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基于序列特征的固有無序蛋白結合位點的統(tǒng)計分析本科畢業(yè)論文(編輯修改稿)

2025-07-24 20:58 本頁面
 

【文章內容簡介】 頻率較高,而氨基酸Ser,Qln,His頻率較低;在蛋白質與DNA的結合位點中,氨基酸Arg,Trp,Gly頻率較高,氨基酸Met,Ser,Fhe,Phe,Asp頻率較低,而氨基酸His在無序區(qū)沒有出現;在蛋白質與RNA的結合位點中氨基酸Trp,Gly頻率較高,氨基酸Met,Ser,Fhe,Pro,Asp,His頻率較低;在蛋白質與ATP\AGP的結合位點中,氨基酸中Gly的頻率較高,氨基酸Al,Trp,Met,Val,Gln,Glu,Asp也有出現,但頻率較低,其余氨基酸沒有出現;在蛋白質與輔因子的結合位點中,氨基酸Ala,Gln頻率較高,而氨基酸The,Phe,Asn,Pro,His,Asp沒有出現,其余氨基酸雖有出現但頻率較低;在蛋白質與配體的結合位點中,氨基酸Trp,His頻率較高,而氨基酸Ala,Ser,Lys,Arg,Phe,Pro,Asp沒有出現,其余氨基酸雖有出現,但頻率較低;在蛋白質與金屬結合位點中,氨基酸Met,Gly,Gln,Lys出現的頻率較高,氨基酸Ala,Thr,Leu,Gly,Asn,Tyr,Asp出現的頻率較低,其余氨基酸沒有出現;在無序組蛋白的其它結合位點中,氨基酸Met,Gly,Cys出現頻率較高,而氨基酸Leu,Pro沒有出現,氨基酸Ser,Arg,Phe出現的頻率較低。 固有無序蛋白質是一類具有特殊序列、結構特征,行使特殊功能的一類特殊蛋白質,目前成為蛋白質研究領域的熱點之一。以上關于固有無序蛋白質中相互作用位點的氨基酸偏好性分析將會為今后蛋白質與其它分子結合位點預測工作提供新的思路和數據支持。隨著可得到的固有無序蛋白質與DNA、RNA、蛋白質復合物等結合物結構數據的不斷增多,我們可以從原子水平上發(fā)現更多的相互作用過程中的規(guī)律,以加深對這種相互作用機制的理解,從而在理論上為藥物的開發(fā)和疾病的治療提供可靠的幫助。參考文獻[1] Uversky VN .Natively unfolded proteins: a point where biology waits for physics. Protein Sci .2002,11: 739756.[2] Dunker AK, Obradovic Z, Romero P, Garner EC, Brown CJ. Intrinsic protein disorder in plete genomes. Genome Inform Ser Workshop Genome Inform .2000, 11: 161171.[3] Dunker AK, Oldfield CJ, Meng J, Romero P, Yang JY, et al. The unfoldomics decade: an update on intrinsically disordered proteins. BMC ,9 Suppl 2: S1.[4] Nishikawa K . Natively unfolded proteins: An overview. BIOPHYSICS .2009,5: 5859.[5] Radivojac P, Iakoucheva LM, Oldfield CJ, Obradovic Z, Uversky VN, et al. Intrinsic disorder and functional proteomics. Biophys , 92: 14391456.[6] Burra PV, Kalmar L, Tompa P . Reduction in structural disorder and functional plexity in the thermal adaptation of prokaryotes. PLoS One .2010, 5: e12069.[7] PavlovicLazetic GM, Mitic NS, Kovacevic JJ, Obradovic Z, Malkov SN, et al. Bioinformatics analysis of disordered proteins in prokaryotes. BMC Bioinformatics. 2012,12: 66.[8] Xue B, Dunker AK, Uversky VN . Orderly order in protein intrinsic disorder distribution: disorder in 3500 proteomes from viruses and the three domains of life. J Biomol Struct Dyn .2012,30: 137149.[9] Sethi D, Garg A, Raghava GP. DPROT: prediction of disordered proteins using evolutionary information. Amino , 35: 599605.[10] Bellay J, Han S, Michaut M, Kim T, Costanzo M, et al. Bringing order to protein disorder through parative genomics and genetic interactions. Genome , 12: 14.[11] Muppirala UK, Honavar VG, Dobbs D Predicting RNAprotein interactions using only sequence information. BMC ,12: 489.[12] Dosztanyi Z, Csizmok V, Tompa P, Simon I The pairwise energy content estimated from amino acid position discriminates between folded and intrinsically unstructured proteins. J Mol Biol 2005,347: 827839.[13] Meszaros B, Tompa P, Simon I, Dosztanyi Z Molecular principles of the interactions of disordered proteins. J Mol Biol 2007,372: 549561.[14] Gunasekaran K, Tsai CJ, Kumar S, Zanuy D, Nussinov RExtended disordered proteins: targeting function with less scaffold. Trends Biochem Sci 2013,28: 8185.[15] 吳建盛, 棟 胡, 伍洪濤, 謝建明, 嘯 孫 蛋白質核酸復合物界面氨基酸與核苷酸偏好性分析. 生物物理學報 2010,26: 234244.[16] Shen J, Zhang J, Luo X, Zhu W, Yu K, et proteinprotein interactions based only on sequences information. Proc Natl Acad Sci U S A 2007,104: 43374341.[17] Wu J, Liu H, Duan X, Ding Y, Wu H, et of DNAbinding residues in proteins from amino acid sequences using a random forest model with a hybrid feature. Bioinformatics 2009,25: 3035.Studies on Binding Sites Based on Sequence Characteristics Intrinsically Disordered Proteins StatisticsYan Zhiduo(College of Physics and Electronic Information, Dezhou University, Dezhou Shandong, 253023)Abstract Taking Disprot and BSDP intrinsically disordered proteins binding sites in the database as the research object, build 9 kinds of binding site data sets, using MATLAB to statistics the binding sites of various amino acids frequency, it was found that the interaction sites of protein and protein is the most, interaction sites of proteins and ATP/GTP is the least, and we can learn that all kinds of binding site of amino acid has obvious preferences. The study is helpful to know the intrinsically chaotic characteristics of protein interactions wi
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