Computational Homology
单细胞转录组测序maker基因英文
单细胞转录组测序maker基因英文Single-cell transcriptomics is a powerful techniquethat allows researchers to study gene expression patterns at the level of individual cells. This technology has revolutionized our understanding of cellular heterogeneity and has the potential to provide valuable insights into various biological processes and diseases. To analyze single-cell transcriptomic data and gain meaningful biological insights, it is essential to have accurate and comprehensive gene annotations. This is where the Maker gene prediction tool comes into play.Maker is a widely used gene annotation pipeline that integrates evidence-based gene prediction methods to accurately identify protein-coding genes in a genome. It combines multiple sources of evidence, such as protein homology, RNA-seq data, and ab initio gene predictions, to generate high-quality gene annotations. In the context of single-cell transcriptomics, Maker can be used to predict genes from the transcriptomic data obtained from individualcells.One of the major requirements for using Maker insingle-cell transcriptomics is the availability of a reference genome. The reference genome serves as a template for gene prediction and provides the necessary genomic context for accurate annotation. The quality of the reference genome is crucial, as any errors or gaps in the genome assembly can lead to incorrect gene predictions. Therefore, it is important to ensure that the reference genome used for Maker gene prediction is of high quality and well-annotated.In addition to a reference genome, another requirement for using Maker in single-cell transcriptomics is the availability of transcriptomic data. Single-cell RNA sequencing (scRNA-seq) is commonly used to generate transcriptomic data from individual cells. This data provides information about the expression levels of genes in each cell and can be used as evidence for gene prediction. Maker can integrate this transcriptomic data with other sources of evidence to improve the accuracy ofgene annotations.Furthermore, it is important to consider the computational resources required for running Maker onsingle-cell transcriptomic data. Single-cell transcriptomics generates large amounts of data, and analyzing this data using Maker can be computationally intensive. High-performance computing resources andefficient algorithms are necessary to handle the computational demands of gene prediction in single-cell transcriptomics. Additionally, the analysis pipeline should be optimized to handle the unique characteristics ofsingle-cell transcriptomic data, such as high levels of technical noise and low RNA capture efficiency.Another important consideration when using Maker in single-cell transcriptomics is the validation of the predicted gene annotations. While Maker integrates multiple sources of evidence to generate gene predictions, it isstill prone to false positives and false negatives. Therefore, it is crucial to validate the predicted gene annotations using independent experimental methods, such asqPCR or in situ hybridization. This validation step ensures the accuracy and reliability of the gene annotations and provides confidence in the downstream analysis of the single-cell transcriptomic data.In conclusion, the use of Maker in single-cell transcriptomics requires a high-quality reference genome, transcriptomic data, computational resources, andvalidation of the predicted gene annotations. By meeting these requirements, researchers can leverage the power of Maker to accurately annotate genes in single-cell transcriptomic data and gain valuable insights intocellular heterogeneity and biological processes.。
分子进化树构建方法
MP法建树流程
Sequence1 Sequence2 Sequence3
Sequence4
Position 1
Position 1 2 3 T G C T A C A G G A A G
If 1 and 2 are grouped a total of four changes are needed.
