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    ์ƒˆ๋กœ์šด ๊ธฐ์ „์˜ ์•ฝ๋ฌผ ํƒ€๊ฒŸ์œผ๋กœ ์•Œ๋ ค์ง„ NSDHL ๋ฐ PptT์˜ ๊ตฌ์กฐ์— ๊ธฐ๋ฐ˜ํ•œ ์ €ํ•ด์ œ ๊ฐœ๋ฐœ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๋Œ€ํ•™ ์•ฝํ•™๊ณผ, 2020. 8. ์ด๋ด‰์ง„.Structure-based drug design (SBDD)๋Š” ์•ฝ๋ฌผ์„ ๋น ๋ฅด๊ณ  ์ ์€ ๋น„์šฉ์œผ๋กœ ํšจ์œจ์ ์œผ๋กœ ๊ฐœ๋ฐœํ•  ์ˆ˜ ์žˆ๋Š” ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ๊ธฐ์ˆ  ์ค‘ ํ•˜๋‚˜์ด๋‹ค. ์ด ๊ธฐ์ˆ ์— ์žˆ์–ด์„œ ํ•ด๋‹น ๋‹จ๋ฐฑ์งˆ์˜ ์‚ผ์ฐจ์› ๊ตฌ์กฐ์˜ ๊ทœ๋ช…์€ ๊ตฌ์กฐ์— ์ตœ์ ํ™”๋œ ํ™”ํ•ฉ๋ฌผ์˜ ๊ฐœ๋ฐœ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•œ๋‹ค. ์ด ๊ธฐ์ˆ ์„ ์ด์šฉํ•˜์—ฌ ์œ ๋งํ•œ ํšจ์†Œ๋ฅผ ํ‘œ์ ์œผ๋กœํ•˜๋Š” ์˜์•ฝํ’ˆ์˜ ๊ฐœ๋ฐœ์„ ์œ„ํ•ด, ๋ณธ ์—ฐ๊ตฌ์ง„์€ ํ˜ธ๋ชจ์‚ฌํ”ผ์—”์Šค ์œ ๋ž˜ NAD+-dependent steroid dehydrogenase-like (NSDHL)์™€ ๊ฒฐํ•ต๊ท  ์œ ๋ž˜ 4-phosphopantetheinyl transferase (PptT)์˜ ๋‘๊ฐ€์ง€ ์ด‰๋งค ํšจ์†Œ์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋‘ ํšจ์†Œ๋Š” ์‚ฌ๋žŒ๊ณผ ๊ฒฐํ•ต๊ท  ์ฃผ์˜ ์ƒ๋ฌผํ•™์  ๋Œ€์‚ฌ์— ์žˆ์–ด ์ค‘์š”ํ•œ ๊ธฐ๋Šฅ์„ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ธํ•ด ์ƒˆ๋กœ์šด ์•ฝ๋ฌผ ๊ฐœ๋ฐœ ํ‘œ์ ์œผ๋กœ ์ฃผ๋ชฉ๋ฐ›์•˜๋‹ค. NSDHL์€ ์ธ๊ฐ„ ์ฝœ๋ ˆ์Šคํ…Œ๋กค ํ•ฉ์„ฑ์— ํ•„์ˆ˜์ ์ธ ํšจ์†Œ์ด๋ฉฐ ํ‘œํ”ผ ์„ฑ์žฅ ์ธ์ž ์ˆ˜์šฉ์ฒด (EGFR) trafficking pathway์˜ ์กฐ์ ˆ ์ธ์ž์ด๋ฉฐ, ์ฝœ๋ ˆ์Šคํ…Œ๋กค ๊ด€๋ จ ์งˆํ™˜ ๋ฐ ์•”์ข…์— ๋Œ€ํ•œ ์ค‘๋Œ€ํ•œ ๊ด€๋ จ์„ฑ์œผ๋กœ ์ธํ•ด ์ƒˆ๋กœ์šด ํ‘œ์  ๋‹จ๋ฐฑ์งˆ๋กœ์„œ ๊ด€์‹ฌ์„ ๋Œ์–ด์™”๋‹ค. PptT๋Š” ์ฝ”์—”์ž์ž„A์˜ phosphopantethein ๋ถ€๋ถ„์„ carrier protein ๋„๋ฉ”์ธ์˜ ์„ธ๋ฆฐ ์ž”๊ธฐ์— ๊ณต์œ ๊ฒฐํ•ฉ์œผ๋กœ ์ „๋‹ฌํ•˜๋ฉฐ, ๊ฒฐํ•ต์˜ ์„ธํฌ ๋‚ด ์ƒ์กด ๋ฐ persistence์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•˜์—ฌ, ๊ธฐ์กด์˜ ์•Œ๋ ค์ง„ ๊ฒฐํ•ต ์•ฝ๋ฌผ๊ณผ๋Š” ๋‹ค๋ฅธ ์ƒˆ๋กœ์šด ๊ธฐ์ž‘์˜ ์•ฝ๋ฌผ ํƒ€๊ฒŸ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๊ธฐ์งˆ์˜ ๊ฒฐํ•ฉ ๋ถ€์œ„ ๋ฐ ๊ฒฐํ•ฉํ˜•ํƒœ์— ๋Œ€ํ•œ ์„ธ๋ถ€์ ์ธ ๋ฌ˜์‚ฌ์™€ ํ•จ๊ป˜ NSDHL๊ณผ PptT ๋ฐ MSMEG_PPTase์˜ X-์„  ์‚ผ์ฐจ์› ๊ฒฐ์ • ๊ตฌ์กฐ๋ฅผ ๋ณด๊ณ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๋Œ€๋กœ, ๊ตฌ์กฐ ๊ธฐ๋ฐ˜์˜ ๊ฐ€์ƒ ์Šคํฌ๋ฆฌ๋‹ ๋ฐ ์ƒํ™”ํ•™์  ํ‰๊ฐ€๋ฅผ ์ˆ˜ํ–‰ํ•˜์—ฌ NSDHL ๋ฐ PptT์— ๋Œ€ํ•œ ์ƒˆ๋กœ์šด ์–ต์ œ์ œ๋ฅผ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ €ํ•ด๋Šฅ์— ๋Œ€ํ•œ ์ถ”๊ฐ€ ์„ธํฌ๊ธฐ๋ฐ˜ ๊ฒ€์ฆ์€ ์šฐ๋ฆฌ์˜ ์–ต์ œ์ œ๊ฐ€ ํ•ฉ๋ฆฌ์ ์œผ๋กœ ๊ฐœ๋ฐœ๋˜์—ˆ์Œ์„ ๋ฐํ˜€๋ƒˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋Š” NSDHL ๊ด€๋ จ ์งˆ๋ณ‘ ๋ฐ ๊ฒฐํ•ต์— ๋Œ€ํ•ญํ•˜๋Š” ์น˜๋ฃŒ์ œ ๊ฐœ๋ฐœ์„ ์œ„ํ•œ ์ข‹์€ ํ”Œ๋žซํผ์œผ๋กœ์„œ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋‹ค.Structure-based drug design (SBDD) is one of the most efficient techniques to accelerate drug development and make it more cost-effective. The determination of the three dimentional structure of the protein facillitates the development of well-optimized compounds based on the structural information. For development of pharmacological agents targeting novel enzymes using SBDD, we focused on two catalytic enzymes: (i) NAD+-dependent steroid dehydrogenase-like (NSDHL) from Homo sapiens and (ii) 4-phosphopantetheinyl transferase (PptT) from Mycobacterium