蛋白质基本性质的分析秋

1、1第六章 蛋白质基本性质分析-11-17第1页2本章主要内容一、蛋白质理化性质分析二、酶切图谱三、亲疏水性分析四、抗原性分析五、表面分析六、柔性分析七、二级结构预测第2页3分析界面The Assay Document is the file created and used by PROTEAN to examine and elucidate protein sequences. It is often referred to as simply an assay. The assay is composed of five principle parts: the status area,

2、 the palette tools, the Method Curtain, the assay surface and the Legend Curtain. Click in the assay graphic below for more on each part.Method curtainAnalysis surfaceLegend curtain第3页4Two selection第4页5Add a known feature第5页6Join a feature第6页7Using the MicroscopeA Microscope is provided for situatio

3、ns where you want to see the sequence responsible for a methods result. This lets you stay zoomed out for the overall big picture, but still see critical residues in a region. Click the Microscope palette tool to activate its display window. You have your choice of displaying the sequence as chemica

4、l formula or space-filling models. Just click the appropriate button in the upper left corner of the Microscope window (as shown in the following figures). The arrow box in the lower left corner controls the size of the amino acid sequence characters and the vertical size of the Microscope window. T

5、he arrow box in the lower right corner controls the horizontal size of the Microscope window. Drag either box to control the size of the mini-window. As the cursors position changes over the assay surface, PROTEAN updates the Microscope window to display residues underneath.You can examine the entir

6、e sequence by choosing either Linear Space Fill or Chemical Formula from the Model Structure submenu (under the Analysis menu).第7页8Tabular DataThe first column shows an amino acid, the second its position and the remaining columns show the value assigned for each residue at the given position. Doubl

7、e-click any value or column to show the parameter window for the method in question. Changing the order of plots on the assay rearranges the column summaries on the Tabular Data window. 第8页9Model structure - Helical Wheel第9页10Model structure - Helical Net第10页11Model structure - Beta Net第11页12Model s

8、tructure Space Fill Model第12页13Model structure Chemical Formula第13页14Titration Curve第14页15Composition第15页16Method Outline .paoApply to other proteinsSave different analysis method and its preset patterns (line color, line weight, fill pattern, fill color)第16页17Protease mapDifferent protease sitesMSD

9、egradationPMF (Peptide Map Finger)第17页18Analysis methodsProtease mapPattern: Prosite database, Ariadne fileCharge densitySecondary Structure: Coiled Coil, Garnier-Robson, Deleage & Roux, Chou-FasmanHydropathy: Goldman-Engleman-Steitz, Kyte-Doolittle, Hopp-WoodsAntigenicity: Settle MHC Motifs, AMPHI,

10、 Rothbard-Taylor, Jameson-WolfAmphilicity EisenbergSurface Probability EminiFlexibility Karplus-Schulz第18页19Charge density电荷平均值法预测正电或负电区域是经过计算在特定范围内残基电荷数加和来实现。该方法结果是以一张平均电荷峰图和两张电荷分布区域图来表示，分别是正电区域和负电区域分布图。因为带电残基有着位于蛋白表面趋势，所以，这种方法有有利于预测蛋白表面特征。 DNASTAR 软件使用White, Handler and Smith (1964)pK值表来进行表面特征预测。同

11、时，pK值表也被用于测定蛋白和多肽序列滴定曲线，取得其等电点（pI）。 参数：用于计算电荷平均值氨基酸残基数量，即范围窗口大小。假如窗口值设得太低，则高带电残基（如赖氨酸和半胱氨酸）将产生很大噪音；假如太高则得到带电区域太少而只好到平平区域图。pH: 用于计算每个残基pK值；正电阈值用于表示正电区域最小值，负电阈值则相反。 不足：该方法可能对于参数改变尤其敏感，所以，使用时可多尝试改变参数得到多个分布图，以显示细微差异。第19页20Charge density第20页21Charge density第21页22二级结构预测(1)：Coiled-coil该方法是依据Parry (1982)方法进

