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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Applications of visualization technology in the structural sciences

The structural sciences are undergoing a transformation driven by advancements in visualization technologies that aid researchers in understanding and communicating experimental data from complex molecular systems. New applications of integrative structural biological and biophysical approaches add a wide variety of complementary information from a broad range of scientific disciplines. These approaches extend structural biophysical methodologies to enable research by the incorporation of a variety of data streams and utilization of tools like molecular graphics, virtual reality, and machine learning. To redefine how structural data—particularly from cryo-electron microscopy and x-ray crystallography—are fed forward for scientific exploration and communication, the advances in tools for data visualization and interpretation have been critical. By bringing molecular systems into an interactive three-dimensional space, these novel technologies enhance research workflows, facilitate structure-based drug design, and create engaging educational experiences. Taken together, these visualization innovations are essential tools for advancing the field by making concepts more accessible and compelling.

Eng, Edward T. [New York Structural Biology Center↗
DOE OSTI2025

California annual grass phenology and allometry influence ecosystem dynamics and fire regime in a vegetation demography model

Grass-dominated ecosystems cover wide areas of the land surface yet have received far less attention from the Earth System Model (ESM) community. This limits model projections of ecosystem dynamics in response to global change and coupled vegetation–climate dynamics. We used the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), a dynamic vegetation demography model, to determine ecosystem sensitivity to alternate, observed grass allometries and biophysical traits, and evaluated model performance in capturing California C 3 annual grasslands structure and fire regimes. Grass allometry, leaf physiology, plant phenology, and plant mortality all drove the seasonal variation in matter and energy exchange and fire dynamics in California annual grasslands. Allometry influenced grassland structure and function mainly through canopy architecture-mediated space and light competition instead of through carbon partitioning strategy. Regional variation in grassland annual burned area was driven by variation in ecosystem productivity. Our study advances the modeling of grassy ecosystems in ESMs by establishing the importance of grass allometry and plant phenology and mortality in driving C 3 annual grassland seasonal dynamics and fire regime. The calibrated annual grass allometry and biophysical traits presented can be applied in future studies to project climate–vegetation–fire feedbacks in annual grass-dominant ecosystems under global change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Roadmap for the future of extreme wildfire events

Background Extreme wildfire events (EWEs) represent a growing threat globally, posing substantial risks to ecosystems, human communities, and infrastructure. Despite increased recognition of their ecological, social, and economic significance, current definitions of EWEs vary widely, reflecting disciplinary biases and regional contexts. This article emerges from an interdisciplinary workshop convened to reassess and refine the definition of EWEs, examine their impacts across ecological and social dimensions, and identify critical knowledge gaps impeding our understanding of these infrequent but important events. Results Our synthesis highlights significant limitations with existing definitions, particularly their reliance on subjective thresholds and their emphasis on extreme fire behavior alone. EWEs encompass a spectrum of complex, multi-dimensional phenomena that extend beyond immediate biophysical characteristics to include cumulative social, economic, and ecological impacts. These impacts often manifest over extended timeframes and include hazardous environmental contamination, severe geomorphic disturbances, ecosystem transformations, and unintended consequences of post-fire management actions. Current wildfire modeling frameworks inadequately capture these compounding factors, particularly the interactions among social systems, ecological conditions, and extreme fire behavior. To overcome these issues, we advocate for an interdisciplinary and context-sensitive approach to defining and studying EWEs. This revised definition emphasizes wildfires exhibiting anomalies in fire behavior, ecological outcomes, or social impacts relative to historically observed baselines, accommodating variability across different geographic regions and ecological settings. Conclusions Adopting an interdisciplinary framework that integrates biophysical and social sciences will enhance the predictive capability of wildfire models and improve resilience planning and response strategies. Filling identified knowledge gaps—such as limited high-quality empirical fire behavior data and insufficient integration of social dynamics into modeling—will better prepare communities and ecosystems to cope with and adapt to EWEs. This inclusive approach underscores the necessity for collaboration across disciplines and sectors, essential to managing extreme wildfires in an era of increasing climatic and ecological uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Characterization of Gramicidin A in Triblock and Diblock Polymersomes and Hybrid Vesicles via Continuous Wave Electron Paramagnetic Resonance Spectroscopy

