단백질시스템 연구실

• [12/28/2025] Global Joint Lab Partnership: Protein Systems Lab & Johns Hopkins University School of Medicine

• [12/30/2024] Overseas Research Training at Institute for Cell Engineering, Johns Hopkins University School of Medicine for Peptide Therapeutics Development in Parkinson's Disease 

• [02/19/2024] Featured Lab Introduction: Protein Systems Lab (formerly Proteomic Informatics Lab) on BRIC

• [11/10/2023] Featured Lab Introduction: Protein Systems Lab (formerly Proteomic Informatics Lab) on KOSEN

• [07/30/2023] Overseas Research Training at School of Chemistry and Chemical Engineering, University of Southampton: Development of Kinomics-Based Platform for Antibiotic Resistance Control

알츠하이머, 파킨슨병, CADASIL, 자폐, 노화, 췌장암 등 진단과 치료가 모두 어려운 난치성 질환을 극복하기 위해, 우리 연구실은 생성형 AI 기반 치료용 펩타이드/단백질 디자인, LC-MS 기반 정밀 진단, 다차원 단백체 분석을 하나의 파이프라인으로 융합합니다.

To overcome intractable diseases that are difficult to both diagnose and treat — including Alzheimer's, Parkinson's, CADASIL, autism, aging, and pancreatic cancer — our laboratory integrates generative AI-driven therapeutic protein design, LC-MS-based precision diagnostics, and multidimensional proteomics into a single pipeline.

Research Keywords: Protein Design, Protein Analytics, Structural Proteomics, Protein Therapeutics

난치성 질환을 극복하기 위해, 우리 연구실의 연구는 다음 세 가지 목적으로 나뉩니다.

Our research is organized around three purposes, each addressing a different stage of conquering intractable disease.

① AI 기반 치료용 단백질 디자인 (AI-Powered Therapeutic Protein Design)

생성형 AI와 생물리화학 분석 기술을 결합하여, 안정성과 효능이 최적화된 치료용 단백질을 직접 설계합니다. 현재는 펩타이드 치료제 개발을 중심으로 난치성 질환의 신약 후보물질을 발굴하고 있으며, 나아가 질병을 근본적으로 제어할 수 있는 기능성 효소(단백질) 디자인으로 연구 영역을 확장하고 있습니다.

We combine generative AI with biophysical analysis to design therapeutic proteins from the ground up. Our current focus is on peptide therapeutics for intractable diseases, and we are expanding this platform toward the design of functional therapeutic enzymes capable of directly correcting disease-driving mechanisms.

② 다차원 단백체 분석 기반 바이오마커 발굴 (Biomarker Discovery through Multidimensional Proteomics)

단일세포부터 복잡한 조직 시료까지 고해상도 다차원 단백체 분석 기술을 적용하고, 여기에 인공지능과 네트워크 모델링을 결합하여 질병 특이적 바이오마커와 치료 표적을 발굴합니다. 이는 난치성 질환의 조기 진단과 새로운 치료 표적 설정의 출발점이 됩니다.

We apply high-resolution multidimensional proteomics — from single cells to complex tissues — and integrate it with AI and network modeling to discover disease-specific biomarkers and therapeutic targets, providing the foundation for early diagnosis and novel intervention.

③ LC-MS 기반 체외진단 · 동반진단법 개발 (LC-MS-Based In Vitro & Companion Diagnostics)

질량분석기의 뛰어난 감도와 특이성을 활용하여, 기존 방법보다 빠르고 정확한 차세대 체외진단 플랫폼을 개발합니다. 동반진단(companion diagnostics) 연구를 통해 환자별 치료 반응을 예측하고, 정밀의료·맞춤형 치료의 실현을 앞당깁니다.

Leveraging the sensitivity and specificity of mass spectrometry, we develop next-generation in vitro diagnostic platforms that are faster and more accurate than existing methods. Through companion diagnostics, we predict patient-specific treatment responses and advance precision, personalized medicine.

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

• 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.)

• 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

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.