5
genetic change
系统发生树术语
Rooted tree vs. Unrooted tree
无 A 有 根 根 树 B 树 two major ways to root trees:
A
10 3 2 5
C D
By midpoint or distance
d (A,D) = 10 + 3 + 5 = 18 Midpoint = 18 / 2 = 9
Distance Uses only pairwise distances Minimizes distance between nearest neighbors Very fast Easily trapped in local optima Good for generating tentative tree, or choosing among multiple trees Maximum parsimony Uses only shared derived characters Minimizes total distance Maximum likelihood Uses all data Maximizes tree likelihood given specific parameter values Very slow Highly dependent on assumed evolution model Good for very small data sets and for testing trees built using other methods
MOE-基于结构的药物设计及在药物发现方面的应用
药物发现/设计
基于大分子结构的 基于小分子配体的 基于片段的
上市
转化医学
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药物开发 (临床前及临床试验)
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生物信息学网站网址(全)
生物信息学网站分子生物学数据库综合目录1. SRS序列查询系统(分子生物学数据库网络浏览器) http://www.embl-heidelberg.ed/srs5/2. 分子生物学数据库及服务器概览/people/pkarp/mimbd/rsmith.html3. BioMedNet图书馆4. DBGET数据库链接http://www.genome.ad.jp/dbget/dbget.links.html5. 哈佛基因组研究数据库与精选服务器6. 约翰. 霍普金斯大学(Johns Hopkins University) OWL网络服器/Dan/proteins/owl.html7. 生物网络服务器索引，USCS /network/science/biology/index.html8. 分子生物学数据库列表(LiMB) gopher:///11/molbio/other9. 病毒学的WWW服务器，UW-Madison /Welcome.html10. UK MRC 人类基组图谱计划研究中心/11. 生物学家和生物化学家的WWW资源http://www.yk.rim.pr.jp/~aisoai/index.html12. 其他生物网络服务器的链接/biolinks.html13. 分子模型服务器与数据库/lap/rsccom/dab/ind006links.html14. EMBO实际结构数据库http://xray.bmc.uu.se/embo/structdb/links.html15. 蛋白质科学家的网络资源/protein/ProSciDocs/WWWResources.html16. ExPASy分子生物学服务器http://expasy.hcuge.ch/cgi-bin/listdoc17. 抗体研究网页18. 生物信息网址http://biochem.kaist.ac.kr/bioinformatics.html19. 乔治.梅森大学(George Mason University)的生物信息学与计算分子生物学专业/~michaels/Bioinformatics/20. INFOBIOGEN数据库目录biogen.fr/services/dbcat/21. 国家生物技术信息研究室/data/data.html22. 人类基因组计划情报/TechResources/Human_Genome23. 生物学软件及数据库档案/Dan/software/biol-links.html24. 蛋白质组研究：功能基因组学的新前沿(著作目录) http://expasy.hcuge.ch/ch2d/LivreTOC.html序列与结构数据库一．主要的公共序列数据库1. EMBL WWW服务器http://www.EMBL-heidelberg.ed/Services/index.html2. Genbank 数据库查询形式(得到Genbank的一个记录) /genbank/query_form.html3. 蛋白质结构数据库WWW服务器(得到一PDB结构) 4. 欧洲生物信息学研究中心(EBI) /5. EBI产业支持/6. SWISS-PROT(蛋白质序列库) http://www.expasy.ch/sprot/sprot-top.html7. 大分子结构数据库/cgi-bin/membersl/shwtoc.pl?J:mms8. Molecules R Us(搜索及观察一蛋白质分子) /modeling/net_services.html9. PIR国际蛋白质序列数据库/Dan/proteins/pir.html10. SCOP(蛋白质的结构分类)，MRC /scop/data/scop.l.html11. 洛斯阿拉莫斯的HIV分子免疫数据库/immuno/index.html12. TIGR数据库/tdb/tdb.html13. NCBI WWW Entrez浏览器/Entrez/index.html14. 剑桥结构数据库(小分子有机的及有机金属的结晶结构) 15. 基因本体论坛/GO/二. 专业数据库1. ANU生物信息学超媒体服务(病毒数据库、分类及病毒的命名法) .au/2. O-GL YCBASE(O联糖基化蛋白质的修订数据库) http://www.cbs.dtu.dk/OGLYCBASE/cbsoglycbase.html3. 