Studying membrane proteins in a native environment is crucial to understanding their structural and/or functional studies. Often, widely accepted mimetic systems have limitations that prevent the study of some membrane proteins. Micelles, bicelles, and liposomes are common biomimetic systems but have problems with membrane compatibility, limited lipid composition, and heterogeneity. To overcome these limitations, polymersomes and hybrid vesicles have become popular alternatives. Polymersomes form from amphiphilic triblock or diblock copolymers and are considered more robust than liposomes. Hybrid vesicles are a combination of lipids and block copolymers that form vesicles composed of a mixture of the two. These hybrid vesicles are appealing because they have the native lipid environment of bilayers but also the stability and customizability of polymersomes. Gramicidin A was incorporated into these polymersomes and characterized using continuous wave electron paramagnetic resonance (CW-EPR) and transmission electron microscopy (TEM). EPR spectroscopy is a powerful biophysical technique used to study the structure and dynamic properties of membrane proteins in their native environment. Spectroscopic studies of gramicidin A have been limited to liposomes; in this study, the membrane peptide is studied in both polymersomes and hybrid vesicles using CW-EPR spectroscopy. Lineshape analysis of spin-labeled gramicidin A revealed linewidth broadening, suggesting that the thicker polymersome membranes restrict the motion of the spin label more when compared to liposome membranes. Statement of Significance: Understanding membrane proteins’ structures and functions is critical in the study of many diseases. In order to study them in a native environment, membrane mimetics must be developed that can be suitable for obtaining superior biophysical data quality to characterize structural dynamics while maintaining their native functions and structures. Many currently widely accepted methods have limitations, such as a loss of native structure and function, heterogeneous vesicle formation, restricted lipid types for the vesicle formation for many proteins, and experimental artifacts, which leaves rooms for the development of new biomembrane mimetics. The triblock and diblock polymersomes and hybrid versicles utilized in this study may overcome these limitations and provide the stability and customizability of polymersomes, keeping the biocompatibility and functionality of liposomes for EPR studies of membrane proteins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Detection of non‐native species formed during fibrillization of the myocilin olfactomedin domain

Abstract Glaucoma is a group of neurodegenerative diseases that together are the leading cause of irreversible blindness worldwide. Myocilin‐associated glaucoma is an inherited form of this disease, caused by intracellular aggregation of misfolded mutant myocilin. In vitro, the myocilin C‐terminal olfactomedin domain (OLF), the relevant domain for glaucoma pathogenesis, can be driven to form amyloid‐like fibrils under mild conditions. Here we characterize a species present during in vitro fibrillization. Purified OLF was subjected to fibrillization at concentrations required for downstream electron microscopy imaging and NMR spectroscopy. Additional biophysical techniques, including analytical ultracentrifugation and X‐ray crystallography, were employed to further characterize the multicomponent mixture. Negative stain transmission electron microscopy (TEM) shows a non‐native species reminiscent of known prefibrillar oligomers from other amyloid systems, NMR indicates a minor population of partially misfolded species is present in solution, and cryo‐EM imaging shows two‐dimensional protein arrays. The predominant soluble species remaining in solution after the fibril reaction is natively folded, as evidenced by X‐ray crystallography. In summary, after incubating OLF under fibrillization‐promoting conditions, there is a heterogeneous mixture consisting of soluble folded protein, mature amyloid‐like fibrils, and partially misfolded intermediate species that at present belie additional molecular detail. The characterization of OLF fibrillar species illustrates the challenges associated with developing a comprehensive understanding of the fibrillization process for large, non‐model amyloidogenic proteins.