tuberculosis. These two enzymes have been reported as novel drug targets due to their essential function in biological metabolism of human and M. tuberculsis, respectively. NSDHL is an essential enzyme in human cholesterol synthesis and a regulator of epidermal growth factor receptor (EGFR) trafficking pathways, has attracted interest as a therapeutic target due to its crucial relevance to cholesterol-related diseases and carcinomas. PptT is a crucial enzyme in intracellular survival and persistence of tuberculosis and covalently transfer the phosphopantethein moiety to a serine residue of carrier protein domain. In this study, we reported X-ray crystal structures of NSDHL, PptT and MSMEG_PPTase, which revealed a detailed description of the coenzyme-binding site and the binding mode. A structure-based virtual screening and biochemical evaluation were performed and identified novel inhibitors for NSDHL and PptT, respectively. Furthermore, further cell-based validation on the inhibitory activity revealed that our inhibitors were rationally developed. Overall, these findings could serve as good platforms for the development of therapeutic agents against NSDHL-related diseases and tuberculosis.Chapter 1. Structure-based discovery of novel inhibitors against human NSDHL with the potential to suppress EGFR activity 1 1.1 Introduction 1 1.2 Materials and Methods 4 1.2.1 Gene cloning, protein expression, and purification 4 1.2.2 Crystallization and X-ray data collection 7 1.2.3 Structure determination, refinement, and analysis 7 1.2.4 Isothermal titration calorimetry (ITC) 8 1.2.5 Size-exclusion chromatography with multiangle light scattering (SEC-MALS) 9 1.2.6 Thermal shift assay (TSA) 9 1.2.7 High-throughput virtual screening 10 1.2.8 NADH-based competitive binding assay for identifying inhibitors 11 1.2.9 Synthetic methods and characterization of a small molecule 12 1.2.10 Liquid Chromatography Mass Spectrometry (LCMS) 16 1.2.11 Kinetic solubility 17 1.2.12 Molecular docking 17 1.2.13 Surface plasmon resonance (SPR) 18 1.2.14 Cellular viability assay 19 1.2.15 Flow cytometry 20 1.2.16 Immunoblotting 21 1.2.17 Public data analysis 23 1.2.18 Statistical analysis 23 1.2.19 Data availability 23 1.3 Results 24 1.3.1 Overall structures of human NSDHL 24 1.3.2 Diverse features of human NSDHL structures 31 1.3.3 NAD+ binding site in NSDHL 35 1.3.4 Conformational change induced by the binding of NAD+ allows the binding of a sterol precursor to NSDHL 38 1.3.5 Human NSDHL favorably employs NAD(H) as its coenzyme in the cholesterol synthesis pathway 43 1.3.6 Development of NSDHL inhibitors based on the structural information 46 1.3.7 Proposed binding mode between compound 9 and NSDHL 54 1.3.8 Therapeutic potential of NSDHL inhibition in EGFR-driven cancer 57 1.3.9 The NSDHL inhibitor accelerates EGFR degradation to suppress EGFR-dependent signaling 63 1.4 Discussion 66 Chapter 2. Structure-based discovery of selective inhibitors against PptT from M. tuberculosis 