Research Focus

Our laboratory is interested in exploring how protein conformational changes, such as those associated with protein aggregation, misfolding, or turnover, impact cellular physiology and lead to human diseases. In this regard, we focus on the development and application of mass spectrometry-based structural and quantitative proteomic methods aimed at monitoring protein conformational changes in complex cellular conditions. We combine these tools with classical biochemical, cell biological, genetic approaches and machine learning algorithms in several lines of research. Our research encompasses the area of artificial intelligence, bioinformatics, biostatistics, software development, and clinical applications. 

Research Topics

(1) Single-Cell Multidimensional Proteome for Peptide Drug Discovery
(2) Clinical Biomarker Discovery through Multidimensional Proteome and AI-guiding
(3) In Vitro & Companion Diagnostic LC-MS Assay in Clinical Laboratory
(4) Artificial Intelligence (AI)-powered Peptide Drug Design
(5) Multi-Omic Bio Big Data Analytics Development

Kim, Hyunsoo Ph.D. (김현수) 
Assistant Professor,  Department of Convergent Bioscience and Informatics, College of Bioscience and Biotechnology, Chungnam National University

Employment

Mar 2023 ~ Feb 2025: Department Chair, Department of Convergent Bioscience and Informatics, College of Bioscience and Biotechnology, Chungnam National University

Sep 2021 ~ Present: Assistant Professor, Department of Bio-AI Convergence, College of Engineering, Chungnam National University Graduate School

Apr 2021 ~ Present: Principal Investigator, Institute of Biotechnology, Chungnam National University

Apr 2021 ~ Present: Professional Scientific Collaborator (John R. Yates Ⅲ Lab.), Department of Molecular Medicine, Scripps Research (USA)

Apr 2019 ~ Jan 2021: Postdoctoral Associate (PI: John R. Yates Ⅲ), Department of Molecular Medicine, Scripps Research (USA)

Jun 2015 ~ Feb 2019: Senior Researcher, Institute of Medical and Biological Engineering in Medical Research Center, Seoul National University

Research Topics

1. 공간 단일세포다차원단백체 기반 펩타이드 약물 발굴 (Spatial Single-Cell Multidimensional Proteome for Peptide Drug Discovery)

: 공간 단일세포 다차원 단백체데이터를 기반으로 핵심 단백질 간 네트워크 변화를 확인하여 치료 표적으로의 기능을 규명하고 이를 제어하는 펩타이드 약물 발굴과유효성 검증(Drug discovery and validation of mimic peptides that control the therapeutic target function of key protein-protein interactions based on spatial single cell proteomics).

2. 다차원단백체 빅데이터와 인공지능 기반 임상 바이오마커 발굴 (Clinical Biomarker Discovery through Multidimensional Proteome and AI-guiding)

: LC-MS기반의 단백체 빅데이터와 머신러닝을 이용하여 임상 바이오마커 발굴과 질병 진단 모델 개발 (Discovering clinical biomarkers and developing disease diagnosis models using machine learning on liquid chromatography and mass spectrometry-based proteome big data).

3. 액체크로마토그래피-질량분석기 기반 체외&동반 진단법 개발 (In Vitro & Companion Diagnostic LC-MS Assay in Clinical Laboratory)

: LC-MS기반으로 기존 분석법의 분석적인 성능을 개량하고 검증해서 국제적으로 표준화된 체외진단법과 동반진단법 신의료기술 개발(Improving and verifying analytical performance based on LC-MS assays and developing internationally standardized in vitro and companion diagnostic assays with new health technology).

4. 인공지능 기반 단백질(펩타이드) 약물 디자인 (AI-powered Protein(Peptide) Drug Design)

: 인공지능을 기반으로특정 기능과 구조에 부합하는 단백질(효소) 및 펩타이드약물 디자인(Utilize artificial intelligence to design therapeutic peptides (protein-based drugs) by analyzing alterations in protein structures).

5. 다중오믹스 바이오 빅데이터 분석 플랫폼 개발 (Multi-Omic Bio Big Data Analytics Development)

: 인공지능 기반으로 다중오믹스 빅 데이터 통합 및 분석 도구 개발과 임상 현장에서 의사 결정에 도움을 줄 수 있는 시스템 및 소프트웨어 개발 (Developing a tool that can help the clinician’s decision-making process in hospitals using multi-omic bio big data and artificial intelligence algorithms).

Selected Paper 

Clinical Assay for AFP-L3 by Using Multiple Reaction Monitoring-Mass Spectrometry for Diagnosing Hepatocellular Carcinoma. Clinical Chemistry (ISSN: 0009-9147), 64 (8), 1230-1238 (2018/08/01). LINK

Clinical application of multiple reaction monitoring-mass spectrometry to human epidermal growth factor receptor 2 measurements as a potential diagnostic tool for breast cancer therapy. Clinical Chemistry (ISSN: 0009-9147), 66 (10), 1339-1348 (2020/10/01). LINK

Prediction of Response to Sorafenib in Hepatocellular Carcinoma: A Putative Marker Panel by Multiple Reaction Monitoring-Mass Spectrometry (MRM-MS). Molecular & Cellular Proteomics (ISSN: 1535-9476), 16 (7), 1312-1323 (2017/07/01). LINK

Clinically Applicable Deep Learning Algorithm Using Quantitative Proteomic Data. Journal of Proteome Research (ISSN: 1535-3893), 18 (8), 3195-3202 (2019/07/17). LINK

지도교수 : 김현수 

(2024년 현재)
김우진, 이원석, Shagufa Malik, 이상운, 이예림, 윤윤기, 정현우, 선규빈, 장한얼, 지재호, 최성윤 

Contact :  kimlab@cnu.ac.kr 
Homepage :   https://www.kimlab.site

Open Positions 

(1) 생물학, 생명과학(공학), 화학(공학), 생명(생물)정보학, 컴퓨터과학(공학), 통계학 전공자 등

(2) 유세포분석기, 질량세포분석기, 액체크로마토그래피, 질량분석기등 다양한 바이오 분석기기 전공자 등

(3) 인공지능 (머신러닝, 딥러닝)을 비롯하여생물정보학 및 생물통계학 전공자 등

연구실 지원자(연구원, 학부생, 대학원지원, 박사후과정)는메일(kimlab@cnu.ac.kr)로 본인의 CV를 송부해 주세요.

If you belong to: (1) Students majored in biological, chemical, computer, statistics area or doctors interested in proteomics experiments and big data analysis. (2) Computer scientists who are interested in develop new algorithms which can be readily applied to medicine. (3) Students who are interested in generating and analyzing omics big data (and want to establish startup companies). Expertise in all areas not necessary as we can train. Students who are willing to expose themselves into highly collaborative projects. If you're an enthusiastic and talented young researcher who is interested in joining our team in the future, you may try an internship at our laboratory. Please send your CV to kimlab@cnu.ac.kr