Scelsi, Hailee F. [School of Chemistry and Biochem↗
DOE OSTI2025

Explainable machine learning to quantify the value of proximal remote sensing in latent energy flux estimation

Proximal remote sensing has the potential to provide critical information on vegetation biophysical factors that can predict land-atmosphere exchange of water and energy. Latent energy (LE) flux is traditionally estimated using process-based models which rely on vegetation parameters that change during the growing season. Data-driven models have the potential to address these issues by offering flexible predictor selection and more efficient utilization of the information in predictor sets. These models require careful choice of predictors to avoid redundancy and allow robust cross-validation. In this study we present a systematic and comprehensive evaluation of machine learning (ML) models to assess the capability of meteorological and proximal sensing data for predicting LE at a half-hourly temporal resolution across multiple growing seasons for an agricultural system. The results presented here demonstrate that a model using four environmental predictors in combination with two proximal sensing variables can capture 88 % of the variability in LE. ML models using only three predictors (one meteorological and two proximal remote sensing) captured 81 % of LE variability, offering the best trade-off between performance and complexity. An ML model utilizing only two predictors, one proximal remote sensing variable and downwelling radiation, captured 77 % of LE variability. These results demonstrate the power of proximal remote sensing and meteorological observations to estimate land-atmosphere water vapor exchange, providing a solution where more direct methods such as eddy covariance are not available and for evaluations of agronomic management and genotypic variations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Controlling matric potential in microfluidics to examine microbial dynamics in unsaturated porous media

The use of microfluidics for the study of soil microbial ecology is an emerging field. Most microfluidic studies of biological systems, however, have been performed under fully saturated conditions that are not representative of natural soil. Therefore, while microfluidics offer many unique capabilities that other methodologies cannot, they are not currently suited to address the effects of matric potential, an important variable defining the microbial moisture niche. Here, a methodology is presented that allows the user to control the aqueous conditions within microfluidic networks by manipulating matric potential using a hanging water column. The method relies on hydrophilic surface treatment of the microfluidic device using polyvinyl alcohol (PVA) and incorporating a bed of small pores at the network boundaries, which serve as a porous ceramic plate analogue (PPA). The method was validated on a simple capillary bundle and then on a more complex pore network. A water retention curve, exhibiting hysteresis, was generated for the pore network over a narrow matric potential range of 0 to – 5 kPa. Both the drainage and wetting curves were reproducible, as were the spatial configuration and the number of fragmented moisture niches in the pore network, particularly on the drainage curve. In contrast, the wetting curve exhibited greater variability in spatial configuration due to the “ink bottle effect,” where capillarity was interrupted by wider pore bodies. Ultimately, the methodology provides realistic pore-scale moisture conditions that can be easily manipulated and maintained, enabling new opportunities to explore soil biophysics and microbial biogeography in unsaturated porous media. As a brief example, images showing the localization of fluorescently tagged Pantoea sp. YR343 at −4.3 kPa are presented, highlighting bacterial distributions in water films and air-water interfaces.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Deconvolution of dynamic heterogeneity in protein structure

Heterogeneity is intrinsic to the dynamic process of a chemical reaction. As reactants are converted to products via intermediates, the nature and extent of heterogeneity vary temporally throughout the duration of the reaction and spatially across the molecular ensemble. The goal of many biophysical techniques, including crystallography and spectroscopy, is to establish a reaction trajectory that follows an experimentally provoked dynamic process. It is essential to properly analyze and resolve heterogeneity inevitably embedded in experimental datasets. We have developed a deconvolution technique based on singular value decomposition (SVD), which we have rigorously practiced in diverse research projects. In this review, we recapitulate the motivation and challenges in addressing the heterogeneity problem and lay out the mathematical foundation of our methodology that enables isolation of chemically sensible structural signals. We also present a few case studies to demonstrate the concept and outcome of the SVD-based deconvolution. Finally, we highlight a few recent studies with mechanistic insights made possible by heterogeneity deconvolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Functional role of myosin-binding protein H in thick filaments of developing vertebrate fast-twitch skeletal muscle

