy and specificity for cancer radiotherapy. This is due to the high linear energy transfer and the short free path of alpha particles that result in a higher and more localized energy deposition than that of beta particles. 225Ac is a very promising alpha-emitter that has successfully shown excellent results on the treatment of a number of malignancies, namely, metastatic castration-resistant prostate cancer, pancreatic cancer and acute myeloid leukemia. A key aspect of TAT is the targeting radiopharmaceutical that transports the 225Ac to the carcinogenic cells, preventing free isotopes from delivering a highly toxic radioactive dose to healthy tissue. However, development of novel radiopharmaceuticals is currently limited by the inability of commercial imaging systems to detect 225Ac in vivo. As a result, their pharmacokinetics cannot be fully understood in clinical applications, delaying their FDA approval and hindering the wide adoption of TAT. 225Ac and its daughters can be imaged through the detection of the gamma rays emitted in their decay chain, but the main challenge of this technique (and the reason why current gamma ray imaging systems are not suitable for this task) is that the gamma ray emission activity is extremely low due to the very small doses injected in human patients (0.1MBq/kg) and in preclinical studies (1MBq/kg in mice) to prevent a morbid toxicity. In this scenario, an apparatus with a high gamma ray detection sensitivity is necessary in order to provide images with exposures no longer than a few minutes. We plan to achieve this unprecedented sensitivity by designing a dedicated gamma camera that integrates Compton and proximity imaging in a multi-modality system. These techniques have been successfully in medical imaging applications, but they have never been combined in the same device in order to improve sensitivity and image quality at the same time. To achieve this goal, we propose to quantitatively image Ac-225 in vivo the first time with a Cadmium Zinc Telluride dual-head camera that enables both Compton and proximity imaging. To reach this goal we plan to 1) assemble Compton and proximity gamma camera; 2) develop a multi- modality reconstruction algorithm for Compton and proximity imaging; 3) demonstrate in vivo imaging of 225Ac with the final prototype and perform first in vivo pharmacokinetics study of two 225Ac radiopharmaceuticals, providing a proof of principle in pre-clinical conditions using phantoms and mice. The outcome from this project will be a prototype gamma camera able to image distributions of 225Ac TAT radiopharmaceuticals in-vivo (and potentially other TAT isotopes), and thus, enabling the complete study of their pharmacokinetics to accelerate their development. With our system, we expect to increase the understanding and confidence in TAT.

NIH Research Projects · FY 2025 · 2022-01

PROJECT SUMMARY Congenital heart disease (CHD) is a group of severe birth defects that collectively represent the leading cause of birth defect-associated illness and death. Despite the extensive use of clinical genetic testing and whole exome sequencing (WES), less than a third of CHD cases can currently be accounted for by mutations in protein-coding genes. Many of the remaining, currently unexplained cases are assumed to be due to non-coding sequence variants that alter the expression of genes essential for cardiac development. To uncover non-coding variants in CHD patients, the National Heart, Lung, and Blood Institute's Bench to Bassinet (B2B) and TopMed programs are using whole genome sequencing (WGS) on large CHD patient cohorts, principally for probands whose prior WES failed to uncover a likely causative coding variant. WGS of 1,831 patient-parent trios from the B2B cohort is currently available, with several hundred additional trios currently being sequenced. Initial analyses of ~750 probands have already identified over 2,000 de novo variants in predicted fetal human heart enhancers, along with a statistically significant excess of genetic loci (27 genes versus 3.7 expected, p=1x10-5) at which the neighboring human fetal heart enhancers showed multiple de novo variants in cases. This suggests that CHD risk is conferred through dysregulation of the respective target genes of these enhancers. However, the causality of these variants in CHD, as well as the molecular underpinnings of their potential pathogenicity, remain to be demonstrated. Building on our extensive previous work in mapping and characterizing cardiac enhancers at scale, we propose to perform systematic in vivo functional validation of de novo sequence variants from CHD patients that reside in predicted heart enhancers to reveal enhancer mutations that contribute to the etiology of CHD. We will 1) use a combination of comprehensive maps of predicted human heart enhancers, genetic and epigenomic analysis tools, and massively parallel reporter assays in cardiomyocytes differentiated from induced pluripotent stem cells (iPSC-CMs) to identify and prioritize cardiac enhancers harboring de novo variants from CHD patients, 2) use our world-class mouse transgenesis pipeline in combination with novel single-cell characterization methods to test the reference and variant alleles of 200 prioritized enhancers (400 alleles in total) at appropriate stages of cardiac development to assess how the risk alleles alter enhancer function in vivo at cellular resolution, 3) use CRISPR/Cas9 genome engineering to generate 20 knock-in mouse models for human CHD variant alleles that alter enhancer activity and matched human reference alleles to assess their impact on the structure and function of the heart using a combination of single-cell transcriptomics and cardiac phenotyping. Successful completion of the proposed studies will provide foundational insights into the role of non-coding regulatory sequences in the most common severe human birth defect, identify specific examples of human enhancer variants conclusively implicated in disease, and provide initial mechanistic insights into their respective mode of action to provide new avenues for exploring future therapeutics.

