We are a multidisciplinary team dedicated to solving complex problems at the intersection of genomic, proteomic, and computational biology.
Our lab pursues fundamental questions in genomic and proteomic biology using computational science with a commitment to open, reproducible research. We believe that scientific progress requires both technical rigor and creative thinking, supported by an inclusive and collaborative environment.
Through our work, we aim to develop new tools, methodologies, and insights that advance both theoretical understanding and practical applications in human health and biological science.
Our research spans multiple domains, united by a commitment to rigorous methodology and open collaboration.
Intrinsically disordered regions (IDRs) in proteins play a pivotal role in protein-protein interactions (PPIs). Using AlphaFold2 and enriched multiple sequence alignments, we predicted and investigated PPIs across the human proteome, focusing on those involving disordered regions.
The assessment of monomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) underscores that the problem of single domain protein fold prediction is nearly solved—no target folds were incorrectly predicted across all Evaluation Units. However, challenges remain in accurately modeling truncated sequences, irregular secondary structures, and interaction induced conformational changes.
Cells generate purine nucleotides through de novo purine biosynthesis (DNPB) and purine salvage. Purine salvage represses DNPB to prevent excessive purine nucleotide synthesis through mechanisms that are incompletely understood. We identified Nudix hydrolase 5 (NUDT5) as a DNPB regulator.
Continuing our genomics-driven exploration of butterfly taxonomy, we integrate phylogenetic trees from all protein-coding genes with existing taxonomic and phenotypic knowledge and uncover further insights into butterfly systematics.
Protein-protein interactions (PPIs) are essential for biological function. Coevolutionary analysis and deep-learning (DL)–based protein structure prediction have enabled comprehensive PPI identification in bacteria and yeast, but these approaches have had limited success for the more complex human proteome.
Genomic sequencing and analysis offer a fast track to cataloguing biodiversity and facilitating species discovery. A comprehensive genomic study of Hesperiidae Latreille, 1809 (Lepidoptera), incorporating primary type specimens, uncovers a plethora of species previously unknown to science.
The assessment of oligomer targets in the Critical Assessment of Structure Prediction Round 16 (CASP16) suggests that complex structure prediction remains an unsolved challenge. Even the leading groups can only predict slightly more than half of the targets to high accuracy.
Protein-protein interactions (PPIs) play a fundamental role in cellular processes, and understanding these interactions is crucial for advances in both basic biological science and biomedical applications. This review presents an overview of recent progress in computational methods for modeling protein complexes and predicting PPIs based on 3D structures, focusing on the transformative role of artificial intelligence-based approaches.
We are a diverse group of researchers, students, and collaborators working together to advance scientific knowledge.
Principal Investigator
Assistant Professor in the McDermott Center for Human Growth and Development
Computational Biologist
Structural Biology and Protein Engineering
Research Scientist
Genomics and Butterfly Genome Studies
Research Associate
Biochemistry and Molecular Biology
Postdoctoral Fellow
Computational Genomics
PhD Candidate
Biochemistry Program
PhD Candidate
Computational Biology, BME Program
Graduate Researcher
Biophysics Program
Graduate Researcher
Computational Biology, BME Program
We welcome inquiries from prospective students, postdoctoral researchers, and collaborators who share our commitment to rigorous, open science.