Myosin-binding protein H (MyBP-H) is a component of the vertebrate skeletal muscle sarcomere with sequence and domain homology to myosin-binding protein C (MyBP-C). Whereas skeletal muscle isoforms of MyBP-C (fMyBP-C, sMyBP-C) modulate muscle contractility via interactions with actin thin filaments and myosin motors within the muscle sarcomere “C-zone,” MyBP-H has no known function. This is in part due to MyBP-H having limited expression in adult fast-twitch muscle and no known involvement in muscle disease. Quantitative proteomics reported here reveal that MyBP-H is highly expressed in prenatal rat fast-twitch muscles and larval zebrafish, suggesting a conserved role in muscle development and prompting studies to define its function. We take advantage of the genetic control of the zebrafish model and a combination of structural, functional, and biophysical techniques to interrogate the role of MyBP-H. Transgenic, FLAG-tagged MyBP-H or fMyBP-C both localize to the C-zones in larval myofibers, whereas genetic depletion of endogenous MyBP-H or fMyBP-C leads to increased accumulation of the other, suggesting competition for C-zone binding sites. Does MyBP-H modulate contractility in the C-zone? Globular domains critical to MyBP-C’s modulatory functions are absent from MyBP-H, suggesting that MyBP-H may be functionally silent. However, our results suggest an active role. In vitro motility experiments indicate MyBP-H shares MyBP-C’s capacity as a molecular “brake.” These results provide new insights and raise questions about the role of the C-zone during muscle development.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Reestablishing larval connectivity in an estuarine landscape: the importance of shoreline and subtidal oysters ( Crassostrea virginica ) in a comprehensive oyster restoration program

The decline of oyster reefs in estuaries has resulted from a combination of chronic and acute disturbances. The loss has resulted in decreased yield for the oyster fishery as well as a decline in ecological benefits that has led to increased efforts to restore oyster reefs. The need for scientific guidance in accomplishing these restoration goals has become even more pressing in the northcentral Gulf of Mexico in the wake of injury to oyster reefs resulting from the Deepwater Horizon oil spill. Restoration of both the shallow, marsh-fringing oyster aggregations and the deeper subtidal oyster reefs is necessary. Historically, fringing oysters have been overlooked in the oyster habitat landscape because of their limited commercial value. Here, we use a biophysical transport model to examine the transport and settlement of oyster larvae in known oyster reefs along the coast of the northcentral Gulf of Mexico. The modeling demonstrated that the majority of oyster larvae settle within the embayment (>98%) or sub-basin (>65%) of their origin. Additionally, the model demonstrated the importance of fringing oysters as a source of larvae to re-seed other fringing oysters along marsh edges as well as subtidal oyster reefs. We conclude that networks of reefs, including both fringing oyster habitat and subtidal oyster reefs within sub-basins, are necessary to provide resilience to the population at the sub-basin level. Finally, we conclude that fringing oyster habitat may serve as an archipelago-like network to enhance larval supply and connectivity for oysters throughout the mesosaline portions of estuaries.

ADCIRC↗
DOE OSTI2024

Insight into Molecular Basis and Dynamics of Full-length CRaf Kinase in Cellular Signaling Mechanism

This study presents the first large-scale simulation using an initial structure predicted by AI/ML algorithms for the 648-amino-acid CRaf kinase, which plays a key role in cellular signaling. Simulation results show the evolution of the predicted structure into much more compact structures with inter-domain interactions that shed insights into auto-inhibition mechanism, paradoxical effect, activation, and recruitment pathways in the CRaf kinase. Newly identified epitopes in the CRaf may suggest additional drug targets. The results were published in Biophysical Journal, DOI:10.1016/j.bpj.2024.06.028.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Electron Bifurcating Hydrogenases

The importance of electron-bifurcating enzymes is manifest by their ability to maximize energy efficiency. Specifically, they couple a downhill oxidation-reduction (redox) reaction with an uphill redox reaction. Since the rapid increase in the discovery of bifurcating enzymes starting in 2008, there has been interest in incorporating their mechanistic principles into artificial/semiartificial systems to drive chemically challenging reactions. This has yet to be achieved, partly because the details of electron bifurcation, i.e. mechanisms, are largely elusive. Nevertheless, much progress has been made in understanding reactivities, structures, and some mechanistic aspects of these enzymes. Notable examples are electron-bifurcating hydrogenases, which are the focus of this chapter. The chapter is organized as follows. Section 11.1 provides an overview of hydrogenases and electron bifurcation. In Section 11.2, some physiological roles of electron-bifurcating hydrogenases are highlighted. Additionally, electron-bifurcating subunit compositions and biochemical reactivities are comprehensively tabulated, and some key points/considerations about these are noted. In Section 11.3, we discuss the known structures of these enzymes, which provide insight into their complex arrangements of redox cofactors, such as iron-sulfur clusters. Also provided are tabulations and discussions of some biophysical properties of the cofactors. In Section 11.4, we discuss the mechanistic proposals of these enzymes, which are primarily based on structural information. Areas of research that are much needed are outlined in Section 11.5. We conclude on the note that what is learned from electron-bifurcating hydrogenases has applicability to other bifurcating enzymes, nonbifurcating analogs, and mechanistic enzymology at large.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unveiling the nanoscale architectures and dynamics of protein assembly with in situ atomic force microscopy

