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dc.contributor.advisor김완규-
dc.contributor.advisor이상혁-
dc.contributor.author류한나-
dc.creator류한나-
dc.date.accessioned2016-08-26T04:08:28Z-
dc.date.available2016-08-26T04:08:28Z-
dc.date.issued2014-
dc.identifier.otherOAK-000000089666-
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/211110-
dc.identifier.urihttp://dcollection.ewha.ac.kr/jsp/common/DcLoOrgPer.jsp?sItemId=000000089666-
dc.description.abstract오늘날 High Throughput Screening (HTS) 기법을 이용한 신약 개발이 활성화 되면서 HTS 데이터의 양은 빠르게 늘어나고 있으며 그에 따라 HTS의 활성 화합물에서 보다 의미 있는 정보를 도출 해 내는 것이 중요한 이슈로 떠오르고 있다. 현재 많은 연구에서 HTS 결과 해석은 표적 단백질에 활성 테스트를 거친 화합물들을 단순히 실험 결과 값에 따라 선별하는 데에 그치고 있어 HTS 결과에서 약물 후보를 선정 하기까지의 절차와 비용을 줄이고 타당성을 높일 수 있는 추가적인 해석 기법이 필요하다. 이 논문에서는 HTS 혹은 Phenotypic screening의 활성 화합물을 비롯하여 특성을 밝히고자 하는 임의의 compound set을 해석할 수 있는 분석 기법을 설명하고 실제 분석이 가능한 웹 도구를 개발하였다. 본 연구는 5천만개의 compound와 그 표적이 되는 9천여개의 타겟 유전자 대한 정보를 사용하여 광범위한 화합물의 분석이 가능하며 compound set을 타겟 family와 scaffold 차원에 적용시킴으로써 기존의 방법과 차별화 된 해석을 제공한다. 또한 compound의 타겟을 추정하는 Similarity Ensemble Approach(SEA)를 이용한 타겟의 유사도 분석에서 타겟 유전자 간의 상호작용과 약물의 side effect, off-target activity 등을 찾아낼 수 있는 분석 방향을 제시한다. 더 나아가 SEA의 성능 평가를 수행 하였고 현재 이 웹 도구는 온라인 상에서 서비스 되고 있다. Compound의 구조와 타겟 정보를 이용한 compound set 분석이 스크리닝을 이용한 신약 개발에 있어 비용 절감과 다양한 해석의 단서를 제공 할 것이다.;As investment of new medicine is activated by High Throughput Screening (HTS) technique, the amount of HTS is rapidly increasing, and accordingly, the current attention is focused on the deduction of significant information from hit compound of HTS. The result interpretation from numerous recent studies merely indicates the sorting, according to the experiment result rate on compound of activation test. It is required in tangible future to come up with additional accounting method that is capable of reducing product and procedure till choosing the medicine nominee of HTS result. On this study, we mainly focus on the analogical method that potentially interpretation the set of arbitrary compound to tell the characteristics of HTS or phenotypic screening’s hit compound. Furthermore, invested the web-tool that can actually analysis. Utilizing the information 50,000,000 compounds and 9,000 target genes, it provides the differentiated account from existing ones, through capacity of extensive range of analyzing the compounds and implying the degree of scaffold and target family into compound set. Moreover, it also suggest the way for direction of future analysis with the interaction of target’s similarity analysis by utilizing Similarity Ensemble Approach(SEA) that assign the target of compound, side effect of drug, and the method to track down the off-target activity. Not only that, but also it has been implemented the evaluation of SEA and web tool is served on-line. The compound set analysis using the target information and structure of compound will provide the reducing costs and diverse analyzing account in way for development of new medicine using screening.-
dc.description.tableofcontentsI. Introduction 1 A. Chemical databases 3 1. Types of chemical databases 3 2. List of chemical databases 3 B. Chemical compound representation 6 1. SMILES 6 2. 3D representation 7 3. MDL/MOL 8 C. Tools for obtaining compound information 9 1. Daylight chemical information systems 9 2. RDKit 9 3. ChemDraw 9 D. Scaffold data 11 1. Scaffold extraction from compound 11 2. Scaffold tree 13 E. Similarity Ensemble Approach (SEA) 14 II. Compound Set Analysis using SEA 16 A. Compound-Target interaction 16 B. Similarity calculation 18 C. Background similarity score 19 D. Target family classification 23 E. Similarity analysis of target family 24 1. Global similarity among all target families 24 2. Target family network via structural similarities of ligand sets 26 F. Performance evaluation of SEA 31 G. Similar target families 37 III. Web-service for Compound Set Analysis 40 A. Data information 40 1. Compound information 41 2. Scaffold data 43 3. Target gene information 44 B. Statistics of collected data 45 1. The number of targets and target families by category 45 2. The number of target families by class level 46 3. Compound set size related target 47 4. Compound set size related target family 49 C. Enrichment test using LLS (Log-likelihood Score) 51 D. Web-developmental environment 52 1. Java Standard Edition 6 (JAVA SE 6) 52 2. Apache Tomcat 8.0.5 52 3. R 2.14.1 52 4. Firefox 29.0.1 52 5. MySQL 5.5 53 6. NetBeans IDE 8.0 53 7. jQuery 1.10.1 53 8. OpenPHACTS v1.3 API 53 9. Data-Driven Documents (D3) 54 E. Web-service of compound set analysis 55 1. Input compound set or screening data 55 2. Target Family Map (TFM) 57 3. Scaffold Tree Map (STM) 57 4. Target assignment using SEA 60 IV. DISCUSSION 63 V. REFERENCE 68 ABSTRACT 71-
dc.formatapplication/pdf-
dc.format.extent5180552 bytes-
dc.languagekor-
dc.publisher이화여자대학교 대학원-
dc.subject.ddc600-
dc.titleAnalyses of Compound Set using Structural Scaffold and Target Information-
dc.typeMaster's Thesis-
dc.format.pageviii, 71 p.-
dc.identifier.thesisdegreeMaster-
dc.identifier.major대학원 생명과학과-
dc.date.awarded2014. 8-
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