Research

Our group focuses on the development and application of computational and experimental research methods geared toward human well-being. We combine expertise as biochemists with software development skills, and we are eager to use this training in overcoming obstacles and bottlenecks in our understanding of how biomolecules function through interaction with their microenvironment. We envision contributing indispensable high-throughput tools for analyzing biochemical/biophysical data and making technological breakthroughs towards a better understanding of protein 3D structures in terms of their roles and behaviors in biological context.

Our research operates across five interconnected themes, all unified by the POKY suite — our integrated, AI-powered NMR platform developed and maintained in this lab.

2. AI and LLM Integration in NMR and Structural Biology

Artificial intelligence is transforming the pace and scope of structural biology. Our group is at the forefront of integrating modern AI and large language model (LLM) methodologies into NMR research workflows.

Current directions include:

• Deep learning for NMR peak detection and assignment: Applying convolutional neural networks and transformer-based architectures to automate recognition of spectral patterns that previously required expert manual interpretation.

• LLM-assisted NMR analysis pipelines: Leveraging large language models to interpret NMR data, assist with experimental design, and generate automated structure reports.

• AI-integrated NMR: Combining AI-based structure prediction (AlphaFold, BOLTZ-2, CHAI-1, etc) with experimental NMR constraints to determine and validate structures of challenging targets, including homomeric protein complexes. The HANA algorithm is being developed for this purpose.

• Chemical shift prediction integration: Developing methods to seamlessly incorporate chemical shift predictors into automated NMR assignment pipelines.

This line of work is deeply interdisciplinary, connecting structural biology, bioinformatics, and computer science — and represents one of our most active recruiting areas for students with programming interests.

3. Integrative Structural Biology and Biomolecular Function

Understanding how proteins accomplish biological work requires knowing not just their three-dimensional structure but also their dynamics, interactions, and context within the cell. We approach this using an integrative structural biology strategy that combines NMR spectroscopy with complementary experimental and computational methods.

• NMR solution and solid-state structure determination: We determine high-resolution protein structures using both solution and solid-state NMR, with automated workflows enabled by POKY. Solid-state NMR expands our reach to membrane proteins, fibrils, and large assemblies that are inaccessible to solution methods.

• NMR/SAXS hybrid approaches: We combine solution NMR with small-angle X-ray scattering (SAXS) data for integrative structure determination of challenging targets. Our NMR/SAXS structure of the mitochondrial-targeted GTPase-activating protein VopE is a recent example (Smith & Lee et al., Protein Science 2022).

• Protein allostery: Allosteric regulation — where binding at one site changes protein behavior at a distant site — is a major mechanism of cellular control. We develop and apply CHESCA-based NMR methods, now fully automated within POKY (CHESPA/CHESCA-SPARKY), to map long-range allosteric networks in signaling proteins. Collaborative work with the Melacini group (McMaster University) has characterized allosteric mechanisms in PKA regulatory subunits and EPAC1. Recent work extended this to Platelet Factor 4 (PF4) tetramers (Ma et al., Biomol. NMR Assignments 2025).

• Protein–RNA interactions and infectious disease: We study protein complexes involved in RNA regulation and viral biology. Recent work characterized how SARS-CoV-2 Nsp9 depletes cellular microRNA let-7b (Mun et al., RNA Biology 2025). Ongoing projects investigate HRV-C (Human Rhinovirus C) components — including the 2Apro protease and CDHR3 receptor-binding domains — as targets for antiviral development. The only solution-state NMR structure of HRV-C2 2Apro was determined in our group.

• Cryo-EM: We have access to cryo-EM instrumentation through the core facility, and computational pipelines for integrating cryo-EM maps with NMR data are part of our expanding integrative toolkit.

4. NMR-Based Metabolomics

Metabolomics — the comprehensive profiling of small-molecule metabolites in biological systems — offers a powerful window into cellular physiology, disease states, and the effects of environmental or pharmacological perturbations. NMR is uniquely suited to metabolomics because it is quantitative, non-destructive, and requires minimal sample preparation.

Our group develops and applies computational tools that make NMR-based metabolomics more accessible and powerful (Chiu et al., Metabolomics 2024):

• A-SIMA (Advanced Support for Interactive Metabolite Analysis): A POKY-integrated tool for automated metabolite identification from 1D and 2D NMR spectra, featuring an intuitive graphical interface and complete user control over the analysis workflow.

• A-MAP (Automated Metabolomics Analysis and Processing): A multivariate statistical analysis tool supporting PCA and OPLS-DA, which takes NMR regions of interest as input to identify metabolic differences between sample groups.

Both tools are pre-built into the POKY suite with accompanying tutorial videos, substantially lowering the barrier to entry for new users and non-expert labs.

Lignin NMR: We have an active project developing standard NMR analysis methods and reference tools for lignin — a complex aromatic biopolymer and major component of plant biomass. Accurate NMR characterization of lignin structure is essential for biomass valorization, biofuel development, and sustainable materials chemistry. This project connects our NMR methodology expertise to important challenges at the interface of chemistry and environmental sustainability.

5. Computer-Aided Drug Design and Traditional Herbal Medicine

Structural data, when combined with computational docking, offers a powerful path to drug discovery — particularly for targets where experimental high-throughput screening is costly or slow. Our group integrates NMR-derived structural information with in-silico methods through ANSER/ADOCK, a multidimensional drug design module within the POKY suite.

ANSER/ADOCK enables:

• Virtual screening of compound libraries against NMR-determined protein structures

• Binding pose prediction and ranking using integrated scoring functions

• Iterative NMR validation of docking predictions through experimental ligand binding assays (e.g., chemical shift perturbation, STD-NMR)

Traditional Herbal Medicine (THM) and cancer drug discovery: One of our most distinctive and exciting research directions applies this CADD pipeline to the systematic, science-based investigation of traditional herbal medicine compounds. Herbal formulas used in East Asian medicine represent thousands of years of empirical pharmacological experience — yet the molecular mechanisms behind many of their effects remain poorly understood.

We screen curated herbal medicine compound libraries against cancer-relevant target proteins using ADOCK, identify promising hits, and validate binding using NMR spectroscopy. This approach bridges traditional knowledge and modern structural biology to accelerate identification of novel anticancer lead compounds.

A recent collaborative study demonstrated that a herbal formula derived from Danggui Bohyul Tang enhances cisplatin efficacy in lung cancer by inhibiting neutrophil extracellular trap (NET) formation — with our NMR structural biology contributing to mechanistic understanding (Kim et al., in revision). This work reflects our ongoing collaboration with colleagues in integrative medicine and oncology.

This research direction sits at the intersection of structural biology, pharmacology, and traditional medicine, and is particularly suited for students interested in translational or interdisciplinary science.

Research Infrastructure

Our lab is equipped to carry out the full spectrum of structural biology research from molecular biology bench to computational analysis:

Wet Lab

• Protein expression, purification, and characterization systems

• Full capabilities for recombinant protein production (bacterial and insect expression; BSL-1)

Dry Lab / Computing

• In-house NVIDIA A100 GPU server for deep learning and AI model training

• High-Performance Computing (HPC) cluster access

• Full POKY suite development and testing environment

NMR

• Access to 400–900 MHz NMR spectrometers

• Solution and solid-state NMR capabilities

Structural Imaging

• Cryo-EM access through core facility

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SI 0010 NMR Schedule