Biology subjects

Ryu, E.

Publications and source records attributed to Ryu, E..

5 recordsLinked to original sources

bioRxiv2026

A Synthetic Microbial Therapy Rewires Antitumor Immunity Across Multiple Cancer Types

Microbial immunotherapies show promise against cancer, yet broad efficacy and mechanistic insight remain elusive. Here, we introduce SPIKE 1.0 (S1.0), a metabolically engineered bacterium that converts tryptophan into immunomodulatory hydroxyindoles via tryptophan monooxygenase to remodel the tumor microenvironment (TME). A single systemic dose of S1.0 elicited potent, durable antitumor responses across multiple murine models, including humanized mice, with minimal toxicity. S1.0 enhanced inflammatory signaling, activated innate and adaptive immunity, and promoted T cell persistence, memory, and resistance to exhaustion. It outperformed checkpoint inhibitors and synergized with chemotherapy. Multi-omics profiling revealed that S1.0 rewired amino acid metabolism in tumor-infiltrating immune cells and disrupted immunosuppressive networks. These results establish S1.0 as a scalable, cost-effective microbial immunotherapy with broad translational potential for solid tumors.

immunology↗

bioRxiv2025

Precision at Every Scale: Efficiency in AI-Driven De Novo Antibody Design

The precise de novo design of antibodies remains a therapeutic challenge. The AI platform, GaluxDesign, was evaluated in a high-efficiency Precision-Scale Workflow by synthesizing and testing only 50 full-length IgG candidates per epitope across eight distinct epitopes from six therapeutic targets. This campaign yielded a 10.5% binder rate (estimated EC50 < 100 nM), identifying target-specific binders for seven of eight epitopes, with multiple candidates exhibiting sub-nanomolar to single-digit nanomolar dissociation constants (Kd). We further assessed the same workflow on nine shared benchmark targets selected for external comparison, where GaluxDesign identified target-specific binders for eight of nine targets, demonstrating strong target-level performance relative to previously reported de novo antibody design approaches. Together, these results establish a high-efficiency, precision-scale workflow for generating novel, high-affinity therapeutic antibodies.

bioengineering↗

bioRxiv2025

Proteome-wide in silico screening for human protein-protein interactions

Protein-protein interactions (PPIs) drive virtually all biological processes, yet most PPIs have not been identified and even more remain structurally unresolved. We developed a two-step computational screen for human PPIs. First, a classifier called KIRC (Knowledge-Informed Rapid Classifier), trained on biological features, was used to rank all 200 million possible protein pairs in the human proteome by their interaction likelihood. Second, the [~]1.6 million top-ranked KIRC pairs were subjected to structure prediction by AlphaFold-Multimer and ranked using SPOC (Structure Prediction and Omics Classifier), which identifies functional predictions based on biological and structural features. This pipeline revealed 16,000 high-confidence PPIs ([~]90% precision), of which more than 5,000 were not previously recognized and more than 12,000 have not been structurally resolved. We use this "predictome" to formulate new hypotheses in different areas of biology, reinterpret low-resolution cryo-EM maps, and identify and validate novel PPIs that may support replication-coupled chromatin assembly. The predicted PPIs, viewable at predictomes.org, are expected to accelerate characterization of the molecular interactions that underlie vertebrate cell physiology.

bioinformatics↗

bioRxiv2025

Efficient elastic tissue motions indicate general motor skill

Insights into the general nature of motor skill could fundamentally change how we develop movement abilities, with implications for musculoskeletal well-being and injury. Here, we sought to identify indicators of general motor skill-those shared by experts across disciplines (e.g., squash, ballet, volleyball) during non-specialized movements (e.g., reaching for water). Identifying such general indicators of motor skill has remained elusive. Using ultrasound imaging with deep learning and optical flow analysis, we tracked elastic tissues (muscles and associated connective tissues) during a simple reaching task performed similarly by world-class athletes and regional-level athletes drawn from diverse disciplines, as well as untrained non-experts. We analyzed previously unexamined inefficiencies: transverse elastic tissue motions orthogonal to muscle fiber direction and physiological tremors, which are oscillations that do not contribute to the net work done by muscles. We discovered that world-class experts minimize both these inefficient motions compared to regional level athletes and non-experts. While regional- level athletes surprisingly showed similar inefficiencies to non-experts, they used elastic tissues more effectively, achieving equivalent movements with smaller actuation-related tissue motions. We establish elastic tissue motion as a key indicator of general motor skill, expanding our understanding of elastic mechanisms and their role in general aspects of motor skill.

animal behavior and cognition↗

bioRxiv2022

Integrating region- and network-level contributions to episodic recollection using multilevel structural equation modeling

The brain is composed of networks of interacting brain regions that support higher order cognition. For instance, a posterior medial (PM) network appears to support recollection and other forms of episodic construction. Past research has focused largely on the roles of individual brain regions in recollection or on their mutual engagement as part of an integrated network. Yet the relationship between these region- and network-level contributions remains poorly understood. Here we applied multilevel structural equation modeling (SEM) to examine the functional organization of the PM network and its relationship to episodic memory outcomes. We evaluated two aspects of functional heterogeneity in the PM network: first, the organization of individual regions into subnetworks, and second, the presence of regionally-specific contributions while accounting for network-level effects. The results suggest that the PM network is composed of ventral and dorsal subnetworks, with the ventral subnetwork making a unique contribution to recollection, and that memory-related activity in individual regions is well accounted for by these network-level effects. These findings highlight the importance of considering both region and network levels of analysis when studying brain-behavior relationships.

neuroscience↗