确定致病机制 1. 皇后确诊乳腺癌 2. 药物遍寻均无效 3. 精准医疗项目启动 生物信息分析 特异化突变基因鉴定 蛋白通路与疾病关联性 XYZ蛋白三维晶体结构 XYZ蛋白药物结合口袋突变前后分析 先导化合物 1. 计算机辅助药物设计: 在 蛋白质三维结构基础上，彻底分析突变后XYZ蛋白结合口袋周围理化性质 突变前：小分子包围在疏水口袋，亲水氨基酸R125、E154与其形成氢键铆钉作用，使其牢牢稳固在蛋白质中 突变后：疏水环境大致不变，但氨基酸N125、I154(无法形成氢键)造成很大空隙，亲水性转换为疏水口袋，稳定性降低 2. 分子动力学方法 分子动力学方法对XYZ蛋白突变后与小分子Hormone间相互作用状况进行了计算模拟分析, 目的:以期得到蛋白质在突变后的小分子结合信息 计算中心首席科学家是这么解释分析结果的: 突变后XYZ蛋白Region A区域没有变化，对该部分不做任何修改； Region B区域中，小分子下方出现巨大空洞，需要对该部分进行补漏； Region B区域巨大空袭，在小分子稳定结合时，下方出现由10~13个水分子组成的“水氢键网络体系”； “水氢键网络体系”中，水分子能量Energy（球体大小）和占有率Occupancy（球体颜色）均能表达出蛋白质局部区域理化性质。 > 具体总结：Region B区域绝大多数水分子能量Energy极小、占有率Occupancy较小，说明该区域极为疏水；但其中W1水分子，能量较好、占有率较高，说明W1水分子极为稳定，周围氨基酸为极亲水性氨基酸，将来可以通过替换W1水分子或通过桥键作用对其进行化学修饰 ; ; 3. 基于体内原生激素Hormone的药物设计:化学片段增长设计法 首先设计的化学片段，将能够有效的到达W1水分子附近或将其替换掉, 福斯坦国立大学化学院研究团队，共设计母核结构50枚，且均具备有机合成实验方法实现的可能性 ; ; 4. 分子对接方法判定最佳化学母核结构 利用分子对接方法，将50枚母核结构分别对接进入突变后蛋白质XYZ结构中， 利用分子对接权重值, 判定3枚小分子药物在对接结果中占据较好的权重位置。奥古斯汀·Khan将此结果呈现给，福斯坦国立大学化学院研究团队，经过再三斟酌分析，决定采用C2、C1和C3进行后期化学结构改造 5.

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Init DNA # DNA x <- DNAString("actttGtag") is(x) ## [1] "DNAString" "XString" "XRaw" "XVector" "Vector" "Annotated" #转录 x %>% complement() %>% RNAString() -> rna # 反向互补 x.r <- reverseComplement(x) x.r ## 9-letter "DNAString" instance ## seq: CTACAAAGT d <- DNAString("TTGAAAA-CTC-N") d ## 13-letter "DNAString" instance ## seq: TTGAAAA-CTC-N #subset subseq(x, start =1, end = 5) ## 5-letter "DNAString" instance ## seq: ACTTT #碱基置换 chartr(old = "A", new = "C", x) ## 9-letter "DNAString" instance ## seq: CCTTTGTCG #DNA_BASES DNA_BASES ## [1] "A" "C" "G" "T" #IUPAC_CODE_MAP IUPAC_CODE_MAP ## A C G T M R W S Y K ## "A" "C" "G" "T" "AC" "AG" "AT" "CG" "CT" "GT" ## V H D B N ## "ACG" "ACT" "AGT" "CGT" "ACGT" RNA x <- RNAString("acuuuGuag") is(x) ## [1] "RNAString" "XString" "XRaw" "XVector" "Vector" "Annotated" # 逆转录 x %>% complement() %>% DNAString() ## 9-letter "DNAString" instance ## seq: TGAAACATC # 翻译 codons(x) ## Views on a 9-letter RNAString subject ## subject: ACUUUGUAG ## views: ## start end width ## [1] 1 3 3 [ACU] ## [2] 4 6 3 [UUG] ## [3] 7 9 3 [UAG] translate(x) ## 3-letter "AAString" instance ## seq: TL* #dna -> rna:T <-> U DNAString("actttGtag") %>% RNAString() ## 9-letter "RNAString" instance ## seq: ACUUUGUAG #rna -> dna:U <-> T rna %>% DNAString() ## 9-letter "DNAString" instance ## seq: TGAAACATC AA: Amino acid x <- AAString("actttGtag") is(x) ## [1] "AAString" "XString" "XRaw" "XVector" "Vector" "Annotated" reads # all kmer reads <- mkAllStrings(c("A", "C", "G", "T"), 6) is(reads) ## [1] "character" "vector" "data.

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Enrichment analysis is a statistical procedure used to identify biological terms which are over-represented in a given gene set. These include signaling pathways , molecular functions, diseases, and a wide variety of other biological terms obtained by integrating prior knowledge of gene function from multiple resources. Enrichr links : links to Enrichr containing the results of enrichment analyses generated by analyzing the up-regulated and down-regulated genes from a differential expression analysis.

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load packages #source("http://bioconductor.org/biocLite.R") #biocLite("GenomicScores") library(GenomicScores) Retrieval of genomic scores through annotation packages There are currently four different annotation packages that store genomic scores and can be accessed using the GenomicScores package Annotation packages Description 1. phastCons100way.UCSC.hg19 phastCons scores derived from the alignment of the human genome (hg19) to other 99 vertebrate species. 2. phastCons100way.UCSC.hg38 phastCons scores derived from the alignment of the human genome (hg38) to other 99 vertebrate species.

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