* Korean Ver.

Our research interests center on structural and functional characterization of membrane proteins, genome-wide identification of protein-protein interaction, systematic & quantitative analysis of molecular evolution and biological network.

We are developing computational biology methods and bioinformatics tools for network medicine and healthcare. Proteins are the major player of cellular function and they carry out their functional role through complex network of protein-protein interactions. The protein repertoire varies depending on cellular states, tissue type, species, and disease state. However, little is known about how this repertoire changes under different cellular or disease states. To gain a better understanding of these dynamic changes, Kim’s lab is developing essential applications for network biology and large-scale high-throughput data integration analysis. Systemic analysis of protein functional network will provide a framework for understanding how protein compositions respond to changes in human disease states.

I. Genome-wide identification of protein interactome

Integrative systems biology
Protein-protein interactions (PPIs) are crucial for many biological functions. Although advances in high-throughput proteomics enable us to construct more comprehensive PPI networks providing holistic view of biological phenomena, there are huge amount of unidentified PPIs still exist. Furthermore, little attention is paid for the network-level understanding of diverse characteristics of PPI. Dr. Kim’s research group tries to solve these problems by integrating various approaches such as modeling physical property of PPI, subcellular localization information, and high-throughput genomics data.

PDZ domain interactome
PDZ domains play an important role on controlling the signal flow by assembling multiple signaling components, such as enzymes, receptors, and ion channels. The understanding PDZ protein functions and interactions is currently limited. In fact, many PDZ domain-ligand interactions are related to important human diseases, such as neurological disease and cancer. Therefore revealing the PDZ domain-ligand interaction network will lead us elucidating numerous cell signaling event and disease progression. Through systematic data integration, quantitative interaction modeling, and subcellular localization mapping, we attempt to discover molecular mechanisms of signaling processes organized by PDZ domain-ligand interactions. 

Membrane protein functions
Cells respond to their environment by sensing signals and translating them into changes in gene expression. Membrane proteins accept and transmit a variety of extracellular signals to intracellular space. Therefore, structural and function diversity is necessary. However, plasma membrane proteins happen to fall into fewer families and folds compared to soluble proteins. Dr. Kim’s group is trying to answer the question how have functional diversity of membrane proteins was achieved during evolution. This will lead to find the answer for cellular signaling dynamics mediated by plasma membrane proteins.


Fig.1. Constructing PDZNet by integrating genome-wide datasets.

 

II. Systematic analysis of protein localizations in the cells

Protein subcellular localization for human disease profiling
Characterizing subcellular localizations of proteins provides a key clue for understanding protein function. Many bioinformatics approaches have focused on identifying protein subcellular localization using various features from primary sequence. However, different programs often deliver conflicting results regarding the localization of the same protein. Dr. Kim’s group currently develops a consensus localization prediction method that is based on a systematic integration of available programs and hopes to obtain substantially high performance. Furthermore, with the high quality subcellular localization information, Dr. Kim’s group is investigating the relationship between disease-associated proteins and their subcellular localizations.

Mitochondria biology
Dr. Kim’s group focuses on the mitochondria, dynamic organelles modulating various biological processes, such as metabolic processes, energy production, and apoptosis through active communications with other cellular compartments. Breakdown of the mitochondrial function often lead to metabolic diseases. Recently, it has been shown that dysfunction of mitochondria is related human diseases, such as diabetes and neurodegeneration. We integrate and manipulate the high-throughput data, such as genomics, proteomics, and interactomics to characterize novel mitochondrial proteins to find mitochondrial disease gene markers.

Fig.2. Identification of subcellular localizations of proteins

 

III. Evolutionary analysis protein structures and sequences

Bioinformatic approaches for the structure and function of proteins
Proteins are functional units to play important roles in the biology of the cell. While the number of sequenced genomes continues to increase, experimentally verified functional annotations and structures of whole genomes remains unknown. Because experimental investigation is costly and time-consuming, accurate computational methods for predicting protein functions and structures become attractive. Dr. Kim’s research group develops various computational methods for identifying functionally important residues and modeling structures by using evolutionary information.

Analyzing dynamics and function of proteins
Protein sequence directly implies its structure that commands function. Protein structure is dynamic and undergoes conformational changes which are fundamental to control various biological processes such as cell signaling, gene expression, and metabolic regulation. Dr. Kim’s group is investigating the relationship between conformational change and sequence evolution of proteins to understand the mechanisms of protein conformational change and to identify the key residues of structural transitions. Dr. Kim’s group developed various measurements to identify functionally important residues of proteins. New methods can capture the property of functionally important residues that would not be apparent by currently available tools. 

Fig.3. Identification of functionally important sites and structures by using evolutionary information