基因组序列数据序(GSDB)(已注释的DNA序列的关系数据序) 4. EBI蛋白质拓扑图/tops/Serverintermed.html5. 酶及新陈代谢途径数据库(EMP) /6. 大肠杆菌数据库收集(ECDC)(大肠杆菌K12的DNA序列汇编) http://susi.bio.uni-giessen.de/ecdc.html7. EcoCyc(大肠杆菌基因及其新陈代谢的百科全书) /ecocyc/ecocyc.html8. Eddy实验室的snoRNA数据库/snoRNAdb/9. GenproEc(大肠杆菌基因及蛋白质) /html/ecoli.html10. NRSub(枯草芽胞杆菌的非冗余数据库) http://pbil.univ-lyonl.fr/nrsub/nrsub.html11. YPD(酿酒酵母蛋白质) /YPDhome.html12. 酵母基因组数据库/Saccharomyces/13. LISTA、LISTA-HOP及LISTA-HON(酵母同源数据库汇编) /14. MPDB(分子探针数据库) http://www.biotech.est.unige.it/interlab/mpdb.html15. tRNA序列及tRNA基因序列汇编http://www.uni-bayreuth.de/departments/biochemie/trna/index/html16. 贝勒医学院(Baylor College of Medicine)的小RNA数据库/dbs/SRPDB/SRPDB.html17. SRPDB(信号识别粒子数据库) /dbs/SRPDB/SRPDB.html18. RDP(核糖体数据库计划) /19. 小核糖体亚蛋白RNA结构http://rrna.uia.ac.be/ssu/index.html20. 大核糖体亚蛋白RNA结构http://rrna.uia.ac.be/lsu/index.html21. RNA修饰数据库/RNAmods/22. 16SMDB及23SMDB(16S和23S核糖体RNA突变数据库)/Departments/Biology/Databases/RNA.html23. SWISS-2DPAGE(二维凝胶电泳数据库) http://expasy.hcuge.ch/ch2d/ch2d-top.html24. PRINTS /bsm/dbbrowser/PRINTS/PRINTS.html25. KabatMan(抗体结构及序列信息数据库) /abs26. ALIGN(蛋白质序列比对一览) /bsm/dbbrowser/ALIGN/ALIGN.html27. CATH(蛋白质结构分类系统) /bsm/cath28. ProDom(蛋白质域数据库) http://protein.toulouse.inra.fr/29. Blocks数据库(蛋白质分类系统) /30. HSSP(按同源性导出的蛋白质二级结构数据库) http://www.sander.embl-heidelberg.de/hssp/31. FSSP(基于结构比对的蛋白质折叠分类) /dali/fssp/fssp.html32. SBASE蛋白质域(已注释的蛋白质序列片断) http://www.icgeb.trieste.it/~sbasessrv/33. TransTerm(翻译控制信号数据库) /Transterm.html34. GRBase(参与基因调控的蛋白质的相关信息数据库) /~regulate/trevgrb.html35. REBASE(限制性内切酶和甲基化酶数据库) /rebase/36. RNaseP数据库/RNaseP/home.html37. REGULONDB(大肠杆菌转录调控数据库) http://www.cifn.unam.mx/Computational_Biology/regulondb/38. TRANSFAC(转录因子及其DNA结合位点数据库) http://transfac.gbf.de/39. MHCPEP(MHC结合肽数据库) .au/mhcpep/40. ATCC(美国菌种保藏中心) /41. 高度保守的核蛋白序列的组蛋白序列数据库/Baxevani/HISTONES42. 3Dee(蛋白质结构域定义数据库) /servers/3Dee.html43. InterPro(蛋白质域以及功能位点的完整资源) /interpro/序列相似性搜索1. EBI序列相似性研究网页/searches/searches.html2. NCBI: BLAST注释/BLAST3. EMBL的BLITZ ULTRA快速搜索/searches/blitz_input.html4. EMBL WWW服务器http://www.embl-heidelberg.de/Services/index.html#55. 蛋白质或核苷酸的模式浏览/compbio/PatScan/HTML/patscan.html6. MEME(蛋白质超二级结构模体发现与研究) /meme/website7. CoreSearch(DNA序列保守元件的识别) http://www.gsf.de/biodv/coresearch.html8. PRINTS/PROSIT浏览(搜索motif数据库) /cgi-bin/attwood/SearchprintsForm.pl9. 苏黎世ETH服务器的DARWIN系统http://cbrg.inf.ethz.ch/10. 利用动态规划找出序列相似性的Pima IIhttp://bmerc-www.bu.ede/protein-seq/pimaII-new.html11. 利用与模式库进行哈希码(hashcode)比较找到序列相似性的DashPat /protein-seq/dashPat-new.html12. PROPSEARCH(基于氨基酸组成的搜索) http://www.embl-heidelberg.de/aaa.html13. 