70 2.1 Introduction 70 2.2 Materials and Methods 72 2.2.1 Gene Cloning, protein expression, and purification 72 2.2.2 Crystallization and X-ray data collection 73 2.2.3 Structure determination, refinement, and analysis 75 2.2.4 High throughput virtual screening 75 2.2.5 BpsA-PptT coupled assay for searching inhibitors 76 2.2.6 2. Cytotoxicity in eukaryotic cell 77 2.2.7 Bactericidal activity in mycobacteria-infected murine macrophages 77 2.3 Results 79 2.3.1 Overall structures of mycobacteria PPTases 79 2.3.2 Structural features and active sites of mycobacteria PPTases 82 2.3.3 Development of small molecule inhibitors to suppress PptT activity. 86 2.3.4 Identification of the selective scaffold inhibiting PptT from M. tuberculosis. 92 2.3.5 PptT inhibitor reveals the bactericidal activity in mycobacteria-infected macrophages 94 2.4 Discussion 96 References 98 ๊ตญ๋ฌธ์ดˆ๋ก 108Docto

    Functional Characterization of Plant ADP-ribosylation Factor 1 Gene

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ƒ๋ช…๊ณผํ•™๋ถ€, 2017. 8. ํ™์ฃผ๋ด‰.์‹๋ฌผ์˜ ์ƒ์žฅ๊ณผ ๋ฐœ๋‹ฌ์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ์ฃผ๋Š” ๊ณ ์˜จ, ๊ฑด์กฐ, ์ €์˜จ, ๊ณ ์—ผ๋„์™€ ๊ฐ™์€ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค๋Š” ์‹๋ฌผ์˜ ์ƒ์žฅ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ž‘๋ฌผ์˜ ํ’ˆ์งˆ๊ณผ ์ƒ์‚ฐ์„ฑ์—๋„ ํฐ ํ”ผํ•ด๋ฅผ ์ฃผ๊ฒŒ ๋œ๋‹ค. ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•œ ์ž‘๋ฌผ์˜ ํ”ผํ•ด๋Š” ์ง€๊ตฌ ์˜จ๋‚œํ™”๋กœ ์ธํ•œ ๊ธฐํ›„๋ณ€ํ™”์— ์˜ํ•ด ๋” ์ฆ๊ฐ€ ๋  ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ๊ทธ๋ ‡๊ธฐ ๋•Œ๋ฌธ์— ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค์— ๋‚ด์„ฑ์„ ๊ฐ–๋Š” ์ž‘๋ฌผ์„ ๊ฐœ๋ฐœํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค์—์„œ ์‹๋ฌผ์ฒด์—์„œ ์ผ์–ด๋‚˜๋Š” ์œ ์ „์ž ์ˆ˜์ค€์˜ ๋ฐ˜์‘์„ ์—ฐ๊ตฌํ•˜๋Š” ๊ฒƒ์€ ๋งค์šฐ ์ค‘์š”ํ•˜๋‹ค. ๊ณ ์˜จ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์ฒ˜๋ฆฌํ•œ ๊ณ ์ถ”(Capsicum annum)์˜ cDNA library์—์„œ ๊ณ ์˜จ ์œ ๋„์„ฑ cDNA ํด๋ก ์ธ CaPP2C (Capsicum annum protein phosphatase 2C), CaMAPK1 (Capsicum annum mitogen-activated protein kinase 1), CaBI-1 (Capsicum annum Bax inhibitor-1)์ด ๋ถ„๋ฆฌ ๋˜์—ˆ๊ณ , 3๊ฐœ์˜ ์œ ์ „์ž์˜ ์ „์‚ฌ๋ฌผ๋“ค์€ ๊ณ ์˜จ์ŠคํŠธ๋ ˆ์Šค ์ด์™ธ์— ๋‹ค์–‘ํ•œ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค ์กฐ๊ฑด์—์„œ ๋ฐœํ˜„์ด ์œ ๋„๋˜์—ˆ๋‹ค. CaPP2C, CaMAPK1, CaBI-1์„ CaMV 35S ํ”„๋กœ๋ชจํ„ฐ์˜ ์ œ์–ด ํ•˜์— Agrobacterium์„ ์ด์šฉํ•œ ํ˜•์งˆ์ „ํ™˜์œผ๋กœ ๋‹ด๋ฐฐ(Nicotiana tabacum)์— ๊ณผ๋‹ค ๋ฐœํ˜„ ์‹œ์ผฐ๋‹ค. RNA blot ๋ถ„์„์„ ํ†ตํ•ด CaPP2C, CaMAPK1, CaBI-1์ด ๊ฐ๊ฐ์˜ ํ˜•์งˆ์ „ํ™˜์ฒด ๋‹ด๋ฐฐ์—์„œ ๊ณผ๋‹ค๋ฐœํ˜„ ๋˜์—ˆ์Œ์„ ํ™•์ธ ํ•˜์˜€์œผ๋ฉฐ ์ด๋“ค ํ˜•์งˆ์ „ํ™˜์ฒด ๋‹ด๋ฐฐ์˜ ์ƒ์ฒด๋ถ„์„์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. 