12、行缠绕卷曲结构预测。 该结构由两个右旋螺旋以堆积角度为20度相互围绕一个左旋超卷曲形成。这些螺旋是由侧链基团相互之间亲水作用稳定着。 螺旋特征是非极性氨基酸残基以每圈3.5个有规律七联子周期排布形成。这种规律使得预测缠绕卷曲结组成为可能。Myosins（肌球蛋白）和keratins（角蛋白）是这种四级结构元素经典。第22页23Coiled-coilPROTEAN首先给每个氨基酸赋予相正确出现缠绕卷曲结构频率。频率值来自一个做过统计已知出现缠绕螺旋频率数据库。取得相对频率值后，残基被分配到28个残基滑动窗口。这一长度值是展现稳定缠绕卷曲结构四个和五个七联肽最小值。将全部窗口值相乘，积取28为根对

13、数。每个残基起始最高分为196。得到数值以峰图或区域图表示。第23页24Coiled-coil参数：只有一个确定出现区域图阈值变量。1.3被认为是已知缠绕卷曲最小值。假如是找象球蛋白中不严格缠绕卷曲结构能够降至1.1；假如是纤维蛋白严格缠绕卷曲结构则可升至1.5。限制性：该方法是统计得到，基于已知翻译GenBank和已知缠绕卷曲结构数据。假如我们蛋白与用于检验测试值有很大出入，这方法就不能准确地预测。再者，即使我们也能够找到三个或四个螺旋并排缠绕，但该方法还是不推荐用于其它类似非极性七联肽重复区其它结构。能够结合Goldman-Engleman-Steitz法预测螺旋穿膜区，也可优化GES方法

14、得到两性（amphipathy）分析。第24页25二级结构预测(2)： Garnier-Robson该算法经过蛋白氨基酸序列预测其二级结构。它是基于已知蛋白晶体结构统计方法，尤其是针对二面角 (C-N-C-C-N) 和氢键网络对-helical (H), -pleated sheet (E - extended chains), -turns (T) 和 coil (C)区域进行优化。. Garnier-Robson方法主要考查存在与一定已知结构特定残基倾向性。在赋予最可能残基构象之前，先考查周围16个残基（上8个，下8个），假如倾向于某种特定结构，初始残基就会被归于那种类型结构，不然，重新评

15、定成其它型结构。第25页26Garnier-RobsonParameters:The only parameter involved in Garnier-Robson predictions is the use of decision constants. If you assign decision constants, you should have prior knowledge of the circular dichroism data. No Decision Constants makes no assumptions of the global a-helix and, s

16、heet content. Calculated Decision Constants are computer derived from global a-helix and sheet probabilities. Constants added are based on three protein classes: proteins with less than 20%, between 20% and 50% and over 50% a-helix or -sheet. Specified Decision Constants are user defined, from circu

17、lar dichroism(CD) data.Limitations:This method is a statistical approach, based on observed residue patterns in 25 proteins. If your protein differs substantially from proteins used to establish the model secondary structure frequencies, this method may give inaccurate structural predictions.第26页27二

18、级结构预测(3)： Deleage & Roux预测分为三步：蛋白类型预测，二级结构预测和优化参数再预测。这种双预测法，是单独预测蛋白类型，从而预测其二级结构倾向性。第一步：是依据蛋白氨基酸组成对蛋白质结构进行分类归属。分类法与Nakasima (1986)相同,除了/类蛋白，依据其与和类相同性被分为 /-和/-两个亚类。 第二步：是依据Kabsch and Sander (1983)二级结构数据库中四种类型(helix,sheet,turn and coil)出现几率进行预测。第三步：每个预测区域基于蛋白类型预测以下标准进行优化： (1) 二级结构预测最大准确性maximal accurac

19、y in secondary structure prediction, (2) 二级结构中预测和观察到结构吻合程度maximal agreement between predicted and observed content in secondary structure and (3) 在给定类型大多数蛋白预测准确性不丧失。no loss of accuracy for the majority of proteins in a given class.第27页28Deleage & RouxParameters: Using any class prediction other than

20、 Computer Calculated forces the method to group the protein into a given class. Use the Computer Calculated parameter unless you have prior knowledge to force a class prediction.Limitations: If the algorithm successfully predicts the class of a protein, structural predictions are somewhat more accur