序列搜索协议(集成模式搜索) /bsm/dbbrowser/protocol.html14. ProtoMap(SEISS-PROT中所有蛋白质的自动层次分类) http://www.protomap.cs.huji.ac.il/15. GenQuest(利用Fasta、Blast、Smith-Waterman方法在任意数据库中搜索) http://www.gdb.rog/Dan/gq/gq.form.html16. SSearch(对特定数据库的搜索) http://watson.genes.nig.ac.jp/homology/ssearch-e_help.html17. Peer Bork搜索列表(motif/模式序列谱搜索) http://www.embl-heidelberg.de/~bork/pattern.html18. PROSITE数据库搜索(搜索序列的功能位点) /searches/prosite.html19. PROWL(Skirball研究中心的蛋白质信息检索) /index.html序列和结构的两两比对1. 蛋白质两两比对(SIM) http://expasy.hcuge.ch/sprot/sim-prot.html2. LALNVIEW比对可视化观察程序ftp://expasy.hcuge.ch/pub/lalnview3. BCM搜索装置(两两序列比对) /seq-search/alignment.html4. DALI蛋白质三维结构比较/dali/5. DIALIGN(无间隙罚分的比对程序) http://www.gsf.de/biodv/dialign/html多重序列比对及系统进行树1. ClustalW(BCM的多重序列比对) /multi-align/multi-align.html2. PHYLIP(推测系统进行树的程序) /phylip.html3. 其它系统进行树程序，PHYLIP文档的汇编http://expasy.hcuge.ch/info/phylogeny.html4. 系统进行树分析程序(生命树列表) /tree/programs/programs.html5. 遗传分类学软件(Willi hennig协会提供的列表) /education.html6. 用于多重序列比对的BCM搜索装置/multi-align/multi-align.html7. AMAS(分析多重序列比对中的序列) /servers/amas_server.html8. 维也纳RNA二级结构软件包http://www.tbi.univie.ac.at/~ivo/RNA/四. 有代表性的预测服务器1. PHD蛋白质预测服务器，用于二级结构、水溶性以及跨膜片断的预测http://www.embl-heidelberg.de/predictprotein/predictprotein.html2. PhdThreader(利用逆折叠方法预测、识别折叠类) http://www.embl-heidelberg.de/predictprotein/phd_help.html3. PSIpred(蛋白质结构预测服务器) /psipred4. THREADER(戴维. 琼斯) /~jones/threader.html5. TMHMM(跨膜螺旋蛋白的预测) http://www.cbs.dtu.dk/services/TMHMM/6. 蛋白质结构分析，BMERC /protein-seq/protein-struct.html7. 蛋白质域和折叠预测的提交表http://genome.dkfz-heidelberg.de/nnga/def-query.html8. NNSSP(利用最近相邻法预测蛋白质的二级结构) /pss/pss.html9. Swiss-Model(基于知识的蛋白质自动同源建模服务器) http://www.expasy.ch/swissmod/SWISS-MODEL.html10. SSPRED(用多重序列比对进行二级结构预测) /jong/predict/sspred.html11. 法国IBCP的SOPM(自寻优化预测方法、二级结构) http://pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=/NPSA/npsa_sopm.html12. TMAP(蛋白质跨膜片断的预测服务) http://www.embl-heidelberg.de/tmap/tmap_info.html13. TMpred(跨膜区域和方向的预测) /software/TMPRED_form.html14. MultPredict(多重序列比对的序列的二级结构) /zpred.html15. BCM搜索装置(蛋白质二级结构预测) /seq-search/struc-predict.html16. COILS(蛋白质的卷曲螺旋区域预测) /software/coils/COILS_doc.html17. Coiled Coils(卷曲螺旋) /depts/biol/units/coils/coilcoil.html18. Paircoil(氨基酸序列中的卷曲螺旋定位) /bab/webcoil.html19. PREDATOR(由单序列预测蛋白质二级结构) http://www.embl-heidelberg.de/argos/predator/predator_info.html20. EV A(蛋白质结构预测服务器的自动评估) /eva/五. 其他预测服务器1. SignalP (革兰氏阳性菌、革兰氏阴性菌和真核生物蛋白质的信号肽及剪切位点) http://www.cbs.dtu.dk/services/SignalP/2. PEDANT(蛋白质提取、描述及分析工具) http://pedant.mips.biochem.mpg.de/六. 分子生物学软件链接1. 生物信息学可视化工具/alan/VisSupp/2. EBI分子生物学软件档案/software/software.html3. BioCatalog /biocat/e-mail_Server_ANAL YSIS.html4. 生物学软件和数据库档案/Dan/softsearch/biol-links.html5. UC Santa Cruz的序列保守性HMM的SAM软件/research/compbio/sam.html七. 网上博士课程1. 生物计算课程资源列表：课程大纲http://www.techfak.uni-bielefeld.de/bcd/Curric/syllabi.html2. 生物序列分析和蛋白质建模的Ph.D课程http://www.cbs.dtu.dk/phdcourse/programme.html3. 分子科学虚拟学校/vsms/sbdd/4. EMBnet 生物计算指南http://biobase.dk/Embnetut/Universl/embnettu.html5. 蛋白质结构的合作课程/PPS/index.html6. 自然科学GNA虚拟学校http://www.techfak.uni-bielefeld.de/bcd/Vsns/index.html7. 分子生物学算法/education/courses/590bi。