3๊ฐœ์˜ ์œ ์ „์ž๋ฅผ ๊ฐ๊ฐ ๊ณผ๋‹ค๋ฐœํ˜„ํ•œ ํ˜•์งˆ์ „ํ™˜์ฒด๋Š” ๋Œ€์กฐ๊ตฐ ์‹๋ฌผ์ฒด์™€ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ๊ทธ๋“ค์˜ ํ˜•ํƒœ์  ์ฐจ์ด๋ฅผ ํ™•์ธ ํ•  ์ˆ˜ ์—†์—ˆ๊ณ  ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค์—๋„ ๋‚ด์„ฑ์„ ๋ณด์ด์ง€ ์•Š์•˜๋‹ค. 3๊ฐœ์˜ ์œ ์ „์ž๋ฅผ ๊ฐ๊ฐ ๊ณผ๋‹ค๋ฐœํ˜„ํ•œ ํ˜•์งˆ์ „ํ™˜ ์‹๋ฌผ์ฒด์˜ ์ „์‚ฌ์ฒด ๋ถ„์„์—์„œ, ๊ณตํ†ต์ ์œผ๋กœ ARF ์œ ์ „์ž์˜ ์ƒํ–ฅ์กฐ์ ˆ์ด ํ™•์ธ ๋˜์–ด, ARF ์œ ์ „์ž๊ฐ€ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค์— ๊ธฐ๋Šฅ์„ ๊ฐ€์งˆ ๊ฒƒ์ด๋ผ ์˜ˆ์ƒํ•˜์—ฌ ๋‹ด๋ฐฐ์—์„œ ARF ์œ ์ „์ž๋ฅผ ๋ถ„๋ฆฌํ•˜์˜€๋‹ค. ๋ถ„๋ฆฌ๋œ 9๊ฐœ์˜ ๋‹ด๋ฐฐ ARF cDNA ํด๋ก ์ค‘ NtARF1 (Nicotiana tabacum ADP-ribosylation factor 1 )์ด CaPP2C ํ˜•์งˆ์ „ํ™˜์ฒด ๋‹ด๋ฐฐ์—์„œ ๋†’์€ ์ˆ˜์ค€์œผ๋กœ ๋ฐœํ˜„๋˜๋Š” ๊ฒƒ์ด ํ™•์ธ ๋˜์—ˆ๊ณ , NtARF1์˜ ์ „์‚ฌ๋ฌผ์€ ๊ณ ์˜จ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•ด ๋ฐœํ˜„์ด ์œ ๋„๋˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. NtARF1์˜ ์‹๋ฌผ์ฒด๋‚ด์—์„œ์˜ ๊ธฐ๋Šฅ์„ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•˜์—ฌ NtARF1์„ ๊ณผ๋‹ค๋ฐœํ˜„ ์‹œํ‚จ ํ˜•์งˆ์ „ํ™˜์ฒด ๋‹ด๋ฐฐ๋ฅผ ์ œ์ž‘ํ•˜์˜€๋‹ค. ์ƒ์ฒด ๋ถ„์„ ๊ฒฐ๊ณผ์—์„œ NtARF1 ํ˜•์งˆ์ „ํ™˜์ฒด ๋‹ด๋ฐฐ๋Š” ๋Œ€์กฐ๊ตฐ ์‹๋ฌผ์ฒด์™€ ๋น„๊ตํ•˜์—ฌ ์ƒ์žฅ๋ฅ , ์‹๋ฌผ ๋†’์ด ๋ฐ ๊ฐ๊ฐ์˜ ์‹๋ฌผ์ฒด์—์„œ ๋ฐœ๋‹ฌ๋œ ๊ฝƒ์˜ ๊ฐœ์ˆ˜๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€์œผ๋ฉฐ ๋น ๋ฅธ ์ข…์ž ๋ฐœ์•„์œจ์„ ๋ณด์—ฌ ์ฃผ์—ˆ๋‹ค. ๋˜ํ•œ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค ์กฐ๊ฑด์ธ ๊ณ ์˜จ, ๊ฑด์กฐ, ์ €์˜จ, ๊ณ ์—ผ๋„์—์„œ NtARF1 ํ˜•์งˆ์ „ํ™˜์ฒด ๋‹ด๋ฐฐ๋Š” ๋Œ€์กฐ๊ตฐ ์‹๋ฌผ์ฒด์™€ ๋น„๊ตํ•˜์—ฌ ๋†’์€ ์ƒ์žฅ๋ฅ ์— ์˜ํ•ด ์ŠคํŠธ๋ ˆ์Šค์— ๋Œ€ํ•œ ๋‚ด์„ฑ์ด ์ฆ๊ฐ€ํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ ๋˜์—ˆ๋‹ค. ์ „์ฒด ์œ ์ „์ฒด ์„œ์—ด์ด ๋ถ„์„๋œ ์•ผ์ƒ์‹๋ฌผ ๋ฐ ์ž‘๋ฌผ๋“ค์˜ database๋ฅผ ์ด์šฉํ•˜์—ฌ ARF family protein์„ ๋ถ„๋ฅ˜ํ•˜์˜€๋‹ค. ๊ณ„ํ†ตํ•™์  ๋ถ„๋ฅ˜๋ฅผ ํ†ตํ•ด ARF family protein๋Š” 4๊ฐœ์˜ Class๋กœ ๋‚˜๋ˆ„์—ˆ์œผ๋ฉฐ Class1 ARF๊ฐ€ ๋ชจ๋“  ์•ผ์ƒ์‹๋ฌผ ๋ฐ ์ž‘๋ฌผ๋“ค์—์„œ ๊ฐ€์žฅ ๋งŽ์ด ์กด์žฌํ•˜์˜€์œผ๋ฉฐ ํŠนํžˆ ์•ผ์ƒ์‹๋ฌผ๊ณผ ๋น„๊ตํ•˜์—ฌ ์ž‘๋ฌผ์—์„œ Class 1 ARF๊ฐ€ ๋งŽ์€ ์ˆซ์ž๊ฐ€ ์กด์žฌํ•˜์˜€๋‹ค.1. ์„œ๋ก  1 2. 1์žฅ CaPP2C, CaMAPK1, CaBI-1 ์œ ์ „์ž๊ฐ€ ๊ณผ๋‹ค๋ฐœํ˜„๋œ ํ˜•์งˆ์ „ํ™˜ ์‹๋ฌผ์ฒด์˜ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค ๋‚ด์„ฑ์— ๊ด€ํ•œ ์—ฐ๊ตฌ 5 2.1 ์„œ๋ก  5 2.2 ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 10 2.3 ๊ฒฐ๊ณผ 17 2.4 ๊ณ ์ฐฐ 54 3. 2์žฅ NtARF1 ์œ ์ „์ž์˜ ์‹๋ฌผ์ƒ์žฅ ๋ฐ ๋น„์ƒ๋ฌผ ์ŠคํŠธ๋ ˆ์Šค์™€์˜ ์—ฐ๊ด€ ๊ด€๊ณ„ 58 3.1 ์„œ๋ก  58 3.2 ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 64 3.3 ๊ฒฐ๊ณผ 72 3.4 ๊ณ ์ฐฐ 106 4. 3์žฅ ์•ผ์ƒ์‹๋ฌผ๊ณผ ์ž‘๋ฌผ๋“ค์—์„œ์˜ ARF์˜ ๋ถ„๋ฅ˜ ๋ฐ ๋น„๊ต 114 4.1 ์„œ๋ก  114 4.2 ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 117 4.3 ๊ฒฐ๊ณผ 119 4.4 ๊ณ ์ฐฐ 141 5. ๊ฒฐ๋ก  144 6. ์ฐธ๊ณ ๋ฌธํ—Œ 146 ABSTRACT 157Docto

    ๋ฌด์„  Ad Hoc ํ†ต์‹ ๋ง ์ง€์›์„ ์œ„ํ•œ ํ”„๋กœํ† ์ฝœ ์—ฐ๊ตฌ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์ „๊ธฐ.์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2001.Docto

    ํ‰ํŒ ๋‚ด ์ „๋ฐฉํ–ฅ ์ „๋‹จ์ˆ˜ํ‰ํŒŒ๋ฅผ ์‚ฌ์šฉํ•œ ํ–ฅ์ƒ๋œ ๊ฐ€์ƒ ์‹œ์—ญ์ „ ์˜์ƒํ™”