NIH Research Projects · FY 2025 · 2021-05

Phenix: providing high quality software to the research community for crystallography and cryo-EM Knowledge of the structure of macromolecules, i.e. how the atoms are arranged in space and also how they change shape over time or in response to external factors, is necessary to understand their function. This information is also routinely exploited to aid in the development of new therapeutics. Crystallography and electron cryo-microscopy (cryo-EM) are powerful methods for determining three- dimensional macromolecular structures. Of the 160,000+ 3D structures available at the Protein Data Bank more than 150,000 have been arrived at using either crystallographic or cryo-EM methods. In both cases, the analysis of the experimental data is a computationally complex problem that relies on sophisticated software. Phenix is a software suite that uses reduced data from X-ray diffraction, electron diffraction, neutron diffraction or cryo-EM 3D reconstructions to determine macromolecular structures and has become widely used over the past 15 years. The Phenix software and associated activities such as outreach and training have become a national and international resource. Based on an analysis of publications, patents, and wwPDB depositions we observe that Phenix resource is of high impact and this is growing, as measured by increasing researcher demand and use. Phenix also plays a very significant role in supporting NIH funded research within the US. Therefore, we formally establish the Phenix Resource, which will: a) support the continued maintenance and optimization the code base to ensure that it keeps pace with current computing paradigms and scientific data standards, b) improve program usability and integration with other community software resources, c) maintain the software and hardware infrastructure for broad, efficient dissemination of the resource, and d) undertake outreach, training and user support to help grow the community of trained researchers who can make best use of the Phenix software.

NIH Research Projects · FY 2025 · 2021-04

PROJECT SUMMARY Lack of standards for neurophysiology data and related metadata is the single greatest impediment to fully extracting return on investment from neurophysiology experiments. One of the greatest questions in science today is understanding how the brain works and gives rise to thoughts, memories, perception, and consciousness. To address this challenge, neurophysiologists within the NIH BRAIN initiative and around the world perform experiments that measure neuronal activity from different parts of the brain and relate that activity to sensation and behavior. A key component of the BRAIN Initiative is to support sharing of these rich datasets and extend their value by enabling reuse of data within and across labs. The goal of this proposal is to disseminate Neurodata Without Borders (NWB) neurophysiology technologies developed as part of the NIH BRAIN Initiative broadly to the neuroscience research community. NWB is an award-winning, community-driven data standard and software ecosystem for neurophysiology. To facilitate data sharing and reuse, the NWB format standardizes how neurophysiology data and associated metadata are stored, and the NWB software enables researchers to access and save data in the NWB format easily and efficiently. Several leading neuroscience labs and institutions now produce data in NWB; however, a substantial energy barrier remains for labs to standardize their data. To lower the barrier of adopting NWB, we propose a multifaceted plan to make NWB easier to use by focusing on the needs of 1) neuroscience researchers and laboratories by enhancing user training, support, and coverage of new technologies and 2) neuroscience tools and technologies by maintaining core NWB technologies and integrating with a wide array of powerful data tools and technologies. With NWB data as a target, scientists can access, manage, and share data using common protocols, while developers have a common format on top of which to build tools. By targeting these two areas simultaneously, we aim to reduce cost, time, and effort for analysis; improve quality, reliability, and accuracy of results; and enable scientists to access new scientific capabilities. Successful completion of the proposed work will enable broad dissemination of NWB to neuroscience labs and researchers and integration of NWB with neuroscience tools, providing the research community with an accessible data standard and software ecosystem that enhances utilization, sharing, quality, reliability, and analysis of neurophysiology data.

