利用蛋白质序列的预测方法 页码,11/20 5574(20)27070-i 283104(22)1914i-0 3120141(22)14510 4166184(19)2155i-0 5212235(24)25300-i 6255276(22)2140i-0 7299319(21)12990-i ---- al ternati ve model 7 strong transmembrane hel i ces, total score: 11974 #f from to I ength score ori entati on 14769(23)2494i-0 284104(21)14070-i 3123141(19)1352i-0 4166185(20)19040 5219236(18)2453i-0 6252274(23)13860-i 7300319(20)915i-0 每种建议的模型都指出格区段起始和终止位点,及其相对膜的取向(由内到外 i nsi de-to outsi de,或由外到内 outsi de-to- i nsi de)。算法作者恰当地指出这些模型基于假设全部跨 膜区在预测中都被找到。因而这些模型应被看作是从该方法所得数据的角度出发所的结果。 第二种预测方法是TMAP,它类似于 SSPRED采用了多序列比对来提高预测的准确性( Persson和 Argos,1994)。同样一G蛋白耦合受体为例,提交给 tmapaemb/- hei del berg.e的查询序列的 格式如下所示: SEQUENCE TI TLE G protei n-coupl ed receptor BLOSUM 62 I NDEL 10 ALI GN 50 file://E:wcb生物信息学(中译本)\第十一章利用蛋白质序列的预测方 2005-1-181 55 74 (20) 2707 o-i 2 83 104 (22) 1914 i-o 3 120 141 (22) 1451 o-i 4 166 184 (19) 2155 i-o 5 212 235 (24) 2530 o-i 6 255 276 (22) 2140 i-o 7 299 319 (21) 1299 o-i ------> alternative model 7 strong transmembrane helices, total score : 11974 # from to length score orientation 1 47 69 (23) 2494 i-o 2 84 104 (21) 1407 o-i 3 123 141 (19) 1352 i-o 4 166 185 (20) 1904 o-i 5 219 236 (18) 2453 i-o 6 252 274 (23) 1386 o-i 7 300 319 (20) 915 i-o ↣⾡ᓎ䆂ⱘ῵ൟ䛑ᣛߎḐऎ↉䍋ྟ੠㒜ℶԡ⚍ˈঞ݊Ⳍᇍ㝰ⱘপ৥˄⬅ࠄݙ໪inside-to￾outsideˈ៪⬅໪ݙࠄoutside-to-inside˅DŽㅫ⊩԰㗙ᙄᔧഄᣛߎ䖭ѯ῵ൟ෎Ѣ؛䆒ܼ䚼䎼 㝰ऎ೼乘⌟Ё䛑㹿ᡒࠄDŽ಴㗠䖭ѯ῵ൟᑨ㹿ⳟ԰ᰃҢ䆹ᮍ⊩᠔ᕫ᭄᥂ⱘ㾦ᑺߎথ᠔ⱘ㒧ᵰDŽ ㄀Ѡ⾡乘⌟ᮍ⊩ᰃTMAPˈᅗ㉏ԐѢSSPRED䞛⫼њ໮ᑣ߫↨ᇍᴹᦤ催乘⌟ⱘޚ⹂ᗻ˄Persson੠ Argosˈ1994˅DŽৠḋϔ*㲟ⱑ㗺ড়ফԧЎ՟ˈᦤѸ㒭tmap@embl-heidelberg.deⱘᶹ䆶ᑣ߫ⱘ Ḑᓣབϟ᠔⼎˖ SEQUENCE TITLE G protein-coupled receptor BLOSUM 62 INDEL 10 ALIGN 50 ㄀कϔゴ߽⫼㲟ⱑ䋼ᑣ߫ⱘ乘⌟ᮍ⊩ 义ⷕˈ11/20 file://E:\wcb\⫳⠽ֵᙃᄺ˄Ё䆥ᴀ˅?㄀कϔゴ߽⫼㲟ⱑ䋼ᑣ߫ⱘ乘⌟ᮍ... 2005-1-18 Click to buy NOW! PDF-XCHANGE www.docu-track.com Click to buy NOW! PDF-XCHANGE www.docu-track.com