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ๊ธฐ๊ณ„ํ•ญ๊ณต๊ณตํ•™๋ถ€(๋ฉ€ํ‹ฐ์Šค์ผ€์ผ ๊ธฐ๊ณ„์„ค๊ณ„์ „๊ณต), 2018. 8. ๊น€์œค์˜.์‹ค์‹œ๊ฐ„ ๊ตฌ์กฐ ์•ˆ์ „ ๋ชจ๋‹ˆํ„ฐ๋ง ๋ถ„์•ผ์—์„œ๋Š” ์œ ๋„ ์ดˆ์ŒํŒŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ‰ํŒ ๋‚ด ๊ฒฐํ•จ์„ ๊ฒ€์ถœํ•˜๋Š” ๋ฐฉ๋ฒ•๋“ค์ด ๋„๋ฆฌ ์—ฐ๊ตฌ๋˜์–ด ์™”๋‹ค. ๋Œ€๋ถ€๋ถ„์˜ ์•ž์„  ์—ฐ๊ตฌ๋“ค์—์„œ๋Š” ๋žจํŒŒ๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ธฐ์—, ์ „ํŒŒ ๊ณผ์ •์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋žจํŒŒ์˜ ๋ถ„์‚ฐ ํŠน์„ฑ๊ณผ ๋‹ค์ค‘ ๋ชจ๋“œ ๋ณ€ํ™˜ ํŠน์„ฑ์„ ์™„ํ™”ํ•˜๊ธฐ ์œ„ํ•ด ์ƒ๋‹นํ•œ ๋…ธ๋ ฅ์„ ๊ธฐ์šธ์–ด์•ผ ํ–ˆ๋‹ค. ์ด์— ๋ฐ˜ํ•ด, ํ‰ํŒ ๋‚ด ๊ฒฐํ•จ ๊ฒ€์ถœ์— ์ ํ•ฉํ•œ ์ „ํŒŒ ํŠน์„ฑ์„ ์ง€๋‹ˆ๊ณ  ์žˆ์œผ๋‚˜ ์ „์ฒด ๋ฐฉํ–ฅ์œผ๋กœ ๊ท ์ผํ•˜๊ฒŒ ์ „๋‹จ์ˆ˜ํ‰ํŒŒ๋ฅผ ๋ฐœ์ƒ์‹œํ‚ค๊ณ  ๊ฒ€์ถœํ•  ์ˆ˜ ์žˆ๋Š” ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•๋“ค์ด ๋ถ€์กฑํ•˜์—ฌ, ์ „๋‹จ์ˆ˜ํ‰ํŒŒ๋Š” ํ‰ํŒ ๋‚ด ๊ฒฐํ•จ ๊ฒ€์ถœ์— ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์ง€ ๋ชปํ•˜์˜€๋‹ค. ํŠนํžˆ ์ „๋‹จ์ˆ˜ํ‰ํŒŒ ํŠธ๋žœ์Šค๋“€์„œ์˜ ์ฃผํŒŒ์ˆ˜ ์‘๋‹ต ํŠน์„ฑ์„ ๋ณด์ƒํ•˜๋Š” ๋ฐ ์ค‘์ ์„ ๋‘๊ณ  ์ „๋ฐฉํ–ฅ ์ „๋‹จ์ˆ˜ํ‰ํŒŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒฐํ•จ ํ‰ํŒ๋“ค์˜ ์˜์ƒ์„ ๊ตฌํ˜„ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ๋ณธ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œํ•œ๋‹ค. ์ฃผํŒŒ์ˆ˜ ์˜์กด์ ์ธ ํŠธ๋žœ์Šค๋“€์„œ์˜ ํŠน์„ฑ์€ ์˜์ƒ ํ™”์งˆ์— ์ƒ๋‹นํžˆ ์˜ํ–ฅ์„ ์ค„ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์— ์‹ ๋ขฐํ•  ์ˆ˜ ์žˆ๋Š” ์ง„๋‹จ ์˜์ƒ์„ ๊ฐ–๊ธฐ ์œ„ํ•ด ํŠธ๋žœ์Šค๋“€์„œ์˜ ์ฃผํŒŒ์ˆ˜ ์‘๋‹ต ํŠน์„ฑ์˜ ๋ณด์ƒ์€ ํ•„์ˆ˜์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ๋‹จ์ˆœํ•˜๋ฉด์„œ๋„ ํšจ๊ณผ์ ์ด์–ด์„œ ๊ณ ์† ์˜์ƒ ์ฒ˜๋ฆฌ์— ์ ํ•ฉํ•œ ์ „๋ฐฉํ–ฅ ์ž๊ธฐ ๋ณ€ํ˜• ํŒจ์น˜ ํŠธ๋žœ์Šค๋“€์„œ์˜ ์ „๋‹ฌ ํ•จ์ˆ˜๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๊ธˆ์† ํ‰ํŒ๋“ค ๋‚ด ๊ตฌ์กฐ ๊ฒฐํ•จ๋“ค์˜ ์œ„์น˜์™€ ํ˜•์ƒ์„ ์‹œ๊ฐํ™”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ, ๊ฐ€์ƒ ์‹œ์—ญ์ „ ์˜์ƒ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜๊ณ  ์˜์ƒ ์ฒ˜๋ฆฌ ๊ณผ์ •์—์„œ ์ „๋ฐฉํ–ฅ ์ž๊ธฐ ๋ณ€ํ˜• ํŒจ์น˜ ํŠธ๋žœ์Šค๋“€์„œ์˜ ์ „๋‹ฌ ํ•จ์ˆ˜๋“ค์˜ ์˜ํ–ฅ์„ ๋ณด์ƒํ•˜๊ธฐ ์œ„ํ•ด 2 ๊ฐ€์ง€ ๋Œ€์•ˆ ๊ธฐ์ˆ ๋“ค์„ ๊ณ ๋ คํ•˜์˜€๋‹ค. ์ด ๊ฒฐ๊ณผ๋กœ ํ–ฅ์ƒ๋œ ๊ฐ€์ƒ ์‹œ์—ญ์ „ ์˜์ƒ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•œ ๋ช‡๋ช‡ ๊ธˆ์† ํ‰ํŒ๋“ค์˜ ์˜์ƒ๋“ค์—์„œ ๊ฒฐํ•จ์˜ ์œ„์น˜๋ฅผ ๋ช…ํ™•ํ•˜๊ฒŒ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ํฌ๋ง์ ์ธ ์‹คํ—˜ ๊ฒฐ๊ณผ๋“ค์€ ์ „๋‹จ์ˆ˜ํ‰ํŒŒ๋ฅผ ๊ธฐ๋ฐ˜ํ•œ ํ–ฅ์ƒ๋œ ๊ฐ€์ƒ ์‹œ์—ญ์ „ ์˜์ƒ ๋ฐฉ๋ฒ•์ด ํ‰ํŒ ๊ตฌ์กฐ๋ฌผ์— ํšจ๊ณผ์ ์ธ ๊ฒ€์‚ฌ ๋ฐฉ๋ฒ•์ด ๋  ์ˆ˜ ์žˆ์Œ์„ ๋ณด์—ฌ ์ค€๋‹ค. Abstract โ…ฐ Table of contents โ…ฒ List of tables โ…ต List of figures โ…ท Chapter 1 Introduction 1 1.1 Research motivation 1 1.2 Research objectives 6 1.3 Thesis outline 7 Chapter 2 Ultrasonic guided waves in plate structures 14 2.1 Chapter overview 14 2.2 Ultrasonic guided waves in plate structures 15 2.2.1 Governing equations for stress wave motion 15 2.2.2 Lamb waves in thin plate with free spaces 18 2.2.3 The propagation of SH waves in an infinite plate 24 2.3 Merits of using SH waves in plate structures 26 Chapter 3 Derivation of the OSH-MPT transfer functions 36 3.1 Chapter overview 36 3.2 Magnetostrictive patch transducer 36 3.3 Configuration of OSH-MPT 38 3.4 The transfer functions of OSH-MPT 40 Chapter 4 Virtual time-reversal method 53 4.1 Chapter overview 53 4.2 VTR process of guided waves in a thin defect-free plate 53 