西安交通大学2015理学与工学门类“最有学术影响力的国际期刊”目录发布
98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146
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期刊名称 AAPG BULLETIN ACCOUNTS OF CHEMICAL RESEARCH ACM COMPUTING SURVEYS ACM TRANSACTIONS ON COMPUTER SYSTEMS ACM TRANSACTIONS ON GRAPHICS ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHO ACS APPLIED MATERIALS & INTERFACES ACS CATALYSIS ACS CHEMICAL BIOLOGY ACS NANO ACTA BIOMATERIALIA ACTA MATERIALIA ACTA MATHEMATICA ADVANCED ENERGY MATERIALS ADVANCED FUNCTIONAL MATERIALS ADVANCED HEALTHCARE MATERIALS ADVANCED MATERIALS ADVANCED SYNTHESIS & CATALYSIS ADVANCES IN APPLIED MECHANICS ADVANCES IN ATOMIC MOLECULAR AND OPTICAL PHYSICS ADVANCES IN CATALYSIS ADVANCES IN CHEMICAL PHYSICS ADVANCES IN COLLOID AND INTERFACE SCIENCE ADVANCES IN COMPUTATIONAL MATHEMATICS ADVANCES IN ECOLOGICAL RESEARCH ADVANCES IN INORGANIC CHEMISTRY ADVANCES IN MATHEMATICS ADVANCES IN ORGANOMETALLIC CHEMISTRY ADVANCES IN PHYSICS ADVANCES IN POLYMER SCIENCE AEROSOL SCIENCE AND TECHNOLOGY AGRICULTURE ECOSYSTEMS & ENVIRONMENT AICHE JOURNAL ALDRICHIMICA ACTA AMERICAN JOURNAL OF MATHEMATICS AMERICAN NATURALIST AMERICAN STATISTICIAN ANALYST ANALYTICAL CHEMISTRY ANGEWANDTE CHEMIE-INTERNATIONAL EDITION ANNALS OF APPLIED PROBABILITY ANNALS OF MATHEMATICS ANNALS OF PHYSICS ANNALS OF PROBABILITY ANNALS OF STATISTICS ANNALS OF THE NEW YORK ACADEMY OF SCIENCES ANNUAL REPORTS ON NMR SPECTROSCOPY ANNUAL REVIEW OF BIOMEDICAL ENGINEERING
JCR2015影响因子(所有期刊从高到低排序) 中科院分区 - 自整理计算机相关汇编
Rank ISSN Abbreviated Journal Title Full Title9990027-8424P NATL ACAD SCI USA PROCEEDINGS OF THE NATIONAL ACAD 81701949-3045IEEE T AFFECT COMPUT IEEE Transactions on Affective Computing 84442162-237X IEEE T NEUR NET LEAR IEEE Transactions on Neural Networks and 84272157-6904ACM T INTEL SYST TEC ACM Transactions on Intelligent Systems an 68301553-877X IEEE COMMUN SURV TUT IEEE COMMUNICATIONS SURVEYS AND 49961069-2509INTEGR COMPUT-AID E INTEGRATED COMPUTER-AIDED ENGIN 53501134-3060ARCH COMPUT METHOD E A RCHIVES OF COMPUTATIONAL METHO 13860045-7825COMPUT METHOD APPL M COMPUTER METHODS IN APPLIED MEC 67701548-7660J STAT SOFTW Journal of Statistical Software52011089-778X IEEE T EVOLUT COMPUT IEEE TRANSACTIONS ON EVOLUTIONAR 28820360-0300ACM COMPUT SURV ACM COMPUTING SURVEYS2020004-5411J ACM JOURNAL OF THE ACM17070129-0657INT J NEURAL SYST International Journal of Neural Systems 32390730-0301ACM T GRAPHIC ACM TRANSACTIONS ON GRAPHICS 16230098-3500ACM T MATH SOFTWARE ACM TRANSACTIONS ON 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东南大学博士学位论文结果不同重组...