21、ate than methods which do not make a class assumption (original Garnier-Robson or original Chou-Fasman). If a protein does not fall into a discreet class, prediction accuracy can be compromised.第28页29二级结构预测(4)： Chou-FasmanChou and Fasman (1990)算法是利用蛋白质氨基酸序列对其二级结构进行预测，是基于已知蛋白质晶体结构统计学方法。最初，它对15个蛋白质247

22、3 AA位于-helix、-sheet和coil出现次数进行列表统计。由此再经过考查蛋白质中特定残基相对频率计算得到每个残基构象参数、出现在特定二级结构类型几率和那种二级结构出现残基类型分数。构象参数实际上是对特定残基出现在-helix、-sheet和coil（以后-turn）偏嗜性。以后， Chou and Fasman将分析工作增加到29个蛋白质结构（4741 AA），再以后Chou增加到64个结构和四种蛋白类型。即Alpha, Beta, Alpha+Beta and Alpha/Beta. Representatives of the Alpha类主要是含大量 -helix和少许-sh

23、eet ，以Hemoglobins and cytochromes 为经典。Beta类含大量-sheet 和少许或不含a-helix， 以 immunoglobulins and serine proteases为经典. Alpha+Beta类含-helix和-sheet，而且位于独立结构域。Alpha/Beta类是含 -helix和-sheet ，但混合出现在相同结构域，以 dehydrogenases and kinases为经典. 默认参数来自64个蛋白结构。该算法原理很简单。使用构象参数，我们可发觉关键位点、确定是螺旋或是折叠、两端进行序列延伸，当出现更大倾向于另一中结构时停顿延伸，此

24、时确定类型也终止了，再重复其它类型判断直至完成整个序列估算。转角区域默认为那些既没有-螺旋也没有-折叠形成区域。第29页30Chou-FasmanParameters: All parameters are radio button toggles. You can only use one of these conformational parameters at a time (although you can have multiple plots using other conformational parameters). 29 Proteins uses the conformat

25、ional parameters from the 29 protein structures. 64 Proteins uses the conformational parameters of 64 protein structures. Alpha Class, Beta Class, Alpha+Beta Class and Alpha/Beta Class use the conformational parameters from Chous class assignments (1990). The two constant parameters Alpha Region Thr

26、eshold: and Beta Region Threshold: are constants which limit whether a region is defined as a-helix or sheet. If Pa 103 and Pa P a region is predicted as helical. If P105 and P Pa a region is predicted as a beta sheet. These default values are from Chou-Fasman (1978), where Pa = 1.03 and P= 1.05 res

27、pectively. These thresholds apply only to the 29 Proteins and 64 Proteins methods.第30页31Chou-FasmanLimitations: This algorithm is statistical; that is, it looks at the probability that a given protein exists in a given secondary structure element. Inaccuracies can arise if your protein sequences is

28、substantially dissimilar from test case proteins and test case protein families. Chou predict 80% accuracy in assigning secondary structure using the 64 protein method.第31页32二级结构预测(5)： PSIPRED 生物信息学网站（http:/bioinf.cs.ucl.ac.uk/psipred/）提供基于神经网络搜索工具PSI-BLASTPSIPRED蛋白二级结构预测方法，结果返回到学术性（商业后缀信箱不可用）Email信

29、箱。第32页33二级结构预测(5)： PSIPRED 生物信息学网站（http:/bioinf.cs.ucl.ac.uk/psipred/）提供基于神经网络搜索工具PSI-BLASTPSIPRED蛋白二级结构预测方法，结果返回到学术性（商业后缀信箱需申请密码）Email信箱第33页34PSIPRED 参考文件第34页35PSIPREDhttp:/bioinf.cs.ucl.ac.uk/psipred/ 第35页36Hydropathy: Goldman-Engleman-Steitz该方法是使用Goldman, Engleman and Steitz (1986)模型对可能穿越细胞膜非极性-螺

30、旋进行预测。非极性螺旋搜索是依据蛋白质序列上氨基酸残基极性指数进行。极性指数则是依据非极性双层脂膜区域与待考查残基相互作用程度得到。出于自由能考虑，该方法不考虑卷曲和-折叠类型，因为这么结构是不可能由细胞质插入到双层脂膜中。基于残基亲水和疏水成份以及位于-螺旋上每一个残基表面区域，赋于每个氨基酸跨膜自由能值。所以，这种方法用于预测跨膜螺旋结构比只依据残基亲疏水性指数来判断方法来得准确。第36页37Goldman-Engleman-SteitzParameters: The only parameter is how many Residues to Average when construct