4.3 VTR process of guided waves in a thin plate with a defect 57 Chapter 5 Enhanced virtual time-reversal method 62 5.1 Chapter overview 62 5.2 Processes of EVTR method 1 63 5.3 Processes of EVTR method 2 64 5.4 Simulation with noises 66 Chapter 6 Preprocessing and image processing 79 6.1 Chapter overview 79 6.2 Pre-processing of the measured data 79 6.3 ATD based imaging algorithm 83 Chapter 7 Basic Experiments 88 7.1 Chapter overview 88 7.2 Basic experimental setup 88 7.3 Verification of the transfer functions of OSH MPT 89 7.4 Investigation on the threshold values of the transfer functions of OSH-MPT 91 7.5 Effect of the vertical position of the magnet on imaging 92 7.6 Signal Reproducibility according to bonding conditions 93 7.7 Performance comparison of VTR and EVTR with simple waveforms 94 Chapter 8 SH0 wave tomography and damage localization 110 8.1 Chapter overview 110 8.2 Mesures of image quality 110 8.3 Defect identification by SH0 wave tomography I 112 8.3.1 Simulation condition 113 8.3.2 Simulated diagnostic images for the plate with shallow notches 113 8.3.3 Simulated diagnostic images for the plate with adjacent notches 115 8.4 Defect identification by SH0 wave tomography II 116 8.4.1 Experimental setup 116 8.4.2 SH0 wave tomography constructed by VTR 117 8.4.3 SH0 wave tomography constructed by EVTR-Method 1 119 8.4.4 SH0 wave tomography constructed by EVTR-Method 2 120 Chapter 9 Conclusion 146 Appendix A Shape effect of the receiving OSH-MPT 148 Appendix B Shape identification by SH0 wave tomography 157 References 166 Abstract in Korean 176Docto

    Abnormal Grain Growth in Fe-3%Si Steel Induced by Laser Annealing and Low Deformation

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์žฌ๋ฃŒ๊ณตํ•™๋ถ€, 2012. 2. ํ™ฉ๋†๋ฌธ.Fe-3%Si steel์—์„œ์˜ [001]๋ฐฉ์œ„ grain์˜ ์„ ํƒ์  ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ ํ˜„์ƒ(Abnormal Grain Growth)์€ 1935๋…„ N.P. Goss์— ์˜ํ•˜์—ฌ ์ตœ์ดˆ๋กœ ๋ณด๊ณ ๋œ ์ดํ›„์— ๋ฐฉํ–ฅ์„ฑ ์ „๊ธฐ๊ฐ•ํŒ ์ƒ์‚ฐ์— ์žˆ์–ด์„œ ํ•ต์‹ฌ์ ์ธ ์š”์†Œ๋กœ ์ด์šฉ๋˜๊ณ  ์žˆ๋‹ค. POSCO์—์„œ ํ˜„์žฌ ์ƒ์‚ฐํ•˜๊ณ  ์žˆ๋Š” ๋ฐฉํ–ฅ์„ฑ ์ „๊ธฐ๊ฐ•ํŒ๋„ Goss grain([001]๋ฐฉ์œ„)์˜ ์„ ํƒ์  ๋น„์ •์ƒ ์ž…์žํ˜„์ƒ์„ ๊ทน๋Œ€ํ™” ํ•˜์—ฌ ์ด์šฉํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์„ ํƒ์  ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ ํ˜„์ƒ์˜ mechanism์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๋“ค์ด ์ง„ํ–‰๋˜์–ด ์™”๊ณ , ๋ณธ ์—ฐ๊ตฌ ๊ทธ๋ฃน์—์„œ๋Š” sub-boundary enhanced solid-state wetting์ด๋ก ์„ ์ œ์•ˆํ•˜๊ณ  ์žˆ๋‹ค. ์ด ์ด๋ก ์€, ๋‚ฎ์€ energy๋ฅผ ๊ฐ–๋Š” sub-boundary์˜ ์กด์žฌ์— ์˜ํ•˜์—ฌ ๊ทน๋Œ€ํ™” ๋œ grain boundary energy anisotropy์— ์˜ํ•œ solid-state wetting ํ˜„์ƒ์ด ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ์‹œ์˜ ์„ฑ์žฅ mechanism์œผ๋กœ ์ž‘์šฉํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ฐฉํ–ฅ์„ฑ ์ „๊ธฐ๊ฐ•ํŒ์˜ ์ง‘ํ•ฉ์กฐ์ง ์ œ์–ด๊ธฐ์ˆ  ๊ฐœ๋ฐœ์— ์žˆ์–ด์„œ ๊ฐ€์žฅ ํฐ ๋ฌธ์ œ์ ์€, ๋ณต์žกํ•œ ์ƒ์‚ฐ ๊ณต์ •๊ณผ 2์ฐจ ์žฌ๊ฒฐ์ •์˜ ์ •ํ™•ํ•œ mechanism์— ๋Œ€ํ•œ ์ดํ•ด ๋ถ€์กฑ์œผ๋กœ ์ธํ•˜์—ฌ ๊ณต์ • ๋ณ€์ˆ˜์˜ ๋ณ€ํ™”๊ฐ€ 