NIH Research Projects · FY 2025 · 2018-01

Abstract The overarching goal in this project, both in the funded award and in this renewal, is to advance the structural biology method of X-ray footprinting mass spectrometry (XFMS) in capability and accessibility such that it becomes a premiere biophysical tool for biomedical investigators around the country. XFMS is a solution state method used to map solvent accessible regions in macromolecules on a timescale of microseconds, yielding information on conformation, protein-protein dynamics, and bound water location and dynamics. It has been used to obtain useful structural information on a diverse range biological systems, from small proteins to large complexes, as well as membrane proteins, and for mapping interaction regions in antibody-target complexes. As part of the original award, we made substantial progress towards our main goal by developing a unique high-throughput and automated XFMS instrument, enabling use of the method to researchers nationwide. In this proposed renewal, we plan to build on this success to implement new capabilities in keeping with the original goal of the grant. Specifically, we plan to integrate fluorescence and Raman spectroscopies, fast mixing with jet delivery capability, and size exclusion directly inline with the XFMS instrument. The integration of these technologies into the XFMS instrument will enable even more challenging biological systems to be studied using the method. While the new specific aims are ambitious, they build naturally from our proven track record in developing complex instrumentation and the successful research team we built during the first grant period. Proof of principle for these technologies is presented, along with preliminary data, and the proposal outlines the significant technical challenges involved and how they will be overcome. The resulting technologies will be a significant gain to the biomedical research community and will be used to meet the increasing demand for access to the XFMS method.

NIH Research Projects · FY 2025 · 2017-09

Project Summary/Abstract One of the new frontiers in structural enzymology is the expansion from a three-dimensional to a truly four- dimensional approach by adding the time dimension to structural studies. While Synchrotron Radiation (SR) crystallography and cryo Electron Microscopy allow the determination of structures in minute detail they are in most cases performed on frozen static samples. With the advent of X-ray free electron lasers (XFELs) like the Linac Coherent Light Source (LCLS) at Stanford, and the development of the “probe before destroy” concept it now is possible to follow structural changes in enzymes in real time and under close to physiological conditions at room temperature (RT). Driven by the success of XFELs and recent advances in detector technology and storage ring and beam line design, several SR sources are also starting to offer time resolved crystallography at RT. These unprecedented capabilities will open new fields of research, not only in biomedical sciences but also in many other areas. Due to the “probe before destroy approach” utilized here, the samples generally need to be replaced after a single X-ray exposure. As biological samples of interest are often only available in scarce amounts, it is mandatory to develop a robust method to introduce the sample into the X-ray interaction region in a continuous manner that minimizes the required sample amount. In order to obtain true “molecular movies” of enzymes of biomedical importance in action, which will contribute to a deeper mechanistic understanding of these molecular machines, it is essential to synchronize the enzyme in the probed sample volume and initiate the reaction of interest in a temporally well-defined manner. Methods for reaction initiation can include mixing with a substrate/chemical compound, or utilize other stimuli such as light, temperature jump, or change in pH or electrical potential. In the frame of this proposal, we will continue the development of robust and versatile sample delivery and reaction triggering methods. We will also integrate multi-modal detection methods, combining X-ray diffraction with complementary in situ spectroscopic techniques to probe both global structures and chemical properties of enzymes concurrently. We will focus on the development of drop-on-demand methods based on acoustic transducer technology, but also explore other droplet dispensing technologies and microfluidics to substantially diminish/eliminate any sample wastage. We will improve the previously developed prototypes for depositing the drops on a moving support, such as a tape or wheel, that can circulate and is self-cleaning, for non-stop continuous operation at the XFEL or SR facility. Several methods for enzyme-substrate mixing will be tested, with emphasis on liquid-gas and liquid-liquid mixing, including with micron size droplet collision methods to achieve faster time resolution. Experiments on well-defined enzyme model systems will be accommodated by modeling approaches to design chemical mixing experiments and use feedback from measurements to optimize the design. These will be implemented at SR and XFEL beam lines and made available for the broader structural enzymology user community.