东南大学博士学位论文基因密码子使用和蛋白质结构的生物信息学分析姓名：***申请学位级别：博士专业：生物医学工程指导教师：***20040401东南大学博士学位论文摘要论文题目：基因密码子使，ｑＪ和蛋白质结构的生物信息学分析研究生姓名：顾万君导师姓名：陆祖宏（教授）学校名称：东南人学随着人类基冈组计划雨Ｉ模式生物基因组计划的完成，公共数据库中生物数据的增艮速度越来越快。
如何从海量的生物数据中解读、提取和获得有片；｜的生物信息，己成为基因组计划下一步亟待解决的问题。
本文中，我们利用生物信息学的方法对基因的周义密码子使用进行了统计分析，研究了不局物种中蛋白质结构和基因周义密码子使用间可能存在的相关性，提出了一种基于密码子的氨基酸二级结构偏向性参数。
同时，我们还在蛋白质二级结构预测的神经网络方法中引入了蛋白质编码基因的密码子使用信息．在预测大肠杆菌的蛋白质的二级结构时提高了预测的准确率。
最后，我们还提出了一种根据单倍体数据将基冈组划分成若干很少出现重组现象的块结构的新方法。
论文的主要内容如下：１．在基因组中，基因的同义密码子使用并不是随机选择的。
研究不周物种中基因的密码子使用模式以及形成这种密码子使用模式的内在因素，对于了解基因组的特征和物种的分子进化具有重要作崩。
我们对一些物种的基因密码子使用模式进行了分析，并且进一步分析了这些物种中影响密码子使用的内在因素。
●通过ＳＡＲＳ病毒基因组和进化上相近的病毒基因组的密码子使用偏性的分析，我们发现在这些病毒基冈组中尽管基因的同义密码子使用存在着偏向性，但是偏向性程度并不高。
这些病毒基因组中，影响同义密码子使用的最主要因素是进化中的碱基突变压力。
同时，基冈的功能也在一定程度上决定了这些病毒基因中密码子的选择。
但是，基因长度和基因翻译过程中的选择作用在这些病毒中并不影响基因的密码子使用。
另外，在这些病毒基囡组中，密码子使用模式在进化上是保守的。
基于密码子使用模式的进化分析表明，ＳＡＲＳ冠状病毒在进化上与其它己知的冠状病毒都不是很近。
计算结构生物学讲义
The big picture of molecular simulations
(分子模拟的总体思路) 分子模拟的总体思路)
使用计算机实现分子 体系微观态（原子的 坐标和动量）的取样
统计力学分析
宏观的热力学， 动力学及其他属性 （物理，化学属性）
A microscopic state（微观态）：the atomic momenta, p; Phase space（相空间）： for a system of N has 6N dimensions. An ensemble(系宗): a collection of points satisfying the conditions of a particular （常见的系宗： NVE， NVT， NPT）
One of the ten “BREAKTHROUGHS” by the Science Journal 2010
David E. Shaw
David E. Shaw serves as Chief Scientist of D. E. Shaw Research and as a Senior Research Fellow at the Center for Computational Biology and Bioinformatics at Columbia University. He received his Ph.D. from Stanford University in 1980, served on the faculty of the Computer Science Department at Columbia until 1986, and founded the D. E. Shaw group in 1988. Since 2001, Dr. Shaw has devoted his time to hands-on research in the field of computational biochemistry. He is now personally involved in the development of new algorithms and machine architectures for high-speed molecular dynamics simulations of biological macromolecules, and in the application of such simulations to basic scientific research in structural biology and biochemistry and to the process of computer-aided drug design. Although he leads the lab’s research efforts in his role as Chief Scientist, his focus is largely technical, with limited involvement in operational and administrative management. Dr. Shaw was appointed to the President's Council of Advisors on Science and Technology by President Clinton in 1994, and again by President Obama in 2009. He is a fellow of the American Association for the Advancement of Science, and was elected to its board of directors in 1998. Dr. Shaw is also a fellow of the American Academy of Arts and Sciences, and serves on the Computer Science and Telecommunications Board of the National Academies.