Proteins play a vital role in different biological processes by forming complexes through precise folding with exclusive inter- and intra-molecular interactions. Understanding the structural and regulatory mechanisms underlying protein complex formation provides insights into biophysical processes. Furthermore, the principle of protein assembly gives guidelines for new biomimetic materials with potential applications in medicine, energy, and nanotechnology. Atomic force microscopy (AFM) is a powerful tool for investigating protein assembly and interactions across spatial scales (single molecules to cells) and temporal scales (milliseconds to days). It has significantly contributed to understanding nanoscale architectures, inter- and intra-molecular interactions, and regulatory elements that determine protein structures, assemblies, and functions. This review describes recent advancements in elucidating protein assemblies with in situ AFM. We discuss the structures, diffusions, interactions, and assembly dynamics of proteins captured by conventional and high-speed AFM in near-native environments and recent AFM developments in the multimodal high-resolution imaging, bimodal imaging, live cell imaging, and machine-learning-enhanced data analysis. These approaches show the significance of broadening the horizons of AFM and enable unprecedented explorations of protein assembly for biomaterial design and biomedical research.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Machine Learning‐Augmented Molecular Dynamics Simulations (MD) Reveal Insights Into the Disconnect Between Affinity and Activation of ZTP Riboswitch Ligands

Abstract The challenge of targeting RNA with small molecules necessitates a better understanding of RNA–ligand interaction mechanisms. However, the dynamic nature of nucleic acids, their ligand‐induced stabilization, and how conformational changes influence gene expression pose significant difficulties for experimental investigation. This work employs a combination of computational and experimental methods to address these challenges. By integrating structure‐informed design, crystallography, and machine learning‐augmented all‐atom molecular dynamics simulations (MD), we synthesized, biophysically and biochemically characterized, and studied the dissociation of a library of small molecule activators of the 5‐aminoimidazole–4–carboxamide ribonucleotide triphosphate (ZTP) riboswitch, a ligand‐binding RNA motif that regulates bacterial gene expression. We uncovered key interaction mechanisms, revealing valuable insights into the role of ligand binding kinetics on riboswitch activation. Further, we established that ligand on‐rates determine activation potency as opposed to binding affinity and elucidated RNA structural differences, which provide mechanistic insights into the interplay of RNA structure on riboswitch activation.

Chemistry↗
DOE OSTI2024

SEC ‐ SAXS / MC Ensemble Structural Studies of the Microtubule Binding Protein Cdt1 Show Monomeric, Folded‐Over Conformations

ABSTRACT Cdt1 is a mixed folded protein critical for DNA replication licensing and it also has a “moonlighting” role at the kinetochore via direct binding to microtubules and the Ndc80 complex. However, it is unknown how the structure and conformations of Cdt1 could allow it to participate in these multiple, unique sets of protein complexes. While robust methods exist to study entirely folded or unfolded proteins, structure–function studies of combined, mixed folded/disordered proteins remain challenging. In this work, we employ orthogonal biophysical and computational techniques to provide structural characterization of mitosis‐competent human Cdt1. Thermal stability analyses shows that both folded winged helix domains1 are unstable. CD and NMR show that the N‐terminal and linker regions are intrinsically disordered. DLS shows that Cdt1 is monomeric and polydisperse, while SEC‐MALS confirms that it is monomeric at high concentrations, but without any apparent inter‐molecular self‐association. SEC‐SAXS enabled computational modeling of the protein structures. Using the program SASSIE, we performed rigid body Monte Carlo simulations to generate a conformational ensemble of structures. We observe that neither fully extended nor extremely compact Cdt1 conformations are consistent with SAXS. The best‐fit models have the N‐terminal and linker disordered regions extended into the solution and the two folded domains close to each other in apparent “folded over” conformations. We hypothesize the best‐fit Cdt1 conformations could be consistent with a function as a scaffold protein that may be sterically blocked without binding partners. Our study also provides a template for combining experimental and computational techniques to study mixed‐folded proteins.