2์ฐจ ์žฌ๊ฒฐ์ • ์ง‘ํ•ฉ์กฐ์ง์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์˜ˆ์ธกํ•˜๊ธฐ ์–ด๋ ต๋‹ค๋Š” ์ ์ด์—ˆ๋‹ค. ์ด์—, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” sub-boundary enhanced solid-state wetting์ด๋ก ์— ์˜ํ•œ ์ดํ•ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ํ•˜์—ฌ Goss ์ด์™ธ์˜ ๋ฐฉ์œ„์˜ grain์ด sub-boundary๋ฅผ ๊ฐ€์ง€๊ณ  ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ์„ ์ผ์œผํ‚ฌ ์ˆ˜ ์žˆ๋„๋ก ์ง‘ํ•ฉ์กฐ์ง์„ ์ œ์–ดํ•˜๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ์ฒซ์งธ๋กœ, 0.3mm๋‘๊ป˜๊นŒ์ง€ ๋ƒ‰๊ฐ„์••์—ฐ์„ ๋งˆ์นœ Fe-3%Si steel์‹œํŽธ์— ๋Œ€ํ•˜์—ฌ pulse laser๋ฅผ ์ด์šฉํ•œ ๋ถ€๋ถ„์  annealing์„ ํ†ตํ•ด ํšŒ๋ณต์„ ์ผ์œผ์ผœ ์ดํ›„์˜ 2์ฐจ ์žฌ๊ฒฐ์ • ๊ณผ์ •์—์„œ ํ•ด๋‹น ๋ถ€๋ถ„์˜ Goss์ด์™ธ ๋ฐฉ์œ„์˜ ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ์„ ์œ ๋„ํ•˜์˜€๋‹ค. ๋‘˜์งธ๋กœ, 1์ฐจ ์žฌ๊ฒฐ์ •์„ ๋งˆ์นœ Fe-3%Si steel์‹œํŽธ์— micro-indenter๋ฅผ ์ด์šฉํ•ด low deformation์„ ๊ฐ€ํ•˜๊ณ  ๋‹ค์‹œ 1์ฐจ ์žฌ๊ฒฐ์ • ๋ฐ 2์ฐจ ์žฌ๊ฒฐ์ • ๊ณผ์ •์„ ํ†ตํ•˜์—ฌ ํ•ด๋‹น ๋ถ€๋ถ„์—์„œ Goss ์ด์™ธ ๋ฐฉ์œ„์˜ ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ์„ ์œ ๋„ํ•˜์˜€๋‹ค. ๋‘ ๊ฒฝ์šฐ ๋ชจ๋‘์— ์žˆ์–ด์„œ Goss ์ด์™ธ ๋ฐฉ์œ„์˜ ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅ ํ˜„์ƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, low deformation์— ์˜ํ•˜์—ฌ ์„ฑ์žฅํ•œ ๋น„์ •์ƒ ์ž…์ž ์„ฑ์žฅ grain์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ solid-state wetting์— ์œ ๋ฆฌํ•œ low energy boundary์— ํ•ด๋‹นํ•˜๋Š” โˆ‘ 1~9์˜ ๋ถ„์œจ์ด ์ƒ๋Œ€์ ์œผ๋กœ ํฌ๊ฒŒ ์„ฑ์žฅํ•œ ๋น„์ •์ƒ ์ž…์ž์„ฑ์žฅgrain์—์„œ ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹คMaste

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์น˜์˜ํ•™๊ณผ ์น˜์ฃผ๊ณผํ•™ ์ „๊ณต,1995.Docto

    An Optimization of Polymer Flooding by Multi-objective Functions in the Depleting Reservoir

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    Thesis (master`s)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ธฐ๊ณ„์„ค๊ณ„ํ•™๊ณผ,1997.Maste

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    DoctorMembrane proteins play crucial roles in communication across the cell membrane through dynamic bio-molecular interactions. More than half of proteins are involved in cellular interactions on the plasma membrane, and these interactions determine the cell fate in response to various environmental factors. Membrane proteins have a various structure in plasma membrane including single spanning transmembrane domain, seven transmembrane domains, embedding with beta barrel and peripheral proteins. And these membrane protein regulate and mediate the function including various enzymatic activity, transporting the molecules, recognition, and signal transduction. However, among these various property, diffusion of the membrane protein on the plasma membrane is one kind of physical characters. The definition of diffusion is that the net movement of molecules from a region of high concentration to a region of low concentration as a result of random motion of the molecules. Thus, using the diffusivity