단백질시스템 연구실

• [12/30/2024] 박종함, 윤윤기 학생, 미국 존스홉킨스 의과대학과 '파킨슨병의 펩타이드 치료제 개발' 연구를 위한 해외파견 연수

• [06/20/2024] 신진연구자 인프라 지원사업 과제 선정 (펩타이드약물 디자인 연구를 위한 HPC 클러스터 시스템 구축)

• [05/28/2024] 오사카공립대학(Osaka metropolitan university) 김동호 교수팀 공동연구, 일본식품영양학회 포스터상 수상

• [02/19/2024] 생물학연구정보센터(BRIC), 단백체정보학 연구실(Proteomic Informatics Lab.) 소개

• [12/13/2023] Published in Scientific Reports, Development of Protein-Protein Interaction Analysis Tool

• [11/10/2023] 한인과학기술네트워크(KOSEN), 단백체정보학 연구실(Proteomic Informatics Lab.) 소개

• [07/30/2023] 이원석 학생, 영국 사우스햄튼대학 화학과와 '키노믹스 기반 항생제 내성 제어 플랫폼 개발' 연구를 위한 해외파견 연수

• [03/04/2023] 개인기초연구사업 우수신진연구 과제 선정 (다종양 단일세포 구조단백체 분석을 통한 면역항암제 치료표적 발굴)

The research focus of the laboratory can be divided into three main areas:

1. Artificial Intelligence-powered Functional Protein Design

• Research on biomarker discovery through single-cellomics and multidimensional proteomics analysis: This area focuses on analyzing samples ranging from individual cells to complex tissues at high resolution. By utilizing single-cellomics and multidimensional proteomics technologies to collect data and combining this with network modeling, the research aims to identify and study disease-related biomarkers.

2. Biomarker Discovery through Single-cell Proteome and Network Model

• Research on functional protein design through the integration of artificial intelligence and mass spectrometry technologies: This field combines cutting-edge AI algorithms with mass spectrometry techniques to rapidly design and validate therapeutic mini-proteins and peptides with enhanced stability and efficacy. The goal is to accelerate the development of innovative pharmaceuticals.

3. In Vitro & Companion Diagnostic LC-MS Assay in Clinical Laboratory

• Research on the development of in vitro diagnostics and companion diagnostics using mass spectrometry: This field leverages the superior sensitivity and specificity of mass spectrometers to develop disease detection methods that are faster and more accurate than existing approaches. Furthermore, through companion diagnostics research, the laboratory is paving the way for personalized medicine.

We are seeking passionate individuals who possess expertise in or are eager to learn the following fields:

• Computer Science (Engineering), Bioinformatics, or related disciplines

Preference given to candidates with experience in programming languages (R, Python) and artificial intelligence/machine learning/deep learning

• Biology (Engineering), Chemistry (Engineering), or related fields

Preference given to candidates with experience in bio-analytical instruments (such as liquid chromatography, mass spectrometry, flow cytometry, mass cytometry, etc.)

Prospective laboratory members (researchers, undergraduate students, graduate school applicants, and postdoctoral candidates) are encouraged to send their CVs via email to kimlab@cnu.ac.kr.