Cell Biology↗
DOE OSTI2025

Structures of a synthetic antibody selected against and bound to the C‐terminal domain of Clostridium perfringens enterotoxin

Abstract Clostridium perfringensenterotoxin (CpE) causes cytotoxic gastrointestinal disease in mammalian epithelium by binding membrane protein receptors called claudins. Claudins direct the formation of cell/cell tight junctions through oligomerization and govern the transport of molecules between individual cells. CpE binds claudins through its C‐terminal domain (cCpE) and induces cytotoxicity through its N‐terminal domain. The non‐toxic cCpE is a useful tool to study claudins, tight junctions, and for translational applications, such as increasing the permeability of restrictive tissues like the blood–brain barrier or selective targeting of claudin overexpressing cancers. Conversely, there are no specialized molecular tools to study CpE or cCpE, or to modulate or inhibit their functions. We previously reported the development of synthetic antigen‐binding fragments (sFabs) that bind cCpE, and low‐resolution structures of them bound to claudin/cCpE complexes. Here, we determine high‐resolution structures of sFab COP‐2 bound to cCpE using X‐ray crystallography and cryogenic electron microscopy. The structures and biophysical findings provide the mechanism of COP‐2 binding to cCpE and the molecular determinants driving their interactions. These insights can advance the design of new antibody‐based tools from our COP‐2 scaffold to study or alter cCpE function and give rise to a “Trojan horse” strategy that exploits cCpE's tight junction barrier disrupting function to selectively deliver conjugated therapeutics through normally impermeable tissues.

Biochemistry & Molecular Biology↗
DOE OSTI2026

A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions

Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, and economic models to capture two-way feedback among water availability, agricultural production, and market responses under groundwater constraints. The primary goal of this paper is to describe the methodological development of the coupled framework and demonstrate the significance of iterative model interaction. Applied to the western United States, we evaluated adaptive responses to restricting groundwater use beyond recharge levels, represented through changes in irrigation management and expansion or shrinkage of crop markets through land reallocation. Results demonstrate that the iterative coupling converges to stable equilibrium responses within 10 iterations. At equilibrium, deficit irrigation emerges as the dominant adaptation strategy in California, with irrigation levels stabilizing at approximately 70% of full irrigation demand, while Arizona and New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases of up to 10% for fruit and vegetable crops; however, these responses moderate as land allocation and production patterns adjust across regions. Deficit irrigation and spatial reallocation of irrigated land partially offset production losses, with variability observed across different states: California maintains yields primarily via deficit irrigation, whereas Arizona and New Mexico will rely mainly on reducing irrigated area to absorb the shock. By capturing feedback between biophysical and economic processes, this approach highlights how irrigation strategies and land-use decisions evolve under water stress and provides a transferable platform for evaluating water management policies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Integrated multi-omic characterizations of the synapse reveal RNA processing factors and ubiquitin ligases associated with neurodevelopmental disorders

The molecular composition of the excitatory synapse is incompletely defined due to its dynamic nature across developmental stages and neuronal populations. To address this gap, we apply proteomic mass spectrometry to characterize the synapse in multiple biological models including the fetal human brain and hiPSC-derived neurons. To prioritize the identified proteins, we develop an orthogonal multi-omic screen of genomic, transcriptomic, interactomic, and structural data. This data-driven framework identifies proteins with key molecular features intrinsic to the synapse, including characteristic patterns of biophysical interactions and cross-tissue expression. The multi-omic analysis captures synaptic proteins across developmental stages and experimental systems, including 493 synaptic candidates supported by proteomics. We further investigate three such proteins that are associated with neurodevelopmental disorders – the CUL3 E3 ubiquitin ligase, the DDX3X and YBX1 nucleic-acid binding proteins – by mapping their networks of physically interacting synapse proteins or transcripts. Our study demonstrates the potential of an integrated multi-omic approach to systematically and more comprehensively resolve the synaptic architecture.

59 BASIC BIOLOGICAL SCIENCES↗