31、ing each point in the plot. The default is 20, to reflect a common transmembrane alpha helix. Factors playing into the choice of window length are the hydrophobic length of the bilayer and the orientation of a potential helix within it. 21 residues are required to span a lipid bilayer 30 long, as th

32、e interval between residues in an a-helix is 1.5.Limitations: This algorithm is much more accurate at predicting membrane bound helices than other hydropathy methods, especially if the helix contains polar residues. It makes no attempt at predicting other structural elements and would not be accurat

33、e for non-helical membrane bound structures. Non-helical membrane bound structures are not very common, due to the high free energy costs of folding in a polar environment and functioning in a non-polar environment.第37页38Hydropathy: Kyte-DoolittleKyte and Doolittle (1982) 预测法是经过蛋白质氨基酸序列来预测。亲疏水性被定义成与

34、水分子亲和程度，正值为亲水(hydrophilic) ，负值为疏水(hydrophobic) 。其值为按预定窗口值进行全部氨基酸残基亲疏水值平均值。作图时平均值标在窗口中央位置。残基亲疏水值是由水蒸发自由能以及残基侧链内部和外部分布情况来计算。第38页39Kyte-DoolittleParameters: The only variable is how large of a window to average hydropathy values over. The author recommends 7 to 11 residues, as anything less than seven

35、will display too much noise and anything greater than 11 will miss small hydropathic regions.Limitations: When Kyte and Doolittle created their hydropathy table, they decided not to use water to ethanol transfer free energies, as ethanol is not necessarily a neutral, non-interacting solvent. For eac

36、h residue, they looked at water-vapor transfer free energies and interior-exterior distribution of side-chains . When these values conflicted, the final hydropathy value was determined by averaging, other empirical data or the authors somewhat arbitrary choices. For common residues, more data was av

37、ailable for determining hydropathy. For less common residues (Tyr, His, Pro, Trp), this becomes more problematic.第39页40Hydropathy: Hopp-WoodsHopp-Woods(1981)法是经过搜索蛋白质中最大局部亲水区来寻找蛋白抗原决定簇。这位置被认为是抗原决定簇所在或者其附近区域。利用亲水性进行抗原决定簇搜索，作者给出两个依据： 1) 抗原决定簇常位于高度暴露在溶剂中区域； 2) 带电亲水性侧链常出现在抗原决定簇中。每个残基被赋予一定亲水值，并以六肽为窗口值进行平

38、均。除了发生修饰Asp、Glu和Pro外，每个氨基酸残基亲疏水值是依据Levit制订表取得。对已知抗原决定簇蛋白进行测试试验显示，最高亲水值峰准确地预测了抗原位点，而较低峰实际上可能是也可能不是抗原位点。第40页41Hopp-WoodsParameters: The default Residues to Average is set at seven, while the original method uses six. There is very little difference in antigenic site prediction with this change, except

39、 that our change requires a slightly larger antigenic site. The authors do not recommend setting this value below five, as too many regions are falsely predicted, or above eight, as averaging residues creates a curve too smooth to locate areas of potential antigenic sites.Limitations: Not all antige

40、nic sites correlate with hydrophilicity. The highest peak has the greatest chance of being an antigenic site if it is correlated with hydrophilicity. Lower peaks tend to become noise. If your protein is substantially divergent from test proteins used to determine hydrophilicity values, predictions m

41、ay be in error.第41页42Antigenicity: Sette MHC MotifsSette MHC II motif 是 Sette, A. (1989)提出方法，用于预测能否与鼠MHC II单体蛋白相互作用多肽抗原位点。包含两个方法，一用于IAd单体表位预测，另一用于IEd。其中IAd方法是基于鸡卵清蛋白一段序列模式 327-332 (V, H, A, A, H, A)进行预测，考查多肽与IAd单体链结合以及不一样碱基替换后影响亲和力等情况。从一套62个六肽库信息中，可建立与IAd单体链结合蛋白序列模体。基于已知试验数据和Dayhoff取代模式以及位于六肽位置，每个残基