of target membrane protein, we could distinguish and analyze binding state and molecular reaction progresses. To elucidate the diffusivity of target membrane protein on living plasma membrane, we utilize super-resolution based single-particle tracking microscope techniques. The 405, 488, 561, and 642 nm laser line and TIRF type objective lens equipped super-resolution microscopy enables detection of multiple individual particles using photoactivatable fluorophores linked membrane protein. And we employ the EGFR family and Syntaxin1A as a model membrane protein. At the beginning, we show a diffusional mobility alteration for analyzing the interaction between membrane protein in aqueous part and ligands in the crowded membrane of living cells. That mobility shift was sensitive to the size of the binder. And also have a molecular specificity. And alteration was caused from direct binding of proteins, not the result of signal transduction. In addition, we combine diffusion coefficient distribution of each states from imaging and Reaction Progress Kinetic Analysis (RPKA). A single particle tracking-based reaction progress kinetic analysis was developed to simultaneously determine the kinetics of multiple states of protein complexes in the membrane of single living cell. The subpopulation ratios of different states were quantitatively extracted from the diffusion coefficient distribution. Using this method, we investigate the series of molecular mechanisms of EGFR induced by cetuximab. The environment of live cell membrane was extremely complex to determine the factor that related with diffusivity, so we employ the in vitro membrane and membrane protein system. Using supported lipid bilayer (SLB) system, we prove the effect of the concentration and size of extracellular domain of membrane protein in diffusion. In diluted membrane protein situation, the result of experiment and simulation shows similar as a Saffman and Delbrรผck model. However, in crowded and complex situation, the diffusion of membrane protein decreased after molecular binding similar to previous live cell experiment. Lastly, I apply the imaging technique to drug screening field. Screening drug candidates rapidly is the first step for developing new pharmaceutical drugs. One of the promising ways to reduce the screening steps and cost is to use directly living cells for screening, instead of using purified target proteins. The compounds screened using living cells will have more biologically activities than those screened from in vitro assays. Here I report a robust method for screening drug candidates in a living cell based on single-protein imaging. I tested three different membrane proteins of epidermal growth factor receptor (EGFR), ErbB2, and ErbB3 and found effective natural compounds for each protein. The screening method I introduce will be widely used for screening the potential drug candidates using a living cell
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