NIH Research Projects · FY 2024 · 2017-09

Cryo-electron microscopy (cryo-EM) has already had a revolutionary impact on cell and molecular biology and become a major source of structural information. Still, the minimum number of parti- cles needed for a three-dimensional reconstruction of a structure, and the minimum size of the particles amenable to reconstruction, remains far above fundamental limits. Over the past four years, we have developed a laser-based phase-plate (LPP) that can contribute to reaching the standard quantum (shot-noise) limit of imaging in cryo-EM. We have tested it on the optical bench, demonstrated phase-contrast imaging, and exceeded all performance parameters that we set out to achieve. Now, at the beginning of the fourth year, we have already made first steps to- wards obtaining a density map of a known structure with the LPP; we fully expect to complete this goal by the end of the fourth year. In this renewal proposal, we aim to achieve an even higher level of performance, one that will add significant value for many classes of problems in structural biology, and that will be well-received by the entire cryo-EM community as a basis for a user-friendly, commercially available product. To do this, we will partially automate data collection by creating new, data-driven feedback tools to maintain alignment of the LPP to the electron diffraction pattern. Upgrading the mechanical and optical design of the LPP will allow us to maintain stable coma-free alignment of the microscope. This upgrade will leverage the relativistic reversal effect, which we recently demonstrated, to elim- inate weak ghost images. In addition, to compensate for the larger chromatic aberration of our microscope in phase-plate mode, we will install a gun monochromator. Using the LPP is expected to enable reconstructions for particles at the lower size limit of what is believed to be theoretically possible for cryo-EM. We expect this to also improve the power of 3D- classification to assign much larger particles into distinctly different conformational and composi- tional states. Throughout the project, we will establish the extent to which the LPP improves cryo- EM capabilities by performing reconstructions of a wide variety of biological specimens. We will determine the number of asymmetric units needed to produce high-resolution density maps, at equivalent values of the resolution, as well as the size of the smallest particles that can be recon- structed. As we advance the LPP, we will use more and more challenging test specimens, from apoferritin and a human, microtubule-associated protein to extremely small proteins, such as my- oglobin or lysozyme.

NIH Research Projects · FY 2025 · 2017-09

Overall Summary/Abstract The overall goal of this proposal is to continue to provide an integrated, efficient synchrotron structural biology Resource to the research community. This Resource, called ALS-ENABLE, is located at the Advanced Light Source (ALS) in Berkeley, California. The team has two decades of experience operating macromolecular X-ray crystallography (MX) and small angle X-ray scattering (SAXS) beamlines, and more recently, an X-ray footprinting mass spectrometry (XFMS) beamline. The team has worked closely together over the last 4 years to create the ALS-ENABLE Resource, and many of the team members are cross-trained in the three X-ray structural biology methods. During the 4 years we have implemented a transparent interface to the ALS structural biology resources, and helped users pursue successful structure determination for both routine and challenging problems. We have worked with a diverse user community, ranging from experts to new synchrotron users with limited training in structural biology techniques. Where necessary we have guided users through the most appropriate routes for answering their biological question. In this renewal application we propose to make several changes to the Resource in response to recent changes in the field of structural biology, leverage a new high- performance beamline (GEMINI), and incorporate the now mature synchrotron technique of X-ray footprinting (XFMS).

NIH Research Projects · FY 2026 · 2014-09

Project Summary/Abstract The goal of this proposal is to understand nature’s well-controlled chemistry that occurs in metalloenzymes, by developing the necessary tools and applying them to follow the structural dynamics of the protein and chemical dynamics of the metal-catalyst. For this purpose, we will use X-ray crystallography and X-ray spectroscopy techniques at X-ray Free Electron Lasers (XFELs). Although the structure of enzymes and the chemistry at the catalytic sites have been studied intensively, an understanding of the atomic-scale chemistry requires a new approach beyond the conventional steady state X-ray crystallography and X-ray spectroscopy at cryogenic temperatures. Following the dynamic changes in the geometric and electronic structure of metalloenzymes at ambient temperature, while overcoming the severe X-ray damage to the redox active catalytic center, is key for deriving the reaction mechanism. The intense and ultra-short femtosecond (fs) X-ray pulses from XFELs provide an opportunity to overcome the current limitations of room temperature data collection for biological samples at synchrotron X- ray sources. The fs X-ray pulses with shot-by-shot sample replacement make it possible to acquire the signal before damage occurs and the sample is destroyed. We will design and apply a suite of time-resolved X-ray diffraction and X-ray absorption/emission spectroscopy methods, that make use of the unique properties of the XFEL beam. This will provide an unprecedented combination of correlated data between the protein and the co-factors, all of which are necessary to understand the interplay between the geometric structure of the protein and electronic structure of the metal complex, and the functional consequences. Spectroscopy, both emission and absorption spectroscopy, will provide the time-evolution of the electronic structure, while simultaneous room temperature X-ray crystallography will help us visualize the changes in the geometric structure of the overall protein. We will use these methodologies to study some of the most important metalloenzymes in biology to gain insights into the catalytic mechanisms, including mono, and dinuclear systems both with homo- and hetero-metallic centers. A representative example of these systems are Fe enzymes for oxygen and C-H bond activation where the involvement of high-valent Fe are proposed, such as the heme containing Cyt P450 systems, non-heme enzymes ribonucleotide reductase (Mn/Fe and Fe/Fe), and methane mono oxygenase (Fe/Fe). We will also focus on Ni and Ni/Fe enzymes relevant to H2 generation and methane metabolism, and functional analogs of the important class of heme-copper oxidase systems engineered into simpler proteins.