42、 可取得1, 2或3分数。3分表示残基位于这种模体可能性很大，1分则可能性较小。六肽中每个残基都取得分数后，在将全部组分分数相乘，假如区域得分400则被认为是IAd阳性区域。 IEd模体预测法是依据IEd抗原结合位点通用模式，该模式来自62个六肽组成已知抗原决定簇数据库。IEd识别模式包含1，2，3位是碱性氨基酸(R, H, K) ，4，6位也是碱性氨基酸，而且5位是不带电氨基酸 (A, C, F, G, I, L, M, P, S, T, V, Y)。 符合这么标准残基在区域图中被赋予正值。利用IAd和IEdMHC II模体预测法在已知IAd和IEd 表位蛋白验证准确率超出75%。第42页4

43、3Sette MHC MotifsParameters: The only parameter is whether to allow one deleterious substitution or not for the IAd motif. If you allow one substitution, you set the threshold for a positive IAd region at 400. If you dont allow a deleterious substitution, the threshold is set to 729 (36). There are

44、no parameters which affect the IEd method.Limitations: This method is intended to find x and y motifs for the d haplotype of mouse MHC II proteins only. It is unlikely to be affective for alternate haplotypes or non-mouse MHC II proteins. It can be an effective tool for designing peptide epitopes wh

45、en used in conjunction with AMPHI and the Rothbard - Taylor method.第43页44Antigenicity: AMPHI依据 Margalit and Berzofsky (1987)模型，可用于预测蛋白一级序列中存在辅助T淋巴细胞免疫优势表位。大多数已知辅助T细胞抗原位点都是由两性螺旋结构组成：即，螺旋一面是极性表面，另一面是非极性表面。这些疏水性是周期性分布，对应与一个或310螺旋结构经典键角。-螺旋周期率是100（也就是每圈3.6个残基），310螺旋则是120。首先，依据Faucher and Pliska疏水值表将一级氨基

46、酸序列转换成疏水值序列；其次，疏水值序列分成相互重合7个或11个肽段块；再者，检索每个疏水块与两性螺旋周期排布符合程度； 最终，由PROTEAN识别出稳定或不稳定两性螺旋结构。 结果可显示为一张疏水图、一个AMPHI区域图、一个螺旋分布图和/或3-10螺旋图（亲疏水性指数）和一张AMPHI强度图。 AMPHI强度图用于确定预测亲疏水性区域相对强度。依据已知T细胞抗原位点，作者发觉结合Faucher-Pliska亲疏水性指数以及对强度图进行正弦最小平方拟合可得到 最为准确结果。第44页45AMPHIParameters: The only parameter is a sensitivity t

47、hreshold. Use the default Low Amphipathic Content for most protein sequences. Low Amphipathic Content uses a minimum amphipathic index of 4 to create the region graph from three (or more) consecutive regions of length 7. For proteins exhibiting a high amphipathic content, use Highly Amphipathic. Hig

48、hly Amphipathic uses an amphipathic index of 8 to create the region plot for three (or more) consecutive regions of length 11. Limitations: The underlying assumption of the AMPHI method is that T-cell antigenic sites are composed of amphipathic helices. While the majority of known T-cell epitopes co

49、ntain amphipathic helices, this method will miss those which do not. It is also limited to T-cell epitopes only. B-cell antigenic sites exhibit a larger tertiary character which this method does not attempt to resolve.第45页46Antigenicity: Rothbard-TaylorRothbard & Taylor方法 (1988)可用于查找潜在包含有通用模式序列T淋巴细胞

50、抗原决定簇。经过蛋白降解过程后，潜在T淋巴细胞表位可结合到主要组织相容性复合体I型或II型分子上(MHC I和MHC II)。从已知抗原决定簇数据库中，作者发觉被MHC I或MHC II分子呈递潜在T细胞表位都含有一个通用模式序列（46个中有37个含有此特征）。该模式序列含有4或5个残基。4残基形式是：残基1：甘氨酸或charged；残基2：疏水氨基酸；残基3：疏水氨基酸；残基4：极性氨基酸或甘氨酸。5残基形式是：残基1：甘氨酸或极性氨基酸；残基2：疏水氨基酸；残基3：疏水氨基酸；残基4：疏水氨基酸或脯氨酸；残基5：极性氨基酸或甘氨酸。带电残基为D, E, H, K, R；疏水氨基酸残基为A,