鼠源抗体的人源化设计
鼠源抗体的人源化设计前言 (1)方法 (2)结果与讨论 (3)鼠源抗体筛选人源框架 (3)人源化抗体CDR的改造 (4)结论 (6)附录 (6)参考文献 (7)前言第一个人用抗体药物来自鼠源抗体，直到现在鼠源抗体仍然是抗体药物的一大来源[Pogson et al.,2016]。
由于鼠源抗体的免疫原性，一般会对其作人源化处理。
目前最通用的方法是将鼠抗的CDR序列移植到人源框架上[Hwang et al., 2005]。
通常CDR移植后的人源化抗体与抗原的亲和力会减弱，如何保持人源化抗体的亲和力是目前最大的技术瓶颈。
通过高通量的筛选方法可以得到适合CDR移植的人源框架，但是这种实验方法周期长，价格昂贵[Townsend et al.,2015]。
为了快速筛选出适合的人源框架，研究者利用序列比对和结构模拟的方法筛选出适合的人源框架，然后通过实验验证抗体与抗原的亲和力，减少了实验工作量，节约了成本和时间，未来会成为具有潜力的抗体人源化设计方法[Kurella et al., 2014;Choi et al.,2015;Choi et al.,2016]。
本文采用自主开发的抗体人源化设计程序，对已知的鼠源抗体进行人源化设计，结果表明计算的方法可以筛选出序列同源性靠后但是亲和力更高的人源化框架。
方法抗体阻断蛋白-蛋白相互作用的受体和配体结构已知（图1）。
配体与抗体结合的区域重叠在受体和配体结合的区域（图2），所以鼠源的抗体可以有效阻断受体和配体的相互作用[Apgar et al.,2016]。
通过鼠源抗体的人源化设计可以最大限度减少抗体的免疫原性。
首先使用鼠源抗体（PDBID为5F3B）的序列在人源框架库中搜索排名靠前的序列作为候选序列，然后将人源序列同源建模到鼠源抗体的骨架上，保留鼠源CDR的序列，然后计算同源模型的能量，判断人源化抗体的稳定性。
人源化CDR突变体采用相同的策略，不同之处在于替换鼠源CDR 序列为突变体序列，并且保留抗原的结构。
Computer-aided drug design
Abstract Two distinct approaches are possible in the area of computer-aided drug design. If the molecular structure of the target macromolecule is known the methods are obvious and direct and have achieved a high level of sophistication. That area may be extended by using computational techniques to predict protein structure as illustrated here by the interleukin-4 receptor. When the only lead is a set of known active compounds or knowledge of a biochemical transformation which is to be interrupted, then the path is less direct. Currently favoured tactics include the use of molecular similarity methods and the employment of neural networks. Recent advances include the prediction of the relative potency of different chiral forms of drugs.
recognize selected sequences, perhaps with the goal of switching off particular genes as in cancer chemotherapy.