51、 V, L, I, F, M, W, T, Y；极性氨基酸残基为D, E, H, R, K, N, Q, S, T。一些氨基酸残基可能会存在于超出一个性质分类中。T细胞模式表位寻找结果以区域图（region plot）方式显示。第46页47Rothbard-TaylorParameters: The only choice for this method is to choose whether to use a five residue or a four residue window. A window of five will locate more matches than a win

52、dow of four, as it locates all 4-mers and all 5-mers which fit motif criteria. Limitations: This method is intended to predict only T-cell epitope patterns only. The authors found that it predicted 80% of the antigenic determinants for their peptide database, but the presence or absence of this moti

53、f isnt sufficient to guarantee antigenicity. It is useful only for T-cell epitopes and should not be confused with B-cell epitopes, which often require greater knowledge of the tertiary structure of the binding site. Use this method in combination with AMPHI and the Sette MHC II method for best resu

54、lts.第47页48Antigenicity: Jameson-WolfJameson-Wolf方法 (1988)可联合使用当前已经有蛋白结构预测方法预测潜在抗原决定簇。其运算法则由6个主要子程序组成。第一步，经过Hopp-Woods方法确定亲水性值；第二步，使用Eminis方法计算表面概率（surface probabilities）；第三步，使用Karplus-Schultz方法预测主链或支链柔性；第四步和第五步，应用Chou-Fasman和Garnier-Robson方法进行二级结构预测。在最终一步，使用柔性参数（flexibility）和亲疏水性/溶剂可及因子制作表面几率分布等高图（也

55、被认为是抗原性指数）。取得综合参数后，再利用峰形加宽函数处理该指数。考虑到来自相对于内部区域表面区域运动产生额外自由能改变，主要表面分布峰分别加宽20, 40, 60, 或80% 。该计算将应用于全部主峰，除了存在于螺旋区域峰以外，因为螺旋结构柔性较差。第48页49Jameson-WolfParameters: The only parameter is a radio switch to Broaden Peaks or Dont Broaden Peaks. The default is to Broaden Peaks. If you choose to not broaden the

56、peaks, the peak broadening function is not carried out for this algorithm.Limitations: This algorithm has the best chance of predicting antigenic determinants than any of its component parts. It will give multiple peaks, all of which can be potential antigenic determinants, but may or may not be imm

57、unogenic determinants. It may be a good idea to superimpose plots of the component methods with the Jameson-Wolf method, to get a feel for how each affects antigenicity predictions. This method uses the default parameters for each of its component methods.第49页50Amphilicity Eisenberg疏水力矩分析是依据描述蛋白质亲水和

58、疏水残基基团分布计算和试验值来进行半经验性定量分析。主要用于描述疏水性或两性不对称性。PROTEAN软件使用 Eisenberg , Weiss and Terwilliger (1984)方法进行Eisenberg力矩预测。结构疏水力矩（当原子坐标已知时）可表示成：n为残基数目，Hn为第n个残基疏水值，sn为由残基-C指向侧链中心向量单位。疏水力矩就是用于评定疏水性每个侧链方向向量之和。在缺乏原子坐标数据情况下，PROTEAN软件利用序列疏水力矩估算蛋白力矩瞬间。可写成：全部变量与上面方程相同，除了是表示后继侧链从结构中心轴辐射角度值（用弧度表示）。-螺旋默认值为100，-折叠默认值为170

59、。第50页51Amphilicity EisenbergParameters: Residues to Average determines how many residues are averaged for the hydrophobic moment and hydrophobicity. Alpha Angle and Beta Angle denote what angles are used in moment and amphipathic region plots to assign emergence angles for successive side chains. An

60、gles chosen determine amphilicity by changing vector assignments of each hydrophobic moment. You should only change these parameters if you are looking for a structure with atypical a-helix and -sheet angles, such as a 310 helix.Limitations: In the absence of atomic data PROTEAN must use emergence a